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) |
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 |
|
philipp.hauser@tu-dresden.de |
Philipp Hauser |
all |
dispatch |
|
|
questions about:
- sector coupling between electricity and gas
- security of supply in the German gas network |
Gas Market Model for Germany |
|
|
|
false |
|
|
|
|
|
|
false |
GAMS; CPLEX |
|
|
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 |
|
|
|
|
|
https://tu-dresden.de/bu/wirtschaft/ee2/forschung/modelle/gamamod-de |
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, Esmanur Eryilmaz, Ali Baran Gündüz, Mark Schäfer, Kai Strunz, Albert Moser |
|
Maon GmbH, Handbook, https://cloud.maon.eu/handbook. |
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 operational costs for electricity spot and frequency reserve markets considering emission caps |
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) |
https://apis.cloud.maon.eu |
Renewables Conventional Generation CHP |
Maon is a market simulation for fundamental electricity, gas, and emission market analysis. It forecasts the facility-wise quarter-hourly dispatch of all supply and demand in whole Europe. 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 thousands of other results. |
Hour |
https://cloud.maon.eu/handbook |
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 |
NEMO |
|
|
University of New South Wales |
Ben Elliston |
|
|
|
b.elliston@unsw.edu.au |
Ben Elliston |
all |
dispatch |
|
|
|
National Electricity Market Optimiser |
|
Australia |
NEM regions |
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
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. |
minimise average cost of electricity |
|
true |
Python |
transmission |
100 |
true |
true |
|
|
|
https://git.ozlabs.org/?p=nemo.git |
Renewables Conventional Generation |
NEMO is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies. |
Hour |
https://nemo.ozlabs.org/ |
NEMO (SEI) |
|
|
Stockholm Environment Institute |
Jason Veysey, Charlie Heaps, Eric Kemp-Benedict |
|
In preparation |
|
jason.veysey@sei.org |
Jason Veysey |
|
dispatch investment |
Deterministic but can readily be applied in Monte Carlo analyses |
|
Climate change mitigation, net-zero pathways, national energy strategies |
Next Energy Modeling system for Optimization |
|
All |
Flexible - user-defined regionalization |
true |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
Constrained cost optimization with perfect foresight |
Minimize total discounted costs |
Full energy system optimization flexible geographic and sectoral scope |
true |
Julia |
transmission distribution DC load flow net transfer capacities |
100 |
false |
true |
SQLite |
https://doi.org/10.1016/j.apenergy.2022.118580
https://doi.org/10.1016/j.est.2021.103474 |
All |
https://github.com/sei-international/NemoMod.jl |
Renewables Conventional Generation CHP |
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. |
Hour |
https://www.sei.org/projects-and-tools/tools/nemo-the-next-energy-modeling-system-for-optimization/ |
OMEGAlpes |
|
|
G2Elab |
B. DELINCHANT, S. HODENCQ, Y. MARECHAL, L. MORRIET, C. PAJOT, V. REINBOLD, F. WURTZ |
|
|
|
omegalpes-users@groupes.renater.fr |
|
some |
|
|
|
|
Optimization ModEls Generation As Linear Programming for Energy Systems |
|
|
|
false |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
|
|
Production consumption conversion storage |
true |
OMEGAlpes, PuLP |
|
|
false |
true |
|
|
Electricity Heat all |
