EnergyScope |
|
|
EPFL, UCLouvain |
Stefano Moret, Gauthier Limpens |
10.1016/j.apenergy.2019.113729 |
Limpens G, Moret S, Jeanmart H, Maréchal F,EnergyScope TD: a novel open-source model for regional energy systems. Appl Energy 2019; Volume 255. |
|
gauthier.limpens@uclouvain.be |
Gauthier Limpens |
all |
dispatch investment |
|
|
Role of storage?
Benefit of electrification?
How to handle high shares of renewables?
What is the impact of uncertainties on investment decisions? |
EnergyScope |
|
Region (Switzerland, Belgium) |
Country |
true |
Apache License 2.0 (Apache-2.0) |
|
Optimization |
Linear programming (43 equations fully documented). |
financial cost, greenhouse gases emissions |
Regional energy system design |
true |
GLPK/GLPSOL or AMPL/Cplex |
|
200,366 |
false |
true |
Excel |
Limpens G, Moret S, Guidati G, Li X, Maréchal F, Jeanmart H. The role of storage in the Swiss energy transition. Proceedings of ECOS2019, june 23-28, 2019, Wroclaw, Poland. 2019 pages 761-774
Limpens, G., Jeanmart, H., & Maréchal, F. (2020). Belgian Energy Transition: What Are the Options?. Energies, 13(1), 261. |
All (Electricity Heating and mobility) |
https://github.com/energyscope/EnergyScope |
|
EnergyScope is open-source model for the strategic energy planning
of urban and regional energy systems.
EnergyScope (v2.0) optimises both the investment and operating strategy of an entire energy system (including electricity, heating and mobility). Additionally, its hourly resolution (using typical days) makes the model suitable for the integration of intermittent renewables, and its concise mathematical formulation and computational effciency are appropriate for uncertainty applications. |
Hour |
|
Ficus |
|
|
Institute for Energy Economy and Application Technology |
Dennis Atabay |
10.5281/zenodo.32077 |
|
60 |
dennis.atabay@tum.de |
Dennis Atabay |
some |
dispatch investment |
None |
|
|
ficus |
|
|
|
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization |
|
costs |
energy system optimization model |
true |
Python (Pyomo) |
|
|
true |
true |
Python (pandas et al) |
|
electricity heating ... |
https://github.com/yabata/ficus |
Renewables Conventional Generation CHP |
A (mixed integer) linear optimisation model for local energy systems |
15 Minute |
https://github.com/yabata/ficus |
FlexiGIS |
|
|
DLR Institute of Networked Energy Systems |
Alaa Alhamwi |
https://doi.org/10.1016/j.apenergy.2017.01.048. |
GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas |
|
alaa.alhamwi@dlr.de |
Alaa Alhamwi |
some |
|
|
|
|
Flexibilisation in Geographic Information Systems |
|
|
building, street, district, city |
false |
BSD 3-Clause "New" or "Revised" License (BSD-3-Clause) |
|
Optimization Simulation |
Modelling and optimisation mathematical model |
simualte local urban demand and supply, localise distributed storage, minimise total system costs |
urban energy systems |
true |
Python |
distribution |
|
false |
true |
Geopandas |
|
Electricity Sector |
https://github.com/FlexiGIS/FlexiGIS.git |
Renewables |
FlexiGIS: an open source GIS-based platform for modelling energy systems and flexibility options in urban areas. It extracts, filters and categorises the geo-referenced urban energy infrastructure, simulates the local electricity consumption and power generation from on-site renewable energy resources, and allocates the required decentralised storage in urban settings using oemof-solph. FlexiGIS investigates systematically different scenarios of self-consumption, it analyses the characteristics and roles of flexibilisation technologies in promoting higher autarky levels in cities. The extracted urban energy infrustructure are based mainly on OpenStreetMap data. |
15 Minute |
https://github.com/FlexiGIS/FlexiGIS.git |
GAMAMOD |
|
|
Technische Universität Dresden (EE2) |
Philipp Hauser |
|
|
|
philipp.hauser@tu-dresden.de |
Philipp Hauser |
some |
dispatch investment |
|
|
|
Gas Market Model |
|
|
|
false |
|
|
Optimization |
|
|
European Natural Gas Market |
false |
GAMS |
transmission distribution |
|
true |
false |
|
|
gas |
|
|
The gas market model GAMAMOD is a bottom-up model used to determine and analyse the optimal natural gas supply structure in Europe and to examine the utilization of the natural gas infrastructure. In its basic version, the model includes the EU-28 countries as well as Switzerland, Norway, the Baltic States and the Balkan region. In addition, important suppliers for the European natural gas market are considered (e.g. Russia, Algeria, and Qatar). On the supply side, the model considers different production capacities with respect to the production level. The model enables the transport of natural gas by modelling pipelines and liquefied natural gas (LNG) shipping. The capacity of single pipelines between neighbouring countries are aggregated in the model. In the case of LNG shipping, the model considers regasification and liquefaction capacities in export and import countries. The model includes an exogenously imputed natural gas demand for each respective country. Moreover, seasonal demand patterns in the respective countries are considered.
