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Combined solar charging stations and energy storage units allocation for electric vehicles by considering uncertainties

机译:考虑不确定因素的电动汽车组合式太阳能充电站和储能单元分配

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Electric vehicles (EVs) are becoming a key feature of smart grids. EVs will be embedded in the smart grids as a mobile load-storage with probabilistic behavior. In order to manage EVs as flexible loads, charging stations (CSs) have essential roles. In this paper, a new method for optimal sitting and sizing of solar CSs using energy storage (ES) options is presented. Also, behavior of EVs in the presence of other loads, electricity price and solar power generation uncertainties are considered. The proposed optimization model maximizes the distribution company (DisCo) benefit by appropriate use of CSs, maximizes the benefit of CSs owners and minimizes the power loss, load demand and voltage sags during peak times considering different technical constraints. The optimization variables are the location and capacity of solar units and energy storage systems. In this paper, charge-discharge process of EVs are considered based on time-of-use (TOU) demand response programs (DRPs). In order to solve the optimization problem considering uncertainty of load growth, electricity price, initial state of charge of batteries and solar power generation, genetic algorithm method using Monte-Carlo simulation is used. The simulation results show that the proposed method has advantages for DisCo and owners of CSs.
机译:电动汽车(EV)逐渐成为智能电网的关键功能。电动汽车将作为具有概率行为的移动负载存储嵌入到智能电网中。为了将电动汽车作为灵活的负载进行管理,充电站(CS)具有至关重要的作用。本文提出了一种使用储能(ES)选项优化太阳能CS的最佳坐姿和尺寸的新方法。此外,还考虑了在存在其他负载,电价和太阳能发电不确定性的情况下电动汽车的行为。所提出的优化模型通过适当地使用CS来最大化配电公司(DisCo)的利益,最大化CS所有者的利益,并在考虑了不同技术约束的情况下将高峰时段的功率损耗,负载需求和电压骤降最小化。优化变量是太阳能电池和储能系统的位置和容量。在本文中,基于使用时间(TOU)需求响应程序(DRP)考虑了电动汽车的充放电过程。为了解决考虑负荷增长,电价,电池初始充电状态和太阳能发电的不确定性的优化问题,使用了基于蒙特卡洛模拟的遗传算法。仿真结果表明,该方法对DisCo和CS拥有者具有优势。

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