Modeling control strategies for prosumers in a Python-based modular simulation tool
The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Cont...
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| Published in: | Energy Informatics Vol. 6; no. Suppl 1; pp. 39 - 22 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Springer International Publishing
01.10.2023
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| ISSN: | 2520-8942, 2520-8942 |
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| Abstract | The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of flexibilities and therefore help mitigate upcoming problems. However, they need to be evaluated carefully before their application in the energy system to avoid any unwanted effects and to choose the most fitting strategy for each application. In this publication, a Python-based modular simulation tool for developing and analysing control strategies for prosumers, which uses pandapower (Thurner et al. 2018), is presented. It is intended for sequential simulations and enables detailed operational analyses, which include evaluating the influence on grid situations, the necessary behavior of energy system components, required measurements and communications. This publication also gives an overview of control strategies, existing simulation tools, how the modular simulation tool fits in and illustrates its functionalities in an application example, which further highlights its versatility and efficiency. Time series simulations with the tool allow analyses regarding the effect of control strategies on power flow results. Moreover, the simulation tool also facilitates evaluating the behavior of energy system components (e.g. distribution substations), necessary communications and measurements as well as any faults that might occur. |
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| AbstractList | Abstract The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of flexibilities and therefore help mitigate upcoming problems. However, they need to be evaluated carefully before their application in the energy system to avoid any unwanted effects and to choose the most fitting strategy for each application. In this publication, a Python-based modular simulation tool for developing and analysing control strategies for prosumers, which uses pandapower (Thurner et al. 2018), is presented. It is intended for sequential simulations and enables detailed operational analyses, which include evaluating the influence on grid situations, the necessary behavior of energy system components, required measurements and communications. This publication also gives an overview of control strategies, existing simulation tools, how the modular simulation tool fits in and illustrates its functionalities in an application example, which further highlights its versatility and efficiency. Time series simulations with the tool allow analyses regarding the effect of control strategies on power flow results. Moreover, the simulation tool also facilitates evaluating the behavior of energy system components (e.g. distribution substations), necessary communications and measurements as well as any faults that might occur. The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of flexibilities and therefore help mitigate upcoming problems. However, they need to be evaluated carefully before their application in the energy system to avoid any unwanted effects and to choose the most fitting strategy for each application. In this publication, a Python-based modular simulation tool for developing and analysing control strategies for prosumers, which uses pandapower (Thurner et al. 2018), is presented. It is intended for sequential simulations and enables detailed operational analyses, which include evaluating the influence on grid situations, the necessary behavior of energy system components, required measurements and communications. This publication also gives an overview of control strategies, existing simulation tools, how the modular simulation tool fits in and illustrates its functionalities in an application example, which further highlights its versatility and efficiency. Time series simulations with the tool allow analyses regarding the effect of control strategies on power flow results. Moreover, the simulation tool also facilitates evaluating the behavior of energy system components (e.g. distribution substations), necessary communications and measurements as well as any faults that might occur. The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of flexibilities and therefore help mitigate upcoming problems. However, they need to be evaluated carefully before their application in the energy system to avoid any unwanted effects and to choose the most fitting strategy for each application. In this publication, a Python-based modular simulation tool for developing and analysing control strategies for prosumers, which uses (Thurner et al. 2018), is presented. It is intended for sequential simulations and enables detailed operational analyses, which include evaluating the influence on grid situations, the necessary behavior of energy system components, required measurements and communications. This publication also gives an overview of control strategies, existing simulation tools, how the modular simulation tool fits in and illustrates its functionalities in an application example, which further highlights its versatility and efficiency. Time series simulations with the tool allow analyses regarding the effect of control strategies on power flow results. Moreover, the simulation tool also facilitates evaluating the behavior of energy system components (e.g. distribution substations), necessary communications and measurements as well as any faults that might occur. |
