Research on energy management strategy of fuel cell power generation system based on Grey–Markov chain power prediction
Zhichao Fu, Qihong Chen, Liyan Zhang, Jing Fan, Haoran Zhang, Zhihua Deng
Abstract
Fuel cell power generation system is a potential renewable power source. To reduce hydrogen consumption and enhance the dynamic performance of the system, Grey–Markov chain power prediction energy management strategy for fuel cell power generation systems was proposed. Firstly, topology of the system is proposed, and mathematical model was established through mechanism analysis. Secondly, framework of power prediction of the system was presented, and Grey–Markov chain model was proposed to predict load power of the fuel cell power system, based on which energy management of the system was implemented. Finally, the proposed energy management strategy was compared with rule-based strategy by experiment. The results show that the proposed power prediction energy management strategy can accurately predict the load power in advance and reduce hydrogen consumption in the fuel cell power generation system.