GAMAMOD enables the analysis of trading capacities between regional markets. Due to restricted transmission capacities, regional incidences of congestions might occur. The model allows for examining supply interruptions and their impact on the European natural gas system. As each country is modelled as a single aggregated node, no congestions occur within a market area. Furthermore, the model considers natural gas storage, which ensures security of supply in the European natural gas market.
Cyprus and Malta are isolated from the integrated European natural gas pipeline grid. Therefore, they are not considered in the model. |
|
https://tu-dresden.de/bu/wirtschaft/ee2/forschung/modelle/gamamod?set_language=en |
GAMAMOD-DE |
|
|
Technische Universität Dresden (EE2) |
Philipp Hauser |
http://hdl.handle.net/10419/197000 |
Hauser, Philipp (2019) : A modelling approach for the German gas gridusing highly resolved spatial, temporal and sectoral data (GAMAMOD-DE), ZBW – LeibnizInformation Centre for Economics, Kiel, Hamburg |
|
philipp.hauser@tu-dresden.de |
Philipp Hauser |
all |
dispatch |
|
|
questions about:
- sector coupling between electricity and gas
- security of supply in the German gas network |
Gas Market Model for Germany |
|
|
|
false |
|
|
|
|
|
|
false |
GAMS; CPLEX |
|
|
true |
false |
|
Hauser, P.; Heidari, S.; Weber, C.; Möst, D.: Does Increasing Natural Gas Demand in the Power Sector Pose a Threat of Congestion to the German Gas Grid? A Model-Coupling Approach, Energies 2019, 12(11) 2159
https://www.mdpi.com/475018 |
|
|
|
|
|
https://tu-dresden.de/bu/wirtschaft/ee2/forschung/modelle/gamamod-de |
GRIMSEL-FLEX |
|
|
University of Geneva |
Martin Soini, Arthur Rinaldi |
|
|
30 |
arthur.rinaldi@unige.ch |
Arthur Rinaldi |
all |
dispatch |
Perfect foresight, Sensitivity analisys, Scenarios |
|
|
General Integrated Modeling environment for the Supply of Electricity and Low-temperature heat |
|
Switzerland, Austria, Italy, France, Germany |
Consumer types and Urban settings |
false |
BSD 2-Clause "Simplified" or "FreeBSD" License (BSD-2-Clause) |
|
Optimization |
Quadratic dipatch sector-coupling model |
Minimization of total system costs |
Energy System Model Optimization Social Planner |
true |
Python (Pyomo) |
transmission net transfer capacities |
5,000,000 |
false |
true |
Python (pandas et al) |
|
Electricity Heat Hydrogen Buildings Transport |
https://github.com/arthurrinaldi/grimsel |
Renewables Conventional Generation |
|
Hour |
|
Genesys |
|
|
RWTH-Aachen University |
Alvarez, Bussar, Cai, Chen, Moraes Jr., Stöcker, Thien |
10.1016/j.egypro.2014.01.156 |
Bussar et. al, 2014, Optimal Allocation and Capacity of Energy Storage Systems in a Future European Power System with 100% Renewable Energy Generation |
|
|
Christian Bussar |
all |
dispatch investment |
24 h foresight for storage operation |
|
How much storage systems of which technology needs to be implemented in the future energy system.
