Ride-Hailing Electric Vehicle Dispatching for Resilience Reserve Enhancement: An Interactive Deep Reinforcement Learning Approach

Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience. However, they have not been well investigated because their charging and order-serving are affected or managed by the power grid dispatching center and the ride-hailing platform. Effective pre-strategies can improve the prevention ability for high-impact and low-probability (HILP) events and provide the foundation for measures in the response and restoration stages. First, this paper proposes a resilience reserve to expand the existing research on power system resilience. Secondly, this paper puts forward an interactive method of deep reinforcement learning, which considers the interests of both the power grid dispatching center and the ride-hailing platform. It improves the resilience reserve by achieving the order dispatch, orderly charging management of ride-hailing electric vehicles, and the pricing strategy of charging stations. Finally, this paper uses a practical example covering about 107.32 km2in the center of Chengdu to verify that the proposed method improves the resilience reserve of the power system without obviously damaging the interests of the ride-hailing platform.