Open Models

	Author institution	Authors	Citation doi	Citation references	Computation time minutes	Contact email	Contact persons	Data availability	Decisions	Deterministic	Example research questions	Full Model Name	Georegions	Georesolution	Is suited for many scenarios	License	Math modeltype	Math modeltype shortdesc	Math objective	Model class	Model source public	Modelling software	Network coverage	Number of variables	Open future	Open source licensed	Processing software	Report references	Sectors	Source download	Technologies	Text description	Timeresolution	Website
AMIRIS	German Aerospace Center		https://doi.org/10.21105/joss.05041	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.	1	Christoph.Schimeczek@dlr.de	Christoph Schimeczek	all	dispatch	stochastic, perfect foresight, deterministic		Agent-based Market model for the Investigation of Renewable and Integrated energy Systems	Germany, Austria	National	true	Apache License 2.0 (Apache-2.0)	Simulation Agent-based	algorithms for market clearing and agent-specific bidding strategies		Agent-based electricity market model	true	Java			false	true	Python	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	electricity	https://gitlab.com/dlr-ve/esy/amiris/amiris/-/releases	Renewables Conventional Generation	Agent-based electricty market model for analysing questions on future energy markets, their market design, and energy-related policy instruments	Hour	https://dlr-ve.gitlab.io/esy/amiris/home/
ASAM	Europa-Universität Flensburg	Samuel Glismann		Glismann (2021), “ Ancillary Services Acquisition Model: considering market interactions in policy design”, preprint Applied Energy Journal. https://arxiv.org/abs/2104.13047			Samuel Glismann	some	dispatch		Redispatch design in the Netherlands	Ancillary Services Acquisition Model	Europe	Individual power stations	true	GNU General Public License version 3.0 (GPL-3.0)	Simulation Agent-based			Agent-based Simulation Market Model Electricity System Model German and European Electricity Market	true	Python (Pyomo)			false	true	Python, PyPSA, Mesa		Electricity Electricity Market Electric power	https://github.com/AncillaryServicesAcquisitionModel/ASAM		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.	15 Minute	https://ancillaryservicesacquisitionmodel.github.io/ASAM/
ASSUME	INATECH Freiburg	Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil	https://doi.org/10.5281/zenodo.8088760	Zenodo		contact@assume-project.de	Nick Harder	all	dispatch	Deterministic	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?	Agent-based Simulation for Studying and Understanding Market Evolution	Depending on input data	NUTS0 - NUTS3, for DE	false	Affero General Public License v3 (AGPL-3.0)	Simulation Agent-based	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	Minimize cost, optimize dispatch per agent	German and European Electricity Market Network-constrained Unit Commitment and Economic Dispatch Agent-based electricity market model	true	Python, Pyomo	transmission distribution		false	true	PostgreSQL	https://doi.org/10.1007/978-3-031-48652-4_10 https://doi.org/10.1016/j.egyai.2023.100295	All / Electricity	https://codeload.github.com/assume-framework/assume/zip/refs/heads/main	Renewables Conventional Generation CHP	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.	15 Minute	https://assume-project.de/
Antares-Simulator	RTE			A New tool for adequacy reporting of electric systems. CIGRE 2008, C1-305 (M. Doquet, R. Gonzalez, S. Lepy, E. Momot, F. Verrier)	20	paul.plessiez@rte-france.com	Paul Plessiez, Jean-Marc Janin, Romain Rousselin-Reinhardt	some	dispatch investment	Monte-Carlo methods, myopic week-ahead foresight	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?	Antares-Simulator	Europe	NUTS0 - NUTS2	true	GNU General Public License version 3.0 (GPL-3.0)	Optimization Simulation	Investment planning: optimization based on Benders decomposition Dispatch : simulation based on MILP	socio-economic welfare, investment costs, greenhouse gas emissions	Capacity Expansion Problem Production Cost Model	true	C++, C	transmission DC load flow net transfer capacities		false	true	Python, TypeScript	- RTE, "Energy Pathways to 2050", 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, "Stochastic Modeling for Future Scenarios of the 2040 Australian National Electricity Market using ANTARES," 2020 International Conference on Smart Grids and Energy Systems (SGES), Perth, Australia, 2020, pp. 761-766, doi: 10.1109/SGES51519.2020.00141.	Electricity Methane Hydrogen Heat	https://antares-simulator.org/pages/antares-simulator/6/	Renewables Conventional Generation CHP	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.	Hour	https://antares-simulator.org/
AnyMOD	TU Berlin	Leonard Göke		Göke (2020), AnyMOD - A graph-based framework for energy system modelling with high levels of renewables and sector integration, Working Paper.		lgo@wip.tu-berlin.de	Leonard Göke	some	dispatch investment	single-stage scenarios	Pathways for the decarbonisation of the European energy system until 2050	AnyMOD	User-dependent	User-dependent	true	MIT license (MIT)	Optimization	Continuous Linear Optimization	cost minimization by default, can set other objectives	Framework	true	Julia/JuMP	transmission net transfer capacities		true	true		Hainsch et al. (2020), European Green Deal: Using Ambitious Climate Targets and Renewable Energy to Climb out of the Economic Crisis, DIW Weekly Report.	User-dependent		Renewables Conventional Generation CHP	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.	Hour	https://github.com/leonardgoeke/AnyMOD.jl
