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) |
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 |
|
|
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 |
SimSES |
|
|
Technical University of Munich |
Marc Möller, Daniel Kucevic, Nils Collath, Anupam Parlikar, Petra Dotzauer, Benedikt Tepe, Stefan Englberger, Martin Cornejo, Andreas Jossen, Holger Hesse, Maik Naumann, Nam Truong |
10.14459/2017mp1401541 |
Naumann, Maik; Truong, Cong Nam (2017): SimSES - Software for techno-economic simulation of stationary energy storage systems. |
27 |
simses.ees@ed.tum.de |
Martin Cornejo |
all |
dispatch |
|
|
Optimal system sizing and operation due to battery aging or economic results |
Simulation of stationary energy storage systems |
|
World |
|
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
Power flow and state of charge calculation based on time series profiles |
|
Electrical energy storage system |
true |
Python |
|
50 |
false |
true |
Python |
Naumann, M; Truong, C.N.; Schimpe, M.; Kucevic, D.; Jossen, A.; Hesse, H.C. (2017): SimSES: Software for techno-economic Simulation of Stationary Energy Storage Systems. In: VDE-ETG-Kongress 2017. Bonn. Preprint accepted for publication in IEEE Conference Proceedings. http://ieeexplore.ieee.org/document/8278770/
Naumann, M.; Karl, R.Ch.; Truong, C.N.; Jossen, A.; Hesse, H.C. (2015): Lithium-ion Battery Cost Analysis in PV-household Application. In: Energy Procedia 73, S. 37–47. DOI: 10.1016/j.egypro.2015.07.555
Truong, C.; Naumann, M.; Karl, R.; Müller, M.; Jossen, A.; Hesse, H. (2016): Economics of Residential Photovoltaic Battery Systems in Germany. The Case of Tesla’s Powerwall. In: Batteries 2 (2), S. 14–30. DOI: 10.3390/batteries2020014 |
Electricity |
https://gitlab.lrz.de/open-ees-ses/simses |
Renewables |
SimSES provides a library of state-the-art energy storage models by combining modularity of multiple topologies as well as the periphery of an ESS. This paper summarizes the structure as well as the capabilites of SimSES. Storage technology models based on current research for lithium-ion batteries, redox flow batteries, as well as hydrogen storage-based electrolysis and fuel cell are presented in detail. In addition, thermal models and their corresponding HVAC systems, housing, and ambient models are depicted. Power electronics are represented with AC/DC and DC/DC converters mapping the main losses of power electronics within a storage system. Additionally, auxiliary components like pumps, compressors, and HVAC are considered. Standard use cases like peak shaving, residential storage, and control reserve power provisions through dispatch of storage are discussed in this work, with the possibility to stack these applications in a multi-use scenario. The analysis is provided by technical and economic evaluations illustrated by KPIs. |
Minute |
https://www.ei.tum.de/ees/simses/ |
SpineOpt.jl |
|
|
|
|
https://doi.org/10.1016/j.esr.2022.100902 |
|
|
|
|
some |
dispatch investment |
Deterministic, perfect foresight, myopic, stochastic. |
|
|
SpineOpt.jl |
|
|
User-dependent |
true |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
|
Optimization |
Linear programming or mixed integer linear programming |
Cost minimization |
Framework |
true |
Julia/JuMP |
transmission DC load flow net transfer capacities |
|
false |
true |
Python, Spine Toolbox |
|
All |
https://github.com/spine-tools/SpineOpt.jl/archive/refs/heads/master.zip |
Renewables Conventional Generation CHP |
SpineOpt is a flexible, open-source, energy system modelling framework for performing operational and planning studies, consisting of a wide spectrum of novel tools and functionalities. The most salient features of SpineOpt include a generic data structure, flexible temporal and spatial structures, a comprehensive representation of uncertainties, and model decomposition capabilities to reduce the computational complexity. These enable the implementation of highly diverse case studies. |
Hour |
https://github.com/spine-tools/SpineOpt.jl |
StELMOD |
|
|
DIW Berlin |
Friedrich Kunz, Jan Abrell |
10.1007/s11067-014-9272-4 |
Abrell, Jan and Kunz, Friedrich (2015): Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market, Networks and Spatial Economics 15(1), pp. 117-147 |
|
fkunz@diw.de |
Friedrich Kunz |
|
dispatch |
deterministic, stochastic |
|
Impact of uncertain renewable generation on markets and generation commitment and dispatch; Analysis of congestion management issues and market design options |
Stochastic Multi-Market Electricity Model |
|
Europe (particular focus on Germany) |
Nodal resolution |
false |
MIT license (MIT) |
|
Optimization |
Mixed integer linear optimization for separate electricity markets (dayahead, intraday, congestion management) linked by a rolling planning procedure |
