<Pages><Category Name="Model">
<Page ID="149" Title="MultiMod"><Template Name="Model"><Field Name="Full_Model_Name">Energy system and resource market model &quot;MultiMod&quot;</Field><Field Name="author_institution">DIW Berlin, NTNU Trondheim</Field><Field Name="authors">Daniel Huppmann, Ruud Egging</Field><Field Name="contact_persons">Daniel Huppmann</Field><Field Name="contact_email">dhuppmann@diw.de</Field><Field Name="website">http://www.diw.de/multimod</Field><Field Name="text_description">The energy system and resource market model &quot;MultiMod&quot; is a large-scale representation of the supply and demand of fossil fuels and renewable energy sources. It captures endogenous substitution between fuels, infrastructure constraints and endogenous investment (e.g., pipeline capacity, power generation technologies), as well as market power by producers of fossil fuels in a unified framework. 

The mathematical framework of the MultiMod model is a dynamic Generalized Nash Equilibrium (GNE) derived from individual players' profit maximisation problems. The formulation is generic and flexible, so that the supply chain of any number of fossil and renewable fuels can be modelled. The framework includes seasonality and allows for a detailed infrastructure representation and a comprehensive transformation sector. Investment in infrastructure (transportation, seasonal storage, transformation) is determined endogenously in the model according to the respective player’s inter-temporal optimisation problem. Furthermore, substitution between different energy carriers on the final demand side is endogenous. Modelling co-production of fuels (e.g. crude oil and associated gas) is possible, as well as a flexible setup of transformation units (multiple inputs, multiple outputs). By formulating the model as an equilibrium problem derived from non-cooperative game theory, the model can incorporate Cournot market power by individual suppliers as well as distinct discount rates by various players concerning their investment.

The current framework is an open-loop perfect foresight model. A stochastic version of the model is under development at NTNU Trondheim. This will allow for consideration of uncertainty and distinct risk profiles for individual players along the supply chain, including investment by consumers in energy efficiency.

For the model description paper, a database representing the global energy system was compiled and used for scenario analysis (Huppmann &amp; Egging, 2014). New datasets or variations on the initial data base are currently under development within specific research projects:

- Focus on US domestic conventional crude and shale oil infrastructure (lead: Johns Hopkins University)

- Focus on Chinese coal policies (lead: Tsinghua University)

- Focus on the global crude oil market and refinery investment (lead: DIW Berlin/TU Berlin)

The model is formulated and solved as a Mixed Complementarity Problem (MCP) and implemented in GAMS, using MS Access and MS Excel for data processing and output reports. The code package includes a number of auxiliary routines and algorithms that greatly facilitate the compilation of the data set as well as calibration of the model.</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS</Field><Field Name="processing_software">MS Access, MS Excel</Field><Field Name="model_class">Equilibrium model</Field><Field Name="sectors">Oil, Gas, Coal, Electricity, Renewables, Industry, Transport, Residential/Commercial</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">Global</Field><Field Name="georesolution">Europe by region, North America by country, rest of world by region</Field><Field Name="timeresolution">Multi year</Field><Field Name="network_coverage">transmission, net transfer capacities</Field><Field Name="math_modeltype">Other</Field><Field Name="math_modeltype_shortdesc">Generalized Nash Equilibrium (GNE) model formulated as a Mixed Complementarity Model (MCP)</Field><Field Name="deterministic">Not covered (yet)</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="number_of_variables">150000</Field><Field Name="computation_time_minutes">600</Field><Field Name="citation_references">Daniel Huppmann &amp; Ruud Egging (2014). Market power, fuel substitution and infrastructure - A large-scale equilibrium model of global energy markets. Energy, 75, 483–500.</Field><Field Name="citation_doi">10.1016/j.energy.2014.08.004</Field><Field Name="report_references">Currently used within EMF 31 (http://emf.stanford.edu)</Field><Field Name="example_research_questions">Scenarios regarding North American shale gas development, Russian supply disruption to Europe, evaluation of renewable support measures (feed-in tariffs vs. emission quota)

Model variations (forks) used for other research projects by international partners (see short description for details)</Field></Template></Page><Page ID="155" Title="Renpass"><Template Name="Model"><Field Name="Full_Model_Name">Renewable Energy Pathways Simulation System</Field><Field Name="author_institution">ZNES Flensburg</Field><Field Name="authors">Frauke Wiese, Gesine Bökenkamp</Field><Field Name="contact_persons">Frauke Wiese</Field><Field Name="contact_email">frauke.wiese@uni-flensburg.de</Field><Field Name="website">https://github.com/fraukewiese/renpass</Field><Field Name="source_download">https://github.com/fraukewiese/renpass</Field><Field Name="text_description">renpass is an open source simulation energy model which has the goal to fulfil the requirements of full transparency and the possibility to image 100% renewable energy systems as well as today's system on a high regional and time resolution.

Currently renpass is being developed further as renpassG!S based on the Open Energy Modelling Framework (oemof).</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">R</Field><Field Name="processing_software">MySQL / R / RMySQL</Field><Field Name="GUI">No</Field><Field Name="model_class">Electricity System Model / Regional Dispatch Model / Transshipment Model</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Poland, Lithuania, Latvia, Estonia, Finland, Sweden, Denmark, Norway, the Netherlands, Belgium, Luxembourg, France, Switzerland, Austria, the Czech Republic, Germany</Field><Field Name="georesolution">Germany: 21 regions / other countries: country</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Minimization of costs for each time step (optimization) within the limits of a given infrastructure (simulation)</Field><Field Name="math_objective">economic costs</Field><Field Name="deterministic">perfect foresight</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">200</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">240</Field><Field Name="computation_time_comments">varying, 240 minutes is roughly for an hourly computation for one year</Field><Field Name="citation_references">Wiese, F. (2015). renpass - Renewable Energy Pathways Simulation System - Open Source as an approach to meet challenges in energy modeling. Dissertation. Europa-Universität Flensburg.</Field><Field Name="citation_doi">10.1002/wene.109</Field><Field Name="report_references">Bökenkamp, G. (2015). The role of Norwegian hydro storage in future renewable electricity supply systems in Germany: Analysis with a simulation model. PhD thesis, University of Flensburg.

Bons, M. (2014). Verstärkte Nutzung Windenergie in Süddeutschland und resultierender Übertragungsbedarf. Master’s thesis, Universität Flensburg.

Hilpert, S. (2013). Simulation von Kraft-Wärme-Kopplungsanlagen im deutschen Energiesystem bis 2030. Masterarbeit, FH Flensburg.

Wingenbach, C., Bunke, W. D., and Hohmeyer, O. (2013). Szenarien für die Entwicklung der stündlichen Preise am deutschen Strommarkt für die Jahre 2015 bis 2041. Technical Report. Center for Sustainable Energy Systems (CSES), Flensburg.

CSES (2014). Modelling Sustainable Electricity Systems for Germany and Neighbours in 2050. Study Group Report by Eva Wiechers, Hendrik Böhm, Wolf Dieter Bunke, Cord Kaldemeyer, Tim Kummerfeld, Martin Söthe, Henning Thiesen. Study group report, Universität Flensburg.

IZT (2014). VerNetzen - Sozial-ökologische, technische und ökonomische Modellierung von Entwicklungspfaden der Energiewende. Institut für Zukunftstechnologien, Universität Flensburg, Deutsche Umwelthilfe. http://www.fona.de/soef/VerNetzen

CSES (2012). Modeling sustainable electricity systems for the Baltic Sea Region. Discussion Paper 3, Centre for Sustainable Energy Systems.</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template><Template Name="lowercase"></Template></Page><Page ID="171" Title="Balmorel"><Template Name="Model"><Field Name="Full_Model_Name">Balmorel</Field><Field Name="author_institution">RAM-løse, DTU</Field><Field Name="authors">Hans Ravn</Field><Field Name="website">http://balmorel.com/</Field><Field Name="source_download">https://github.com/balmorelcommunity/Balmorel</Field><Field Name="text_description">Balmorel is a deterministic, partial equilibrium model for optimizing an energy system. The optimization maximizes the social welfare of the energy system. The model optimizes both investments and operational dispatch under physical and regulatory constraints.</Field><Field Name="User documentation">https://github.com/balmorelcommunity/Balmorel/tree/master/base/documentation</Field><Field Name="Code documentation">https://github.com/balmorelcommunity/Balmorel/tree/master/base/documentation</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">ISC License (ISC)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/balmorelcommunity/Balmorel</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS</Field><Field Name="processing_software">Excel, Python (Pandas)</Field><Field Name="External optimizer">All those supported by GAMS</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy System Model</Field><Field Name="sectors">User-dependent</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">User-dependent (Pan-European, applied in 20+ countries)</Field><Field Name="georesolution">Hierarchical: countries, regions, areas</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow, net transfer capacities</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear programming (with an option of mixed-integer programming)</Field><Field Name="math_objective">Social welfare maximization</Field><Field Name="deterministic">Deterministic, perfect foresight, global sensitivity analysis</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">1,000,000+</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">Variable</Field><Field Name="computation_time_comments">model- and hardware-dependent</Field><Field Name="citation_references">Wiese, Frauke, Rasmus Bramstoft, Hardi Koduvere, Amalia Rosa Pizarro Alonso, Olexandr Balyk, Jon Gustav Kirkerud, Åsa Grytli Tveten, Torjus Folsland Bolkesjø, Marie Münster, and Hans V. Ravn. “Balmorel Open Source Energy System Model.” Energy Strategy Reviews 20 (2018): 26–34.</Field><Field Name="citation_doi">10.1016/j.esr.2018.01.003.</Field><Field Name="report_references">H. Ravn et al.: Balmorel: A Model for Analyses of the Electricity and CHP Markets in the Baltic Sea Region (2001), http://www.eabalmorel.dk/files/download/Balmorel%20A%20Model%20for%20Analyses%20of%20the%20Electricity%20and%20CHP%20Markets%20in%20the%20Baltic%20Sea%20Region.pdf</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="177" Title="SciGRID power"><Template Name="Model"><Field Name="Full_Model_Name">Scientific Grid Model of European Power Transmission Networks</Field><Field Name="Acronym">SciGRID_power</Field><Field Name="author_institution">DLR Institute of Networked Energy Systems</Field><Field Name="authors">Wided Medjroubi, Carsten Matke</Field><Field Name="contact_persons">Wided Medjroubi, Carsten Matke</Field><Field Name="contact_email">developers(at)scigrid.de, carstenmatke@gmail.com</Field><Field Name="website">https://www.power.scigrid.de/</Field><Field Name="source_download">https://www.power.scigrid.de/pages/downloads.html</Field><Field Name="logo">SciGRID power logo.png</Field><Field Name="text_description">SciGRID is an open source model of the European transmission network. On the 15.06.2015 the first version (release V0.1) of SciGRID was released and the second version (release V0.2) was made available on the 20.11.2015. The third release of SciGRID (release 0.3) was made available on the 1st of August 2016 and includes a European and German dataset.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python, PostgreSQL</Field><Field Name="processing_software">Python, PostgreSQL, Osmosis, osm2pgsql</Field><Field Name="GUI">No</Field><Field Name="model_class">Transmission Network Model</Field><Field Name="sectors">Electricity Sector</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="georegions">Europe and Germany (any other EU country also possible)</Field><Field Name="georesolution">nodal resolution</Field><Field Name="network_coverage">transmission</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">We consider a topological graph (V,L) as a mathematical structure that consists of a set V of vertices and a set L of nonempty subsets of V called links.</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">C. Matke, et al., Paper: (2017) Structure Analysis of the German Transmission Network Using the Open Source Model SciGRID. In: Bertsch V., Fichtner W., Heuveline V., Leibfried T. (eds) Advances in Energy System Optimization. Trends in Mathematics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-51795-7_11</Field><Field Name="report_references">M. Rohden, et al., Paper: &quot;Cascading Failures in AC Electricity Grids.&quot; arXiv preprint
D. Jung and S. Kettemann, Paper: &quot;Long-Range Response in AC Electricity Grids.&quot; Phys. Rev. E. 94, 012307(2016).</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="181" Title="Genesys"><Template Name="Model"><Field Name="Full_Model_Name">Genetic Optimisation of a European Energy Supply System</Field><Field Name="Acronym">GENESYS(1)</Field><Field Name="author_institution">RWTH-Aachen University</Field><Field Name="authors">Alvarez, Bussar, Cai, Chen, Moraes Jr., Stöcker, Thien</Field><Field Name="contact_persons">Christian Bussar</Field><Field Name="contact_email">cbu@isea.rwth-aachen.de ;  genesys@isea.rwth-aachen.de</Field><Field Name="website">http://www.genesys.rwth-aachen.de</Field><Field Name="source_download">Form on website</Field><Field Name="logo">Genesys.png</Field><Field Name="text_description">The GENESYS Simulation tool has the central target so optimise the future European power system (electricity) with a high share of renewable generation. It can find an economic optimal distribution of generators, storage and grid in a 21 region Europe.
The optimisation is based on a covariance matrix adaption evolution strategy (CMA-ES) while the operation is simulated as a hierarchical setup of system elements aiming to balance the load at minimal cost.
GENESYS comes with a set of input time-series and a parameter set for 2050 which can be adjusted by the user.
It was developed as open source within a publicly funded project and its development is currently continued at RWTH Aachen University.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU Library or &quot;Lesser&quot; General Public License version 2.1 (LGPL-2.1)</Field><Field Name="model_source_public">No</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">C++, boost library, MySQL and QT4, (optional CPLEX solver implementation)</Field><Field Name="processing_software">Excel/Matlab and a Visualisation tool programmed in QT4 (c++)</Field><Field Name="GUI">No</Field><Field Name="model_class">Electricity System Model</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">Europe, North Africa, Middle East</Field><Field Name="georesolution">EUMENA, 21 regions</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, net transfer capacities</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">optimisation of system combination with evolutionary strategy
simulation of operation with hierarchical management strategy and linear load balancing between regions (network simplex)</Field><Field Name="math_objective">minimise levelised cost of electricity</Field><Field Name="deterministic">24 h foresight for storage operation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">1-3 min for operation simulation (5y data) &lt;br&gt; 80h optimisation run</Field><Field Name="citation_references">Bussar et. al, 2014, Optimal Allocation and Capacity of Energy Storage Systems in a Future European Power System with 100% Renewable Energy Generation</Field><Field Name="citation_doi">10.1016/j.egypro.2014.01.156</Field><Field Name="example_research_questions">How much storage systems of which technology needs to be implemented in the future energy system.
How big are the transfer capacities between regions.
How much renewable generator power of which technology are necessary?
How much conventional generators are allowed within assumed CO2 emission limits?</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="187" Title="EMLab-Generation"><Template Name="Model"><Field Name="Full_Model_Name">EMLab-Generation</Field><Field Name="author_institution">Delft University of Technology</Field><Field Name="authors">Jörn C. Richstein, Emile Chappin, Pradyumna Bhagwat, Laurens de Vries</Field><Field Name="contact_persons">Jörn C. Richstein</Field><Field Name="contact_email">j.c.richstein@tudelft.nl</Field><Field Name="website">http://emlab.tudelft.nl/</Field><Field Name="source_download">https://github.com/EMLab/emlab-generation</Field><Field Name="logo">Logo-emlab.png</Field><Field Name="text_description">The main purpose is to explore the long-term effects of interacting energy and climate policies by means of a simulation model of power companies investing in generation capacity. With this model, we study the influence of policy on investment in the electricity market in order to explicate possible effects of current and alternative/additional policies on the various sector goals, i.e. renewables targets, CO2 emission targets, security of supply and affordability. The methodology, agent-based modelling, allows for a different set of assumptions different as to the mainstream models for such questions: this model can explore heterogeneity of actors, consequences of imperfect expectations and investment behaviour outside of ideal conditions.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Java</Field><Field Name="processing_software">R</Field><Field Name="GUI">No</Field><Field Name="model_class">Agent-based Simulation</Field><Field Name="sectors">Electricity Market, Carbon Market</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">Central Western Europe</Field><Field Name="georesolution">Zones</Field><Field Name="timeresolution">Year</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="math_modeltype">Simulation, Agent-based</Field><Field Name="deterministic">Limited foresight, optional risk aversion</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">60</Field><Field Name="computation_time_comments">Depends on the enabled modules</Field><Field Name="citation_references">Richstein et al. 2014, Cross-border electricity market effects due to price caps in an emission trading system: An agent-based approach, Energy Policy Volume 71, August 2014, Pages 139–158</Field><Field Name="citation_doi">10.1016/j.enpol.2014.03.037</Field><Field Name="example_research_questions">- What is the effect of carbon price caps?
- How is the market stability reserve going to effect the EU ETS?
- What long-term effects does a capacity market have?</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="205" Title="NEMO"><Template Name="Model"><Field Name="Full_Model_Name">National Electricity Market Optimiser</Field><Field Name="author_institution">University of New South Wales</Field><Field Name="authors">Ben Elliston</Field><Field Name="contact_persons">Ben Elliston</Field><Field Name="contact_email">b.elliston@unsw.edu.au</Field><Field Name="website">https://nemo.ozlabs.org/</Field><Field Name="source_download">https://git.ozlabs.org/?p=nemo.git</Field><Field Name="text_description">NEMO is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Australia</Field><Field Name="georesolution">NEM regions</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Optimisations are carried out using a single-objective evaluation function (with penalties). The search space (of generator capacities) is searched using the CMA-ES algorithm.</Field><Field Name="math_objective">minimise average cost of electricity</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">up to 100</Field><Field Name="computation_time_minutes">5 minutes to several hours</Field><Field Name="computation_time_comments">runtime depends on # of CPUs devoted to optimisation</Field></Template></Page><Page ID="214" Title="Oemof"><Template Name="Model"><Field Name="Full_Model_Name">Open Energy Modelling Framework</Field><Field Name="author_institution">Reiner Lemoine Institut / ZNES Flensburg</Field><Field Name="authors">Stephan Günther, Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Caroline Möller, Guido Plessmann, Clemens Wingenbach et al.</Field><Field Name="contact_persons">Stephan Günther, Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Caroline Möller, Guido Plessmann, Clemens Wingenbach et al.</Field><Field Name="contact_email">oemof(affe)rl-institut.de</Field><Field Name="website">https://oemof.org/</Field><Field Name="source_download">https://github.com/oemof/oemof/releases</Field><Field Name="logo">8503379.png</Field><Field Name="text_description">oemof is a framework for energy system model development and its application in energy system analysis. Currently, it bases on collaborative work of three institutions. You can clone/fork the code at github.

Containing a linear optimisation problem formulation library, feedin-data generation library and other auxiliary libraries it is meant to be developed further according to interests of user/ developer community.</Field><Field Name="Support">https://forum.openmod.org/tags/c/qa/oemof</Field><Field Name="User documentation">https://oemof.readthedocs.io/en/latest/</Field><Field Name="Code documentation">https://oemof-solph.readthedocs.io</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/oemof/oemof</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python, Pyomo, Coin-OR</Field><Field Name="processing_software">PostgreSQL, PostGIS</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy Modelling Framework</Field><Field Name="sectors">Electricity, Heat, Mobility</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution</Field><Field Name="Transfer (Gas)">Distribution</Field><Field Name="Transfer (Heat)">Distribution</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Depends on user</Field><Field Name="georegions">Depends on user</Field><Field Name="georesolution">Depends on user</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, distribution, DC load flow, net transfer capacities</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">https://oemof.org/libraries/</Field><Field Name="math_objective">costs, emissions</Field><Field Name="deterministic">Deterministic</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_comments">Depends strongly on use of modeling framework. Typically if investment decisions are enabled, a model run takes a multiple of minutes to compute.</Field><Field Name="Integrating models">FlexiGis</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="338" Title="OSeMOSYS"><Template Name="Model"><Field Name="Full_Model_Name">open-source energy modelling system</Field><Field Name="Acronym">OSeMOSYS</Field><Field Name="author_institution">KTH Royal Institute of Technology</Field><Field Name="authors">Mark Howells, Holger Rogner, Neil Strachan, Charles Heaps, Hillard Huntington, Socrates Kypreos, Alison Hughes, Semida Silveira, Joe DeCarolis, Morgan Bazillian, Alexander Roehrl</Field><Field Name="contact_persons">Mark Howells, Will Usher, Abhishek Shivakumar, Manuel Welsch, Vignesh Sridharan</Field><Field Name="contact_email">osemosys@gmail.com</Field><Field Name="website">http://www.osemosys.org</Field><Field Name="source_download">http://github.com/OSeMOSYS/OSeMOSYS</Field><Field Name="text_description">OSeMOSYS has been created by a community of leading institutions and is capable of powerful energy systems analysis and prototyping new energy model formulations. It is typically used for the analysis of energy systems looking over the medium (10-15yrs) and long (50-100yrs) term. It is used by experienced modellers as an exploratory tool, by developing country modellers where data limitations are an issue, and as a teaching tool.</Field><Field Name="User documentation">osemosys.readthedocs.io</Field><Field Name="Code documentation">osemosys.readthedocs.io</Field><Field Name="Number of developers">10s</Field><Field Name="Number of users">100s</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">http://github.com/OSeMOSYS/OSeMOSYS</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GNU MathProg</Field><Field Name="processing_software">Python</Field><Field Name="GUI">No</Field><Field Name="sectors">all,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">investment</Field><Field Name="georegions">Africa (all countries), Sweden, Baltic States, Nicaragua, Bolivia, South America, EU-27+3</Field><Field Name="georesolution">Country</Field><Field Name="timeresolution">Day</Field><Field Name="network_coverage">transmission, distribution</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear optimisation (with an option of mixed-integer programming)</Field><Field Name="math_objective">Minimise total discounted cost of system</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_comments">Depends on solver used (glpsol/CPLEX/Gurobi etc.)</Field><Field Name="citation_doi">doi:10.1016/j.enpol.2011.06.033</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="423" Title="PowerMatcher"><Template Name="Model"><Field Name="Full_Model_Name">PowerMatcherSuite</Field><Field Name="author_institution">Flexiblepower Alliance Network</Field><Field Name="website">http://flexiblepower.github.io/</Field><Field Name="source_download">https://github.com/flexiblepower/powermatcher</Field><Field Name="text_description">&quot;The PowerMatcher is a smart grid coordination mechanism. It balances distributed energy resources (DER) and (flexible) loads ... The PowerMatcher core application provides the market mechanism for the determination of the market equilibrium, while the devices work as actors for demand and/or supply&quot;</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Java</Field><Field Name="technologies">Renewables</Field><Field Name="is_suited_for_many_scenarios">No</Field></Template></Page><Page ID="431" Title="Calliope"><Template Name="Model"><Field Name="Full_Model_Name">Calliope</Field><Field Name="author_institution">ETH Zürich</Field><Field Name="authors">Stefan Pfenninger, Bryn Pickering</Field><Field Name="contact_persons">contact@callio.pe</Field><Field Name="contact_email">contact@callio.pe</Field><Field Name="website">http://www.callio.pe/</Field><Field Name="source_download">https://github.com/calliope-project/calliope</Field><Field Name="logo">Calliope-Logo-Simplified.png</Field><Field Name="text_description">Calliope is a framework to develop energy system models using a modern and open source Python-based toolchain. It is under active development and freely available under the Apache 2.0 license.

Feedback and contributions are very welcome!</Field><Field Name="User documentation">https://calliope.readthedocs.io/en/stable/</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/calliope-project/calliope</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="processing_software">Python (pandas et al)</Field><Field Name="GUI">No</Field><Field Name="model_class">Framework</Field><Field Name="sectors">User-dependent</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">fixed or time varying</Field><Field Name="georegions">User-dependent</Field><Field Name="georesolution">User-dependent</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, distribution, net transfer capacities</Field><Field Name="Observation period">Less than one year, More than one year</Field><Field Name="Additional dimensions (Ecological)">CO2, land use, and more (user-defined)</Field><Field Name="Additional dimensions (Economical)">LCOE</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_objective">User-dependent, including financial cost, CO2, and water consumption</Field><Field Name="deterministic">Deterministic; stochastic programming add-on</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_comments">user-dependent</Field><Field Name="citation_references">Pfenninger and Pickering, (2018). Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software, 3(29), 825</Field><Field Name="citation_doi">10.21105/joss.00825</Field><Field Name="report_references">Simon Morgenthaler, Wilhelm Kuckshinrichs and Dirk Witthaut (2020). Optimal system layout and locations for fully renewable high temperature co-electrolysis. Applied Energy, doi: 10.1016/j.apenergy.2019.114218

C. Del Pero, F. Leonforte, F. Lombardi, N. Stevanato, J. Barbieri, N. Aste, H. Huerto, E. Colombo (2019). Modelling Of An Integrated Multi-Energy System For A Nearly Zero Energy Smart District. 2019 International Conference on Clean Electrical Power (ICCEP) (pp. 246–252). doi: 10.1109/ICCEP.2019.8890129

Adriaan Hilbers, David Brayshaw and Axel Gandy (2019). Importance subsampling: improving power system planning under climate-based uncertainty. Applied Energy, doi: 10.1016/j.apenergy.2019.04.110

Francesco Lombardi, Matteo Vincenzo Rocco and Emanuela Colombo (2019). A multi-layer energy modelling methodology to assess the impact of heat-electricity integration strategies: the case of the residential cooking sector in Italy. Energy, doi: 10.1016/j.energy.2019.01.004

Bryn Pickering and Ruchi Choudhary (2019). District energy system optimisation under uncertain demand: Handling data-driven stochastic profiles. Applied Energy 236, 1138–1157. doi: 10.1016/j.apenergy.2018.12.037

Bryn Pickering and Ruchi Choudhary (2018). Mitigating risk in district-level energy investment decisions by scenario optimisation, in: Proceedings of BSO 2018. Presented at the 4th Building Simulation and Optimization Conference, Cambridge, UK, pp. 38–45. PDF in Conference proceedings

Bryn Pickering and Ruchi Choudhary (2017). Applying Piecewise Linear Characteristic Curves in District Energy Optimisation. Proceedings of the 30th ECOS Conference, San Diego, CA, 2-6 July 2017. PDF link

Stefan Pfenninger (2017). Dealing with multiple decades of hourly wind and PV time series in energy models: a comparison of methods to reduce time resolution and the planning implications of inter-annual variability. Applied Energy. doi: 10.1016/j.apenergy.2017.03.051

Paula Díaz Redondo, Oscar Van Vliet and Anthony Patt (2017). Do We Need Gas as a Bridging Fuel? A Case Study of the Electricity System of Switzerland. Energies, 10 (7), p. 861. doi: 10.3390/en10070861

Paula Díaz Redondo and Oscar Van Vliet (2016). Modelling the Energy Future of Switzerland after the Phase Out of Nuclear Power Plants. Energy Procedia. doi: 10.1016/j.egypro.2015.07.843

Mercè Labordena and Johan Lilliestam (2015). Cost and Transmission Requirements for Reliable Solar Electricity from Deserts in China and the United States. Energy Procedia. doi: 10.1016/j.egypro.2015.07.850

Stefan Pfenninger and James Keirstead (2015). Renewables, nuclear, or fossil fuels? Comparing scenarios for the Great Britain electricity system. Applied Energy, 152, pp. 83-93. doi: 10.1016/j.apenergy.2015.04.102

Stefan Pfenninger and James Keirstead (2015). Comparing concentrating solar and nuclear power as baseload providers using the example of South Africa. Energy. doi: 10.1016/j.energy.2015.04.077</Field><Field Name="Interfaces">netCDF input/output</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="444" Title="URBS"><Template Name="Model"><Field Name="Full_Model_Name">urbs</Field><Field Name="author_institution">TUM EI ENS</Field><Field Name="authors">Johannes Dorfner; Magdalena Dorfner; Soner Candas; Sebastian Müller; Yunus Özsahin; Thomas Zipperle; Simon Herzog</Field><Field Name="contact_persons">Johannes Dorfner</Field><Field Name="contact_email">johannes.dorfner@tum.de</Field><Field Name="website">https://github.com/tum-ens/urbs</Field><Field Name="source_download">https://github.com/tum-ens/urbs</Field><Field Name="text_description">urbs is a linear programming optimisation model for capacity expansion planning and unit commitment for distributed energy systems. Its name, latin for city, stems from its origin as a model for optimisation for urban energy systems. Since then, it has been adapted to multiple scales from neighbourhoods to continents.</Field><Field Name="User documentation">http://urbs.readthedocs.io/</Field><Field Name="Code documentation">http://urbs.readthedocs.io/</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/tum-ens/urbs</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="processing_software">Python (pandas et al)</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy Modelling Framework,</Field><Field Name="sectors">User-dependent, Electricity</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="Market models">N/A</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">User-dependent</Field><Field Name="georesolution">User-dependent</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, net transfer capacities</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear optimization model of a user-defined reference energy system.</Field><Field Name="math_objective">Minimise total discounted cost of system</Field><Field Name="deterministic">None</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">100000</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">20</Field><Field Name="computation_time_comments">Highly dependent on model size (esp. storage) and solver (CPLEX, GLPK, Gurobi)</Field><Field Name="citation_references">Dorfner, Johannes (2016). &quot;Open Source Modelling and Optimisation of Energy Infrastructure at Urban Scale&quot;, doctoral thesis, Technical University of Munich</Field><Field Name="citation_doi">10.5281/zenodo.46118</Field><Field Name="report_references">]]
* [http://mediatum.ub.tum.de/?id=1285570 Open Source Modelling and Optimisation of Energy Infrastructure at Urban Scale]; Johannes Dorfner; doctoral thesis, Technical University of Munich, 2016
* [https://mediatum.ub.tum.de/node?id=1171502 Electricity system optimization in the EUMENA region]; Matthias Huber, Johannes Dorfner, Thomas Hamacher; technical report, Munich, 2012
* [https://mediatum.ub.tum.de/doc/1233948/1233948.pdf Modelling a Low-Carbon Power System for Indonesia, Malaysia and Singapore]; Juergen Stich, Melanie Mannhart, Thomas Zipperle, Tobias Massier, Matthias Huber, Thomas Hamacher; 33rd IEW International Energy Workshop, Peking, 2014
* [http://dx.doi.org/10.1016/j.enpol.2011.12.040 Transmission grid extensions for the integration of variable renewable energies in Europe: Who benefits where?]; Katrin Schaber, Florian Steinke, Thomas Hamacher; Energy Policy, Volume 43, April 2012, 123–135.</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="483" Title="DIETER"><Template Name="Model"><Field Name="Full_Model_Name">Dispatch and Investment Evaluation Tool with Endogenous Renewables</Field><Field Name="Acronym">DIETER</Field><Field Name="author_institution">DIW Berlin</Field><Field Name="authors">Wolf-Peter Schill, Alexander Zerrahn</Field><Field Name="contact_persons">Wolf-Peter Schill, Alexander Zerrahn</Field><Field Name="contact_email">wschill@diw.de</Field><Field Name="website">http://www.diw.de/dieter</Field><Field Name="text_description">The Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) has initially been developed in the research project StoRES to study the role of power storage and other flexibility options in a greenfield setting with high shares of renewables. Meanwhile, several model extensions have been developed and applied to different research questions. The model determines cost-minimizing combinations of power generation, demand-side management, and storage capacities as well as their respective dispatch in both the wholesale and the reserve markets. DIETER thus captures multiple system values of energy storage and other flexibility options related to arbitrage, firm capacity, and reserves. DIETER is an open source model which may be freely used and modified by anyone. The code is licensed under the MIT license, and input data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License. The model is implemented in the General Algebraic Modeling System (GAMS). Running the model thus also requires a GAMS system, an LP solver, and respective licenses.</Field><Field Name="Primary outputs">Capacities, dispatch (and prices)</Field><Field Name="User documentation">http://www.diw.de/dieter</Field><Field Name="Code documentation">http://www.diw.de/dieter</Field><Field Name="Source of funding">Various research projects</Field><Field Name="Number of developers">2 permanent + some temporary ones</Field><Field Name="Number of users">unknown</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS; CPLEX</Field><Field Name="processing_software">MS Excel</Field><Field Name="External optimizer">CPLEX and others</Field><Field Name="GUI">No</Field><Field Name="model_class">Optimization</Field><Field Name="sectors">electricity plus sector coupling (EVs, P2Heat)</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Transport</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">DSM: Detailed representation of load shifting and load curtailment</Field><Field Name="Market models">electricity wholesale and reserve markets</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Exogenous</Field><Field Name="georegions">Initial version: greenfield, loosely calibrated to Germany; central European version also available</Field><Field Name="georesolution">In most applications so far, Germany as one node; version with additional central European country nodes available</Field><Field Name="timeresolution">Hour</Field><Field Name="Additional dimensions (Other)">Solar prosumage</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear cost minimization problem. Decision variables include investment and dispatch of generation, storage, DSM and different sector coupling options including vehicle-grid interactions in both wholesale and balancing markets.</Field><Field Name="math_objective">Cost minimization</Field><Field Name="deterministic">- (work in progress)</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_comments">depends on model specification (seconds to days)</Field><Field Name="citation_references">Zerrahn, A., Schill, W.-P. (2017): Long-run power storage requirements for high shares of renewables: review and a new model. Renewable and Sustainable Energy Reviews 79, 1518-1534</Field><Field Name="citation_doi">https://doi.org/10.1016/j.rser.2016.11.098</Field><Field Name="report_references">https://doi.org/10.1016/j.rser.2017.05.205, 
https://doi.org/10.5547/2160-5890.6.1.wsch, 
https://doi.org/10.1007/s12398-016-0174-7</Field><Field Name="example_research_questions">Which capacities of various flexibility / sector coupling options prove to be optimal under different shares of renewables, and what are their effects on quantities and prices?</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="615" Title="Temoa"><Template Name="Model"><Field Name="Full_Model_Name">Tools for Energy Model Optimization and Analysis</Field><Field Name="author_institution">NC State University</Field><Field Name="authors">Joe DeCarolis, Kevin Hunter, Binghui Li, Sarat Sreepathi</Field><Field Name="contact_persons">Joe DeCarolis</Field><Field Name="contact_email">jdecarolis@ncsu.edu</Field><Field Name="website">http://temoaproject.org/</Field><Field Name="source_download">https://github.com/TemoaProject/temoa/</Field><Field Name="logo">TemoaFinalNoText.png</Field><Field Name="text_description">Tools for Energy Model Optimization and Analysis (Temoa) is an open source framework used to conduct analysis with a bottom-up, technology rich energy system model.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 2.0 (GPL-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="processing_software">SQLite</Field><Field Name="model_class">energy system optimization model</Field><Field Name="sectors">all</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="decisions">investment</Field><Field Name="georegions">U.S., currently</Field><Field Name="georesolution">single region</Field><Field Name="timeresolution">Multi year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">The model objective is to minimize the present cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of exogenously specified end-use demands.</Field><Field Name="math_objective">Cost minimization</Field><Field Name="deterministic">stochastic optimization, moeling-to-generate alternatives</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="computation_time_minutes">5</Field><Field Name="computation_time_comments">varies with chosen solver</Field><Field Name="citation_references">Hunter, K., Sreepathi, S., DeCarolis, J. F. (2013). Modeling for insight using tools for energy model optimization and analysis (TEMOA). Energy Economics, 40, 339-349.</Field><Field Name="citation_doi">10.1016/j.eneco.2013.07.014</Field><Field Name="example_research_questions">1. How does uncertainty in technology-specific characteristics (e.g., capital cost of solar PV) affect outcomes of interest (e.g., fuel prices, fossil fuel consumption, air emissions)?
2. Which technologies and fuels appear to be robust options given uncertainty in future climate
policy and rates of technology learning?
3. How much flexibility exists in energy system design and at what cost?</Field></Template></Page><Page ID="735" Title="PLEXOS Open EU"><Template Name="Model"><Field Name="Full_Model_Name">PLEXOS Open EU</Field><Field Name="author_institution">University College Cork</Field><Field Name="authors">Paul Deane</Field><Field Name="contact_persons">Paul Deane</Field><Field Name="contact_email">jp.deane@ucc.ie</Field><Field Name="website">http://www.ucc.ie/en/energypolicy/</Field><Field Name="source_download">http://wiki.openmod-initiative.org/wiki/Power_plant_portfolios</Field><Field Name="text_description">Full Details available at
http://wiki.openmod-initiative.org/wiki/Power_plant_portfolios</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">PLEXOS</Field><Field Name="processing_software">MS Excel</Field><Field Name="GUI">No</Field><Field Name="model_class">Market Model</Field><Field Name="sectors">Electricity Market,</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">North West Europe</Field><Field Name="georesolution">Member State</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Least Cost Optimization, Can be run in MIP or linear relaxed mode</Field><Field Name="math_objective">Minimize total Generation cost</Field><Field Name="deterministic">None</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">60</Field><Field Name="citation_references">http://www.sciencedirect.com/science/article/pii/S0960148115001640</Field><Field Name="citation_doi">doi:10.1016/j.renene.2015.02.048</Field><Field Name="example_research_questions">Cost of electricity in 2020
Congestion on Lines
Impact of carbon prices</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="820" Title="EnergyNumbers-Balancing"><Template Name="Model"><Field Name="Full_Model_Name">EnergyNumbers-Balancing</Field><Field Name="author_institution">UCL Energy Institute</Field><Field Name="authors">[[user:Andrew ZP Smith</Field><Field Name="1">Andrew ZP Smith]]</Field><Field Name="contact_persons">Andrew ZP Smith</Field><Field Name="contact_email">andrew.smith@ucl.ac.uk</Field><Field Name="website">http://energynumbers.info/balancing</Field><Field Name="source_download">https://github.com/RCUK-CEE/energynumbers-balancing</Field><Field Name="logo">EnergyNumbers-logo400.png</Field><Field Name="text_description">The model uses historic demand data, and historic (half-)hourly capacity factors for PV and wind, to simulate the extent to which demand could be met by some combination of wind, PV and storage. Please do email me if you'd like to request early access to the source, and mention your github username.</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Fortran, PHP, Javascript, HTML, CSS</Field><Field Name="processing_software">Matlab, Python</Field><Field Name="GUI">No</Field><Field Name="model_class">Simulating storage and exogenously-variable renewables</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Renewables</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Britain, Germany, Spain</Field><Field Name="georesolution">National</Field><Field Name="timeresolution">Hour</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">Historic wind/PV capacity factors are scaled up to meet a scenario's specified aggregate penetration levels, and compared to historic (half-)hourly demand. Storage is dispatched period-by-period based on surplus/deficits of energy. Timescale is dependent on country: GB half-hourly; Germany hourly; Spain 10-minutely.</Field><Field Name="deterministic">Deterministic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">28</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">0.00001</Field><Field Name="computation_time_comments">runtime ~1ms to simulate 4.5 years half-hourly</Field><Field Name="example_research_questions">In Britain, how much wind &amp; PV generation would be constrained, and what proportion of demand would get met in real time, assuming half-hourly demand as it was 2011-2015, and X% aggregate wind penetration, Y% aggregate PV penetration, and power-to-gas storage with an input efficiency of A%, an output efficiency of B%, storage capacity of C TWh, an input capacity of D GW, and an output capacity of E GW.</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="856" Title="EMMA"><Template Name="Model"><Field Name="Full_Model_Name">The European Electricity Market Model</Field><Field Name="author_institution">Neon Neue Energieökonomik GmbH</Field><Field Name="authors">Lion Hirth</Field><Field Name="contact_persons">Lion Hirth</Field><Field Name="contact_email">hirth@neon-energie.de</Field><Field Name="website">https://neon-energie.de/emma/</Field><Field Name="source_download">https://neon-energie.de/emma/</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Creative Commons Attribution 3.0 (CC-BY-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS</Field><Field Name="GUI">No</Field><Field Name="model_class">Power market model</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">France, Poland, Belgium, The Netherlands, Germany, Sweden, Norway</Field><Field Name="georesolution">Countries</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear program</Field><Field Name="math_objective">Total system cost</Field><Field Name="deterministic">Sensitivities (many)</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">5</Field><Field Name="computation_time_hardware">PC</Field><Field Name="example_research_questions">Long-term market value of wind and solar power; Optimal share of wind and solar power in electricity generation; Explaining electricity price development</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="862" Title="DESSTinEE"><Template Name="Model"><Field Name="Full_Model_Name">Demand for Energy Services, Supply and Transmission in Europe</Field><Field Name="author_institution">Imperial College London</Field><Field Name="authors">Iain Staffell, Richard Green</Field><Field Name="contact_persons">Iain Staffell</Field><Field Name="contact_email">i.staffell@imperial.ac.uk</Field><Field Name="website">http://tinyurl.com/desstinee</Field><Field Name="source_download">http://tinyurl.com/desstinee</Field><Field Name="text_description">The DESSTINEE model (Demand for Energy Services, Supply and Transmission in EuropE) a model of the European energy system in 2050, with a focus on the electricity system. The model is designed to test assumptions about the technical requirements for energy transport (particularly for electricity), and the scale of the economic challenge to develop the necessary infrastructure.  Forty countries are considered in and around Europe, and 10 forms of primary and secondary energy.  The model uses a predictive simulation technique, rather than solving a partial or general equilibrium.  Data is therefore specified by the user (exogenously), and the model calculates a set of answers for the given set of assumptions.

