Optimal scheduling of hydrogen coupled electrothermal integrated energy system based on deep reinforcement learning algorithm
In order to promote the coupling of hydrogen energy with other energy sources in the integrated energy system,improve the flexibility of energy utilization and reduce the carbon emission of the system,an operation optimization method of hydrogen coupled electrothermal integrated energy system(HCEH-IES)is proposed.The mathematical model of each device in the HCEH-IES is established,and the basic principle of deep reinforcement learning algorithm and the process of twin delayed deep deterministic policy gradient(TD3)algorithm are described in detail.The uncertain optimal scheduling problem of HCEH-IES is trans-formed into Markov decision process,and the TD3 algorithm is used to convert optimization objective and constraints into reward functions for dynamic scheduling decision-making in continuous state space and action space,then a reasonable energy distribution management scheme is formed.The agents are trained with his-torical data,and the scheduling strategies obtained by deep Q learning network and deep deterministic policy gradient algorithm are compared.The results show that,compared with the deep Q learning network and the deep deterministic policy gradient algorithm,the scheduling strategy based on TD3 algorithm is more econo-mic,and its results are closer to the economic cost of the CPLEX-based day-ahead optimal scheduling method,and it is more suitable to solve the dynamic optimal scheduling problem of the integrated energy system,which effectively realizes the flexible utilization of energy and improves the economy and low-carbon perfor-mance of the integrated energy system.
HCEH-IESrenewable energydeep reinforcement learningtwin delayed deep deterministic policy gradientenergy optimization managementMarkov decision process
万方数据
维普