Backbone	VTT Technical Research Centre of Finland	Juha Kiviluoma, Erkka Rinne, Topi Rasku, Niina Helistö, Jussi Ikäheimo, Dana Kirchem, Ran Li, Ciara O'Dwyer, Jussi Ikäheimo, Tomi J. Lindroos, Eric Harrison	https://doi.org/10.3390/en12173388	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.	10	Tomi.J.Lindroos@vtt.fi	Tomi J. Lindroos	some	dispatch investment	Short-term and long-term stochastics are available	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).	Backbone - energy systems model	Depends on user	Depends on user	true	GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0)	Optimization	The model minimizes the objective function and includes constraints related to energy balance, emissions, unit operation, transfers, system operation, portfolio design, etc.	Cost minimization; emission minimization;	Framework	true	GAMS	transmission DC load flow net transfer capacities	1,000,000	false	true	Spine Toolbox or Excel		All	https://gitlab.vtt.fi/backbone/backbone/-/tree/release-3.x	Renewables Conventional Generation CHP	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).	15 Minute
Balmorel	RAM-løse, DTU	Hans Ravn	10.1016/j.esr.2018.01.003.	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.				all	dispatch investment	Deterministic, perfect foresight, global sensitivity analysis		Balmorel	User-dependent (Pan-European, applied in 20+ countries)	Hierarchical: countries, regions, areas	true	ISC License (ISC)	Optimization	Linear programming (with an option of mixed-integer programming)	Social welfare maximization	Energy System Model	true	GAMS	net transfer capacities transmission DC load flow		true	true	Excel, Python (Pandas)	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	User-dependent	https://github.com/balmorelcommunity/Balmorel	Renewables Conventional Generation CHP	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.	Hour	http://balmorel.com/
Breakthrough Energy Model	Breakthrough Energy Foundation	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	10.1109/PESGM41954.2020.9281850	Y. Xu et al., "U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies," 2020 IEEE Power & Energy Society General Meeting (PESGM), 2020, pp. 1-5.	1,200	sciences@breakthroughenergy.org	Yixing Xu	all	dispatch	Scenario Analysis (Deterministic)		Breakthrough Energy Model	Currently U.S., but extendable to any region	Nodal	false	MIT license (MIT)	Optimization Simulation	The Breakthrough Energy Model runs DCOPF simulations	Minimize cost	Framework	true	Julia/JuMP	transmission DC load flow	1,000,000,000	false	true	Python	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	Electricity	https://github.com/Breakthrough-Energy	Renewables Conventional Generation	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.	Hour	https://breakthrough-energy.github.io/docs/index.html
CAPOW	North Carolina State University	Jordan Kern, Yufei Su	https://doi.org/10.1016/j.envsoft.2020.104667	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		jkern@ncsu.edu	Jordan Kern	all	dispatch	Short-term and long-term stochastics are available		California and West Coast Power Systems model		Zonal	true	MIT license (MIT)	Simulation	Iterative mixed-integer program, with user defined operating horizon	Cost minimization	CAISO and Mid-Columbia markets/U.S. West Coast	true	Python (Pyomo)	transmission		false	true			Electric power	https://github.com/romulus97/CAPOW_PY36	Renewables Conventional Generation	Python-based multi-zone unit commitment/economic dispatch model of CAISO and Mid-C markets coupled with "stochastic engine" for representing effects of multiple spatiotemporally correlated hydrometeorological processes on demand, hydropower and wind and solar power production.	Hour	https://kern.wordpress.ncsu.edu/
CESAR-P	Urban Energy Systems Lab, Empa (Swiss Federal Laboratories for Materials Science and Technology)	Leonie Fierz, Aaron Bojarski, Ricardo Parreira da Silva, Sven Eggimann	https://doi.org/10.5281/zenodo.5148531	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			Kristina Orehounig	some				Combined Energy Simulation and Retrofit in Python	Switzerland	depending on input data	false	Affero General Public License v3 (AGPL-3.0)	Simulation			Swiss building stock	false	Python, EnergyPlus			false	true			electricity heating cooling domestic hot water	https://github.com/hues-platform/cesar-p-core		The package allows for simulating the building energy demand of a district, including options for retrofitting, cost and emission calculation.	Hour	https://github.com/hues-platform/cesar-p-core
Calliope	ETH Zürich	Stefan Pfenninger, Bryn Pickering	10.21105/joss.00825	Pfenninger and Pickering, (2018). Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software, 3(29), 825		contact@callio.pe	contact@callio.pe	some	dispatch investment	Deterministic; stochastic programming add-on		Calliope	User-dependent	User-dependent	true	Apache License 2.0 (Apache-2.0)	Optimization		User-dependent, including financial cost, CO2, and water consumption	Framework	true	Python (Pyomo)	net transfer capacities transmission distribution		true	true	Python (pandas et al)	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	User-dependent	https://github.com/calliope-project/calliope	Renewables Conventional Generation CHP	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!	Hour	http://www.callio.pe/
