AMIRIS |
German Aerospace Center |
true |
false |
Apache License 2.0 (Apache-2.0) |
all |
Agent-based electricity market model |
true |
dispatch |
|
Germany, Austria |
Simulation Agent-based |
algorithms for market clearing and agent-specific bidding strategies |
|
stochastic, perfect foresight, deterministic |
Java |
|
Python |
Hour |
ASAM |
Europa-Universität Flensburg |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
some |
Agent-based Simulation Market Model Electricity System Model German and European Electricity Market |
true |
dispatch |
|
Europe |
Simulation Agent-based |
|
|
|
Python (Pyomo) |
|
Python, PyPSA, Mesa |
15 Minute |
ASSUME |
INATECH Freiburg |
true |
false |
Affero General Public License v3 (AGPL-3.0) |
all |
German and European Electricity Market Network-constrained Unit Commitment and Economic Dispatch Agent-based electricity market model |
true |
dispatch |
|
Depending on input data |
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 |
Deterministic |
Python, Pyomo |
transmission distribution |
PostgreSQL |
15 Minute |
Antares-Simulator |
RTE |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
some |
Capacity Expansion Problem Production Cost Model |
true |
dispatch investment |
|
Europe |
Optimization Simulation |
Investment planning: optimization based on Benders decomposition
Dispatch : simulation based on MILP |
socio-economic welfare, investment costs, greenhouse gas emissions |
Monte-Carlo methods, myopic week-ahead foresight |
C++, C |
transmission DC load flow net transfer capacities |
Python, TypeScript |
Hour |
AnyMOD |
TU Berlin |
true |
true |
MIT license (MIT) |
some |
Framework |
true |
dispatch investment |
|
User-dependent |
Optimization |
Continuous Linear Optimization |
cost minimization by default, can set other objectives |
single-stage scenarios |
Julia/JuMP |
transmission net transfer capacities |
|
Hour |
Backbone |
VTT Technical Research Centre of Finland |
true |
false |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
some |
Framework |
true |
dispatch investment |
|
Depends on user |
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; |
Short-term and long-term stochastics are available |
GAMS |
transmission DC load flow net transfer capacities |
Spine Toolbox or Excel |
15 Minute |
Balmorel |
RAM-løse, DTU |
true |
true |
ISC License (ISC) |
all |
Energy System Model |
true |
dispatch investment |
|
User-dependent (Pan-European, applied in 20+ countries) |
Optimization |
Linear programming (with an option of mixed-integer programming) |
Social welfare maximization |
Deterministic, perfect foresight, global sensitivity analysis |
GAMS |
net transfer capacities transmission DC load flow |
Excel, Python (Pandas) |
Hour |
Breakthrough Energy Model |
Breakthrough Energy Foundation |
true |
false |
MIT license (MIT) |
all |
Framework |
true |
dispatch |
|
Currently U.S., but extendable to any region |
Optimization Simulation |
The Breakthrough Energy Model runs DCOPF simulations |
Minimize cost |
Scenario Analysis (Deterministic) |
Julia/JuMP |
transmission DC load flow |
Python |
Hour |
CAPOW |
North Carolina State University |
true |
false |
MIT license (MIT) |
all |
CAISO and Mid-Columbia markets/U.S. West Coast |
true |
dispatch |
|
|
Simulation |
Iterative mixed-integer program, with user defined operating horizon |
Cost minimization |
Short-term and long-term stochastics are available |
Python (Pyomo) |
transmission |
|
Hour |
CESAR-P |
Urban Energy Systems Lab, Empa (Swiss Federal Laboratories for Materials Science and Technology) |
true |
false |
Affero General Public License v3 (AGPL-3.0) |
some |
Swiss building stock |
false |
|
|
Switzerland |
Simulation |
|
|
|
Python, EnergyPlus |
|
|
Hour |
Calliope |
ETH Zürich |
true |
true |
Apache License 2.0 (Apache-2.0) |
some |
Framework |
true |
dispatch investment |
|
User-dependent |
Optimization |
|
User-dependent, including financial cost, CO2, and water consumption |