https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes |
|
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. |
|
https://omegalpes.readthedocs.io/en/latest/index.html |
OSeMOSYS |
|
|
KTH Royal Institute of Technology |
Mark Howells, Holger Rogner, Neil Strachan, Charles Heaps, Hillard Huntington, Socrates Kypreos, Alison Hughes, Semida Silveira, Joe DeCarolis, Morgan Bazillian, Alexander Roehrl |
doi:10.1016/j.enpol.2011.06.033 |
|
|
osemosys@gmail.com |
Mark Howells, Will Usher, Abhishek Shivakumar, Manuel Welsch, Vignesh Sridharan |
all |
investment |
|
|
|
open-source energy modelling system |
|
Africa (all countries), Sweden, Baltic States, Nicaragua, Bolivia, South America, EU-27+3 |
Country |
true |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
Linear optimisation (with an option of mixed-integer programming) |
Minimise total discounted cost of system |
|
true |
GNU MathProg |
transmission distribution |
|
true |
true |
Python |
|
all |
http://github.com/OSeMOSYS/OSeMOSYS |
Renewables Conventional Generation CHP |
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. |
Day |
http://www.osemosys.org |
Oemof |
|
|
Reiner Lemoine Institut / ZNES Flensburg |
Stephan Günther, Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Caroline Möller, Guido Plessmann, Clemens Wingenbach et al. |
|
|
|
|
Stephan Günther, Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Caroline Möller, Guido Plessmann, Clemens Wingenbach et al. |
some |
dispatch investment |
Deterministic |
|
|
Open Energy Modelling Framework |
|
Depends on user |
Depends on user |
false |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
https://oemof.org/libraries/ |
costs, emissions |
Energy Modelling Framework |
true |
Python, Pyomo, Coin-OR |
transmission distribution net transfer capacities DC load flow |
|
true |
true |
PostgreSQL, PostGIS |
|
Electricity Heat Mobility |
https://github.com/oemof/oemof/releases |
Renewables Conventional Generation CHP |
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. |
Hour |
https://oemof.org/ |
OnSSET |
|
|
KTH Royal Institute of Technology |
Dimitrios Mentis, Mark Howells, Holger Rogner, Alexandros Korkovelos, Christopher Arderne, Oliver Broad, Manuel Welsch, Francesco Fuso Nerini |
10.1016/j.esd.2015.09.007 |
Mentis, Dimitrios; Welsch, Manuel; Fuso Nerini, Francesco; Broad, Oliver; Howells, Mark; Bazilian, Morgan; Rogner, Holger (December 2015). "A GIS-based approach for electrification planning: a case study on Nigeria". Energy for Sustainable Development. 29: 142–150. doi:10.1016/j.esd.2015.09.007. ISSN 0973-0826. |
|
mentis@kth.se |
Dimitrios Mentis |
|
|
|
|
|
Open Source Spatial Electrification Tool |
|
Sub-Saharan Africa, developing Asia, Latin America |
1 km to 10 km |
false |
MIT license (MIT) |
|
Optimization |
|
Cost minimization |
|
true |
Python |
|
|
false |
true |
Python |
IEA World Energy Outlook 2014, IEA World Energy Outlook 2015, IEA and World Bank Global Tracking Framework 2015 |
|
https://github.com/KTH-dESA/PyOnSSET |
Renewables Conventional Generation |
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. |
Multi year |
|
OpenTUMFlex |
|
|
Technical University of Munich |
Michel Zade, Babu Kumaran Nalini, Zhengjie You, Peter Tzscheuschler |
doi:10.3390/en13215617 |
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. |
|
|
Michel Zade, Babu Kumaran Nalini, Zhengjie You, Peter Tzscheuschler |
some |
|
|
|
How can prosumer offer flexibility to the grid?
