wiki.openmod-initiative.org - User contributions [en]

AMIRIS

2024-04-17T14:14:16Z

Florian Maurer:

{{Model
|Full_Model_Name=Agent-based Market model for the Investigation of Renewable and Integrated energy Systems
|Acronym=AMIRIS
|author_institution=German Aerospace Center
|contact_persons=Christoph Schimeczek
|contact_email=Christoph.Schimeczek@dlr.de
|website=https://dlr-ve.gitlab.io/esy/amiris/home/
|source_download=https://gitlab.com/dlr-ve/esy/amiris/amiris/-/releases
|logo=amiris-logo.jpg
|text_description=Agent-based electricty market model for analysing questions on future energy markets, their market design, and energy-related policy instruments
|Primary outputs=electricity prices, power plant dispatch, cost and income
|Support=OpenMod Forum
|Framework=FAME
|User documentation=https://gitlab.com/dlr-ve/esy/amiris/amiris/-/wikis/home
|Source of funding=German Aerospace Center, German Federal Ministry for Economic Affairs and Climate Action, European Commission
|Number of developers=8
|Number of users=15
|open_source_licensed=Yes
|license=Apache License 2.0 (Apache-2.0)
|model_source_public=Yes
|Link to source=https://gitlab.com/dlr-ve/esy/amiris/amiris
|data_availability=all
|open_future=No
|modelling_software=Java
|processing_software=Python
|Additional software=FAME
|GUI=No
|model_class=Agent-based electricity market model
|sectors=electricity
|technologies=Renewables, Conventional Generation
|Energy carrier (Gas)=Natural gas, Biogas
|Energy carrier (Liquid)=Petrol
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Hydro, Sun, Wind
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=bidding behaviour
|Market models=day-ahead electricity market
|decisions=dispatch
|Changes in efficiency=fixed
|georegions=Germany, Austria
|georesolution=National
|timeresolution=Hour
|Observation period=More than one year
|Additional dimensions (Ecological)=CO2 emissions
|Additional dimensions (Economical)=spot price, income
|math_modeltype=Simulation, Agent-based
|math_modeltype_shortdesc=algorithms for market clearing and agent-specific bidding strategies
|deterministic=stochastic, perfect foresight, deterministic
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=1
|computation_time_hardware=Desktop PC
|computation_time_comments=one year, one country
|citation_references=Schimeczek et al. (2023). AMIRIS: Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. Journal of Open Source Software, 8(84), 5041.
|citation_doi=https://doi.org/10.21105/joss.05041
|report_references=https://doi.org/10.1016/j.apenergy.2021.117267; https://doi.org/10.3390/en13153920; https://doi.org/10.3390/en13205350; https://doi.org/10.1155/2017/7494313
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

File:Amiris-logo.jpg

2024-04-17T14:14:10Z

Florian Maurer: AMIRIS Logo by DLR under CC license

== Summary ==
AMIRIS Logo by DLR under CC license

== Licensing ==
{{license_otherswork_opencontent}}

ASSUME

2024-04-17T12:53:24Z

Florian Maurer:

{{Model
|Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution
|Acronym=ASSUME
|author_institution=INATECH Freiburg
|authors=Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil
|contact_persons=Nick Harder
|contact_email=contact@assume-project.de
|website=https://assume-project.de/
|source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main
|logo=assume-project.png
|text_description=ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup and Reinforcement Learning. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
|Primary outputs=electricity prices, power plant dispatch, cost and income
|Support=OpenMod Forum, GitHub Issues
|Framework=mango-agents
|User documentation=https://assume.readthedocs.io/
|Code documentation=https://assume.readthedocs.io/en/latest/assume.html
|Source of funding=Federal Ministry for Economic Affairs and Climate Action (BMWK)
|Number of developers=5
|Number of users=10
|open_source_licensed=Yes
|license=Affero General Public License v3 (AGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/assume-framework/assume/releases
|data_availability=all
|open_future=No
|modelling_software=Python, Pyomo
|processing_software=PostgreSQL
|External optimizer=GLPK, CBC, Gurobi, C-Plex
|GUI=No
|model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,
|sectors=All / Electricity,
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=bidding behaviour
|Market models=day-ahead electricity market, support market, redispatch, nodal pricing
|decisions=dispatch
|Changes in efficiency=fixed per simulation run per powerplant
|georegions=depending on input data
|georesolution=NUTS0 - NUTS3, for DE
|timeresolution=15 Minute
|network_coverage=transmission, distribution
|Observation period=Less than one month, Less than one year
|Additional dimensions (Ecological)=CO2 emissions
|Additional dimensions (Economical)=spot price, income, production cost per generation unit, profit per unit
|Additional dimensions (Social)=bidding behavior, reinforcement learning output
|Additional dimensions (Other)=grid congestion
|math_modeltype=Simulation, Agent-based
|math_modeltype_shortdesc=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
|math_objective=Minimize cost, optimize dispatch per agent
|deterministic=Deterministic
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=Zenodo
|citation_doi=https://doi.org/10.5281/zenodo.8088760
|report_references=https://doi.org/10.1007/978-3-031-48652-4_10

