Optimum Scheduling of Truck-Based Mobile Energy Couriers (MEC) Using Deep Deterministic Policy Gradient

We propose a new architecture of truck-based mobile energy couriers (MEC) for power distribution networks with high penetration of renewable energy sources (RES). Each MEC is a truck equipped with high-density inverters, converters, capacitor banks, and energy storage devices. The MEC platform can improve the flexibility, resilience, and RES hosting capability of a distribution grid through spatial-temporal energy reallocation based on the stochastic behaviors of RES and loads. The employment of MEC necessitates the development of complex scheduling and control schemes that can adaptively cope with the dynamic natures of both the power grid and the transportation network. The problem is formulated as a non-convex optimization problem to minimize the total generation cost, subject to the various constraints imposed by conventional and renewable energy sources, energy storage, and transportation networks, etc. The problem is solved by combining optimal power flow (OPF) with deep reinforcement learning (DRL) under the framework of deep deterministic policy gradient (DDPG). Simulation results demonstrate that the proposed MEC platform with DDPG can achieve significant cost reduction compared to conventional systems with static energy storage.