How big are the transfer capacities between regions.
How much renewable generator power of which technology are necessary?
How much conventional generators are allowed within assumed CO2 emission limits? |
Genetic Optimisation of a European Energy Supply System |
|
Europe, North Africa, Middle East |
EUMENA, 21 regions |
false |
GNU Library or "Lesser" General Public License version 2.1 (LGPL-2.1) |
|
Optimization Simulation |
optimisation of system combination with evolutionary strategy
simulation of operation with hierarchical management strategy and linear load balancing between regions (network simplex) |
minimise levelised cost of electricity |
Electricity System Model |
false |
C++, boost library, MySQL and QT4, (optional CPLEX solver implementation) |
transmission net transfer capacities |
|
false |
true |
Excel/Matlab and a Visualisation tool programmed in QT4 (c++) |
|
Electricity |
http://Form%20on%20website |
Renewables Conventional Generation |
The GENESYS Simulation tool has the central target so optimise the future European power system (electricity) with a high share of renewable generation. It can find an economic optimal distribution of generators, storage and grid in a 21 region Europe.
The optimisation is based on a covariance matrix adaption evolution strategy (CMA-ES) while the operation is simulated as a hierarchical setup of system elements aiming to balance the load at minimal cost.
GENESYS comes with a set of input time-series and a parameter set for 2050 which can be adjusted by the user.
It was developed as open source within a publicly funded project and its development is currently continued at RWTH Aachen University. |
Hour |
http://www.genesys.rwth-aachen.de |
GridCal |
|
|
|
Santiago Peñate Vera, Michel Lavoie |
|
|
|
santiago.penate.vera@gmail.com |
Santiago Peñate Vera |
all |
|
Deterministic, stochastic |
|
|
GridCal |
|
|
|
true |
GNU General Public License version 3.0 (GPL-3.0) |
|
Optimization Simulation |
Object oriented structures -> intermediate objects holding arrays -> Numerical modules |
Match generation to demand and minimise cost |
Transmission Network Model and Data (input and output) |
true |
Python |
transmission distribution AC load flow DC load flow |
|
true |
true |
Python |
|
Electricity |
https://github.com/SanPen/GridCal.git |
Conventional Generation |
GridCal is a research oriented power systems software.
Research oriented? How? Well, it is a fruit of research. It is designed to be modular. As a researcher I found that the available software (not even talking about commercial options) are hard to expand or adapt to achieve complex simulations. GridCal is designed to allow you to build and reuse modules, which eventually will boost your productivity and the possibilities that are at hand. |
|
https://github.com/SanPen/GridCal |
HighRES |
|
|
UCL, UiO |
James Price, Marianne Zeyringer |
|
Zeyringer, M., Price, J., Fais, B., Li, P.-H. & Sharp, E. Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather. Nat. Energy 3, 395–403 (2018) |
60 |
|
|
all |
dispatch investment |
|
|
|
high spatial and temporal electricity system model |
|
EEA+Norway and UK |
Country level, 20 zones for GB |
true |
MIT license (MIT) |
|
Optimization |
|
Minimization of total system costs |
European electricity system model GB electricity system model |
false |
GAMS; CPLEX |
transmission net transfer capacities |
|
false |
true |
Python |
|
Electricity |
|
Renewables Conventional Generation |
The model is used to plan least-cost electricity systems for Europe and specifically designed to analyse the effects of high shares of variable renewables and explore integration/flexibility options. It does this by comparing and trading off potential options to integrate renewables into the system including the extension of the transmission grid, interconnection with other countries, building flexible generation (e.g. gas power stations), renewable curtailment and energy storage.
highRES is written in GAMS and its objective is to minimise power system investment and operational costs to meet hourly demand, subject to a number of system constraints. The transmission grid is represented using a linear transport model. To realistically model variable renewable supply, the model uses spatially and temporally-detailed renewable generation time series that are based on weather data.