Can prosumer flexibility be quantified? |
OpenTUMFlex |
|
User dependent |
User dependent |
false |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
|
Cost optimal optimization and flexibility calculation |
Energy System Model urban energy systems load shifting optimisation Local energy systems |
true |
Python (Pyomo) |
distribution |
|
false |
true |
|
|
Energy Electricity Market Households electricity plus sector coupling (EVs |
https://github.com/tum-ewk/OpenTUMFlex |
Renewables CHP |
An open-source flexibility estimation model that quantifies all possible flexibilities from the available prosumer devices and prices them. |
15 Minute |
https://www.ei.tum.de/en/ewk/forschung/projekte/c-sells/ |
PLEXOS Open EU |
|
|
University College Cork |
Paul Deane |
doi:10.1016/j.renene.2015.02.048 |
http://www.sciencedirect.com/science/article/pii/S0960148115001640 |
60 |
jp.deane@ucc.ie |
Paul Deane |
all |
dispatch |
None |
|
Cost of electricity in 2020
Congestion on Lines
Impact of carbon prices |
PLEXOS Open EU |
|
North West Europe |
Member State |
false |
|
|
Optimization |
Least Cost Optimization, Can be run in MIP or linear relaxed mode |
Minimize total Generation cost |
Market Model |
true |
PLEXOS |
net transfer capacities |
|
true |
false |
MS Excel |
|
Electricity Market |
http://wiki.openmod-initiative.org/wiki/Power_plant_portfolios |
Renewables Conventional Generation |
Full Details available at
http://wiki.openmod-initiative.org/wiki/Power_plant_portfolios |
Hour |
http://www.ucc.ie/en/energypolicy/ |
POMATO |
|
|
TU Berlin |
Richard Weinhold, Robert Mieth |
10.1016/j.softx.2021.100870 |
Weinhold, Richard, and Robert Mieth. 2021. “Power Market Tool (POMATO) for the Analysis of Zonal Electricity Markets.” SoftwareX 16 (December): 100870. |
|
riw@wip.tu-berlin.de |
Richard Weinhold |
some |
dispatch |
Chance Constrained |
|
|
Power Market Tool |
|
User-dependent |
Nodal resolution |
false |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
|
Optimization |
Linear Economic Dispatch. Linear Optimal Power Flow. Linear Security Constrained Optimal Power Flow |
Cost minimization |
Network-constrained Unit Commitment and Economic Dispatch |
true |
Julia/JuMP |
transmission DC load flow net transfer capacities |
|
false |
true |
Python |
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. |
Electricity Market Heat |
https://github.com/richard-weinhold/pomato |
Renewables Conventional Generation CHP |
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. |
Hour |
https://github.com/richard-weinhold/pomato |
Pandapipes |
|
|
Fraunhofer IEE, Uni Kassel |
Dennis Cronbach, Daniel Lohmeier, Jolando Kisse, Simon Drauz,Tanja Kneiske |
|
https://www.pandapipes.org/references/ |
|
tanja.kneiske@ieg.fraunhofer.de |
Tanja Kneiske |
|
|
|
|
|
pandapipes |
|
|
|
false |
MIT license (MIT) |
|
Simulation |
|
|
|
false |
Python |
distribution |
|
true |
true |
|
|
|
https://github.com/e2nIEE/pandapipes |
Renewables |
An easy to use open source tool for fluid system modeling, analysis and optimization with a high degree of automation. |
|
http://www.pandapipes.org |
Pandapower |
|
|
|
Energy Management and Power System Operation (University of Kassel), Fraunhofer IEE |
10.1109/TPWRS.2018.2829021 |
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 |
|
|
Leon Thurner, Alexander Scheidler |
some |
|
|
|
|
Pandapower |
|
|
|
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
|
|
Transmission Network Model |
true |
Python |
transmission distribution |
|
false |
true |
Pandas |
|
|
https://github.com/e2nIEE/pandapower/ |
Renewables Conventional Generation |
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. |
|
http://www.pandapower.org |
PowNet |
|
|
Singapore University of Technology and Design |
AFM Kamal Chowdhury, Jordan Kern, Thanh Duc Dang, Stefano Galelli |
http://doi.org/10.5334/jors.302 |
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. |
|
k.chy0013@gmail.com |
AFM Kamal Chowdhury |
all |
dispatch |
Sensitivity analysis |
|
|
PowNet |
|