| ArticleNumber | 39 |
| Author | Mende, Denis Braun, Martin Ringelstein, Jan Schoen, Andrea |
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| Cites_doi | 10.3390/en13123290 10.1109/TPWRS.2018.2829021 10.3390/en15082866 10.1109/TSG.2013.2274391 10.1016/j.apenergy.2018.03.123 10.1016/j.simpa.2023.100467 10.1186/s42162-019-0083-1 10.1049/icp.2021.1609 10.1016/j.jpowsour.2014.04.078 10.3390/en16010088 10.2139/ssrn.4034171 10.1109/IECON.2018.8591486 10.1049/icp.2022.2721 |
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| Keywords | Distribution system operation Control strategies Prosumers Distribution grids Power system modeling pandapower |
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In: ETG Congress 2023 TU Dortmund University (2023); Institute of Energy Systems, Energy Efficiency and Energy Economics: agent-based Energy System Modeling and Simulation. https://ie3.etit.tu-dortmund.de/labs-tools/simona/ Accessed 05 Apr 2023 Schoen A, Ringelstein J, Spalthoff C, Braun M (2020) Identifikation und Definition von Betriebsführungsstrategien für die Elektromobilität. In: 16. Symposium Energieinnovation, 12.-14.02.2020, vol. 2021. Graz, Austria Fraunhofer IEE and University of Kassel (2023c) SimBench. https://simbench.de/en/ Accessed 25 Mar2023 Fraunhofer IEE and University of Kassel: pandapower Control Module (2023b) https://pandapower.readthedocs.io/en/latest/control.html Accessed 03 Mar 2023 Liu Z, Ringelstein J, Ernst M, Requardt B, Zauner E, Baumbush K, Wende-von Berg S, Braun M (2022) Monitoring of low-voltage grids using artificial neural networks and its field test application based on the beeDIP-platform. 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In: Tagung Zukünftige Stromnetze NREL (2023a) Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). https://helics.org/ Accessed 18 Mar 2023 German Federal Government (2023) “We’re Tripling the Speed of the Expansion of Renewable Energies”. https://www.bundesregierung.de/breg-de/themen/klimaschutz/amendment-of-the-renewables-act-2060448 Accessed 04 Apr 2023 CEN-CENELEC-ETSI Smart Grid Coordination Group: CEN-CENELEC-ETSI Smart Grid Coordination Group—Smart Grid Reference Architecture. https://energy.ec.europa.eu/system/files/2014-11/xpert_group1_reference_architecture_0.pdf Accessed 19 Jun 2023 Forschungsstelle für Energiewirtschaft e.V. 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(2023) MATLAB and Simulink for Microgrid, Smart Grid, and Charging Infrastructure. https://www.mathworks.com/solutions/electrification/microgrid-smart-grid-charging-infrastructure.html Accessed 18 Mar 2023 ThurnerLScheidlerASchäferFMenkeJ-HDollichonJMeierFMeineckeSBraunMpandapower—an open source python tool for convenient modeling, analysis and optimization of electric power systemsIEEE Trans Power Syst20183366510652110.1109/TPWRS.2018.2829021 ADAC (2023) Jeder Fünfte Neuwagen Ist Ein Elektroauto. https://www.adac.de/news/neuzulassungen-kba/ Accessed 26 Mar 2023 Requardt B (2021) Architekturen und Verfahren für modulare Pilotsysteme und Erweiterungen von Netzleitstellen. PhD thesis, University of Kassel Mende D (2022) Modellierung Von Maßnahmen der Leistungsflusssteuerung in Einer Nichtlinearen Mathematischen Optimierung zur Anwendung Im Operativen Engpassmanagement Elektrischer Energieversorgungssysteme: Zugl.: Hannover, Univ., Diss., 2021. Fraunhofer Verlag, Stuttgart Plotly Technologies Inc. (2023) Plotly Dash. https://dash.plotly.com/introduction Accessed 19 Mar 2023 BDEW, German Association of Energy and Water Industries: Smart Grid Traffic Light Concept (2023) https://www.bdew.de/media/documents/Stn_20150310_Smart-Grids-Traffic-Light-Concept_english.pdf Accessed 17 Mar 2023 Birk Jones C, Vining W, Lave M, Haines T, Neuman C, Bennett J, Scoffield DR (2022) Impact of Electric Vehicle customer response to Time-of-Use rates on distribution power grids. Energy Reports 8 Fraunhofer IEE (2023b) Ladeinfrastruktur 2.0. https://iee.fraunhofer.de/de/projekte/suche/laufende/ladeinfrastruktur2-0.html Accessed 03 Mar 2023 Schoen A, Ulffers J, Maschke H, Junge E, Bott C, Thurner L, Braun M (2021b) Considering control approaches for electric vehicle charging in grid planning. 