Particle swarm optimization based on data driven for EV charging station siting

Linfei Yin and Yifan Zhang

Energy, 2024, vol. 310, issue C

Abstract: Charging stations are an important support facility for electric vehicles (EVs). Nowadays, the EV industry is developing rapidly, however, the imperfect construction of charging stations or the unreasonable choice of location has seriously reduced the desire of people to buy EVs and led to the problem of difficult charging of EVs in many areas. At present, the research field of EV charging station siting suffers from the inability to quickly and accurately calculate the optimal solution for charging station siting. In this regard, a particle swarm optimization based on deep neural networks modified boundaries (DNNMBPSO) is proposed for solving the problem. DNNMBPSO reduces the convergence value of the objective function by applying deep learning to modify the boundary of the particle swarm optimization. DNNMBPSO is an algorithm that combines heuristic and data driven. In this study, DNNMBPSO is applied for siting study in a system having 50 alternative points, 500 alternative points, and 1000 alternative points and a case of siting of electric vehicle charging stations in Nanning, Guangxi, China. The convergence value of the DNNMBPSO-based objective function is found to be at least 5.5 %, 1.7 %, 8.23 % and 14.7 %, lower compared to genetic algorithms, African vulture optimization algorithm, particle swarm optimization, and grey wolf optimization algorithms, respectively. Traditional heuristic optimization algorithms cannot find optimal solutions in large-scale systems, while DNNMBPSO shows feasibility in large-scale systems.

Keywords: Charging station addressing optimization; Deep neural networks; Particle swarm optimization; Infeasible region (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:310:y:2024:i:c:s0360544224029724

DOI: 10.1016/j.energy.2024.133197

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