Multi-Stage Operation Strategy of Electric Vehicle Aggregators in Energy-Frequency Market

As the ownership of Electric Vehicles (EVs) continues to grow and their market penetration increases, accurately measuring and fully utilizing the scheduling capabilities of grid-connected EVs to participate in power system optimization is becoming increasingly crucial. In response, this paper considers the charging and discharging responsiveness of EV users and proposes a multi-stage market operation strategy for EV Aggregators (EVA) aimed at maximizing the operational revenue of EVA. Firstly, a model of individual EVs in multiple grid-connected states is proposed, considering the differences in psychological tolerance among users participating in bidirectional vehicle-to-grid interactions. An EV interaction response model based on the Weber-Fechner law is established to analyze the responsiveness of EVs under different charging and discharging compensation prices. Then, considering the time-series response characteristics of individual EVs influenced by charging and discharging compensation throughout the full grid-connected period, a compensation strategy is formulated, and an EV cluster response model reflecting the bidirectional interaction capability of charging and discharging is proposed. Next, a multi-stage sequential interaction model is established for optimizing the operational revenue of EVA in the energyfrequency market, considering the multi-period coupling constraints of the participation of EVA in the energy and frequency regulation markets. Finally, the effectiveness of the proposed operational strategy is verified through case studies using PJM market operation data.