The DESSTINEE model is available as a set of standalone Excel spreadsheets which perform three tasks:
1.  Project annual energy demands at country-level forwards to 2050;
2.  Synthesise hourly profiles for electricity demand in 2010 and 2050;
3.  Simulate the least-cost generation and transmission of electricity around the continent.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Creative Commons Attribution Share-Alike 3.0 (CC-BY-SA-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Excel / VBA</Field><Field Name="processing_software">Excel / VBA</Field><Field Name="model_class">Simulation</Field><Field Name="sectors">All / Electricity</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Europe, North Africa</Field><Field Name="georesolution">National</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">Annual projection: simple arithmetic
Hourly load curve production: partial decomposition
Electricty system dispatch: Merit order stack with transmission constraints</Field><Field Name="math_objective">Costs, welfare, carbon emissions, fuel mixes</Field><Field Name="deterministic">Stochastic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">Thousands / millions</Field><Field Name="computation_time_minutes">Annual projection = seconds; Load curves = 2 minutes; Power dispatch = minutes to hours</Field><Field Name="citation_references">T. Bossmann and I. Staffell, 2016.  The shape of future electricity demand: Exploring load curves in 2050s Germany and Britain.  Energy, 90(20), 1317–1333.</Field><Field Name="citation_doi">http://dx.doi.org/10.1016/j.energy.2015.06.082</Field><Field Name="example_research_questions">How much transmission will Europe need in 2050
How will electricity demand change in 2050 under different decarbonisation pathways</Field></Template></Page><Page ID="982" Title="Ficus"><Template Name="Model"><Field Name="Full_Model_Name">ficus</Field><Field Name="author_institution">Institute for Energy Economy and Application Technology</Field><Field Name="authors">Dennis Atabay</Field><Field Name="contact_persons">Dennis Atabay</Field><Field Name="contact_email">dennis.atabay@tum.de</Field><Field Name="website">https://github.com/yabata/ficus</Field><Field Name="source_download">https://github.com/yabata/ficus</Field><Field Name="text_description">A (mixed integer) linear optimisation model for local energy systems</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="processing_software">Python (pandas et al)</Field><Field Name="model_class">energy system optimization model,</Field><Field Name="sectors">electricity, heating,...</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="decisions">dispatch, investment</Field><Field Name="timeresolution">15 Minute</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_objective">costs</Field><Field Name="deterministic">None</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="computation_time_minutes">60</Field><Field Name="computation_time_comments">Highly dependent on model size</Field><Field Name="citation_doi">10.5281/zenodo.32077</Field></Template></Page><Page ID="995" Title="Energy Transition Model"><Template Name="Model"><Field Name="Full_Model_Name">Energy Transition Model</Field><Field Name="author_institution">Quintel Intelligence</Field><Field Name="authors">Quintel Intelligence</Field><Field Name="contact_persons">Chael Kruip</Field><Field Name="contact_email">chael.kruip@quintel.com</Field><Field Name="website">www.energytransitionmodel.com</Field><Field Name="source_download">https://github.com/quintel/documentation</Field><Field Name="logo">LogoETM.png</Field><Field Name="text_description">Web-based model based on a holistic description of a country's energy system.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Developed in-house written in Ruby (on Rails)</Field><Field Name="processing_software">Excel / VBA</Field><Field Name="model_class">Demand driven energy model</Field><Field Name="sectors">Households, Buildings, Agriculture, Transport, Industry, Energy</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">EU27, The Netherlands, UK, Poland, France, Germany, Spain, Brazil</Field><Field Name="georesolution">Country</Field><Field Name="timeresolution">Year</Field><Field Name="network_coverage">transmission, distribution, net transfer capacities</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">The ETM is based on an energy graph where nodes can convert one type of energy into another.</Field><Field Name="math_objective">Given demand and other choices, calculate primary energy use, costs, CO2-emission etc.</Field><Field Name="deterministic">The user can assess the impact of almost every input variable and assumption</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">300</Field><Field Name="computation_time_minutes">0.0083333</Field><Field Name="computation_time_comments">From input to output, including communication with the server the calculation takes less than a second (except with very slow internet connections).</Field><Field Name="citation_references">https://github.com/quintel/documentation</Field><Field Name="report_references">http://www.energieakkoordser.nl/~/media/files/energieakkoord/nieuwsberichten/2015/20141212-quintel.ashx, 

http://www.energieakkoordser.nl/~/media/files/energieakkoord/nieuwsberichten/2015/20150210-vergadering/20150210-uitvoeringsagenda.ashx

https://www.youtube.com/watch?v=UMkehKZC3Kc&amp;list=UUwUlayF7P2RnRHFz0_a1v9A&amp;feature=share</Field><Field Name="example_research_questions">What would happen (to reliability, CO2, cost) if we close all non-profitable power plants?

Which combinations of options can we use to reach a certain goal (in sustainability, cost, import dependence etc.)?</Field></Template></Page><Page ID="1090" Title="PyPSA"><Template Name="Model"><Field Name="Full_Model_Name">Python for Power System Analysis</Field><Field Name="Acronym">PyPSA</Field><Field Name="author_institution">FIAS</Field><Field Name="authors">Tom Brown, Jonas Hörsch, David Schlachtberger</Field><Field Name="contact_persons">Tom Brown</Field><Field Name="contact_email">brown@fias.uni-frankfurt.de</Field><Field Name="website">https://www.pypsa.org/</Field><Field Name="source_download">https://github.com/PyPSA/PyPSA</Field><Field Name="text_description">PyPSA is a free software toolbox for simulating and optimising modern energy systems that include features such as variable wind and solar generation, storage units, sector coupling and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series.</Field><Field Name="Support">https://groups.google.com/forum/#!forum/pypsa</Field><Field Name="User documentation">https://pypsa.org/doc/</Field><Field Name="Source of funding">BMBF</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/PyPSA/PyPSA</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python, Pyomo</Field><Field Name="processing_software">Pandas</Field><Field Name="External optimizer">All those supported by Pyomo</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy System Model,</Field><Field Name="sectors">Electricity, Heat, Transport, User-defined</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">Europe, China, South Africa</Field><Field Name="georesolution">User dependent</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, distribution, AC load flow, DC load flow, net transfer capacities</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Non-linear power flow; linear optimal power flow / investment optimisation</Field><Field Name="math_objective">Cost minimization</Field><Field Name="deterministic">Not explicitly covered, but stochastic optimisation possible</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Journal of Open Research Software, 2018, 6 (1)</Field><Field Name="citation_doi">https://doi.org/10.5334/jors.188</Field><Field Name="report_references">https://pypsa.org/publications/</Field><Field Name="example_research_questions">Power flow analysis, market analysis, total system investment optimisation, contingency analysis, sector coupling</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="1359" Title="SIREN"><Template Name="Model"><Field Name="Full_Model_Name">SEN Integrated Renewable Energy Network Toolkit</Field><Field Name="author_institution">Sustainable Energy Now Inc</Field><Field Name="authors">Angus King</Field><Field Name="contact_persons">Angus King</Field><Field Name="contact_email">angusking53@gmail.com</Field><Field Name="website">http://www.sen.asn.au/modelling_overview</Field><Field Name="source_download">https://github.com/ozsolarwind/siren</Field><Field Name="logo">Sen48x48.png</Field><Field Name="text_description">SIREN uses external datasets to model the potential for renewable energy generation for a geographic region. The approach is to model the data on an hourly basis for a desired year (ignoring leap days, that is, 8,760 hours). Users explore potential location and scale of renewable energy sources (stations, storage, transmission) to meet electricity demand. It is possible to model any geographic area and uses a number of open or publicly available data sources:
&lt;ul&gt;
&lt;li&gt;Maps can be created from OpenStreet Map (MapQuest) tiles
&lt;li&gt;Weather data files can be created from NASA (MERRA2) or ECMWF (ERA5) satellite data
&lt;li&gt;It uses US NREL SAM models to calculate energy generation
&lt;/ul&gt;
SIREN is available, packaged for Windows, on Sourceforge (https://sourceforge.net/projects/sensiren/). There's a help file (https://rawgit.com/ozsolarwind/siren/master/help.html) which describes &quot;how it works&quot;.
The program source is available on github (https://github.com/ozsolarwind/siren)</Field><Field Name="User documentation">https://rawgit.com/ozsolarwind/siren/master/help.html</Field><Field Name="Number of developers">1</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Affero General Public License v3 (AGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/ozsolarwind/siren</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python, NREL SAM</Field><Field Name="processing_software">Python</Field><Field Name="GUI">No</Field><Field Name="model_class">Electricity System Model,</Field><Field Name="sectors">Electricity,</Field><Field Name="technologies">Renewables</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georesolution">Individual power stations</Field><Field Name="timeresolution">Hour</Field><Field Name="math_modeltype">Simulation, Other</Field><Field Name="math_modeltype_shortdesc">Uses NREL SAM models to estimate hourly renewable generation for a range/number of renewable energy stations</Field><Field Name="math_objective">Match generation to demand and minimise cost</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="report_references">https://sen.asn.au/modelling/</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="1679" Title="EnergyRt"><Template Name="Model"><Field Name="Full_Model_Name">energy systems modeling R-toolbox</Field><Field Name="authors">Oleg Lugovoy, Vladimir Potashnikov</Field><Field Name="contact_persons">Oleg Lugovoy</Field><Field Name="contact_email">olugovoy@gmail.com</Field><Field Name="website">energyRt.org</Field><Field Name="source_download">https://github.com/olugovoy/energyRt</Field><Field Name="text_description">energyRt is a package for R to develop Reference Energy System (RES) models and analyze energy-technologies. The package includes a standard RES (or &quot;Bottom-Up&quot;) linear, cost-minimizing model, which can be solved by GAMS or GLPK. The model has similarities with TIMES/MARKAL, OSeMOSYS, but has its own specifics, f.i. definition of technologies.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Affero General Public License v3 (AGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS; GLPK</Field><Field Name="processing_software">R</Field><Field Name="model_class">Reference Energy System</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">linear, cost-minimizing, partial equilibrium</Field><Field Name="math_objective">costs</Field><Field Name="deterministic">perfect foresight</Field><Field Name="is_suited_for_many_scenarios">Yes</Field></Template></Page><Page ID="2225" Title="StELMOD"><Template Name="Model"><Field Name="Full_Model_Name">Stochastic Multi-Market Electricity Model</Field><Field Name="Acronym">stELMOD</Field><Field Name="author_institution">DIW Berlin</Field><Field Name="authors">Friedrich Kunz, Jan Abrell</Field><Field Name="contact_persons">Friedrich Kunz</Field><Field Name="contact_email">fkunz@diw.de</Field><Field Name="website">http://www.diw.de/elmod</Field><Field Name="source_download">https://github.com/frkunz/stELMOD</Field><Field Name="text_description">stELMOD is a stochastic optimization model to analyze the impact of uncertain renewable generation on the dayahead and intraday electricity markets as well as network congestion management. The consecutive clearing of the electricity markets is incorporated by a rolling planning procedure resembling the market process of most European markets.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/frkunz/stELMOD</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS</Field><Field Name="processing_software">MS Excel</Field><Field Name="GUI">No</Field><Field Name="model_class">Optimization</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Storage (Electricity)">PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Europe (particular focus on Germany)</Field><Field Name="georesolution">Nodal resolution</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow, net transfer capacities</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Mixed integer linear optimization for separate electricity markets (dayahead, intraday, congestion management) linked by a rolling planning procedure</Field><Field Name="math_objective">Minimization of total generation cost</Field><Field Name="deterministic">deterministic, stochastic</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Abrell, Jan and Kunz, Friedrich (2015): Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market, Networks and Spatial Economics 15(1), pp. 117-147</Field><Field Name="citation_doi">10.1007/s11067-014-9272-4</Field><Field Name="report_references">Kunz, Friedrich, Zerrahn, Alexander (2016): Coordinating Cross-Country Congestion Management. DIW Discussion Paper 1551</Field><Field Name="example_research_questions">Impact of uncertain renewable generation on markets and generation commitment and dispatch; Analysis of congestion management issues and market design options</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="2309" Title="OnSSET"><Template Name="Model"><Field Name="Full_Model_Name">Open Source Spatial Electrification Tool</Field><Field Name="Acronym">OnSSET</Field><Field Name="author_institution">KTH Royal Institute of Technology</Field><Field Name="authors">Andreas Sahlberg, Alexandros Korkovelos, Dimitrios Mentis,  Babak Khavari, Mark Howells, Holger Rogner, Christopher Arderne, Oliver Broad, Manuel Welsch, Francesco Fuso Nerini, Julian Cantor</Field><Field Name="contact_persons">Andreas Sahlberg</Field><Field Name="website">https://www.linkedin.com/company/onsset-open-source-spatial-electrification-tool</Field><Field Name="source_download">https://github.com/onsset</Field><Field Name="logo">Onsset logo 3.png</Field><Field Name="text_description">OnSSET has been designed for identifying least-cost technology options to electrify areas presently unserved by grid-based electricity and to estimate associated investment needs related to electrification. OnSSET uses energy-related data and information on a geographical basis such as settlement sizes and locations, distances from existing and planned transmission network, power plants, economic activity, local renewable energy flows,road network, nighttime light etc.</Field><Field Name="Primary outputs">Optimal Electrification Mix, Investment Needs</Field><Field Name="User documentation">https://onsset.readthedocs.io/en/latest/</Field><Field Name="Source of funding">SIDA, UNDESA, UNDP, IEA, ABB, World Bank, GEAPP, SEforALL</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/OnSSET/onsset</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Python</Field><Field Name="GUI">No</Field><Field Name="sectors">Electricity,</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="georegions">Sub-Saharan Africa, developing Asia, Latin America</Field><Field Name="georesolution">Settlement level</Field><Field Name="timeresolution">Multi year</Field><Field Name="network_coverage">transmission, distribution</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">Greenhouse gas emissions</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Technologies selected based on lowest Levelized Cost of Electricity (LCOE) for each settlement</Field><Field Name="math_objective">Cost minimization</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Mentis, Dimitrios; Welsch, Manuel; Fuso Nerini, Francesco; Broad, Oliver; Howells, Mark; Bazilian, Morgan; Rogner, Holger (December 2015). &quot;A GIS-based approach for electrification planning: a case study on Nigeria&quot;. Energy for Sustainable Development. 29: 142–150. doi:10.1016/j.esd.2015.09.007. ISSN 0973-0826.</Field><Field Name="citation_doi">10.1016/j.esd.2015.09.007</Field><Field Name="report_references">IEA World Energy Outlook 2014, 2015, 2019, 2021, 2022, IEA and World Bank Global Tracking Framework 2015</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="2439" Title="MOCES"><Template Name="Model"><Field Name="Full_Model_Name">Modeling of Complex Energy Systems</Field><Field Name="Acronym">MOCES</Field><Field Name="author_institution">Chair of Automation and Energy Systems (Saarland University)</Field><Field Name="authors">Lukas Exel</Field><Field Name="contact_persons">Lukas Exel</Field><Field Name="contact_email">lukas.exel@aut.uni-saarland.de</Field><Field Name="website">http://tiny.cc/2q07iy</Field><Field Name="logo">Moces logo beta.png</Field><Field Name="text_description">MOCES is a modeling tool that allows a simulative investigation of complex energy systems. It is build on top of the modeling language Modelica. It is not restricted to a specific modeling depth, neither spatial nor temporal. Nevertheless, in the time domain it focuses on dynamics with time constants larger then seconds and in the spatial domain it concentrates on the super ‘entity connected to the grid’ level.</Field><Field Name="Number of developers">1</Field><Field Name="Number of users">1</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Modelica, Dymola, (OpenModelica), C++, MySQL, SQLite</Field><Field Name="processing_software">Lsodar, Dassl</Field><Field Name="Additional software">Modelica Tool like Dymola (tested) or OpenModelica (no testet)</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Energy Modeling Framework</Field><Field Name="sectors">Electricity, User-dependent</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Depends on user</Field><Field Name="georesolution">Depends on user</Field><Field Name="timeresolution">Second</Field><Field Name="Observation period">Less than one month, Less than one year</Field><Field Name="math_modeltype">Simulation, Agent-based</Field><Field Name="math_modeltype_shortdesc">HDAE (Hybrid Differential Equations) combined with  an agent-based approach.</Field><Field Name="deterministic">deterministic, stochastic</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="number_of_variables">100000</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_hardware">standard pc</Field><Field Name="computation_time_comments">strongly depends on the modeling depth and model complexity</Field><Field Name="citation_references">L. Exel, F. Felgner and G. Frey, &quot;Multi-domain modeling of distributed energy systems - The MOCES approach,&quot; 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), Miami, FL, 2015, pp. 774-779.</Field><Field Name="citation_doi">10.1109/SmartGridComm.2015.7436395</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="2495" Title="Dispa-SET"><Template Name="Model"><Field Name="Full_Model_Name">Dispa-SET</Field><Field Name="Acronym">Dispa-SET</Field><Field Name="author_institution">European Commission, Joint Research Centre</Field><Field Name="authors">Sylvain Quoilin, Konstantinos Kavvadias</Field><Field Name="contact_persons">Sylvain Quoilin, Andreas Zucker</Field><Field Name="contact_email">Andreas.ZUCKER@ec.europa.eu</Field><Field Name="website">https://github.com/squoilin</Field><Field Name="source_download">https://joinup.ec.europa.eu/software/dispaset/description</Field><Field Name="text_description">The Dispa-SET model is an open-source unit commitment and dispatch model developed within the “Joint Research Centre” and focused on the balancing and flexibility problems in European grids.</Field><Field Name="Primary outputs">Dispatch and commitment of all units in the power system</Field><Field Name="User documentation">https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/modelling-future-eu-power-systems-under-high-shares-renewables-dispa-set-21-open-source</Field><Field Name="Code documentation">http://dispa-set.readthedocs.io/en/latest/index.html</Field><Field Name="Number of developers">4</Field><Field Name="Number of users">5</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">European Union Public Licence Version 1.1 (EUPL-1.1)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/squoilin/Dispa-SET/archive/master.zip</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo), GAMS</Field><Field Name="processing_software">Python</Field><Field Name="External optimizer">CPLEX</Field><Field Name="GUI">No</Field><Field Name="model_class">EU power system</Field><Field Name="sectors">Power system</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas</Field><Field Name="Energy carrier (Liquid)">Diesel, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">DSM Modelled as a virtual storage unit</Field><Field Name="Market models">Day-Ahead, Ancillary</Field><Field Name="decisions">dispatch</Field><Field Name="Changes in efficiency">Temperature and load dependent</Field><Field Name="georegions">Currently, 7 EU countries</Field><Field Name="georesolution">NUTS1</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">From the same dataset, the model can be expressed as a MILP or LP problem</Field><Field Name="math_objective">Minimization of operational costs</Field><Field Name="deterministic">Through proper sizing of reserve needs</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">30</Field><Field Name="computation_time_hardware">Standard</Field><Field Name="citation_references">Quoilin, S., Hidalgo Gonzalez, I., &amp; Zucker, A. (2017). Modelling Future EU Power Systems Under High Shares of Renewables: The Dispa-SET 2.1 open-source model. Publications Office of the European Union.</Field><Field Name="citation_doi">10.2760/25400</Field><Field Name="report_references">Existing or ongoing case studies for Bolivia, Greece, Ireland, Netherlands, Belgium</Field><Field Name="example_research_questions">Influence of self-consumption and distributed generation; Influence of electric vehicles; Influence of high shares of renewables;  Flexibility provided by DSM and power-to-heat; ...</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="2811" Title="TransiEnt"><Template Name="Model"><Field Name="Full_Model_Name">TransiEnt Library</Field><Field Name="Acronym">TransiEnt</Field><Field Name="author_institution">Hamburg University of Technology</Field><Field Name="authors">Lisa Andresen, Carsten Bode, Pascal Dubucq, Jan-Peter Heckel, Ricardo Peniche, Anne Senkel, Oliver Schülting</Field><Field Name="contact_persons">Carsten Bode, Jan-Peter Heckel, Anne Senkel, Oliver Schülting</Field><Field Name="contact_email">transientlibrary@tuhh.de</Field><Field Name="website">https://www.tuhh.de/transient-ee/en/</Field><Field Name="source_download">https://www.tuhh.de/transient-ee/en/download.html</Field><Field Name="logo">LogoHighQuality.png</Field><Field Name="text_description">The TransiEnt library is written in the Modelica modeling language and allows simulations of coupled energy networks with high share of renewable energies. The library can be downloaded for free and open source under the Modelica License 2. 

The TransiEnt library contains object oriented components models of all major elements of the enegy infrastructure with its corresponding producers, consumers, grids and storage systems. These components can be used to simulate different scenarios from single power plants starting up to the simulation of primary control in the ENTSO-E grid.</Field><Field Name="Primary outputs">Deeper understanding of coupled energy systems and analysis of energy storage options by sector coupling and demand side integration</Field><Field Name="Support">transientlibrary@tuhh.de</Field><Field Name="Framework">Modelica modeling language</Field><Field Name="Source of funding">German Federal Ministry of Economic Affiars and Energy (PTJ 03ET4003)</Field><Field Name="Number of developers">8</Field><Field Name="Number of users">100</Field><Field Name="open_source_licensed">Yes</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://www.tuhh.de/transient-ee/en/download.html</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Modelica</Field><Field Name="processing_software">Dymola</Field><Field Name="Additional software">Visual Studio 2010 / Visual C++ 2010 Express Edition</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Dynamic system simulation model library</Field><Field Name="sectors">electricity, district heating, Gas,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Hydrogen</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">Most consumer models are based on typical profiles and some on underlying pyhsics (e.g. exponential dependency of electric loads to frequency and voltage)</Field><Field Name="Market models">Linear step-by-step merit order model included</Field><Field Name="Changes in efficiency">Efficiency is based on operating state</Field><Field Name="georegions">Hamburg / Germany</Field><Field Name="georesolution">Metropolregion Hamburg</Field><Field Name="timeresolution">Second</Field><Field Name="network_coverage">transmission, distribution, net transfer capacities</Field><Field Name="Observation period">Less than one month, Less than one year</Field><Field Name="Additional dimensions (Ecological)">CO2 Emissions</Field><Field Name="Additional dimensions (Economical)">Total cost of heat and electricity sector</Field><Field Name="Additional dimensions (Other)">Control behaviour, e.g. stability of electric grid frequency control</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">Models in the library are based on differential algebraic equations and are solved using a variable step solver. By using the object oriented Modelica language the library allows an investigation of different timescales and levels of physical detail.</Field><Field Name="deterministic">Prediction errors can be introduced by (filtered) white noise timeseries to see changes in control behaviour</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="number_of_variables">Depending on scenario up to 30 000</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">60</Field><Field Name="computation_time_comments">The Library can be used to simulate single components as wells as complete systems. The values above apply to a coupled example whith a district heating network and a gas network.</Field><Field Name="citation_references">Andresen, Lisa ; Dubucq, Pascal ; Peniche, Ricardo ; Ackermann, Günter ; Kather, Alfons ; Schmitz, Gerhard: Status of the TransiEnt Library: Transient simulation of coupled energy networks with high share of renewable energy. In: Proceedings of the 11th International Modelica Conference. Paris : Modelica Association, 2015, S. 695–705</Field><Field Name="citation_doi">10.3384/ecp15118695</Field><Field Name="report_references">See: https://www.tuhh.de/transient-ee/en/publications.html
for a complete list</Field><Field Name="example_research_questions">* How does the possible amount of hydrogen that can be fed into the gas distribution grid depend on the ambient temperature (considering changes in heating load, gas density and heat of combustion)

* How does the use of synthetic wind inertia technology impact the electric grid stability</Field><Field Name="Integrated models">ClaRa Library</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="3782" Title="SimSES"><Template Name="Model"><Field Name="Full_Model_Name">Simulation of stationary energy storage systems</Field><Field Name="Acronym">SimSES</Field><Field Name="author_institution">Technical University of Munich</Field><Field Name="authors">Marc Möller, Daniel Kucevic, Nils Collath, Anupam Parlikar, Petra Dotzauer, Benedikt Tepe, Stefan Englberger, Martin Cornejo, Andreas Jossen, Holger Hesse, Maik Naumann, Nam Truong</Field><Field Name="contact_persons">Martin Cornejo</Field><Field Name="contact_email">simses.ees@ed.tum.de</Field><Field Name="website">https://www.ei.tum.de/ees/simses/</Field><Field Name="source_download">https://gitlab.lrz.de/open-ees-ses/simses</Field><Field Name="text_description">SimSES provides a library of state-the-art energy storage models by combining modularity of multiple topologies as well as the periphery of an ESS. This paper summarizes the structure as well as the capabilites of SimSES. Storage technology models based on current research for lithium-ion batteries, redox flow batteries, as well as hydrogen storage-based electrolysis and fuel cell are presented in detail. In addition, thermal models and their corresponding HVAC systems, housing, and ambient models are depicted. Power electronics are represented with AC/DC and DC/DC converters mapping the main losses of power electronics within a storage system. Additionally, auxiliary components like pumps, compressors, and HVAC are considered. Standard use cases like peak shaving, residential storage, and control reserve power provisions through dispatch of storage are discussed in this work, with the possibility to stack these applications in a multi-use scenario. The analysis is provided by technical and economic evaluations illustrated by KPIs.</Field><Field Name="User documentation">https://gitlab.lrz.de/open-ees-ses/simses</Field><Field Name="Code documentation">https://gitlab.lrz.de/open-ees-ses/simses</Field><Field Name="Number of developers">6</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://gitlab.lrz.de/open-ees-ses/simses</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Python</Field><Field Name="GUI">No</Field><Field Name="model_class">Electrical energy storage system</Field><Field Name="sectors">Electricity,</Field><Field Name="technologies">Renewables</Field><Field Name="Demand sectors">Households, Industry, Commercial sector, Other</Field><Field Name="Energy carriers (Renewable)">Sun, Wind</Field><Field Name="Storage (Electricity)">Battery, Chemical</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">Load profiles</Field><Field Name="Market models">Profiles</Field><Field Name="decisions">dispatch</Field><Field Name="Changes in efficiency">Temperature, power</Field><Field Name="georegions">World</Field><Field Name="timeresolution">Minute</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="Additional dimensions (Economical)">NPV, ROI, IRR, LCOE</Field><Field Name="Additional dimensions (Other)">Battery aging, battery energy efficiency</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">Power flow and state of charge calculation based on time series profiles</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">&gt;50</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">27</Field><Field Name="computation_time_hardware">Workstation</Field><Field Name="computation_time_comments">20 years with 5 minute time step resolution</Field><Field Name="citation_references">Naumann, Maik; Truong, Cong Nam (2017): SimSES - Software for techno-economic simulation of stationary energy storage systems.</Field><Field Name="citation_doi">10.14459/2017mp1401541</Field><Field Name="report_references">Naumann, M; Truong, C.N.; Schimpe, M.; Kucevic, D.; Jossen, A.; Hesse, H.C. (2017): SimSES: Software for techno-economic Simulation of Stationary Energy Storage Systems. In: VDE-ETG-Kongress 2017. Bonn. Preprint accepted for publication in IEEE Conference Proceedings. http://ieeexplore.ieee.org/document/8278770/

Naumann, M.; Karl, R.Ch.; Truong, C.N.; Jossen, A.; Hesse, H.C. (2015): Lithium-ion Battery Cost Analysis in PV-household Application. In: Energy Procedia 73, S. 37–47. DOI: 10.1016/j.egypro.2015.07.555

Truong, C.; Naumann, M.; Karl, R.; Müller, M.; Jossen, A.; Hesse, H. (2016): Economics of Residential Photovoltaic Battery Systems in Germany. The Case of Tesla’s Powerwall. In: Batteries 2 (2), S. 14–30. DOI: 10.3390/batteries2020014</Field><Field Name="example_research_questions">Optimal system sizing and operation due to battery aging or economic results</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="3930" Title="DynPP"><Template Name="Model"><Field Name="Full_Model_Name">Dynamic Power Plant Model</Field><Field Name="Acronym">DynPP</Field><Field Name="author_institution">University of Rostock</Field><Field Name="authors">Moritz Hübel</Field><Field Name="contact_persons">Moritz Hübel</Field><Field Name="contact_email">moritz.huebel@uni-rostock.de</Field><Field Name="website">ltt-rostock.de</Field><Field Name="text_description">Full Scope Dynamic Simulation Models of different thermal power plants</Field><Field Name="Primary purpose">Startup Optimization</Field><Field Name="Primary outputs">Optimization Strategies, Control Parameters</Field><Field Name="Support">no</Field><Field Name="Framework">Dymola/Modelica</Field><Field Name="User documentation">no</Field><Field Name="Code documentation">no</Field><Field Name="Source of funding">Utility Companies</Field><Field Name="Number of developers">6</Field><Field Name="Number of users">6</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Modelica, Dymola, (OpenModelica), C++, MySQL, SQLite</Field><Field Name="processing_software">Matlab</Field><Field Name="External optimizer">JModelica, Python</Field><Field Name="GUI">No</Field><Field Name="model_class">Specific Power Plants</Field><Field Name="sectors">Coal, Gas, Heat, Electricity,</Field><Field Name="technologies">Conventional Generation, CHP</Field><Field Name="Energy carrier (Gas)">Natural gas</Field><Field Name="Energy carrier (Liquid)">Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="Market models">Fixed Price Market</Field><Field Name="decisions">investment</Field><Field Name="Changes in efficiency">variable</Field><Field Name="georegions">specific plants</Field><Field Name="georesolution">one point</Field><Field Name="timeresolution">Second</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="Observation period">Less than one month</Field><Field Name="Additional dimensions (Ecological)">Emissions, Heat</Field><Field Name="Additional dimensions (Economical)">Electricity, Heat, Ancillary Services</Field><Field Name="Additional dimensions (Social)">Operator Warnings</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">physical-equation-based</Field><Field Name="math_objective">operation, cost, emissions, thermal stress</Field><Field Name="deterministic">Deterministic</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="number_of_variables">10000-40000</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">600</Field><Field Name="computation_time_hardware">Workstation</Field><Field Name="citation_references">Modelling and simulation of a coal-fired power plant for start-up optimisation</Field><Field Name="citation_doi">http://dx.doi.org/10.1016/j.apenergy.2017.10.033</Field><Field Name="report_references">Hübel, M., Meinke, S., Nocke, J., Hassel, E., Identification of Energy Storage Capacities within large-scale Power Plants and Development of Control Strategies to increase marketable Grid Services, ASME 2015 Power and Energy Conversion Conference, June 28-July 2, 2015, San Diego, USA

Hübel, M., Prause, J., Gierow, C., Meinke, M. Hassel, E., Simulation of Ancillary Services in Thermal Power Plants in Energy Systems with High Impact of Renewable Energy, Power Energy Conference 2017, Charlotte, USA</Field><Field Name="example_research_questions">Which potential of Flexibility can be provided by specific thermal power plants?</Field><Field Name="Larger scale usage">Utility Companies</Field><Field Name="Model validation">Measurement Data of reference plants</Field><Field Name="Comment on model validation">agreement with less than 10% to all reliable process measurements</Field><Field Name="Integrated models">Fixed Price Market Environment</Field><Field Name="Interfaces">Dymola FMI</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="4104" Title="ELMOD"><Template Name="Model"><Field Name="Full_Model_Name">Electricity Model</Field><Field Name="Acronym">ELMOD</Field><Field Name="author_institution">Technische Universität Berlin</Field><Field Name="authors">Florian Leuthold, Hannes Weigt, Christian von Hirschhausen, Jonas Egerer, Clemens Gerbaulet, Casimir Lorenz, Jens Weibezahn</Field><Field Name="contact_persons">Jens Weibezahn</Field><Field Name="contact_email">jew@wip.tu-berlin.de</Field><Field Name="website">https://www.diw.de/elmod</Field><Field Name="text_description">The &quot;Electricity Model&quot; (ELMOD) is a deterministic linear or mixed integer dispatch model framework of the German (and European) electricity and co-generation heat sector.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://www.diw.de/elmod</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS</Field><Field Name="External optimizer">CPLEX, GUROBI</Field><Field Name="GUI">No</Field><Field Name="model_class">German and European Electricity Market</Field><Field Name="sectors">Electricity, Heat</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Germany, Europe</Field><Field Name="georesolution">power plant block, transmission network node</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow</Field><Field Name="Observation period">Less than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="4227" Title="GAMAMOD"><Template Name="Model"><Field Name="Full_Model_Name">Gas Market Model</Field><Field Name="Acronym">GAMAMOD</Field><Field Name="author_institution">Technische Universität Dresden (EE2)</Field><Field Name="authors">Lucas De La Fuente; Philipp Hauser</Field><Field Name="contact_persons">Lucas De La Fuente</Field><Field Name="contact_email">mailto:lucas.delafuente@tu-dresden.de</Field><Field Name="website">https://tu-dresden.de/bu/wirtschaft/ee2/forschung/modelle/gamamod?set_language=en</Field><Field Name="source_download">https://doi.org/10.5281/zenodo.14593645</Field><Field Name="text_description">The gas market model GAMAMOD is a bottom-up model used to determine and analyze the optimal natural gas supply structure in Germany and to examine the utilization of the natural gas infrastructure. In its basic version, the model is a Linear Program with  a high spatial resolution and daily time steps. It contains more than 800 nodes and 1200 edges, while also taking into account parallel transmission lines, storage, and changes to demand and the grid as year progress. It's main outputs are optimal flow, imports, storage usage and retrofitting.
GAMAMOD enables the analysis of trading capacities between regional markets. Due to restricted transmission capacities, regional incidences of congestions might occur. The model allows for examining supply interruptions and their impact on the European natural gas system. As each country is modelled as a single aggregated node, no congestions occur within a market area. Furthermore, the model considers natural gas storage, which ensures security of supply in the European natural gas market.
Cyprus and Malta are isolated from the integrated European natural gas pipeline grid. Therefore, they are not considered in the model.</Field><Field Name="Primary outputs">optimal gas flows; retrofitting potential; gas storage; import patterns</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Creative Commons Attribution 4.0 (CC-BY-4.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://doi.org/10.5281/zenodo.14593645</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS; CPLEX</Field><Field Name="GUI">No</Field><Field Name="model_class">German Transmission Grid,</Field><Field Name="sectors">Gas,</Field><Field Name="Demand sectors">Households, Industry, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">Germany</Field><Field Name="georesolution">NUTS0 - NUTS3, for DE</Field><Field Name="timeresolution">Day</Field><Field Name="network_coverage">transmission</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_objective">Total system cost</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Hauser, Philipp (2019) : A modelling approach for the German gas gridusing highly resolved spatial, temporal and sectoral data (GAMAMOD-DE), ZBW – LeibnizInformation Centre for Economics, Kiel, Hamburg</Field><Field Name="citation_doi">http://hdl.handle.net/10419/197000</Field><Field Name="report_references">Hauser, P.; Heidari, S.; Weber, C.; Möst, D.: Does Increasing Natural Gas Demand in the Power Sector Pose a Threat of Congestion to the German Gas Grid? A Model-Coupling Approach, Energies 2019, 12(11) 2159
https://www.mdpi.com/475018</Field><Field Name="example_research_questions">- Sector Coupling between electricity and gas
- Security of Supply in the German gas network
- Retrofitting Potential of German Gas Grid</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="4292" Title="TIMES-PT"><Template Name="Model"><Field Name="Full_Model_Name">Portugal - The Integrated MARKAL-EFOM System</Field><Field Name="Acronym">TIMES-PT</Field><Field Name="author_institution">CENSE - NOVA University Lisbon</Field><Field Name="authors">Simoes, S., Fortes, P.</Field><Field Name="contact_persons">Patrícia Fortes</Field><Field Name="contact_email">p.fs@fct.unl.pt</Field><Field Name="source_download">https://iea-etsap.org/index.php/documentation</Field><Field Name="text_description">TIMES_PT is a technology rich, bottom-up model generator, which uses linear-programming to produce a least-cost energy system to satisfy the demand for energy services, optimized according to a number of user constraints (e.g. CO2 emissions cap), over medium to long-term time horizons. TIMES_PT characterizes the entire chain of the Portuguese energy system from 2005 to 2050 (in 5-year steps), including energy imports and production (e.g., oil and bio refineries), transformation (e.g., power and heat production), distribution, exports and end-use consumption, in industry, residential, services, agriculture and transport sectors and their respective sub-sectors.

