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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 3, 2024.
Abstract: In order to promote the absorption of photovoltaic in low-voltage distribution network, and reduce the voltage over-limit problem caused by high proportion of distributed photovoltaics, this paper proposes a method for optimizing the allocation of distributed energy storage system in low voltage distribution network. Firstly, based on the node voltage of the maximum load day and all day, the optimal clustering number k is obtained by the elbow method, and the K-means clustering algorithm is used to realize the zoning of the distribution network. Secondly, the objective function is to improve the node voltage, reduce the power loss, and minimize the comprehensive cost of energy storage investment, and at the same time consider various constraints such as power balance and energy storage battery, and construct a multi-objective optimization model for the optimal configuration of distributed energy storage system in low voltage distribution network. After normalizing each objective function, the weight coefficient of each objective function is determined based on the analytic hierarchy method. The whale algorithm is used to solve the model to determine the best installation location and capacity of distributed energy storage. Finally, taking an actual area as an example, the effectiveness of the proposed model in leveling the voltage exceeding of low voltage distribution network nodes is verified.
Lin Zhu, Xiaofang Meng and Nannan Zhang, “The Optimal Allocation Method for Energy Storage in Low Voltage Distribution Power Network” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150361
@article{Zhu2024,
title = {The Optimal Allocation Method for Energy Storage in Low Voltage Distribution Power Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150361},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150361},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Lin Zhu and Xiaofang Meng and Nannan Zhang}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.