CapacityExpansion	Stanford University, RWTH Aachen	Lucas Elias Kuepper, Holger Teichgraeber, Patricia Levi, Ali Ramadhan	https://doi.org/10.21105/joss.02034	https://joss.theoj.org/papers/10.21105/joss.02034#		elias.kuepper@rwth-aachen.de	Lucas Elias Kuepper	all	dispatch investment		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	CapacityExpansion	Input data dependent	input data dependent	true	MIT license (MIT)	Optimization	Optimization, Linear optimization model input-data depending energy system	Total system cost	Capacity Expansion Problem	true	Julia/JuMP			false	true	Julia		electricity heat gas	https://github.com/YoungFaithful/CapacityExpansion.jl		CapacityExpansion is a julia implementation of an input-data-scaling capacity expansion modeling framework.		https://youngfaithful.github.io/CapacityExpansion.jl/stable/
DESSTinEE	Imperial College London	Iain Staffell, Richard Green	http://dx.doi.org/10.1016/j.energy.2015.06.082	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.		i.staffell@imperial.ac.uk	Iain Staffell	all	dispatch	Stochastic	How much transmission will Europe need in 2050 How will electricity demand change in 2050 under different decarbonisation pathways	Demand for Energy Services, Supply and Transmission in Europe	Europe, North Africa	National	true	Creative Commons Attribution Share-Alike 3.0 (CC-BY-SA-3.0)	Simulation	Annual projection: simple arithmetic Hourly load curve production: partial decomposition Electricty system dispatch: Merit order stack with transmission constraints	Costs, welfare, carbon emissions, fuel mixes	Simulation	true	Excel / VBA	net transfer capacities		true	true	Excel / VBA		All / Electricity	http://tinyurl.com/desstinee	Renewables Conventional Generation	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.	Hour	http://tinyurl.com/desstinee
DIETER	DIW Berlin	Wolf-Peter Schill, Alexander Zerrahn	https://doi.org/10.1016/j.rser.2016.11.098	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		wschill@diw.de	Wolf-Peter Schill, Alexander Zerrahn	all	dispatch investment	- (work in progress)	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?	Dispatch and Investment Evaluation Tool with Endogenous Renewables	Initial version: greenfield, loosely calibrated to Germany; central European version also available	In most applications so far, Germany as one node; version with additional central European country nodes available	true	MIT license (MIT)	Optimization	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.	Cost minimization	Optimization	true	GAMS; CPLEX			false	true	MS Excel	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	electricity plus sector coupling (EVs P2Heat)		Renewables Conventional Generation	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.	Hour	http://www.diw.de/dieter
Demod	EPFL	Matteo Barsanti, Lionel Constantin	https://doi.org/10.1186/s42162-021-00180-6	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).	10		Matteo Barsanti, Lionel Constantin	some		Not yet implemented		domestic energy demand model	Germany, UK	depending on input data	true	GNU General Public License version 3.0 (GPL-3.0)	Simulation	First order and semi- Markov-chain Monte Carlo simulation.	Assess domestic energy demand evolution and demand-side-management scenarios	Simulation	true	Python			false	true	Python		end-use demand	https://github.com/epfl-herus/demod		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	Minute	https://demod.readthedocs.io/en/latest/
Dispa-SET	European Commission, Joint Research Centre	Sylvain Quoilin, Konstantinos Kavvadias	10.2760/25400	Quoilin, S., Hidalgo Gonzalez, I., & 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.	30	Andreas.ZUCKER@ec.europa.eu	Sylvain Quoilin, Andreas Zucker	all	dispatch	Through proper sizing of reserve needs	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; ...	Dispa-SET	Currently, 7 EU countries	NUTS1	true	European Union Public Licence Version 1.1 (EUPL-1.1)	Optimization	From the same dataset, the model can be expressed as a MILP or LP problem	Minimization of operational costs	EU power system	true	Python (Pyomo), GAMS	net transfer capacities		false	true	Python	Existing or ongoing case studies for Bolivia, Greece, Ireland, Netherlands, Belgium	Power system	https://joinup.ec.europa.eu/software/dispaset/description	Renewables Conventional Generation CHP	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.	Hour	https://github.com/squoilin
DynPP	University of Rostock	Moritz Hübel	http://dx.doi.org/10.1016/j.apenergy.2017.10.033	Modelling and simulation of a coal-fired power plant for start-up optimisation	600	moritz.huebel@uni-rostock.de	Moritz Hübel		investment	Deterministic	Which potential of Flexibility can be provided by specific thermal power plants?	Dynamic Power Plant Model	Specific plants	one point	false		Optimization Simulation	physical-equation-based	operation, cost, emissions, thermal stress	Specific Power Plants	false	Modelica, Dymola, (OpenModelica), C++, MySQL, SQLite	net transfer capacities		false	false	Matlab	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	Coal Gas Heat Electricity		Conventional Generation CHP	Full Scope Dynamic Simulation Models of different thermal power plants	Second	http://ltt-rostock.de
