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 objec-tive 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 inter-vals 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.
building energy systemdeep reinforcement learningfuel cellthermal-electric coor-dinated schedulingPV power generation
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