Deterministic; stochastic programming add-on |
Python (Pyomo) |
net transfer capacities transmission distribution |
Python (pandas et al) |
Hour |
CapacityExpansion |
Stanford University, RWTH Aachen |
true |
false |
MIT license (MIT) |
all |
Capacity Expansion Problem |
true |
dispatch investment |
|
Input data dependent |
Optimization |
Optimization, Linear optimization model input-data depending energy system |
Total system cost |
|
Julia/JuMP |
|
Julia |
|
DESSTinEE |
Imperial College London |
true |
true |
Creative Commons Attribution Share-Alike 3.0 (CC-BY-SA-3.0) |
all |
Simulation |
true |
dispatch |
|
Europe, North Africa |
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 |
Stochastic |
Excel / VBA |
net transfer capacities |
Excel / VBA |
Hour |
DIETER |
DIW Berlin |
true |
false |
MIT license (MIT) |
all |
Optimization |
true |
dispatch investment |
|
Initial version: greenfield, loosely calibrated to Germany; central European version also available |
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 |
- (work in progress) |
GAMS; CPLEX |
|
MS Excel |
Hour |
Demod |
EPFL |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
some |
Simulation |
true |
|
|
Germany, UK |
Simulation |
First order and semi- Markov-chain Monte Carlo simulation. |
Assess domestic energy demand evolution and demand-side-management scenarios |
Not yet implemented |
Python |
|
Python |
Minute |
Dispa-SET |
European Commission, Joint Research Centre |
true |
false |
European Union Public Licence Version 1.1 (EUPL-1.1) |
all |
EU power system |
true |
dispatch |
|
Currently, 7 EU countries |
Optimization |
From the same dataset, the model can be expressed as a MILP or LP problem |
Minimization of operational costs |
Through proper sizing of reserve needs |
Python (Pyomo), GAMS |
net transfer capacities |
Python |
Hour |
DynPP |
University of Rostock |
false |
false |
|
|
Specific Power Plants |
false |
investment |
|
Specific plants |
Optimization Simulation |
physical-equation-based |
operation, cost, emissions, thermal stress |
Deterministic |
Modelica, Dymola, (OpenModelica), C++, MySQL, SQLite |
net transfer capacities |
Matlab |
Second |
EA-PSM Electric Arc Flash |
JSC Energy Advice |
false |
false |
|
all |
|
false |
|
|
Global, European Union, Lithuania, Turkey, Poland, India |
Optimization Simulation |
|
|
|
Java |
transmission distribution AC load flow DC load flow |
Java, JavaFX |
|
EA-PSM Electric Short Circuit |
JSC Energy Advice |
false |
false |
|
all |
|
false |
|
|
Global, European Union, Lithuania, Turkey, Poland, India |
Optimization Simulation |
|
|
|
Java |
transmission distribution AC load flow DC load flow |
Java, JavaFX |
|
ELMOD |
Technische Universität Berlin |
true |
true |
MIT license (MIT) |
some |
German and European Electricity Market |
true |
dispatch |
|
Germany, Europe |
Optimization |
|
|
|
GAMS |
transmission DC load flow |
|
Hour |
ELTRAMOD |
Technische Universität Dresden (ee2) |
false |
true |
|
some |
German and European Electricity Market |
false |
dispatch investment |
|
EU-27 + Norway + Switzerland + United Kingdom + Balkan countries |
Optimization |
Linear optimization model. Decision variables include investment and dispatch of generation, storage, DSM and different sector coupling options including both wholesale and balancing markets. |
Minimization of total system costs |
Deterministic; Perfect foresight; Sensitivity analysis ; |
GAMS; CPLEX |
transmission net transfer capacities |
|
Hour |
EMLab-Generation |
Delft University of Technology |
true |
true |
Apache License 2.0 (Apache-2.0) |
some |
Agent-based Simulation |
true |
dispatch investment |
|
Central Western Europe |
Simulation Agent-based |
|
|
Limited foresight, optional risk aversion |
Java |
net transfer capacities |
R |
Year |
EMMA |
Neon Neue Energieökonomik GmbH |
true |
true |
Creative Commons Attribution 3.0 (CC-BY-3.0) |
all |
Power market model |
true |