https://doi.org/10.1016/j.egyai.2023.100295
|example_research_questions=What influence does the availability of different order types on a market have?

How can deep reinforcement learning for multiple markets be implemented in software?

What is the best way for demand-side management to be implemented in bidding agents?

How can different energy market designs be modelled in energy market simulations?
|Model validation=benchmark to entsoe, comparison of real dispatch
|Specific properties=reinforcement learning, RL, interoperability, market abstraction, multiple markets
|Integrated models=PyPSA, AMIRIS
|Interfaces=CSV, PostgreSQL
|Model input file format=Yes
|Model file format=Yes
|Model output file format=Yes
}}

Oemof

2024-04-17T12:29:15Z

Florian Maurer:

{{Model
|Full_Model_Name=Open Energy Modelling Framework
|author_institution=Reiner Lemoine Institut / ZNES Flensburg
|authors=Stephan Günther, Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Caroline Möller, Guido Plessmann, Clemens Wingenbach et al.
|contact_persons=Stephan Günther, Simon Hilpert, Cord Kaldemeyer, Uwe Krien, Caroline Möller, Guido Plessmann, Clemens Wingenbach et al.
|contact_email=oemof(affe)rl-institut.de
|website=https://oemof.org/
|source_download=https://github.com/oemof/oemof/releases
|logo=8503379.png
|text_description=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.
|Support=https://forum.openmod.org/tags/c/qa/oemof
|User documentation=https://oemof.readthedocs.io/en/latest/
|Code documentation=https://oemof-solph.readthedocs.io
|open_source_licensed=Yes
|license=GNU General Public License version 3.0 (GPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/oemof/oemof
|data_availability=some
|open_future=Yes
|modelling_software=Python, Pyomo, Coin-OR
|processing_software=PostgreSQL, PostGIS
|GUI=No
|model_class=Energy Modelling Framework
|sectors=Electricity, Heat, Mobility
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Gas)=Natural gas, Biogas, Hydrogen
|Energy carrier (Liquid)=Diesel, Ethanol, Petrol
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Distribution
|Transfer (Gas)=Distribution
|Transfer (Heat)=Distribution
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=Yes
|Storage (Heat)=Yes
|decisions=dispatch, investment
|Changes in efficiency=Depends on user
|georegions=Depends on user
|georesolution=Depends on user
|timeresolution=Hour
|network_coverage=transmission, distribution, DC load flow, net transfer capacities
|math_modeltype=Optimization, Simulation
|math_modeltype_shortdesc=https://oemof.org/libraries/
|math_objective=costs, emissions
|deterministic=Deterministic
|is_suited_for_many_scenarios=No
|montecarlo=No
|computation_time_comments=Depends strongly on use of modeling framework. Typically if investment decisions are enabled, a model run takes a multiple of minutes to compute.
|Integrating models=FlexiGis
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

FlexiGIS

2024-04-17T12:24:50Z

Florian Maurer:

{{Model
|Full_Model_Name=Flexibilisation in Geographic Information Systems
|Acronym=FlexiGIS
|author_institution=DLR Institute of Networked Energy Systems
|authors=Alaa Alhamwi
|contact_persons=Alaa Alhamwi
|contact_email=alaa.alhamwi@dlr.de
|website=https://github.com/FlexiGIS/FlexiGIS.git
|source_download=https://github.com/FlexiGIS/FlexiGIS.git
|logo=Suit1.png
|text_description=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.
|Source of funding=DLR
|open_source_licensed=Yes
|license=BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)
|model_source_public=Yes
|Link to source=https://github.com/FlexiGIS/FlexiGIS.git
|data_availability=some
|open_future=No
|modelling_software=Python
|processing_software=Geopandas
|External optimizer=GLPK, oemof-solph
|GUI=No
|model_class=urban energy systems
|sectors=Electricity Sector,
|technologies=Renewables
|Demand sectors=Households, Industry, Commercial sector
|Energy carriers (Renewable)=Hydro, Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|Market models=energy technology market
|georesolution=building, street, district, city
|timeresolution=15 Minute
|network_coverage=distribution
|Observation period=Less than one year
|math_modeltype=Optimization, Simulation
|math_modeltype_shortdesc=Modelling and optimisation mathematical model
|math_objective=simualte local urban demand and supply, localise distributed storage, minimise total system costs
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas
|citation_doi=https://doi.org/10.1016/j.apenergy.2017.01.048.
|Model validation=simulated consumption and generation were validated against real measured data
|Comment on model validation=real data of the respective city are required
|Integrated models=oemof
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