EA-PSM Electric Arc Flash	JSC Energy Advice					info@energyadvice.lt		all				EA-PSM Electric Arc Flash	Global, European Union, Lithuania, Turkey, Poland, India	global, continents, nations	true		Optimization Simulation				false	Java	transmission distribution AC load flow DC load flow		false	false	Java, JavaFX			http://www.energyadvice.lt/en		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.		http://www.energyadvice.lt/en
EA-PSM Electric Short Circuit	JSC Energy Advice					info@energyadvice.lt		all				EA-PSM Electric Short Circuit	Global, European Union, Lithuania, Turkey, Poland, India	global, continents, nations	false		Optimization Simulation				false	Java	transmission distribution AC load flow DC load flow		false	false	Java, JavaFX			http://www.energyadvice.lt/en/		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.		http://www.energyadvice.lt/en/
ELMOD	Technische Universität Berlin	Florian Leuthold, Hannes Weigt, Christian von Hirschhausen, Jonas Egerer, Clemens Gerbaulet, Casimir Lorenz, Jens Weibezahn				jew@wip.tu-berlin.de	Jens Weibezahn	some	dispatch			Electricity Model	Germany, Europe	power plant block, transmission network node	false	MIT license (MIT)	Optimization			German and European Electricity Market	true	GAMS	transmission DC load flow		true	true			Electricity Heat		Renewables Conventional Generation CHP	The "Electricity Model" (ELMOD) is a deterministic linear or mixed integer dispatch model framework of the German (and European) electricity and co-generation heat sector.	Hour	https://www.diw.de/elmod
ELTRAMOD	Technische Universität Dresden (ee2)	Dominik Möst, David Gunkel, Theresa Ladwig, Daniel Schubert, Hannes Hobbie, Christoph Zöphel, Steffi Misconel, Carl-Philipp Anke	urn:nbn:de:bsz:14-qucosa-236074	Demand Side Management in Deutschland zur Systemintegration erneuerbarer Energien		dominik.moest@tu-dresden.de	Dominik Möst	some	dispatch investment	Deterministic; Perfect foresight; Sensitivity analysis ;		Electricity Transshipment Model	EU-27 + Norway + Switzerland + United Kingdom + Balkan countries	NUTS0 - NUTS3	false		Optimization	Linear optimization model. Decision variables include investment and dispatch of generation, storage, DSM and different sector coupling options including both wholesale and balancing markets.	Minimization of total system costs	German and European Electricity Market	false	GAMS; CPLEX	transmission net transfer capacities		true	false		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.	Electricity including sector coupling (EVs PtX)		Renewables Conventional Generation CHP	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.	Hour	https://tu-dresden.de/bu/wirtschaft/ee2/forschung/modelle/eltramod
EMLab-Generation	Delft University of Technology	Jörn C. Richstein, Emile Chappin, Pradyumna Bhagwat, Laurens de Vries	10.1016/j.enpol.2014.03.037	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	60	j.c.richstein@tudelft.nl	Jörn C. Richstein	some	dispatch investment	Limited foresight, optional risk aversion	- 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?	EMLab-Generation	Central Western Europe	Zones	true	Apache License 2.0 (Apache-2.0)	Simulation Agent-based			Agent-based Simulation	true	Java	net transfer capacities		true	true	R		Electricity Market Carbon Market	https://github.com/EMLab/emlab-generation	Renewables Conventional Generation	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.	Year	http://emlab.tudelft.nl/
EMMA	Neon Neue Energieökonomik GmbH	Lion Hirth			5	hirth@neon-energie.de	Lion Hirth	all	dispatch investment	Sensitivities (many)	Long-term market value of wind and solar power; Optimal share of wind and solar power in electricity generation; Explaining electricity price development	The European Electricity Market Model	France, Poland, Belgium, The Netherlands, Germany, Sweden, Norway	Countries	false	Creative Commons Attribution 3.0 (CC-BY-3.0)	Optimization	Linear program	Total system cost	Power market model	true	GAMS	net transfer capacities		true	true			Electricity	https://neon-energie.de/emma/	Renewables Conventional Generation CHP		Hour	https://neon-energie.de/emma/
EOLES elec	CIRED	Behrang Shirizadeh, Philippe Quirion	10.1016/j.eneco.2020.105004	Shirizadeh, B. & Quirion, P. (2020). Low-carbon options for French power sector: What role for renewables, nuclear energy and carbon capture and storage? Energy Economics, 105004.		shirizadeh@centre-cired.fr	Behrang Shirizadeh	all	dispatch investment	Deterministic; Perfect foresight; Sensitivity analysis ;		Energy Optimization for Low Emission Systems - electricity		Country level	false	Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0)	Optimization Simulation	Simultaneous optimization of dispatch and investment (linear programming), solved in CPLEX solver of GAMS	investment cost and operational costs (fixed and variable) minimization	Electricity System Model	true	GAMS	transmission		false	true			Electricity Sector Carbon Market		Renewables Conventional Generation	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).	Hour	http://www.centre-cired.fr/fr/behrang-shirizadeh/
