Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports

Zhuoyu Jiang, Rujie Liu, Weiwei Guan, Lei Xiong, Changli Shi () and Jingyuan Yin
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Zhuoyu Jiang: Three Gorges Electric Energy Co., Ltd., Wuhan 430015, China
Rujie Liu: Three Gorges Electric Energy Co., Ltd., Wuhan 430015, China
Weiwei Guan: China Yangtze Power Co., Ltd., Beijing 100033, China
Lei Xiong: Three Gorges Electric Energy Co., Ltd., Wuhan 430015, China
Changli Shi: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
Jingyuan Yin: Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China

Energies, 2025, vol. 18, issue 7, 1-24

Abstract: Aiming at resolving the problem of stable and efficient operation of integrated green hydrogen production, storage, and supply hydrogen refueling stations at different time scales, this paper proposes a multi-time-scale hierarchical energy management strategy for integrated green hydrogen production, storage, and supply hydrogen refueling station (HFS). The proposed energy management strategy is divided into two layers. The upper layer uses the hourly time scale to optimize the operating power of HFS equipment with the goal of minimizing the typical daily operating cost, and proposes a parameter adaptive particle swarm optimization (PSA-PSO) solution algorithm that introduces Gaussian disturbance and adaptively adjusts the learning factor, inertia weight, and disturbance step size of the algorithm. Compared with traditional optimization algorithms, it can effectively improve the ability to search for the optimal solution. The lower layer uses the minute-level time scale to suppress the randomness of renewable energy power generation and hydrogen load consumption in the operation of HFS. A solution algorithm based on stochastic model predictive control (SMPC) is proposed. The Latin hypercube sampling (LHS) and simultaneous backward reduction methods are used to generate and reduce scenarios to obtain a set of high-probability random variable scenarios and bring them into the MPC to suppress the disturbance of random variables on the system operation. Finally, real operation data of a HFS in southern China are used for example analysis. The results show that the proposed energy management strategy has a good control effect in different typical scenarios.

Keywords: green hydrogen; HFS; multi-time scale; particle swarm optimization; stochastic model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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