Capacity optimization of PV and battery storage for EVCS with multi-venues charging behavior difference towards economic targets

Wenxin Huang, Jianguo Wang, Jianping Wang, Mi Zhou, Jinxin Cao and Li Cai

Energy, 2024, vol. 313, issue C

Abstract: EV users served by multi-venues Electric Vehicle Charging Stations (EVCS) have different charging behaviors, encompassing aspects such as charging duration, energy consumption, and behavioral dispersion, which affect the integrated role of photovoltaic (PV) and battery storage (BS). This study analyzes the charging behavior of drivers at different venues EVCS in Wuhan, China. Additionally, it generates EV fleet load profiles through Monte Carlo simulation, accounting for fluctuations in charging loads due to charging congestion and seasonal differences. A robust optimization (RO) model is proposed for the integration of PV-BS capacity at multi-venues EVCS, with the objective of annual planning and operation economics, further considering PV-EV uncertainty, BS arbitrage and cycle-life decay, and EV fleet charging power control. The planning and operation processes are explicitly given in a hierarchical form and solved by the G&CG algorithm. The results show that the optimal integration capacity of PV and BS is subject to variability based on the charging behaviors observed at different EVCS venues. Furthermore, the sensitivity analysis discusses the implications of broader variations in charging behaviors and their dispersion on capacity integration, as well as the influence of diverse solar irradiation conditions. The insights derived from this study can inform PV-BS capacity integration strategies for EVCS across various venues.

Keywords: Multi-venues charging stations; Charging behavior; Behavioral dispersion; Photovoltaic systems; Battery storage; Robust optimization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224036119

DOI: 10.1016/j.energy.2024.133833

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