EOLES elecRES	CIRED	Behrang Shirizadeh, Quentin Perrier, Philippe Quirion	10.5547/01956574.43.1.bshi	Shirizadeh, B., Perrier, Q. & Quirion, P. (2022) How sensitive are optimal fully renewable systems to technology cost uncertainty? The Energy Journal, Vol 43, No. 1		shirizadeh@centre-cired.fr	Behrang Shirizadeh	all	dispatch investment	Deterministic; Perfect foresight; Sensitivity analysis ; Robust decision making		Energy Optimization for Low Emission Systems - renewable electricity		Coutry	false	Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0)	Optimization Simulation	Simultaneous optimization of dispatch and investment (linar programming), solved in CPLEX solver of GAMS	investment cost and operational costs (fixed and variable) minimization	Electricity System Model	true	GAMS	transmission		false	true			Electricity Sector	https://github.com/BehrangShirizadeh/EOLES_elecRES	Renewables	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.	Hour
ESO-X	Imperial College London	Clara F. Heuberger	https://doi.org/10.1016/j.apenergy.2017.07.075	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	10	c.heuberger14@imperial.ac.uk	Clara F. Heuberger	all	dispatch investment	scenario analysis		Electricity Systems Optimisation Framework	UK	single-node (ESONE: 29 nodes)	true	MIT license (MIT)	Optimization	MILP	minimise total system cost	power system model	true	GAMS; CPLEX		240,000	false	true	R	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 & 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	Electricity		Renewables Conventional Generation	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.	Hour	https://zenodo.org/record/1048943%2C%20https://zenodo.org/record/1212298
Energy Policy Simulator	Energy Innovation, LLC	Jeffrey Rissman, Robbie Orvis			0.05	jeff@energyinnovation.org	Jeffrey Rissman, Robbie Orvis	all	dispatch investment	Monte carlo		Energy Policy Simulator		single region	true	GNU General Public License version 3.0 (GPL-3.0)	Simulation	Annual forward simulating model with some investment optimization and full accounting of policy interactions.		System Dynamics	true	Vensim		1,000	false	true	Vensim		Electricity buildings transportation industry district heat land agriculture hydrogen etc...	https://github.com/Energy-Innovation/eps-us/archive/2.1.2.zip	Renewables Conventional Generation	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&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: ->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). ->Direct cash flow (cost or savings) impacts on consumers, industry (as a whole), government, and several specific industries ->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.) ->Vehicle technology market shares and fleet composition (electric vehicles, etc.) ->Energy use by fuel type from various energy-using technologies (specific types of vehicles, building components, etc.) ->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.	Year	https://energypolicy.solutions
Energy Transition Model	Quintel Intelligence	Quintel Intelligence		https://github.com/quintel/documentation	0.00833	chael.kruip@quintel.com	Chael Kruip	all	dispatch	The user can assess the impact of almost every input variable and assumption	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.)?	Energy Transition Model	EU27, The Netherlands, UK, Poland, France, Germany, Spain, Brazil	Country	true	MIT license (MIT)	Simulation	The ETM is based on an energy graph where nodes can convert one type of energy into another.	Given demand and other choices, calculate primary energy use, costs, CO2-emission etc.	Demand driven energy model	true	Developed in-house written in Ruby (on Rails)	transmission distribution net transfer capacities	300	true	true	Excel / VBA	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&list=UUwUlayF7P2RnRHFz0_a1v9A&feature=share	Households Buildings Agriculture Transport Industry Energy	https://github.com/quintel/documentation	Renewables Conventional Generation CHP	Web-based model based on a holistic description of a country's energy system.	Year	http://www.energytransitionmodel.com
EnergyNumbers-Balancing	UCL Energy Institute				1.0e-5	andrew.smith@ucl.ac.uk	Andrew ZP Smith	some	dispatch	Deterministic	In Britain, how much wind & 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.	EnergyNumbers-Balancing	Britain, Germany, Spain	National	true		Simulation			Simulating storage and exogenously-variable renewables	false	Fortran, PHP, Javascript, HTML, CSS		28	false	false	Matlab, Python		Electricity	https://github.com/RCUK-CEE/energynumbers-balancing	Renewables	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.	Hour	http://energynumbers.info/balancing
EnergyRt		Oleg Lugovoy, Vladimir Potashnikov				olugovoy@gmail.com	Oleg Lugovoy			perfect foresight		energy systems modeling R-toolbox			true	Affero General Public License v3 (AGPL-3.0)	Optimization	linear, cost-minimizing, partial equilibrium	costs	Reference Energy System	true	GAMS; GLPK			true	true	R			https://github.com/olugovoy/energyRt		energyRt is a package for R to develop Reference Energy System (RES) models and analyze energy-technologies. The package includes a standard RES (or "Bottom-Up") 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.		http://energyRt.org
