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Optimal power allocation and capacity configuration based on variable cut-off frequency low-pass filtering for photovoltaic with hybrid energy storage system

Author

Listed:
  • Kan, Xiaobo
  • Ma, Xun
  • Yao, Jingying
  • Xiong, Bifeng
  • Zhao, Yu

Abstract

Photovoltaic (PV) power generation is affected by intermittent solar radiation, leading to fluctuations in output power and reducing the stability and reliability of PV systems. In general, a hybrid energy storage system (HESS) combined with a PV system is employed to smooth PV power fluctuation. However, the traditional algorithms for balancing the capacity of HESS, power fluctuations, and life cycle costs (LCC) remain challenging. To address this challenge, a modified method of variable cut-off frequency low-pass filtering (VFLPF) is developed to determine the capacity of battery and supercapacitor (SC) in HESS, as well as balance the relationship between PV power fluctuations and total energy system capacity (TESC). Additionally, ZE and Zp are adopted as metrics for evaluating the total energy system capacity and reference power, respectively, specifically employed to mitigate the maximum fluctuation rate (MFR) in PV systems. Moreover, the presented algorithm is validated by the photovoltaic module combined with the hybrid energy storage system (PVM-HESS) and the photovoltaic array combined with the hybrid energy storage system (PVA-HESS). In the presented PVM-HESS and PVA-HESS, the metrics ZE are respectively reduced by 2.65–2.94 % and 20–54.10 % compared to the traditional fixed cut-off frequency low-pass filtering (FFLPF) strategies of S.Ⅰ (cut-off frequency of 160 Hz) and S.Ⅱ (cut-off frequency of 750 Hz), while the Zp is reduced by 3.3–45.62 % and 3.31–78 %, respectively. The life cycle costs determined by the presented algorithm are reduced by 7.71 % and 11.10–21.52 %. Furthermore, a three-port DC-DC converter of 450 W is designed to connect the DC Bus with the PV module and the hybrid energy storage system, in which the MFR of PV output power is reduced by 23.77 %. Finally, a 20 kWp PVA combined with the hybrid energy storage system optimises power allocation and capacity configuration while reducing the MFRs throughout the year by 0.07–64.53 %. In conclusion, the presented algorithm and strategy are an effective technical solution for reducing total energy capacity and reference power when smoothing power fluctuations, as well as providing theoretical support for coordinated operation of storage energy equipment in a hybrid energy storage system.

Suggested Citation

  • Kan, Xiaobo & Ma, Xun & Yao, Jingying & Xiong, Bifeng & Zhao, Yu, 2026. "Optimal power allocation and capacity configuration based on variable cut-off frequency low-pass filtering for photovoltaic with hybrid energy storage system," Renewable Energy, Elsevier, vol. 256(PH).
  • Handle: RePEc:eee:renene:v:256:y:2026:i:ph:s0960148125021901
    DOI: 10.1016/j.renene.2025.124526
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    References listed on IDEAS

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    1. Benavides, Darío & Arévalo, Paul & Criollo, Adrián & Tostado-Véliz, Marcos & Jurado, Francisco, 2024. "Multi-mode monitoring and energy management for photovoltaic-storage systems," Renewable Energy, Elsevier, vol. 230(C).
    2. Ouyang, Tiancheng & Zhang, Mingliang & Qin, Peijia & Tan, Xianlin, 2024. "Flow battery energy storage system for microgrid peak shaving based on predictive control algorithm," Applied Energy, Elsevier, vol. 356(C).
    3. Huang, Jing & Teng, Xiao & Hu, Qingyi & Guo, Su & Boland, John, 2025. "Dynamic energy storage capacity optimization based on ultra-short-term prediction in grid-connected PV system," Renewable Energy, Elsevier, vol. 253(C).
    4. Gao, Mingfei & Han, Zhonghe & Zhang, Ce & Li, Peng & Wu, Di & Li, Peng, 2023. "Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage," Energy, Elsevier, vol. 277(C).
    5. Xiong, Hualin & Xu, Beibei & Kheav, Kimleng & Luo, Xingqi & Zhang, Xingjin & Patelli, Edoardo & Guo, Pengcheng & Chen, Diyi, 2021. "Multiscale power fluctuation evaluation of a hydro-wind-photovoltaic system," Renewable Energy, Elsevier, vol. 175(C), pages 153-166.
    6. Tiezhou Wu & Wenshan Yu & Lujun Wang & Linxin Guo & Zhiquan Tang, 2019. "Power Distribution Strategy of Microgrid Hybrid Energy Storage System Based on Improved Hierarchical Control," Energies, MDPI, vol. 12(18), pages 1-14, September.
    7. Li, Dezhi & Li, Shuo & Zhang, Shubo & Sun, Jianrui & Wang, Licheng & Wang, Kai, 2022. "Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine," Energy, Elsevier, vol. 250(C).
    8. Zhang, Yijie & Ma, Tao & Elia Campana, Pietro & Yamaguchi, Yohei & Dai, Yanjun, 2020. "A techno-economic sizing method for grid-connected household photovoltaic battery systems," Applied Energy, Elsevier, vol. 269(C).
    9. Sun, Lejia & Jia, Jingqi & Wang, QuanLi & Zhang, Yimeng, 2024. "A novel multiphase DC/DC boost converter for interaction of solar energy and hydrogen fuel cell in hybrid electric vehicles," Renewable Energy, Elsevier, vol. 229(C).
    10. Song, Chenchen & Guo, Zhiling & Liu, Zhengguang & Hongyun, Zhang & Liu, Ran & Zhang, Haoran, 2024. "Application of photovoltaics on different types of land in China: Opportunities, status and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    11. Lappalainen, Kari & Valkealahti, Seppo, 2022. "Sizing of energy storage systems for ramp rate control of photovoltaic strings," Renewable Energy, Elsevier, vol. 196(C), pages 1366-1375.
    12. Lyu, Chenghao & Zhang, Yuchen & Bai, Yilin & Yang, Kun & Song, Zhengxiang & Ma, Yuhang & Meng, Jinhao, 2024. "Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage," Applied Energy, Elsevier, vol. 363(C).
    13. Hernández, J.C. & Sanchez-Sutil, F. & Muñoz-Rodríguez, F.J. & Baier, C.R., 2020. "Optimal sizing and management strategy for PV household-prosumers with self-consumption/sufficiency enhancement and provision of frequency containment reserve," Applied Energy, Elsevier, vol. 277(C).
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