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Adaptive V2G Peak Shaving and Smart Charging Control for Grid Integration of PEVs

机译：PEV的电网集成的自适应V2G削峰和智能充电控制

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摘要

The stochastic nature of plug-in electric vehicle (PEV) driving behavior and distribution grid load profile make it challenging to control vehicle-grid integration in a mutually beneficial way. This article proposes a new adaptive control strategy that manages PEV charging/discharging for peak shaving and load leveling in a distribution grid. For accurate and high fidelity transportation mobility modeling, real vehicle driving test data are collected from the field. Considering the estimated total required PEV battery charging energy, the vehicle-to-grid capabilities of PEVs, and the forecasted non-PEV base load, a reference operating point for the grid is estimated. This reference operating point is updated once at the end of peak hours to guarantee a full final state-of-charge to each PEV. Proposed method provides cost-efficient operation for the utility grid, utmost user convenience free from range anxiety, and ease of implementation at the charging station nodes. It is tested on a real residential transformer, which serves approximately one thousand customers, under various PEV penetration levels and charging scenarios. Performance is assessed in terms of mean-square-error and peak shaving index. Results are compared with those of various reference operating point choices and shown to be superior.
机译：插电式电动汽车（PEV）的行驶特性和配电网负荷曲线的随机性使得以互惠互利的方式控制车辆与电网的集成带来挑战。本文提出了一种新的自适应控制策略，该策略可管理PEV充电/放电，以实现配电网中的削峰和负载均衡。为了进行准确，高保真的运输机动性建模，需要从现场收集真实的车辆驾驶测试数据。考虑到估计的所需PEV电池总充电能量，PEV的车辆到电网容量以及预测的非PEV基本负荷，可以估计电网的参考工作点。该参考工作点在高峰时段结束时更新一次，以确保每个PEV的完整最终充电状态。所提出的方法为公用电网提供了具有成本效益的操作，最大程度地为用户提供了便利，而没有范围的烦恼，并且易于在充电站节点处实施。它在一个实际的住宅变压器上进行了测试，该变压器可以在各种PEV渗透水平和充电方案下为大约1,000个客户提供服务。根据均方误差和峰剃刮指数评估性能。将结果与各种参考工作点选择的结果进行比较，结果显示出更好的结果。

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