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Adaptive Variable Universe Fuzzy Droop Control Based on a Novel Multi-Strategy Harris Hawk Optimization Algorithm for a Direct Current Microgrid with Hybrid Energy Storage

Author

Listed:
  • Chen Wang

    (Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China)

  • Shangbin Jiao

    (Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China)

  • Youmin Zhang

    (Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Xiaohui Wang

    (Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, China)

  • Yujun Li

    (Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China)

Abstract

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy Harris Hawk Optimization Algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within ±0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit.

Suggested Citation

  • Chen Wang & Shangbin Jiao & Youmin Zhang & Xiaohui Wang & Yujun Li, 2024. "Adaptive Variable Universe Fuzzy Droop Control Based on a Novel Multi-Strategy Harris Hawk Optimization Algorithm for a Direct Current Microgrid with Hybrid Energy Storage," Energies, MDPI, vol. 17(21), pages 1-39, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5296-:d:1506031
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    1. Upasana Lakhina & Nasreen Badruddin & Irraivan Elamvazuthi & Ajay Jangra & Truong Hoang Bao Huy & Josep M. Guerrero, 2023. "An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
    2. Tobajas, Javier & Garcia-Torres, Felix & Roncero-Sánchez, Pedro & Vázquez, Javier & Bellatreche, Ladjel & Nieto, Emilio, 2022. "Resilience-oriented schedule of microgrids with hybrid energy storage system using model predictive control," Applied Energy, Elsevier, vol. 306(PB).
    3. Celikel, Resat & Yilmaz, Musa & Gundogdu, Ahmet, 2022. "A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions," Renewable Energy, Elsevier, vol. 184(C), pages 361-373.
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    1. Zhengyao Wang & Fulin Fan & Hang Zhang & Kai Song & Jinhai Jiang & Chuanyu Sun & Rui Xue & Jingran Zhang & Zhengjian Chen, 2025. "Flexible On-Grid and Off-Grid Control for Electric–Hydrogen Coupling Microgrids," Energies, MDPI, vol. 18(4), pages 1-23, February.

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