dispatch investment |
|
France, Poland, Belgium, The Netherlands, Germany, Sweden, Norway |
Optimization |
Linear program |
Total system cost |
Sensitivities (many) |
GAMS |
net transfer capacities |
|
Hour |
EOLES elec |
CIRED |
true |
false |
Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0) |
all |
Electricity System Model |
true |
dispatch investment |
|
|
Optimization Simulation |
Simultaneous optimization of dispatch and investment (linear programming), solved in CPLEX solver of GAMS |
investment cost and operational costs (fixed and variable) minimization |
Deterministic; Perfect foresight; Sensitivity analysis ; |
GAMS |
transmission |
|
Hour |
EOLES elecRES |
CIRED |
true |
false |
Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0) |
all |
Electricity System Model |
true |
dispatch investment |
|
|
Optimization Simulation |
Simultaneous optimization of dispatch and investment (linar programming), solved in CPLEX solver of GAMS |
investment cost and operational costs (fixed and variable) minimization |
Deterministic; Perfect foresight; Sensitivity analysis ; Robust decision making |
GAMS |
transmission |
|
Hour |
ESO-X |
Imperial College London |
true |
false |
MIT license (MIT) |
all |
power system model |
true |
dispatch investment |
|
UK |
Optimization |
MILP |
minimise total system cost |
scenario analysis |
GAMS; CPLEX |
|
R |
Hour |
Energy Policy Simulator |
Energy Innovation, LLC |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
all |
System Dynamics |
true |
dispatch investment |
|
|
Simulation |
Annual forward simulating model with some investment optimization and full accounting of policy interactions. |
|
Monte carlo |
Vensim |
|
Vensim |
Year |
Energy Transition Model |
Quintel Intelligence |
true |
true |
MIT license (MIT) |
all |
Demand driven energy model |
true |
dispatch |
|
EU27, The Netherlands, UK, Poland, France, Germany, Spain, Brazil |
Simulation |
The ETM is based on an energy graph where nodes can convert one type of energy into another. |
Given demand and other choices, calculate primary energy use, costs, CO2-emission etc. |
The user can assess the impact of almost every input variable and assumption |
Developed in-house written in Ruby (on Rails) |
transmission distribution net transfer capacities |
Excel / VBA |
Year |
EnergyNumbers-Balancing |
UCL Energy Institute |
false |
false |
|
some |
Simulating storage and exogenously-variable renewables |
false |
dispatch |
|
Britain, Germany, Spain |
Simulation |
|
|
Deterministic |
Fortran, PHP, Javascript, HTML, CSS |
|
Matlab, Python |
Hour |
EnergyRt |
|
true |
true |
Affero General Public License v3 (AGPL-3.0) |
|
Reference Energy System |
true |
|
|
|
Optimization |
linear, cost-minimizing, partial equilibrium |
costs |
perfect foresight |
GAMS; GLPK |
|
R |
|
EnergyScope |
EPFL, UCLouvain |
true |
false |
Apache License 2.0 (Apache-2.0) |
all |
Regional energy system design |
true |
dispatch investment |
|
Region (Switzerland, Belgium) |
Optimization |
Linear programming (43 equations fully documented). |
financial cost, greenhouse gases emissions |
|
GLPK/GLPSOL or AMPL/Cplex |
|
Excel |
Hour |
Ficus |
Institute for Energy Economy and Application Technology |
true |
true |
GNU General Public License version 3.0 (GPL-3.0) |
some |
energy system optimization model |
true |
dispatch investment |
|
|
Optimization |
|
costs |
None |
Python (Pyomo) |
|
Python (pandas et al) |
15 Minute |
FlexiGIS |
DLR Institute of Networked Energy Systems |
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
some |
urban energy systems |
true |
|
|
|
Optimization Simulation |
Modelling and optimisation mathematical model |
simualte local urban demand and supply, localise distributed storage, minimise total system costs |
|
Python |
distribution |
Geopandas |
15 Minute |
GAMAMOD |
Technische Universität Dresden (EE2) |
false |
true |
|
some |
European Natural Gas Market |
false |
dispatch investment |
