Placement of FCS Considering Power Loss, Land Cost, and EV Population

Fareed Ahmad, I. Ashraf, A. Iqbal
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Abstract

Electric vehicles (EVs) have recently gotten a lot of attention from the government and the auto industry. This is because EVs produce less CO2 and cost less to run and maintain. However, as EV adoption rises, the pressure on the distribution network increases due to changes in power loss, voltage profile, etc. So, EV fast charging stations (FCSs) must be put in the right places for the distribution network to work well. As a result, a two-stage technique is suggested for the deployment of FCSs in this paper. The Land Cost Index (LCI) and the EV Population Index have been taken into consideration while introducing the Charging Station Investor Decision Index (CSIDI) in the first stage (EVPI). Further, the CSIDI was developed to determine the location of FCS in the electrical distribution system while minimizing the cost of land and maximizing the EV population. The distribution system restrictions are considered while formulating an optimization problem in the following step to minimize the overall active power loss. The improved version of the bald eagle search (IBES) algorithm has also been used to solve the minimization issue, and the outcomes have been contrasted with those of the particle swarm optimization (PSO) technique.
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考虑电力损耗、土地成本和电动汽车数量的FCS布局
电动汽车(ev)最近受到了政府和汽车行业的广泛关注。这是因为电动汽车产生的二氧化碳更少,运行和维护成本更低。然而,随着电动汽车采用率的提高,由于功率损耗、电压分布等的变化,配电网的压力也在增加。因此,必须将电动汽车快速充电站设置在合适的位置,才能保证配电网的正常运行。因此,本文建议采用两阶段技术部署fcs。在第一阶段引入充电站投资者决策指数(CSIDI)时,考虑了土地成本指数(LCI)和电动汽车人口指数(EVPI)。此外,开发了CSIDI来确定FCS在配电系统中的位置,同时最小化土地成本和最大化电动汽车数量。在制定下一步优化问题时考虑了配电系统的限制条件,以使总有功损耗最小。采用改进的白头鹰搜索(IBES)算法求解最小化问题,并与粒子群优化(PSO)算法的求解结果进行对比。
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