Optimal Dispatch of Integrated Electricity-gas System With Soft Actor-critic Deep Reinforcement Learning

Optimal dispatching of multi-energy flow is one of the core technologies to realize the efficient operation of integrated energy system. In this paper, a reinforcement learning based on the framework of soft actor-critic was proposed for optimizing the operation of integrated electricity-gas energy system. The agent adaptively learned the control strategies through its interaction with the power system. This method is able to take continuous control actions of the multi-energy flow system and flexibly deal with the complicated stochastic problem with uncertain wind power, photovoltaic power and loads. Thus the stochastic dispatching of integrated electricity-gas energy system can be implemented. First, the framework of the reinforcement learning for optimal dispatching was built and the methodology of the soft actor-critic was introduced. Then the interactive environment for the agent was built. The action and state space, reward approach, neural network structure and training process were designed. Finally, the results calculated by the proposed method were analyzed in two different integrated electricity-gas energy systems. © 2021 Chin. Soc. for Elec. Eng.