|
|
Optimization |
|
|
|
GAMS |
transmission distribution |
|
|
GAMAMOD-DE |
Technische Universität Dresden (EE2) |
false |
true |
|
all |
|
false |
dispatch |
|
|
|
|
|
|
GAMS; CPLEX |
|
|
|
GRIMSEL-FLEX |
University of Geneva |
true |
false |
BSD 2-Clause "Simplified" or "FreeBSD" License (BSD-2-Clause) |
all |
Energy System Model Optimization Social Planner |
true |
dispatch |
|
Switzerland, Austria, Italy, France, Germany |
Optimization |
Quadratic dipatch sector-coupling model |
Minimization of total system costs |
Perfect foresight, Sensitivity analisys, Scenarios |
Python (Pyomo) |
transmission net transfer capacities |
Python (pandas et al) |
Hour |
Genesys |
RWTH-Aachen University |
true |
false |
GNU Library or "Lesser" General Public License version 2.1 (LGPL-2.1) |
all |
Electricity System Model |
false |
dispatch investment |
|
Europe, North Africa, Middle East |
Optimization Simulation |
optimisation of system combination with evolutionary strategy
simulation of operation with hierarchical management strategy and linear load balancing between regions (network simplex) |
minimise levelised cost of electricity |
24 h foresight for storage operation |
C++, boost library, MySQL and QT4, (optional CPLEX solver implementation) |
transmission net transfer capacities |
Excel/Matlab and a Visualisation tool programmed in QT4 (c++) |
Hour |
GridCal |
|
true |
true |
GNU General Public License version 3.0 (GPL-3.0) |
all |
Transmission Network Model and Data (input and output) |
true |
|
|
|
Optimization Simulation |
Object oriented structures -> intermediate objects holding arrays -> Numerical modules |
Match generation to demand and minimise cost |
Deterministic, stochastic |
Python |
transmission distribution AC load flow DC load flow |
Python |
|
HighRES |
UCL, UiO |
true |
false |
MIT license (MIT) |
all |
European electricity system model GB electricity system model |
false |
dispatch investment |
|
EEA+Norway and UK |
Optimization |
|
Minimization of total system costs |
|
GAMS; CPLEX |
transmission net transfer capacities |
Python |
Hour |
IRENA FlexTool |
VTT Technical Research Centre of Finland |
true |
false |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
some |
Multi-purpose |
true |
dispatch investment |
|
User dependent |
Optimization |
Typically linear cost minimization, but unit online decisions can be mixed-integer linear (and effectively investment decisions too). |
cost minimization |
perfect foresight, but can use limited horizon |
GNU MathProg |
transmission net transfer capacities |
Python, SQL |
Hour |
JMM |
Risoe National Laboratory; University of Stuttgart; University of Duisburg-Essen |
false |
false |
|
|
|
false |
|
|
|
|
|
|
|
|
|
|
|
Lemlab |
Technical University of Munich |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
all |
agent-based simulation |
true |
|
|
|
Simulation Agent-based |
Agents: intertemporal convex optimization
Markets: (iterative) double-sided auctions, p2p clearing
Forecasting: naive, deterministic forecasting, neural networks |
|
perfect forecast, deterministic, stochastic |
Python, Pyomo |
|
PostgreSQL, Ethereum |
|
LoadProfileGenerator |
FZ Jülich |
true |
false |
MIT license (MIT) |
some |
|
true |
|
|
|
|
|
|
|
C# |
|
|
Minute |
MEDEAS |
GEEDS group; University of Valladolid (http://www.eis.uva.es/energiasostenible/?lang=en) |
true |
false |
MIT license (MIT) |
all |
|
false |
|
|
Global; European Union; Bulgaria; Austria |
Other |
System dynamics. Top-down |
CO2 equivalent emissions, energy, social, economic costs, RE-share |
Deterministic |
Phyton |
|
Phyton |
Year |
MOCES |
Chair of Automation and Energy Systems (Saarland University) |
false |
true |
|
|
Energy Modeling Framework |
false |
dispatch |
|
Depends on user |
Simulation Agent-based |
HDAE (Hybrid Differential Equations) combined with an agent-based approach. |
|
deterministic, stochastic |
Modelica, Dymola, (OpenModelica), C++, MySQL, SQLite |
