A review of power curve decomposition techniques for energy storage systems

doi:10.19799/j.cnki.2095-4239.2025.0502

Abstract

Abstract:

Against the backdrop of numerous challenges in integrating wind and photovoltaic power into the grid, the development of energy storage technology has emerged as a key means of enhancing renewable energy utilization and strengthening the operational flexibility of power systems. Power curve decomposition methods are increasingly being used to optimize hybrid energy storage configurations. This report explores various power curve decomposition techniques for energy storage and their applications in the energy storage field, including traditional decomposition methods and those based on square-wave foundations. First, four traditional power curve decomposition methods are reviewed: Fourier-transform, wavelet packet decomposition, empirical mode decomposition, and low-pass filtering. The applications of these methods in the energy storage field, as explored by relevant researchers, are also introduced and the current research landscape is analyzed at both the national and international levels. The report highlights existing gaps in adapting these methods to the characteristics of hybrid energy storage systems. It concludes that configurations must be designed to enable energy storage devices to rapidly store and release electrical energy during charging and discharging, and that the power curve of a single storage device often exhibits a waveform with matrix-like characteristics. A method for decomposing, transforming, and analyzing energy storage power curves is proposed based on three transformations with a square-wave base, and the application in energy storage configuration is discussed. Finally, the current challenges in the decomposition of energy storage power curves are summarized and prospects for future research are proposed. The findings provide theoretical support for optimizing energy storage configurations, improving the accuracy of power curve decomposition, and optimizing the performance of energy storage systems in various operational scenarios.

Key words: power curve decomposition, orthogonal non-continuous basis functions, Fourier transform, wavelet transform, Walsh transform

CLC Number:

TM 73

Tanying LIU, Zhenxin SUN, Liangjie WEI, Hui LIU, Liping ZHANG, Chu WANG. A review of power curve decomposition techniques for energy storage systems[J]. Energy Storage Science and Technology, 2025, 14(8): 3065-3077.

Figures/Tables 5

References 80

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