Adaptive look-ahead economic dispatch based on deep reinforcement learning

Abstract

With the advancement of the energy transition, renewable power-dominated new power systems are faced with unprecedented technical challenges. Faced with the increasing uncertainty brought by renewables and the exponential growth of the number of flexible resources, data-driven methods have shown great potential in recent years due to their adaptability in scenarios with massive decisions and high efficiency. Therefore, this paper proposes a data-driven look-ahead economic dispatch model with full consideration of N-1 outage contingency based on reinforcement learning and a deep deterministic policy gradient (DDPG) algorithm. We first use the domain knowledge of dynamic economic dispatch to formulate the reinforcement learning model, and then utilize an improved DDPG algorithm with mode classification for the training and validation. The N-1 outage contingency is considered based on the idea of transfer learning to reduce the size of N-1 scenarios and further improve the efficiency. Case studies based on the IEEE30-bus and SG126-bus systems validate the effectiveness of the proposed model and algorithm, with superior performance in terms of security and economy compared to the conventional model-based method.