Scenario 3.

India’s expanding population has necessitated the development of alternate transportation methods with electric vehicles (EVs) being the most indigenous and need for the current scenario. The major hindrance is the undue influence on the power distribution system caused by incorrect charging station setup. Renewable Energy Sources (RES) have a lower environmental impact than the non-renewable sources of energy and due to which Plug-in Hybrid Electric Vehicles (PHEV) charging stations are installed in the highest-ranking buses to facilitate their effective placements. Based on meta-heuristic optimization, this study offers an effective PHEV charging stations allocation approach for RES applications. The primary objective of the developed system is to create a charging network at a reasonable cost while maintaining the operational features of the distribution network. These troublesare handled by applying meta-heuristic algorithms and optimum planning based on renewable energy systems to satisfy the outcomes of the variables. As a result, by adding charging station parameters, this research proposes to conceptualize the distribution of optimal charging stationsas multiple-objectives of the problem. Furthermore, the PHEV RES and charging station location problem is handled in this study by deploying a novel hybrid algorithm termed as Atom Search Woven Aquila Optimization Algorithm (AT-AQ) that includes the ideas of both Aquila Optimizer (AO) and Atom Search Optimization (ASO) Algorithms. In reality, Aquila Optimizer is a unique population-based optimization approach energized by Aquila’s behaviour when seeking prey and it solves the problems of slow convergence and local optimum trapping. According to the findings of the experiments, the proposed model outperformed the other methods in terms of minimized cost function.

随着印度人口持续扩张，发展替代交通方式已成必然趋势，其中电动汽车（Electric Vehicles, EVs）是当前场景下最具本土适配性且亟需的解决方案。当前面临的主要阻碍在于：不合理的充电站布局会对配电系统造成不当负荷影响。相较于不可再生能源，可再生能源（Renewable Energy Sources, RES）的环境影响更低，因此本研究优先在高优先级巴士线路部署插电式混合动力电动汽车（Plug-in Hybrid Electric Vehicles, PHEV）充电站，以保障其布局的有效性。

本研究基于元启发式优化（meta-heuristic optimization）算法，提出了一种适配可再生能源应用场景的PHEV充电站优化配置方案。所构建系统的核心目标为：在保障配电系统运行特性的前提下，以合理成本构建充电网络。本研究通过引入充电站相关参数，将最优充电站布局问题建模为多目标优化问题，以此应对各类优化挑战。此外，针对PHEV充电站选址与可再生能源协同优化问题，本研究采用一种融合鸢优化算法（Aquila Optimizer, AO）与原子搜索优化算法（Atom Search Optimization, ASO）思想的新型混合算法——原子搜索融合鸢优化算法（Atom Search Woven Aquila Optimization Algorithm, AT-AQ）进行求解。

事实上，鸢优化算法是一种受鸢类捕猎行为启发的新型群体智能优化算法，可有效解决传统优化算法收敛速度慢、易陷入局部最优的缺陷。实验结果表明，所提模型在最小化成本函数方面的表现优于其他对比方法。