Currently there is one version for Europe and one for GB. |
Hour |
https://github.com/highRES-model |
IRENA FlexTool |
|
|
VTT Technical Research Centre of Finland |
Juha Kiviluoma, Arttu Tupala, Antti Soininen |
|
|
|
juha.kiviluoma@vtt.fi |
Juha Kiviluoma |
some |
dispatch investment |
perfect foresight, but can use limited horizon |
|
|
IRENA FlexTool |
|
User dependent |
User dependent |
true |
GNU Library or "Lesser" General Public License version 3.0 (LGPL-3.0) |
|
Optimization |
Typically linear cost minimization, but unit online decisions can be mixed-integer linear (and effectively investment decisions too). |
cost minimization |
Multi-purpose |
true |
GNU MathProg |
transmission net transfer capacities |
|
false |
true |
Python, SQL |
|
All sectors (user can add more) |
https://github.com/irena-flextool/flextool |
Renewables Conventional Generation CHP |
IRENA FlexTool is an energy and power systems model for understanding the role of variable power generation in future energy systems. It performs capacity expansion planning as well as operational planning.
VTT develops the model for IRENA (and receives a lot of feedback from IRENA to improve the model) |
Hour |
https://irena-flextool.github.io/flextool/ |
JMM |
|
|
Risoe National Laboratory; University of Stuttgart; University of Duisburg-Essen |
Peter Meiborn; Helge V. Larsen; Rüdiger Barth; Heike Brand; Christoph Weber; Oliver Voll |
|
|
|
|
|
|
|
|
|
|
Joint Market Model |
|
|
|
false |
|
|
|
|
|
|
false |
|
|
|
false |
false |
|
|
|
|
|
|
|
http://www.wilmar.risoe.dk/Deliverables/Wilmar%20d6_2_b_JMM_doc.pdf |
Lemlab |
|
|
Technical University of Munich |
Sebastian Dirk Lumpp, Markus Doepfert, Michel Zade |
|
|
20 |
sebastian.lumpp@tum.de |
Sebastian Dirk Lumpp |
all |
|
perfect forecast, deterministic, stochastic |
|
|
local energy market laboratory |
|
|
|
false |
GNU General Public License version 3.0 (GPL-3.0) |
|
Simulation Agent-based |
Agents: intertemporal convex optimization
Markets: (iterative) double-sided auctions, p2p clearing
Forecasting: naive, deterministic forecasting, neural networks |
|
agent-based simulation |
true |
Python, Pyomo |
|
|
false |
true |
PostgreSQL, Ethereum |
|
local energy markets |
https://github.com/tum-ewk/lemlab |
Renewables Conventional Generation CHP |
An open-source tool for the agent-based development and testing of local energy market applications. lemlab allows the user to simulate a LEM using a full agent-based modelling (ABM) in either simulation (SIM) or real-time (RTS) modes. This allows the rapid testing of algorithms as well as the real-time integration of hardware and software components. |
|
https://github.com/tum-ewk/lemlab |
LoadProfileGenerator |
|
|
FZ Jülich |
Noah Pflugradt, Peter Stenzel, Martin Robinius, Detlef Stolten |
|
|
|
Noah.Pflugradt@gmail.com |
Noah Pflugradt |
some |
|
|
|
|
LoadProfileGenerator |
|
|
|
false |
MIT license (MIT) |
|
|
|
|
|
true |
C# |
|
|
false |
true |
|
|
|
https://github.com/FZJ-IEK3-VSA/LoadProfileGenerator |
|
Generates residential profiles for electricity, water, car charging, occupancy and more.