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| References_xml | – reference: Birk Jones C, Vining W, Lave M, Haines T, Neuman C, Bennett J, Scoffield DR (2022) Impact of Electric Vehicle customer response to Time-of-Use rates on distribution power grids. Energy Reports 8 – reference: Liu Z, Ringelstein J, Ernst M, Requardt B, Zauner E, Baumbush K, Wende-von Berg S, Braun M (2022) Monitoring of low-voltage grids using artificial neural networks and its field test application based on the beeDIP-platform. In: 6th E-Mobility Power System Integration Symposium (EMOB 2022), 2022, 94–99 – reference: Mende D (2022) Modellierung Von Maßnahmen der Leistungsflusssteuerung in Einer Nichtlinearen Mathematischen Optimierung zur Anwendung Im Operativen Engpassmanagement Elektrischer Energieversorgungssysteme: Zugl.: Hannover, Univ., Diss., 2021. Fraunhofer Verlag, Stuttgart – reference: CEN-CENELEC-ETSI Smart Grid Coordination Group: CEN-CENELEC-ETSI Smart Grid Coordination Group—Smart Grid Reference Architecture. https://energy.ec.europa.eu/system/files/2014-11/xpert_group1_reference_architecture_0.pdf Accessed 19 Jun 2023 – reference: Römer C, Hiry J, Kittl C, Liebig T, Rehtanz C (2019) Charging control of electric vehicles using contextual bandits considering the electrical distribution grid. arxiv:1905.01163 – reference: Hildermeier J, Burger J, Jahn A, Rosenow J (2023) A review of tariffs and services for smart charging of electric vehicles in Europe. Energies 16(1) – reference: ADAC (2023) Jeder Fünfte Neuwagen Ist Ein Elektroauto. https://www.adac.de/news/neuzulassungen-kba/ Accessed 26 Mar 2023 – reference: Mende D, Schmiesing J, Brantl J, Wang H, Mora E, Geiger D, Pape C, Drauz SR, Majidi M, Braun M (2022) Modelltiefe in Verteilnetzen: Szenariobasierte Evaluation des Analyseumfangs und Komplexitätsreduktion für Netzstudien. In: Tagung Zukünftige Stromnetze – reference: ThurnerLScheidlerASchäferFMenkeJ-HDollichonJMeierFMeineckeSBraunMpandapower—an open source python tool for convenient modeling, analysis and optimization of electric power systemsIEEE Trans Power Syst20183366510652110.1109/TPWRS.2018.2829021 – reference: VDE (2023) VDE-AR-N 4105 Anwendungsregel:2018–11 Generators Connected to the Low-voltage Distribution Network. https://www.vde-verlag.de/standards/0100492/vde-ar-n-4105-anwendungsregel-2018-11.html Accessed 28 Mar 2023 – reference: BDEW, German Association of Energy and Water Industries: Smart Grid Traffic Light Concept (2023) https://www.bdew.de/media/documents/Stn_20150310_Smart-Grids-Traffic-Light-Concept_english.pdf Accessed 17 Mar 2023 – reference: Fraunhofer IEE (2023c) MotiV—Modelltiefe in Verteilnetzen. https://www.iee.fraunhofer.de/de/projekte/suche/2020/motiv.html Accessed 05 Apr 2023 – reference: Forschungsstelle für Energiewirtschaft e.V. (2023) GridSim-Electric Grid and Energy System Model for Distribution Grids. https://www.ffe.de/en/tools/gridsim-electricity-grid-and-energy-system-model-for-distribution-grids/ Accessed 18 Mar 2023 – reference: Fraunhofer IEE (2023a) OpSim: Test- and Simulation-environment for grid control and aggregation strategies. url:opsim.net/en Accessed 17 Mar 2023 – reference: Schoen A, Ulffers J, Maschke H, Mueller L, Braun M (2023) Integrating control strategies for electric vehicles into a simultaneity-factor-based grid planning approach. In: ETG Congress 2023 – reference: Plotly Technologies Inc. (2023) Plotly Dash. https://dash.plotly.com/introduction Accessed 19 Mar 2023 – reference: VogtMMartenFBraunMA survey and statistical analysis of smart grid co-simulationsAppl Energy2018222677810.1016/j.apenergy.2018.03.123 – reference: TU Dortmund University (2023); Institute of Energy Systems, Energy Efficiency and Energy Economics: agent-based Energy System Modeling and Simulation. https://ie3.etit.tu-dortmund.de/labs-tools/simona/ Accessed 05 Apr 2023 – reference: Bundesnetzagentur: Leitfaden Einspeisemanagement (2023) https://www.bundesnetzagentur.de/DE/Fachthemen/ElektrizitaetundGas/ErneuerbareEnergien/Einspeisemanagement/start.html Accessed 04 Apr 2023 – reference: Fraunhofer IEE (2023b) Ladeinfrastruktur 2.0. https://iee.fraunhofer.de/de/projekte/suche/laufende/ladeinfrastruktur2-0.html Accessed 03 Mar 2023 – reference: Montoya J, Brandl R, Vogt M, Marten F, Maniatopoulos M, Fabian A (2018) Asynchronous Integration of a real-time simulator to a geographically distributed controller through a co-simulation environment. 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In: CIRED 2021–the 26th International Conference and Exhibition on Electricity Distribution, 2021, 1470–1474 – reference: PapadopoulosPJenkinsNCipciganLMGrauIZabalaECoordination of the charging of electric vehicles using a multi-agent systemIEEE Trans Smart Grid2013441802180910.1109/TSG.2013.2274391 – reference: NREL (2023b) Charging Infrastructure Technologies: Smart Electric Vehicle Charging for a Reliable and Resilient Grid (RECHARGE). https://www.energy.gov/sites/default/files/2021-06/elt202_bennett_2021_o_5-14_752pm_KS_TM.pdf Accessed 18 Mar 2023 – reference: Schoen A, Ringelstein J, Spalthoff C, Braun M (2020) Identifikation und Definition von Betriebsführungsstrategien für die Elektromobilität. In: 16. Symposium Energieinnovation, 12.-14.02.2020, vol. 2021. 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| SubjectTerms | Computer Science Control strategies Distribution grids Distribution system operation Electric power Electric power systems Electric vehicles Electrical transmission Heat exchangers Heat pumps Information Systems and Communication Service Low voltage pandapower Photovoltaics Power flow Power system modeling Prosumers Simulation Substations |
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| Title | Modeling control strategies for prosumers in a Python-based modular simulation tool |
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