Optimal integration of EV charging infrastructure in sustainable distribution systems via growth optimizer-based Hong point estimate

Abdullah M. Shaheen, Aya R. Ellien, Adel A. El-Ela and Eman S. Ali

Energy, 2025, vol. 318, issue C

Abstract: This research presents an advanced methodology for the optimal integration of Battery Electric Vehicle (BEV) charging stations within distribution systems. Besides the BEV impacts, renewable energy resources (RESs) and capacitors are simultaneously allocated considering the uncertainties accompanied to RESs and load ratio (LR). The proposed approach develops a multi-objective optimization framework, enhanced by a Fuzzy-Based Multi-Objective (FBMO) model, to determine the optimal infrastructure for BEV charging stations. Also, for decoding the compound randomness, Hong's three-Point Estimate Method (H3PEM) is integrated with growth algorithm (GO) to minimize power losses and maximize all of total voltage stability index (TVSI) and the system hosting capacity for BEVs. The methodology is rigorously tested across three distinct distribution networks: IEEE 69-bus, IEEE 85-bus, and a real 94-node Portuguese network. The study encompasses various scenarios, considering different numbers of charging stations and their associated capacities. The proposed GO algorithm outperforms well-known metaheuristic algorithms of COA, GWO, and PSO, resulting in improved efficiency and voltage stability recording ameliorated percentage of 24.568 %, 24.778 %, and 23.514 %; respectively. Accompanying this integration, the distribution system's performance has been concretely enhanced with a 93.6 % reduction in power losses and a 31.52 % promotion in TVSI.

Keywords: Battery electric vehicles; Hong's three-point estimate method; Multi-objective growth optimizer algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:318:y:2025:i:c:s0360544225002099

DOI: 10.1016/j.energy.2025.134567

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