  Due to the increasing popularity of electric vehicles (EVs) and the
technological advancement of EV electronics, the vehicle-to-grid (V2G)
technique and large-scale scheduling algorithms have been developed to achieve
a high level of renewable energy and power grid stability. This paper proposes
a deep reinforcement learning (DRL) method for the continuous
charging/discharging coordination strategy in aggregating large-scale EVs in
V2G mode with renewable energy sources (RES). The DRL coordination strategy can
efficiently optimize the electric vehicle aggregator's (EVA's) real-time
charging/discharging power with the state of charge (SOC) constraints of the
EVA and the individual EV. Compared with uncontrolled charging, the load
variance is reduced by 97.37$\%$ and the charging cost by 76.56$\%$. The DRL
coordination strategy further demonstrates outstanding transfer learning
ability to microgrids with RES and large-scale EVA, as well as the complicated
weekly scheduling. The DRL coordination strategy demonstrates flexible,
adaptable, and scalable performance for the large-scale V2G under realistic
operating conditions.
