EVLearn: Extending the CityLearn Framework with Electric Vehicle Simulation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910404441014272 |
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| author | Fonseca, Tiago Ferreira, Luis Cabral, Bernardo Severino, Ricardo Nweye, Kingsley Ghose, Dipanjan Nagy, Zoltan |
| author_facet | Fonseca, Tiago Ferreira, Luis Cabral, Bernardo Severino, Ricardo Nweye, Kingsley Ghose, Dipanjan Nagy, Zoltan |
| contents | Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) 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 prospective, 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 G2V 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, a simulation module for researching in both V2G and G2V energy management strategies, that models EVs, their charging infrastructure and associated energy flexibility dynamics; second, this paper integrates EVLearn with the existing CityLearn framework, providing V2G and G2V simulation capabilities into the study of broader energy management strategies. Results validated EVLearn and its integration into CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_06521 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EVLearn: Extending the CityLearn Framework with Electric Vehicle Simulation Fonseca, Tiago Ferreira, Luis Cabral, Bernardo Severino, Ricardo Nweye, Kingsley Ghose, Dipanjan Nagy, Zoltan Multiagent Systems Systems and Control Intelligent energy management strategies, such as Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) 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 prospective, 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 G2V 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, a simulation module for researching in both V2G and G2V energy management strategies, that models EVs, their charging infrastructure and associated energy flexibility dynamics; second, this paper integrates EVLearn with the existing CityLearn framework, providing V2G and G2V simulation capabilities into the study of broader energy management strategies. Results validated EVLearn and its integration into CityLearn, where the impact of these strategies is highlighted through a comparative simulation scenario. |
| title | EVLearn: Extending the CityLearn Framework with Electric Vehicle Simulation |
| topic | Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2404.06521 |