EnergyScope	EPFL, UCLouvain	Stefano Moret, Gauthier Limpens	10.1016/j.apenergy.2019.113729	Limpens G, Moret S, Jeanmart H, Maréchal F,EnergyScope TD: a novel open-source model for regional energy systems. Appl Energy 2019; Volume 255.		gauthier.limpens@uclouvain.be	Gauthier Limpens	all	dispatch investment		Role of storage? Benefit of electrification? How to handle high shares of renewables? What is the impact of uncertainties on investment decisions?	EnergyScope	Region (Switzerland, Belgium)	Country	true	Apache License 2.0 (Apache-2.0)	Optimization	Linear programming (43 equations fully documented).	financial cost, greenhouse gases emissions	Regional energy system design	true	GLPK/GLPSOL or AMPL/Cplex		200,366	false	true	Excel	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., & Maréchal, F. (2020). Belgian Energy Transition: What Are the Options?. Energies, 13(1), 261.	All (Electricity Heating and mobility)	https://github.com/energyscope/EnergyScope		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.	Hour
Ficus	Institute for Energy Economy and Application Technology	Dennis Atabay	10.5281/zenodo.32077		60	dennis.atabay@tum.de	Dennis Atabay	some	dispatch investment	None		ficus			true	GNU General Public License version 3.0 (GPL-3.0)	Optimization		costs	energy system optimization model	true	Python (Pyomo)			true	true	Python (pandas et al)		electricity heating ...	https://github.com/yabata/ficus	Renewables Conventional Generation CHP	A (mixed integer) linear optimisation model for local energy systems	15 Minute	https://github.com/yabata/ficus
FlexiGIS	DLR Institute of Networked Energy Systems	Alaa Alhamwi	https://doi.org/10.1016/j.apenergy.2017.01.048.	GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas		alaa.alhamwi@dlr.de	Alaa Alhamwi	some				Flexibilisation in Geographic Information Systems		building, street, district, city	false	BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)	Optimization Simulation	Modelling and optimisation mathematical model	simualte local urban demand and supply, localise distributed storage, minimise total system costs	urban energy systems	true	Python	distribution		false	true	Geopandas		Electricity Sector	https://github.com/FlexiGIS/FlexiGIS.git	Renewables	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.	15 Minute	https://github.com/FlexiGIS/FlexiGIS.git
GAMAMOD	Technische Universität Dresden (EE2)	Philipp Hauser				philipp.hauser@tu-dresden.de	Philipp Hauser	some	dispatch investment			Gas Market Model			false		Optimization			European Natural Gas Market	false	GAMS	transmission distribution		true	false			gas			The gas market model GAMAMOD is a bottom-up model used to determine and analyse the optimal natural gas supply structure in Europe and to examine the utilization of the natural gas infrastructure. In its basic version, the model includes the EU-28 countries as well as Switzerland, Norway, the Baltic States and the Balkan region. In addition, important suppliers for the European natural gas market are considered (e.g. Russia, Algeria, and Qatar). 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. The capacity of single pipelines between neighbouring countries are aggregated in the model. In the case of LNG shipping, the model considers regasification and liquefaction capacities in export and import countries. The model includes an exogenously imputed natural gas demand for each respective country. Moreover, seasonal demand patterns in the respective countries are considered. 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.		https://tu-dresden.de/bu/wirtschaft/ee2/forschung/modelle/gamamod?set_language=en
GAMAMOD-DE	Technische Universität Dresden (EE2)	Lucas De La Fuente; Philipp Hauser	http://hdl.handle.net/10419/197000	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		lucas.delafuente@tu-dresden.de	Lucas De La Fuente	all	dispatch		questions about: - sector coupling between electricity and gas - security of supply in the German gas network	Gas Market Model	Germany	NUTS0 - NUTS3, for DE	false		Optimization Simulation			German Transmission Grid	false	GAMS; CPLEX	transmission		true	false		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					Day	https://tu-dresden.de/bu/wirtschaft/bwl/ee2/forschung/modelle/gamamod
GRIMSEL-FLEX	University of Geneva	Martin Soini, Arthur Rinaldi			30	arthur.rinaldi@unige.ch	Arthur Rinaldi	all	dispatch	Perfect foresight, Sensitivity analisys, Scenarios		General Integrated Modeling environment for the Supply of Electricity and Low-temperature heat	Switzerland, Austria, Italy, France, Germany	Consumer types and Urban settings	false	BSD 2-Clause "Simplified" or "FreeBSD" License (BSD-2-Clause)	Optimization	Quadratic dipatch sector-coupling model	Minimization of total system costs	Energy System Model Optimization Social Planner	true	Python (Pyomo)	transmission net transfer capacities	5,000,000	false	true	Python (pandas et al)		Electricity Heat Hydrogen Buildings Transport	https://github.com/arthurrinaldi/grimsel	Renewables Conventional Generation		Hour
