LAPSE:2023.20239v1
Published Article
LAPSE:2023.20239v1
Optimal Configuration of Energy Storage Systems in High PV Penetrating Distribution Network
Jinhua Zhang, Liding Zhu, Shengchao Zhao, Jie Yan, Lingling Lv
March 17, 2023
Abstract
In this paper, a method for rationally allocating energy storage capacity in a high-permeability distribution network is proposed. By constructing a bi-level programming model, the optimal capacity of energy storage connected to the distribution network is allocated by considering the operating cost, load fluctuation, and battery charging and discharging strategy. By constructing four scenarios with energy storage in the distribution network with a photovoltaic permeability of 29%, it was found that the bi-level decision-making model proposed in this paper saves 2346.66 yuan and 2055.05 yuan, respectively, in daily operation cost compared to the scenario without energy storage and the scenario with single-layer energy storage. After accessing IEEE-33 nodes for simulation verification, it was found that the bi-level decision-making model proposed in this paper has a good inhibition effect on voltage fluctuation and load fluctuation after energy storage configuration. In addition, this paper analyzes the energy storage that can be accessed by photovoltaic distribution networks with different permeability and finds that when photovoltaic permeability reaches 45% and corresponding energy storage is configured, the economic and energy storage benefits of the system are the best.
Keywords
bi-level decision-making models, Energy Storage, high PV penetration, optimal configuration
Suggested Citation
Zhang J, Zhu L, Zhao S, Yan J, Lv L. Optimal Configuration of Energy Storage Systems in High PV Penetrating Distribution Network. (2023). LAPSE:2023.20239v1
Author Affiliations
Zhang J: School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Zhu L: School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Zhao S: School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Yan J: State Key Laboratory of New Energy Power System, School of New Energy, North China Electric Power University, Beijing 100096, China
Lv L: School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Journal Name
Energies
Volume
16
Issue
5
First Page
2168
Year
2023
Publication Date
2023-02-23
ISSN
1996-1073
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PII: en16052168, Publication Type: Journal Article
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LAPSE:2023.20239v1
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https://doi.org/10.3390/en16052168
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