A Modified Long Short-Term Memory-Deep Deterministic Policy Gradient-Based Scheduling Method for Active Distribution Networks

Abstract

To improve the decision-making level of active distribution networks (ADNs), this paper proposes a novel framework for coordinated scheduling based on the long short-term memory network (LSTM) with deep reinforcement learning (DRL). Considering the interaction characteristics of ADNs with distributed energy resources (DERs), the scheduling objective is constructed to reduce the operation cost and optimize the voltage distribution. To tackle this problem, a LSTM module is employed to perform feature extraction on the ADN environment, which can realize the recognition and learning of massive temporal structure data. The concerned ADN real-time scheduling model is duly formulated as a finite Markov decision process (FMDP). Moreover, a modified deep deterministic policy gradient (DDPG) algorithm is proposed to solve the complex decision-making problem. Numerous experimental results within a modified IEEE 33-bus system demonstrate the validity and superiority of the proposed method.