Recently, the penetration of photovoltaic (PV) units and plug-in electric vehicles (PEVs) has been quickly increased worldwide. Due to the intermittent nature of PV and the stochastic nature of PEVs, several operation problems can be noticed in distribution systems, including excessive energy losses and voltage violations. In this paper, an optimization-based algorithm is proposed to accurately determine the optimal locations and capacities of multiple PV units in the presence of PEVs to minimize energy losses while considering various system constraints. The proposed algorithm considers the uncertainty of PV and loads, and the stochastic nature of PEVs. Furthermore, the operational constraints of PEVs are incorporated in the optimization model: 1) arrival and departure times, 2) initial state of charge (SOC), 3) minimum preset state of charge by the owner, and 4) the time-of-use electricity tariff, and 5) different charging control schemes. The optimal PV planning model is formulated as a two-layer optimization problem that ensures an optimal PV allocation while optimizing PEV charging simultaneously. A two-layer metaheuristic method is developed to solve the optimization model considering annual datasets of the studied distribution systems. The results demonstrate the efficacy of the proposed algorithm.

中文翻译：

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考虑PEV随机特性的不确定PV的最佳布置和尺寸

近来，光伏（PV）单元和插电式电动汽车（PEV）的普及在世界范围内迅速增加。由于PV的间歇性和PEV的随机性，在配电系统中可能会发现一些运行问题，包括过多的能量损失和违反电压的情况。在本文中，提出了一种基于优化的算法，可以在考虑各种系统约束的情况下，准确确定PEV存在时多个PV单元的最佳位置和容量，以最大程度地减少能量损失。提出的算法考虑了光伏发电和负荷的不确定性以及电动汽车的随机性。此外，PEV的操作约束已纳入优化模型：1）到达和离开时间； 2）初始充电状态（SOC）；3）所有者预设的最低充电状态，以及4）使用时间的电价，以及5）不同的充电控制方案。最佳光伏计划模型被表述为两层优化问题，该问题可确保最佳光伏分配，同时优化PEV充电。考虑到所研究的配电系统的年度数据集，开发了一种两层元启发式方法来求解优化模型。结果证明了该算法的有效性。考虑到所研究的配电系统的年度数据集，开发了一种两层元启发式方法来求解优化模型。结果证明了该算法的有效性。考虑到所研究的配电系统的年度数据集，开发了一种两层元启发式方法来求解优化模型。结果证明了该算法的有效性。