Abstract:

Against the backdrop of numerous challenges in the grid integration of wind and photovoltaic power, energy storage technology has emerged as a key means to enhance renewable energy utilization and strengthen the operational flexibility of power systems. Its power curve decomposition methods have gradually been applied to optimize hybrid energy storage configurations. This paper explores various energy storage power curve decomposition techniques and their applications in the energy storage field, including traditional decomposition methods and those based on square-wave foundations. First, the paper reviews four traditional power curve decomposition methods: Fourier transform, wavelet packet decomposition, empirical mode decomposition, and low-pass filtering. It also introduces the applications of these methods in the field of energy storage, as explored by relevant researchers, and analyzes the current research landscape at both national and international levels. The paper highlights existing gaps in adapting these methods to the characteristics of hybrid energy storage systems. It concludes that configurations must be based on the ability of 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. This paper proposes a method for energy storage power curve decomposition, transformation, and analysis based on three transformations with a square-wave base, and discusses its application in energy storage configuration. Finally, the paper summarizes the current challenges in energy storage power curve decomposition and offers prospects for future research. The findings provide theoretical support for optimizing energy storage configurations, improving the accuracy of power curve decomposition, and ensuring the optimal 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