Optimal Aggregation Design for Massive V2G Participation in Energy Market

As a new type of transportation, electric vehicles (EV) can effectively adjust the supply and demand balance of power systems using their vehicle-to-grid (V2G) characteristics. To better promote the participation of EV resources in the energy market and interact with power systems, we propose a novel framework of an electric vehicle aggregator (EVA) that can aggregate schedulable EVs within its jurisdiction to provide auxiliary services for the power grid. Due to EV charging behavior's uncertain nature, we employ a probability mass function (PMF) based model to provide more accurate forecasts of future EV behaviors. To reduce EVA operation costs and maximize the travel utility for EV users participating in this service, we develop an EVA optimization schedule model that combines a day-ahead optimization schedule and real-time optimization schedule. Finally, we create three case studies to verify the results of the proposed method. Matlab is used to simulate and analyze each case study concerning uncoordinated charging, coordinated charging while considering day-ahead optimization schedules, and an ensemble of coordinated charging activities that consider the day-ahead optimization schedule and real-time optimization schedule. Through comparative analysis, it is verified that the proposed strategy can effectively reduce EVAs' operating costs and meet the travel requirements of EV users. The impact of different degrees of error of EV plug-out information on the proposed method is also analyzed.