TIMES_PT has been developed since 2004 and has benefited from the peer-review of numerous national partners from industrial sectors, power production, oil refining and end-use energy sectors. TIMES_PT model informed climate policy in Portugal in the last 10 years and has supported the design of climate mitigation policies.

The development of the TIMES_PT model started within the EU research project NEEDS and the national research project E2POL. Although its implementation was motivated by research goals, during the past decade the model has become a major tool supporting national climate mitigation policies, and to a lesser extent, air pollution policies. The Low Carbon Roadmap 2050 is a flagship policy document currently used as the Portuguese long term view on mitigation goals, while the PNAC— National Plan on Climate Change includes the visions up to 2030 from stakeholders from other policy areas, as transportation and industry. The negotiations for the revisions of the National Emission Ceilings Directive for 2020 and 2030, as well as the National Strategy for Air Quality (2015) were supported by projections generated by TIMES_PT. More recently, TIMES_PT was linked with a national CGE model, which has motivated its use in the Green Tax Reform.</Field><Field Name="User documentation">https://iea-etsap.org/index.php/documentation</Field><Field Name="Code documentation">https://iea-etsap.org/index.php/documentation</Field><Field Name="Number of developers">3</Field><Field Name="Number of users">5</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">Yes</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS</Field><Field Name="External optimizer">CPLEX</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy supply and demand</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="georegions">Portugal</Field><Field Name="georesolution">National</Field><Field Name="timeresolution">Seasonal</Field><Field Name="network_coverage">transmission, distribution</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">greenhouse gas emissions</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_objective">Minimise total discounted cost of the energy system</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="4299" Title="TIMES Évora"><Template Name="Model"><Field Name="Full_Model_Name">Évora - The Integrated MARKAL-EFOM System</Field><Field Name="Acronym">TIMES - Évora</Field><Field Name="author_institution">CENSE - NOVA University Lisbon</Field><Field Name="authors">Simoes, S., Dias, L.</Field><Field Name="contact_persons">Sofia Simões</Field><Field Name="contact_email">sgcs@fct.unl.pt</Field><Field Name="text_description">The TIMES-Évora is a city specific energy system model, which comprehensively represent Évora municipality energy systems, focusing on energy use in residential and non-residential buildings, transport systems and other energy uses (e.g. public lighting, small-scale industry etc.). It also will represent the city waste chain and water and sewage systems in what concerns its energy consumption. The key objective of the model is the identification of an optimum mix of applicable measures and technologies that will pave the way towards the achievement of the cities’ sustainable targets.</Field><Field Name="User documentation">https://iea-etsap.org/index.php/documentation</Field><Field Name="Code documentation">https://iea-etsap.org/index.php/documentation</Field><Field Name="Number of developers">2</Field><Field Name="Number of users">2</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://iea-etsap.org/index.php/documentation</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS</Field><Field Name="External optimizer">CPLEX</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy supply and demand</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="georegions">Évora (Portugal)</Field><Field Name="georesolution">Municipality</Field><Field Name="timeresolution">Seasonal</Field><Field Name="Additional dimensions (Ecological)">Greenhouse Gas Emissions</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_objective">Minimise total discounted cost of the energy system</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="4305" Title="JMM"><Template Name="Model"><Field Name="Full_Model_Name">Joint Market Model</Field><Field Name="Acronym">JMM</Field><Field Name="author_institution">Risoe National Laboratory; University of Stuttgart; University of Duisburg-Essen</Field><Field Name="authors">Peter Meiborn; Helge V. Larsen; Rüdiger Barth; Heike Brand; Christoph Weber; Oliver Voll</Field><Field Name="website">http://www.wilmar.risoe.dk/Deliverables/Wilmar%20d6_2_b_JMM_doc.pdf</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="open_future">No</Field><Field Name="GUI">No</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="4623" Title="ESO-X"><Template Name="Model"><Field Name="Full_Model_Name">Electricity Systems Optimisation Framework</Field><Field Name="Acronym">ESO, ESO-X, ESO-XEL</Field><Field Name="author_institution">Imperial College London</Field><Field Name="authors">Clara F. Heuberger</Field><Field Name="contact_persons">Clara F. Heuberger</Field><Field Name="contact_email">c.heuberger14@imperial.ac.uk</Field><Field Name="website">https://zenodo.org/record/1048943, https://zenodo.org/record/1212298</Field><Field Name="logo">ESO logo name2.pdf</Field><Field Name="text_description">The Electricity Systems Optimisation (ESO) framework contains a suite of power system capacity expansion and unit commitment models at different levels of spatial and temporal resolution and modelling complexity. Available for download is the single-node model with long-term capacity expansion from 2015 to 2050 in 5 yearly time steps and at hourly discretisation including endogenous technology cost learning (ESO-XEL) as perfect foresight and myopic foresight planning option.</Field><Field Name="Primary outputs">number/type of new power generation+storage units, cost, carbon intensity, utilisation, wind/solar curtailment</Field><Field Name="User documentation">https://zenodo.org/record/1048943</Field><Field Name="Code documentation">https://zenodo.org/record/1048943</Field><Field Name="Number of developers">1</Field><Field Name="Number of users">5</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://zenodo.org/record/1048943</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS; CPLEX</Field><Field Name="processing_software">R</Field><Field Name="GUI">No</Field><Field Name="model_class">power system model</Field><Field Name="sectors">Electricity,</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Energy carrier (Gas)">Natural gas</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Storage (Electricity)">Battery, CAES, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Market models">energy and carbon</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">fixed</Field><Field Name="georegions">UK</Field><Field Name="georesolution">single-node (ESONE: 29 nodes)</Field><Field Name="timeresolution">Hour</Field><Field Name="Additional dimensions (Economical)">ancillary services (reserve, inertia)</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">MILP</Field><Field Name="math_objective">minimise total system cost</Field><Field Name="deterministic">scenario analysis</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">240,000</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">10</Field><Field Name="computation_time_hardware">Intel i7-4770 CPU, 3.4GHz, 8GB RAM</Field><Field Name="computation_time_comments">depends on scenario, e.g., amount of storage capacity</Field><Field Name="citation_references">Heuberger CF, Rubin ES, Staffell I, Shah N, Mac Dowell Nclose, 2017, Power capacity expansion planning considering endogenous technology cost learning, APPLIED ENERGY, Vol: 204, Pages: 831-845, ISSN: 0306-2619</Field><Field Name="citation_doi">https://doi.org/10.1016/j.apenergy.2017.07.075</Field><Field Name="report_references">Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, The changing costs of technology and the optimal investment timing in the power sector

Heuberger CF, Mac Dowell N, 2018, Real-World Challenges with a Rapid Transition to 100% Renewable Power Systems, Joule, Vol: 2, Pages: 367-370 

Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2018, Impact of myopia and disruptive events in power systems planning, Nature Energy, doi:10.1038/s41560-018-0159-3

Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks, COMPUTERS &amp; CHEMICAL ENGINEERING, Vol: 107, Pages: 247-256, ISSN: 0098-1354

Heuberger CF, Staffell I, Shah N, Mac Dowell N, 2017, Valuing Flexibility in CCS Power Plants, IEAGHG Technical Report, http://www.ieaghg.org/exco_docs/2017-09.pdf</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="4728" Title="MEDEAS"><Template Name="Model"><Field Name="Full_Model_Name">Modelling the Energy Development under Environmental and Social constraints</Field><Field Name="Acronym">MEDEAS</Field><Field Name="author_institution">GEEDS group; University of Valladolid (http://www.eis.uva.es/energiasostenible/?lang=en)</Field><Field Name="contact_persons">Jordi Solé</Field><Field Name="contact_email">jsole@icm.csic.es</Field><Field Name="website">http://medeas.eu/</Field><Field Name="source_download">http://medeas.eu/model/medeas-model</Field><Field Name="logo">Logo MEDEAS.png</Field><Field Name="Primary outputs">EROIs; Primary Energy Sources; Final Energy Consumption by sector and type; GHGs emissions by sector and type; material consumption. It is possible to include many other outputs, according to the interests of the user.</Field><Field Name="Support">Yes</Field><Field Name="User documentation">http://medeas.eu/deliverables</Field><Field Name="Source of funding">Funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 691287.</Field><Field Name="Number of developers">Less than 10</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">No</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Phyton</Field><Field Name="processing_software">Phyton</Field><Field Name="Additional software">Vensim DSS software for Windows Version 6.4E (x32)</Field><Field Name="GUI">No</Field><Field Name="sectors">electricity, heat, liquid fuels, gas, solid fuels</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="Changes in efficiency">Apart from coefficients, changes in efficiency are considered by using EROIs and Energy intensities</Field><Field Name="georegions">Global; European Union; Bulgaria; Austria</Field><Field Name="georesolution">global, continents, nations</Field><Field Name="timeresolution">Year</Field><Field Name="Additional dimensions (Ecological)">GHG emissions, water, material and energy requirements</Field><Field Name="Additional dimensions (Social)">Population</Field><Field Name="math_modeltype">Other</Field><Field Name="math_modeltype_shortdesc">System dynamics. Top-down</Field><Field Name="math_objective">CO2 equivalent emissions, energy, social, economic costs, RE-share</Field><Field Name="deterministic">Deterministic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">less than an hour</Field><Field Name="computation_time_hardware">personal computer/laptop</Field><Field Name="Integrated models">G-ROADS</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="4969" Title="GridCal"><Template Name="Model"><Field Name="Full_Model_Name">GridCal</Field><Field Name="Acronym">GridCal</Field><Field Name="authors">Santiago Peñate Vera, Michel Lavoie</Field><Field Name="contact_persons">Santiago Peñate Vera</Field><Field Name="contact_email">santiago.penate.vera@gmail.com</Field><Field Name="website">https://github.com/SanPen/GridCal</Field><Field Name="source_download">https://github.com/SanPen/GridCal.git</Field><Field Name="logo">GridCal banner.png</Field><Field Name="text_description">GridCal is a research oriented power systems software.

Research oriented? How? Well, it is a fruit of research. It is designed to be modular. As a researcher I found that the available software (not even talking about commercial options) are hard to expand or adapt to achieve complex simulations. GridCal is designed to allow you to build and reuse modules, which eventually will boost your productivity and the possibilities that are at hand.</Field><Field Name="Primary outputs">PF, OPF, PF time series, OPF time series, SC, stability, stochastic PF, etc.</Field><Field Name="Support">Linux, Windows, OSX</Field><Field Name="Framework">Python</Field><Field Name="User documentation">https://github.com/SanPen/GridCal/blob/master/Documentation/GridCal/Manual_of_GridCal.pdf</Field><Field Name="Code documentation">https://github.com/SanPen/GridCal/blob/master/Documentation/GridCal/Manual_of_GridCal.pdf</Field><Field Name="Number of developers">2</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/SanPen/GridCal.git</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Python</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Transmission Network Model and Data (input and output),</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Changes in efficiency">Fixed</Field><Field Name="network_coverage">transmission, distribution, AC load flow, DC load flow</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Object oriented structures -&gt; intermediate objects holding arrays -&gt; Numerical modules</Field><Field Name="math_objective">Match generation to demand and minimise cost</Field><Field Name="deterministic">Deterministic, stochastic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">Yes</Field><Field Name="Model validation">Pytest, comparison with commercial and open-source alternatives</Field><Field Name="Specific properties">Compability with multiple formats, GUI, multiple PF algorithms, grid reduction, transformer voltage regulators, voltage controlled generators, conductor resistance temperature correction</Field><Field Name="Integrated models">CIM (Common Information Model v16), PSS/e RAW versions 30, 32 and 33, Matpower, DigSilent .DGS</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="5012" Title="Switch"><Template Name="Model"><Field Name="Full_Model_Name">Switch</Field><Field Name="author_institution">Environmental Defense Fund</Field><Field Name="authors">Matthias Fripp, Josiah Johnston, Rodrigo Henríquez, Benjamín Maluenda</Field><Field Name="contact_persons">Matthias Fripp</Field><Field Name="contact_email">mfripp@edf.org</Field><Field Name="website">http://switch-model.org</Field><Field Name="source_download">https://github.com/switch-model/switch</Field><Field Name="text_description">Switch is a capacity-planning model for power systems with large shares of renewable energy, storage and/or demand response. It optimizes investment decisions for renewable and conventional generation, storage, hydro and other assets, based on how they would be used during a collection of sample days in many future years. The use of multiple investment periods and chronologically sequenced hours enables optimization and assessment of a long-term renewable transition based on a direct consideration of how these resources would be used hour-by-hour. The Switch platform is highly modular, allowing easy selection between prewritten components or addition of custom components as first-class elements in the model.</Field><Field Name="Primary outputs">optimal investment plans, hourly operational details, emissions, costs</Field><Field Name="Support">contact authors via http://switch-model.org</Field><Field Name="Framework">Pyomo</Field><Field Name="User documentation">http://switch-model.org</Field><Field Name="Code documentation">http://switch-model.org</Field><Field Name="Source of funding">U.S. Dept. of Energy, U.S. Enviro. Protection Agency, Ulupono Initiative, Blue Planet Foundation</Field><Field Name="Number of developers">4</Field><Field Name="Number of users">20</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/switch-model/switch</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python, Pyomo</Field><Field Name="processing_software">Python, any user-selected software</Field><Field Name="External optimizer">glpk, cbc, cplex, gurobi, any Pyomo- (or AMPL-) compatible solver</Field><Field Name="GUI">No</Field><Field Name="model_class">Power system capacity expansion, energy system</Field><Field Name="sectors">electricity, gas, hydrologic, transport, end-use demand, carbon sequestration; user-extendable</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">flexible timing of electricity consumption based on price or direct optimization</Field><Field Name="Market models">customer, IOU or RTO; carbon tax, cap-and-trade, RPS or renewable subsidies</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">part-load efficiency and startup fuel are included; users could extend to include environmental factors</Field><Field Name="georesolution">buildings, microgrids, city, state, national or continental</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, distribution, AC load flow, DC load flow, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">currently includes greenhouse gas emissions and land use (based on investment choices); extendable to include other factors</Field><Field Name="Additional dimensions (Economical)">multi-decade NPV, annual capital and O&amp;M expenditure, hourly prices, consumer expenditure, net welfare</Field><Field Name="Additional dimensions (Social)">user-extendable as needed</Field><Field Name="Additional dimensions (Other)">user-extendable as needed</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">intertemporal mathematical optimization</Field><Field Name="math_objective">total cost or consumer surplus, including environmental adders</Field><Field Name="deterministic">stochastic treatment of hourly renewable variability; allocation of reserves for sub-hourly variability; scenarios or progressive hedging for uncertain annual weather or fuel or equipment costs</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">20</Field><Field Name="computation_time_hardware">single-threaded on 3.0 GHz Intel i7 CPU</Field><Field Name="computation_time_comments">computation time is roughly cubic with the spatial and temporal resolution selected; users typically adjust resolution to achieve 2-10 min solution time in testing phases, 10-60 min solution time for final optimizations</Field><Field Name="citation_references">J. Johnston, R. Henríquez, B. Maluenda and M. Fripp “Switch 2.0: a modern platform for planning high-renewable power systems,” Preprint, 2018. https://arxiv.org/abs/1804.05481</Field><Field Name="report_references">&lt;h5&gt;Overview&lt;/h5&gt;
&lt;ul&gt;
&lt;li&gt;Josiah Johnston, Rodrigo Henriquez-Auba, Benjamín Maluenda and Matthias Fripp. [https://doi.org/10.1016/j.softx.2019.100251  &quot;Switch 2.0: A modern platform for planning high-renewable power systems.&quot;] &lt;em&gt;SoftwareX&lt;/em&gt; 10 (2019): 100251.&lt;/li&gt;
&lt;li&gt;Matthias Fripp. [https://doi.org/10.1021/es204645c  &quot;Switch: A Planning Tool for Power Systems with Large Shares of Intermittent Renewable Energy.&quot;] &lt;em&gt;Environmental Science &amp;amp; Technology&lt;/em&gt; 46, no. 11 (2012): 6371-6378.&lt;/li&gt;
&lt;/ul&gt;
&lt;h5&gt;United States and Canada&lt;/h5&gt;
&lt;h6&gt;Full U.S.&lt;/h6&gt;
&lt;ul&gt;
&lt;li&gt;Thuy Doan, Matthias Fripp and Michael Roberts. [https://thuyttdoan.com/publication/wip_gas_pipeline/  &quot;Are We Building Too Much Natural Gas Pipeline? A comparison of actual US expansion of pipeline to an optimized model of the interstate network.&quot;] (2022).&lt;/li&gt;
&lt;/ul&gt;
&lt;h6&gt;Western North America&lt;/h6&gt;
&lt;ul&gt;
&lt;li&gt;James Nelson, Josiah Johnston, Ana Mileva, Matthias Fripp, Ian Hoffman, Autumn Petros-Good, Christian Blanco and Daniel M. Kammen. [https://doi.org/10.1016/j.enpol.2012.01.031  &quot;High-resolution modeling of the western North American power system demonstrates low-cost and low-carbon futures.&quot;] &lt;em&gt;Energy Policy&lt;/em&gt; 43 (2012): 436-447.&lt;/li&gt;
&lt;li&gt;James H. Nelson. [https://www.proquest.com/openview/87e4209361019cbb93bd58f7b4d577fe/1  &quot;Scenarios for Deep Carbon Emission Reductions from Electricity by 2050 in Western North America Using the SWITCH Electric Power Sector Planning Model.&quot;] Ph.D. dissertation,, University of California, Berkeley (2013).&lt;/li&gt;
&lt;li&gt;Ana Mileva, James H. Nelson, Josiah Johnston and Daniel M. Kammen. [https://doi.org/10.1021/es401898f  &quot;SunShot solar power reduces costs and uncertainty in future low-carbon electricity systems.&quot;] &lt;em&gt;Environmental Science &amp;amp; Technology&lt;/em&gt; 47, no. 16 (2013): 9053-9060.&lt;/li&gt;
&lt;li&gt;James Nelson, Ana Mileva, Josiah Johnston, Daniel Kammen, Max Wei and Jeffrey Greenblatt. [https://www.osti.gov/servlets/purl/1163655  &quot;Scenarios for Deep Carbon Emission Reductions from Electricity by 2050 in Western North America using the SWITCH Electric Power Sector Planning Model: California’s Carbon Challenge Phase II Volume II.&quot;] Lawrence Berkeley National Laboratory (2014).&lt;/li&gt;
&lt;li&gt;Ana Mileva. [https://www.proquest.com/openview/e11dff64108db028d29da3e1aa293b89/1  &quot;Greenhouse gas emission reductions, system flexibility requirements, and drivers of storage deployment in the North American power system through 2050.&quot;] Ph.D. dissertation, University of California, Berkeley (2014).&lt;/li&gt;
&lt;li&gt;Josiah Johnston. [https://www.proquest.com/openview/0a39e3cd025b0300a48da62420b29ee2/1  &quot;Open and collaborative climate change mitigation planning for electric power grids.&quot;] Ph.D. dissertation, University of California, Berkeley (2015).&lt;/li&gt;
&lt;li&gt;Daniel L. Sanchez, James H. Nelson, Josiah Johnston, Ana Mileva and Daniel M. Kammen. [https://doi.org/10.1038/nclimate2488  &quot;Biomass enables the transition to a carbon-negative power system across western North America.&quot;] &lt;em&gt;Nature Climate Change&lt;/em&gt; 5, no. 3 (2015): 230-234.&lt;/li&gt;
&lt;li&gt;Daniel L. Sanchez. [https://escholarship.org/uc/item/0rs8n38z  &quot;Deployment, Design, and Commercialization of Carbon-­Negative Energy Systems.&quot;] Ph.D. dissertation, University of California, Berkeley, 2015.&lt;/li&gt;
&lt;li&gt;Ana Mileva, Josiah Johnston, James H. Nelson and Daniel M. Kammen. [https://doi.org/10.1016/j.apenergy.2015.10.180  &quot;Power system balancing for deep decarbonization of the electricity sector.&quot;] &lt;em&gt;Applied Energy&lt;/em&gt; 162 (2016): 1001-1009.&lt;/li&gt;
&lt;li&gt;Patricia Hidalgo-Gonzalez. [https://www.proquest.com/openview/52ddd01f70958eeaaf63b3f9730688af/  &quot;Learning and Control Systems for the Integration of Renewable Energy into Grids of the Future.&quot;] Ph.D. dissertation, University of California, Berkeley (2020).&lt;/li&gt;
&lt;li&gt;Julia Szinai, David N. Yates, Patricia Hidalgo-Gonzalez, Daniel M. Kammen, Ranjit Deshmukh, and Andrew D. Jones. [https://ui.adsabs.harvard.edu/abs/2020AGUFMGC064..07S/abstract  &quot;Evaluating Climate Change Adaptation Strategies for Electricity and Water Systems in the Western US with a Cross-Sectoral Energy-Water Nexus Modeling Approach.&quot;] In &lt;em&gt;AGU Fall Meeting Abstracts&lt;/em&gt;, vol. 2020: (2020) pp. GC064-07.&lt;/li&gt;
&lt;li&gt;Julia Katalin Szinai. [https://www.proquest.com/openview/bad48e313f3c68e1f82b354616187f3d  &quot;Crossed wires: Cross-sectoral dynamics of planning climate-resilient electricity systems.&quot;] University of California, Berkeley, 2021.&lt;/li&gt;
&lt;li&gt;Patricia L. Hidalgo-Gonzalez, Josiah Johnston and Daniel M. Kammen. [https://doi.org/10.1016/j.tej.2021.106925  &quot;Cost and impact of weak medium term policies in the electricity system in Western North America.&quot;] &lt;em&gt;The Electricity Journal&lt;/em&gt; 34, no. 3 (2021): 106925.&lt;/li&gt;
&lt;li&gt;Rodrigo Marti Henriquez Auba. [https://digitalassets.lib.berkeley.edu/techreports/ucb/incoming/EECS-2022-264.pdf  &quot;Challenges on Decarbonization of Electric Power Systems.&quot;] (2022).&lt;/li&gt;
&lt;li&gt;P.A. Sánchez-Pérez, Martin Staadecker, Julia Szinai, Sarah Kurtz and Patricia Hidalgo-Gonzalez. [https://doi.org/10.1016/j.apenergy.2022.119022  &quot;Effect of modeled time horizon on quantifying the need for long-duration storage.&quot;] &lt;em&gt;Applied Energy&lt;/em&gt; 317 (2022): 119022.&lt;/li&gt;
&lt;li&gt;Natalia Gonzalez, Paul Serna-Torre, Pedro Sanchez-Perez, Ryan Davidson, Bryan Murray, Martin Staadecker, Julia Szinai, Rachel Wei, Daniel Kammen, Deborah Sunter and Patricia Hidalgo-Gonzalez. [https://doi.org/10.21203/rs.3.rs-3353442/v1  &quot;Offshore Wind and Wave Energy Can Reduce Total Installed Capacity Required in Zero Emissions Grids.&quot;] (2023).&lt;/li&gt;
&lt;/ul&gt;
&lt;h6&gt;Texas&lt;/h6&gt;
&lt;ul&gt;
&lt;li&gt;Joshua D. Rhodes, Thomas Deetjen and Caitlin Smith. [https://www.ideasmiths.net/wp-content/uploads/2022/02/LANCIUM_IS_ERCOT_flexDC_FINAL_2021.pdf  &quot;Impacts of Large, Flexible Data Center Operations on the Future of ERCOT.&quot;] Lancium White Paper. (2021).&lt;/li&gt;
&lt;li&gt;Joshua D. Rhodes and Thomas Deetjen. [https://www.ideasmiths.net/wp-content/uploads/2021/07/APA_IS_ERCOT_grid_FINAL.pdf  &quot;Least-cost optimal expansion of the ERCOT grid.&quot;] IdeaSmiths LLC. (2021).&lt;/li&gt;
&lt;li&gt;Sarah Emilee Dodamead.[https://repositories.lib.utexas.edu/handle/2152/118332  &quot;Exploring the Trade-offs Between Battery Storage and Transmission for the Electrical Grid.&quot;] PhD diss., 2022.&lt;/li&gt;
&lt;/ul&gt;
&lt;h6&gt;California&lt;/h6&gt;
&lt;ul&gt;
&lt;li&gt;Matthias Fripp [https://search.proquest.com/openview/615beec4b81f803b0332ac6182b0c5a3/1  &quot;Optimal investment in wind and solar power in California.&quot;] Ph.D. dissertation, University of California, Berkeley (2008).&lt;/li&gt;
&lt;li&gt;Max Wei, James H. Nelson, Michael K. Ting, Christopher Yang, Jeffery B. Greenblatt, James E. McMahon, Daniel M. Kammen, Christopher M. Jones, Ana Mileva, Josiah Johnston and Ranjit Bharvirkar. [https://eta.lbl.gov/publications/california-s-carbon-challenge-0  &quot;California’s Carbon Challenge: Scenarios for Achieving 80% Emissions Reduction in 2050.&quot;] Lawrence Berkeley National Laboratory (2012).&lt;/li&gt;
&lt;li&gt;Max Wei, James H. Nelson, Jeffery B. Greenblatt, Ana Mileva, Josiah Johnston, Michael Ting, Christopher Yang, Chris Jones, James E McMahon and Daniel M Kammen. [https://doi.org/10.1088/1748-9326/8/1/014038  &quot;Deep carbon reductions in California require electrification and integration across economic sectors.&quot;] &lt;em&gt;Environmental Research Letters&lt;/em&gt; 8, no. 1 (2013): 014038.&lt;/li&gt;
&lt;li&gt;Daniel M. Kammen, Blas L. Pérez Henrıquez and Josiah Johnston. [https://books.google.com/books?hl=en&amp;amp;lr=&amp;amp;id=fWEKBAAAQBAJ&amp;amp;oi=fnd&amp;amp;pg=PA175&amp;amp;dq=info:a3ke3QRGWMsJ:scholar.google.com&amp;amp;ots=bO0uZOvn7V&amp;amp;sig=h0F8DsLso1IrWL5EJ69I7RKtrFA#v=onepage&amp;amp;q&amp;amp;f=false  &quot;California’s climate policy and the development of clean energy systems institutional foundations.&quot;] Carbon governance, climate change and business transformation (2014): 175-187.&lt;/li&gt;
&lt;li&gt;Max Wei, Jeffrey Greenblatt, Sally Donovan, James Nelson, Ana Mileva, Josiah Johnston and Daniel Kammen. [https://escholarship.org/uc/item/8gr134wb  &quot;Scenarios for Meeting California's 2050 Climate Goals: California's Carbon Challenge Phase II Volume I: Non-Electricity Sectors and Overall Scenario Results.&quot;] Lawrence Berkeley National Laboratory report no. LBNL-6743E (2014).&lt;/li&gt;
&lt;li&gt;Geoff Morrison, Sonia Yeh, Anthony R Eggert, Christopher Yang, James Nelson, Jeffery Greenblatt, Raphael Isaac, Mark Z Jacobson, Josiah Johnston, Daniel M Kammen, Ana Mileva, Jack Moore, David Roland-Holst, Max Wei, John Weyant, James Williams, Ray Williams and Christina Zapata. [https://citeseerx.ist.psu.edu/document?repid=rep1&amp;amp;type=pdf&amp;amp;doi=9b9d431ffd63f753b8ae7612b0a1cc9e4c0d77b4  &quot;Long-term Energy Planning In California: Insights and Future Modeling Needs.&quot;] U.C. Davis Inst. of Transportation Studies report no. UCD-ITS-RR-14-08 (2014).&lt;/li&gt;
&lt;li&gt;Geoffrey M. Morrison, Sonia Yeh, Anthony R. Eggert, Christopher Yang, James H. Nelson, Jeffery B. Greenblatt, Raphael Isaac, Mark Z. Jacobson, Josiah Johnston, Daniel M. Kammen, Ana Mileva, Jack Moore, David Roland-Holst, Max Wei, John P. Weyant, James H. Williams, Ray Williams and Christina B. Zapata. [https://doi.org/10.1007/s10584-015-1403-5  &quot;Comparison of low-carbon pathways for California.&quot;] &lt;em&gt;Climatic Change&lt;/em&gt; 131 (2015): 545-557&lt;/li&gt;
&lt;li&gt;Max Wei, Shuba V. Raghavan and Patricia Hidalgo-Gonzalez. [https://www.energy.ca.gov/publications/2019/building-healthier-and-more-robust-future-2050-low-carbon-energy-scenarios  &quot;Building a Healthier and More Robust Future: 2050 Low-Carbon Energy Scenarios for California.&quot;] California Energy Commission report no. CEC-500-2019-033 (2019).&lt;/li&gt;
&lt;li&gt;P. A. Sanchez-Perez, Sarah Kurtz, Natalia Gonzalez, Martin Staadecker and Patricia Hidalgo-Gonzalez. [https://doi.org/10.1109/EESAT55007.2022.9998031  &quot;Effect of Time Resolution on Capacity Expansion Modeling to Quantify Value of Long-Duration Energy Storage.&quot;] &lt;em&gt;2022 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)&lt;/em&gt; (2022).&lt;/li&gt;
&lt;/ul&gt;
&lt;h6&gt;Hawaii&lt;/h6&gt;
&lt;ul&gt;
&lt;li&gt;Matthias Fripp. [https://uhero.hawaii.edu/wp-content/uploads/2019/08/WP_2016-1.pdf  &quot;Making an Optimal Plan for 100% Renewable Power in Hawai‘i - Preliminary Results from the SWITCH Power System Planning Model.&quot;] UHERO Working Paper No. 2016-1 (2016).&lt;/li&gt;
&lt;li&gt;Matthias Fripp. [https://uhero.hawaii.edu/wp-content/uploads/2019/08/WP_2017-3.pdf  &quot;Effect of Electric Vehicles on Design, Operation and Cost of a 100% Renewable Power System.&quot;]  UHERO Working Paper No. 2017-3 (2017).&lt;/li&gt;
&lt;li&gt;Imelda, Matthias Fripp and Michael J. Roberts. [https://www.nber.org/papers/w24712  &quot;Variable pricing and the cost of renewable energy.&quot;] No. w24712. National Bureau of Economic Research, 2018.&lt;/li&gt;
&lt;li&gt;Matthias Fripp. [https://doi.org/10.1186/s13705-018-0184-x  &quot;Intercomparison between Switch 2.0 and GE MAPS models for simulation of high-renewable power systems in Hawaii.&quot;] (2018).&lt;/li&gt;
&lt;li&gt;John Larsen, Shashank Mohan, Whitney Herndon, Peter Marsters, and Hannah Pitt. [https://rhg.com/wp-content/uploads/2018/04/rhodium_transcendingoil_final_report_4-18-2018-final.pdf  &quot;Transcending Oil: Hawaii’s Path to a Clean Energy Economy.&quot;] Rhodium Group (2018).&lt;/li&gt;
&lt;/ul&gt;
&lt;h5&gt;Latin America&lt;/h5&gt;
&lt;h6&gt;Chile&lt;/h6&gt;
&lt;ul&gt;
&lt;li&gt;Juan Pablo Carvallo, Patricia Hidalgo-González and Daniel M Kammen. [https://www.nrdc.org/sites/default/files/envisioning-sustainable-chile-report-sp.pdf  &quot;Imaginando un Chile sustentable.&quot;] Natural Resources Defense Council (NRDC), 2014.&lt;/li&gt;
&lt;li&gt;Juan Pablo Carvallo, Patricia Hidalgo-González and Daniel M Kammen. [https://www.nrdc.org/sites/default/files/envisioning-sustainable-chile-report.pdf  &quot;Envisioning a sustainable Chile: Five findings about the future of the Chilean electricity and energy system.&quot;] Natural Resources Defense Council (NRDC), 2014.&lt;/li&gt;
&lt;li&gt;Daniel M. Kammen, Rebekah Shirley, Juan Pablo Carvallo and Diego Ponce de Leon Barido.[https://clas.berkeley.edu/sites/default/files/publications/brlasspring2014-kammenetal.pdf  &quot;Switching to Sustainability.&quot;] U.C. Berkeley Center for Latin American Studies (2014).&lt;/li&gt;
&lt;li&gt;Benjamín Maluenda Philippi. [https://doi.org/10.7764/tesisUC/ING/21412  &quot;Expansion planning under long-term uncertainty for hydrothermal systems with volatile resources.&quot;] (2017).&lt;/li&gt;
&lt;li&gt;Benjamín Maluenda Philippi, Matias Negrete-Pincetic, Daniel E. Olivares, and Álvaro Lorca. [https://doi.org/10.1016/j.ijepes.2018.06.008  &quot;Expansion planning under uncertainty for hydrothermal systems with variable resources.&quot;] &lt;em&gt;International Journal of Electrical Power &amp;amp; Energy Systems&lt;/em&gt; 103 (2018): 644-651.&lt;/li&gt;
&lt;li&gt;Felipe Verástegui, Álvaro Lorca, Matias Negrete-Pincetic and Daniel Olivares. [https://doi.org/10.1016/j.enpol.2020.111702  &quot;Firewood heat electrification impacts in the Chilean power system.&quot;] &lt;em&gt;Energy Policy&lt;/em&gt; 144 (2020): 111702.&lt;/li&gt;
&lt;li&gt;Felipe Verástegui, Álvaro Lorca, Daniel Olivares and Matias Negrete-Pincetic. [https://doi.org/10.1016/j.energy.2021.121242  &quot;Optimization-based analysis of decarbonization pathways and flexibility requirements in highly renewable power systems.&quot;] &lt;em&gt;Energy&lt;/em&gt; 234 (2021): 121242.&lt;/li&gt;
&lt;li&gt;José Miguel Valdes, Álvaro Lorca, Cristian Salas, Francisco Pinto, Rocío Herrera, Alejandro Bañados, Raúl Urtubia, Patricio Castillo, Lucas Maulén and Diego González. [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4481537  &quot;Greenhouse Gas Mitigation Beyond the Nationally Determined Contributions in Chile: An Assessment of Alternatives.&quot;] &lt;em&gt;SSRN Electronic Journal&lt;/em&gt; (2023).&lt;/li&gt;
&lt;/ul&gt;
&lt;h6&gt;Mexico&lt;/h6&gt;
&lt;ul&gt;
&lt;li&gt;Sergio Castellanos, Pedro Sanchez-Perez, Aldo Pasos-Trejo, Mateo Torres, Josiah Johnston, Apollo Jain, Alejandra Monroy, Florin James-Langer, Diego Ponce de Leon, Oliver Probst and Daniel M. Kammen. [https://doi.org/10.1109/PVSC.2018.8548261  &quot;Modeling high-penetration of clean energy in the electrical grid: A case for Mexico.&quot;] &lt;em&gt;2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC &amp;amp; 34th EU PVSEC)&lt;/em&gt; (2018).&lt;/li&gt;
&lt;li&gt;Sergio Castellanos, Apollo Jain, James Adam Mahady and Daniel M. Kammen. [https://ui.adsabs.harvard.edu/abs/2019AGUFMGC31O1295C/abstract  &quot;Vehicle Electrification in Mexico: Evaluating Long-Term Grid Capacity Planning and Interplay with Renewable Deployment.&quot;] In AGU Fall Meeting Abstracts, vol. 2019, pp. GC31O-1295. 2019.&lt;/li&gt;
&lt;/ul&gt;
&lt;h6&gt;Nicaragua&lt;/h6&gt;
&lt;ul&gt;
&lt;li&gt;Diego Ponce de Leon Barido, Josiah Johnston, Maria V Moncada, Duncan Callaway and Daniel M Kammen. [http://dx.doi.org/10.1088/1748-9326/10/10/104002  &quot;Evidence and future scenarios of a low-carbon energy transition in Central America: a case study in Nicaragua.&quot;] &lt;em&gt;Environmental Research Letters&lt;/em&gt; 10, no. 10 (2015): 104002.&lt;/li&gt;
&lt;/ul&gt;
&lt;h5&gt;China&lt;/h5&gt;
&lt;ul&gt;
&lt;li&gt;Gang He, Anne-Perrine Avrin, James Nelson, Jianwei Tian, Josiah Johnston, Ana Mileva and Daniel Kammen. [https://www.iaee.org/proceedings/article/8118  &quot;China’s Ability to Achieve National Energy Objectives Depends on Coordination of Infrastructure and Policy Initiatives.&quot;] 37th IAEE International Conference on Energy &amp;amp; the Economy (2014).&lt;/li&gt;
&lt;li&gt;Gang He. [https://www.proquest.com/openview/61b3c7808fcb8c90624160ab4dfb5d46/1  &quot;Decarbonizing China's Power Sector: Potential, Prospects and Policy.&quot;] Ph.D. dissertation, University of California, Berkeley (2015).&lt;/li&gt;
&lt;li&gt;Gang He, Anne-Perrine Avrin, James H. Nelson, Josiah Johnston, Ana Mileva, Jianwei Tian, and Daniel M. Kammen. [https://doi.org/10.1021/acs.est.6b01345  &quot;SWITCH-China: A Systems Approach to Decarbonizing China’s Power System.&quot;] Environmental Science &amp;amp; Technology 50, no. 11 (2016): 5467-5473.&lt;/li&gt;
&lt;li&gt;Anne-Perrine Avrin, Scott J. Moura and Daniel M. Kammen. [https://doi.org/10.1109/APPEEC.2016.7779459  &quot;Minimizing cost uncertainty with a new methodology for use in policy making: China's electricity pathways.&quot;] &lt;em&gt;2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)&lt;/em&gt; (2016).&lt;/li&gt;
&lt;li&gt;Anne-Perrine Avrin [https://search.proquest.com/openview/bfe875ed2b6934329657ccf46bba00c4/1  &quot;China's Power Sector Decarbonization: Modeling Emission Reduction Potential, Technical Feasibility and Cost Efficiency of Inter-sectoral Approaches.&quot;] Ph.D. dissertation, University of California, Berkeley (2018).&lt;/li&gt;
&lt;li&gt;Gang He, Jiang Lin, Froylan Sifuentes, Xu Liu, Nikit Abhyankar and Amol Phadke. [https://eta-publications.lbl.gov/sites/default/files/rapid_cost_decrease_of_renewable_energy_and_storage_offers_an_opportunity_to_accelerate_the_decarbonization_of_chinas_power_system_lbnl-2001357.pdf  &quot;Rapid cost decrease of renewable energy and storage offers an opportunity to accelerate the decarbonization of China’s power system.&quot;] Lawrence Berkeley National Laboratory (2020).&lt;/li&gt;
&lt;li&gt;Gang He, Jiang Lin, Froylan Sifuentes, Xu Liu, Nikit Abhyankar, and Amol Phadke. [https://doi.org/10.1038/s41467-020-16184-x  &quot;Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system.&quot;] &lt;em&gt;Nature Communications&lt;/em&gt; 11, no. 1 (2020): 2486.&lt;/li&gt;
&lt;li&gt;Gang He, Jiang Lin, Froylan Sifuentes, Xu Liu, Nikit Abhyankar and Amol Phadke. [https://doi.org/10.1038/s41467-020-16184-x  &quot;Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system.&quot;] &lt;em&gt;Nature Communications&lt;/em&gt; 11, no. 1 (2020).&lt;/li&gt;
&lt;li&gt;Bo Li, Ziming Ma, Gang He, Patricia Hidalgo-Gonzalez, Natalie Fedorova, Minyou Chen and Daniel M. Kammen. [https://dx.doi.org/10.2139/ssrn.3699159  &quot;Offshore Wind Replaces Coal and Reduces Transmission, Enabling China to Meet the 1.5°C Climate Imperative.&quot;] &lt;em&gt;SSRN Electronic Journal&lt;/em&gt; (2020).&lt;/li&gt;
&lt;li&gt;Bo Li, Ziming Ma, Patricia Hidalgo-Gonzalez, Alex Lathem, Natalie Fedorova, Gang He, Haiwang Zhong, Minyou Chen and Daniel M. Kammen. [https://doi.org/10.1016/j.enpol.2020.111962  &quot;Modeling the impact of EVs in the Chinese power system: Pathways for implementing emissions reduction commitments in the power and transportation sectors.&quot;] &lt;em&gt;Energy Policy&lt;/em&gt; 149 (2021): 111962.&lt;/li&gt;
&lt;li&gt;Guangzhi Yin, Bo Li, Natalie Fedorova, Patricia Hidalgo-Gonzalez, Daniel M. Kammen and Maosheng Duan. [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3767160  &quot;Accelerating China’s Fossil Fuel Plant Retirement and Renewable Energy Expansion via Capacity Mechanism.&quot;] &lt;em&gt;SSRN Electronic Journal&lt;/em&gt; (2021).&lt;/li&gt;
&lt;li&gt;Guangzhi Yin, Bo Li, Natalie Fedorova, Patricia Hidalgo-Gonzalez, Daniel M. Kammen and Maosheng Duan. [https://doi.org/10.1016/j.isci.2021.103287  &quot;Orderly retire China's coal-fired power capacity via capacity payments to support renewable energy expansion.&quot;] &lt;em&gt;iScience&lt;/em&gt; 24, no. 11 (2021): 103287.&lt;/li&gt;
&lt;li&gt;Chao Zhang, Gang He, Josiah Johnston and Lijin Zhong. [https://doi.org/10.1016/j.jclepro.2021.129765  &quot;Long-term transition of China's power sector under carbon neutrality target and water withdrawal constraint.&quot;] &lt;em&gt;Journal of Cleaner Production&lt;/em&gt; 329 (2021): 129765.&lt;/li&gt;
&lt;li&gt;Xiaoli Zhang, Xueqin Cui, Bo Li, Patricia Hidalgo-Gonzalez, Daniel M Kammen, Ji Zou and Ke Wang. [https://doi.org/10.1016/j.apenergy.2021.118401  &quot;Immediate actions on coal phaseout enable a just low-carbon transition in China’s power sector.&quot;] &lt;em&gt;Applied Energy&lt;/em&gt; 308 (2022): 118401.&lt;/li&gt;
&lt;li&gt;Jiang Lin, Nikit Abhyankar, Gang He, Xu Liu and Shengfei Yin. [https://doi.org/10.1016/j.isci.2022.103749  &quot;Large balancing areas and dispersed renewable investment enhances grid flexibility in a renewable-dominant power system in China.&quot;] &lt;em&gt;iScience&lt;/em&gt; 25, no. 2 (2022): 103749.&lt;/li&gt;
&lt;li&gt;Liqun Peng, Denise L. Mauzerall, Yaofeng D. Zhong and Gang He. [https://doi.org/10.1038/s41467-023-40337-3  &quot;Heterogeneous effects of battery storage deployment strategies on decarbonization of provincial power systems in China.&quot;] &lt;em&gt;Nature Communications&lt;/em&gt; 14, no. 1 (2023).&lt;/li&gt;
&lt;/ul&gt;
&lt;h5&gt;Japan&lt;/h5&gt;
&lt;ul&gt;
&lt;li&gt;Tatsuya Wakeyama. [http://sw.pg2.at/abstracts/a0106.html  &quot;Impact of Increasing Share of Renewables on the Japanese Electricity System - Model Based Analysis.&quot;] Energynautics GmbH, Brussels (2015).&lt;/li&gt;
&lt;li&gt;Rena Kuwahata, Peter Merk, Tatsuya Wakeyama, Dimitri Pescia, Steffen Rabe and Shota Ichimura. [https://doi.org/10.1049/iet-rpg.2019.0711  &quot;Renewables integration grid study for the 2030 Japanese power system.&quot;] &lt;em&gt;IET Renewable Power Generation&lt;/em&gt; 14, no. 8 (2020): 1249-1258.&lt;/li&gt;
&lt;/ul&gt;
&lt;h5&gt;Laos&lt;/h5&gt;
&lt;ul&gt;
&lt;li&gt;Nkiruka Avila, Noah Kittner, Rebekah Shirley, Michael B. Dwyer, David Roberts, Jalel Sager and Daniel M. Kammen. [https://orbi.uliege.be/bitstream/2268/255697/1/2020%20Resource%20Governance_LMPPI_Tinh_Minh%20%281%29.pdf  &quot;Beyond the Battery: Power Expansion Alternatives for Economic Resilience and Diversity in Laos.&quot;] &lt;em&gt;Resource Governance, Agriculture and Sustainable Livelihoods in the Lower Mekong Basin&lt;/em&gt; (2019): 27-65.&lt;/li&gt;
&lt;li&gt;Aaditee Kudrimoti, Alex Lathem, Rachel Ng and Ashley Yip. [https://researchmap.jp/danidelbarrioalvarez/academic_contribution/30436560/attachment_file.pdf  &quot;SWITCH-Laos: Power Systems Investment Planning for Economic Resilience in Laos.&quot;] 14th GMSARN&lt;/li&gt;
&lt;li&gt;International Conference (2019).&lt;/li&gt;
&lt;/ul&gt;
&lt;h5&gt;India&lt;/h5&gt;
&lt;ul&gt;
&lt;li&gt;Chao Zhang, Joonseok Yang, Johannes Urpelainen, Puneet Chitkara, Jiayi Zhang and Jiao Wang. [https://doi.org/10.1021/acs.est.0c08724  &quot;Thermoelectric Power Generation and Water Stress in India: A Spatial and Temporal Analysis.&quot;] &lt;em&gt;Environmental Science &amp;amp; Technology&lt;/em&gt; 55, no. 8 (2021): 4314-4323.&lt;/li&gt;
&lt;/ul&gt;
&lt;h5&gt;Kenya&lt;/h5&gt;
&lt;ul&gt;
&lt;li&gt;Daniel Kammen and Brooke Maushund. [https://rael.berkeley.edu/wp-content/uploads/2017/03/ARF-2017-OUTCOMES.pdf  &quot;Renewable Electrification and Integration Implementation Strategies.&quot;] in The Path 2021: Outcomes of the Africa Renewable Energy Forum (2016).&lt;/li&gt;
&lt;li&gt;Juan-Pablo Carvallo, Brittany J. Shaw, Nkiruka I. Avila and Daniel M. Kammen. [https://doi.org/10.1021/acs.est.7b00345  &quot;Sustainable Low-Carbon Expansion for the Power Sector of an Emerging Economy: The Case of Kenya.&quot;] &lt;em&gt;Environmental Science &amp;amp; Technology&lt;/em&gt; 51 no. 17 (2017): 10232–10242.&lt;/li&gt;
&lt;li&gt;Juan Pablo Carvallo. [https://escholarship.org/uc/item/2qh8d0ng  &quot;Strategic Planning for Universal Electricity Access.&quot;] Ph.D. dissertation, University of California, Berkeley (2019).&lt;/li&gt;
&lt;li&gt;Juan-Pablo Carvallo, Jay Taneja, Duncan Callaway and Daniel M. Kammen. [https://doi.org/10.1109/JPROC.2019.2925759  &quot;Distributed Resources Shift Paradigms on Power System Design, Planning, and Operation: An Application of the GAP Model.&quot;] &lt;em&gt;Proceedings of the IEEE&lt;/em&gt; 107, no. 9 (2019): 1906-1922.&lt;/li&gt;
&lt;li&gt;Juan Pablo Carvallo, Nan Zhang, Sean P. Murphy, Benjamin D. Leibowicz and Peter H. Larsen. [https://doi.org/10.1016/j.apenergy.2020.115071  &quot;The economic value of a centralized approach to distributed resource investment and operation.&quot;] &lt;em&gt;Applied Energy&lt;/em&gt; 269 (2020): 115071.&lt;/li&gt;
&lt;/ul&gt;
&lt;h5&gt;Spain&lt;/h5&gt;
&lt;ul&gt;
&lt;li&gt;Gustavo Gomes Pereira. [https://upcommons.upc.edu/handle/2117/332163  &quot;Power System Modelling: A techno-economic analysis of the island of Menorca, Spain.&quot;]. Master's thesis, Universitat Politècnica de Catalunya (2020).&lt;/li&gt;
&lt;/ul&gt;</Field><Field Name="example_research_questions">identify least-cost combination of resources to reach 100% renewable power; calculate cost of achieving various renewable or carbon targets; select assets to minimize cost for a microgrid, possibly interacting with outside electricity supplier; calculate effect of price-responsive demand on consumer welfare while adopting renewable power</Field><Field Name="Comment on model validation">where technical detail is important, users should configure switch to reflect local operating rules and validate results against existing practices; Switch has also been validated against GE-MAPS in a Hawaii case study in production-cost mode (https://doi.org/10.1186/s13705-018-0184-x)</Field><Field Name="Specific properties">using selected samples of full days enables direct modeling of curtailment, storage, hydro and demand response in a multi-decade model; highly modular platform enables easy and structured customization for specific studies</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5224" Title="EA-PSM Electric Arc Flash"><Template Name="Model"><Field Name="Full_Model_Name">EA-PSM Electric Arc Flash</Field><Field Name="Acronym">EA-PSM Electric Arc Flash</Field><Field Name="author_institution">JSC Energy Advice</Field><Field Name="contact_email">info@energyadvice.lt</Field><Field Name="website">http://www.energyadvice.lt/en</Field><Field Name="source_download">http://www.energyadvice.lt/en</Field><Field Name="logo">EA-PSM HnE-02.png</Field><Field Name="text_description">EA-PSM Arc flash model can be used to calculate arc flash incident energy, flash boundary, both arc and fault currents, safe working distance. Calculations are validated in accordance with IEEE 1584 standard. It is possible to choose from different equipment types and calculate incident energy at any selected distance.</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="Link to source">http://www.energyadvice.lt/en/electrical-engineering-software-for-plant/</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Java</Field><Field Name="processing_software">Java, JavaFX</Field><Field Name="GUI">Yes</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="georegions">Global, European Union, Lithuania, Turkey, Poland, India</Field><Field Name="georesolution">global, continents, nations</Field><Field Name="network_coverage">transmission, distribution, AC load flow, DC load flow</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5233" Title="EA-PSM Electric Short Circuit"><Template Name="Model"><Field Name="Full_Model_Name">EA-PSM Electric Short Circuit</Field><Field Name="Acronym">EA-PSM Electric Short Circuit</Field><Field Name="author_institution">JSC Energy Advice</Field><Field Name="contact_email">info@energyadvice.lt</Field><Field Name="website">http://www.energyadvice.lt/en/</Field><Field Name="source_download">http://www.energyadvice.lt/en/</Field><Field Name="logo">EA-PSM HnE-02.png</Field><Field Name="text_description">EA-PSM Electric Short Circuit calculation model allows to get immediate results of three-phase, phase-to-phase, phase-to-isolated neutral and phase-to-grounded neutral short circuit currents. Calculations of the model are verified in accordance with IEC 60909 standard.</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="Link to source">http://www.energyadvice.lt/en/electrical-engineering-software-for-plant/</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Java</Field><Field Name="processing_software">Java, JavaFX</Field><Field Name="GUI">Yes</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="georegions">Global, European Union, Lithuania, Turkey, Poland, India</Field><Field Name="georesolution">global, continents, nations</Field><Field Name="network_coverage">transmission, distribution, AC load flow, DC load flow</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">Yes</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5245" Title="Region4FLEX"><Template Name="Model"><Field Name="Full_Model_Name">region4FLEX</Field><Field Name="author_institution">DLR Institute of Networked Energy Systems</Field><Field Name="authors">Wilko Heitkoetter, Wided Medjroubi</Field><Field Name="contact_persons">Wilko Heitkoetter</Field><Field Name="contact_email">wilko.heitkoetter@dlr.de</Field><Field Name="text_description">The open source model region4FLEX quantifies, to which extent regional load shifting potentials can fulfill the local flexibility demand of the German high voltage grid (110, 220, 380 kV), e.g. for mitigating curtailment of renewable energies. The model offers an underlying database, which contains load shifting potentials on the administrative district level for Germany. The load shifting potentials are calculated by taking into account the structural parameters of the respective regions, such as employment rates in different industry sectors or the composition of the residential building stock. The local flexibility demand data of the power grid are calculated using the open_eGO energy system model. In region4FLEX, a cost optimisation defines, which of the available load shifting potentials in a region can be used, to meet the local flexibility demand. The resulting operating data, e.g. numbers of load shifting events, are used for a subsequent economic-assessment of the flexibility options from the operator’s perspective.

