Ge Cao1, Yifeng Dang1This email address is being protected from spambots. You need JavaScript enabled to view it., Shaoxiong Guo2, Yan Liang2, and Rong Jia1
1School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
2Xi’an Power Supply Company of State Grid Shaanxi Electric Power Company, Xi’an 710032, China
Received: August 31, 2024 Accepted: April 7, 2025 Publication Date: May 1, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Under the background of "double carbon", the installed capacity of wind power grows year by year, characterized by intermittency and volatility, bringing challenges to the reliable operation of the power system. This study proposes an optimal capacity configuration method for supercapacitor energy storage systems (SCES) to mitigate wind power fluctuations and maintain power system stability. The initial wind power curves are first analyzed and processed using empirical modal analysis to obtain a series of intrinsic modal functions at different frequencies, distinguishing between low frequency and high-frequency signals, which are reconstructed. Secondly, Leigh takes the minimization of the fluctuation volume and investment and operation cost of the supercapacitor energy storage system as the objective function and establishes the capacity allocation model of the supercapacitor energy storage system based on the power generation constraints and energy balance constraints of the supercapacitor energy storage system; Finally, the computational analysis was performed by actual area data, and the K-means algorithm was used to solve for grouping the data points into clusters, reconstructing each typical day and setting the maximum fluctuation limit. The proposed capacity allocation model is utilized to flatten the wind power data, and by comparing the grid-connected power curves of the wind power output before and after flattening, the capacity configurations of the supercapacitors for the two scenarios are 24.06 MW, and 30.34 MW, the rationality and effectiveness of the proposed method in flattening the fluctuations are verified.
Keywords: Supercapacitor; Fluctuation smoothing; Energy storage; K-means algorithm.
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