|
Lsodar, Dassl |
Second |
Maon |
Maon GmbH |
false |
false |
|
all |
Mixed-Integer Quadratic Programming (MIQP) |
false |
dispatch investment |
|
Europe, North Africa, Middle East |
Optimization Simulation Other Agent-based |
|
Minimization of operational costs for electricity spot and frequency reserve markets considering emission caps |
Monte Carlo, preprocessing or sensitivity |
C++ |
transmission distribution AC load flow DC load flow net transfer capacities |
Ansible, Ceph, cURL, Docker, GraphQL, Kubernetes, MinIO, MongoDB, Node.js, Preact, Python, TypeScript, WebAssembly |
Hour |
Medea |
University of Natural Resources and Life Sciences, Vienna |
true |
false |
MIT license (MIT) |
all |
Austrian and German electricity market |
true |
dispatch investment |
|
Austria, Germany |
Optimization |
|
Total system cost |
Deterministic |
GAMS |
net transfer capacities |
Python |
Hour |
MicroGridsPy |
Politecnico di Milano |
true |
false |
European Union Public Licence Version 1.1 (EUPL-1.1) |
all |
Energy Modeling Framework |
true |
dispatch investment |
|
|
Optimization |
The model is based on two-stage stochastic optimisation and LP or MILP mathematical formulation |
Single or multi objective optimization (NPC, operation costs, CO2 emissions) |
Two-stage stochastic optimization |
Python (Pyomo) |
|
Excel |
Hour |
Mosaik |
OFFIS |
true |
false |
GNU Library or "Lesser" General Public License version 2.1 (LGPL-2.1) |
some |
distributed energy systems smart grid simulation |
true |
|
|
|
Optimization Simulation Agent-based |
|
|
|
Python |
transmission distribution |
HDF5, InfluxDB, Grafana |
Second |
MultiMod |
DIW Berlin, NTNU Trondheim |
false |
true |
|
some |
Equilibrium model |
false |
dispatch investment |
|
Global |
Other |
Generalized Nash Equilibrium (GNE) model formulated as a Mixed Complementarity Model (MCP) |
|
Not covered (yet) |
GAMS |
transmission net transfer capacities |
MS Access, MS Excel |
Multi year |
NEMO |
University of New South Wales |
true |
true |
GNU General Public License version 3.0 (GPL-3.0) |
all |
|
true |
dispatch |
|
Australia |
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 |
|
Python |
transmission |
|
Hour |
NEMO (SEI) |
Stockholm Environment Institute |
true |
false |
Apache License 2.0 (Apache-2.0) |
|
Full energy system optimization flexible geographic and sectoral scope |
true |
dispatch investment |
|
All |
Optimization |
Constrained cost optimization with perfect foresight |
Minimize total discounted costs |
Deterministic but can readily be applied in Monte Carlo analyses |
Julia |
transmission distribution DC load flow net transfer capacities |
SQLite |
Hour |
OMEGAlpes |
G2Elab |
true |
false |
Apache License 2.0 (Apache-2.0) |
some |
Production consumption conversion storage |
true |
|
|
|
Optimization |
|
|
|
OMEGAlpes, PuLP |
|
|
|
OSeMOSYS |
KTH Royal Institute of Technology |
true |
true |
Apache License 2.0 (Apache-2.0) |
all |
|
true |
investment |
|
Africa (all countries), Sweden, Baltic States, Nicaragua, Bolivia, South America, EU-27+3 |
Optimization |
Linear optimisation (with an option of mixed-integer programming) |
Minimise total discounted cost of system |
|
GNU MathProg |
transmission distribution |
Python |
Day |
Oemof |
Reiner Lemoine Institut / ZNES Flensburg |
true |
true |
GNU General Public License version 3.0 (GPL-3.0) |
some |
Energy Modelling Framework |
true |
dispatch investment |
|
Depends on user |
Optimization Simulation |
https://oemof.org/libraries/ |
costs, emissions |
Deterministic |
Python, Pyomo, Coin-OR |
transmission distribution net transfer capacities DC load flow |
PostgreSQL, PostGIS |
Hour |
OnSSET |
KTH Royal Institute of Technology |
true |
false |
MIT license (MIT) |
|
|
true |
|
|
Sub-Saharan Africa, developing Asia, Latin America |
Optimization |
|
Cost minimization |
|
Python |
|
Python |
Multi year |
OpenTUMFlex |
Technical University of Munich |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