Model is under development. After release it will be directly downloadable.

MODULE 1: Heat demand and power-to-heat capacities
(Article: https://doi.org/10.1016/j.apenergy.2019.114161 ; Open Access Preprint: https://arxiv.org/abs/1912.03763 ; Open Dataset DOI: https://doi.org/10.5281/zenodo.2650200)

MODULE 2: Regionalised load shifting potentials for 19 technologies from the residential, commercial and industrial sector, as well as sector coupling (dsmlib tool) 
(Article: https://doi.org/10.1016/j.adapen.2020.100001, dsmlib tool and dataset: https://zenodo.org/record/3988921)

MODULE 3: Investment and dispatch optimisation of demand response; economic assessment from macro-economic and operator's perspective
(Article: https://doi.org/10.3390/en15228577; Code repository: https://doi.org/10.5281/zenodo.6424639)</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">No</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">PostgreSQL</Field><Field Name="GUI">No</Field><Field Name="model_class">load shifting optimisation</Field><Field Name="sectors">electricity plus sector coupling (EVs, P2Heat, P2Gas)</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="georegions">Germany</Field><Field Name="georesolution">Administrative districts</Field><Field Name="timeresolution">15 Minute</Field><Field Name="network_coverage">transmission</Field><Field Name="math_modeltype">Optimization</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5291" Title="GAMAMOD-DE"><Template Name="Model"><Field Name="Full_Model_Name">Gas Market Model</Field><Field Name="Acronym">GAMAMOD</Field><Field Name="author_institution">Technische Universität Dresden (EE2)</Field><Field Name="authors">Lucas De La Fuente; Philipp Hauser</Field><Field Name="contact_persons">Lucas De La Fuente</Field><Field Name="contact_email">mailto:lucas.delafuente@tu-dresden.de</Field><Field Name="website">https://tu-dresden.de/bu/wirtschaft/ee2/forschung/modelle/gamamod?set_language=en</Field><Field Name="text_description">The gas market model GAMAMOD is a bottom-up model used to determine and analyze the optimal natural gas supply structure in Germany and to examine the utilization of the natural gas infrastructure. In its basic version, the model is a Linear Program with  a high spatial resolution and daily time steps. It contains more than 800 nodes and 1200 edges, while also taking into account parallel transmission lines, storage, and changes to demand and the grid as year progress. It's main outputs are optimal flow, imports, storage usage and retrofitting.

In addition, important suppliers for the European natural gas market are considered. On the supply side, the model considers different production capacities with respect to the production level. The model enables the transport of natural gas by modelling pipelines and liquefied natural gas (LNG) shipping.

A version of GAMAMOD focused on optimal retrofitting calculations also exists, this one has the form of a Mixed-Integer Linear Program.</Field><Field Name="Primary outputs">optimal gas flows; retrofitting potential; gas storage; import patterns</Field><Field Name="Number of developers">1</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS; CPLEX</Field><Field Name="GUI">No</Field><Field Name="model_class">German Transmission Grid</Field><Field Name="sectors">Gas,</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Germany</Field><Field Name="georesolution">NUTS0 - NUTS3, for DE</Field><Field Name="timeresolution">Day</Field><Field Name="network_coverage">transmission</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_objective">Total system cost</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Hauser, Philipp (2019) : A modelling approach for the German gas gridusing highly resolved spatial, temporal and sectoral data (GAMAMOD-DE), ZBW – LeibnizInformation Centre for Economics, Kiel, Hamburg</Field><Field Name="citation_doi">http://hdl.handle.net/10419/197000</Field><Field Name="report_references">Hauser, P.; Heidari, S.; Weber, C.; Möst, D.: Does Increasing Natural Gas Demand in the Power Sector Pose a Threat of Congestion to the German Gas Grid? A Model-Coupling Approach, Energies 2019, 12(11) 2159
https://www.mdpi.com/475018</Field><Field Name="example_research_questions">- Sector Coupling between electricity and gas
- Security of Supply in the German gas network
- Retrofitting Potential of German Gas Grid</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template><Free_Text id="1" /></Page><Page ID="5294" Title="ELTRAMOD"><Template Name="Model"><Field Name="Full_Model_Name">Electricity Transshipment Model</Field><Field Name="Acronym">ELTRAMOD</Field><Field Name="author_institution">Technische Universität Dresden (ee2)</Field><Field Name="authors">Dominik Möst, David Gunkel, Theresa Ladwig, Daniel Schubert, Hannes Hobbie, Christoph Zöphel, Steffi Misconel, Carl-Philipp Anke</Field><Field Name="contact_persons">Dominik Möst</Field><Field Name="contact_email">dominik.moest@tu-dresden.de</Field><Field Name="website">https://tu-dresden.de/bu/wirtschaft/ee2/forschung/modelle/eltramod</Field><Field Name="text_description">ELTRAMOD is a fundamental bottom-up electricity market model incorporating the electricity markets of the EU-27 states, Norway, Switzerland, United Kingdom and the Balkan region as well as the Net Transfer Capacities (NTC) between these countries. Each country is treated as one node with country-specific hourly time series of electricity demand and renewable feed-in. The country-specific wind and photovoltaic feed-in is characterised by the installed capacity and an hourly capacity factor. The capacity factors are calculated with the help of publically available time series of wind speed and solar radiation. ELTRAMOD is a linear optimisation model which calculates the cost-minimal generation dispatch and investments in additional transmission lines, storage facilities and other flexibility options. The set of conventional power plants consists of fossil fired, nuclear and hydro plants where different technological characteristics are implemented, such as efficiency, emission factors and availability. Daily prices for CO2 allowances, as well as daily wholesale fuel prices supplemented by country-specific mark-ups are implemented in ELTRAMOD. The country- and technology-specific parameters and the temporal resolution of 8760 hours allow an in-depth analysis of various challenges of the future European electricity system. For example, the trade-off between network extension and storage investment as well as import and export flows of electricity in Europe can be analysed.</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">GAMS; CPLEX</Field><Field Name="GUI">No</Field><Field Name="model_class">German and European Electricity Market,</Field><Field Name="sectors">Electricity including sector coupling (EVs, PtX)</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Hydrogen</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="Market models">European electricity market incl. carbon market (EU ETS)</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">EU-27 + Norway + Switzerland + United Kingdom + Balkan countries</Field><Field Name="georesolution">NUTS0 - NUTS3</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear optimization model. Decision variables include investment and dispatch of generation, storage, DSM and different sector coupling options including both wholesale and balancing markets.</Field><Field Name="math_objective">Minimization of total system costs</Field><Field Name="deterministic">Deterministic; Perfect foresight; Sensitivity analysis ;</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Demand Side Management in Deutschland zur Systemintegration erneuerbarer Energien</Field><Field Name="citation_doi">urn:nbn:de:bsz:14-qucosa-236074</Field><Field Name="report_references">Schreiber, S., Zöphel, C., Möst, D., 2021. Optimal Energy Portfolios in the Electricity Sector: Trade-offs and Interplay between Different Flexibility Options, in: Möst, D., Schreiber, S., Herbst, A., Jakob, M., Martino, A., Poganietz, W.-R. (Eds.), The Future European Energy System - Renewable Energy, Flexibility Options and Technological Progress. Springer International Publishing. https://doi.org/10.1007/978-3-030-60914-6. 

Anke, C.-P.; Hobbie, H.; Schreiber, S.; Möst, D.: Coal phase-outs and carbon prices: Interactions between EU emission trading and national carbon mitigation policies. In: Energy Policy Vol. 144 (2020), Nr. 111647

Zöphel, Christoph; Schreiber, Steffi; Herbst, A.; Klinger, A-L; Manz, P.; Heitel, S.; Fermi, F.; Wyrwa, A.; Raczynski, M.; Reiter, U. D4.3 Report on cost optimal energy technology portfolios for system flexibility in the sectors heat, electricity and mobility. In: Report des REFLEX Projektes (2019)

Energy System Analysis Agency (ESA²): Shaping our energy system - combining European modelling expertise, Brüssel, 2013.

Gunkel, D.; Kunz, F.; Müller, T., von Selasinsky, A.; Möst, D.: Storage Investment or
Transmission Expansion: How to Facilitate Renewable Energy Integration in Europe?.

Tagungsband VDE-Kongress Smart Grid - Intelligente Energieversorgung der Zukunft, 2012.

Müller, T.: Influence of increasing renewable feed-in on the operation of conventional and
storage power plants. 1st KIC InnoEnergy Scientist Conference, Leuven, 2012.

Müller, T.; Gunkel, D.; Möst, D.: Renewable curtailment and its impact on grid and storage
capacities in 2030, Enerday Conference, Dresden 2013.</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5372" Title="OMEGAlpes"><Template Name="Model"><Field Name="Full_Model_Name">Optimization ModEls Generation As Linear Programming for Energy Systems</Field><Field Name="Acronym">OMEGAlpes</Field><Field Name="author_institution">G2Elab</Field><Field Name="authors">B. DELINCHANT, S. HODENCQ, Y. MARECHAL, L. MORRIET, C. PAJOT, V. REINBOLD, F. WURTZ</Field><Field Name="contact_email">omegalpes-users@groupes.renater.fr</Field><Field Name="website">https://omegalpes.readthedocs.io/en/latest/index.html</Field><Field Name="source_download">https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes</Field><Field Name="logo">OMEGAlpes.PNG</Field><Field Name="text_description">OMEGAlpes stands for Generation of Optimization Models As Linear Programming for Energy Systems. It aims to be an energy systems modelling tool for linear optimisation (LP, MILP). It is currently based on the LP modeler PuLP.</Field><Field Name="User documentation">https://omegalpes.readthedocs.io/en/latest/index.html</Field><Field Name="Code documentation">https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">OMEGAlpes, PuLP</Field><Field Name="External optimizer">CBC, Gurobi...</Field><Field Name="GUI">No</Field><Field Name="model_class">Production, consumption, conversion, storage</Field><Field Name="sectors">Electricity, Heat, all</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Observation period">Less than one year, More than one year</Field><Field Name="Additional dimensions (Social)">Multi-actor modelling</Field><Field Name="math_modeltype">Optimization</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5470" Title="Maon"><Template Name="Model"><Field Name="author_institution">Maon GmbH</Field><Field Name="authors">Mihail Ketov, Fabian Breitkreutz, Yash Patel, Sangeetha Kadarkarai, Hajar Mouchrik, Nicolai Schmid, Dariush Wahdany, Kaan Gecü, Ömer Bilgin, Ali Baran Gündüz, Anton Kucherenko, Söhnke Hartmann, Kai Strunz, Albert Moser</Field><Field Name="contact_persons">Dr. Mihail Ketov</Field><Field Name="contact_email">info@maon.eu</Field><Field Name="website">https://maon.eu</Field><Field Name="logo">Maon Colors Borderless Transparent.png</Field><Field Name="text_description">Maon is a market simulation for fundamental electricity wholesale market analysis. It forecasts the facility-level quarter-hourly dispatch of all supply and demand across a continent. Further, it can predict capacities and uncertainties of generators, interconnectors, storages, and consumers.

Web browsers provide access to the data management, simulation, and analysis environment. It enables high-speed, high-resolution, and large-scale foresights. Scenarios can be parameterized by multiple users at the same time, calculated by one click, and collaboratively visually analyzed.

Users get support by work-leveraging parameterization tools, comprehensive quality checks, and interactive visualizations. Maon provides not only results like prices, dispatches, and capacities, but also capture rates, costs, price distributions, revenues, utilizations, and many other measures.</Field><Field Name="Support">commercial</Field><Field Name="User documentation">https://docs.cloud.maon.eu/</Field><Field Name="Code documentation">https://docs.cloud.maon.eu/</Field><Field Name="Source of funding">private</Field><Field Name="Number of developers">25</Field><Field Name="Number of users">1000</Field><Field Name="open_source_licensed">No</Field><Field Name="model_source_public">No</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">C++</Field><Field Name="processing_software">Ansible, Ceph, cURL, Docker, GraphQL, Kubernetes, MinIO, MongoDB, Node.js, Preact, Python, tusd, TypeScript, WebAssembly</Field><Field Name="Additional software">Only browser and internet connection required</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Mixed-Integer Quadratic Programming (MIQP)</Field><Field Name="sectors">Electricity plus couplings (industry, commercial, households, transport)</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Individual efficiency per operating point</Field><Field Name="georegions">Europe, North Africa, Middle East</Field><Field Name="georesolution">Individual power stations</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, distribution, AC load flow, DC load flow, net transfer capacities</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="math_modeltype">Optimization, Simulation, Other, Agent-based</Field><Field Name="math_objective">Minimization of dispatch and investment cost</Field><Field Name="deterministic">Monte Carlo, preprocessing or sensitivity</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">1,000,000,000</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">300</Field><Field Name="computation_time_hardware">high performance computing cluster</Field><Field Name="computation_time_comments">dispatch for 8760 coupled hours in full European region with spot, FCR, aFRR, mFRR, emission, renewable, thermal, hydro, battery, CHP, PtG, DSR, FBMC, AHC, HVDC and on-off decision model, without facility-wise aggregations</Field><Field Name="citation_references">Maon GmbH, Documentation, https://docs.cloud.maon.eu.</Field><Field Name="report_references">https://maon.eu/publications</Field><Field Name="Interfaces">Front-end at https://cloud.maon.eu and APIS at https://apis.cloud.maon.eu</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5477" Title="EnergyScope"><Template Name="Model"><Field Name="Full_Model_Name">EnergyScope</Field><Field Name="Acronym">ES</Field><Field Name="author_institution">EPFL, UCLouvain</Field><Field Name="authors">Stefano Moret, Gauthier Limpens</Field><Field Name="contact_persons">Gauthier Limpens</Field><Field Name="contact_email">gauthier.limpens@uclouvain.be</Field><Field Name="source_download">https://github.com/energyscope/EnergyScope</Field><Field Name="text_description">EnergyScope is open-source model for the strategic energy planning
of urban and regional energy systems.
EnergyScope (v2.0) optimises both the investment and operating strategy of an entire energy system (including electricity, heating and mobility). Additionally, its hourly resolution (using typical days) makes the model suitable for the integration of intermittent renewables, and its concise mathematical formulation and computational effciency are appropriate for uncertainty applications.</Field><Field Name="Primary outputs">Energy system design</Field><Field Name="User documentation">https://github.com/energyscope/EnergyScope/tree/master/Documentation</Field><Field Name="Code documentation">https://github.com/energyscope/EnergyScope/tree/master/Documentation</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/energyscope/EnergyScope</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GLPK/GLPSOL or AMPL/Cplex</Field><Field Name="processing_software">Excel</Field><Field Name="GUI">No</Field><Field Name="model_class">Regional energy system design</Field><Field Name="sectors">All (Electricity, Heating and mobility)</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">End use demand hourly fixed (a priori)</Field><Field Name="Market models">None</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">None</Field><Field Name="georegions">Region (Switzerland, Belgium)</Field><Field Name="georesolution">Country</Field><Field Name="timeresolution">Hour</Field><Field Name="Observation period">Less than one month, Less than one year</Field><Field Name="Additional dimensions (Ecological)">Total greenhouse gases emissions</Field><Field Name="Additional dimensions (Economical)">Total system cost</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear programming (43 equations fully documented).</Field><Field Name="math_objective">financial cost, greenhouse gases emissions</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">200366</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">1 (AMPL/Cplex) - 15 (GLPK/GLPSOL)</Field><Field Name="computation_time_hardware">Intel®Core™ Quad i7-6600U CPU @2.60 GHz, with a memory of 16 Go, and a 64-bit system.</Field><Field Name="computation_time_comments">Depends on the case complexity. Rarely exceeds 5 minutes.</Field><Field Name="citation_references">Limpens G, Moret S, Jeanmart H, Maréchal F,EnergyScope TD: a novel open-source model for regional energy systems. Appl Energy 2019; Volume 255.</Field><Field Name="citation_doi">10.1016/j.apenergy.2019.113729</Field><Field Name="report_references">Limpens G, Moret S, Guidati G, Li X, Maréchal F, Jeanmart H. The role of storage in the Swiss energy transition. Proceedings of ECOS2019, june 23-28, 2019, Wroclaw, Poland. 2019 pages 761-774

