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This paper addresses the problem of optimizing charging/discharging schedulesof electric vehicles (EVs) when participate in demand response (DR). As thereexist uncertainties in EVs' remaining energy, arrival and departure time, andfuture electricity prices, it is quite difficult to make charging decisions tominimize charging cost while guarantee that the EV's batterystate-of-the-charge (SOC) is within certain range. To handle with this dilemma,this paper formulates the EV charging scheduling problem as a constrainedMarkov decision process (CMDP). By synergistically combining the augmentedLagrangian method and soft actor critic algorithm, a novel safe off-policyreinforcement learning (RL) approach is proposed in this paper to solve theCMDP. The actor network is updated in a policy gradient manner with theLagrangian value function. A double-critics network is adopted to synchronouslyestimate the action-value function to avoid overestimation bias. The proposedalgorithm does not require strong convexity guarantee of examined problems andis sample efficient. Comprehensive numerical experiments with real-worldelectricity price demonstrate that our proposed algorithm can achieve highsolution optimality and constraints compliance.

A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle