A Bi-Level Capacity Optimization Method for Hybrid Energy Storage Systems Combining the IBWO and MVMD Algorithms

Qiaoqiao Xing, Shidong Li (), Da Qiu, Yang Long, Qinyi Liao, Xiangjin Yin, Yunxiang Li and Kai Qian
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Qiaoqiao Xing: School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China
Shidong Li: School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China
Da Qiu: School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China
Yang Long: School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China
Qinyi Liao: School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China
Xiangjin Yin: School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China
Yunxiang Li: School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China
Kai Qian: School of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, China

Energies, 2025, vol. 18, issue 7, 1-24

Abstract: With the swift evolution of renewable energy technologies, the design and optimization of microgrids have emerged as vital components for fostering energy transition and promoting sustainable development. This study presents a bi-level capacity optimization model for microgrids, integrating wind–solar generation with hybrid electric–hydrogen energy storage systems to simultaneously enhance economic efficiency and system stability. The outer layer minimizes the annual total cost through the application of an Improved Beluga Whale Optimization (IBWO) algorithm, which is enhanced by strategies including the reverse elitism strategy, horizontal and vertical crossover operations, and a whirlwind scavenging strategy to improve performance. The inner layer builds on the optimized results from the outer layer, employing a Multivariable Variational Mode Decomposition (MVMD) algorithm to regulate the power output of the energy storage system. By integrating electric–hydrogen hybrid storage technology, the inner layer effectively mitigates power fluctuations. Furthermore, this study designs a modal decomposition-based charging and discharging scheduling strategy to ensures the system’s continuous and stable operation. Simulations performed on MATLAB 2018b and CPLEX 12.8 platforms indicate that the proposed dual-layer model decreases annual total expenses by 27.5% compared to a single-layer model while keeping grid-connected power variations within 10% of the installed capacity. This research provides innovative perspectives on microgrid optimization design and offers substantial technical support for ensuring stability and economic efficiency in intricate operational settings.

Keywords: microgrid; bi-level optimization model; hybrid energy storage; improved beluga whale optimization algorithm; MVMD algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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