Limpens, G., Jeanmart, H., &amp; Maréchal, F. (2020). Belgian Energy Transition: What Are the Options?. Energies, 13(1), 261.</Field><Field Name="example_research_questions">Role of storage?
Benefit of electrification?
How to handle high shares of renewables?
What is the impact of uncertainties on investment decisions?</Field><Field Name="Model validation">Demonstration on previous year (2011)</Field><Field Name="Comment on model validation">It is not a validation, but a comparison.</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5635" Title="FlexiGIS"><Template Name="Model"><Field Name="Full_Model_Name">Flexibilisation in Geographic Information Systems</Field><Field Name="Acronym">FlexiGIS</Field><Field Name="author_institution">DLR Institute of Networked Energy Systems</Field><Field Name="authors">Alaa Alhamwi</Field><Field Name="contact_persons">Alaa Alhamwi</Field><Field Name="contact_email">alaa.alhamwi@dlr.de</Field><Field Name="website">https://github.com/FlexiGIS/FlexiGIS.git</Field><Field Name="source_download">https://github.com/FlexiGIS/FlexiGIS.git</Field><Field Name="logo">Suit1.png</Field><Field Name="text_description">FlexiGIS: an open source GIS-based platform for modelling energy systems and flexibility options in urban areas. It extracts, filters and categorises the geo-referenced urban energy infrastructure, simulates the local electricity consumption and power generation from on-site renewable energy resources, and allocates the required decentralised storage in urban settings using oemof-solph. FlexiGIS investigates systematically different scenarios of self-consumption, it analyses the characteristics and roles of flexibilisation technologies in promoting higher autarky levels in cities. The extracted urban energy infrustructure are based mainly on OpenStreetMap data.</Field><Field Name="Source of funding">DLR</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/FlexiGIS/FlexiGIS.git</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Geopandas</Field><Field Name="External optimizer">GLPK, oemof-solph</Field><Field Name="GUI">No</Field><Field Name="model_class">urban energy systems</Field><Field Name="sectors">Electricity Sector,</Field><Field Name="technologies">Renewables</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Market models">energy technology market</Field><Field Name="georesolution">building, street, district, city</Field><Field Name="timeresolution">15 Minute</Field><Field Name="network_coverage">distribution</Field><Field Name="Observation period">Less than one year</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Modelling and optimisation mathematical model</Field><Field Name="math_objective">simualte local urban demand and supply, localise distributed storage, minimise total system costs</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas</Field><Field Name="citation_doi">https://doi.org/10.1016/j.apenergy.2017.01.048.</Field><Field Name="Model validation">simulated consumption and generation were validated against real measured data</Field><Field Name="Comment on model validation">real data of the respective city are required</Field><Field Name="Integrated models">oemof</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5662" Title="Backbone"><Template Name="Model"><Field Name="Full_Model_Name">Backbone - energy systems model</Field><Field Name="Acronym">Backbone</Field><Field Name="author_institution">VTT Technical Research Centre of Finland</Field><Field Name="authors">Juha Kiviluoma, Erkka Rinne, Topi Rasku, Niina Helistö, Jussi Ikäheimo, Dana Kirchem, Ran Li, Ciara O'Dwyer, Jussi Ikäheimo, Tomi J. Lindroos, Eric Harrison</Field><Field Name="contact_persons">Tomi J. Lindroos</Field><Field Name="contact_email">Tomi.J.Lindroos@vtt.fi</Field><Field Name="source_download">https://gitlab.vtt.fi/backbone/backbone/-/tree/release-3.x</Field><Field Name="text_description">Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS).</Field><Field Name="Primary outputs">Costs, emissions, generation, consumption, transfers</Field><Field Name="Support">Voluntary</Field><Field Name="Framework">Backbone is a framework.</Field><Field Name="User documentation">https://gitlab.vtt.fi/backbone/backbone/wikis/home</Field><Field Name="Code documentation">Formulas: https://doi.org/10.3390/en12173388; Code documentation: within code</Field><Field Name="Source of funding">Academy of Finland; ESIPP project (Ireland)</Field><Field Name="Number of developers">11</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU Library or &quot;Lesser&quot; General Public License version 3.0 (LGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://gitlab.vtt.fi/backbone/backbone/-/tree/release-3.x</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS</Field><Field Name="processing_software">Spine Toolbox or Excel</Field><Field Name="GUI">No</Field><Field Name="model_class">Framework</Field><Field Name="sectors">All</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">Markets only</Field><Field Name="Market models">Any product can have a market; also reserve markets</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Piecewise linear (SOS2 and incremental), Time-dependent efficiency, environment dependent efficiency</Field><Field Name="georegions">Depends on user</Field><Field Name="georesolution">Depends on user</Field><Field Name="timeresolution">15 Minute</Field><Field Name="network_coverage">transmission, DC load flow, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">Depends on data</Field><Field Name="Additional dimensions (Economical)">Depends on data</Field><Field Name="Additional dimensions (Social)">-</Field><Field Name="Additional dimensions (Other)">-</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">The model minimizes the objective function and includes constraints related to energy balance, emissions, unit operation, transfers, system operation, portfolio design, etc.</Field><Field Name="math_objective">Cost minimization; emission minimization;</Field><Field Name="deterministic">Short-term and long-term stochastics are available</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">1000000</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">10</Field><Field Name="computation_time_hardware">Normal laptop</Field><Field Name="computation_time_comments">Simple models can solve full year in seconds, complicated European models with 20+ regions and many sectors typically solve full year in ~hour</Field><Field Name="citation_references">Helistö, N.; Kiviluoma, J.; Ikäheimo, J.; Rasku, T.; Rinne, E.; O’Dwyer, C.; Li, R.; Flynn, D. Backbone—An Adaptable Energy Systems Modelling Framework. Energies 2019, 12, 3388.</Field><Field Name="citation_doi">https://doi.org/10.3390/en12173388</Field><Field Name="report_references">Journal publications (updated 14.11.2023):

Model documentation. Please cite this if looking for a generic Backbone reference.
Helistö, N., Kiviluoma, J., Ikäheimo, J., Rasku, T., Rinne, E., O’Dwyer, C., Li, R., &amp; Flynn, D. (2019). Backbone - An adaptable energy systems modelling framework. Energies, 12(17), 3388. https://doi.org/10.3390/en12173388


Model verification for power systems (using an IEEE test system)

C. Barrows et al. (2020). The IEEE Reliability Test System: A Proposed 2019 Update. IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 119-127, Jan. 2020. https://doi.org/10.1109/TPWRS.2019.2925557


Papers with a methodological focus

Finke, J. and Bertsch, V. (2023). Implementing a highly adaptable method for the multi-objective optimisation of energy systems. Applied Energy, Volume 332, 2023, 120521, ISSN 0306-2619, https://doi.org/10.1016/j.apenergy.2022.120521.

Helistö, N., Kiviluoma, J., Morales-España, G. &amp; O’Dwyer, C. (2021). Impact of operational details and temporal representations on investment planning in energy systems dominated by wind and solar. Applied Energy, 290, 116712. https://doi.org/10.1016/j.apenergy.2021.116712

Helistö, N., Kiviluoma, J., &amp; Reittu, H. (2020). Selection of representative slices for generation expansion planning using regular decomposition. Energy, 211, 118585. https://doi.org/10.1016/j.energy.2020.118585

Rasku, T., Miettinen, J., Rinne, E., &amp; Kiviluoma, J. (2020). Impact of 15-day energy forecasts on the hydro-thermal scheduling of a future Nordic power system. Energy, 192, 116668. https://doi.org/10.1016/j.energy.2019.116668



Case studies

Putkonen, N., Lindroos, T.J., Neniškis, E., Žalostība, D., Norvaiša, E., Galinis, A., Teremranova, J. &amp; Kiviluoma, J. (2022) Modeling the Baltic countries’ Green Transition and Desynchronization from the Russian Electricity Grid. https://dx.doi.org/10.54337/ijsepm.7059

Pursiheimo, E., Lindroos, T. J., Sundell, D., Rämä, M., Tulkki, V. (2022) Optimal Investment Analysis for Heat Pumps and Nuclear Heat in Decarbonised Helsinki Metropolitan District Heating System. Energy Storages and Saving, https://doi.org/10.1016/j.enss.2022.03.001

Kiviluoma, J., O'Dwyer, C., Ikäheimo, J., Lahon, R., Li, Ran, Kirchem D., Helistö, N., Rinne, E., Flynn, D. (2022) Multi-sectoral flexibility measures to facilitate wind and solar power integration. IET Renew. Power Gener.,  https://doi.org/10.1049/rpg2.12399

Ikäheimo, J., Weiss, R., Kiviluoma, J., Pursiheimo, E., &amp; Lindroos, T. J. (2022). Impact of power-to-gas on the cost and design of the future low-carbon urban energy system. Applied Energy, 305, [117713]. https://doi.org/10.1016/j.apenergy.2021.117713

Lindroos, T. J., Mäki, E., Koponen, K., Hannula, I., Kiviluoma, J., &amp; Raitila, J. (2021). Replacing fossil fuels with bioenergy in district heating – Comparison of technology options. Energy, 231, [120799]. https://doi.org/10.1016/j.energy.2021.120799

Rasku, T., &amp; Kiviluoma, J. (2019). A comparison of widespread flexible residential electric heating and energy efficiency in a future Nordic power system. Energies, 12(1), 5. https://doi.org/10.3390/en12010005




Shared model data

Ikäheimo, J., Purhonen, A., Lindroos, T.J., Rämä, M. and Harrison, E. Northern European Model. https://github.com/vttresearch/north_european_model

Lindroos, T.J., and Pursiheimo, E. Helsinki Region DHC model. https://gitlab.vtt.fi/backbone/models/helsinki-dhc-model



Please see a longer list at:
https://gitlab.vtt.fi/backbone/backbone/-/wikis/More-information/List-of-publications</Field><Field Name="example_research_questions">Cost efficient future energy systems with high shares of variable power generation. Exploring the impact of operational details on energy system planning. Optimizing the use of storages and energy intensive processes that have days-long time delays (model temporal structure can change during the horizon).</Field><Field Name="Model validation">Comparison against two other models: https://doi.org/10.3390/en12173388</Field><Field Name="Comment on model validation">Produces similar unit commitment results as a commercial tool in wide-spread use.</Field><Field Name="Specific properties">Flexible temporal and technological detail. An energy systems model with capability for detailed unit commitment of the power system. Can include operational detail in generation/transmission planning.</Field><Field Name="Interfaces">Excel or Spine Toolbox</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5773" Title="REopt"><Template Name="Model"><Field Name="Full_Model_Name">REopt</Field><Field Name="Acronym">REopt</Field><Field Name="author_institution">The National Renewable Energy Laboratory</Field><Field Name="authors">Dylan Cutler, Kate Anderson, Dan Olis, Emma Elgqvist, Andy Walker, Xiangkun Li, William Becker, Kathleen Krah, Nick Laws, Sakshi Mishra, Josiah Pohl</Field><Field Name="contact_persons">Josiah Pohl</Field><Field Name="contact_email">jpohl@nrel.gov</Field><Field Name="website">https://reopt.nrel.gov/</Field><Field Name="source_download">https://github.com/NREL/REopt_Lite_API</Field><Field Name="logo">REopt.jpg</Field><Field Name="text_description">The REopt™ model provides concurrent, multiple technology integration and optimization capabilities to help organizations meet their cost savings and energy performance goals. Formulated as a mixed integer linear program, the REopt model recommends an optimally sized mix of renewable energy, conventional generation, and energy storage technologies; estimates the net present value of implementing those technologies; and provides a dispatch strategy for operating the technology mix at maximum economic efficiency.</Field><Field Name="Primary purpose">Integrated Energy System Optimization</Field><Field Name="Primary outputs">Sizing and Dispatch of Distributed Energy Resources</Field><Field Name="Support">The National Renewable Energy Laboratory</Field><Field Name="User documentation">https://reopt.nrel.gov/tool/REopt%20Lite%20Web%20Tool%20User%20Manual.pdf</Field><Field Name="Code documentation">https://github.com/NREL/REopt_Lite_API/wiki</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/NREL/REopt_Lite_API</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia/JuMP</Field><Field Name="processing_software">Python</Field><Field Name="External optimizer">Xpress, Cbc, SCIP</Field><Field Name="Additional software">postgresql, redis</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy System Model</Field><Field Name="sectors">Energy</Field><Field Name="technologies">Renewables, CHP</Field><Field Name="Demand sectors">Industry</Field><Field Name="Energy carrier (Liquid)">Diesel</Field><Field Name="Energy carriers (Renewable)">Sun, Wind</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Reduction Factor</Field><Field Name="georegions">World</Field><Field Name="georesolution">Site</Field><Field Name="timeresolution">Hour</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Economical)">20 Year Analysis</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Mixed Integer Linear Program</Field><Field Name="math_objective">Minimize Lifecycle Cost</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">https://www.nrel.gov/docs/fy14osti/61783.pdf</Field><Field Name="report_references">https://www.nrel.gov/docs/fy18osti/70813.pdf</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5812" Title="CAPOW"><Template Name="Model"><Field Name="Full_Model_Name">California and West Coast Power Systems model</Field><Field Name="Acronym">CAPOW</Field><Field Name="author_institution">North Carolina State University</Field><Field Name="authors">Jordan Kern, Yufei Su</Field><Field Name="contact_persons">Jordan Kern</Field><Field Name="contact_email">jkern@ncsu.edu</Field><Field Name="website">https://kern.wordpress.ncsu.edu/</Field><Field Name="source_download">https://github.com/romulus97/CAPOW_PY36</Field><Field Name="text_description">Python-based multi-zone unit commitment/economic dispatch model of CAISO and Mid-C markets coupled with &quot;stochastic engine&quot; for representing effects of multiple spatiotemporally correlated hydrometeorological processes on demand, hydropower and wind and solar power production.</Field><Field Name="Primary outputs">Generator level electricity production and emissions, zonal market prices, and total system costs.</Field><Field Name="Source of funding">NSF INFEWS program</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/romulus97/CAPOW_PY36</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="GUI">No</Field><Field Name="model_class">CAISO and Mid-Columbia markets/U.S. West Coast</Field><Field Name="sectors">Electric power</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas</Field><Field Name="Energy carriers (Solid)">Coal</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">Centrally controlled</Field><Field Name="Market models">Day-ahead</Field><Field Name="decisions">dispatch</Field><Field Name="georesolution">Zonal</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">Environmental flow constraints at dams</Field><Field Name="Additional dimensions (Economical)">Marginal cost based pricing</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">Iterative mixed-integer program, with user defined operating horizon</Field><Field Name="math_objective">Cost minimization</Field><Field Name="deterministic">Short-term and long-term stochastics are available</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">Yes</Field><Field Name="citation_references">Su, Y., Kern, J., Denaro, S., Hill, J., Reed, P., Sun, Y., Cohen, J., Characklis, G. (2020). “An open source model for quantifying risks in bulk electric power systems from spatially and temporally correlated hydrometeorological processes” Environmental Modelling and Software. Vol. 126</Field><Field Name="citation_doi">https://doi.org/10.1016/j.envsoft.2020.104667</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5849" Title="PowNet"><Template Name="Model"><Field Name="Full_Model_Name">PowNet</Field><Field Name="Acronym">PowNet</Field><Field Name="author_institution">Singapore University of Technology and Design</Field><Field Name="authors">AFM Kamal Chowdhury, Jordan Kern, Thanh Duc Dang, Stefano Galelli</Field><Field Name="contact_persons">AFM Kamal Chowdhury</Field><Field Name="contact_email">k.chy0013@gmail.com</Field><Field Name="website">https://github.com/kamal0013/PowNet</Field><Field Name="source_download">https://zenodo.org/record/3462879#.XoL6T4gzZaQ</Field><Field Name="text_description">PowNet is a least-cost optimization model for simulating the Unit Commitment and Economic Dispatch (UC/ED) of large-scale (regional to country) power systems. In PowNet, a power system is represented by a set of nodes that include power plants, high-voltage substations, and import/export stations (for cross-border systems). The model schedules and dispatches the electricity supply from power plant units to meet hourly electricity demand in substations (at a minimum cost). It considers the techno-economic constraints of both generating units and high-voltage transmission network. The power flow calculation is based on a Direct Current (DC) network (with N-1 criterion), which provides a reasonable balance between modelling accuracy and data and computational requirements.</Field><Field Name="Primary purpose">Power systems analysis</Field><Field Name="Primary outputs">Hourly schedule and dispatch of power plant units, flow through transmission lines, and reserves.</Field><Field Name="Source of funding">Singapore's Ministry of Education (MoE)</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/kamal0013/PowNet</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="processing_software">Python</Field><Field Name="External optimizer">GUROBI, CPLEX</Field><Field Name="GUI">No</Field><Field Name="model_class">Network-constrained Unit Commitment and Economic Dispatch</Field><Field Name="sectors">Electricity, Electric power, Energy</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Laos, Cambodia, Thailand, any user-defined country or region</Field><Field Name="georesolution">High-voltage substation</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, distribution, DC load flow</Field><Field Name="Additional dimensions (Ecological)">water-energy nexus</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Mixed Integer Linear Program (MILP), DC Power Flow, Unit Commitment, Economic Dispatch</Field><Field Name="math_objective">Cost minimization</Field><Field Name="deterministic">Sensitivity analysis</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">minutes</Field><Field Name="citation_references">Chowdhury, A.F.M.K., Kern, J., Dang, T.D. and Galelli, S., 2020. PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis. Journal of Open Research Software, 8(1), p.5.</Field><Field Name="citation_doi">http://doi.org/10.5334/jors.302</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5875" Title="USENSYS"><Template Name="Model"><Field Name="Full_Model_Name">United States energy system optimization model</Field><Field Name="Acronym">USENSYS</Field><Field Name="author_institution">Environmental Defense Fund</Field><Field Name="authors">Oleg Lugovoy</Field><Field Name="contact_persons">Oleg Lugovoy</Field><Field Name="contact_email">olugovoy@edf.org</Field><Field Name="website">www.usensys.org</Field><Field Name="source_download">https://github.com/usensys/usensys</Field><Field Name="logo">usensys.svg</Field><Field Name="text_description">United States Energy SYStem (USENSYS) is an open source capacity expansion model (CEM, also knows as Reference Energy System model, RES), developped based on energyRt package for R.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Affero General Public License v3 (AGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/usensys/usensys</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">R/energyRt</Field><Field Name="processing_software">R</Field><Field Name="External optimizer">GAMS or Python/Pyomo or Julia/JuMP or GLPK/MathProg</Field><Field Name="Additional software">one of the above</Field><Field Name="GUI">No</Field><Field Name="model_class">Capacity expansion, Reference Energy System,</Field><Field Name="sectors">Electric power,</Field><Field Name="technologies">Renewables</Field><Field Name="Energy carriers (Renewable)">Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">investment</Field><Field Name="georegions">US 48 lower states &amp; DC</Field><Field Name="georesolution">Administrative districts</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear programming</Field><Field Name="math_objective">Cost minimization</Field><Field Name="deterministic">Deterministic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">1-5+ hours</Field><Field Name="computation_time_hardware">64+Gb RAM, 4-5GHz, 6+ cores Intel</Field><Field Name="computation_time_comments">Depends on solver used (glpsol/CPLEX/Gurobi etc.)</Field><Field Name="report_references">in progress, by now: https://github.com/usensys/usensys</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5895" Title="EOLES elecRES"><Template Name="Model"><Field Name="Full_Model_Name">Energy Optimization for Low Emission Systems - renewable electricity</Field><Field Name="Acronym">EOLES_elecRES</Field><Field Name="author_institution">CIRED</Field><Field Name="authors">Behrang Shirizadeh, Quentin Perrier, Philippe Quirion</Field><Field Name="contact_persons">Behrang Shirizadeh</Field><Field Name="contact_email">shirizadeh@centre-cired.fr</Field><Field Name="source_download">https://github.com/BehrangShirizadeh/EOLES_elecRES</Field><Field Name="text_description">EOLES_elecRES is a dispatch and investment model that minimizes the annualized power
generation and storage costs, including the cost of connection to the grid. It includes six
power generation technologies: offshore and onshore wind power, solar photovoltaics
(PV), run-of-river and lake-generated hydro-electricity, and biogas combined with opencycle gas turbines. It also includes three energy storage technologies: pump-hydro
storage (PHS), batteries and methanation combined with open-cycle gas turbines.</Field><Field Name="Primary outputs">Annualized cost, Installed capacities and generation and storage profiles</Field><Field Name="User documentation">http://www2.centre-cired.fr/IMG/pdf/cired_wp_2020_80_shirizadeh_quirion_perrier.pdf</Field><Field Name="Code documentation">https://github.com/BehrangShirizadeh/EOLES_elecRES</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/BehrangShirizadeh/EOLES_elecRES</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS</Field><Field Name="GUI">No</Field><Field Name="model_class">Electricity System Model</Field><Field Name="sectors">Electricity Sector</Field><Field Name="technologies">Renewables</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Biogas</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Storage (Electricity)">Battery, Chemical, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">Inelastic demand - Optimization from social planner perspective</Field><Field Name="Market models">Electricity market</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">fixed</Field><Field Name="georesolution">Coutry</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Economical)">system-wide LCOE, technology specific LCOE and hourly spot price</Field><Field Name="Additional dimensions (Other)">load curtailment, storage loss and etc.</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Simultaneous optimization of dispatch and investment (linar programming), solved in CPLEX solver of GAMS</Field><Field Name="math_objective">investment cost and operational costs (fixed and variable) minimization</Field><Field Name="deterministic">Deterministic; Perfect foresight; Sensitivity analysis ; Robust decision making</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Shirizadeh, B., Perrier, Q. &amp; Quirion, P. (2022) How sensitive are optimal fully renewable systems to technology cost uncertainty? The Energy Journal, Vol 43, No. 1</Field><Field Name="citation_doi">10.5547/01956574.43.1.bshi</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5897" Title="EOLES elec"><Template Name="Model"><Field Name="Full_Model_Name">Energy Optimization for Low Emission Systems - electricity</Field><Field Name="Acronym">EOLES_elec</Field><Field Name="author_institution">CIRED</Field><Field Name="authors">Behrang Shirizadeh, Philippe Quirion</Field><Field Name="contact_persons">Behrang Shirizadeh</Field><Field Name="contact_email">mailto:shirizadeh@centre-cired.fr</Field><Field Name="website">http://www.centre-cired.fr/fr/behrang-shirizadeh/</Field><Field Name="text_description">The EOLES family of models optimizes the investment and operation of an energy system
in order to minimize the total cost while satisfying energy demand. EOLES_elec is the
electricity version of this family of models. It minimizes the annualized power generation
and storage costs, including the cost of connection to the grid. It includes eight power
generation technologies: offshore and onshore wind power, solar photovoltaics (PV), runof-river and lake-generated hydro-electricity, nuclear power (EPR, i.e. third generation
European pressurized water reactors), open-cycle gas turbines and combined-cycle gas
turbines equipped with post-combustion carbon capture and storage. The latter two
generation technologies burn methane which can come from three sources: fossil natural
gas, biogas from anaerobic digestion and renewable gas from power-to-gas technology
(methanation). EOLES_elec also includes four energy storage technologies: pumped hydro storage (PHS), Li-Ion batteries and two types of methanation (with and without CCS).</Field><Field Name="Primary outputs">Annualized cost, Installed capacities and generation and storage profiles</Field><Field Name="User documentation">http://www2.centre-cired.fr/IMG/pdf/cired_wp_2020_79_shirizadeh_quirion.pdf</Field><Field Name="Code documentation">https://github.com/BehrangShirizadeh/EOLES_elec</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/BehrangShirizadeh/EOLES_elec</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS</Field><Field Name="GUI">No</Field><Field Name="model_class">Electricity System Model,</Field><Field Name="sectors">Electricity Sector, Carbon Market,</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carriers (Solid)">Uranium</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Storage (Electricity)">Battery, Chemical, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">Inelastic demand</Field><Field Name="Market models">Electricity and carbon markets</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">fixed</Field><Field Name="georesolution">Country level</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">CO2 emissions, CO2 storage need</Field><Field Name="Additional dimensions (Economical)">system-wide LCOE, technology specific LCOE, hourly spot price, Social cost, Technical cost and Carbon and Energy market revenues</Field><Field Name="Additional dimensions (Social)">Social cost of Carbon, Social cost of System</Field><Field Name="Additional dimensions (Other)">load curtailment, storage loss and etc.</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Simultaneous optimization of dispatch and investment (linear programming), solved in CPLEX solver of GAMS</Field><Field Name="math_objective">investment cost and operational costs (fixed and variable) minimization</Field><Field Name="deterministic">Deterministic; Perfect foresight; Sensitivity analysis ;</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Shirizadeh, B. &amp; Quirion, P. (2020). Low-carbon options for French power sector: What role for renewables, nuclear energy and carbon capture and storage? Energy Economics, 105004.</Field><Field Name="citation_doi">10.1016/j.eneco.2020.105004</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5910" Title="Pandapower"><Template Name="Model"><Field Name="Full_Model_Name">Pandapower</Field><Field Name="authors">Energy Management and Power System Operation (University of Kassel), Fraunhofer IEE</Field><Field Name="contact_persons">Leon Thurner, Alexander Scheidler</Field><Field Name="website">http://www.pandapower.org</Field><Field Name="source_download">https://github.com/e2nIEE/pandapower/</Field><Field Name="text_description">pandapower builds on the data analysis library pandas and the power system analysis toolbox PYPOWER to create an easy to use network calculation program aimed at automation of analysis and optimization in power systems. What started as a convenience wrapper around PYPOWER has evolved into a stand-alone power systems analysis toolbox with extensive power system model library, an improved power flow solver and many other power systems analysis functions.</Field><Field Name="Primary outputs">Power Flow</Field><Field Name="Framework">PYPOWER</Field><Field Name="Code documentation">pandapower.readthedocs.io</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/e2nIEE/pandapower/</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Pandas</Field><Field Name="GUI">No</Field><Field Name="model_class">Transmission Network Model</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Market models">none</Field><Field Name="network_coverage">transmission, distribution</Field><Field Name="math_modeltype">Simulation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">L. Thurner, A. Scheidler, F. Schäfer et al, pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems, in IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 6510-6521, Nov. 2018</Field><Field Name="citation_doi">10.1109/TPWRS.2018.2829021</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5913" Title="Mosaik"><Template Name="Model"><Field Name="Full_Model_Name">mosaik</Field><Field Name="Acronym">Mosaik</Field><Field Name="author_institution">OFFIS</Field><Field Name="contact_email">mosaik@offis.de</Field><Field Name="website">http://mosaik.offis.de/</Field><Field Name="source_download">https://gitlab.com/mosaik</Field><Field Name="logo">Mosaik logo.png</Field><Field Name="text_description">Mosaik is a flexible Smart Grid co-simulation framework.

Mosaik allows you to reuse and combine existing simulation models and simulators to create large-scale Smart Grid scenarios – and by large-scale we mean thousands of simulated entities distributed over multiple simulator processes. These scenarios can then serve as test bed for various types of control strategies (e.g., multi-agent systems (MAS) or centralized control).

Mosaik is written in Python and completely open source (LGPL), including some simple simulators, a binding to pandapower and PYPOWER and a demonstration scenario.</Field><Field Name="User documentation">mosaik.readthedocs.io</Field><Field Name="Code documentation">mosaik.readthedocs.io</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU Library or &quot;Lesser&quot; General Public License version 2.1 (LGPL-2.1)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://gitlab.com/mosaik</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">HDF5, InfluxDB, Grafana</Field><Field Name="GUI">Yes</Field><Field Name="model_class">distributed energy systems, smart grid simulation</Field><Field Name="sectors">electricity, heat, mobility, household</Field><Field Name="technologies">Renewables, CHP</Field><Field Name="Demand sectors">Households</Field><Field Name="Energy carriers (Renewable)">Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="timeresolution">Second</Field><Field Name="network_coverage">transmission, distribution</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="math_modeltype">Optimization, Simulation, Agent-based</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">A. Ofenloch et al., &quot;MOSAIK 3.0: Combining Time-Stepped and Discrete Event Simulation,&quot; 2022 Open Source Modelling and Simulation of Energy Systems (OSMSES), 2022, pp. 1-5</Field><Field Name="citation_doi">10.1109/OSMSES54027.2022.9769116.</Field><Field Name="Integrated models">demod, pandapower</Field><Field Name="Interfaces">Python, Java, C#, Matlab, FMI</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5940" Title="PowerSimulations.jl"><Template Name="Model"><Field Name="Full_Model_Name">PowerSimulations.jl</Field><Field Name="Acronym">PowerSimulations.jl</Field><Field Name="author_institution">NREL</Field><Field Name="authors">Clayton Barrows, Jose-Daniel Lara, Daniel Thom, Dheepak Krishnamurthy, Sourabh Dalvi</Field><Field Name="contact_persons">Clayton Barrows</Field><Field Name="contact_email">clayton.barrows@nrel.gov</Field><Field Name="website">https://github.com/nrel-siip/PowerSimulations.jl</Field><Field Name="source_download">https://github.com/nrel-siip/PowerSimulations.jl</Field><Field Name="logo">Siip-power.png</Field><Field Name="text_description">Flexible, modular, and scalable package for power system quasi-static analysis with sequential problem specification capabilities.</Field><Field Name="Primary outputs">Unit-commitment and economic dispatch</Field><Field Name="Support">https://join.slack.com/t/nrel-siip/shared_invite/zt-glam9vdu-o8A9TwZTZqqNTKHa7q3BpQ</Field><Field Name="Framework">https://www.nrel.gov/analysis/siip.html</Field><Field Name="User documentation">https://nrel-siip.github.io/PowerSimulations.jl/latest/</Field><Field Name="Code documentation">https://nrel-siip.github.io/PowerSimulations.jl/latest/</Field><Field Name="Source of funding">NREL/DOE</Field><Field Name="Number of developers">13</Field><Field Name="Number of users">200</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/NREL-SIIP/PowerSimulations.jl</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia</Field><Field Name="processing_software">Julia</Field><Field Name="External optimizer">Any</Field><Field Name="Additional software">Solver dependencies</Field><Field Name="GUI">No</Field><Field Name="model_class">quasii-static sequential unit-commitment and economic dispatch problems</Field><Field Name="sectors">Power system</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Market models">Energy and ancillary service scheduling markets</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Any</Field><Field Name="georesolution">nodal resolution (all nodes are included)</Field><Field Name="timeresolution">Second</Field><Field Name="network_coverage">transmission, AC load flow, DC load flow, net transfer capacities</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Principal application is sequential quasi-static system optimization problems (production cost modeling).</Field><Field Name="math_objective">Least Cost</Field><Field Name="deterministic">scenario analysis</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">1000000</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">1</Field><Field Name="computation_time_hardware">High Performance Computer</Field><Field Name="computation_time_comments">Best in class</Field><Field Name="Larger scale usage">U.S. Eastern Interconnection</Field><Field Name="Model validation">Detailed comparisions with PLEXOS, Prescient, Backbone, MATPOWER, PSO, and otherrs.</Field><Field Name="Integrated models">PowerSystems.jl, PowerModels.jl</Field><Field Name="Interfaces">HELICS</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="5959" Title="AnyMOD"><Template Name="Model"><Field Name="Full_Model_Name">AnyMOD</Field><Field Name="author_institution">TU Berlin</Field><Field Name="authors">Leonard Göke</Field><Field Name="contact_persons">Leonard Göke</Field><Field Name="contact_email">lgo@wip.tu-berlin.de</Field><Field Name="website">https://github.com/leonardgoeke/AnyMOD.jl</Field><Field Name="logo">AnyMOD logo.png</Field><Field Name="text_description">AnyMOD is a framework to create large scale energy system models with multiple periods of capacity expansion. It pursues a graph-based approach that was developed to address the challenges in modelling high-levels of intermittent generation and sectoral integration.</Field><Field Name="User documentation">https://leonardgoeke.github.io/AnyMOD.jl/stable/</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/leonardgoeke/AnyMOD.jl</Field><Field Name="data_availability">some</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Julia/JuMP</Field><Field Name="External optimizer">yes</Field><Field Name="GUI">No</Field><Field Name="model_class">Framework</Field><Field Name="sectors">User-dependent</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">price-sensitive demand</Field><Field Name="Market models">exogenous supply and demand curves</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">may depend on the time, the plant's age, and the operational mode</Field><Field Name="georegions">User-dependent</Field><Field Name="georesolution">User-dependent</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Continuous Linear Optimization</Field><Field Name="math_objective">cost minimization by default, can set other objectives</Field><Field Name="deterministic">single-stage scenarios</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Göke (2020), AnyMOD - A graph-based framework for energy system modelling with high levels of renewables and sector integration, Working Paper.</Field><Field Name="report_references">Hainsch et al. (2020), European Green Deal: Using Ambitious Climate Targets and Renewable Energy to Climb out of the Economic Crisis, DIW Weekly Report.</Field><Field Name="example_research_questions">Pathways for the decarbonisation of the European energy system until 2050</Field><Field Name="Model validation">Integrated error reporting</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="5971" Title="PyLESA"><Template Name="Model"><Field Name="Full_Model_Name">Python for Local Energy Systems Analysis</Field><Field Name="Acronym">PyLESA</Field><Field Name="author_institution">University of Strathclyde</Field><Field Name="authors">Andrew Lyden</Field><Field Name="contact_persons">Andrew Lyden</Field><Field Name="contact_email">andrew.lyden@strath.ac.uk</Field><Field Name="source_download">https://github.com/andrewlyden/PyLESA</Field><Field Name="text_description">PyLESA is an open source tool capable of modelling local energy systems containing both electrical and thermal technologies. It was developed with the aim of aiding the design of local energy systems. The focus of the tool is on modelling systems with heat pumps and thermal storage alongside time-of-use electricity tariffs and predictive control strategies. It is anticipated that the tool provides a framework for future development including electrical battery studies and participation in grid balancing mechanisms.

This tool was developed as part of a PhD, &quot;Modelling and Design of Local Energy Systems Incorporating Heat Pumps, Thermal Storage, Future Tariffs, and Model Predictive Control &quot; by Andrew Lyden.</Field><Field Name="Primary purpose">Design of local energy systems with heat pumps, thermal storage and MPC</Field><Field Name="Primary outputs">Technical; economic</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/andrewlyden/PyLESA</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Python</Field><Field Name="External optimizer">APOPT</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Local energy systems</Field><Field Name="sectors">electricity, heat</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households</Field><Field Name="Energy carriers (Renewable)">Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="Market models">Variety of exisitng and future electricity tariffs</Field><Field Name="decisions">dispatch</Field><Field Name="Changes in efficiency">State</Field><Field Name="georesolution">Local/Community/District</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">AC load flow, DC load flow</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_objective">Minimization of operational costs</Field><Field Name="deterministic">perfect foresight</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">Minutes to Hours</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6037" Title="Energy Policy Simulator"><Template Name="Model"><Field Name="Full_Model_Name">Energy Policy Simulator</Field><Field Name="Acronym">EPS</Field><Field Name="author_institution">Energy Innovation, LLC</Field><Field Name="authors">Jeffrey Rissman, Robbie Orvis</Field><Field Name="contact_persons">Jeffrey Rissman, Robbie Orvis</Field><Field Name="contact_email">jeff@energyinnovation.org</Field><Field Name="website">https://energypolicy.solutions</Field><Field Name="source_download">https://github.com/Energy-Innovation/eps-us/archive/2.1.2.zip</Field><Field Name="text_description">About the Energy Policy Simulator

The Energy Policy Simulator (EPS) is a computer model developed by Energy Innovation LLC as part of its Energy Policy Solutions project, an effort which aims to inform policymakers and regulators about which climate and energy policies will reduce greenhouse gas emissions most effectively and at the lowest cost.

The EPS allows the user to control dozens of different policies that affect energy use and emissions in various sectors of the economy (such as a carbon tax, fuel economy standards for vehicles, reducing methane leakage from industry, and accelerated R&amp;D advancement of various technologies). The model includes every major sector of the economy: transportation, electricity supply, buildings, industry, agriculture, and land use. The model reports outputs at annual intervals and provides numerous outputs, including:

-&gt;Emissions of 12 different pollutants (CO2, nitrogen oxides (NOx), sulfur oxides (SOx), fine particulate matter (PM2.5), and eight others), as well as carbon dioxide equivalent (CO2e; a measure of the global warming potential of various pollutants).

-&gt;Direct cash flow (cost or savings) impacts on consumers, industry (as a whole), government, and several specific industries

-&gt;Human deaths avoided thanks to reduced particulate pollution
The composition and output of the electricity sector (e.g. capacity and generation from coal, natural gas, wind, solar, etc.)

-&gt;Vehicle technology market shares and fleet composition (electric vehicles, etc.)

-&gt;Energy use by fuel type from various energy-using technologies (specific types of vehicles, building components, etc.)

-&gt;Breakdowns of how each policy within a policy package contributes to total abatement and the cost-effectiveness of each policy (e.g. wedge diagrams and cost curves)

The EPS is a system dynamics computer model created in a commercial program called Vensim. Vensim is a tool produced by Ventana Systems for the creation and simulation of system dynamics models. The Energy Policy Simulator has been designed to be used with the free Vensim Model Reader. Directions on how to obtain Vensim Model Reader and the Energy Policy Simulator can be found on the Download and Installation Instructions page.

The model is distributed with a complete set of input data representing the United States, but it has a modular structure that allows it to be adapted to different countries and regions by swapping the input data. The EPS reads in all of its input data from external text files, which are generated by accompanying Excel files. All of these files are included in the model distribution.

