Authors

Junda Zhang

Abstract

With a high proportion of renewable energy connected to the power system, the power fluctuation problem of photovoltaic microgrid is becoming increasingly prominent. It is difficult for a single energy storage technology to meet the requirements of high power density and high energy density at the same time. Hybrid energy storage system (HESS) has become an effective solution to stabilize power fluctuation through the cooperative work of energy and power energy storage equipment. In this paper, a collaborative control strategy of “system-level frequency division correction-equipment-level energy shaping” is proposed. In the system level, Moby Dick optimization algorithm (BWO) is used to optimize the parameters of Variational Modal Decomposition (VMD) to achieve accurate frequency division of power signals, and in the equipment level, an energy shaping (ES) controller is designed based on port-controlled Hamiltonian (PCH) model. Through Matlab/Simulink simulation verification, the power deficit range of the system before suppression was -863.31W to 1182.25W. After adopting the control strategy proposed in this paper, the power deficit was suppressed to - 47.12W to 81.87W, reducing the fluctuation amplitude by 93.78%. Compared to traditional PI control, this strategy lowers DC bus voltage fluctuations by 45.3% to 65.8% and shortens suppression time by 24.7% to 33.3%. It significantly enhances the microgrid’s stability and dynamic response performance under sudden power changes.

Citation

  • Journal: 2025 2nd International Conference on Energy Technology and Electrical Power (ETEP)
  • Year: 2025
  • Volume:
  • Issue:
  • Pages: 251–256
  • Publisher: IEEE
  • DOI: 10.1109/etep67941.2025.11440638

BibTeX

@inproceedings{Zhang_2025,
  title={{Research on Power Fluctuation Suppression of Photovoltaic Microgrid Based on Hybrid Energy Storage}},
  DOI={10.1109/etep67941.2025.11440638},
  booktitle={{2025 2nd International Conference on Energy Technology and Electrical Power (ETEP)}},
  publisher={IEEE},
  author={Zhang, Junda},
  year={2025},
  pages={251--256}
}

Download the bib file

References

  • Ahmad S, Shafiullah M, Ahmed CB, Alowaifeer M (2023) A Review of Microgrid Energy Management and Control Strategies. IEEE Access 11:21729–21757. https://doi.org/10.1109/access.2023.324851 – 10.1109/access.2023.3248511
  • Taye BA, Choudhury NBD (2023) Adaptive filter based method for hybrid energy storage system management in DC microgrid. e-Prime - Advances in Electrical Engineering, Electronics and Energy 5:100259. https://doi.org/10.1016/j.prime.2023.10025 – 10.1016/j.prime.2023.100259
  • Hajiaghasi S, Salemnia A, Hamzeh M (2019) Hybrid energy storage system for microgrids applications: A review. Journal of Energy Storage 21:543–570. https://doi.org/10.1016/j.est.2018.12.01 – 10.1016/j.est.2018.12.017
  • Lu X, Guerrero JM, Sun K, Vasquez JC, Teodorescu R, Huang L (2014) Hierarchical Control of Parallel AC-DC Converter Interfaces for Hybrid Microgrids. IEEE Trans Smart Grid 5(2):683–692. https://doi.org/10.1109/tsg.2013.227232 – 10.1109/tsg.2013.2272327
  • Rajabinezhad M, Mirafzal B, Fateh F, Zuo S (2024) A Q‐Learning and Fuzzy Logic Control of Hybrid Energy Storage System Using Two Stage Low‐Pass Filter to Smooth Power Fluctuations in Microgrid. Intl J Robust & Nonlinear. https://doi.org/10.1002/rnc.771 – 10.1002/rnc.7713
  • Neelagiri S, Usha P, Biradar S (2025) Fuzzy logic-based energy management system for a microgrid with hybrid energy storage: design, control, and comparative analysis. Int J Pow Elec & Dri Syst 16(3):1991. https://doi.org/10.11591/ijpeds.v16.i3.pp1991-200 – 10.11591/ijpeds.v16.i3.pp1991-2004
  • Zhu W, Yang Y, Zhi P, Liang Z (2022) A Control Strategy of Photovoltaic Hybrid Energy Storage System Based on Adaptive Wavelet Packet Decomposition. International Journal of Electrochemical Science 17(11):221144. https://doi.org/10.20964/2022.11.3 – 10.20964/2022.11.30
  • Xu X-F, Wang K, Ma W-H, Wu C-L, Huang X-R, Ma Z-X, Li Z-H (2024) Multi-objective particle swarm optimization algorithm based on multi-strategy improvement for hybrid energy storage optimization configuration. Renewable Energy 223:120086. https://doi.org/10.1016/j.renene.2024.12008 – 10.1016/j.renene.2024.120086
  • Zhang Z, Cheng X, Xing Z, Wang Z (2024) Energy management strategy optimization for hybrid energy storage system of tram based on competitive particle swarm algorithms. Journal of Energy Storage 75:109698. https://doi.org/10.1016/j.est.2023.10969 – 10.1016/j.est.2023.109698
  • Siddiqui NA, Tahir H, Akram M, Manzoor HU (2025) Optimized Control of Hybrid Energy Storage Systems Using Whale Optimization Algorithm for Enhanced Battery Longevity and Stability in Microgrids. Engineering Reports 7(5). https://doi.org/10.1002/eng2.7019 – 10.1002/eng2.70199
  • Blaabjerg F, Teodorescu R, Liserre M, Timbus AV (2006) Overview of Control and Grid Synchronization for Distributed Power Generation Systems. IEEE Trans Ind Electron 53(5):1398–1409. https://doi.org/10.1109/tie.2006.88199 – 10.1109/tie.2006.881997
  • Holmes DG, Lipo TA (2003) Pulse Width Modulation for Power Converter – 10.1109/9780470546284
  • Utkin VI (1992) Sliding Modes in Control and Optimization. Springer Berlin Heidelber – 10.1007/978-3-642-84379-2
  • Krstic, Nonlinear and adaptive control design[M] (1995)
  • Ortega, Energy-shaping control of three-phase AC/DC voltage-source converters[J]. IEEE Transactions on Control Systems Technology (2008)
  • Li Y, Ding Z, Yu Y, Liu Y (2023) Hybrid energy storage power allocation strategy based on parameter-optimized VMD algorithm for marine micro gas turbine power system. Journal of Energy Storage 73:109189. https://doi.org/10.1016/j.est.2023.10918 – 10.1016/j.est.2023.109189
  • Abdolmaleki B, Bergna-Diaz G (2022) Distributed Control and Optimization of DC Microgrids: A Port-Hamiltonian Approach. IEEE Access 10:64222–64233. https://doi.org/10.1109/access.2022.318320 – 10.1109/access.2022.3183209