Laos, Cambodia, Thailand, any user-defined country or region |
High-voltage substation |
false |
MIT license (MIT) |
|
Optimization Simulation |
Mixed Integer Linear Program (MILP), DC Power Flow, Unit Commitment, Economic Dispatch |
Cost minimization |
Network-constrained Unit Commitment and Economic Dispatch |
true |
Python (Pyomo) |
transmission distribution DC load flow |
|
false |
true |
Python |
|
Electricity Electric power Energy |
https://zenodo.org/record/3462879#.XoL6T4gzZaQ |
Renewables Conventional Generation |
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. |
Hour |
https://github.com/kamal0013/PowNet |
PowerMatcher |
|
|
Flexiblepower Alliance Network |
|
|
|
|
|
|
|
|
|
|
|
PowerMatcherSuite |
|
|
|
false |
Apache License 2.0 (Apache-2.0) |
|
|
|
|
|
true |
Java |
|
|
false |
true |
|
|
|
https://github.com/flexiblepower/powermatcher |
Renewables |
"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" |
|
http://flexiblepower.github.io/ |
PowerSimulations.jl |
|
|
NREL |
Clayton Barrows, Jose-Daniel Lara, Daniel Thom, Dheepak Krishnamurthy, Sourabh Dalvi |
|
|
1 |
clayton.barrows@nrel.gov |
Clayton Barrows |
|
dispatch |
scenario analysis |
|
|
PowerSimulations.jl |
|
Any |
nodal resolution (all nodes are included) |
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Optimization |
Principal application is sequential quasi-static system optimization problems (production cost modeling). |
Least Cost |
quasii-static sequential unit-commitment and economic dispatch problems |
true |
Julia |
transmission AC load flow DC load flow net transfer capacities |
1,000,000 |
false |
true |
Julia |
|
Power system |
https://github.com/nrel-siip/PowerSimulations.jl |
Renewables Conventional Generation |
Flexible, modular, and scalable package for power system quasi-static analysis with sequential problem specification capabilities. |
Second |
https://github.com/nrel-siip/PowerSimulations.jl |
PowerSimulationsDynamics.jl |
|
|
NREL |
Jose-Daniel Lara, Rodrigo Henríquez-Auba |
|
|
|
nrel-siip@nrel.gov |
Clayton Barrows |
|
|
scenario analysis |
|
|
PowerSimulationsDynamics.jl |
|
|
Nodal resolution |
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
PowerSimulationsDynamics.jl enables transient stability analysis of power systems through differential-algebraic equations and with forward differentiation to enable small-signal stability analysis. |
N/A |
Dynamic system simulation model library |
true |
Julia |
transmission AC load flow |
|
false |
true |
Julia |
|
Electric power Electricity electricity |
https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl |
Renewables Conventional Generation CHP |
|
Less than second |
https://github.com/NREL-SIIP/PowerSimulationsDynamics.jl |
PowerSystems.jl |
|
|
NREL |
Clayton Barrows, Jose-Daniel Lara, Daniel Thom, Dheepak Krishnamurthy, Sourabh Dalvi |
|
|
0.1 |
nrel-siip@nrel.gov |
Clayton Barrows |
|
dispatch |
scenario analysis |
|
|
PowerSystems.jl |
|
Any |
Nodal resolution |
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
PowerSystems.jl includes basic power flow and network matrix calculation capabilities. |
|
Optimization Simulation |
true |
Julia |
transmission AC load flow DC load flow net transfer capacities |
100,000 |
false |
true |
Julia |
|
Electricity Electricity Sector Electric power |
https://github.com/NREL-SIIP/PowerSystems.jl |
Renewables Conventional Generation |
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. |
Less than second |
https://github.com/NREL-SIIP/PowerSystems.jl |
Pvlib python |
|
|
|
This is a community supported tool. Contributors to each release are listed here: https://pvlib-python.readthedocs.io/en/stable/whatsnew.html. |
https://doi.org/10.21105/joss.00884 |
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 |
|
|
See: https://github.com/pvlib/pvlib-python#getting-support |
some |
|
|
|
|
pvlib python |
|
|
|
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
|
|
|
true |
Python |
|
|
false |
true |
NumPy, Pandas |
|
Electricity |
https://github.com/pvlib/pvlib-python |