Agentbased simulation using a psychological behavior model. |
Minute |
http://loadprofilegenerator.de |
MEDEAS |
|
|
GEEDS group; University of Valladolid (http://www.eis.uva.es/energiasostenible/?lang=en) |
|
|
|
|
jsole@icm.csic.es |
Jordi Solé |
all |
|
Deterministic |
|
|
Modelling the Energy Development under Environmental and Social constraints |
|
Global; European Union; Bulgaria; Austria |
global, continents, nations |
true |
MIT license (MIT) |
|
Other |
System dynamics. Top-down |
CO2 equivalent emissions, energy, social, economic costs, RE-share |
|
false |
Phyton |
|
|
false |
true |
Phyton |
|
electricity heat liquid fuels gas solid fuels |
http://medeas.eu/model/medeas-model |
Renewables Conventional Generation CHP |
|
Year |
http://medeas.eu/ |
MOCES |
|
|
Chair of Automation and Energy Systems (Saarland University) |
Lukas Exel |
10.1109/SmartGridComm.2015.7436395 |
L. Exel, F. Felgner and G. Frey, "Multi-domain modeling of distributed energy systems - The MOCES approach," 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), Miami, FL, 2015, pp. 774-779. |
|
lukas.exel@aut.uni-saarland.de |
Lukas Exel |
|
dispatch |
deterministic, stochastic |
|
|
Modeling of Complex Energy Systems |
|
Depends on user |
Depends on user |
false |
|
|
Simulation Agent-based |
HDAE (Hybrid Differential Equations) combined with an agent-based approach. |
|
Energy Modeling Framework |
false |
Modelica, Dymola, (OpenModelica), C++, MySQL, SQLite |
|
100,000 |
true |
false |
Lsodar, Dassl |
|
Electricity User-dependent |
|
Renewables Conventional Generation |
MOCES is a modeling tool that allows a simulative investigation of complex energy systems. It is build on top of the modeling language Modelica. It is not restricted to a specific modeling depth, neither spatial nor temporal. Nevertheless, in the time domain it focuses on dynamics with time constants larger then seconds and in the spatial domain it concentrates on the super ‘entity connected to the grid’ level. |
Second |
http://tiny.cc/2q07iy |
Maon |
|
|
Maon GmbH |
Mihail Ketov, Fabian Breitkreutz, Yash Patel, Sangeetha Kadarkarai, Hajar Mouchrik, Nicolai Schmid, Dariush Wahdany, Kaan Gecü, Ömer Bilgin, Esmanur Eryilmaz, Ali Baran Gündüz, Mark Schäfer, Kai Strunz, Albert Moser |
|
Maon GmbH, Handbook, https://cloud.maon.eu/handbook. |
1,000 |
info@maon.eu |
Dr. Mihail Ketov |
all |
dispatch investment |
Monte Carlo, preprocessing or sensitivity |
|
|
Maon |
|
Europe, North Africa, Middle East |
Individual power stations |
true |
|
|
Optimization Simulation Other Agent-based |
|
Minimization of operational costs for electricity spot and frequency reserve markets considering emission caps |
Mixed-Integer Quadratic Programming (MIQP) |
false |
C++ |
transmission distribution AC load flow DC load flow net transfer capacities |
1,000,000,000 |
false |
false |
Ansible, Ceph, cURL, Docker, GraphQL, Kubernetes, MinIO, MongoDB, Node.js, Preact, Python, TypeScript, WebAssembly |
https://maon.eu/publications |
heat Electricity gas and emissions plus couplings (industry transport) |
https://apis.cloud.maon.eu |
Renewables Conventional Generation CHP |
Maon is a market simulation for fundamental electricity, gas, and emission market analysis. It forecasts the facility-wise quarter-hourly dispatch of all supply and demand in whole Europe. Further, it can predict capacities and uncertainties of generators, interconnectors, storages, and consumers.
Web browsers provide access to the data management, simulation, and analysis environment. It enables high-speed, high-resolution, and large-scale foresights. Scenarios can be parameterized by multiple users at the same time, calculated by one click, and collaboratively visually analyzed.
