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 |