基于激励的电动汽车在工作场所停车场聚集的两阶段最优竞价算法
A Two-Stage Optimal Bidding Algorithm for Incentive-based Aggregation of Electric Vehicles in Workplace Parking Lots
摘要：
本文提出了一种基于激励的聚合器，用于工作场所停车场的电动汽车（EV）参与能源和监管市场。通过季节性自回归移动平均（ARIMA）模型对市场信息进行预测，建立了与电动汽车对激励措施的响应相协调的提前一天（DA）规划模型，以确定启动聚合计划的日期和在第一阶段向电动汽车车主广播的最佳激励措施。在给定激励机制和电动汽车响应的情况下，设计了一种满足电动汽车能量需求的实时最优竞价算法，以最大化聚合商在市场中的利润。所提出的模型进行了测试，从PJM的能源和监管市场收集的数据。结果表明，基于激励的聚合器可以受益于电动汽车所有者和聚合器从市场上获得的信用。同时，仿真结果也表明，所提出的最优报价算法能够有效地处理调节信号的不确定性，并具有较高的跟踪精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
