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<p>In order to accommodate the increasing number of electric vehicles (EVs), the transportation sector must prioritize the rapid installation of EV charging stations (EVCSs). However, integrating EV charging stations into existing distribution networks is quite challenging and requires strategic planning because they increase the load demand. Renewable energy sources (RES) and battery energy storage systems (BESS) are installed at EVCS to ensure power supply security, reliability, and economics for EV charging stations. This paper has designed a criteria weight ranking mechanism to accept charging requests for EVs depending on the criteria weights specified by the EV owner. A multi-objective prairie dog optimization (MOPDO) algorithm is employed to optimize the placement of two EVCS in an IEEE 33 bus unbalanced radial distribution system (URDS), as well as the capacity of PV units, BESS units, and EVs in each EVCS, to minimize total active power loss and total cost while satisfying all system constraints. According to three case studies and simulation results, the suggested method optimally allocates two EVCS with PV and BESS in IEEE 33-bus URDS. Furthermore, the smart scheduling of the EVs reduces the overall load burden on the grid network. The findings emphasize the robustness and effectiveness of the proposed approach in raising the energy economics of EV charging stations and enhancing the distribution system's performance.</p>

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Impact of Electric Vehicle Charging Station with Photovoltaic and Battery Energy Storage System on Power Distribution System Smartness

  • 期刊论文

  • 发表日期：

    2025年6月11日

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  • Sigma Ray,
  • Kumari Kasturi,
  • Manas Ranjan Nayak

摘要

In order to accommodate the increasing number of electric vehicles (EVs), the transportation sector must prioritize the rapid installation of EV charging stations (EVCSs). However, integrating EV charging stations into existing distribution networks is quite challenging and requires strategic planning because they increase the load demand. Renewable energy sources (RES) and battery energy storage systems (BESS) are installed at EVCS to ensure power supply security, reliability, and economics for EV charging stations. This paper has designed a criteria weight ranking mechanism to accept charging requests for EVs depending on the criteria weights specified by the EV owner. A multi-objective prairie dog optimization (MOPDO) algorithm is employed to optimize the placement of two EVCS in an IEEE 33 bus unbalanced radial distribution system (URDS), as well as the capacity of PV units, BESS units, and EVs in each EVCS, to minimize total active power loss and total cost while satisfying all system constraints. According to three case studies and simulation results, the suggested method optimally allocates two EVCS with PV and BESS in IEEE 33-bus URDS. Furthermore, the smart scheduling of the EVs reduces the overall load burden on the grid network. The findings emphasize the robustness and effectiveness of the proposed approach in raising the energy economics of EV charging stations and enhancing the distribution system's performance.