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Since hydrogen has many advantages (e.g., free pollution, extensive sources,convenient storage and transportation), hydrogen-based multi-energy systems(HMESs) have received wide attention. However, existing works on the optimaloperation of HMESs neglect building thermal dynamics, which means that theflexibility of building thermal loads can not be utilized for reducing systemoperation cost. In this paper, we investigate an optimal operation problem ofan HMES with the consideration of building thermal dynamics. Specifically, wefirst formulate an expected operational cost minimization problem related to anHMES. Due to the existence of uncertain parameters, inexplicit building thermaldynamics models, temporally coupled operational constraints related to threekinds of energy storage systems and indoor temperatures, as well as thecoupling between electric energy subsystems and thermal energy subsystems, itis challenging to solve the formulated problem. To overcome the challenge, wereformulate the problem as a Markov game and propose an energy managementalgorithm to solve it based on multi-agent discrete actor-critic with rules(MADACR). Note that the proposed algorithm does not require any prior knowledgeof uncertain parameters, parameter prediction, and explicit building thermaldynamics model. Simulation results based on real-world traces show theeffectiveness of the proposed algorithm.

Optimal Operation of a Hydrogen-based Building Multi-Energy System Based on Deep Reinforcement Learning