some |
Energy System Model urban energy systems load shifting optimisation Local energy systems |
true |
|
|
User dependent |
Optimization Simulation |
|
Cost optimal optimization and flexibility calculation |
|
Python (Pyomo) |
distribution |
|
15 Minute |
PLEXOS Open EU |
University College Cork |
false |
true |
|
all |
Market Model |
true |
dispatch |
|
North West Europe |
Optimization |
Least Cost Optimization, Can be run in MIP or linear relaxed mode |
Minimize total Generation cost |
None |
PLEXOS |
net transfer capacities |
MS Excel |
Hour |
POMATO |
TU Berlin |
true |
false |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
some |
Network-constrained Unit Commitment and Economic Dispatch |
true |
dispatch |
|
User-dependent |
Optimization |
Linear Economic Dispatch. Linear Optimal Power Flow. Linear Security Constrained Optimal Power Flow |
Cost minimization |
Chance Constrained |
Julia/JuMP |
transmission DC load flow net transfer capacities |
Python |
Hour |
Pandapipes |
Fraunhofer IEE, Uni Kassel |
true |
true |
MIT license (MIT) |
|
|
false |
|
|
|
Simulation |
|
|
|
Python |
distribution |
|
|
Pandapower |
|
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
some |
Transmission Network Model |
true |
|
|
|
Simulation |
|
|
|
Python |
transmission distribution |
Pandas |
|
PowNet |
Singapore University of Technology and Design |
true |
false |
MIT license (MIT) |
all |
Network-constrained Unit Commitment and Economic Dispatch |
true |
dispatch |
|
Laos, Cambodia, Thailand, any user-defined country or region |
Optimization Simulation |
Mixed Integer Linear Program (MILP), DC Power Flow, Unit Commitment, Economic Dispatch |
Cost minimization |
Sensitivity analysis |
Python (Pyomo) |
transmission distribution DC load flow |
Python |
Hour |
PowerMatcher |
Flexiblepower Alliance Network |
true |
false |
Apache License 2.0 (Apache-2.0) |
|
|
true |
|
|
|
|
|
|
|
Java |
|
|
|
PowerSimulations.jl |
NREL |
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
quasii-static sequential unit-commitment and economic dispatch problems |
true |
dispatch |
|
Any |
Optimization |
Principal application is sequential quasi-static system optimization problems (production cost modeling). |
Least Cost |
scenario analysis |
Julia |
transmission AC load flow DC load flow net transfer capacities |
Julia |
Second |
PowerSimulationsDynamics.jl |
NREL |
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Dynamic system simulation model library |
true |
|
|
|
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 |
scenario analysis |
Julia |
transmission AC load flow |
Julia |
Less than second |
PowerSystems.jl |
NREL |
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Optimization Simulation |
true |
dispatch |
|
Any |
Simulation |
PowerSystems.jl includes basic power flow and network matrix calculation capabilities. |
|
scenario analysis |
Julia |
transmission AC load flow DC load flow net transfer capacities |
Julia |
Less than second |
Pvlib python |
|
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
some |
|
true |
|
|
|
Simulation |
|
|
|
Python |
|
NumPy, Pandas |
|
PyLESA |
University of Strathclyde |
true |
false |
MIT license (MIT) |
some |
Local energy systems |
true |
dispatch |
|
|
Simulation |
|
Minimization of operational costs |
perfect foresight |
Python |
AC load flow DC load flow |
Python |
Hour |
PyPSA |
FIAS |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
all |
Energy System Model |
true |
dispatch investment |
|
Europe, China, South Africa |
Optimization Simulation |
Non-linear power flow; linear optimal power flow / investment optimisation |
Cost minimization |
Not explicitly covered, but stochastic optimisation possible |
Python, Pyomo |
transmission distribution AC load flow DC load flow net transfer capacities |
Pandas |
Hour |
QuaSi - GenSim |
Siz energieplus |
true |
false |
MIT license (MIT) |
all |
building energy demand |
