Hybrid Energy Storage Capacity Optimization Configuration for Wind Power Fluctuation Smoothing Based on GSWOA-VMD
DOI:
https://doi.org/10.13052/dgaej2156-3306.4118Keywords:
Global search whale optimization algorithm, variational mode decomposition, wind power fluctuation smoothing, hybrid energy storage, capacity optimization configurationAbstract
To reduce the impact of wind power fluctuations on the power grid, this paper proposes a hybrid energy storage system (HESS) capacity optimization configuration method for wind power fluctuation smoothing. The method utilizes a Global Search Whale Optimization Algorithm (GSWOA) to optimize Variational Mode Decomposition (VMD) parameters. First, GSWOA determines the optimal parameter combination (K, α) for VMD. The optimized VMD then decomposes the wind power into a component suitable for direct grid integration and a component requiring smoothing. The smoothing-required component undergoes secondary allocation. Subsequently, an HESS capacity optimization model, considering system costs, is established. This approach achieves efficient wind power fluctuation smoothing and economically optimized operation of the HESS. Case study results demonstrate that the proposed method (GSWOA-VMD) offers superior calculation accuracy and convergence speed compared to alternatives. Furthermore, the HESS configuration exhibits a higher smoothing rate and lower comprehensive cost than single energy storage systems.
Downloads
References
Guo Z, Cai Y, Huang S, et al. Research on electricity balance and measurement optimization of new energy power system considering renewable energy consumption mechanism[J]. Distributed Generation and Alternative Energy Journal, 2024, 39(03):483–506.
Liu Z. Long-term Wind power optimization with DQN[J]. Distributed Generation and Alternative Energy Journal, 2025,40(02): 307–332.
Ma L, Xie L R, Ye L, et al. Wind power fluctuation suppression strategy based on hybrid energy storage bi-level programming model[J]. Power System Technology, 2022, 46(03):1016–1029.
Zhang Y, Xu Y J, Guo H, et al. A hybrid energy storage system with optimized operating strategy for mitigating wind power fluctuations[J]. Renewable Energy, 2018, 125:121–132.
Li H Z, Sun D Y, Li B K, et al. Collaborative optimization of VRB-PS hybrid energy storage system for large-scale wind power grid integration[J]. Energy, 2023, 265:126292.
Lu Q Y, Yang Y G, Chen J S, et al. Capacity optimization of hybrid energy storage systems for offshore wind power volatility smoothing[J]. Energy Reports, 2023, 9(S7):575–583.
Kumar G V B and Palanisamy K. Ramp-Rate control for mitigation of solar PV fluctuations with hybrid energy storage system[J]. Distributed Generation and Alternative Energy Journal, 2023, 38(03):817–840.
Liu W, Wu H S, He Z C, et al. A multistage current charging method for Li-Ion battery considering balance of internal consumption and charging speed[J].Transactions of China Electrotechnical Society, 2017, 32(09):112–120.
Vlad A, Singh N, Rolland J, et al. Hybrid supercapacitor-battery materials for fast electrochemical charge storage[J]. Scientific Reports, 2014, 4:4315.
Zhang L, Hu X S, Wang Z P, et al. A review of supercapacitor modeling, estimation, and applications: A control/management perspective [J]. Renewable and Sustainable Energy Reviews, 2018, 81:1868–1878.
Hong F, Jia X Y, Liang L, et al. Hierarchical capacity optimization configuration of hybrid energy storage for wind farm frequency support[J]. Proceedings of the CSEE, 2024, 44(14):5596–5607.
Sang B Y, Wang D S, Yang B, et al. Optimal allocation of energy storage system for smoothing the output fluctuations of new energy[J]. Proceedings of the CSEE, 2014, 34(22):3700–3706.
Hu R, Yin S J, Fu Y. Optimal allocation of hybrid energy storage capacity based on power fluctuations and wind curtailment characteristics[J]. Electrical Measurement & Instrumentation, 2017, 54(19):46–53.
Qi X J, Zheng X W, Wang X R, et al. Improved wavelet packet method of smoothing wind power fluctuations baesd on time-frequency analysis[J]. Acta Energiae Solaris Sinica, 2022, 43(07):302–309.
Yi H, Chen H. Energy management of compound power supply based on grey wolf algorithm[J]. Chinese Journal of Ship Research, 2025, 20(03):249–257.
Li X, Zhang J C, Wang N. Capacity configuration strategy for super capacitor-flywheel- battery hybrid energy storage system[J]. Electric Power, 2018, 51(11):117–124.
Jiang X K, Liu C, Zhang X S, et al. Study on strategy for stabilizing wind power fluctuations based on dual energy storage[J]. Electrical Measurement & Instrumentation, 2025, 62(04):19–25.
Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3):531–544.
Xu Z W, Xiang K X, Wang B, et al. Study on PV power prediction based on VMD-IGWO-LSTM[J]. Distributed Generation and Alternative Energy Journal, 2024, 39(03):507–530.
Du J Y, Lei Y, Li Y K, et al. Hybrid energy storage strategy based on parameter optimized variational mode decomposition[J]. Modern Electric Power, 2021, 38(1):51–59.
Li X, Wang J, Qiu Y, et al.Optimal allocation of hybrid energy storage capacity based on variational mode decomposition[J]. Acta Energiae Solaris Sinica, 2022, 43(02):88–96.
Li W G, Jiao P L, Liu X Y, et al. Capacity optimization configuration of energy storage auxiliary traditional unit frequency modulation based on variational mode decomposition[J]. Power System Protection and Control, 2020, 48(06):43–52.
Li Y M, Ding Z M, Yu Y H, et al. Hybrid energy storage power distribution strategy for micro gas turbine power generation system based on variational mode decomposition[J]. Journal of Xi’an Jiaotong University, 2023, 57(10):183–195.
LIU Y B, ZHANG X, KANG Y L, et al. Capacity allocation of hybrid energy storage system for stabilizing wind power fluctuations through twice decomposition[J]. Proceedings of the CSU-EPSA, 2024, 36(09):61–69.
National technical committee for standardization of electricity regulatory, technical rule for connecting wind farm to power system: GB/T 19963-2011[S]. Beijing: General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, 2011.
Gao X Z, Wang L, Tian J, et al. Research on hybrid energy storage power distribution strategy based on parameter optimization variational mode decomposition[J]. Energy Storage Science and Technology, 2022, 11(01):147–155.
Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95(5):51–67.
Xu F H, and Zhang W. Research on the optimal location of urban electric vehicle charging stations based on the whale optimization algorithm in the context of green energy[J]. Distributed Generation and Alternative Energy Journal, 2025, 40(01):193–212.
Liu L, Bai K Q, Dan Z H,et al. Whale optimization algorithm with global search strategy[J]. Journal of Chinese Computer Systems, 2020, 41(09):1820–1825.
Li D R, Xiao P and Zhai R J, et al. Path planning of welding robots based on developed whale optimization algorithm[C]. 2021 6th International Conference on Control, Robotics and Cybernetics (CRC), 2021, 101–105.
Chen W T, Wang W X, Xie H B, et al. Characterization of surface EMG signal based on fuzzy entropy[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(02):266–272.
