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 |