FlexiGIS

2024-04-17T12:24:13Z

Florian Maurer:

{{Model
|Full_Model_Name=Flexibilisation in Geographic Information Systems
|Acronym=FlexiGIS
|author_institution=DLR Institute of Networked Energy Systems
|authors=Alaa Alhamwi
|contact_persons=Alaa Alhamwi
|contact_email=alaa.alhamwi@dlr.de
|website=https://github.com/FlexiGIS/FlexiGIS.git
|source_download=https://github.com/FlexiGIS/FlexiGIS.git
|logo=Suit1.png
|text_description=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.
|Source of funding=DLR
|open_source_licensed=Yes
|license=BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)
|model_source_public=Yes
|Link to source=https://github.com/FlexiGIS/FlexiGIS.git
|data_availability=some
|open_future=No
|modelling_software=Python
|processing_software=Geopandas
|External optimizer=GLPK, oemof-solph
|GUI=No
|model_class=urban energy systems
|sectors=Electricity Sector,
|technologies=Renewables
|Demand sectors=Households, Industry, Commercial sector
|Energy carriers (Renewable)=Hydro, Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|Market models=energy technology market
|georesolution=building, street, district, city
|timeresolution=15 Minute
|network_coverage=distribution
|Observation period=Less than one year
|math_modeltype=Optimization, Simulation
|math_modeltype_shortdesc=Modelling and optimisation mathematical model
|math_objective=simualte local urban demand and supply, localise distributed storage, minimise total system costs
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas
|citation_doi=https://doi.org/10.1016/j.apenergy.2017.01.048.
|Model validation=simulated consumption and generation were validated against real measured data
|Comment on model validation=real data of the respective city are required
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

FlexiGIS

2024-04-17T12:18:00Z

Florian Maurer:

{{Model
|Full_Model_Name=Flexibilisation in Geographic Information Systems
|Acronym=FlexiGIS
|author_institution=DLR Institute of Networked Energy Systems
|authors=Alaa Alhamwi
|contact_persons=Alaa Alhamwi
|contact_email=alaa.alhamwi@dlr.de
|website=https://github.com/FlexiGIS/FlexiGIS.git
|source_download=https://github.com/FlexiGIS/FlexiGIS.git
|logo=Suit1.png
|text_description=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 urbs. 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.
|Source of funding=DLR
|open_source_licensed=Yes
|license=BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)
|model_source_public=Yes
|Link to source=https://github.com/FlexiGIS/FlexiGIS.git
|data_availability=some
|open_future=No
|modelling_software=Python
|processing_software=Geopandas
|External optimizer=GLPK, urbs
|GUI=No
|model_class=urban energy systems
|sectors=Electricity Sector,
|technologies=Renewables
|Demand sectors=Households, Industry, Commercial sector
|Energy carriers (Renewable)=Hydro, Sun, Wind
|Transfer (Electricity)=Distribution
|Storage (Electricity)=Battery
|Storage (Gas)=No
|Storage (Heat)=No
|Market models=energy technology market
|georesolution=building, street, district, city
|timeresolution=15 Minute
|network_coverage=distribution
|Observation period=Less than one year
|math_modeltype=Optimization, Simulation
|math_modeltype_shortdesc=Modelling and optimisation mathematical model
|math_objective=simualte local urban demand and supply, localise distributed storage, minimise total system costs
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas
|citation_doi=https://doi.org/10.1016/j.apenergy.2017.01.048.
|Model validation=simulated consumption and generation were validated against real measured data
|Comment on model validation=real data of the respective city are required
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

Antares-Simulator

2024-04-17T12:14:10Z

Florian Maurer:

{{Model
|Full_Model_Name=Antares-Simulator
|Acronym=Antares-Simulator
|author_institution=RTE
|contact_persons=Paul Plessiez, Jean-Marc Janin, Romain Rousselin-Reinhardt
|contact_email=paul.plessiez@rte-france.com
|website=https://antares-simulator.org/
|source_download=https://antares-simulator.org/pages/antares-simulator/6/
|logo=AntaresSimulator Logo-CMJN.jpg
|text_description=Antares-Simulator is an open-source tool for the modelling, the simulation and the planning of multi-energy systems. It is a sequential Monte-Carlo simulator designed for short to long term studies of large interconnected energy grids. It simulates the economic behavior of the whole transmission-generation system, throughout the year and with a resolution of one hour.
|Primary outputs=Optimal investment planning, production plans, capital and operational costs, resource adequacy
|User documentation=https://antares-doc.readthedocs.io/en/latest/
|Code documentation=https://antares-simulator.readthedocs.io/en/latest/build/0-INSTALL/
|Number of developers=10
|Number of users=40
|open_source_licensed=Yes
|license=GNU General Public License version 3.0 (GPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/AntaresSimulatorTeam/Antares_Simulator/
|data_availability=some
|open_future=No
|modelling_software=C++, C
|processing_software=Python, TypeScript
|External optimizer=Sirius, Xpress
|GUI=Yes
|model_class=Capacity Expansion Problem, Production Cost Model
|sectors=Electricity, Methane, Hydrogen, Heat
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Transport, Commercial sector
|Energy carrier (Gas)=Natural gas, Hydrogen
|Energy carriers (Renewable)=Hydro, Sun, Wind
|Transfer (Electricity)=Transmission
|Transfer (Gas)=Transmission
|Transfer (Heat)=Transmission
|Storage (Electricity)=Battery, CAES, PHS
|Storage (Gas)=Yes
|Storage (Heat)=Yes
|User behaviour=Central Planner
|Market models=Central Planner prospective, similar to Day-Ahead market
|decisions=dispatch, investment
|Changes in efficiency=Yes
|georegions=Europe
|georesolution=NUTS0 - NUTS2
|timeresolution=Hour
|network_coverage=transmission, DC load flow, net transfer capacities
|Observation period=More than one year
|math_modeltype=Optimization, Simulation
|math_modeltype_shortdesc=Investment planning: optimization based on Benders decomposition
Dispatch : simulation based on MILP
|math_objective=socio-economic welfare, investment costs, greenhouse gas emissions
|deterministic=Monte-Carlo methods, myopic week-ahead foresight
|is_suited_for_many_scenarios=Yes
|montecarlo=Yes
|computation_time_minutes=20
|computation_time_hardware=Standard PC
|computation_time_comments=For 1 year Monte-Carlo pan-European system. Monte-Carlo years solving can be parallelized, significantly reducing the computation time when many MC years are simulated
|citation_references=A New tool for adequacy reporting of electric systems. CIGRE 2008, C1-305 (M. Doquet, R. Gonzalez, S. Lepy, E. Momot, F. Verrier)
|report_references=- RTE, "Energy Pathways to 2050", https://assets.rte-france.com/prod/public/2022-01/Energy%20pathways%202050_Key%20results.pdf

- Lauvergne, Rémi, Yannick Perez, Mathilde Françon, et Alberto Tejeda De La Cruz. « Integration of Electric Vehicles into Transmission Grids: A Case Study on Generation Adequacy in Europe in 2040 ». Applied Energy 326 (15 novembre 2022): 120030. https://doi.org/10.1016/j.apenergy.2022.120030.

- Lynch, Arthur, Yannick Perez, Sophie Gabriel, et Gilles Mathonniere. « Nuclear Fleet Flexibility: Modeling and Impacts on Power Systems with Renewable Energy ». Applied Energy 314 (15 mai 2022): 118903. https://doi.org/10.1016/j.apenergy.2022.118903.

- Houghton, T., K. R. W. Bell, et M. Doquet. « Offshore Transmission for Wind: Comparing the Economic Benefits of Different Offshore Network Configurations ». Renewable Energy 94 (1 août 2016): 268‑79. https://doi.org/10.1016/j.renene.2016.03.038.

- A. T. Samuel, A. Aldamanhori, A. Ravikumar and G. Konstantinou, "Stochastic Modeling for Future Scenarios of the 2040 Australian National Electricity Market using ANTARES," 2020 International Conference on Smart Grids and Energy Systems (SGES), Perth, Australia, 2020, pp. 761-766, doi: 10.1109/SGES51519.2020.00141.
|example_research_questions=What are the best investment options to efficiently decarbonize the European energy sector?
What is the operational cost of a given pan-european energy mix?
What can be the added value of reinforcing the transmission grid on a given border?
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

EMLab-Generation

2024-04-17T12:12:01Z

Florian Maurer:

{{Model
|Full_Model_Name=EMLab-Generation
|author_institution=Delft University of Technology
|authors=Jörn C. Richstein, Emile Chappin, Pradyumna Bhagwat, Laurens de Vries
|contact_persons=Jörn C. Richstein
|contact_email=j.c.richstein@tudelft.nl
|website=http://emlab.tudelft.nl/
|source_download=https://github.com/EMLab/emlab-generation
|logo=Logo-emlab.png
|text_description=The main purpose is to explore the long-term effects of interacting energy and climate policies by means of a simulation model of power companies investing in generation capacity. With this model, we study the influence of policy on investment in the electricity market in order to explicate possible effects of current and alternative/additional policies on the various sector goals, i.e. renewables targets, CO2 emission targets, security of supply and affordability. The methodology, agent-based modelling, allows for a different set of assumptions different as to the mainstream models for such questions: this model can explore heterogeneity of actors, consequences of imperfect expectations and investment behaviour outside of ideal conditions.
|open_source_licensed=Yes
|license=Apache License 2.0 (Apache-2.0)
|model_source_public=Yes
|data_availability=some
|open_future=Yes
|modelling_software=Java
|processing_software=R
|GUI=No
|model_class=Agent-based Simulation
|sectors=Electricity Market, Carbon Market
|technologies=Renewables, Conventional Generation
|Storage (Gas)=No
|Storage (Heat)=No
|decisions=dispatch, investment
|georegions=Central Western Europe
|georesolution=Zones
|timeresolution=Year
|network_coverage=net transfer capacities
|math_modeltype=Simulation, Agent-based
|deterministic=Limited foresight, optional risk aversion
|is_suited_for_many_scenarios=Yes
|montecarlo=No
|computation_time_minutes=60
|computation_time_comments=Depends on the enabled modules
|citation_references=Richstein et al. 2014, Cross-border electricity market effects due to price caps in an emission trading system: An agent-based approach, Energy Policy Volume 71, August 2014, Pages 139–158
|citation_doi=10.1016/j.enpol.2014.03.037
|example_research_questions=- What is the effect of carbon price caps?
- How is the market stability reserve going to effect the EU ETS?
- What long-term effects does a capacity market have?
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

Pandapower

2024-04-17T12:10:10Z

Florian Maurer:

{{Model
|Full_Model_Name=Pandapower
|authors=Energy Management and Power System Operation (University of Kassel), Fraunhofer IEE
|contact_persons=Leon Thurner, Alexander Scheidler
|website=http://www.pandapower.org
|source_download=https://github.com/e2nIEE/pandapower/
|text_description=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.
|Primary outputs=Power Flow
|Framework=PYPOWER
|Code documentation=pandapower.readthedocs.io
|open_source_licensed=Yes
|license=BSD 3-Clause "New" or "Revised" License (BSD-3-Clause)
|model_source_public=Yes
|Link to source=https://github.com/e2nIEE/pandapower/
|data_availability=some
|open_future=No
|modelling_software=Python
|processing_software=Pandas
|GUI=No
|model_class=Transmission Network Model
|technologies=Renewables, Conventional Generation
|Transfer (Electricity)=Distribution, Transmission
|Storage (Gas)=No
|Storage (Heat)=No
|Market models=none
|network_coverage=transmission, distribution
|math_modeltype=Simulation
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=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
|citation_doi=10.1109/TPWRS.2018.2829021
|Model input file format=No
|Model file format=No
|Model output file format=No
}}

ASSUME

2024-04-17T12:04:05Z

Florian Maurer:

{{Model
|Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution
|Acronym=ASSUME
|author_institution=INATECH Freiburg
|authors=Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil
|contact_persons=Nick Harder
|contact_email=contact@assume-project.de
|website=https://assume-project.de/
|source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main
|logo=assume-project.png
|text_description=ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
|Primary outputs=electricity prices, power plant dispatch, cost and income
|Support=OpenMod Forum, GitHub Issues
|Framework=mango-agents
|User documentation=https://assume.readthedocs.io/
|Code documentation=https://assume.readthedocs.io/en/latest/assume.html
|Source of funding=Federal Ministry for Economic Affairs and Climate Action (BMWK)
|Number of developers=5
|Number of users=10
|open_source_licensed=Yes
|license=Affero General Public License v3 (AGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/assume-framework/assume/releases
|data_availability=all
|open_future=No
|modelling_software=Python, Pyomo
|processing_software=PostgreSQL
|External optimizer=GLPK, CBC, Gurobi, C-Plex
|GUI=No
|model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,
|sectors=All / Electricity,
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Distribution, Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=bidding behaviour
|Market models=day-ahead electricity market, support market, redispatch, nodal pricing
|decisions=dispatch
|Changes in efficiency=fixed per simulation run per powerplant
|georegions=depending on input data
|georesolution=NUTS0 - NUTS3, for DE
|timeresolution=15 Minute
|network_coverage=transmission, distribution
|Observation period=Less than one month, Less than one year
|Additional dimensions (Ecological)=CO2 emissions
|Additional dimensions (Economical)=spot price, income, production cost per generation unit, profit per unit
|Additional dimensions (Social)=bidding behavior, reinforcement learning output
|Additional dimensions (Other)=grid congestion
|math_modeltype=Simulation, Agent-based
|math_modeltype_shortdesc=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
|math_objective=Minimize cost, optimize dispatch per agent
|deterministic=Deterministic
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=Zenodo
|citation_doi=https://doi.org/10.5281/zenodo.8088760
|report_references=https://doi.org/10.1007/978-3-031-48652-4_10

https://doi.org/10.1016/j.egyai.2023.100295
|example_research_questions=What influence does the availability of different order types on a market have?

How can deep reinforcement learning for multiple markets be implemented in software?

What is the best way for demand-side management to be implemented in bidding agents?

How can different energy market designs be modelled in energy market simulations?
|Model validation=benchmark to entsoe, comparison of real dispatch
|Specific properties=reinforcement learning, RL, interoperability, market abstraction, multiple markets
|Integrated models=PyPSA, AMIRIS
|Interfaces=CSV, PostgreSQL
|Model input file format=Yes
|Model file format=Yes
|Model output file format=Yes
}}

ASSUME

2024-04-17T12:01:24Z

Florian Maurer:

{{Model
|Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution
|Acronym=ASSUME
|author_institution=INATECH Freiburg
|authors=Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil
|contact_persons=Nick Harder
|contact_email=contact@assume-project.de
|website=https://assume-project.de/
|source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main
|logo=assume-project.png
|text_description=ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
|Primary outputs=electricity prices, power plant dispatch, cost and income
|Support=OpenMod Forum, GitHub Issues
|Framework=mango-agents
|User documentation=https://assume.readthedocs.io/
|Code documentation=https://assume.readthedocs.io/en/latest/assume.html
|Source of funding=Federal Ministry for Economic Affairs and Climate Action (BMWK)
|Number of developers=5
|Number of users=10
|open_source_licensed=Yes
|license=Affero General Public License v3 (AGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/assume-framework/assume/releases
|data_availability=all
|open_future=No
|modelling_software=Python, Pyomo
|processing_software=PostgreSQL
|External optimizer=GLPK, CBC, Gurobi, C-Plex
|GUI=No
|model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,
|sectors=All / Electricity,
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Distribution, Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=bidding behaviour
|Market models=day-ahead electricity market, support market, redispatch, nodal pricing
|decisions=dispatch
|Changes in efficiency=fixed per simulation run per powerplant
|georegions=depending on input data
|georesolution=NUTS0 - NUTS3, for DE
|timeresolution=15 Minute
|network_coverage=transmission, distribution
|Observation period=Less than one month, Less than one year
|Additional dimensions (Ecological)=CO2 emissions
|Additional dimensions (Economical)=spot price, income, production cost per generation unit, profit per unit
|Additional dimensions (Social)=bidding behavior, reinforcement learning output
|Additional dimensions (Other)=grid congestion
|math_modeltype=Simulation, Agent-based
|math_modeltype_shortdesc=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
|math_objective=Minimize cost, optimize dispatch per agent
|deterministic=Deterministic
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=Zenodo
|citation_doi=https://doi.org/10.5281/zenodo.8088760
|report_references=https://doi.org/10.1007/978-3-031-48652-4_10

https://doi.org/10.1016/j.egyai.2023.100295

|example_research_questions=What influence does the availability of different order types on a market have?
How can deep reinforcement learning for multiple markets be implemented in software?
What is the best way for demand-side management to be implemented in bidding agents?
How can different energy market designs be modelled in energy market simulations?
|Model validation=benchmark to entsoe, comparison of real dispatch
|Specific properties=reinforcement learning, RL, interoperability, market abstraction, multiple markets
|Integrated models=PyPSA, AMIRIS
|Interfaces=CSV, PostgreSQL
|Model input file format=Yes
|Model file format=Yes
|Model output file format=Yes
}}

ASSUME

2024-04-17T12:01:10Z

Florian Maurer:

{{Model
|Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution
|Acronym=ASSUME
|author_institution=INATECH Freiburg
|authors=Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil
|contact_persons=Nick Harder
|contact_email=contact@assume-project.de
|website=https://assume-project.de/
|source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main
|logo=assume-project.png
|text_description=ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
|Primary outputs=electricity prices, power plant dispatch, cost and income
|Support=OpenMod Forum, GitHub Issues
|Framework=mango-agents
|User documentation=https://assume.readthedocs.io/
|Code documentation=https://assume.readthedocs.io/en/latest/assume.html
|Source of funding=Federal Ministry for Economic Affairs and Climate Action (BMWK)
|Number of developers=5
|Number of users=10
|open_source_licensed=Yes
|license=Affero General Public License v3 (AGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/assume-framework/assume/releases
|data_availability=all
|open_future=No
|modelling_software=Python, Pyomo
|processing_software=PostgreSQL
|External optimizer=GLPK, CBC, Gurobi, C-Plex
|GUI=No
|model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,
|sectors=All / Electricity,
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Distribution, Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=bidding behaviour
|Market models=day-ahead electricity market, support market, redispatch, nodal pricing
|decisions=dispatch
|Changes in efficiency=fixed per simulation run per powerplant
|georegions=depending on input data
|georesolution=NUTS0 - NUTS3, for DE
|timeresolution=15 Minute
|network_coverage=transmission, distribution
|Observation period=Less than one month, Less than one year
|Additional dimensions (Ecological)=CO2 emissions
|Additional dimensions (Economical)=spot price, income, production cost per generation unit, profit per unit
|Additional dimensions (Social)=bidding behavior, reinforcement learning output
|Additional dimensions (Other)=grid congestion
|math_modeltype=Simulation, Agent-based
|math_modeltype_shortdesc=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
|math_objective=Minimize cost, optimize dispatch per agent
|deterministic=Deterministic
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=Zenodo
|citation_doi=https://doi.org/10.5281/zenodo.8088760
|report_references=https://doi.org/10.1007/978-3-031-48652-4_10
https://doi.org/10.1016/j.egyai.2023.100295
|example_research_questions=What influence does the availability of different order types on a market have?
How can deep reinforcement learning for multiple markets be implemented in software?
What is the best way for demand-side management to be implemented in bidding agents?
How can different energy market designs be modelled in energy market simulations?
|Model validation=benchmark to entsoe, comparison of real dispatch
|Specific properties=reinforcement learning, RL, interoperability, market abstraction, multiple markets
|Integrated models=PyPSA, AMIRIS
|Interfaces=CSV, PostgreSQL
|Model input file format=Yes
|Model file format=Yes
|Model output file format=Yes
}}

ASSUME

2024-04-17T11:56:53Z

Florian Maurer:

{{Model
|Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution
|Acronym=ASSUME
|author_institution=INATECH Freiburg
|authors=Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil
|contact_persons=Nick Harder
|contact_email=contact@assume-project.de
|website=https://assume-project.de/
|source_download=https://codeload.github.com/assume-framework/assume/zip/refs/heads/main
|logo=assume-project.png
|text_description=ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
|Primary outputs=electricity prices, power plant dispatch, cost and income
|Support=OpenMod Forum, GitHub Issues
|Framework=mango-agents
|User documentation=https://assume.readthedocs.io/
|Code documentation=https://assume.readthedocs.io/en/latest/assume.html
|Source of funding=Federal Ministry for Economic Affairs and Climate Action (BMWK)
|Number of developers=5
|Number of users=10
|open_source_licensed=Yes
|license=Affero General Public License v3 (AGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/assume-framework/assume/releases
|data_availability=all
|open_future=No
|modelling_software=Python, Pyomo
|processing_software=PostgreSQL
|External optimizer=GLPK, CBC, Gurobi, C-Plex
|GUI=No
|model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,
|sectors=All / Electricity,
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Distribution, Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=bidding behaviour
|Market models=day-ahead electricity market, support market, redispatch, nodal pricing
|decisions=dispatch
|Changes in efficiency=fixed per simulation run per powerplant
|georegions=depending on input data
|georesolution=NUTS0 - NUTS3, for DE
|timeresolution=15 Minute
|network_coverage=transmission, distribution
|Observation period=Less than one month, Less than one year
|Additional dimensions (Ecological)=CO2 emissions
|Additional dimensions (Economical)=spot price, income, production cost per generation unit, profit per unit
|Additional dimensions (Social)=bidding behavior, reinforcement learning output
|Additional dimensions (Other)=grid congestion
|math_modeltype=Simulation, Agent-based
|math_modeltype_shortdesc=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
|math_objective=Minimize cost, optimize dispatch per agent
|deterministic=Deterministic
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=Zenodo
|citation_doi=https://doi.org/10.5281/zenodo.8088760
|example_research_questions=How can different energy market designs be modelled in
|Model validation=benchmark to entsoe, comparison of real dispatch
|Specific properties=reinforcement learning, RL, interoperability, market abstraction
|Integrated models=PyPSA, AMIRIS
|Interfaces=CSV, PostgreSQL
|Model input file format=Yes
|Model file format=Yes
|Model output file format=Yes
}}