Renewables |
pvlib python is a community supported tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. |
|
https://pvlib-python.readthedocs.io/en/stable/ |
PyLESA |
|
|
University of Strathclyde |
Andrew Lyden |
|
|
|
andrew.lyden@strath.ac.uk |
Andrew Lyden |
some |
dispatch |
perfect foresight |
|
|
Python for Local Energy Systems Analysis |
|
|
Local/Community/District |
false |
MIT license (MIT) |
|
Simulation |
|
Minimization of operational costs |
Local energy systems |
true |
Python |
AC load flow DC load flow |
|
false |
true |
Python |
|
electricity heat |
https://github.com/andrewlyden/PyLESA |
Renewables Conventional Generation |
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, "Modelling and Design of Local Energy Systems Incorporating Heat Pumps, Thermal Storage, Future Tariffs, and Model Predictive Control " by Andrew Lyden. |
Hour |
|
PyPSA |
|
|
FIAS |
Tom Brown, Jonas Hörsch, David Schlachtberger |
https://doi.org/10.5334/jors.188 |
Journal of Open Research Software, 2018, 6 (1) |
|
brown@fias.uni-frankfurt.de |
Tom Brown |
all |
dispatch investment |
Not explicitly covered, but stochastic optimisation possible |
|
Power flow analysis, market analysis, total system investment optimisation, contingency analysis, sector coupling |
Python for Power System Analysis |
|
Europe, China, South Africa |
User dependent |
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
Non-linear power flow; linear optimal power flow / investment optimisation |
Cost minimization |
Energy System Model |
true |
Python, Pyomo |
transmission distribution AC load flow DC load flow net transfer capacities |
|
false |
true |
Pandas |
https://pypsa.org/publications/ |
Electricity Heat Transport User-defined |
https://github.com/PyPSA/PyPSA |
Renewables Conventional Generation CHP |
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. |
Hour |
https://www.pypsa.org/ |
QuaSi - GenSim |
|
|
Siz energieplus |
Tobias Maile, Simon Marx, Etienne Ott, Moira Peter, Heiner Steinacker, Matthias Stickel |
10.3390/en16176115 |
Maile, T.; Steinacker, H.; Stickel, M.W.; Ott, E.; Kley, C. Automated Generation of Energy Profiles for Urban Simulations. Energies 2023, 16, 6115. |
3 |
info@quasi-software.org |
Etienne Ott, Matthias Stickel |
all |
|
|
|
|
Generic Model for Thermal Building Simulation |
|
All |
|
false |
MIT license (MIT) |
|
Simulation |
EnergyPlus is used to perform a thermal building simulation |
|
building energy demand |
true |
EnergyPlus, OpenStudio, MS Excel, Ruby |
|
|
false |
true |
MS Excel |
To cite a specific version of GenSim, use:
Maile, T., Marx, S., Ott, E., Peter, M., Steinacker, H., & Stickel, M. (2023). GenSim v2.15 - Generic Building Simulation (part of QuaSi) (release). Zenodo. https://doi.org/10.5281/zenodo.10200807 |
electricity heat cold |
https://github.com/QuaSi-Software/GenSim |
|
GenSim - for "generic building simulation" - 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. "Generic" in this context refers to a "generally valid" 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/ |
15 Minute |
http://www.quasi-software.org/ |
QuaSi - ReSiE |
|
|
Siz energieplus |
Etienne Ott, Heiner Steinacker, Matthias Stickel |
10.1088/1742-6596/2600/2/022009 |
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 |
|
info@quasi-software.org |
Etienne Ott, Heiner Steinacker, Matthias Stickel |
all |
dispatch investment |
sensitivity analysis |
|
|
computational core for the simulation of energy systems |
|
Depends on user |
depends on user |
true |
MIT license (MIT) |
|
Simulation Other |
rule-based algorithms, system dynamics |
energy balances |
multi energy systems in urban scale |
true |
Julia |
transmission distribution |
|
false |
true |
Julia |
|
all sectors incl. heat cold hydrogen electricity |
https://github.com/QuaSi-Software |
Renewables Conventional Generation CHP |
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! |
15 Minute |
http://www.quasi-software.org/ |
QuaSi - SoDeLe |
|
|
Siz energieplus |
Heiner Steinacker, Jonas Mucke, Etienne Ott, Matthias Stickel |
