Optimization of Electric Vehicle Charging to Shave Peak Load for Integration in Smart Grid

Bidya Debnath, S. Biswas, M. Uddin
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引用次数: 7

Abstract

Current movement towards electric vehicle (EV) usage will demand high power consumption in future due to EV charging. As the usage will grow, sudden spike in load curve at busy hours will be a severe problem. This paper focuses on the optimized charging scheduling for EVs to shave peak load for their integration in smart grid. A two layer optimization method is proposed based on the location, charging status of the EV and load scenario of the substations to shave electric peak load. Optimization problem is formulated for each of the layer. The optimization problem of the first layer determines the allowable load level in each hour of a day for each charging station by reducing the peak load of substation. For a new EV, the charging station selection method is proposed by using the allowable peak loads obtained from first layer optimization problem. The optimization problem of the second layer provides an optimized on-off keying charging scheme of the ports for the selected charging stations. The first optimization problem is an off-line root mean square type non-linear problem and the second problem is a binary linear programming. Both the problems are solved for several scenarios by using MATLAB optimization toolboxes. The numerical results show that a significant amount peak load shaving can be achieved by using the proposed method and the percentage of peak load reduction increases with increasing the EV penetration.
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面向智能电网集成的电动汽车充电削峰优化
目前电动汽车(EV)的使用将需要高功耗,因为电动汽车充电。随着使用量的增长,繁忙时段负载曲线的突然尖峰将是一个严重的问题。本文主要研究了电动汽车在智能电网集成过程中,如何优化充电计划以降低峰值负荷。提出了一种基于电动汽车位置、充电状态和变电站负荷情景的两层优化方法,以实现电力峰值负荷的削减。对每一层都制定了优化问题。第一层优化问题通过降低变电站的峰值负荷来确定每个充电站在一天中每小时的允许负荷水平。针对一种新型电动汽车,提出了利用第一层优化问题得到的允许峰值负荷选择充电站的方法。第二层优化问题为所选充电站提供了一种优化的端口开关键控充电方案。第一个优化问题是脱机均方型非线性问题,第二个问题是二元线性规划问题。利用MATLAB的优化工具箱对这两个问题进行了求解。数值结果表明,采用该方法可实现较大幅度的削峰,且削峰率随电动汽车渗透率的增加而增加。
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