true |
|
|
All |
Simulation |
EnergyPlus is used to perform a thermal building simulation |
|
|
EnergyPlus, OpenStudio, MS Excel, Ruby |
|
MS Excel |
15 Minute |
QuaSi - ReSiE |
Siz energieplus |
true |
false |
MIT license (MIT) |
all |
multi energy systems in urban scale |
true |
dispatch investment |
|
Depends on user |
Simulation Other |
rule-based algorithms, system dynamics |
energy balances |
sensitivity analysis |
Julia |
transmission distribution |
Julia |
15 Minute |
QuaSi - SoDeLe |
Siz energieplus |
true |
false |
MIT license (MIT) |
all |
PV energy production |
true |
|
|
All |
Simulation |
physics-based with efficiency curves from CEC |
|
|
Python |
|
Python, MS Excel |
Hour |
REopt |
The National Renewable Energy Laboratory |
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
some |
Energy System Model |
true |
dispatch investment |
|
World |
Optimization |
Mixed Integer Linear Program |
Minimize Lifecycle Cost |
|
Julia/JuMP |
|
Python |
Hour |
Region4FLEX |
DLR Institute of Networked Energy Systems |
true |
false |
Apache License 2.0 (Apache-2.0) |
all |
load shifting optimisation |
false |
|
|
Germany |
Optimization |
|
|
|
Python |
transmission |
PostgreSQL |
15 Minute |
Renpass |
ZNES Flensburg |
true |
true |
GNU General Public License version 3.0 (GPL-3.0) |
all |
Electricity System Model / Regional Dispatch Model / Transshipment Model |
true |
dispatch |
|
Poland, Lithuania, Latvia, Estonia, Finland, Sweden, Denmark, Norway, the Netherlands, Belgium, Luxembourg, France, Switzerland, Austria, the Czech Republic, Germany |
Optimization Simulation |
Minimization of costs for each time step (optimization) within the limits of a given infrastructure (simulation) |
economic costs |
perfect foresight |
R |
net transfer capacities |
MySQL / R / RMySQL |
Hour |
SIREN |
Sustainable Energy Now Inc |
true |
false |
Affero General Public License v3 (AGPL-3.0) |
some |
Electricity System Model |
true |
dispatch investment |
|
|
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 |
|
Python, NREL SAM |
|
Python |
Hour |
SMS++ |
Dipartimento di Informatica, Università di Pisa |
true |
false |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
some |
in princople all short- to long-term optimization |
true |
dispatch investment |
|
Any |
Optimization |
in principle any optimization model, particular emphasis on decomposition approaches |
in principle any, currently cost minimization |
in principle any, currently scenarios |
SMS++ |
transmission distribution DC load flow net transfer capacities |
hand-coded C++ |
Multi year |
SciGRID gas |
DLR Institute of Networked Energy Systems |
true |
false |
Creative Commons Attribution 4.0 (CC-BY-4.0) |
all |
European Gas Transmission Network Model and Data (input and output) |
true |
|
|
Europe |
Other Simulation |
|
|
|
GeoJSON & CSV |
|
|
|
SciGRID power |
DLR Institute of Networked Energy Systems |
true |
true |
Apache License 2.0 (Apache-2.0) |
all |
Transmission Network Model |
true |
|
|
Europe and Germany (any other EU country also possible) |
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. |
|
|
Python, PostgreSQL |
transmission |
Python, PostgreSQL, Osmosis, osm2pgsql |
|
SimSEE |
Institute of Electrical Engineering |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
some |
Optimal energy dispatch |
true |
dispatch investment |
|
|
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. |
stochastic, hydro inflows, wind velocity, solar radiation, temerature an Demand. |
freepascal |
net transfer capacities |
freepascal |
Hour |
SimSES |
Technical University of Munich |
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
all |
Electrical energy storage system |
true |
dispatch |
|
World |
Simulation |
Power flow and state of charge calculation based on time series profiles |
|
|
Python |
|
Python |
Minute |
SpineOpt.jl |
|
true |
false |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
some |
