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.印度不断增长的人口使得开发替代交通方式成为必然需求，其中电动汽车（EVs）是当前情境下最具本土适配性的方案。当前的主要阻碍在于，不合理的充电站布局会对配电网造成不当影响。相较于不可再生能源，可再生能源（Renewable Energy Sources, RES）对环境的影响更低，因此研究将插电式混合动力电动汽车（Plug-in Hybrid Electric Vehicles, PHEV）充电站优先部署在高优先级公交线路上，以保障其布局的合理性与有效性。基于元启发式优化理论，本研究针对可再生能源应用场景提出了一种高效的PHEV充电站规划方法。所构建系统的核心目标是，在保障配电网运行特性的前提下，以合理成本搭建充电网络。本研究通过元启发式算法与基于可再生能源系统的优化规划，对上述问题进行求解，以满足各变量的优化目标。据此，本研究通过引入充电站相关参数，将最优充电站布局问题转化为多目标优化问题。此外，本研究采用一种融合鸢优化器（Aquila Optimizer, AO）与原子搜索优化（Atom Search Optimization, ASO）算法思想的新型混合算法——原子搜索鸢优化算法（Atom Search Woven Aquila Optimization Algorithm, AT-AQ），来求解插电式混合动力电动汽车、可再生能源与充电站选址联合优化问题。事实上，鸢优化器是一种基于种群的新型优化算法，其灵感来源于鸢捕猎时的行为模式，可有效解决传统算法收敛速度慢、易陷入局部最优的问题。实验结果表明，所提模型在成本函数最小化指标上优于其他对比算法。