Multi-Agent Deep Reinforced Co-Dispatch of Energy and Hydrogen Storage in Low-Carbon Building Clusters

Abstract

Low-carbon communities integrate various energy producers and consumers and have been forming an important architecture for future multi-energy system to tackle challenges in improving energy utilization efficiency, alleviating environmental pollution, and achieving reliability of energy supply systems. This paper proposes a multi-building network model containing hydrogen energy storage systems, and verifies the applicability of the multi-agent deep deterministic policy gradient (MADDPG) algorithm in solving the multi-energy coordinated dispatch of a multi-building network by comparing various energy dispatch methods. First, the model of hydrogen storage system based on liquid organic hydrogen carrier (LOHC) is completed by unifying the process of electrolysis, storage and release of energy from hydrogen storage system, and an energy dispatch method is further constructed by utilizing the hydrogen storage system, heat storage system, and renewable energy generation equipment in the building for coordinated energy supply to the load. Secondly, the observation space and action space of grid-interactive building multi-energy dispatch in the network are respectively established by considering the ability of MADDPG algorithm to learn deterministic policies for continuous actions. Finally, the advantage of grid-interactive buildings containing hydrogen storage systems to reduce operating costs is validated in a numerical simulation based on three dispatch scenarios for six buildings. Four deep reinforcement learning (DRL) algorithms are used to compare the operating costs with model predictive control (MPC) algorithms in a network model containing six grid-interactive buildings, and validate the advantages of MADDPG in optimizing the cost of co-dispatching multi-energy sources in networked grid-interactive buildings.