Minimization of total generation cost |
Optimization |
true |
GAMS |
transmission DC load flow net transfer capacities |
|
false |
true |
MS Excel |
Kunz, Friedrich, Zerrahn, Alexander (2016): Coordinating Cross-Country Congestion Management. DIW Discussion Paper 1551 |
Electricity |
https://github.com/frkunz/stELMOD |
Renewables Conventional Generation CHP |
stELMOD is a stochastic optimization model to analyze the impact of uncertain renewable generation on the dayahead and intraday electricity markets as well as network congestion management. The consecutive clearing of the electricity markets is incorporated by a rolling planning procedure resembling the market process of most European markets. |
Hour |
http://www.diw.de/elmod |
Switch |
|
|
Environmental Defense Fund |
Matthias Fripp, Josiah Johnston, Rodrigo Henríquez, Benjamín Maluenda |
|
J. Johnston, R. Henríquez, B. Maluenda and M. Fripp “Switch 2.0: a modern platform for planning high-renewable power systems,” Preprint, 2018. https://arxiv.org/abs/1804.05481 |
20 |
mfripp@edf.org |
Matthias Fripp |
all |
dispatch investment |
stochastic treatment of hourly renewable variability; allocation of reserves for sub-hourly variability; scenarios or progressive hedging for uncertain annual weather or fuel or equipment costs |
|
identify least-cost combination of resources to reach 100% renewable power; calculate cost of achieving various renewable or carbon targets; select assets to minimize cost for a microgrid, possibly interacting with outside electricity supplier; calculate effect of price-responsive demand on consumer welfare while adopting renewable power |
Switch |
|
|
buildings, microgrids, city, state, national or continental |
true |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
intertemporal mathematical optimization |
total cost or consumer surplus, including environmental adders |
Power system capacity expansion energy system |
true |
Python, Pyomo |
transmission distribution AC load flow DC load flow net transfer capacities |
|
false |
true |
Python, any user-selected software |
|
electricity gas hydrologic transport end-use demand carbon sequestration; user-extendable |
https://github.com/switch-model/switch |
Renewables Conventional Generation CHP |
Switch is a capacity-planning model for power systems with large shares of renewable energy, storage and/or demand response. It optimizes investment decisions for renewable and conventional generation, storage, hydro and other assets, based on how they would be used during a collection of sample days in many future years. The use of multiple investment periods and chronologically sequenced hours enables optimization and assessment of a long-term renewable transition based on a direct consideration of how these resources would be used hour-by-hour. The Switch platform is highly modular, allowing easy selection between prewritten components or addition of custom components as first-class elements in the model. |
Hour |
http://switch-model.org |
System Advisor Model (SAM) |
|
|
National Renewable Energy Laboratory |
|
https://doi.org/10.2172/1126294 |
Blair, N.; Dobos, A.; Freeman, J.; Neises, T.; Wagner, M.; Ferguson, T.; Gilman, P.; Janzou, S. (2014). System Advisor Model, SAM 2014.1.14: General Description. NREL/TP-6A20-61019. National Renewable Energy Laboratory. Golden, CO. Accessed October 31, 2016. |
1 |
sam.support@nrel.gov |
|
|
dispatch |
stochastic, deterministic |
|
|
System Advisor Model |
|
|
|
true |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Simulation |
Time series simulation of power system performance coupled with annual pro forma cash flow calculations. |
time series power generation, installation cost, annual operating and financial cost |
International renewble energy project modeling |
true |
C++, WxWidgets |
|
|
false |
true |
|
|
power generation |
https://github.com/nrel/sam |
Renewables |
The System Advisor Model (SAM) is a free techno-economic software model that facilitates decision-making for people in the renewable energy industry. |
Minute |
https://sam.nrel.gov |
TIMES |
|
|
IEA-ETSAP |
IEA-ETSAP |
|
Documentation for the TIMES Model, R. Loulou, G. Goldstein, A. Kanudia, A. Lehtila, U. Remme, 2016 |
|
ggian@etsap.org |
George Giannakidis |
|
dispatch investment |
Deterministic, perfect foresight, myopic, stochastic. |
|
https://iea-etsap.org/index.php/documentation |
The Integrated MARKAL EFOM Model |
|
Local, National, Regional, Global models |
Local, National, Regional, Global models |
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization |
Partial equilibrium, least cost optimisation, with MIP, NLP options. Perfect foresight and myopic options. |
Total discounted system cost minimisation |
Local National Regional Global models developed using TIMES |
true |