|
Heiner Steinacker, Jonas Mucke: SoDeLe v2.0.0: Solarsimulation denkbar leicht. 2024 |
1 |
info@quasi-software.org |
Heiner Steinacker |
all |
|
|
|
|
Solar simulation as easy as can be |
|
All |
|
true |
MIT license (MIT) |
|
Simulation |
physics-based with efficiency curves from CEC |
|
PV energy production |
true |
Python |
|
15 |
false |
true |
Python, MS Excel |
|
Electricity |
https://github.com/QuaSi-Software/SoDeLe/releases |
Renewables |
SoDeLe (loosely translated as "Solar simulation as easy as can be") 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. |
Hour |
http://www.quasi-software.org/ |
REopt |
|
|
The National Renewable Energy Laboratory |
Dylan Cutler, Kate Anderson, Dan Olis, Emma Elgqvist, Andy Walker, Xiangkun Li, William Becker, Kathleen Krah, Nick Laws, Sakshi Mishra, Josiah Pohl |
|
https://www.nrel.gov/docs/fy14osti/61783.pdf |
|
jpohl@nrel.gov |
Josiah Pohl |
some |
dispatch investment |
|
|
|
REopt |
|
World |
Site |
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Optimization |
Mixed Integer Linear Program |
Minimize Lifecycle Cost |
Energy System Model |
true |
Julia/JuMP |
|
|
false |
true |
Python |
https://www.nrel.gov/docs/fy18osti/70813.pdf |
Energy |
https://github.com/NREL/REopt_Lite_API |
Renewables CHP |
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. |
Hour |
https://reopt.nrel.gov/ |
Region4FLEX |
|
|
DLR Institute of Networked Energy Systems |
Wilko Heitkoetter, Wided Medjroubi |
|
|
|
wilko.heitkoetter@dlr.de |
Wilko Heitkoetter |
all |
|
|
|
|
region4FLEX |
|
Germany |
Administrative districts |
false |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
|
|
load shifting optimisation |
false |
Python |
transmission |
|
false |
true |
PostgreSQL |
|
electricity plus sector coupling (EVs P2Heat P2Gas) |
|
|
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) |
15 Minute |
|
Renpass |
|
|
ZNES Flensburg |
Frauke Wiese, Gesine Bökenkamp |
10.1002/wene.109 |
Wiese, F. (2015). renpass - Renewable Energy Pathways Simulation System - Open Source as an approach to meet challenges in energy modeling. Dissertation. Europa-Universität Flensburg. |
240 |
frauke.wiese@uni-flensburg.de |
Frauke Wiese |
all |
dispatch |
perfect foresight |
|
|
Renewable Energy Pathways Simulation System |
|
Poland, Lithuania, Latvia, Estonia, Finland, Sweden, Denmark, Norway, the Netherlands, Belgium, Luxembourg, France, Switzerland, Austria, the Czech Republic, Germany |
Germany: 21 regions / other countries: country |
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
Minimization of costs for each time step (optimization) within the limits of a given infrastructure (simulation) |
economic costs |
Electricity System Model / Regional Dispatch Model / Transshipment Model |
true |
R |
net transfer capacities |
200 |
true |
true |
MySQL / R / RMySQL |
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. |
Electricity |
https://github.com/fraukewiese/renpass |
Renewables Conventional Generation |
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). |
Hour |
https://github.com/fraukewiese/renpass |
SIREN |
|
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Sustainable Energy Now Inc |
Angus King |
|
|
|
angus@ozsolarwind.com |
Angus King |
some |
dispatch investment |
|
|
|
SEN Integrated Renewable Energy Network Toolkit |
|
|
Individual power stations |
true |
Affero General Public License v3 (AGPL-3.0) |
|
Simulation Other |
Uses NREL SAM models to estimate hourly renewable generation for a range/number of renewable energy stations |
Match generation to demand and minimise cost |
Electricity System Model |
true |
Python, NREL SAM |
|
|
false |
true |
Python |
https://sen.asn.au/modelling/ |
Electricity |
https://github.com/ozsolarwind/siren |
Renewables |
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:
<ul>
<li>Maps can be created from OpenStreet Map (MapQuest) tiles
<li>Weather data files can be created from NASA (MERRA2) or ECMWF (ERA5) satellite data
<li>It uses US NREL SAM models to calculate energy generation
