Intelligent Scheduling for Efficient Thermal-Electric Coordination of a Photovoltaic/Hydrogen/Storage Building Energy System Considering Source-Load Uncertainties

The cogeneration technology of photovoltaic (PV) coupled fuel cells holds significant potential for widespread application in low-carbon building energy systems. However, the thermal-electric coordinated scheduling of this system faces challenges related to multi-energy flows, strong coupling, and source-load uncertainties. To address these issues, this paper establishes a novel multi-energy coupling model for the system and formulates an optimization problem with the objective of minimizing intraday comprehensive cost, subject to constraints on thermal-electric balances and device storage boundaries. Through training under various sets of random PV and thermal-electric loads, this paper proposes an improved deep reinforcement learning algorithm, specifically deep deterministic policy gradient (DDPG), enabling rapid evaluation of charge/discharge intervals for storage devices and facilitating swift decision-making for scheduling. Simulation results demonstrate that the improved DDPG significantly improves the training convergence speed under a typical winter day scenario, reducing the overall scheduling cost by 10.36 %. Besides, simulation results under 60 uncertain scenarios, with uncertainty intervals ranging from 10% to 30%, indicate that, compared to DDPG, rule-based method, and dynamic programming, the improved DDPG can achieve approximately theoretically optimal results, enhancing robustness and adaptability to uncertainty. © 2024 Science Press. All rights reserved.