EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929204732362752 |
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| author | Balogun, Emmanuel Buechler, Elizabeth Bhela, Siddharth Onori, Simona Rajagopal, Ram |
| author_facet | Balogun, Emmanuel Buechler, Elizabeth Bhela, Siddharth Onori, Simona Rajagopal, Ram |
| contents | To enable the electrification of transportation systems, it is important to understand how technologies such as grid storage, solar photovoltaic systems, and control strategies can aid the deployment of electric vehicle charging at scale. In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid. This python-based platform can run a receding horizon control scheme for real-time operation and a one-shot control scheme for planning problems, with multi-timescale dynamics for different systems to simulate realistic scenarios. We demonstrate the utility of EV-EcoSim through a case study focused on economic evaluation of battery size to reduce electricity costs while considering impacts of fast charging on the power distribution grid. We present qualitative and quantitative evaluations on the battery size in tabulated results. The tabulated results delineate the trade-offs between candidate battery sizing solutions, providing comprehensive insights for decision-making under uncertainty. Additionally, we demonstrate the implications of the battery controller model fidelity on the system costs and show that the fidelity of the battery controller can completely change decisions made when planning an electric vehicle charging site. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04705 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure Balogun, Emmanuel Buechler, Elizabeth Bhela, Siddharth Onori, Simona Rajagopal, Ram Systems and Control Multiagent Systems Software Engineering Optimization and Control To enable the electrification of transportation systems, it is important to understand how technologies such as grid storage, solar photovoltaic systems, and control strategies can aid the deployment of electric vehicle charging at scale. In this work, we present EV-EcoSim, a co-simulation platform that couples electric vehicle charging, battery systems, solar photovoltaic systems, grid transformers, control strategies, and power distribution systems, to perform cost quantification and analyze the impacts of electric vehicle charging on the grid. This python-based platform can run a receding horizon control scheme for real-time operation and a one-shot control scheme for planning problems, with multi-timescale dynamics for different systems to simulate realistic scenarios. We demonstrate the utility of EV-EcoSim through a case study focused on economic evaluation of battery size to reduce electricity costs while considering impacts of fast charging on the power distribution grid. We present qualitative and quantitative evaluations on the battery size in tabulated results. The tabulated results delineate the trade-offs between candidate battery sizing solutions, providing comprehensive insights for decision-making under uncertainty. Additionally, we demonstrate the implications of the battery controller model fidelity on the system costs and show that the fidelity of the battery controller can completely change decisions made when planning an electric vehicle charging site. |
| title | EV-EcoSim: A grid-aware co-simulation platform for the design and optimization of electric vehicle charging infrastructure |
| topic | Systems and Control Multiagent Systems Software Engineering Optimization and Control |
| url | https://arxiv.org/abs/2401.04705 |