Additional Information

The EPS is released under the GNU General Public License version 3 (GPLv3) or any later version and is free and open-source software. For more information, please see the Software License page.

The EPS has benefited from the work of many contributors and reviewers.</Field><Field Name="Primary outputs">Energy use, emissions, costs</Field><Field Name="Framework">System Dynamics</Field><Field Name="User documentation">https://us.energypolicy.solutions/docs</Field><Field Name="Code documentation">https://us.energypolicy.solutions/docs</Field><Field Name="Number of developers">2</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/Energy-Innovation/eps-us/archive/2.1.2.zip</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Vensim</Field><Field Name="processing_software">Vensim</Field><Field Name="GUI">Yes</Field><Field Name="model_class">System Dynamics</Field><Field Name="sectors">Electricity, buildings, transportation, industry, district heat, land, agriculture, hydrogen, etc...</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georesolution">single region</Field><Field Name="timeresolution">Year</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">Annual forward simulating model with some investment optimization and full accounting of policy interactions.</Field><Field Name="deterministic">Monte carlo</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">&gt;1000</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">0.05</Field><Field Name="computation_time_hardware">Standard laptop</Field><Field Name="computation_time_comments">Model run is completed in a few seconds for a single run</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6049" Title="POMATO"><Template Name="Model"><Field Name="Full_Model_Name">Power Market Tool</Field><Field Name="Acronym">POMATO</Field><Field Name="author_institution">TU Berlin</Field><Field Name="authors">Richard Weinhold, Robert Mieth</Field><Field Name="contact_persons">Richard Weinhold</Field><Field Name="contact_email">riw@wip.tu-berlin.de</Field><Field Name="website">https://github.com/richard-weinhold/pomato</Field><Field Name="source_download">https://github.com/richard-weinhold/pomato</Field><Field Name="logo">Pomato.gif</Field><Field Name="text_description">POMATO stands for (POwer MArket TOol) and is an easy to use tool for the comprehensive analysis of the modern electricity market. It comprises the necessary power engineering framework to account for power flow physics, thermal transport constraints and security policies of the underlying transmission infrastructure, depending on the requirements defined by the user. POMATO was specifically designed to realistically model Flow-Based Market-Coupling (FBMC) and is therefore equipped with a fast security constrained optimal power flow algorithm and allows zonal market clearing with endogenously generated flow-based parameters, and redispatch.</Field><Field Name="Primary outputs">Economic dispatch subhect to various network representations</Field><Field Name="User documentation">https://pomato.readthedocs.io/en/latest/</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU Library or &quot;Lesser&quot; General Public License version 3.0 (LGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/richard-weinhold/pomato</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia/JuMP</Field><Field Name="processing_software">Python</Field><Field Name="External optimizer">Clp per default, commercial solvers are compadible</Field><Field Name="Additional software">No</Field><Field Name="GUI">No</Field><Field Name="model_class">Network-constrained Unit Commitment and Economic Dispatch,</Field><Field Name="sectors">Electricity Market, Heat</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Energy carriers (Solid)">Uranium</Field><Field Name="Energy carriers (Renewable)">Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">User-dependent</Field><Field Name="georesolution">Nodal resolution</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow, net transfer capacities</Field><Field Name="Observation period">Less than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear Economic Dispatch. Linear Optimal Power Flow. Linear Security Constrained Optimal Power Flow</Field><Field Name="math_objective">Cost minimization</Field><Field Name="deterministic">Chance Constrained</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Weinhold, Richard, and Robert Mieth. 2021. “Power Market Tool (POMATO) for the Analysis of Zonal Electricity Markets.” SoftwareX 16 (December): 100870.</Field><Field Name="citation_doi">10.1016/j.softx.2021.100870</Field><Field Name="report_references">Schönheit, Weinhold, Dierstein (2020), The impact of different strategies for generation shift keys (GSKs) on the flow-based market coupling domain: A model-based analysis of Central Western Europe. https://doi.org/10.1016/j.apenergy.2019.114067.


Weinhold, Richard, and Robert Mieth. 2020. “Fast Security-Constrained Optimal Power Flow Through Low-Impact and Redundancy Screening.” IEEE Transactions on Power Systems 35 (6): 4574–84. https://doi.org/10.1109/TPWRS.2020.2994764.


Weinhold, Richard. 2021. “Evaluating Policy Implications on the Restrictiveness of Flow-Based Market Coupling with High Shares of Intermittent Generation: A Case Study for Central Western Europe.” ArXiv preprint 2109.04940v1. https://arxiv.org/abs/2109.04940.


Weinhold, Richard, and Robert Mieth. 2021. “Uncertainty-Aware Capacity Allocation in Flow-Based Market Coupling.” ArXiv preprint 2109.04968v2. https://arxiv.org/abs/2109.04968.</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6058" Title="CapacityExpansion"><Template Name="Model"><Field Name="Full_Model_Name">CapacityExpansion</Field><Field Name="author_institution">Stanford University, RWTH Aachen</Field><Field Name="authors">Lucas Elias Kuepper, Holger Teichgraeber, Patricia Levi, Ali Ramadhan</Field><Field Name="contact_persons">Lucas Elias Kuepper</Field><Field Name="contact_email">elias.kuepper@rwth-aachen.de</Field><Field Name="website">https://youngfaithful.github.io/CapacityExpansion.jl/stable/</Field><Field Name="source_download">https://github.com/YoungFaithful/CapacityExpansion.jl</Field><Field Name="logo">Cep text.svg</Field><Field Name="text_description">CapacityExpansion is a julia implementation of an input-data-scaling capacity expansion modeling framework.</Field><Field Name="Primary outputs">interface between the optimization result and further analysis</Field><Field Name="Framework">JUMP/julia</Field><Field Name="Code documentation">https://youngfaithful.github.io/CapacityExpansion.jl/stable/</Field><Field Name="Number of developers">3</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/YoungFaithful/CapacityExpansion.jl</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia/JuMP</Field><Field Name="processing_software">Julia</Field><Field Name="External optimizer">Gurobi/Clp</Field><Field Name="GUI">No</Field><Field Name="model_class">Capacity Expansion Problem</Field><Field Name="sectors">electricity, heat, gas</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">input data dependent</Field><Field Name="georegions">input data dependent</Field><Field Name="georesolution">input data dependent</Field><Field Name="Additional dimensions (Ecological)">input data dependent</Field><Field Name="Additional dimensions (Social)">input data dependent</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Optimization, Linear optimization model input-data depending energy system</Field><Field Name="math_objective">Total system cost</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">input data dependent</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">https://joss.theoj.org/papers/10.21105/joss.02034#</Field><Field Name="citation_doi">https://doi.org/10.21105/joss.02034</Field><Field Name="example_research_questions">CapacityExpansioncan be applied to plan and validate a variety of energy systems. Thefocus on time-series aggregation, storage modelling, and integration of multiple energy carriersmake it especially valuable for the planning and validation of future energy systems with highershares of non-dispatchable generation and sector coupling technologie</Field><Field Name="Model validation">http://dx.doi.org/10.1016/j.eneco.2016.08.001</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6127" Title="LoadProfileGenerator"><Template Name="Model"><Field Name="Full_Model_Name">LoadProfileGenerator</Field><Field Name="Acronym">LPG</Field><Field Name="author_institution">FZ Jülich</Field><Field Name="authors">Noah Pflugradt, Peter Stenzel, Martin Robinius, Detlef Stolten</Field><Field Name="contact_persons">Noah Pflugradt</Field><Field Name="contact_email">Noah.Pflugradt@gmail.com</Field><Field Name="website">loadprofilegenerator.de</Field><Field Name="source_download">https://github.com/FZJ-IEK3-VSA/LoadProfileGenerator</Field><Field Name="text_description">Generates residential profiles for electricity, water, car charging, occupancy and more.
Agentbased simulation using a psychological behavior model.</Field><Field Name="Primary outputs">load profiles for use in other simulations</Field><Field Name="Framework">C#</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/FZJ-IEK3-VSA/LoadProfileGenerator</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">C#</Field><Field Name="GUI">Yes</Field><Field Name="Demand sectors">Households</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="timeresolution">Minute</Field><Field Name="Observation period">Less than one year, More than one year</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6131" Title="Medea"><Template Name="Model"><Field Name="Full_Model_Name">medea</Field><Field Name="Acronym">medea</Field><Field Name="author_institution">University of Natural Resources and Life Sciences, Vienna</Field><Field Name="authors">Sebastian Wehrle, Johannes Schmidt</Field><Field Name="contact_persons">Sebastian Wehrle</Field><Field Name="contact_email">sebastian.wehrle@boku.ac.at</Field><Field Name="website">https://github.com/inwe-boku/medea</Field><Field Name="source_download">https://github.com/inwe-boku/medea</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/inwe-boku/medea</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS</Field><Field Name="processing_software">Python</Field><Field Name="External optimizer">CPLEX, Gurobi</Field><Field Name="GUI">No</Field><Field Name="model_class">Austrian and German electricity market</Field><Field Name="sectors">Electricity, Heat,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Energy carrier (Gas)">Natural gas</Field><Field Name="Energy carrier (Liquid)">Diesel</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Storage (Electricity)">Battery, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">state-dependent (CHP)</Field><Field Name="georegions">Austria, Germany</Field><Field Name="georesolution">Countries</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_objective">Total system cost</Field><Field Name="deterministic">Deterministic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">15</Field><Field Name="citation_references">https://arxiv.org/abs/2006.08009</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6158" Title="System Advisor Model (SAM)"><Template Name="Model"><Field Name="Full_Model_Name">System Advisor Model</Field><Field Name="Acronym">SAM</Field><Field Name="author_institution">National Renewable Energy Laboratory</Field><Field Name="contact_email">sam.support@nrel.gov</Field><Field Name="website">https://sam.nrel.gov</Field><Field Name="source_download">https://github.com/nrel/sam</Field><Field Name="logo">Sam icon 256.jpg</Field><Field Name="text_description">The System Advisor Model (SAM) is a free techno-economic software model that facilitates decision-making for people in the renewable energy industry.</Field><Field Name="Primary outputs">Hourly or subhourly time series power generation and performance data, annual cash flow with cost and financial metrics.</Field><Field Name="Support">Free forum on website, email, periodic webinars and online round table meetings with SAM team.</Field><Field Name="User documentation">https://sam.nrel.gov/download.html</Field><Field Name="Code documentation">https://github.com/nrel/sam</Field><Field Name="Source of funding">U.S. Department of Energy and other sponsors.</Field><Field Name="Number of developers">8-12</Field><Field Name="Number of users">125,500 email subscribers</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/NREL/SAM/blob/develop/LICENSE</Field><Field Name="open_future">No</Field><Field Name="modelling_software">C++, WxWidgets</Field><Field Name="Additional software">https://github.com/NREL/SAM/wiki/Software-Dependencies</Field><Field Name="GUI">Yes</Field><Field Name="model_class">International renewble energy project modeling</Field><Field Name="sectors">power generation</Field><Field Name="technologies">Renewables</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">time series electric load data</Field><Field Name="Market models">power generation</Field><Field Name="decisions">dispatch</Field><Field Name="timeresolution">Minute</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">Time series simulation of power system performance coupled with annual pro forma cash flow calculations.</Field><Field Name="math_objective">time series power generation, installation cost, annual operating and financial cost</Field><Field Name="deterministic">stochastic, deterministic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">1</Field><Field Name="computation_time_comments">Varies with performance model</Field><Field Name="citation_references">Blair, N.; Dobos, A.; Freeman, J.; Neises, T.; Wagner, M.; Ferguson, T.; Gilman, P.; Janzou, S. (2014). System Advisor Model, SAM 2014.1.14: General Description. NREL/TP-6A20-61019. National Renewable Energy Laboratory. Golden, CO. Accessed October 31, 2016.</Field><Field Name="citation_doi">https://doi.org/10.2172/1126294</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6170" Title="OpenTUMFlex"><Template Name="Model"><Field Name="Full_Model_Name">OpenTUMFlex</Field><Field Name="Acronym">OpenTUMFlex</Field><Field Name="author_institution">Technical University of Munich</Field><Field Name="authors">Michel Zade, Babu Kumaran Nalini, Zhengjie You, Peter Tzscheuschler</Field><Field Name="contact_persons">Michel Zade, Babu Kumaran Nalini, Zhengjie You, Peter Tzscheuschler</Field><Field Name="contact_email">michel.zade@tum.de, babu.kumaran-nalini@tum.de, zhengjie.you@tum.de, ptzscheu@tum.de</Field><Field Name="website">https://www.ei.tum.de/en/ewk/forschung/projekte/c-sells/</Field><Field Name="source_download">https://github.com/tum-ewk/OpenTUMFlex</Field><Field Name="text_description">An open-source flexibility estimation model that quantifies all possible flexibilities from the available prosumer devices and prices them.</Field><Field Name="Primary outputs">Flexibility tables</Field><Field Name="Source of funding">SINTEG C/sells</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/tum-ewk/OpenTUMFlex</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy System Model, urban energy systems, load shifting optimisation, Local energy systems,</Field><Field Name="sectors">Energy, Electricity Market, Households, electricity plus sector coupling (EVs,</Field><Field Name="technologies">Renewables, CHP</Field><Field Name="Demand sectors">Households, Industry</Field><Field Name="Energy carriers (Renewable)">Sun</Field><Field Name="Transfer (Electricity)">Distribution</Field><Field Name="Transfer (Gas)">Distribution</Field><Field Name="Transfer (Heat)">Distribution</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">Prosumer Flexibility</Field><Field Name="Market models">Local energy market</Field><Field Name="georegions">User dependent</Field><Field Name="georesolution">User dependent</Field><Field Name="timeresolution">15 Minute</Field><Field Name="network_coverage">distribution</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_objective">Cost optimal optimization and flexibility calculation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Zade, M.; You, Z.; Kumaran Nalini, B.; Tzscheutschler, P.; Wagner, U. Quantifying the Flexibility of Electric Vehicles in Germany and California—A Case Study. Energies 2020, 13, 5617.</Field><Field Name="citation_doi">doi:10.3390/en13215617</Field><Field Name="example_research_questions">How can prosumer offer flexibility to the grid? 
Can prosumer flexibility be quantified?</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6250" Title="TIMES"><Template Name="Model"><Field Name="Full_Model_Name">The Integrated MARKAL EFOM Model</Field><Field Name="Acronym">TIMES</Field><Field Name="author_institution">IEA-ETSAP</Field><Field Name="authors">IEA-ETSAP</Field><Field Name="contact_persons">George Giannakidis</Field><Field Name="contact_email">ggian@etsap.org</Field><Field Name="website">www.etsap.org</Field><Field Name="source_download">https://github.com/etsap-TIMES/TIMES_model</Field><Field Name="logo">TIMES.png</Field><Field Name="text_description">The TIMES model generator combines two different, but complementary, systematic approaches to modelling energy: a technical engineering approach and an economic approach. TIMES is a technology rich, bottom-up model generator, which uses linear-programming to produce a least-cost energy system, optimized according to a number of user constraints, over medium to long-term time horizons.</Field><Field Name="Primary outputs">Capacities, Investement requirements, Energy flows, Costs, Emissions</Field><Field Name="User documentation">https://iea-etsap.org/index.php/documentation</Field><Field Name="Code documentation">https://iea-etsap.org/index.php/documentation</Field><Field Name="Source of funding">IEA-ETSAP</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/etsap-TIMES/TIMES_model</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS</Field><Field Name="processing_software">EXCEL, VEDA, ANSWER</Field><Field Name="External optimizer">CPLEX</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Local, National, Regional Global models developed using TIMES</Field><Field Name="sectors">All sectors</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="Market models">Full competition</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">Local, National, Regional, Global models</Field><Field Name="georesolution">Local, National, Regional, Global models</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">Pollutants emission, water demand can be modelled</Field><Field Name="Additional dimensions (Economical)">Total system discounted cost, marginal prices</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Partial equilibrium, least cost optimisation, with MIP, NLP options. Perfect foresight and myopic options.</Field><Field Name="math_objective">Total discounted system cost minimisation</Field><Field Name="deterministic">Deterministic, perfect foresight, myopic, stochastic.</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">5 mins - hours</Field><Field Name="citation_references">Documentation for the TIMES Model, R. Loulou, G. Goldstein, A. Kanudia, A. Lehtila, U. Remme, 2016</Field><Field Name="report_references">https://iea-etsap.org/index.php/documentation</Field><Field Name="example_research_questions">https://iea-etsap.org/index.php/documentation</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6260" Title="SciGRID gas"><Template Name="Model"><Field Name="Full_Model_Name">Scientific Grid Model of European Gas Transmission Networks</Field><Field Name="Acronym">SciGRID_gas</Field><Field Name="author_institution">DLR Institute of Networked Energy Systems</Field><Field Name="authors">Jan Diettrich, Wided Medjroub, Adam Pluta</Field><Field Name="contact_persons">Jan Diettrich, Wided Medjroub, Adam Pluta</Field><Field Name="website">https://www.gas.scigrid.de/</Field><Field Name="source_download">https://zenodo.org/record/4288440#.YFhii9wxmUk</Field><Field Name="logo">Scigrid gas logo.png</Field><Field Name="text_description">The SciGRID_gas project provides an open-source gastransmission data model for Europe with rich geographical and meta information originating from various publicly available sources. It is build by the German Aerospace Center DLR Institute of Networked Energy Systems Oldenburg and funded as a three year project by the German Federal Ministry for Economic Affairs and Energy (BMWi).

The following SciGRID_gas data sets are available:

   • INET_Raw: InternetDaten data set; Data originates from an internet research of Wikipedia, gas TSOs
     fact sheets, maps, press releases and more.
   • INET_filled: INET_raw dataset with all empty values estimated by heuristic processes and filled
     into the dataset
   • GIE_Raw: Gas Infrastructure Europe data set; Data orginates from Gas Infrastructure Europe
   • NO_Raw: Norway data set; Data originates from Gassco AS, The Norwegian Ministry of Petroleum and Energy
     (www.norskpetroleum.no)
   • LKD_Raw: Long-term Planning and Short-term Optimization data set; Data originates from gas data of LKD_EU
     (ISBN: 978-3-86780-554-4) project
   • EMAP_Raw: Entsog Capacity Map 2019
   • SciGRID_gas IGG: merged data sets of INET_raw, GIE and International Gas Union data set (GSE) data and
     heuristic process to fill missing parameter values
   • SciGRID_gas IGGI: merged data sets of INET, GIE, GSE and International Gas Union data set (IGU)
   • SciGRID_gas IGGIN: merged data sets of INET, GIE, GSE, IGU and NO
   • SciGRID_gas IGGINL: merged data sets of INET, GIE, GSE, IGU, NO and LKD

All data sets can we downloaded at https://zenodo.org/search?page=1&amp;size=20&amp;q=SciGRID_gas.</Field><Field Name="Primary outputs">Interconnected geographical and meta information about pipelines, storages, LNG terminals, production sites, compressors, border points, interconnection points and entry points</Field><Field Name="User documentation">https://zenodo.org/record/4288440#.YFhii9wxmUk</Field><Field Name="Source of funding">German Federal Ministry for Economic Affairs and Energy (BMWi)</Field><Field Name="Number of developers">3</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Creative Commons Attribution 4.0 (CC-BY-4.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://zenodo.org/search?page=1&amp;size=20&amp;q=SciGRID_gas</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GeoJSON &amp; CSV</Field><Field Name="GUI">No</Field><Field Name="model_class">European Gas Transmission Network Model and Data (input and output)</Field><Field Name="sectors">Gas</Field><Field Name="Energy carrier (Gas)">Natural gas</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">No</Field><Field Name="georegions">Europe</Field><Field Name="georesolution">Individual gas transmission elements (pipelines, compressorstations, borderpoints etc.)</Field><Field Name="math_modeltype">Simulation, Other</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Jan Diettrich, Wided Medjroubi &amp; Adam Pluta. (2020). SciGRID_gas IGGINL (Version 1.0.0) [Data Set]. Zenobo. http://doi.org/10.5281/zenobo.4288440</Field><Field Name="citation_doi">10.5281/zenodo.4288440</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6283" Title="PowerSimulationsDynamics.jl"><Template Name="Model"><Field Name="Full_Model_Name">PowerSimulationsDynamics.jl</Field><Field Name="author_institution">NREL</Field><Field Name="authors">Jose-Daniel Lara, Rodrigo Henríquez-Auba</Field><Field Name="contact_persons">Clayton Barrows</Field><Field Name="contact_email">nrel-siip@nrel.gov</Field><Field Name="website">https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl</Field><Field Name="source_download">https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl</Field><Field Name="logo">SIIP energy.jpg</Field><Field Name="Primary purpose">Power systems transient stability analysis</Field><Field Name="Primary outputs">Voltage, Rotor Angles, Frequency</Field><Field Name="Support">https://join.slack.com/t/nrel-siip/shared_invite/zt-glam9vdu-o8A9TwZTZqqNTKHa7q3BpQ</Field><Field Name="Framework">https://www.nrel.gov/analysis/siip.html</Field><Field Name="User documentation">https://nrel-siip.github.io/PowerSimulationsDynamics.jl/stable/</Field><Field Name="Code documentation">https://nrel-siip.github.io/PowerSimulationsDynamics.jl/stable/</Field><Field Name="Number of developers">2</Field><Field Name="Number of users">30</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia</Field><Field Name="processing_software">Julia</Field><Field Name="External optimizer">SUNDIALS</Field><Field Name="GUI">No</Field><Field Name="model_class">Dynamic system simulation model library,</Field><Field Name="sectors">Electric power, Electricity, electricity,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Industry, Commercial sector, Other</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="georesolution">Nodal resolution</Field><Field Name="timeresolution">Less than second</Field><Field Name="network_coverage">transmission, AC load flow</Field><Field Name="Observation period">Less than one month, Less than one year</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">PowerSimulationsDynamics.jl enables transient stability analysis of power systems through differential-algebraic equations and with forward differentiation to enable small-signal stability analysis.</Field><Field Name="math_objective">N/A</Field><Field Name="deterministic">scenario analysis</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">Highly dependent on application</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">Highly dependent on application</Field><Field Name="Integrated models">PowerModels.jl</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6289" Title="PowerSystems.jl"><Template Name="Model"><Field Name="Full_Model_Name">PowerSystems.jl</Field><Field Name="author_institution">NREL</Field><Field Name="authors">Clayton Barrows, Jose-Daniel Lara, Daniel Thom, Dheepak Krishnamurthy, Sourabh Dalvi</Field><Field Name="contact_persons">Clayton Barrows</Field><Field Name="contact_email">nrel-siip@nrel.gov</Field><Field Name="website">https://github.com/NREL-SIIP/PowerSystems.jl</Field><Field Name="source_download">https://github.com/NREL-SIIP/PowerSystems.jl</Field><Field Name="logo">Siip-power1.jpg</Field><Field Name="text_description">The PowerSystems.jl package provides a rigorous data model using Julia structures to enable power systems analysis and modeling. In addition to stand-alone system analysis tools and data model building, the PowerSystems.jl package is used as the foundational data container for the PowerSimulations.jl and PowerSimulationsDynamics.jl packages. PowerSystems.jl supports a limited number of data file formats for parsing.</Field><Field Name="Support">https://join.slack.com/t/nrel-siip/shared_invite/zt-glam9vdu-o8A9TwZTZqqNTKHa7q3BpQ</Field><Field Name="Framework">https://nrel-sienna.github.io/Sienna/#</Field><Field Name="User documentation">https://nrel-sienna.github.io/PowerSystems.jl/stable/</Field><Field Name="Code documentation">https://nrel-sienna.github.io/PowerSystems.jl/stable/</Field><Field Name="Number of developers">13</Field><Field Name="Number of users">200</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/NREL-Sienna/PowerSystems.jl</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia</Field><Field Name="processing_software">Julia</Field><Field Name="GUI">No</Field><Field Name="model_class">power system model</Field><Field Name="sectors">Electricity, Electricity Sector, Electric power,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Market models">ISO</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">Any</Field><Field Name="georesolution">Nodal resolution</Field><Field Name="timeresolution">Less than second</Field><Field Name="network_coverage">transmission, AC load flow, DC load flow, net transfer capacities</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">PowerSystems.jl includes basic power flow and network matrix calculation capabilities.</Field><Field Name="deterministic">scenario analysis</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">100000</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">0.1</Field><Field Name="computation_time_hardware">Laptop</Field><Field Name="Integrated models">Sienna, PowerSimulations.jl, PowerSimulationsDynamics.jl</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="6302" Title="ASAM"><Template Name="Model"><Field Name="Full_Model_Name">Ancillary Services Acquisition Model</Field><Field Name="Acronym">ASAM</Field><Field Name="author_institution">Europa-Universität Flensburg</Field><Field Name="authors">Samuel Glismann</Field><Field Name="contact_persons">Samuel Glismann</Field><Field Name="website">https://ancillaryservicesacquisitionmodel.github.io/ASAM/</Field><Field Name="source_download">https://github.com/AncillaryServicesAcquisitionModel/ASAM</Field><Field Name="text_description">Agent-based model to simulate processes of ancillary services acquisition and electricity markets. ASAM uses the agent-based model framework Mesa and the toolbox for power system analyses PyPSA.</Field><Field Name="Primary outputs">Policy performance indicators</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/AncillaryServicesAcquisitionModel/ASAM</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="processing_software">Python, PyPSA, Mesa</Field><Field Name="External optimizer">Solvers supported by Pyomo</Field><Field Name="GUI">No</Field><Field Name="model_class">Agent-based Simulation, Market Model, Electricity System Model, German and European Electricity Market,</Field><Field Name="sectors">Electricity, Electricity Market, Electric power,</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">Bounded rationality</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Europe</Field><Field Name="georesolution">Individual power stations</Field><Field Name="timeresolution">15 Minute</Field><Field Name="Observation period">Less than one month</Field><Field Name="math_modeltype">Simulation, Agent-based</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Glismann (2021), “ Ancillary Services Acquisition Model: considering market interactions in policy design”, preprint Applied Energy Journal.  https://arxiv.org/abs/2104.13047</Field><Field Name="example_research_questions">Redispatch design in the Netherlands</Field><Field Name="Specific properties">Clearing algorithms</Field><Field Name="Integrated models">PyPSA</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6364" Title="Breakthrough Energy Model"><Template Name="Model"><Field Name="Full_Model_Name">Breakthrough Energy Model</Field><Field Name="author_institution">Breakthrough Energy Foundation</Field><Field Name="authors">Yixing Xu, Dhileep Sivam, Kaspar Mueller, Bainan Xia, Daniel Olsen, Yifan Li, Dan Livengood, Victoria Hunt, Ben Rouillé d’Orfeuil, Merrielle Ondreicka, Anna Hurlimann, Daniel Muldrew, Jon Hagg, Kamilah Jenkins</Field><Field Name="contact_persons">Yixing Xu</Field><Field Name="contact_email">sciences@breakthroughenergy.org</Field><Field Name="website">https://breakthrough-energy.github.io/docs/index.html</Field><Field Name="source_download">https://github.com/Breakthrough-Energy</Field><Field Name="text_description">The Breakthrough Energy Model is a production cost model with capacity expansion algorithms and heuristics, originally designed to explore the generation and transmission expansion needs to meet U.S. states’ clean energy goals. The data management occurs within Python, the DCOPF optimization problem is created via Julia, and the preferred solver currently being used is Gurobi, while it is flexible to choose various free or proprietary solvers.  A fully integrated capacity expansion model is in development.</Field><Field Name="Primary outputs">DCOPF, scenario studies</Field><Field Name="Support">https://join.slack.com/t/besciencescommunity/shared_invite/zt-or95p3pi-kO1pj1b6O64THiHU9bgzkA</Field><Field Name="Framework">https://science.breakthroughenergy.org/open-source-release</Field><Field Name="User documentation">https://breakthrough-energy.github.io/docs/index.html</Field><Field Name="Code documentation">https://breakthrough-energy.github.io/docs/index.html</Field><Field Name="Number of developers">20</Field><Field Name="Number of users">40</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/Breakthrough-Energy</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia/JuMP</Field><Field Name="processing_software">Python</Field><Field Name="External optimizer">Gurobi</Field><Field Name="GUI">No</Field><Field Name="model_class">Framework</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Other</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Market models">Assumes a single-actor cost minimization</Field><Field Name="decisions">dispatch</Field><Field Name="georegions">Currently U.S., but extendable to any region</Field><Field Name="georesolution">Nodal</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">The Breakthrough Energy Model runs DCOPF simulations</Field><Field Name="math_objective">Minimize cost</Field><Field Name="deterministic">Scenario Analysis (Deterministic)</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="number_of_variables">1e9</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">1,200</Field><Field Name="computation_time_hardware">AMD EPYC 7452 32-Core Processor</Field><Field Name="computation_time_comments">Computation time and number of variables reflects a typical run using 8 or 16 cores for a full 82,000 node model of the continental U.S.</Field><Field Name="citation_references">Y. Xu et al., &quot;U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies,&quot; 2020 IEEE Power &amp; Energy Society General Meeting (PESGM), 2020, pp. 1-5.</Field><Field Name="citation_doi">10.1109/PESGM41954.2020.9281850</Field><Field Name="report_references">Yixing Xu, Daniel Olsen, Bainan Xia, Dan Livengood, Victoria Hunt, Yifan Li, and Lane Smith. 2021. “A 2030 United States Macro Grid: Unlocking Geographical Diversity to Accomplish Clean Energy Goals.” Seattle, WA: Breakthrough Energy Sciences.
https://science.breakthroughenergy.org/publications/MacroGridReport.pdf</Field><Field Name="Model validation">Historical comparison to annual, state-level generation levels by generation type from EIA-923 form for 2016</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="6376" Title="CESAR-P"><Template Name="Model"><Field Name="Full_Model_Name">Combined Energy Simulation and Retrofit in Python</Field><Field Name="Acronym">CESAR-P</Field><Field Name="author_institution">Urban Energy Systems Lab, Empa (Swiss Federal Laboratories for Materials Science and Technology)</Field><Field Name="authors">Leonie Fierz, Aaron Bojarski, Ricardo Parreira da Silva, Sven Eggimann</Field><Field Name="contact_persons">Kristina Orehounig</Field><Field Name="website">https://github.com/hues-platform/cesar-p-core</Field><Field Name="source_download">https://github.com/hues-platform/cesar-p-core</Field><Field Name="text_description">The package allows for simulating the building energy demand of a district, including options for retrofitting, cost and emission calculation.</Field><Field Name="Primary outputs">Demand profiles</Field><Field Name="User documentation">https://cesar-p-core.readthedocs.io/en/latest/</Field><Field Name="Code documentation">https://cesar-p-core.readthedocs.io/en/latest/development/index.html</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Affero General Public License v3 (AGPL-3.0)</Field><Field Name="model_source_public">No</Field><Field Name="Link to source">https://github.com/hues-platform/cesar-p-core</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python, EnergyPlus</Field><Field Name="Additional software">EnergyPlus</Field><Field Name="GUI">No</Field><Field Name="model_class">Swiss building stock</Field><Field Name="sectors">electricity, heating, cooling, domestic hot water</Field><Field Name="Demand sectors">Households, Commercial sector</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="georegions">Switzerland</Field><Field Name="georesolution">depending on input data</Field><Field Name="timeresolution">Hour</Field><Field Name="math_modeltype">Simulation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Leonie Fierz, Urban Energy Systems Lab, Empa. (2021, July 30). hues-platform/cesar-p-core: CESAR-P-V2.0.1 (CESAR-P-V2.0.1). Zenodo. https://doi.org/10.5281/zenodo.5148531</Field><Field Name="citation_doi">https://doi.org/10.5281/zenodo.5148531</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6454" Title="SMS++"><Template Name="Model"><Field Name="Full_Model_Name">SMS++ energy Blocks</Field><Field Name="Acronym">SMS++</Field><Field Name="author_institution">Dipartimento di Informatica, Università di Pisa</Field><Field Name="authors">The SMS++ Team</Field><Field Name="contact_persons">Antonio Frangioni</Field><Field Name="contact_email">frangio@di.unipi.it</Field><Field Name="website">https://smspp.gitlab.io</Field><Field Name="source_download">https://gitlab.com/smspp/smspp-project</Field><Field Name="logo">Logo noback.png</Field><Field Name="text_description">SMS++ is a software framework for modelling and solving large-scale problems with multiple nested forms of structure. The primary application of SMS++ has been to energy problems and several specific components have been developed.</Field><Field Name="Primary purpose">flexible framework for in principle any energy optimization problem</Field><Field Name="Primary outputs">commitment, energy, and all related measures</Field><Field Name="Support">The SMS++ Team</Field><Field Name="Framework">SMS++</Field><Field Name="User documentation">unfortunately none, working on it</Field><Field Name="Code documentation">https://smspp.gitlab.io</Field><Field Name="Source of funding">mostly PGMO projects and plan4res</Field><Field Name="Number of developers">5 / 6 but not all on energy problems</Field><Field Name="Number of users">2 / 3 academic, 1 industrial so far</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU Library or &quot;Lesser&quot; General Public License version 3.0 (LGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://gitlab.com/smspp/smspp-project</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">SMS++</Field><Field Name="processing_software">hand-coded C++</Field><Field Name="External optimizer">choice between Cplex, SCIP, SDDP, BundleSolver, hepefully many other to come</Field><Field Name="Additional software">netCDF, boost, Eigen</Field><Field Name="GUI">No</Field><Field Name="model_class">in princople all short- to long-term optimization</Field><Field Name="sectors">electricity, heat components partly developed, but extensible to anything</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">optimization</Field><Field Name="Market models">none so far, but extendible</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">possible via scenario</Field><Field Name="georegions">any</Field><Field Name="georesolution">any</Field><Field Name="timeresolution">Multi year</Field><Field Name="network_coverage">transmission, distribution, DC load flow, net transfer capacities</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="Additional dimensions (Ecological)">emission consraints (but extendable)</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">in principle any optimization model, particular emphasis on decomposition approaches</Field><Field Name="math_objective">in principle any, currently cost minimization</Field><Field Name="deterministic">in principle any, currently scenarios</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">unlimited</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">depends</Field><Field Name="computation_time_hardware">from laptop to HPC</Field><Field Name="citation_references">under construction</Field><Field Name="report_references">https://edition.pagesuite-professional.co.uk/html5/reader/production/default.aspx?pubname=&amp;edid=f0cd4626-ba9b-4718-8e54-5e7da5346ec4</Field><Field Name="example_research_questions">https://www.plan4res.eu/wp-content/uploads/2019/06/plan4res-Definition-Case-Studies-Summary-CS1.pdf</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="6484" Title="Demod"><Template Name="Model"><Field Name="Full_Model_Name">domestic energy demand model</Field><Field Name="Acronym">demod</Field><Field Name="author_institution">EPFL</Field><Field Name="authors">Matteo Barsanti, Lionel Constantin</Field><Field Name="contact_persons">Matteo Barsanti, Lionel Constantin</Field><Field Name="contact_email">matteo.barsanti@epfl.ch, constantin.lionel@hotmail.com</Field><Field Name="website">https://demod.readthedocs.io/en/latest/#</Field><Field Name="source_download">https://github.com/epfl-herus/demod</Field><Field Name="text_description">demod is an open-source python library for socio-technical simulation of domestic energy demand (e.g., electrical and thermal). It allows to generate household occupancy, activity, thermal and electrical demand profiles with high temporal resolution</Field><Field Name="Primary outputs">Household occupancy, activity, thermal and electrical demand profiles</Field><Field Name="User documentation">https://demod.readthedocs.io/en/latest/overview/index.html</Field><Field Name="Code documentation">https://demod.readthedocs.io/en/latest/overview/index.html</Field><Field Name="Source of funding">Swiss National Science Foundation (SNSF project number:182878)</Field><Field Name="Number of developers">2</Field><Field Name="Number of users">1</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/epfl-herus/demod</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Python</Field><Field Name="GUI">No</Field><Field Name="model_class">Simulation,</Field><Field Name="sectors">end-use demand</Field><Field Name="Demand sectors">Households</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">Activity-based ennergy demand modeling</Field><Field Name="georegions">Germany, UK</Field><Field Name="georesolution">depending on input data</Field><Field Name="timeresolution">Minute</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">First order and semi- Markov-chain Monte Carlo simulation.</Field><Field Name="math_objective">Assess domestic energy demand evolution and demand-side-management scenarios</Field><Field Name="deterministic">Not yet implemented</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">tens/hundreds</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">10</Field><Field Name="computation_time_hardware">Intel(R) Core(TM) i7-8665U CPU @ 1.90GHz   2.11 GHz. RAM 16.0 GB</Field><Field Name="computation_time_comments">32 households simulated for 1 year with 1 min time resolution</Field><Field Name="citation_references">Barsanti, M., Schwarz, J.S., Gérard Constantin, L.G. et al. Socio-technical modeling of smart energy systems: a co-simulation design for domestic energy demand. Energy Inform 4, 12 (2021).</Field><Field Name="citation_doi">https://doi.org/10.1186/s42162-021-00180-6</Field><Field Name="Model validation">Undergoing</Field><Field Name="Integrated models">co-simulation framework like mosaik</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6532" Title="GRIMSEL-FLEX"><Template Name="Model"><Field Name="Full_Model_Name">General Integrated Modeling environment for the Supply of Electricity and Low-temperature heat</Field><Field Name="Acronym">GRIMSEL</Field><Field Name="author_institution">University of Geneva</Field><Field Name="authors">Martin Soini, Arthur Rinaldi</Field><Field Name="contact_persons">Arthur Rinaldi</Field><Field Name="contact_email">arthur.rinaldi@unige.ch</Field><Field Name="source_download">https://github.com/arthurrinaldi/grimsel</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 2-Clause &quot;Simplified&quot; or &quot;FreeBSD&quot; License (BSD-2-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/arthurrinaldi/grimsel</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="processing_software">Python (pandas et al)</Field><Field Name="External optimizer">CPLEX</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy System Model, Optimization, Social Planner</Field><Field Name="sectors">Electricity, Heat, Hydrogen, Buildings, Transport</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Hydrogen</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, Chemical, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">Demand side response</Field><Field Name="decisions">dispatch</Field><Field Name="Changes in efficiency">Linear cost curves</Field><Field Name="georegions">Switzerland, Austria, Italy, France, Germany</Field><Field Name="georesolution">Consumer types and Urban settings</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">GHG emissions</Field><Field Name="Additional dimensions (Economical)">Shadow prices of each energy carriers</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Quadratic dipatch sector-coupling model</Field><Field Name="math_objective">Minimization of total system costs</Field><Field Name="deterministic">Perfect foresight, Sensitivity analisys, Scenarios</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="number_of_variables">5000000</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">30</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="6559" Title="Pvlib python"><Template Name="Model"><Field Name="Full_Model_Name">pvlib python</Field><Field Name="authors">This is a community supported tool. Contributors to each release are listed here: https://pvlib-python.readthedocs.io/en/stable/whatsnew.html.</Field><Field Name="contact_persons">See: https://github.com/pvlib/pvlib-python#getting-support</Field><Field Name="website">https://pvlib-python.readthedocs.io/en/stable/</Field><Field Name="source_download">https://github.com/pvlib/pvlib-python</Field><Field Name="logo">Pvlib logo horiz.png</Field><Field Name="text_description">pvlib python is a community supported tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems.</Field><Field Name="Primary outputs">Timeseries photovoltaic system power output.</Field><Field Name="Support">pvlib usage questions can be asked on Stack Overflow (http://stackoverflow.com/) and tagged with the pvlib tag (http://stackoverflow.com/questions/tagged/pvlib).  The pvlib-python google group (https://groups.google.com/forum/#!forum/pvlib-python) is used for discussing various topics of interest to the pvlib-python community. We also make new version announcements on the google group.  If you suspect that you may have discovered a bug or if you'd like to change something about pvlib, then please make an issue on our GitHub issues page (https://github.com/pvlib/pvlib-python/issues).</Field><Field Name="Framework">Python</Field><Field Name="User documentation">https://github.com/pvlib/pvlib-python/issues</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/pvlib/pvlib-python</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">NumPy, Pandas</Field><Field Name="GUI">No</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Renewables</Field><Field Name="Energy carriers (Renewable)">Sun</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="math_modeltype">Simulation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">William F. Holmgren, Clifford W. Hansen, and Mark A. Mikofski. “pvlib python: a python package for modeling solar energy systems.” Journal of Open Source Software, 3(29), 884, (2018). https://doi.org/10.21105/joss.00884</Field><Field Name="citation_doi">https://doi.org/10.21105/joss.00884</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6574" Title="AMIRIS"><Template Name="Model"><Field Name="Full_Model_Name">Agent-based Market model for the Investigation of Renewable and Integrated energy Systems</Field><Field Name="Acronym">AMIRIS</Field><Field Name="author_institution">German Aerospace Center</Field><Field Name="contact_persons">Christoph Schimeczek</Field><Field Name="contact_email">Christoph.Schimeczek@dlr.de</Field><Field Name="website">https://helmholtz.software/software/amiris</Field><Field Name="source_download">https://gitlab.com/dlr-ve/esy/amiris/amiris/-/releases</Field><Field Name="logo">amiris-logo.jpg</Field><Field Name="text_description">Agent-based electricty market model for analysing questions on future energy markets, their market design, and energy-related policy instruments</Field><Field Name="Primary outputs">electricity prices, power plant dispatch, cost and income</Field><Field Name="Support">OpenMod Forum</Field><Field Name="Framework">FAME</Field><Field Name="User documentation">https://gitlab.com/dlr-ve/esy/amiris/amiris/-/wikis/home</Field><Field Name="Source of funding">German Aerospace Center, German Federal Ministry for Economic Affairs and Climate Action, European Commission</Field><Field Name="Number of developers">8</Field><Field Name="Number of users">15</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://gitlab.com/dlr-ve/esy/amiris/amiris</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Java</Field><Field Name="processing_software">Python</Field><Field Name="Additional software">FAME</Field><Field Name="GUI">No</Field><Field Name="model_class">Agent-based electricity market model</Field><Field Name="sectors">electricity</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas</Field><Field Name="Energy carrier (Liquid)">Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">bidding behaviour</Field><Field Name="Market models">day-ahead electricity market</Field><Field Name="decisions">dispatch</Field><Field Name="Changes in efficiency">fixed</Field><Field Name="georegions">Germany, Austria</Field><Field Name="georesolution">National</Field><Field Name="timeresolution">Hour</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">CO2 emissions</Field><Field Name="Additional dimensions (Economical)">spot price, income</Field><Field Name="math_modeltype">Simulation, Agent-based</Field><Field Name="math_modeltype_shortdesc">algorithms for market clearing and agent-specific bidding strategies</Field><Field Name="deterministic">stochastic, perfect foresight, deterministic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">1</Field><Field Name="computation_time_hardware">Desktop PC</Field><Field Name="computation_time_comments">one year, one country</Field><Field Name="citation_references">Schimeczek et al. (2023). AMIRIS: Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. Journal of Open Source Software, 8(84), 5041.</Field><Field Name="citation_doi">https://doi.org/10.21105/joss.05041</Field><Field Name="report_references">https://doi.org/10.1016/j.apenergy.2021.117267; https://doi.org/10.3390/en13153920; https://doi.org/10.3390/en13205350; https://doi.org/10.1155/2017/7494313</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6589" Title="Lemlab"><Template Name="Model"><Field Name="Full_Model_Name">local energy market laboratory</Field><Field Name="Acronym">lemlab</Field><Field Name="author_institution">Technical University of Munich</Field><Field Name="authors">Sebastian Dirk Lumpp, Markus Doepfert, Michel Zade</Field><Field Name="contact_persons">Sebastian Dirk Lumpp</Field><Field Name="contact_email">sebastian.lumpp@tum.de</Field><Field Name="website">https://github.com/tum-ewk/lemlab</Field><Field Name="source_download">https://github.com/tum-ewk/lemlab</Field><Field Name="logo">Lemlab logo.png</Field><Field Name="text_description">An open-source tool for the agent-based development and testing of local energy market applications. lemlab allows the user to simulate a LEM using a full agent-based modelling (ABM) in either simulation (SIM) or real-time (RTS) modes. This allows the rapid testing of algorithms as well as the real-time integration of hardware and software components.</Field><Field Name="User documentation">https://lemlab.readthedocs.io</Field><Field Name="Code documentation">https://lemlab.readthedocs.io</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/tum-ewk/lemlab</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python, Pyomo</Field><Field Name="processing_software">PostgreSQL, Ethereum</Field><Field Name="External optimizer">Gurobi, CPLEX, GLPK</Field><Field Name="Additional software">PyCharm, PostgreSQL, Ethereum</Field><Field Name="GUI">No</Field><Field Name="model_class">agent-based simulation</Field><Field Name="sectors">local energy markets</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Other</Field><Field Name="Energy carriers (Renewable)">Sun, Wind</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="math_modeltype">Simulation, Agent-based</Field><Field Name="math_modeltype_shortdesc">Agents: intertemporal convex optimization
Markets: (iterative) double-sided auctions, p2p clearing
Forecasting: naive, deterministic forecasting, neural networks</Field><Field Name="deterministic">perfect forecast, deterministic, stochastic</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">20</Field><Field Name="computation_time_hardware">CPU: Intel Core i7-8550U, SSD: Samsung 3000MB/s read, 1800 MB/s write</Field><Field Name="computation_time_comments">50 prosumers, one day</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6604" Title="HighRES"><Template Name="Model"><Field Name="Full_Model_Name">high spatial and temporal electricity system model</Field><Field Name="Acronym">highRES</Field><Field Name="author_institution">UCL, UiO</Field><Field Name="authors">James Price, Marianne Zeyringer</Field><Field Name="website">https://github.com/highRES-model</Field><Field Name="text_description">The model is used to plan least-cost electricity systems for Europe and specifically designed to analyse the effects of high shares of variable renewables and explore integration/flexibility options. It does this by comparing and trading off potential options to integrate renewables into the system including the extension of the transmission grid, interconnection with other countries, building flexible generation (e.g. gas power stations), renewable curtailment and energy storage.

