Graph reinforcement learning for real-time optimal dispatch of active distribution network

The renewable energy system, energy storage system and other energy resources of active distribution network can effectively improve the flexibility and reliability of operation. Meanwhile, renewable energy and load also bring uncertainty to the distribution network, resulting in large dimensions of real-time optimal dispatch and poor modeling accuracy of active distribution network. To solve this problem, a graph reinforcement learning method combining graph neural network and reinforcement learning is proposed to avoid accurate modeling of complex systems. Firstly, the real-time optimal dispatch is described as Markov decision process and dynamic sequential decision problem. Secondly, a graph representation method based on the physical connection is proposed to express the implied correlation of state variables. Then a graph reinforcement learning is proposed to learn the optimal strategy for mapping system state graph to decision output. Finally, the graph reinforcement learning is developed to distributed graph reinforcement learning. The simulations show that graph reinforcement learning achieves better results in optimality and efficiency. © 2024 South China University of Technology. All rights reserved.