Users get support by work-leveraging parameterization tools, comprehensive quality checks, and interactive visualizations. Maon provides not only results like prices, dispatches, and capacities, but also capture rates, costs, price distributions, revenues, utilizations, and thousands of other results. |
Hour |
https://cloud.maon.eu/handbook |
Medea |
|
|
University of Natural Resources and Life Sciences, Vienna |
Sebastian Wehrle, Johannes Schmidt |
|
https://arxiv.org/abs/2006.08009 |
15 |
sebastian.wehrle@boku.ac.at |
Sebastian Wehrle |
all |
dispatch investment |
Deterministic |
|
|
medea |
|
Austria, Germany |
Countries |
true |
MIT license (MIT) |
|
Optimization |
|
Total system cost |
Austrian and German electricity market |
true |
GAMS |
net transfer capacities |
|
false |
true |
Python |
|
Electricity Heat |
https://github.com/inwe-boku/medea |
Renewables Conventional Generation CHP |
|
Hour |
https://github.com/inwe-boku/medea |
MicroGridsPy |
|
|
Politecnico di Milano |
Sergio Balderrama, Sylvain Quoilin, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Emanuela Colombo, Riccardo Mereu, Nicolò Stevanato, Ivan Sangiorgio, Gianluca Pellecchia |
https://doi.org/10.1016/j.energy.2019.116073 |
Sergio Balderrama, Francesco Lombardi, Fabio Riva, Walter Canedo, Emanuela Colombo, Sylvain Quoilin, A two-stage linear programming optimization framework for isolated hybrid microgrids in a rural context: The case study of the “El Espino” community, Energy (2019), 188, |
|
nicolo.stevanato@polimi.it |
Nicolo' Stevanato |
all |
dispatch investment |
Two-stage stochastic optimization |
|
-Long-term sizing of rural microgrids
-Load evolution |
MicroGridsPy |
|
|
Village-scale |
true |
European Union Public Licence Version 1.1 (EUPL-1.1) |
|
Optimization |
The model is based on two-stage stochastic optimisation and LP or MILP mathematical formulation |
Single or multi objective optimization (NPC, operation costs, CO2 emissions) |
Energy Modeling Framework |
true |
Python (Pyomo) |
|
|
false |
true |
Excel |
-Nicolò Stevanato, Francesco Lombardi, Emanuela Colombo, Sergio Balderrama, Sylvain Quoilin, Two-Stage Stochastic Sizing of a Rural Micro-Grid Based on Stochastic Load Generation, 2019 IEEE Milan PowerTech, Milan, Italy, 2019, pp. 1-6. https://doi.org/10.1109/PTC.2019.8810571
-Nicolò Stevanato, Francesco Lombardi, Giulia Guidicini, Lorenzo Rinaldi, Sergio Balderrama, Matija Pavičević, Sylvain Quoilin, Emanuela Colombo, Long-term sizing of rural microgrids: Accounting for load evolution through multi-step investment plan and stochastic optimization, Energy for Sustainable Development (2020), 58, pp. 16-29, https://doi.org/10.1016/j.esd.2020.07.002
-Nicolò Stevanato, Lorenzo Rinaldi, Stefano Pistolese, Sergio Balderrama, Sylvain Quoilin, Emanuela Colombo, Modeling of a Village-Scale Multi-Energy System for the Integrated Supply of Electric and Thermal Energy, Applied Sciences (2020), https://doi.org/10.3390/app10217445 |
Micro-grids design |
https://github.com/SESAM-Polimi/MicroGridsPy-SESAM.git |
Renewables Conventional Generation |
The MicroGridsPy model main objective is to provide an open-source alternative to the problem of sizing and dispatch of energy in micro-grids in isolated places. It’s written in python(pyomo) and use excel and text files as input and output data handling and visualisation.
Main features:
-Optimal sizing of PV panels, wind turbines, other renewable technologies, back-up genset and electrochemical storage system for least cost electricity supply in rural isolated areas;
-Optimal dispatch from the identified supply systems;
-Possibility to optimize on NPC or operation costs;
-LCOE evaluation for the identified system.
Possible features:
-Two-stage stochastic optimization;
-Multi-year evolving load demand and multi-step capacity expansion;
-Possibility of connecting to the national grid;
-Two-objective optimization (economic and environmental objective functions);
-Brownfield optimization;
-Built-in load archetypes for rural users;
-Endogenous calculation of renewable energy sources production. |
Hour |
https://github.com/SESAM-Polimi/MicroGridsPy-SESAM |
Mosaik |
|
|
OFFIS |
|
10.1109/OSMSES54027.2022.9769116. |
A. Ofenloch et al., "MOSAIK 3.0: Combining Time-Stepped and Discrete Event Simulation," 2022 Open Source Modelling and Simulation of Energy Systems (OSMSES), 2022, pp. 1-5 |
|
mosaik@offis.de |
|
some |
|
|
|
|
mosaik |
|
|
|
true |
GNU Library or "Lesser" General Public License version 2.1 (LGPL-2.1) |
|
Optimization Simulation Agent-based |
|
|
distributed energy systems smart grid simulation |
true |
Python |
transmission distribution |
|
false |
true |
HDF5, InfluxDB, Grafana |
|
electricity heat mobility household |
https://gitlab.com/mosaik |
Renewables CHP |
Mosaik is a flexible Smart Grid co-simulation framework.
