Graph Reinforcement Learning for Securing Critical Loads by E-Mobility

Inefficient scheduling of electric vehicles (EVs) is detrimental to not only the profitability of charging stations but also the experience of EV users and the stable operation of the grid. Regulating the charging market by dynamic pricing is a feasible choice for EV coordinated scheduling. Power outages caused by natural disasters have always been a serious threat to critical loads such as hospitals and data centers. With the development of vehicle-to-grid (V2G) technology, the potential to attract EV users to the stations near the critical loads through dynamic pricing and to aggregate EVs into a flexible emergency supply to maintain the critical load is being explored. However, determining charging prices in real-time that are both attractive to users and profitable to stations is a challenging task, which is further complicated by the relationships and interactions between multiple stations. Therefore, this paper proposes the graph reinforcement learning (GRL) approach to seek the optimal pricing strategy to address the above problems. The experiment results show that the proposed method can effectively achieve profit maximization and EV scheduling for critical load maintenance.