Joint Optimization and Learning Approach for Smart Operation of Hydrogen-Based Building Energy Systems

In recent years, hydrogen-based multi-energy systems (HMESs) have received wide attention. However, existing works on the optimal operation of HMESs neglect building thermal dynamics, which means that the flexibility of thermal loads can not be utilized for reducing system operation cost. In this paper, we investigate an optimal operation problem of an HMES with the consideration of building thermal dynamics. Specifically, we first formulate an expected operational cost minimization problem related to an HMES. Due to the existence of uncertain parameters, inexplicit building thermal dynamics models, spatially and temporally coupled operational constraints, and nonlinear constraints, it is challenging to solve the formulated problem. Then, we propose an algorithm to solve the problem based on model-based optimization and data-driven based learning. The key idea of the proposed algorithm is summarized as follows: (1) transforming the long-term cost minimization problem into several single-slot subproblems using Lyapunov optimization techniques; (2) dividing each single-slot subproblem into two parts according to the availability of model information; (3) solving one part based on convex optimization and solving another part using multi-agent attention-based deep deterministic policy gradient. Simulation results based on real-world traces show the effectiveness of the proposed algorithm.