Genesys	RWTH-Aachen University	Alvarez, Bussar, Cai, Chen, Moraes Jr., Stöcker, Thien	10.1016/j.egypro.2014.01.156	Bussar et. al, 2014, Optimal Allocation and Capacity of Energy Storage Systems in a Future European Power System with 100% Renewable Energy Generation			Christian Bussar	all	dispatch investment	24 h foresight for storage operation	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?	Genetic Optimisation of a European Energy Supply System	Europe, North Africa, Middle East	EUMENA, 21 regions	false	GNU Library or "Lesser" General Public License version 2.1 (LGPL-2.1)	Optimization Simulation	optimisation of system combination with evolutionary strategy simulation of operation with hierarchical management strategy and linear load balancing between regions (network simplex)	minimise levelised cost of electricity	Electricity System Model	false	C++, boost library, MySQL and QT4, (optional CPLEX solver implementation)	transmission net transfer capacities		false	true	Excel/Matlab and a Visualisation tool programmed in QT4 (c++)		Electricity	http://Form%20on%20website	Renewables Conventional Generation	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.	Hour	http://www.genesys.rwth-aachen.de
GridCal		Santiago Peñate Vera, Michel Lavoie				santiago.penate.vera@gmail.com	Santiago Peñate Vera	all		Deterministic, stochastic		GridCal			true	GNU General Public License version 3.0 (GPL-3.0)	Optimization Simulation	Object oriented structures -> intermediate objects holding arrays -> Numerical modules	Match generation to demand and minimise cost	Transmission Network Model and Data (input and output)	true	Python	transmission distribution AC load flow DC load flow		true	true	Python		Electricity	https://github.com/SanPen/GridCal.git	Conventional Generation	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.		https://github.com/SanPen/GridCal
HighRES	UCL, UiO	James Price, Marianne Zeyringer		Zeyringer, M., Price, J., Fais, B., Li, P.-H. & 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)	60			all	dispatch investment			high spatial and temporal electricity system model	EEA+Norway and UK	Country level, 20 zones for GB	true	MIT license (MIT)	Optimization		Minimization of total system costs	European electricity system model GB electricity system model	false	GAMS; CPLEX	transmission net transfer capacities		false	true	Python		Electricity		Renewables Conventional Generation	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.	Hour	https://github.com/highRES-model
IRENA FlexTool	VTT Technical Research Centre of Finland	Juha Kiviluoma, Arttu Tupala, Antti Soininen				juha.kiviluoma@vtt.fi	Juha Kiviluoma	some	dispatch investment	perfect foresight, but can use limited horizon		IRENA FlexTool	User dependent	User dependent	true	GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0)	Optimization	Typically linear cost minimization, but unit online decisions can be mixed-integer linear (and effectively investment decisions too).	cost minimization	Multi-purpose	true	GNU MathProg	transmission net transfer capacities		false	true	Python, SQL		All sectors (user can add more)	https://github.com/irena-flextool/flextool	Renewables Conventional Generation CHP	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)	Hour	https://irena-flextool.github.io/flextool/
JMM	Risoe National Laboratory; University of Stuttgart; University of Duisburg-Essen	Peter Meiborn; Helge V. Larsen; Rüdiger Barth; Heike Brand; Christoph Weber; Oliver Voll										Joint Market Model			false						false				false	false								http://www.wilmar.risoe.dk/Deliverables/Wilmar%20d6_2_b_JMM_doc.pdf
Lemlab	Technical University of Munich	Sebastian Dirk Lumpp, Markus Doepfert, Michel Zade			20	sebastian.lumpp@tum.de	Sebastian Dirk Lumpp	all		perfect forecast, deterministic, stochastic		local energy market laboratory			false	GNU General Public License version 3.0 (GPL-3.0)	Simulation Agent-based	Agents: intertemporal convex optimization Markets: (iterative) double-sided auctions, p2p clearing Forecasting: naive, deterministic forecasting, neural networks		agent-based simulation	true	Python, Pyomo			false	true	PostgreSQL, Ethereum		local energy markets	https://github.com/tum-ewk/lemlab	Renewables Conventional Generation CHP	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.		https://github.com/tum-ewk/lemlab
LoadProfileGenerator	FZ Jülich	Noah Pflugradt, Peter Stenzel, Martin Robinius, Detlef Stolten				Noah.Pflugradt@gmail.com	Noah Pflugradt	some				LoadProfileGenerator			false	MIT license (MIT)					true	C#			false	true				https://github.com/FZJ-IEK3-VSA/LoadProfileGenerator		Generates residential profiles for electricity, water, car charging, occupancy and more. Agentbased simulation using a psychological behavior model.	Minute	http://loadprofilegenerator.de
MEDEAS	GEEDS group; University of Valladolid (http://www.eis.uva.es/energiasostenible/?lang=en)					jsole@icm.csic.es	Jordi Solé	all		Deterministic		Modelling the Energy Development under Environmental and Social constraints	Global; European Union; Bulgaria; Austria	global, continents, nations	true	MIT license (MIT)	Other	System dynamics. Top-down	CO2 equivalent emissions, energy, social, economic costs, RE-share		false	Phyton			false	true	Phyton		electricity heat liquid fuels gas solid fuels	http://medeas.eu/model/medeas-model	Renewables Conventional Generation CHP		Year	http://medeas.eu/
