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 |
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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 |
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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) |
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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 |
|
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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 |
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JSC Energy Advice |
|
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info@energyadvice.lt |
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all |
|
|
|
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EA-PSM Electric Arc Flash |
|
Global, European Union, Lithuania, Turkey, Poland, India |
global, continents, nations |
true |
|
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Optimization Simulation |
|
|
|
false |
Java |
transmission distribution AC load flow DC load flow |
|
false |
false |
Java, JavaFX |
|
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http://www.energyadvice.lt/en |
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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. |
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http://www.energyadvice.lt/en |
EA-PSM Electric Short Circuit |
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JSC Energy Advice |
|
|
|
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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/ |
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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 |
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Technische Universität Berlin |
Florian Leuthold, Hannes Weigt, Christian von Hirschhausen, Jonas Egerer, Clemens Gerbaulet, Casimir Lorenz, Jens Weibezahn |
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|
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jew@wip.tu-berlin.de |
Jens Weibezahn |
some |
dispatch |
|
|
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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 |