基于SC-SAC算法的REHMIS-IES优化调度策略OA

The renewable energy-hydrogen-methanol integrated station(REHMIS)produces green hydrogen using electricity generated from renewable energy sources,and further synthesizes methanol from the green hydrogen and carbon dioxide,thereby achieving the substitution of green hydrogen for hydrogen produced from conventional fossil fuels.To simultaneously meet the methanol load demand of REHMIS and the multi-energy demand of its supporting buildings,a novel integrated energy system(IES)topology named REHMIS-IES was designed.To obtain an efficient operation strategy for REHMIS-IES,an execution framework based on the strictly constrained soft actor-critic(SC-SAC)algorithm was proposed.The established mathematical model was transformed into a Markov decision process,and a state constraint mechanism(SCM)was incorporated to prevent drastic fluctuations in the state of the energy storage system.In the execution stage of the SC-SAC algorithm,the trained Q-network and action constraints were transformed into a mixed-integer linear programming(MILP)model to ensure that scheduling decisions could comply with all operational constraints.The results from multi-scenario simulations showed that the proposed system could effectively reduce operating costs while meeting multi-energy demands.Compared with other deep reinforcement learning algorithms,the SC-SAC algorithm could lower the system energy imbalance by approximately 16.2%and reduce operating costs by at least 11.7%.