Mosaik allows you to reuse and combine existing simulation models and simulators to create large-scale Smart Grid scenarios – and by large-scale we mean thousands of simulated entities distributed over multiple simulator processes. These scenarios can then serve as test bed for various types of control strategies (e.g., multi-agent systems (MAS) or centralized control).
Mosaik is written in Python and completely open source (LGPL), including some simple simulators, a binding to pandapower and PYPOWER and a demonstration scenario. |
Second |
http://mosaik.offis.de/ |
MultiMod |
|
|
DIW Berlin, NTNU Trondheim |
Daniel Huppmann, Ruud Egging |
10.1016/j.energy.2014.08.004 |
Daniel Huppmann & Ruud Egging (2014). Market power, fuel substitution and infrastructure - A large-scale equilibrium model of global energy markets. Energy, 75, 483–500. |
600 |
dhuppmann@diw.de |
Daniel Huppmann |
some |
dispatch investment |
Not covered (yet) |
|
Scenarios regarding North American shale gas development, Russian supply disruption to Europe, evaluation of renewable support measures (feed-in tariffs vs. emission quota)
Model variations (forks) used for other research projects by international partners (see short description for details) |
Energy system and resource market model "MultiMod" |
|
Global |
Europe by region, North America by country, rest of world by region |
false |
|
|
Other |
Generalized Nash Equilibrium (GNE) model formulated as a Mixed Complementarity Model (MCP) |
|
Equilibrium model |
false |
GAMS |
transmission net transfer capacities |
150,000 |
true |
false |
MS Access, MS Excel |
Currently used within EMF 31 (http://emf.stanford.edu) |
Oil Gas Coal Electricity Renewables Industry Transport Residential/Commercial |
|
Renewables Conventional Generation |
The energy system and resource market model "MultiMod" is a large-scale representation of the supply and demand of fossil fuels and renewable energy sources. It captures endogenous substitution between fuels, infrastructure constraints and endogenous investment (e.g., pipeline capacity, power generation technologies), as well as market power by producers of fossil fuels in a unified framework.
The mathematical framework of the MultiMod model is a dynamic Generalized Nash Equilibrium (GNE) derived from individual players' profit maximisation problems. The formulation is generic and flexible, so that the supply chain of any number of fossil and renewable fuels can be modelled. The framework includes seasonality and allows for a detailed infrastructure representation and a comprehensive transformation sector. Investment in infrastructure (transportation, seasonal storage, transformation) is determined endogenously in the model according to the respective player’s inter-temporal optimisation problem. Furthermore, substitution between different energy carriers on the final demand side is endogenous. Modelling co-production of fuels (e.g. crude oil and associated gas) is possible, as well as a flexible setup of transformation units (multiple inputs, multiple outputs). By formulating the model as an equilibrium problem derived from non-cooperative game theory, the model can incorporate Cournot market power by individual suppliers as well as distinct discount rates by various players concerning their investment.
The current framework is an open-loop perfect foresight model. A stochastic version of the model is under development at NTNU Trondheim. This will allow for consideration of uncertainty and distinct risk profiles for individual players along the supply chain, including investment by consumers in energy efficiency.
For the model description paper, a database representing the global energy system was compiled and used for scenario analysis (Huppmann & Egging, 2014). New datasets or variations on the initial data base are currently under development within specific research projects:
- Focus on US domestic conventional crude and shale oil infrastructure (lead: Johns Hopkins University)
- Focus on Chinese coal policies (lead: Tsinghua University)
- Focus on the global crude oil market and refinery investment (lead: DIW Berlin/TU Berlin)
The model is formulated and solved as a Mixed Complementarity Problem (MCP) and implemented in GAMS, using MS Access and MS Excel for data processing and output reports. The code package includes a number of auxiliary routines and algorithms that greatly facilitate the compilation of the data set as well as calibration of the model. |
Multi year |
http://www.diw.de/multimod |