LAPSE:2023.28292v1
Published Article
LAPSE:2023.28292v1
Optimal Planning of Hybrid Electricity−Hydrogen Energy Storage System Considering Demand Response
Zijing Lu, Zishou Li, Xiangguo Guo, Bo Yang
April 11, 2023
Abstract
In recent years, the stability of the distribution network has declined due to the large proportion of the uses of distributed generation (DG) with the continuous development of renewable energy power generation technology. Meanwhile, the traditional distribution network operation mode cannot keep the balance of the source and load. The operation mode of the active distribution network (ADN) can effectively reduce the decline in operation stability caused by the high proportion of DG. Therefore, this work proposes a bi-layer model for the planning of the electricity−hydrogen hybrid energy storage system (ESS) considering demand response (DR) for ADN. The upper layer takes the minimum load fluctuation, maximum user purchase cost satisfaction, and user comfort as the goals. Based on the electricity price elasticity matrix model, the optimal electricity price formulation strategy is obtained for the lower ESS planning. In the lower layer, the optimal ESS planning scheme is obtained with the minimum life cycle cost (LCC) of ESS, the voltage fluctuation of ADN, and the load fluctuation as the objectives. Finally, the MOPSO algorithm is used to test the model, and the correctness of the proposed method is verified by the extended IEEE-33 node test system. The simulation results show that the fluctuation in the voltage and load is reduced by 62.13% and 37.06%, respectively.
Keywords
active distribution network (ADN), demand response (DR), energy storage system (ESS), life cycle cost (LCC), multiple objective particle swarm optimization (MOPSO)
Suggested Citation
Lu Z, Li Z, Guo X, Yang B. Optimal Planning of Hybrid Electricity−Hydrogen Energy Storage System Considering Demand Response. (2023). LAPSE:2023.28292v1
Author Affiliations
Lu Z: State Grid Hubei Electric Power Co., Ltd., Wuhan 430070, China
Li Z: State Grid Hubei Electric Power Co., Ltd., Wuhan 430070, China
Guo X: Center Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Wuhan 430074, China
Yang B: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
Journal Name
Processes
Volume
11
Issue
3
First Page
852
Year
2023
Publication Date
2023-03-13
ISSN
2227-9717
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Original Submission
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PII: pr11030852, Publication Type: Journal Article
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LAPSE:2023.28292v1
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https://doi.org/10.3390/pr11030852
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