Open Access

ARTICLE

Dynamic Boundary Optimization via IDBO-VMD: A Novel Power Allocation Strategy for Hybrid Energy Storage with Enhanced Grid Stability

Zujun Ding, Qi Xiang, Chengyi Li, Mengyu Ma, Chutong Zhang, Xinfa Gu, Jiaming Shi, Hui Huang, Aoyun Xia, Wenjie Wang, Wan Chen, Ziluo Yu, Jie Ji^*
Department of Electricity, School of Automation, Huaiyin Institute of Technology, Huaian, 223002, China
* Corresponding Author: Jie Ji. Email:

Energy Engineering https://doi.org/10.32604/ee.2025.070442

Received 16 July 2025; Accepted 22 August 2025; Published online 18 September 2025

Abstract

In order to address environmental pollution and resource depletion caused by traditional power generation, this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer (IDBO) with Variational Mode Decomposition (VMD). The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations. This study innovatively improves the traditional variational mode decomposition (VMD) algorithm, and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO self-optimization of key parameters K and a. On this basis, Fourier transform technology is used to define the boundary point between high frequency and low frequency signals, and a targeted energy distribution strategy is proposed: high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations; Low-frequency components are distributed to lead-carbon batteries, optimizing long-term energy storage and scheduling efficiency. This strategy effectively improves the response speed and stability of the energy storage system. The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency. Specifically, it effectively reduces the charge–discharge frequency of the battery, prolongs battery life, and optimizes the operating ranges of the state-of-charge (SOC) for both lead-carbon batteries and supercapacitors. In addition, the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system, but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.

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Keywords

Energy efficiency; hybrid energy storage system; intelligent algorithm; power fluctuation mitigation; renewable energy

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