Deep reinforcement learning-based optimal scheduling of integrated energy systems for electricity, heat, and hydrogen storage

Abstract

The increasing load demands and the extensive usage of renewable energy in integrated energy systems pose a challenge to the most efficient scheduling of integrated energy systems (IES) because of the unpredictability and volatility of both the load side and renewable energy.Integrating heat storage and hydrogen storage technologies into integrated energy systems can effectively alleviate the phenomena of wind and solar power desertion caused by the instability of renewable energy sources. This study introduces a real-time optimal scheduling method based on the Soft Actor-Critic (SAC) algorithm, aimed at reducing system operating costs and enhancing the consumption rate of renewable energy. First, the established mathematical model is converted into a Markov Decision Process (MDP), the objective function is converted by designing the action state space and the reward function, and the deep reinforcement learning framework is established. Finally, the decision-making outcomes of intelligence in various energy storage scenarios of renewable energy consumption and extreme cases are analyzed and compared, and the results show that the heat storage and hydrogen storage system significantly improve the rate of renewable energy consumption and the economy of the system. This method is also shown to significantly improve the rate of renewable energy consumption.