</ul>
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 "how it works" |
Hour |
http://www.sen.asn.au/modelling_overview |
SMS++ |
|
|
Dipartimento di Informatica, Università di Pisa |
The SMS++ Team |
|
under construction |
|
frangio@di.unipi.it |
Antonio Frangioni |
some |
dispatch investment |
in principle any, currently scenarios |
|
https://www.plan4res.eu/wp-content/uploads/2019/06/plan4res-Definition-Case-Studies-Summary-CS1.pdf |
SMS++ energy Blocks |
|
Any |
any |
true |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
|
Optimization |
in principle any optimization model, particular emphasis on decomposition approaches |
in principle any, currently cost minimization |
in princople all short- to long-term optimization |
true |
SMS++ |
transmission distribution DC load flow net transfer capacities |
|
false |
true |
hand-coded C++ |
https://edition.pagesuite-professional.co.uk/html5/reader/production/default.aspx?pubname=&edid=f0cd4626-ba9b-4718-8e54-5e7da5346ec4 |
electricity heat components partly developed but extensible to anything |
https://gitlab.com/smspp/smspp-project |
Renewables Conventional Generation |
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. |
Multi year |
https://smspp.gitlab.io |
SciGRID gas |
|
|
DLR Institute of Networked Energy Systems |
Jan Diettrich, Wided Medjroub, Adam Pluta |
10.5281/zenodo.4288440 |
|
|
|
Jan Diettrich, Wided Medjroub, Adam Pluta |
all |
|
|
|
|
Scientific Grid Model of European Gas Transmission Networks |
|
Europe |
Individual gas transmission elements (pipelines, compressorstations, borderpoints etc.) |
false |
Creative Commons Attribution 4.0 (CC-BY-4.0) |
|
Other Simulation |
|
|
European Gas Transmission Network Model and Data (input and output) |
true |
GeoJSON & CSV |
|
|
false |
true |
|
|
Gas |
https://zenodo.org/record/4288440#.YFhii9wxmUk |
|
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&size=20&q=SciGRID_gas. |
|
https://www.gas.scigrid.de/ |
SciGRID power |
|
|
DLR Institute of Networked Energy Systems |
Wided Medjroubi, Carsten Matke |
|
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 |
|
|
Wided Medjroubi, Carsten Matke |
all |
|
|
|
|
Scientific Grid Model of European Power Transmission Networks |
|
Europe and Germany (any other EU country also possible) |
nodal resolution |
false |
Apache License 2.0 (Apache-2.0) |
|
Simulation |
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. |
|
Transmission Network Model |
true |
Python, PostgreSQL |
transmission |
|
true |
true |
Python, PostgreSQL, Osmosis, osm2pgsql |
M. Rohden, et al., Paper: "Cascading Failures in AC Electricity Grids." arXiv preprint
D. Jung and S. Kettemann, Paper: "Long-Range Response in AC Electricity Grids." Phys. Rev. E. 94, 012307(2016). |
Electricity Sector |
https://www.power.scigrid.de/pages/downloads.html |
|
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. |
|
https://www.power.scigrid.de/ |
SimSEE |
|
|
Institute of Electrical Engineering |
Ruben Chaer, Pablo Alfaro y Gonzalo Casaravilla |
|
Chaer, R. (2008.). Simulación de sistemas de energía eléctrica. Tesis de maestría. Universidad de la Republica (Uruguay). Facultad de Ingenieria. |
15 |
rchaer@simsee.org |
Ruben Chaer |
some |
dispatch investment |
stochastic, hydro inflows, wind velocity, solar radiation, temerature an Demand. |
|
|
Simulator of System of Electrical Energy. |
|
|
|
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
Optimal Stochastic Dynamic Programming solver for computation of the operational Policy and a Monte Carlo style simulator of the system using the computed Policy |
minimization of the future operational cost. |
Optimal energy dispatch |
true |
freepascal |
net transfer capacities |
1,000 |
false |
true |
freepascal |
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 |
Electricity Market |
https://sourceforge.net/projects/simsee/ |
Renewables Conventional Generation CHP |
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). |
Hour |
https://simsee.org/index_en.html |