Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports
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...
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MDPI AG
2025-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/7/1583 |
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| author | Zhuoyu Jiang Rujie Liu Weiwei Guan Lei Xiong Changli Shi Jingyuan Yin |
| author_facet | Zhuoyu Jiang Rujie Liu Weiwei Guan Lei Xiong Changli Shi Jingyuan Yin |
| author_sort | Zhuoyu Jiang |
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| description | 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. |
| format | Article |
| id | doaj-art-daef0cf370f94b93a768c968631b75b0 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-daef0cf370f94b93a768c968631b75b02025-08-20T03:08:50ZengMDPI AGEnergies1996-10732025-03-01187158310.3390/en18071583Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of PortsZhuoyu Jiang0Rujie Liu1Weiwei Guan2Lei Xiong3Changli Shi4Jingyuan Yin5Three Gorges Electric Energy Co., Ltd., Wuhan 430015, ChinaThree Gorges Electric Energy Co., Ltd., Wuhan 430015, ChinaChina Yangtze Power Co., Ltd., Beijing 100033, ChinaThree Gorges Electric Energy Co., Ltd., Wuhan 430015, ChinaInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, ChinaAiming 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.https://www.mdpi.com/1996-1073/18/7/1583green hydrogenHFSmulti-time scaleparticle swarm optimizationstochastic model predictive control |
| spellingShingle | Zhuoyu Jiang Rujie Liu Weiwei Guan Lei Xiong Changli Shi Jingyuan Yin Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports Energies green hydrogen HFS multi-time scale particle swarm optimization stochastic model predictive control |
| title | Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports |
| title_full | Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports |
| title_fullStr | Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports |
| title_full_unstemmed | Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports |
| title_short | Multi-Time-Scale Layered Energy Management Strategy for Integrated Production, Storage, and Supply Hydrogen Refueling Stations Based on Flexible Hydrogen Load Characteristics of Ports |
| title_sort | multi time scale layered energy management strategy for integrated production storage and supply hydrogen refueling stations based on flexible hydrogen load characteristics of ports |
| topic | green hydrogen HFS multi-time scale particle swarm optimization stochastic model predictive control |
| url | https://www.mdpi.com/1996-1073/18/7/1583 |
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