MOCES	Chair of Automation and Energy Systems (Saarland University)	Lukas Exel	10.1109/SmartGridComm.2015.7436395	L. Exel, F. Felgner and G. Frey, "Multi-domain modeling of distributed energy systems - The MOCES approach," 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), Miami, FL, 2015, pp. 774-779.		lukas.exel@aut.uni-saarland.de	Lukas Exel		dispatch	deterministic, stochastic		Modeling of Complex Energy Systems	Depends on user	Depends on user	false		Simulation Agent-based	HDAE (Hybrid Differential Equations) combined with an agent-based approach.		Energy Modeling Framework	false	Modelica, Dymola, (OpenModelica), C++, MySQL, SQLite		100,000	true	false	Lsodar, Dassl		Electricity User-dependent		Renewables Conventional Generation	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.	Second	http://tiny.cc/2q07iy
Maon	Maon GmbH	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		Maon GmbH, Documentation, https://docs.cloud.maon.eu.	1,000	info@maon.eu	Dr. Mihail Ketov	all	dispatch investment	Monte Carlo, preprocessing or sensitivity		Maon	Europe, North Africa, Middle East	Individual power stations	true		Optimization Simulation Other Agent-based		Minimization of dispatch and investment cost	Mixed-Integer Quadratic Programming (MIQP)	false	C++	transmission distribution AC load flow DC load flow net transfer capacities	1,000,000,000	false	false	Ansible, Ceph, cURL, Docker, GraphQL, Kubernetes, MinIO, MongoDB, Node.js, Preact, Python, TypeScript, WebAssembly	https://maon.eu/publications	heat Electricity gas and emissions plus couplings (industry transport)		Renewables Conventional Generation CHP	Maon is a market simulation for fundamental electricity, gas, and emission 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 parameters.	Hour	https://maon.eu
Medea	University of Natural Resources and Life Sciences, Vienna	Sebastian Wehrle, Johannes Schmidt		https://arxiv.org/abs/2006.08009	15	sebastian.wehrle@boku.ac.at	Sebastian Wehrle	all	dispatch investment	Deterministic		medea	Austria, Germany	Countries	true	MIT license (MIT)	Optimization		Total system cost	Austrian and German electricity market	true	GAMS	net transfer capacities		false	true	Python		Electricity Heat	https://github.com/inwe-boku/medea	Renewables Conventional Generation CHP		Hour	https://github.com/inwe-boku/medea
MicroGridsPy	Politecnico di Milano	Sergio Balderrama, Sylvain Quoilin, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Emanuela Colombo, Riccardo Mereu, Nicolò Stevanato, Ivan Sangiorgio, Gianluca Pellecchia	https://doi.org/10.1016/j.energy.2019.116073	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,		nicolo.stevanato@polimi.it	Nicolo' Stevanato	all	dispatch investment	Two-stage stochastic optimization	-Long-term sizing of rural microgrids -Load evolution	MicroGridsPy		Village-scale	true	European Union Public Licence Version 1.1 (EUPL-1.1)	Optimization	The model is based on two-stage stochastic optimisation and LP or MILP mathematical formulation	Single or multi objective optimization (NPC, operation costs, CO2 emissions)	Energy Modeling Framework	true	Python (Pyomo)			false	true	Excel	-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	Micro-grids design	https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git	Renewables Conventional Generation	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.	Hour	https://github.com/SESAM-Polimi/MicroGridsPy-SESAM
Mosaik	OFFIS		10.1109/OSMSES54027.2022.9769116.	A. Ofenloch et al., "MOSAIK 3.0: Combining Time-Stepped and Discrete Event Simulation," 2022 Open Source Modelling and Simulation of Energy Systems (OSMSES), 2022, pp. 1-5		mosaik@offis.de		some				mosaik			true	GNU Library or "Lesser" General Public License version 2.1 (LGPL-2.1)	Optimization Simulation Agent-based			distributed energy systems smart grid simulation	true	Python	transmission distribution		false	true	HDF5, InfluxDB, Grafana		electricity heat mobility household	https://gitlab.com/mosaik	Renewables CHP	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.	Second	http://mosaik.offis.de/
MultiMod	DIW Berlin, NTNU Trondheim	Daniel Huppmann, Ruud Egging	10.1016/j.energy.2014.08.004	Daniel Huppmann & Ruud Egging (2014). Market power, fuel substitution and infrastructure - A large-scale equilibrium model of global energy markets. Energy, 75, 483–500.	600	dhuppmann@diw.de	Daniel Huppmann	some	dispatch investment	Not covered (yet)	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)	Energy system and resource market model "MultiMod"	Global	Europe by region, North America by country, rest of world by region	false		Other	Generalized Nash Equilibrium (GNE) model formulated as a Mixed Complementarity Model (MCP)		Equilibrium model	false	GAMS	transmission net transfer capacities	150,000	true	false	MS Access, MS Excel	Currently used within EMF 31 (http://emf.stanford.edu)	Oil Gas Coal Electricity Renewables Industry Transport Residential/Commercial		Renewables Conventional Generation	The energy system and resource market model "MultiMod" 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 & 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.	Multi year	http://www.diw.de/multimod
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