Deep Reinforcement Learning Method for Demand Response Management of Interruptible Load

As an important part of incentive demand response (DR), interruptible load (IL) can achieve a rapid response and improve demand side resilience. Yet, model-based optimization algorithms concerning with IL require the explicit physical or mathematical model of the system, which makes it difficult to adapt to realistic operation conditions. In this paper, a model-free deep reinforcement learning (DRL) method with dueling deep Q network (DDQN) structure is designed to optimize the DR management of IL under the time of use (TOU) tariff and variable electricity consumption patterns. The DDQN-based automatic demand response (ADR) architecture is firstly constructed, which provides a possibility for real-time application of DR. To obtain the maximum long-term profit, the DR management problem of IL is formulated as a Markov decision process (MDP), in which the state, action, and reward function are defined, respectively. The DDQN-based DRL algorithm is applied to solve this MDP for the DR strategy with maximum cumulative reward. The simulation results validate that the proposed algorithm with DDQN overcomes the noise and instability in traditional DQN, and realizes the goal of reducing both the peak load demand and the operation costs on the premise of regulating voltage to the safe limit.