GAMS |
transmission DC load flow net transfer capacities |
|
false |
true |
EXCEL, VEDA, ANSWER |
https://iea-etsap.org/index.php/documentation |
All sectors |
https://github.com/etsap-TIMES/TIMES_model |
Renewables Conventional Generation CHP |
The TIMES model generator combines two different, but complementary, systematic approaches to modelling energy: a technical engineering approach and an economic approach. TIMES is a technology rich, bottom-up model generator, which uses linear-programming to produce a least-cost energy system, optimized according to a number of user constraints, over medium to long-term time horizons. |
Hour |
http://www.etsap.org |
TIMES Évora |
|
|
CENSE - NOVA University Lisbon |
Simoes, S., Dias, L. |
|
|
|
sgcs@fct.unl.pt |
Sofia Simões |
|
|
|
|
|
Évora - The Integrated MARKAL-EFOM System |
|
Évora (Portugal) |
Municipality |
false |
|
|
Optimization |
|
Minimise total discounted cost of the energy system |
Energy supply and demand |
true |
GAMS |
|
|
true |
false |
|
|
|
|
Renewables Conventional Generation CHP |
The TIMES-Évora is a city specific energy system model, which comprehensively represent Évora municipality energy systems, focusing on energy use in residential and non-residential buildings, transport systems and other energy uses (e.g. public lighting, small-scale industry etc.). It also will represent the city waste chain and water and sewage systems in what concerns its energy consumption. The key objective of the model is the identification of an optimum mix of applicable measures and technologies that will pave the way towards the achievement of the cities’ sustainable targets. |
Seasonal |
|
TIMES-PT |
|
|
CENSE - NOVA University Lisbon |
Simoes, S., Fortes, P. |
|
|
|
p.fs@fct.unl.pt |
Patrícia Fortes |
|
|
|
|
|
Portugal - The Integrated MARKAL-EFOM System |
|
Portugal |
National |
false |
|
|
Optimization |
|
Minimise total discounted cost of the energy system |
Energy supply and demand |
true |
GAMS |
transmission distribution |
|
true |
false |
|
|
|
https://iea-etsap.org/index.php/documentation |
Renewables Conventional Generation CHP |
TIMES_PT is a technology rich, bottom-up model generator, which uses linear-programming to produce a least-cost energy system to satisfy the demand for energy services, optimized according to a number of user constraints (e.g. CO2 emissions cap), over medium to long-term time horizons. TIMES_PT characterizes the entire chain of the Portuguese energy system from 2005 to 2050 (in 5-year steps), including energy imports and production (e.g., oil and bio refineries), transformation (e.g., power and heat production), distribution, exports and end-use consumption, in industry, residential, services, agriculture and transport sectors and their respective sub-sectors.
TIMES_PT has been developed since 2004 and has benefited from the peer-review of numerous national partners from industrial sectors, power production, oil refining and end-use energy sectors. TIMES_PT model informed climate policy in Portugal in the last 10 years and has supported the design of climate mitigation policies.
The development of the TIMES_PT model started within the EU research project NEEDS and the national research project E2POL. Although its implementation was motivated by research goals, during the past decade the model has become a major tool supporting national climate mitigation policies, and to a lesser extent, air pollution policies. The Low Carbon Roadmap 2050 is a flagship policy document currently used as the Portuguese long term view on mitigation goals, while the PNAC— National Plan on Climate Change includes the visions up to 2030 from stakeholders from other policy areas, as transportation and industry. The negotiations for the revisions of the National Emission Ceilings Directive for 2020 and 2030, as well as the National Strategy for Air Quality (2015) were supported by projections generated by TIMES_PT. More recently, TIMES_PT was linked with a national CGE model, which has motivated its use in the Green Tax Reform. |
Seasonal |
|
Temoa |
|
|
NC State University |
Joe DeCarolis, Kevin Hunter, Binghui Li, Sarat Sreepathi |
10.1016/j.eneco.2013.07.014 |
Hunter, K., Sreepathi, S., DeCarolis, J. F. (2013). Modeling for insight using tools for energy model optimization and analysis (TEMOA). Energy Economics, 40, 339-349. |
5 |
jdecarolis@ncsu.edu |
Joe DeCarolis |
all |
investment |
stochastic optimization, moeling-to-generate alternatives |
|
1. How does uncertainty in technology-specific characteristics (e.g., capital cost of solar PV) affect outcomes of interest (e.g., fuel prices, fossil fuel consumption, air emissions)?
2. Which technologies and fuels appear to be robust options given uncertainty in future climate
policy and rates of technology learning?
3. How much flexibility exists in energy system design and at what cost? |
Tools for Energy Model Optimization and Analysis |
|
U.S., currently |
single region |
true |
GNU General Public License version 2.0 (GPL-2.0) |
|
Optimization |
The model objective is to minimize the present cost of energy supply by deploying and utilizing energy technologies and commodities over time to meet a set of exogenously specified end-use demands. |
Cost minimization |
energy system optimization model |
true |
Python (Pyomo) |
|
|
false |
true |
SQLite |
|
all |
https://github.com/TemoaProject/temoa/ |
Renewables Conventional Generation |
Tools for Energy Model Optimization and Analysis (Temoa) is an open source framework used to conduct analysis with a bottom-up, technology rich energy system model. |
Multi year |
http://temoaproject.org/ |
TransiEnt |
|
|
Hamburg University of Technology |
Lisa Andresen, Carsten Bode, Pascal Dubucq, Jan-Peter Heckel, Ricardo Peniche, Anne Senkel, Oliver Schülting |
10.3384/ecp15118695 |
Andresen, Lisa ; Dubucq, Pascal ; Peniche, Ricardo ; Ackermann, Günter ; Kather, Alfons ; Schmitz, Gerhard: Status of the TransiEnt Library: Transient simulation of coupled energy networks with high share of renewable energy. In: Proceedings of the 11th International Modelica Conference. Paris : Modelica Association, 2015, S. 695–705 |
60 |
transientlibrary@tuhh.de |
Carsten Bode, Jan-Peter Heckel, Anne Senkel, Oliver Schülting |
some |
|
Prediction errors can be introduced by (filtered) white noise timeseries to see changes in control behaviour |
|
* How does the possible amount of hydrogen that can be fed into the gas distribution grid depend on the ambient temperature (considering changes in heating load, gas density and heat of combustion)
* How does the use of synthetic wind inertia technology impact the electric grid stability |
TransiEnt Library |
|
Hamburg / Germany |
Metropolregion Hamburg |
false |
|
|
Simulation |
Models in the library are based on differential algebraic equations and are solved using a variable step solver. By using the object oriented Modelica language the library allows an investigation of different timescales and levels of physical detail. |
|
Dynamic system simulation model library |
true |
Modelica |
transmission distribution net transfer capacities |
30,000 |
false |
true |
Dymola |
See: https://www.tuhh.de/transient-ee/en/publications.html
for a complete list |
electricity district heating Gas |
https://www.tuhh.de/transient-ee/en/download.html |
Renewables Conventional Generation CHP |
The TransiEnt library is written in the Modelica modeling language and allows simulations of coupled energy networks with high share of renewable energies. The library can be downloaded for free and open source under the Modelica License 2.
The TransiEnt library contains object oriented components models of all major elements of the enegy infrastructure with its corresponding producers, consumers, grids and storage systems. These components can be used to simulate different scenarios from single power plants starting up to the simulation of primary control in the ENTSO-E grid. |
Second |
https://www.tuhh.de/transient-ee/en/ |
URBS |
|
|
TUM EI ENS |
Johannes Dorfner; Magdalena Dorfner; Soner Candas; Sebastian Müller; Yunus Özsahin; Thomas Zipperle; Simon Herzog |
10.5281/zenodo.46118 |
Dorfner, Johannes (2016). "Open Source Modelling and Optimisation of Energy Infrastructure at Urban Scale", doctoral thesis, Technical University of Munich |
20 |
johannes.dorfner@tum.de |
Johannes Dorfner |
some |
dispatch investment |
None |
|
|
urbs |
|
User-dependent |
User-dependent |
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization |
Linear optimization model of a user-defined reference energy system. |
Minimise total discounted cost of system |
Energy Modelling Framework |
true |
Python (Pyomo) |
transmission net transfer capacities |
100,000 |
true |
true |
Python (pandas et al) |
|
User-dependent Electricity |
https://github.com/tum-ens/urbs |
Renewables Conventional Generation CHP |
urbs is a linear programming optimisation model for capacity expansion planning and unit commitment for distributed energy systems. Its name, latin for city, stems from its origin as a model for optimisation for urban energy systems. Since then, it has been adapted to multiple scales from neighbourhoods to continents. |
Hour |
https://github.com/tum-ens/urbs |
USENSYS |
|
|
Environmental Defense Fund |
Oleg Lugovoy |
|
|
|
olugovoy@edf.org |
Oleg Lugovoy |
all |
investment |
Deterministic |
|
|
United States energy system optimization model |
|
US 48 lower states & DC |
Administrative districts |
true |
Affero General Public License v3 (AGPL-3.0) |
|
Optimization |
Linear programming |
Cost minimization |
Capacity expansion Reference Energy System |
true |
R/energyRt |
transmission |
|
false |
true |
R |
in progress, by now: https://github.com/usensys/usensys |
Electric power |
https://github.com/usensys/usensys |
Renewables |
United States Energy SYStem (USENSYS) is an open source capacity expansion model (CEM, also knows as Reference Energy System model, RES), developped based on energyRt package for R. |
Hour |
http://www.usensys.org |