ASSUME

2024-04-17T11:53:34Z

Florian Maurer: Add ASSUME project

{{Model
|Full_Model_Name=Agent-based Simulation for Studying and Understanding Market Evolution
|Acronym=ASSUME
|author_institution=INATECH Freiburg
|authors=Florian Maurer, Nick Harder, Kim K. Miskiw, Johanna Adams, Manish Khanra, Parag Pratil
|contact_persons=Nick Harder
|contact_email=contact@assume-project.de
|website=https://assume-project.de/
|source_download=pip install assume-framework
|logo=assume-project.png
|text_description=ASSUME is an open-source toolbox for agent-based simulations of European electricity markets, with a primary focus on the German market setup. Developed as an open-source model, its primary objectives are to ensure usability and customizability for a wide range of users and use cases in the energy system modeling community.
|Primary outputs= electricity prices, power plant dispatch, cost and income
|Support=OpenMod Forum, GitHub Issues
|Framework=mango-agents
|User documentation=https://assume.readthedocs.io/
|Code documentation=https://assume.readthedocs.io/en/latest/assume.html
|Source of funding=Federal Ministry for Economic Affairs and Climate Action (BMWK)
|Number of developers=5
|Number of users=10
|open_source_licensed=Yes
|license=Affero General Public License v3 (AGPL-3.0)
|model_source_public=Yes
|Link to source=https://github.com/assume-framework/assume/
|data_availability=all
|open_future=No
|modelling_software=Python, Pyomo
|processing_software=PostgreSQL
|External optimizer=GLPK, CBC, Gurobi, C-Plex
|GUI=No
|model_class=German and European Electricity Market, Network-constrained Unit Commitment and Economic Dispatch, Agent-based electricity market model,
|sectors=All / Electricity,
|technologies=Renewables, Conventional Generation, CHP
|Demand sectors=Households, Industry, Commercial sector
|Energy carrier (Gas)=Natural gas
|Energy carriers (Solid)=Biomass, Coal, Lignite, Uranium
|Energy carriers (Renewable)=Geothermal heat, Hydro, Sun, Wind
|Transfer (Electricity)=Distribution, Transmission
|Storage (Electricity)=Battery, CAES, Chemical, Kinetic, PHS
|Storage (Gas)=No
|Storage (Heat)=No
|User behaviour=bidding behaviour
|Market models=day-ahead electricity market, support market, redispatch, nodal pricing
|decisions=dispatch
|Changes in efficiency=fixed per simulation run per powerplant
|georegions=depending on input data
|georesolution=NUTS0 - NUTS3, for DE
|timeresolution=15 Minute
|network_coverage=transmission, distribution
|Observation period=Less than one month, Less than one year
|Additional dimensions (Ecological)=CO2 emissions
|Additional dimensions (Economical)=spot price, income, production cost per generation unit, profit per unit
|Additional dimensions (Social)=bidding behavior, reinforcement learning output
|Additional dimensions (Other)=grid congestion
|math_modeltype=Simulation, Agent-based
|math_modeltype_shortdesc=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
|math_objective=Minimize cost, optimize dispatch per agent
|deterministic=Deterministic
|is_suited_for_many_scenarios=No
|montecarlo=No
|citation_references=Zenodo
|citation_doi=https://doi.org/10.5281/zenodo.8088760
|example_research_questions=How can different energy market designs be modelled in
|Model validation=benchmark to entsoe
|Specific properties=reinforcement learning, RL, interoperability, market abstraction
|Integrated models=PyPSA, AMIRIS
|Interfaces=CSV, PostgreSQL
|Model input file format=Yes
|Model file format=Yes
|Model output file format=Yes
}}

File:Assume-project.png

2024-04-17T11:34:04Z

Florian Maurer: Logo of ASSUME project - Agent-based Simulation for Studying and Understanding Market Evolution

== Summary ==
Logo of ASSUME project - Agent-based Simulation for Studying and Understanding Market Evolution

== Licensing ==
{{license_ownwork_default}}