Multiobjective optimization for sizing and placing electric vehicle charging stations considering comprehensive uncertainties

Q2 Energy Energy Informatics Pub Date : 2024-11-28 DOI:10.1186/s42162-024-00428-x

Abdallah Mohammed, Omar Saif, Maged A Abo‑Adma, Rasha Elazab

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Abstract

The rapid growth of electric vehicles (EVs) demands a robust and efficient charging infrastructure. To address this, we propose a particle swarm optimization algorithm designed for optimal placement and sizing of EV charging stations. This study hypothesizes that comprehensive consideration of uncertainties in vehicle types, user behaviors, road dynamics, and environmental impacts will enhance infrastructure effectiveness. Our method integrates data from road networks, driver patterns, station owners, and EV manufacturers to meet diverse charging requirements. Results indicate that 14 fast charging stations are needed along the studied freeway, with a total installation cost of $289,820 and annual operational costs of $4,223,050, leading to annual CO₂ emissions of 1,843,572.57 kg. This strategic approach balances technical, environmental, and economic criteria, providing an essential tool for policymakers and urban planners in establishing sustainable EV charging networks.

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考虑综合不确定性的电动汽车充电站规模和布局的多目标优化方法

电动汽车（EV）的快速发展需要一个强大而高效的充电基础设施。为此，我们提出了一种粒子群优化算法，旨在优化电动汽车充电站的布局和规模。本研究假设，综合考虑车辆类型、用户行为、道路动态和环境影响等方面的不确定性，将提高基础设施的效率。我们的方法整合了来自道路网络、驾驶员模式、充电站业主和电动汽车制造商的数据，以满足不同的充电需求。结果表明，所研究的高速公路沿线需要 14 个快速充电站，总安装成本为 289,820 美元，年运营成本为 4,223,050 美元，年二氧化碳排放量为 1,843,572.57 千克。这种战略方法兼顾了技术、环境和经济标准，为政策制定者和城市规划者建立可持续的电动汽车充电网络提供了重要工具。

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