论文标题
两阶段的最佳竞标算法，用于基于激励的电动汽车在工作场所停车场的聚合
A Two-Stage Optimal Bidding Algorithm for Incentive-based Aggregation of Electric Vehicles in Workplace Parking Lots
论文作者
论文摘要
本文提议在工作场所停车场的电动汽车（电动汽车）聚合器集合，以参与能源和监管市场。通过实施季节性自回旋综合运动平均值（ARIMA）模型以预测市场信息，制定了与电动汽车对激励措施的反应协调的日益计划（DA）计划模型，以确定激活聚合计划的日子，以及在第一阶段向EV所有者广播的最佳奖励。鉴于确定的激励措施和电动汽车的回应，符合第二阶段的电动汽车能源需求的实时（RT）最佳竞标算法的设计旨在最大程度地利用市场中的汇总利润。使用PJM的能源和监管市场收集的数据对所提出的模型进行了测试。结果表明，基于激励的聚合器可以从从市场获得的信用中受益于电动汽车所有者和聚合者。同时，结果还表明所提出的最佳竞标算法能够处理调节信号的不确定性并以高精度遵循信号。
This paper proposes an incentive-based aggregator for electric vehicles (EVs) in workplace parking lots to participate in the energy and regulation markets. With the implementation of seasonal Autoregressive Integrated Moving Average (ARIMA) model to predict the market information, a Day-Ahead (DA) planning model coordinated with the EV's responses to the incentive is formulated to determine the days to activate the aggregation program and the optimal incentives that are broadcasted to the EV owners in the first stage. Given the determined incentives and EV's responses, an real-time (RT) optimal bidding algorithm complying with EVs' energy demand in the second stage is designed to maximize the aggregator's profits in the markets. The proposed models are tested using the data collected from PJM's energy and regulation markets. The results show the incentive-based aggregator can benefit both the EV owners and the aggregator from the credits obtained from the markets. Meanwhile, the results also indicate the proposed optimal bidding algorithm is capable of handling the uncertainty of regulation signals and following the signals with high precision.
