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Coordinated Vehicle-to-Grid Scheduling to Minimize Grid Load Variance

机译:车辆到电网的协调调度,以最小化电网负荷差异

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This paper presents the Vehicle-to-Grid scheduling algorithm to minimize the grid load variance by utilizing the grid-connected electric vehicle battery. The algorithm performs in two modes, which are load leveling and peak load shaving. In the load leveling mode, the grid-connected electric vehicle is charged from the power grid and hence, increase the grid loading. Meanwhile, the grid loading is reduced in peak load shaving mode since electric vehicle discharges energy from the battery to support the power grid. Various constraints have been considered to ensure the practicality of this study. The Vehicle-to-Grid study was implemented in a commercial-residential area with electric vehicle mobility of 1300. Both uncoordinated charging and coordinated Vehicle-to-Grid scheduling were performed and compared. The results showed that the uncoordinated charging of electric vehicle will induce a new peak in the power grid load profile. On the other hand, the results showed that the proposed coordinated Vehicle-to-Grid scheduling algorithm successfully minimized the grid load variance while satisfying all the constraints and power grid requirements.
机译:本文提出了一种“车辆到电网”调度算法,该算法通过利用并网电动汽车电池来最大程度地减小电网负载变化。该算法以两种模式执行,即负载均衡和峰值负载削减。在负载均衡模式下,并网电动车辆从电网充电,因此增加了电网负载。同时,由于电动车辆从电池释放能量以支撑电网,因此在峰值负荷剃刮模式下减小了电网负荷。已考虑各种限制因素以确保本研究的实用性。车辆到电网的研究是在电动汽车机动性为1300的商业住宅区中进行的。未协调的充电和车辆到电网的调度都进行了比较。结果表明,电动汽车的不协调充电将在电网负载曲线中引起一个新的峰值。另一方面,结果表明,所提出的车辆到电网协调调度算法成功地最小化了电网负荷方差,同时满足了所有约束和电网需求。

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