EVLearn: extending the cityLearn framework with electric vehicle simulation
Abstract Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (V1G) emerge as a potential solution to the Electric Vehicles’ (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-02-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-024-00445-w |
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| _version_ | 1823861528480186368 |
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| author | Tiago Fonseca Luis Lino Ferreira Bernardo Cabral Ricardo Severino Kingsley Nweye Dipanjan Ghose Zoltan Nagy |
| author_facet | Tiago Fonseca Luis Lino Ferreira Bernardo Cabral Ricardo Severino Kingsley Nweye Dipanjan Ghose Zoltan Nagy |
| author_sort | Tiago Fonseca |
| collection | DOAJ |
| description | Abstract Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (V1G) emerge as a potential solution to the Electric Vehicles’ (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced perspective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and V1G strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, an open-source extension for the existing CityLearn simulation framework. EVLearn provides both V2G and V1G energy management simulation capabilities into the study of broader energy management strategies of CityLearn by modeling EVs, their charging infrastructure and associated energy flexibility dynamics. Results validated the extension of CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario. |
| format | Article |
| id | doaj-art-1acc18f2b31e4172b92235766eb20fbc |
| institution | Kabale University |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-1acc18f2b31e4172b92235766eb20fbc2025-02-09T12:56:39ZengSpringerOpenEnergy Informatics2520-89422025-02-018112510.1186/s42162-024-00445-wEVLearn: extending the cityLearn framework with electric vehicle simulationTiago Fonseca0Luis Lino Ferreira1Bernardo Cabral2Ricardo Severino3Kingsley Nweye4Dipanjan Ghose5Zoltan Nagy6INESC TEC/Polytechnic of Porto - School of Engineering PortoINESC TEC/Polytechnic of Porto - School of Engineering PortoINESC TEC/Polytechnic of Porto - School of Engineering PortoINESC TEC/Polytechnic of Porto - School of Engineering PortoThe University of Texas at AustinThe University of Texas at AustinThe University of Texas at AustinAbstract Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (V1G) emerge as a potential solution to the Electric Vehicles’ (EVs) integration into the energy grid. These strategies promise enhanced grid resilience and economic benefits for both vehicle owners and grid operators. Despite the announced perspective, the adoption of these strategies is still hindered by an array of operational problems. Key among these is the lack of a simulation platform that allows to validate and refine V2G and V1G strategies. Including the development, training, and testing in the context of Energy Communities (ECs) incorporating multiple flexible energy assets. Addressing this gap, first we introduce the EVLearn, an open-source extension for the existing CityLearn simulation framework. EVLearn provides both V2G and V1G energy management simulation capabilities into the study of broader energy management strategies of CityLearn by modeling EVs, their charging infrastructure and associated energy flexibility dynamics. Results validated the extension of CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario.https://doi.org/10.1186/s42162-024-00445-wSimulation ToolVehicle to GridElectric vehiclesSmart GridEnergy Management |
| spellingShingle | Tiago Fonseca Luis Lino Ferreira Bernardo Cabral Ricardo Severino Kingsley Nweye Dipanjan Ghose Zoltan Nagy EVLearn: extending the cityLearn framework with electric vehicle simulation Energy Informatics Simulation Tool Vehicle to Grid Electric vehicles Smart Grid Energy Management |
| title | EVLearn: extending the cityLearn framework with electric vehicle simulation |
| title_full | EVLearn: extending the cityLearn framework with electric vehicle simulation |
| title_fullStr | EVLearn: extending the cityLearn framework with electric vehicle simulation |
| title_full_unstemmed | EVLearn: extending the cityLearn framework with electric vehicle simulation |
| title_short | EVLearn: extending the cityLearn framework with electric vehicle simulation |
| title_sort | evlearn extending the citylearn framework with electric vehicle simulation |
| topic | Simulation Tool Vehicle to Grid Electric vehicles Smart Grid Energy Management |
| url | https://doi.org/10.1186/s42162-024-00445-w |
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