Optimal operation of distributed energy storage in the distribution network considering source load uncertainty under the high light voltage infiltration

The uncertainty of distributed photovoltaic output and load demand increases the difficulty of optimizing the operation of energy storage systems. However, the existing technology is often difficult to accurately predict the future photovoltaic output and load demand. Therefore, the optimal operation of distributed energy storage of distribution network considering the uncertainty of source load under the high light voltage penetration is designed. Based on the two objectives of minimum power loss and minimum load fluctuation, the comprehensive constraint conditions including node power flow balance, node voltage, branch current, BESS operation and network unbalance are established. An energy storage charging and discharging strategy based on the principle of source-charge balance is proposed, and the source-charge uncertainty is modeled by the distributed robust optimization method. Combining the advantages of NSGA-II algorithm and particle swarm optimization algorithm, an improved multi-objective optimization algorithm is proposed. The experimental results show that the distributed energy storage battery with the design method has a high utilization rate, improves the photovoltaic absorption rate on the basis of reducing the total system cost, and the charge and discharge power curve is more stable from 5:00 to 20:00.

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Publication Details

Journal
Discover Applied Sciences
Published
2025-11-27
DOI
https://doi.org/10.1007/s42452-025-07909-y
Primary Topic
Microgrid Control and Optimization
Type
article
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Optimal operation of distributed energy storage in the distribution network considering source load uncertainty under the high light voltage infiltration

Fufeng Miao, Yang Zhao, Wanxun Liu, Ruihua Si et al.
Discover Applied Sciences
Microgrid Control and Optimization
article

Optimal operation of distributed energy storage in the distribution network considering source load uncertainty under the high light voltage infiltration

Fufeng Miao, Yang Zhao, Wanxun Liu, Ruihua Si, Yaoqiang Wang, Wenfeng Li
article en

Abstract

The uncertainty of distributed photovoltaic output and load demand increases the difficulty of optimizing the operation of energy storage systems. However, the existing technology is often difficult to accurately predict the future photovoltaic output and load demand. Therefore, the optimal operation of distributed energy storage of distribution network considering the uncertainty of source load under the high light voltage penetration is designed. Based on the two objectives of minimum power loss and minimum load fluctuation, the comprehensive constraint conditions including node power flow balance, node voltage, branch current, BESS operation and network unbalance are established. An energy storage charging and discharging strategy based on the principle of source-charge balance is proposed, and the source-charge uncertainty is modeled by the distributed robust optimization method. Combining the advantages of NSGA-II algorithm and particle swarm optimization algorithm, an improved multi-objective optimization algorithm is proposed. The experimental results show that the distributed energy storage battery with the design method has a high utilization rate, improves the photovoltaic absorption rate on the basis of reducing the total system cost, and the charge and discharge power curve is more stable from 5:00 to 20:00.

Discover Applied SciencesVol. 8(1)
State Grid Corporation of China (China) (CN), Zhengzhou University (CN), Henan Energy & Chemical Industry Group (China) (CN), Shanghai Electric (China) (CN), Economic Research Institute (BG), Economic Research Institute (KZ)
Openalex Percentile: Top 54%
Microgrid Control and Optimization
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Optimal operation of distributed energy storage in the distribution network considering source load uncertainty under the high light voltage infiltration — Fufeng Miao, Yang Zhao, et al. · Discover Applied Sciences (2025) | TGRS Research Map | TGRS