highRES is written in GAMS and its objective is to minimise power system investment and operational costs to meet hourly demand, subject to a number of system constraints. The transmission grid is represented using a linear transport model. To realistically model variable renewable supply, the model uses spatially and temporally-detailed renewable generation time series that are based on weather data.

Currently there is one version for Europe and one for GB.</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">No</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS; CPLEX</Field><Field Name="processing_software">Python</Field><Field Name="GUI">No</Field><Field Name="model_class">European electricity system model, GB electricity system model</Field><Field Name="sectors">Electricity,</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">EEA+Norway and UK</Field><Field Name="georesolution">Country level, 20 zones for GB</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_objective">Minimization of total system costs</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">60</Field><Field Name="citation_references">Zeyringer, M., Price, J., Fais, B., Li, P.-H. &amp; Sharp, E. Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather. Nat. Energy 3, 395–403 (2018)</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6613" Title="NEMO (SEI)"><Template Name="Model"><Field Name="Full_Model_Name">Next Energy Modeling system for Optimization</Field><Field Name="Acronym">NEMO (SEI)</Field><Field Name="author_institution">Stockholm Environment Institute</Field><Field Name="authors">Jason Veysey, Charlie Heaps, Eric Kemp-Benedict</Field><Field Name="contact_persons">Jason Veysey</Field><Field Name="contact_email">jason.veysey@sei.org</Field><Field Name="website">https://www.sei.org/projects-and-tools/tools/nemo-the-next-energy-modeling-system-for-optimization/</Field><Field Name="source_download">https://github.com/sei-international/NemoMod.jl</Field><Field Name="logo">Nemo logo lowres.png</Field><Field Name="text_description">NEMO is a high performance, open-source energy system optimization modeling tool developed in Julia. It is intended for users who seek substantial optimization capabilities without the financial burden of proprietary software or the performance bottlenecks of common open-source alternatives. It can be used in stand-alone mode or with the Low Emissions Analysis Platform (LEAP) as a front-end.</Field><Field Name="Primary purpose">Full energy system optimization</Field><Field Name="Support">https://leap.sei.org/support/</Field><Field Name="User documentation">https://sei-international.github.io/NemoMod.jl/stable/</Field><Field Name="Code documentation">https://github.com/sei-international/NemoMod.jl</Field><Field Name="Source of funding">Stockholm Environment Institute</Field><Field Name="Number of developers">3</Field><Field Name="Number of users">1000</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/sei-international/NemoMod.jl</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia</Field><Field Name="processing_software">SQLite</Field><Field Name="External optimizer">Cbc, GLPK, CPLEX, Gurobi, Xpress, Mosek, others compatible with JuMP</Field><Field Name="GUI">No</Field><Field Name="model_class">Full energy system optimization, flexible geographic and sectoral scope</Field><Field Name="sectors">All</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">Cost optimizing</Field><Field Name="Market models">Cost optimizing</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Efficiencies can vary over time</Field><Field Name="georegions">All</Field><Field Name="georesolution">Flexible - user-defined regionalization</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, distribution, DC load flow, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">Emissions of GHGs and other air pollutants</Field><Field Name="Additional dimensions (Economical)">Real and discounted costs, including NPV</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Constrained cost optimization with perfect foresight</Field><Field Name="math_objective">Minimize total discounted costs</Field><Field Name="deterministic">Deterministic but can readily be applied in Monte Carlo analyses</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">100</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">From 1 second to several hours, depending on model complexity</Field><Field Name="computation_time_hardware">Intel i9-9900K, 64 GB RAM, SSD</Field><Field Name="computation_time_comments">Natively supports parallel processing, so multi-core processors are an advantage</Field><Field Name="citation_references">In preparation</Field><Field Name="report_references">https://doi.org/10.1016/j.apenergy.2022.118580
https://doi.org/10.1016/j.est.2021.103474</Field><Field Name="example_research_questions">Climate change mitigation, net-zero pathways, national energy strategies</Field><Field Name="Model validation">Comparison to other models' results, inspection of source code and documentation</Field><Field Name="Integrated models">Low Emissions Analysis Platform (LEAP)</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="6712" Title="SimSEE"><Template Name="Model"><Field Name="Full_Model_Name">Simulator of System of Electrical Energy.</Field><Field Name="Acronym">SimSEE</Field><Field Name="author_institution">Institute of Electrical Engineering</Field><Field Name="authors">Ruben Chaer, Pablo Alfaro y Gonzalo Casaravilla</Field><Field Name="contact_persons">Ruben Chaer</Field><Field Name="contact_email">rchaer@simsee.org</Field><Field Name="website">https://simsee.org/index_en.html</Field><Field Name="source_download">https://sourceforge.net/projects/simsee/</Field><Field Name="logo">Logosimseesinmarco 02.jpg</Field><Field Name="text_description">SimSEE is a platform for the Simulation of Systems of Electrical Energy. As such, it allows creating simulators tailored to a generation system, simply by adding the different types of Actors (thermal, wind, solar and hydraulic generators, demand, interconnections, etc.) to a Play-Room (simulation environment). These Actors behave in the Room according to their type.

It is 100% programmed with Object Oriented technology which makes it easy to incorporate new models (types of Actors).

To simulate the optimal operation of an Electric Power System, SimSEE solves a Dynamic Stochastic Programming problem, obtaining as a result an Optimal Operation Policy. Using this Policy, different realizations of the stochastic processes (chronicles or possible histories of the future of the system) are simulated.

Since 2010, SimSEE has become the tool commonly used in Uruguay to simulate the operation of the energy system, mainly due to the good stochastic models developed for the modeling of wind and solar energy.
These models achieve an adequate representation, both in the long term (Investment Planning) and in the short term (System Operation).</Field><Field Name="Primary outputs">Optimal energy dispatch of the energy resources.</Field><Field Name="Support">https://simsee.org/contacto_en.php</Field><Field Name="Framework">freepascal</Field><Field Name="User documentation">https://simsee.org/simsee/verdoc/vol1_en.php</Field><Field Name="Code documentation">https://sourceforge.net/p/simsee/src/HEAD/tree/</Field><Field Name="Source of funding">academic projects</Field><Field Name="Number of developers">more than 10</Field><Field Name="Number of users">more than 100</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://sourceforge.net/p/simsee/src/HEAD/tree/</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">freepascal</Field><Field Name="processing_software">freepascal</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Optimal energy dispatch</Field><Field Name="sectors">Electricity Market,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Industry</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Can be affected by the temperature</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="Additional dimensions (Ecological)">compute greenhouse emissions</Field><Field Name="Additional dimensions (Economical)">sopo prices, marginal costs, time-series</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Optimal Stochastic Dynamic Programming solver for computation of the operational Policy and a Monte Carlo style simulator of the system using the computed Policy</Field><Field Name="math_objective">minimization of the future operational cost.</Field><Field Name="deterministic">stochastic, hydro inflows, wind velocity, solar radiation, temerature an Demand.</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">1000</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">15</Field><Field Name="computation_time_hardware">desktop with 8 cpus</Field><Field Name="computation_time_comments">the 15 min with 8 cpus is for a optimizaion/simulation of the uruguayan system over 10 years horizon with a daily time-step</Field><Field Name="citation_references">Chaer, R. (2008.). Simulación de sistemas de energía eléctrica. Tesis de maestría. Universidad de la Republica (Uruguay). Facultad de Ingenieria.</Field><Field Name="report_references">Chaer R. (2018) Handling the Intermittence of Wind and Solar Energy Resources, from Planning to Operation. Uruguay’s Success. September 2018 Conference: 36th USAEE/IAEE NORTH AMERICAN CONFERENCEAt: Washington DC USA</Field><Field Name="Larger scale usage">long term investment planning (typically 20 years)</Field><Field Name="Model validation">The model was tested using a set of configurations of the Uruguayan electrical system. For example, conditions of drought or abundance of the hydroelectric resource and starting the simulations with the lakes full or empty and comparing the results with the result of the expert operators.</Field><Field Name="Comment on model validation">Once this series of tests was carried out, the model began to be used for the programming of the operation on all time scales by the ISO of Uruguay</Field><Field Name="Specific properties">It uses its own stochastic modeling technique which facilitates the assimilation of forecasts</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template><Free_Text id="1">&lt;ol&gt;&lt;li&gt;REDIRECT &lt;a href=&quot;/wiki/SimSES&quot; title=&quot;SimSES&quot;&gt;SimSES&lt;/a&gt;
&lt;/li&gt;&lt;/ol&gt;
</Free_Text></Page><Page ID="6734" Title="Pandapipes"><Template Name="Model"><Field Name="Full_Model_Name">pandapipes</Field><Field Name="Acronym">pandapipes</Field><Field Name="author_institution">Fraunhofer IEE, Uni Kassel</Field><Field Name="authors">Dennis Cronbach, Daniel Lohmeier, Jolando Kisse, Simon Drauz,Tanja Kneiske</Field><Field Name="contact_persons">Tanja Kneiske</Field><Field Name="contact_email">tanja.kneiske@ieg.fraunhofer.de</Field><Field Name="website">www.pandapipes.org</Field><Field Name="source_download">https://github.com/e2nIEE/pandapipes</Field><Field Name="logo">Pandapipes.png</Field><Field Name="text_description">An easy to use open source tool for fluid system modeling, analysis and optimization with a high degree of automation.</Field><Field Name="User documentation">https://pandapipes.readthedocs.io/en/latest/</Field><Field Name="Code documentation">https://pandapipes.readthedocs.io/en/latest/</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">No</Field><Field Name="Link to source">https://github.com/e2nIEE/pandapipes</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python</Field><Field Name="GUI">No</Field><Field Name="technologies">Renewables</Field><Field Name="Demand sectors">Households</Field><Field Name="Energy carrier (Gas)">Natural gas, Hydrogen</Field><Field Name="Transfer (Electricity)">Distribution</Field><Field Name="Transfer (Gas)">Distribution</Field><Field Name="Transfer (Heat)">Distribution</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="network_coverage">distribution</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Economical)">Grid expansion costs</Field><Field Name="math_modeltype">Simulation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">https://www.pandapipes.org/references/</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6745" Title="MicroGridsPy"><Template Name="Model"><Field Name="Full_Model_Name">MicroGridsPy</Field><Field Name="Acronym">MGpy</Field><Field Name="author_institution">Politecnico di Milano</Field><Field Name="authors">Sergio Balderrama, Sylvain Quoilin, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Emanuela Colombo, Riccardo Mereu, Nicolò Stevanato, Ivan Sangiorgio, Gianluca Pellecchia</Field><Field Name="contact_persons">Nicolo' Stevanato</Field><Field Name="contact_email">nicolo.stevanato@polimi.it</Field><Field Name="website">https://github.com/SESAM-Polimi/MicroGridsPy-SESAM</Field><Field Name="source_download">https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git</Field><Field Name="logo">MGpy.png</Field><Field Name="text_description">The MicroGridsPy model main objective is to provide an open-source alternative to the problem of sizing and dispatch of energy in micro-grids in isolated places. It’s written in python(pyomo) and use excel and text files as input and output data handling and visualisation.

Main features:

-Optimal sizing of PV panels, wind turbines, other renewable technologies, back-up genset and electrochemical storage system for least cost electricity supply in rural isolated areas;

-Optimal dispatch from the identified supply systems;

-Possibility to optimize on NPC or operation costs;

-LCOE evaluation for the identified system.


Possible features:

-Two-stage stochastic optimization;

-Multi-year evolving load demand and multi-step capacity expansion;

-Possibility of connecting to the national grid;

-Two-objective optimization (economic and environmental objective functions);

-Brownfield optimization;

-Built-in load archetypes for rural users;

-Endogenous calculation of renewable energy sources production.</Field><Field Name="Primary outputs">Optimal sizing of PV panels, wind turbines, other renewable technologies, back-up genset and electrochemical storage system for least cost electricity supply in rural isolated areas; Optimal dispatch from the identified supply systems; Possibility to optimize on NPC or operation costs; LCOE evaluation for the identified system.</Field><Field Name="User documentation">https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/README.md</Field><Field Name="Code documentation">https://github.com/SESAM-Polimi/MicroGridsPy-SESAM/blob/MicroGridsPy-2.0/Code/_README.txt</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">European Union Public Licence Version 1.1 (EUPL-1.1)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/SESAM-Polimi/MicroGridsPy-SESAM</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="processing_software">Excel</Field><Field Name="External optimizer">Gurobi, CPLEX, cbc, glpk</Field><Field Name="GUI">No</Field><Field Name="model_class">Energy Modeling Framework,</Field><Field Name="sectors">Micro-grids design</Field><Field Name="technologies">Renewables, Conventional Generation</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carrier (Liquid)">Diesel</Field><Field Name="Energy carriers (Renewable)">Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">Built-in load archetypes for rural users</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">No</Field><Field Name="georesolution">Village-scale</Field><Field Name="timeresolution">Hour</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">Greenhouse gas emissions</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">The model is based on two-stage stochastic optimisation and LP or MILP mathematical formulation</Field><Field Name="math_objective">Single or multi objective optimization (NPC, operation costs, CO2 emissions)</Field><Field Name="deterministic">Two-stage stochastic optimization</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">5-40</Field><Field Name="citation_references">Sergio Balderrama, Francesco Lombardi, Fabio Riva, Walter Canedo, Emanuela Colombo, Sylvain Quoilin, A two-stage linear programming optimization framework for isolated hybrid microgrids in a rural context: The case study of the “El Espino” community, Energy (2019), 188,</Field><Field Name="citation_doi">https://doi.org/10.1016/j.energy.2019.116073</Field><Field Name="report_references">-Nicolò Stevanato, Francesco Lombardi, Emanuela Colombo, Sergio Balderrama, Sylvain Quoilin, Two-Stage Stochastic Sizing of a Rural Micro-Grid Based on Stochastic Load Generation, 2019 IEEE Milan PowerTech, Milan, Italy, 2019, pp. 1-6. https://doi.org/10.1109/PTC.2019.8810571

-Nicolò Stevanato, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Sergio Balderrama, Matija Pavičević, Sylvain Quoilin, Emanuela Colombo, Long-term sizing of rural microgrids: Accounting for load evolution through multi-step investment plan and stochastic optimization, Energy for Sustainable Development (2020), 58, pp. 16-29, https://doi.org/10.1016/j.esd.2020.07.002

-Nicolò Stevanato, Lorenzo Rinaldi, Stefano Pistolese, Sergio Balderrama, Sylvain Quoilin, Emanuela Colombo, Modeling of a Village-Scale Multi-Energy System for the Integrated Supply of Electric and Thermal Energy, Applied Sciences (2020), https://doi.org/10.3390/app10217445</Field><Field Name="example_research_questions">-Long-term sizing of rural microgrids

-Load evolution</Field><Field Name="Specific properties">Two-stage stochastic optimization, Multi-year evolving load demand and multi-step capacity expansion, Possibility of connecting to the national grid, Two-objective optimization (economic and environmental objective functions), Brownfield optimization, Built-in load archetypes for rural users, Endogenous calculation of renewable energy sources production.</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6916" Title="QuaSi - ReSiE"><Template Name="Model"><Field Name="Full_Model_Name">computational core for the simulation of energy systems</Field><Field Name="Acronym">ReSiE</Field><Field Name="author_institution">siz energieplus</Field><Field Name="authors">Etienne Ott, Heiner Steinacker, Matthias Stickel</Field><Field Name="contact_persons">Etienne Ott, Heiner Steinacker, Matthias Stickel</Field><Field Name="contact_email">info@quasi-software.org</Field><Field Name="website">http://www.quasi-software.org/</Field><Field Name="source_download">https://github.com/QuaSi-Software</Field><Field Name="logo">230505 Logo Resie.jpg</Field><Field Name="text_description">ReSiE is a software tool that simulates energy supply concepts for buildings, focusing on renewable energy, sector coupling and individual operating strategies. It is part of the QuaSi project that includes additional tools and can be used for individual buildings up to district-level or cities. Unlike many other tools based on systems of equations, ReSiE uses rule-based algorithms, system dynamics and an agent-based approach. This approach enables detailed simulations without linearization, capturing energy flow and system state in each time step. The central mathematical model is based on energy balances and the order of the energy calculations that is determined during preprocessing. In addition, ReSiE is suitable to perform black-box optimizations for optimal component sizing. The model can be easily extended by any energy carriers and additional components or storages of variable complexity. More information is available in the documentation: https://quasi-software.readthedocs.io/en/latest/

Note: ReSiE is currently under development!</Field><Field Name="Primary outputs">sizing, LCC, LCA, Energy flows</Field><Field Name="Framework">QuaSi</Field><Field Name="User documentation">https://quasi-software.readthedocs.io/en/latest/</Field><Field Name="Code documentation">https://quasi-software.readthedocs.io/en/latest/</Field><Field Name="Source of funding">German Federal Ministry for Economic Affairs and Climate Action, BMWK</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/QuaSi-Software/resie</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia</Field><Field Name="processing_software">Julia</Field><Field Name="GUI">No</Field><Field Name="model_class">multi energy systems in urban scale</Field><Field Name="sectors">all sectors incl. heat, cold, hydrogen, electricity</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">energy demands are input for ReSiE</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">part-load depended efficiencies are included</Field><Field Name="georegions">depends on user</Field><Field Name="georesolution">depends on user</Field><Field Name="timeresolution">15 Minute</Field><Field Name="network_coverage">transmission, distribution</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">greenhouse gas emissions</Field><Field Name="Additional dimensions (Economical)">LCC</Field><Field Name="math_modeltype">Simulation, Other</Field><Field Name="math_modeltype_shortdesc">rule-based algorithms, system dynamics</Field><Field Name="math_objective">energy balances</Field><Field Name="deterministic">sensitivity analysis</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_comments">several minutes on business laptop, depending on model complexity and time-step</Field><Field Name="citation_references">Ott, E.; Steinacker, H.; Stickel, M.; Kley, C. and Fisch, M.N.: Dynamic open-source simulation engine for generic modeling of district-scale energy systems with focus on sector coupling and complex operational strategies, 2023, Journal of Physics: Conference Series, 2600 022009</Field><Field Name="citation_doi">10.1088/1742-6596/2600/2/022009</Field><Field Name="Model validation">extended test framwork included, validation against other tools and measurement data is in planning</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6931" Title="IRENA FlexTool"><Template Name="Model"><Field Name="Full_Model_Name">IRENA FlexTool</Field><Field Name="Acronym">FlexTool</Field><Field Name="author_institution">VTT Technical Research Centre of Finland</Field><Field Name="authors">Juha Kiviluoma, Arttu Tupala, Antti Soininen</Field><Field Name="contact_persons">Juha Kiviluoma</Field><Field Name="contact_email">juha.kiviluoma@vtt.fi</Field><Field Name="website">https://irena-flextool.github.io/flextool/</Field><Field Name="source_download">https://github.com/irena-flextool/flextool</Field><Field Name="logo">Flextool logo.png</Field><Field Name="text_description">IRENA FlexTool is an energy and power systems model for understanding the role of variable power generation in future energy systems. It performs capacity expansion planning as well as operational planning.

VTT develops the model for IRENA (and receives a lot of feedback from IRENA to improve the model)</Field><Field Name="Primary outputs">Investments, retirements, generation, demand, storage, transfer, prices, reserves, penalties/violations</Field><Field Name="Support">https://github.com/irena-flextool/flextool/issues</Field><Field Name="Framework">Uses Spine Toolbox</Field><Field Name="User documentation">https://irena-flextool.github.io/flextool/</Field><Field Name="Source of funding">IRENA, LeapRE</Field><Field Name="Number of developers">3</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU Library or &quot;Lesser&quot; General Public License version 3.0 (LGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/irena-flextool/flextool</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GNU MathProg</Field><Field Name="processing_software">Python, SQL</Field><Field Name="External optimizer">HiGHS default (supports others)</Field><Field Name="Additional software">Spine Toolbox</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Multi-purpose</Field><Field Name="sectors">All sectors (user can add more)</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">User can define additional constraints to simulate some user behaviours</Field><Field Name="Market models">Commodity and energy markets can be added (including CO2)</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Two-part (min. load and full load efficiency with a curve between))</Field><Field Name="georegions">User dependent</Field><Field Name="georesolution">User dependent</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">User dependent</Field><Field Name="Additional dimensions (Economical)">Price time series</Field><Field Name="Additional dimensions (Social)">-</Field><Field Name="Additional dimensions (Other)">-</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Typically linear cost minimization, but unit online decisions can be mixed-integer linear (and effectively investment decisions too).</Field><Field Name="math_objective">cost minimization</Field><Field Name="deterministic">perfect foresight, but can use limited horizon</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">Case dependent</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">Case dependent</Field><Field Name="computation_time_hardware">Case dependent</Field><Field Name="computation_time_comments">Should be quite fast for linear problems.</Field><Field Name="Model validation">Has been compared against PLEXOS</Field><Field Name="Comment on model validation">Not published</Field><Field Name="Interfaces">Spine Toolbox</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="6994" Title="SpineOpt.jl"><Template Name="Model"><Field Name="Full_Model_Name">SpineOpt.jl</Field><Field Name="authors">Ihlemann, M., Kouveliotis-Lysikatos, I., Huang, J., Dillon, J., O'Dwyer, C., Rasku, T., Marin, M., Poncelet, K., Kiviluoma, J., &amp; SpineOpt contributors</Field><Field Name="contact_persons">info@tools-for-energy-system-modelling.org</Field><Field Name="contact_email">info@tools-for-energy-system-modelling.org</Field><Field Name="website">https://github.com/spine-tools/SpineOpt.jl</Field><Field Name="source_download">https://github.com/spine-tools/SpineOpt.jl/archive/refs/heads/master.zip</Field><Field Name="logo">MOPO logo spineopt.png</Field><Field Name="text_description">SpineOpt is a flexible, open-source, energy system modelling framework for performing operational and planning studies, consisting of a wide spectrum of novel tools and functionalities. The most salient features of SpineOpt include a generic data structure, flexible temporal and spatial structures, a comprehensive representation of uncertainties, and model decomposition capabilities to reduce the computational complexity. These enable the implementation of highly diverse case studies.</Field><Field Name="User documentation">https://spine-tools.github.io/SpineOpt.jl/latest/index.html</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU Library or &quot;Lesser&quot; General Public License version 3.0 (LGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/spine-tools/SpineOpt.jl</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia/JuMP</Field><Field Name="processing_software">Python, Spine Toolbox</Field><Field Name="Additional software">Python, Spine Toolbox</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Framework</Field><Field Name="sectors">All</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georesolution">User-dependent</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow, net transfer capacities</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear programming or mixed integer linear programming</Field><Field Name="math_objective">Cost minimization</Field><Field Name="deterministic">Deterministic, perfect foresight, myopic, stochastic.</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Ihlemann, M., Kouveliotis-Lysikatos, I., Huang, J., Dillon, J., O'Dwyer, C., Rasku, T., Marin, M., Poncelet, K., &amp; Kiviluoma, J. (2022). SpineOpt: A flexible open-source energy system modelling framework. Energy Strategy Reviews, 43, [100902].</Field><Field Name="citation_doi">https://doi.org/10.1016/j.esr.2022.100902</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="7003" Title="QuaSi - GenSim"><Template Name="Model"><Field Name="Full_Model_Name">Generic Model for Thermal Building Simulation</Field><Field Name="Acronym">GenSim</Field><Field Name="author_institution">siz energieplus</Field><Field Name="authors">Tobias Maile, Simon Marx, Etienne Ott, Moira Peter, Heiner Steinacker, Matthias Stickel</Field><Field Name="contact_persons">Etienne Ott, Matthias Stickel</Field><Field Name="contact_email">info@quasi-software.org</Field><Field Name="website">http://www.quasi-software.org/</Field><Field Name="source_download">https://github.com/QuaSi-Software/GenSim</Field><Field Name="logo">230505 GenSim.jpg</Field><Field Name="text_description">GenSim - for &quot;generic building simulation&quot; - is a building simulation software using the EnergyPlus® simulation engine to generate high-resolution heating and cooling demand profiles as well as electricity demand profiles for buildings with various types of use. &quot;Generic&quot; in this context refers to a &quot;generally valid&quot; building model. This means that the software is versatile enough to simulate any type of building in a very flexible and simplified way, enabling users to efficiently adapt the software for any building design.

GenSim was specifically devloped for the use during project pre-planning where detailed simulations of buildings are challenging due to typically constrained time budgets and limited availability of information. Traditional simulation tools require extensive input data, making the process time-consuming. GenSim addresses this by providing presets for multiple building typologies and a streamlined approach for quick, simple, yet accurate building simulations. This is particularly valuable in early planning stages when only rough data about the planned buildings is available. If more detailed information (wall structure, detailed geometry, specific use, ...) is available about the building to be examined, this can be used for more precise results.

