基于遗传算法的光伏电动汽车充电站

Pandey Sweta, Bhusnur Surekh, Anjum Naushin
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引用次数: 0

摘要

提出了连接到混合电网-可再生能源系统(RES)(如太阳能、微型水电和风能)的快速充电电动汽车(EV)站的详细建模。这有助于利润最大化和降低电网能耗。由于光伏(PV)系统在部分遮阳条件下表现出几个峰值;跟踪全局最大值点是非常困难的。因此,解决这一非线性问题需要进行广泛的搜索。由于解决方案的多样性,GA是首选的太阳能MPPT。与遗传算法(GA)和最大功率点跟踪(MPPT)技术相比,所得到的经济考虑使利润最优。同样明显的是,拟议的策略通过限制系统网络和电网之间可能交换的电量,降低了电网对系统网络的影响。采用遗传算法和遗传MPPT技术分别在公用电网上得到了光伏阵列的电压和电流。
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Photovoltaic-Based Electric Vehicle Charging Station using GA Algorithm
Detailed modeling of rapid charging Electric Vehicle (EV) stations connected to a hybrid grid-Renewable Energy Systems (RES), such as solar, mini-hydro, and wind, has been proposed. This assists in maximizing profit and lowering grid energy consumption. Since Photovoltaic (PV) systems exhibit several peaks under partial shading conditions; it is very difficult to track the global maximum point. Therefore, a broad search is needed to solve this nonlinear problem. Due to the variety of solutions, GA is preferred for solar MPPT. In comparison to the Genetic Algorithm (GA) and Maximum Power Point Tracking (MPPT) technique, the economic considerations obtained optimizes profit. It is also obvious that the proposed strategy lowers the grid's influence on the system network by capping the amount of electricity that may be exchanged between the system network and the grid. The voltage and current of PV array with GA method and GA MPPT technique results on the utility grid respectively.
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