Framework |
true |
dispatch investment |
|
|
Optimization |
Linear programming or mixed integer linear programming |
Cost minimization |
Deterministic, perfect foresight, myopic, stochastic. |
Julia/JuMP |
transmission DC load flow net transfer capacities |
Python, Spine Toolbox |
Hour |
StELMOD |
DIW Berlin |
true |
false |
MIT license (MIT) |
|
Optimization |
true |
dispatch |
|
Europe (particular focus on Germany) |
Optimization |
Mixed integer linear optimization for separate electricity markets (dayahead, intraday, congestion management) linked by a rolling planning procedure |
Minimization of total generation cost |
deterministic, stochastic |
GAMS |
transmission DC load flow net transfer capacities |
MS Excel |
Hour |
Switch |
Environmental Defense Fund |
true |
false |
Apache License 2.0 (Apache-2.0) |
all |
Power system capacity expansion energy system |
true |
dispatch investment |
|
|
Optimization |
intertemporal mathematical optimization |
total cost or consumer surplus, including environmental adders |
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 |
Python, Pyomo |
transmission distribution AC load flow DC load flow net transfer capacities |
Python, any user-selected software |
Hour |
System Advisor Model (SAM) |
National Renewable Energy Laboratory |
true |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
International renewble energy project modeling |
true |
dispatch |
|
|
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 |
stochastic, deterministic |
C++, WxWidgets |
|
|
Minute |
TIMES |
IEA-ETSAP |
true |
false |
GNU General Public License version 3.0 (GPL-3.0) |
|
Local National Regional Global models developed using TIMES |
true |
dispatch investment |
|
Local, National, Regional, Global models |
Optimization |
Partial equilibrium, least cost optimisation, with MIP, NLP options. Perfect foresight and myopic options. |
Total discounted system cost minimisation |
Deterministic, perfect foresight, myopic, stochastic. |
GAMS |
transmission DC load flow net transfer capacities |
EXCEL, VEDA, ANSWER |
Hour |
TIMES Évora |
CENSE - NOVA University Lisbon |
false |
true |
|
|
Energy supply and demand |
true |
|
|
Évora (Portugal) |
Optimization |
|
Minimise total discounted cost of the energy system |
|
GAMS |
|
|
Seasonal |
TIMES-PT |
CENSE - NOVA University Lisbon |
false |
true |
|
|
Energy supply and demand |
true |
|
|
Portugal |
Optimization |
|
Minimise total discounted cost of the energy system |
|
GAMS |
transmission distribution |
|
Seasonal |
Temoa |
NC State University |
true |
false |
GNU General Public License version 2.0 (GPL-2.0) |
all |
energy system optimization model |
true |
investment |
|
U.S., currently |
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 |
stochastic optimization, moeling-to-generate alternatives |
Python (Pyomo) |
|
SQLite |
Multi year |
TransiEnt |
Hamburg University of Technology |
true |
false |
|
some |
Dynamic system simulation model library |
true |
|
|
Hamburg / Germany |
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. |
|
Prediction errors can be introduced by (filtered) white noise timeseries to see changes in control behaviour |
Modelica |
transmission distribution net transfer capacities |
Dymola |
Second |
URBS |
TUM EI ENS |
true |
true |
GNU General Public License version 3.0 (GPL-3.0) |
some |
Energy Modelling Framework |
true |
dispatch investment |
|
User-dependent |
Optimization |
Linear optimization model of a user-defined reference energy system. |
Minimise total discounted cost of system |
None |
Python (Pyomo) |
transmission net transfer capacities |
Python (pandas et al) |
Hour |
USENSYS |
Environmental Defense Fund |
true |
false |
Affero General Public License v3 (AGPL-3.0) |
all |
Capacity expansion Reference Energy System |
true |
investment |
|
US 48 lower states & DC |
Optimization |
Linear programming |
Cost minimization |
Deterministic |
R/energyRt |
transmission |
R |
Hour |