More information is available in the documentation: https://quasi-software.readthedocs.io/en/latest/gensim_user_manual/</Field><Field Name="Primary outputs">energy demands of a building (heating, cooling, electricity)</Field><Field Name="Framework">QuaSi</Field><Field Name="User documentation">https://quasi-software.readthedocs.io/en/latest/gensim_user_manual/</Field><Field Name="Code documentation">https://quasi-software.readthedocs.io/en/latest/</Field><Field Name="Source of funding">German Federal Ministry for Economic Affairs and Climate Action, BMWK</Field><Field Name="Number of developers">4</Field><Field Name="Number of users">20</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/QuaSi-Software/GenSim</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">EnergyPlus, OpenStudio, MS Excel, Ruby</Field><Field Name="processing_software">MS Excel</Field><Field Name="Additional software">OpenStudio</Field><Field Name="GUI">Yes</Field><Field Name="model_class">building energy demand</Field><Field Name="sectors">electricity, heat, cold</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">typical days</Field><Field Name="georegions">All</Field><Field Name="timeresolution">15 Minute</Field><Field Name="Observation period">Less than one year, More than one year</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">EnergyPlus is used to perform a thermal building simulation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">3</Field><Field Name="computation_time_hardware">business laptop</Field><Field Name="computation_time_comments">more with complex geomety of the building</Field><Field Name="citation_references">Maile, T.; Steinacker, H.; Stickel, M.W.; Ott, E.; Kley, C. Automated Generation of Energy Profiles for Urban Simulations. Energies 2023, 16, 6115.</Field><Field Name="citation_doi">10.3390/en16176115</Field><Field Name="report_references">To cite a specific version of GenSim, use:

Maile, T., Marx, S., Ott, E., Peter, M., Steinacker, H., &amp; Stickel, M. (2023). GenSim v2.15 - Generic Building Simulation (part of QuaSi) (release). Zenodo. https://doi.org/10.5281/zenodo.10200807</Field><Field Name="Model validation">extended test framwork included, validation against other DesignBuilder and Measurement data has been performed in the above paper. The tool was used for multiple years in many project.</Field><Field Name="Interfaces">command-line-interface</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="7012" Title="Antares-Simulator"><Template Name="Model"><Field Name="Full_Model_Name">Antares-Simulator</Field><Field Name="Acronym">Antares-Simulator</Field><Field Name="author_institution">RTE</Field><Field Name="contact_persons">Paul Plessiez, Jean-Marc Janin, Romain Rousselin-Reinhardt</Field><Field Name="contact_email">paul.plessiez@rte-france.com</Field><Field Name="website">https://antares-simulator.org/</Field><Field Name="source_download">https://antares-simulator.org/pages/antares-simulator/6/</Field><Field Name="logo">AntaresSimulator Logo-CMJN.jpg</Field><Field Name="text_description">Antares-Simulator is an open-source tool for the modelling, the simulation and the planning of multi-energy systems. It is a sequential Monte-Carlo simulator designed for short to long term studies of large interconnected energy grids. It simulates the economic behavior of the whole transmission-generation system, throughout the year and with a resolution of one hour.</Field><Field Name="Primary outputs">Optimal investment planning, production plans, capital and operational costs, resource adequacy</Field><Field Name="User documentation">https://antares-doc.readthedocs.io/en/latest/</Field><Field Name="Code documentation">https://antares-simulator.readthedocs.io/en/latest/build/0-INSTALL/</Field><Field Name="Number of developers">10</Field><Field Name="Number of users">40</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">GNU General Public License version 3.0 (GPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/AntaresSimulatorTeam/Antares_Simulator/</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">C++, C</Field><Field Name="processing_software">Python, TypeScript</Field><Field Name="External optimizer">Sirius, Xpress</Field><Field Name="GUI">Yes</Field><Field Name="model_class">Capacity Expansion Problem, Production Cost Model</Field><Field Name="sectors">Electricity, Methane, Hydrogen, Heat</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas, Hydrogen</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="User behaviour">Central Planner</Field><Field Name="Market models">Central Planner prospective, similar to Day-Ahead market</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">Yes</Field><Field Name="georegions">Europe</Field><Field Name="georesolution">NUTS0 - NUTS2</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization, Simulation</Field><Field Name="math_modeltype_shortdesc">Investment planning: optimization based on Benders decomposition
Dispatch : simulation based on MILP</Field><Field Name="math_objective">socio-economic welfare, investment costs, greenhouse gas emissions</Field><Field Name="deterministic">Monte-Carlo methods, myopic week-ahead foresight</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">Yes</Field><Field Name="computation_time_minutes">20</Field><Field Name="computation_time_hardware">Standard PC</Field><Field Name="computation_time_comments">For 1 year Monte-Carlo pan-European system. Monte-Carlo years solving can be parallelized, significantly reducing the computation time when many MC years are simulated</Field><Field Name="citation_references">A New tool for adequacy reporting of electric systems. CIGRE 2008,  C1-305 (M. Doquet, R. Gonzalez, S. Lepy, E. Momot, F. Verrier)</Field><Field Name="report_references">- RTE, &quot;Energy Pathways to 2050&quot;, https://assets.rte-france.com/prod/public/2022-01/Energy%20pathways%202050_Key%20results.pdf

- Lauvergne, Rémi, Yannick Perez, Mathilde Françon, et Alberto Tejeda De La Cruz. « Integration of Electric Vehicles into Transmission Grids: A Case Study on Generation Adequacy in Europe in 2040 ». Applied Energy 326 (15 novembre 2022): 120030. https://doi.org/10.1016/j.apenergy.2022.120030.

- Lynch, Arthur, Yannick Perez, Sophie Gabriel, et Gilles Mathonniere. « Nuclear Fleet Flexibility: Modeling and Impacts on Power Systems with Renewable Energy ». Applied Energy 314 (15 mai 2022): 118903. https://doi.org/10.1016/j.apenergy.2022.118903.

- Houghton, T., K. R. W. Bell, et M. Doquet. « Offshore Transmission for Wind: Comparing the Economic Benefits of Different Offshore Network Configurations ». Renewable Energy 94 (1 août 2016): 268‑79. https://doi.org/10.1016/j.renene.2016.03.038.

- A. T. Samuel, A. Aldamanhori, A. Ravikumar and G. Konstantinou, &quot;Stochastic Modeling for Future Scenarios of the 2040 Australian National Electricity Market using ANTARES,&quot; 2020 International Conference on Smart Grids and Energy Systems (SGES), Perth, Australia, 2020, pp. 761-766, doi: 10.1109/SGES51519.2020.00141.</Field><Field Name="example_research_questions">What are the best investment options to efficiently decarbonize the European energy sector?
What is the operational cost of a given pan-european energy mix?
What can be the added value of reinforcing the transmission grid on a given border?</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="7060" Title="ASSUME"><Template Name="Model"><Field Name="Full_Model_Name">Agent-based Simulation for Studying and Understanding Market Evolution</Field><Field Name="Acronym">ASSUME</Field><Field Name="author_institution">INATECH Freiburg</Field><Field Name="authors">Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil</Field><Field Name="contact_persons">Nick Harder</Field><Field Name="contact_email">contact@assume-project.de</Field><Field Name="website">https://assume-project.de/</Field><Field Name="source_download">https://codeload.github.com/assume-framework/assume/zip/refs/heads/main</Field><Field Name="logo">assume-project.png</Field><Field Name="text_description">ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup and Reinforcement Learning. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.</Field><Field Name="Primary outputs">electricity prices, power plant dispatch, cost and income</Field><Field Name="Support">OpenMod Forum, GitHub Issues</Field><Field Name="Framework">mango-agents</Field><Field Name="User documentation">https://assume.readthedocs.io/</Field><Field Name="Code documentation">https://assume.readthedocs.io/en/latest/assume.html</Field><Field Name="Source of funding">Federal Ministry for Economic Affairs and Climate Action (BMWK)</Field><Field Name="Number of developers">5</Field><Field Name="Number of users">10</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Affero General Public License v3 (AGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/assume-framework/assume/releases</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python, Pyomo</Field><Field Name="processing_software">PostgreSQL</Field><Field Name="External optimizer">GLPK, CBC, Gurobi, C-Plex</Field><Field Name="GUI">No</Field><Field Name="model_class">German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,</Field><Field Name="sectors">All / Electricity,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carrier (Gas)">Natural gas</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">bidding behaviour</Field><Field Name="Market models">day-ahead electricity market, support market, redispatch, nodal pricing</Field><Field Name="decisions">dispatch</Field><Field Name="Changes in efficiency">fixed per simulation run per powerplant</Field><Field Name="georegions">depending on input data</Field><Field Name="georesolution">NUTS0 - NUTS3, for DE</Field><Field Name="timeresolution">15 Minute</Field><Field Name="network_coverage">transmission, distribution</Field><Field Name="Observation period">Less than one month, Less than one year</Field><Field Name="Additional dimensions (Ecological)">CO2 emissions</Field><Field Name="Additional dimensions (Economical)">spot price, income, production cost per generation unit, profit per unit</Field><Field Name="Additional dimensions (Social)">bidding behavior, reinforcement learning output</Field><Field Name="Additional dimensions (Other)">grid congestion</Field><Field Name="math_modeltype">Simulation, Agent-based</Field><Field Name="math_modeltype_shortdesc">depending on parameterization bidding behavior and market behavior can be defined.

bidding behavior:

* bid marginal cost
* complex bids

market behavior:

* pay as bid
* pay as clear
* redispatch
* nodal pricing</Field><Field Name="math_objective">Minimize cost, optimize dispatch per agent</Field><Field Name="deterministic">Deterministic</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Zenodo</Field><Field Name="citation_doi">https://doi.org/10.5281/zenodo.8088760</Field><Field Name="report_references">https://doi.org/10.1007/978-3-031-48652-4_10

https://doi.org/10.1016/j.egyai.2023.100295</Field><Field Name="example_research_questions">What influence does the availability of different order types on a market have?

How can deep reinforcement learning for multiple markets be implemented in software?

What is the best way for demand-side management to be implemented in bidding agents?

How can different energy market designs be modelled in energy market simulations?</Field><Field Name="Model validation">benchmark to entsoe, comparison of real dispatch</Field><Field Name="Specific properties">reinforcement learning, RL, interoperability, market abstraction, multiple markets</Field><Field Name="Integrated models">PyPSA, AMIRIS</Field><Field Name="Interfaces">CSV, PostgreSQL</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="7084" Title="QuaSi - SoDeLe"><Template Name="Model"><Field Name="Full_Model_Name">Solar simulation as easy as can be</Field><Field Name="Acronym">SoDeLe</Field><Field Name="author_institution">siz energieplus</Field><Field Name="authors">Heiner Steinacker, Jonas Mucke, Etienne Ott, Matthias Stickel</Field><Field Name="contact_persons">Heiner Steinacker</Field><Field Name="contact_email">info@quasi-software.org</Field><Field Name="website">http://www.quasi-software.org/</Field><Field Name="source_download">https://github.com/QuaSi-Software/SoDeLe/releases</Field><Field Name="logo">230505 SoDeLe.jpg</Field><Field Name="text_description">SoDeLe (loosely translated as &quot;Solar simulation as easy as can be&quot;) is an easy-to-use tool for calculating energy profiles of photovoltaic systems. It is based on the well-validated python-pvlib, but offers a user-friendly GUI based on Excel (also a CLI with JSON input). SoDeLe can simulate PV systems with parameters from real PV modules and inverters with different orientations. Alternatively, preset standard modules and a constant DC-AC efficiency can be selected. The database of parameters contains more than 35,000 PV modules from various manufacturers.
The simulation is based on a weather file, which can be either an EWP file (EnergyPlus Weather File) or a .dat file from the German Weather Service (DWD).

With SoDeLe, the energy yield of planned or existing photovoltaic systems can be determined quickly and easily in high temporal resolution without much expert knowledge. The results can be used for dynamic energy system simulations, storage sizing or dynamic cost and greenhouse gas calculations. In addition, different orientations and different PV modules and inverters can be analyzed or the energy yield of existing systems can be checked if real historical weather data is used as input.

The results of SoDeLe were verified for different module-inverter configurations, orientations and locations using comparative simulations with the commercial software PV*SOL and with the annual totals calculated by PVGIS.</Field><Field Name="Primary outputs">energy profiles</Field><Field Name="Framework">QuaSi</Field><Field Name="User documentation">https://quasi-software.readthedocs.io/en/latest/</Field><Field Name="Code documentation">https://quasi-software.readthedocs.io/en/latest/</Field><Field Name="Source of funding">German Federal Ministry for Economic Affairs and Climate Action, BMWK</Field><Field Name="Number of developers">2</Field><Field Name="Number of users">10</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/QuaSi-Software/SoDeLe</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Python, MS Excel</Field><Field Name="External optimizer">none</Field><Field Name="Additional software">none</Field><Field Name="GUI">Yes</Field><Field Name="model_class">PV energy production</Field><Field Name="sectors">Electricity</Field><Field Name="technologies">Renewables</Field><Field Name="Energy carriers (Renewable)">Sun</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Changes in efficiency">temperature-dependent</Field><Field Name="georegions">All</Field><Field Name="timeresolution">Hour</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Simulation</Field><Field Name="math_modeltype_shortdesc">physics-based with efficiency curves from CEC</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">15</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">1</Field><Field Name="computation_time_hardware">business laptop</Field><Field Name="citation_references">Heiner Steinacker, Jonas Mucke: SoDeLe v2.0.0: Solarsimulation denkbar leicht. 2024</Field><Field Name="Integrated models">pvlib</Field><Field Name="Interfaces">CLI, MS Excel, JSON</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="7123" Title="GENeSYS-MOD"><Template Name="Model"><Field Name="Full_Model_Name">Global Energy System Model</Field><Field Name="Acronym">GENeSYS-MOD</Field><Field Name="author_institution">TU Berlin</Field><Field Name="authors">Löffler, Konstantin; Hainsch, Karlo; Burandt, Thorsten; Kemfert, Claudia; von Hirschhausen, Christian</Field><Field Name="contact_persons">Löffler, Konstantin</Field><Field Name="contact_email">kl@wip.tu-berlin.de</Field><Field Name="source_download">https://github.com/GENeSYS-MOD</Field><Field Name="logo">Logo simplified 2.png</Field><Field Name="text_description">The Global Energy System Model (GENeSYS‑MOD) is a linear cost-minimizing optimization model being developed at Technische Universität Berlin, Germany. The project was originally based on the OSeMOSYS framework and the first version was released in 2017 using GAMS. The codebase was later translated into Julia. Both versions and a representative dataset are available on GitHub.
GENeSYS‑MOD couples the demand sectors covering electricity, buildings, industry, and transport and finds the cost-optimal investment into conventional and renewable energy generation, storage, and infrastructure. The research focus is on long-term system development and pathway analysis.

The model was first used to analyze decarbonization scenarios at the global level, broken down into ten regions. However, the framework is highly flexible, allowing for calculations at various levels of detail, from individual households to global aggregations, depending on the desired research question and availability of input data.</Field><Field Name="Primary outputs">inst</Field><Field Name="User documentation">https://github.com/GENeSYS-MOD</Field><Field Name="Code documentation">https://github.com/GENeSYS-MOD</Field><Field Name="Source of funding">Technische Universität Berlin</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Apache License 2.0 (Apache-2.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/GENeSYS-MOD</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">GAMS, Julia</Field><Field Name="External optimizer">gurobi</Field><Field Name="GUI">No</Field><Field Name="model_class">multi‑commodity optimization</Field><Field Name="sectors">Electricity, Heat, Transport, Industry, Buildings,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Distribution</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="decisions">dispatch, investment</Field><Field Name="Changes in efficiency">fixed</Field><Field Name="georesolution">NUTS1, federal states</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="Additional dimensions (Ecological)">greenhouse gas emissions</Field><Field Name="Additional dimensions (Economical)">marginal costs, investment cost, operational costs</Field><Field Name="Additional dimensions (Social)">energy sector employment possible</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Linear optimization</Field><Field Name="math_objective">Minimization of total system costs</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Löffler, Konstantin; Hainsch, Karlo; Burandt, Thorsten; Kemfert, Claudia; von Hirschhausen, Christian (2017). &quot;Designing a model for the global energy system — GENeSYS-MOD: an application of the Open-Source Energy Modeling System (OSeMOSYS)&quot;. Energies. 10 (1468): 1468.</Field><Field Name="citation_doi">10.3390/en10101468</Field><Field Name="report_references">https://zenodo.org/communities/genesys-mod</Field><Field Name="example_research_questions">Modeling the low-carbon transition of the European energy system — a quantitative assessment of the stranded assets problem

Decarbonizing China's energy system — Modeling the transformation of the electricity, transportation, heat, and industrial sectors

Energy transition scenarios: what policies, societal attitudes, and technology developments will realize the EU Green Deal?</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="7147" Title="RHEIA"><Template Name="Model"><Field Name="Full_Model_Name">Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems</Field><Field Name="Acronym">RHEIA</Field><Field Name="author_institution">UCLouvain</Field><Field Name="authors">Diederik Coppitters, Panagiotis Tsirikoglou, Ward De Paepe, Konstantinos Kyprianidis, Anestis Kalfas, Francesco Contino</Field><Field Name="contact_persons">Diederik Coppitters</Field><Field Name="contact_email">diederik.coppitters@uclouvain.be</Field><Field Name="website">https://rheia.readthedocs.io/en/latest/</Field><Field Name="source_download">https://github.com/rheia-framework/RHEIA</Field><Field Name="text_description">The Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems (RHEIA) framework provides multi-objective optimization (deterministic and stochastic) and uncertainty quantification algorithms. These algorithms can be applied on hydrogen-based energy systems, which are included in RHEIA. In addition, RHEIA allows to connect your own models to the algorithms as well.</Field><Field Name="Primary outputs">Bridge uncertainty assessment and energy modelling</Field><Field Name="User documentation">https://rheia.readthedocs.io/en/latest/</Field><Field Name="Code documentation">https://rheia.readthedocs.io/en/latest/</Field><Field Name="Source of funding">FNRS</Field><Field Name="Number of developers">5</Field><Field Name="Number of users">10</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/rheia-framework/RHEIA</Field><Field Name="data_availability">all</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Python</Field><Field Name="GUI">No</Field><Field Name="model_class">distributed energy systems, energy planning</Field><Field Name="sectors">All</Field><Field Name="technologies">Renewables</Field><Field Name="Demand sectors">Households, Industry</Field><Field Name="Energy carrier (Gas)">Hydrogen</Field><Field Name="Energy carriers (Renewable)">Sun, Wind</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="Additional dimensions (Other)">uncertainty</Field><Field Name="math_modeltype">Optimization</Field><Field Name="deterministic">probabilistic</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">minutes</Field><Field Name="citation_references">Coppitters et al., (2022). RHEIA: Robust design optimization of renewable Hydrogen and dErIved energy cArrier systems. Journal of Open Source Software, 7(75), 4370</Field><Field Name="citation_doi">doi.org/10.21105/joss.04370</Field><Field Name="report_references">https://scholar.google.com/scholar?cites=14879586069709356648&amp;as_sdt=2005&amp;sciodt=0,5&amp;hl=en</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="7156" Title="OpenTEPES"><Template Name="Model"><Field Name="Full_Model_Name">Open Generation, Storage, and Transmission Operation and Expansion Planning Model with RES and ESS</Field><Field Name="Acronym">openTEPES</Field><Field Name="author_institution">Universidad Pontificia Comillas</Field><Field Name="authors">Andres Ramos, Erik Alvarez, Francisco Labora</Field><Field Name="contact_persons">Andres Ramos</Field><Field Name="contact_email">andres.ramos@comillas.edu</Field><Field Name="website">https://opentepes.readthedocs.io/en/latest/index.html</Field><Field Name="source_download">https://github.com/IIT-EnergySystemModels/openTEPES</Field><Field Name="logo">OpenTEPES.png</Field><Field Name="text_description">The openTEPES model presents a decision support system for defining the integrated generation, storage, and transmission expansion plan (GEP+SEP+TEP) of a large-scale electric system at a tactical level (i.e., time horizons of 10-20 years), defined as a set of generation, storage, and (electricity, hydrogen, and heat) networks dynamic investment decisions for multiple future years.</Field><Field Name="Primary outputs">Investment, operation, emission, marginal, economica, and operational flexibility results</Field><Field Name="User documentation">https://opentepes.readthedocs.io/en/latest/index.html</Field><Field Name="Code documentation">https://github.com/IIT-EnergySystemModels/openTEPES</Field><Field Name="Source of funding">European and Spanish research projects</Field><Field Name="Number of developers">Three</Field><Field Name="Number of users">+132k downloads</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">Affero General Public License v3 (AGPL-3.0)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/IIT-EnergySystemModels/openTEPES</Field><Field Name="data_availability">all</Field><Field Name="open_future">Yes</Field><Field Name="modelling_software">Python / Pyomo</Field><Field Name="processing_software">Python / Pandas</Field><Field Name="External optimizer">HiGHS, Gurobi</Field><Field Name="GUI">No</Field><Field Name="model_class">Bottom-up energy (electricity &amp; hydrogen &amp; heat) system model</Field><Field Name="sectors">Electricity, Hydrogen, Heat</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Commercial sector</Field><Field Name="Energy carrier (Gas)">Hydrogen</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Transfer (Gas)">Transmission</Field><Field Name="Transfer (Heat)">Transmission</Field><Field Name="Storage (Electricity)">Battery, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="Market models">Centralized dispatch and investment</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">Europe, African power pools, Spain, Portugal</Field><Field Name="georesolution">Node</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, DC load flow, net transfer capacities</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">Two-stage stochastic optimization model to determine the best invesment and operation decisions</Field><Field Name="math_objective">Minimise total discounted cost of the energy system</Field><Field Name="deterministic">Two-stage stochastic optimization</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="number_of_variables">Millions</Field><Field Name="montecarlo">No</Field><Field Name="computation_time_minutes">60</Field><Field Name="computation_time_hardware">Any personal computer or server</Field><Field Name="computation_time_comments">Computation time will depend on the optimization problem size</Field><Field Name="citation_references">A. Ramos, E. Quispe, S. Lumbreras “OpenTEPES: Open-source Transmission and Generation Expansion Planning” SoftwareX 18: June 2022 10.1016/j.softx.2022.101070</Field><Field Name="citation_doi">10.1016/j.softx.2022.101070</Field><Field Name="example_research_questions">Energy transition analysis
* Linkage with energy system models (integrated assessment models IAM) to refine the representation of the power sector 
* National Energy and Climate Plan (NECP) 2030 for Spain

Storage analysis
* Cost-benefit analysis (CBA) of candidate pumped-hydro storage units
* Future ESS role (batteries vs. pumped-hydro storage vs. CSP)
* Penetration of EV and type of charge
* Impact of local energy communities (LEC) on transmission investments with detailed representation of storage hydro

Security of supply
* Technologies providing firmness and flexibility to the system</Field><Field Name="Larger scale usage">European electric system</Field><Field Name="Interfaces">CSV files</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="7192" Title="AdOpT-NET0"><Template Name="Model"><Field Name="Full_Model_Name">Advanced Optimization Tool for Networks and Energy</Field><Field Name="Acronym">AdOpT-NET0</Field><Field Name="author_institution">Utrecht Univeristy</Field><Field Name="authors">Jan F. Wiegner, Julia L. Tiggeloven, Luca Bertoni, Inge M. Ossentjuk, Matteo Gazzani</Field><Field Name="contact_persons">Jan F. Wiegner, Matteo Gazzani</Field><Field Name="contact_email">j.f.wiegner@uu.nl</Field><Field Name="website">https://adopt-net0.readthedocs.io/en/latest/</Field><Field Name="source_download">https://github.com/UU-ER/AdOpT-NET0</Field><Field Name="logo">Adopt_fulllogo@4x-100.jpg</Field><Field Name="text_description">AdOpT-NET0 is a Python Library for bottom-up multi energy system modelling. It can model conversion technologies and networks for any carrier and optimize the design and operation of your energy system.</Field><Field Name="User documentation">https://adopt-net0.readthedocs.io/en/latest/</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/UU-ER/AdOpT-NET0</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python (Pyomo)</Field><Field Name="External optimizer">Pyomo compatible solvers</Field><Field Name="Additional software">Pyomo compatible solvers</Field><Field Name="GUI">No</Field><Field Name="model_class">Multi Energy System Model</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">User dependent</Field><Field Name="georesolution">User dependent</Field><Field Name="timeresolution">Hour</Field><Field Name="Observation period">More than one year</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_modeltype_shortdesc">MILP, LP</Field><Field Name="math_objective">Cost minimization; emission minimization; user defined</Field><Field Name="deterministic">Monte carlo</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">Yes</Field><Field Name="citation_references">Wiegner et al., (2025). AdOpT-NET0: A technology-focused Python package for the optimization of multi-energy systems. Journal of Open Source Software, 10(106), 7402</Field><Field Name="citation_doi">https://doi.org/10.21105/joss.07402</Field><Field Name="report_references">Wiegner, J. F., Gibescu, M., &amp; Gazzani, M. (forthcoming). Unleashing the full potential of the north sea – identifying key energy infrastructure synergies for 2030 and 2040. Forthcoming.
https://doi.org/10.48550/arXiv.2411.00540

Tiggeloven, J. L., Faaij, A. P. C., Kramer, G. J., &amp; Gazzani, M. (2025). Optimizing emissions
reduction in ammonia-ethylene chemical clusters: Synergistic integration of electrification,
carbon capture, and hydrogen. Industrial and Engineering Chemistry Research. https://doi.org/10.1021/acs.iecr.4c03817

Tiggeloven, J. L., Faaij, A. P. C., Kramer, G. J., &amp; Gazzani, M. (2023). Optimization of
electric ethylene production: Exploring the role of cracker flexibility, batteries, and renewable
energy integration. Industrial and Engineering Chemistry Research, 62(40), 16360–16382.
https://doi.org/10.1021/ACS.IECR.3C02226

Wiegner, J. F., Grimm, A., Weimann, L., &amp; Gazzani, M. (2022). Optimal design and operation
of solid sorbent direct air capture processes at varying ambient conditions. Industrial &amp;
Engineering Chemistry Research, 61(34), 12649–12667. https://doi.org/10.1021/acs.iecr.2c00681</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="7222" Title="ZEN-garden"><Template Name="Model"><Field Name="Full_Model_Name">Zero-emissions Energy Networks</Field><Field Name="Acronym">ZEN-garden</Field><Field Name="author_institution">ETH Zürich</Field><Field Name="authors">Jacob Mannhardt, Alissa Ganter, Lukas Kunz, Lukas Schmidt-Engelbertz, Janis Fluri, Vinzenz Muser, Johannes Burger, Francesco De Marco, Christoph Funke, Nour Boulos, Paolo Gabrielli, Giovanni Sansavini</Field><Field Name="contact_persons">ZEN-garden team</Field><Field Name="contact_email">zen-garden@ethz.ch</Field><Field Name="website">https://linktr.ee/zengarden_</Field><Field Name="logo">Zen garden logo text.png</Field><Field Name="text_description">ZEN-garden is an open-source linear optimization model of long-term energy system transition pathways. ZEN-garden, with a modular and flexible design, can be used to optimize different types of energy systems, value chains, or other network-based systems. ZEN-garden particularly provides a detailed description of transition pathways with, among other features, cumulative or annual carbon limits, capacity expansion constraints, and construction years. Data handling is user-oriented with features covering unit consistency, scaling, and parallelizable scenario analysis. Results output by ZEN-garden are investigated on an intuitive and flexible visualization platform.</Field><Field Name="Primary outputs">Energy systems transition pathways</Field><Field Name="User documentation">https://zen-garden.readthedocs.io/en/latest/</Field><Field Name="Code documentation">https://github.com/ZEN-universe/ZEN-garden</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/ZEN-universe/ZEN-garden</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="GUI">No</Field><Field Name="model_class">Framework,</Field><Field Name="sectors">All,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Demand sectors">Households, Industry, Transport, Commercial sector, Other</Field><Field Name="Energy carrier (Gas)">Natural gas, Biogas, Hydrogen</Field><Field Name="Energy carrier (Liquid)">Diesel, Ethanol, Petrol</Field><Field Name="Energy carriers (Solid)">Biomass, Coal, Lignite, Uranium</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Transfer (Gas)">Distribution, Transmission</Field><Field Name="Transfer (Heat)">Distribution, Transmission</Field><Field Name="Storage (Electricity)">Battery, CAES, Chemical, Kinetic, PHS</Field><Field Name="Storage (Gas)">Yes</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">All</Field><Field Name="georesolution">Node</Field><Field Name="timeresolution">Hour</Field><Field Name="network_coverage">transmission, distribution, DC load flow</Field><Field Name="math_modeltype">Optimization</Field><Field Name="math_objective">Minimize net-present costs or minimize carbon emissions</Field><Field Name="deterministic">Scenario Analysis (Deterministic)</Field><Field Name="is_suited_for_many_scenarios">Yes</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">Mannhardt, J., Ganter, A., Burger, J., De Marco, F., Kunz, L., Schmidt-Engelbertz, L., Gabrielli, P., &amp; Sansavini, G. (2025). ZEN-garden: Optimizing energy transition pathways with user-oriented data handling. SoftwareX, 29, 102059. DOI:10.1016/j.softx.2025.102059</Field><Field Name="citation_doi">https://doi.org/10.1016/j.softx.2025.102059</Field><Field Name="report_references">Mannhardt, J., Gabrielli, P., &amp; Sansavini, G. (2024). Understanding the vicious cycle of myopic foresight and constrained technology deployment in transforming the European energy system. iScience, 27(12), 111369. https://doi.org/10.1016/j.isci.2024.111369

Mannhardt, J., Gabrielli, P., &amp; Sansavini, G. (2023). Collaborative and selfish mitigation strategies to tackle energy scarcity: The case of the European gas crisis. iScience, 26(5), 106750. https://doi.org/10.1016/j.isci.2023.106750

Ganter, A., Lonergan, K. E., Büchi, H. M., &amp; Sansavini, G. (2024). Shifting to low-carbon hydrogen production supports job creation but does not guarantee a just transition. One Earth, 7(11), 1981–1993. https://doi.org/10.1016/j.oneear.2024.10.009

Ganter, A., Gabrielli, P., &amp; Sansavini, G. (2024). Near-term infrastructure rollout and investment strategies for net-zero hydrogen supply chains. Renewable and Sustainable Energy Reviews, 194, 114314. https://doi.org/10.1016/j.rser.2024.114314</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="7246" Title="Sienna"><Template Name="Model"><Field Name="Full_Model_Name">Sienna</Field><Field Name="author_institution">NREL</Field><Field Name="authors">Clayton Barrows, Jose-Daniel Lara, Kate Doubleday, Rodrigo Henriquez-Auba, Matt Bossart, Gabriel Konar-Steenberg</Field><Field Name="contact_persons">Clayton Barrows</Field><Field Name="contact_email">mailto:clayton.barrows@nrel.gov</Field><Field Name="website">https://nrel-sienna.github.io/Sienna/#</Field><Field Name="source_download">https://github.com/nrel-sienna</Field><Field Name="logo">Sienna-logo.png</Field><Field Name="text_description">The most advanced power systems modeling platform ever built</Field><Field Name="Support">https://join.slack.com/t/nrel-sienna/shared_invite/zt-1lyt10wio-y3yV_yug3F68vLau27gUzA</Field><Field Name="Framework">https://nrel-sienna.github.io/Sienna/#</Field><Field Name="User documentation">https://nrel-sienna.github.io/Sienna/SiennaDocs/docs/build/index.html</Field><Field Name="Code documentation">https://nrel-sienna.github.io/Sienna/SiennaDocs/docs/build/index.html</Field><Field Name="Number of developers">36</Field><Field Name="Number of users">1000</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">BSD 3-Clause &quot;New&quot; or &quot;Revised&quot; License (BSD-3-Clause)</Field><Field Name="model_source_public">Yes</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Julia</Field><Field Name="processing_software">Julia</Field><Field Name="GUI">No</Field><Field Name="model_class">Production Cost, Capacity Expansion, Dynamics</Field><Field Name="sectors">Electricity,</Field><Field Name="technologies">Renewables, Conventional Generation, CHP</Field><Field Name="Transfer (Electricity)">Transmission</Field><Field Name="Storage (Electricity)">Battery, PHS</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="User behaviour">command line</Field><Field Name="Market models">any</Field><Field Name="decisions">dispatch, investment</Field><Field Name="georegions">any</Field><Field Name="georesolution">any</Field><Field Name="timeresolution">Multi year</Field><Field Name="network_coverage">transmission, AC load flow, DC load flow, net transfer capacities</Field><Field Name="Observation period">Less than one month, Less than one year, More than one year</Field><Field Name="math_modeltype">Optimization, Simulation, Other</Field><Field Name="math_modeltype_shortdesc">Supports optimization based expansion and operational simulations, and transient simulations with differential-algebraic equations and forward differentiation.</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="Integrated models">PowerSimulations.jl, PowerSimulationsDynamics.jl</Field><Field Name="Model input file format">Yes</Field><Field Name="Model file format">Yes</Field><Field Name="Model output file format">Yes</Field></Template></Page><Page ID="7258" Title="Energy Access Explorer (EAE)"><Template Name="Model"><Field Name="Full_Model_Name">Energy Access Explorer</Field><Field Name="Acronym">EAE</Field><Field Name="author_institution">World Resources Institute</Field><Field Name="authors">Dimitrios Mentis, Davida Wood, Bharath Jairaj, Santiago Sinclair Lecaros,  Douglas Ronoh, Akansha Saklani, Santiago Sinclair Lecaros, Alemayehu Agizew, Abdul Khalid, Shikha Anand,Lily Odarno, Fabian Jendle, Elise Mazur, Anila Qehaja, Francis Gassert,</Field><Field Name="contact_persons">Dimitrios Mentis</Field><Field Name="contact_email">dimitrios.mentis@wri.org</Field><Field Name="website">https://www.energyaccessexplorer.org/</Field><Field Name="source_download">https://github.com/energyaccessexplorer</Field><Field Name="logo">EAE Logo Compact RGB.png</Field><Field Name="text_description">WRI, in collaboration with over 300 partners, has developed the Energy Access Explorer (EAE), the World’s First Digital Public Good to deliver climate compatible energy transitions for everyone. EAE takes a data-informed, integrated, and inclusive approach to achieving universal energy access, supporting equitable socio-economic development. EAE provides governments, businesses, and financiers with a transparent, interactive, geospatial platform to visualize and analyze high-priority areas for energy interventions in Africa and South Asia. EAE functions also as a dynamic information system, reducing software engineering and data transaction costs for both data providers and users and facilitating data management and governance.</Field><Field Name="Primary outputs">High resolution Priority areas for energy interventions</Field><Field Name="User documentation">https://www.open.edu/openlearncreate/course/view.php?id=13664</Field><Field Name="Code documentation">https://github.com/energyaccessexplorer</Field><Field Name="Source of funding">Philanthropic resources, Governments</Field><Field Name="Number of developers">1</Field><Field Name="Number of users">&gt;35,000</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://github.com/energyaccessexplorer</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">https://github.com/energyaccessexplorer</Field><Field Name="processing_software">https://github.com/energyaccessexplorer</Field><Field Name="GUI">No</Field><Field Name="technologies">Renewables</Field><Field Name="Demand sectors">Households</Field><Field Name="Energy carriers (Solid)">Biomass</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Hydro, Sun, Wind</Field><Field Name="Transfer (Electricity)">Distribution, Transmission</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">No</Field><Field Name="decisions">investment</Field><Field Name="georegions">Africa, Asia</Field><Field Name="georesolution">1 km</Field><Field Name="timeresolution">Multi year</Field><Field Name="Additional dimensions (Economical)">Productive Uses of Renewable Energy</Field><Field Name="Additional dimensions (Social)">Health and Education Facilities Electrification</Field><Field Name="math_modeltype">Other</Field><Field Name="math_modeltype_shortdesc">Multicriteria Decision Analytical Algorithm</Field><Field Name="math_objective">Priority areas for energy interventions</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="number_of_variables">50-100</Field><Field Name="montecarlo">No</Field><Field Name="citation_references">https://www.energyaccessexplorer.org/attribution/</Field><Field Name="report_references">Technical Note: Energy Access Explorer: Data and Methods</Field><Field Name="example_research_questions">Integrated and Inclusive Energy Planning:
Where are the underserved communities that can be prioritized for electrification through grid, mini-grid, or standalone systems?

Market Intelligence:
Which geographic areas show the highest potential customer base for off-grid solar and mini-grid deployment?

Clean Cooking:
Where are households and institutions most dependent on polluting fuels, and how can clean cooking solutions be targeted to them?

Impact Investment:
Which regions offer the greatest social and economic return on investment for energy access interventions?

Productive Uses of Renewable Energy (PURE):
Where can renewable energy most effectively power agriculture, small enterprises, and local value chains?

Health and Education Electrification:
Which health and education facilities lack reliable electricity, and what clean energy options are best suited for them?</Field><Field Name="Integrated models">Linked with OnSSET and OnStove</Field><Field Name="Interfaces">https://www.energyaccessexplorer.org/</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page><Page ID="7276" Title="Heat4Future"><Template Name="Model"><Field Name="Full_Model_Name">Heat4Future</Field><Field Name="Acronym">Heat4Future</Field><Field Name="author_institution">Hamburg University of Applied Sciences</Field><Field Name="authors">Nina Kicherer, Pablo Benalcazar, Peter Lorenzen, Olessya Kozlenko, Sadi Tomtulu, Jan Trosdorff</Field><Field Name="contact_persons">Nina Kicherer, Pablo Benalcazar</Field><Field Name="contact_email">benalcazar@min-pan.krakow.pl</Field><Field Name="source_download">https://gitlab.com/c4dht/strategic-heat-planning-project</Field><Field Name="open_source_licensed">Yes</Field><Field Name="license">MIT license (MIT)</Field><Field Name="model_source_public">Yes</Field><Field Name="Link to source">https://gitlab.com/c4dht/strategic-heat-planning-project</Field><Field Name="data_availability">some</Field><Field Name="open_future">No</Field><Field Name="modelling_software">Python</Field><Field Name="processing_software">Python</Field><Field Name="GUI">No</Field><Field Name="model_class">district heating system planning</Field><Field Name="sectors">district heating, Heat,</Field><Field Name="technologies">Renewables, CHP</Field><Field Name="Demand sectors">Other</Field><Field Name="Energy carriers (Solid)">Biomass</Field><Field Name="Energy carriers (Renewable)">Geothermal heat, Sun</Field><Field Name="Storage (Gas)">No</Field><Field Name="Storage (Heat)">Yes</Field><Field Name="decisions">dispatch</Field><Field Name="timeresolution">Hour</Field><Field Name="Observation period">Less than one year</Field><Field Name="math_modeltype">Simulation</Field><Field Name="is_suited_for_many_scenarios">No</Field><Field Name="montecarlo">No</Field><Field Name="citation_doi">https://doi.org/10.1016/j.mex.2025.103222</Field><Field Name="Model input file format">No</Field><Field Name="Model file format">No</Field><Field Name="Model output file format">No</Field></Template></Page></Category>
</Pages>