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Reactive Power Optimal Control of a Wind Farm for Minimizing Collector System Losses

Author

Listed:
  • Yunqi Xiao

    (Department of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Yi Wang

    (Department of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Yanping Sun

    (North China Electric Power Research Institute Co., Ltd., Beijing 100032, China)

Abstract

A reactive power/voltage control strategy is proposed that uses wind turbines as distributed reactive power sources to optimize the power flow in large-scale wind farms and reduce the overall losses of the collector system. A mathematical model of loss optimization for the wind farm collector systems is proposed based on a reactive power/voltage sensitivity analysis; a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to validate the optimization performances. The simulation model is established based on a large-scale wind farm. The results of multiple scenarios show that the proposed strategy is superior to the traditional methods with regard to the reactive power/voltage control of the wind farm and the loss reduction of the collector system. Furthermore, the advantages in terms of annual energy savings and environmental protection are also estimated.

Suggested Citation

  • Yunqi Xiao & Yi Wang & Yanping Sun, 2018. "Reactive Power Optimal Control of a Wind Farm for Minimizing Collector System Losses," Energies, MDPI, vol. 11(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3177-:d:183284
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    References listed on IDEAS

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    3. Dan Wang & Qing’e Hu & Jia Tang & Hongjie Jia & Yun Li & Shuang Gao & Menghua Fan, 2017. "A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement," Energies, MDPI, vol. 10(12), pages 1-19, December.
    4. Abdelsalam, Ali M. & El-Shorbagy, M.A., 2018. "Optimization of wind turbines siting in a wind farm using genetic algorithm based local search," Renewable Energy, Elsevier, vol. 123(C), pages 748-755.
    5. Han, Xiaojuan & Zhang, Hua & Yu, Xiaoling & Wang, Lina, 2016. "Economic evaluation of grid-connected micro-grid system with photovoltaic and energy storage under different investment and financing models," Applied Energy, Elsevier, vol. 184(C), pages 103-118.
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    Cited by:

    1. Saša Vlahinić & Dubravko Franković & Vitomir Komen & Anamarija Antonić, 2019. "Reactive Power Compensation with PV Inverters for System Loss Reduction," Energies, MDPI, vol. 12(21), pages 1-17, October.
    2. Yukun Dong & Yu Zhang & Fubin Liu & Zhengjun Zhu, 2022. "Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 15(8), pages 1-18, April.
    3. Mir Sayed Shah Danish & Tomonobu Senjyu & Sayed Mir Shah Danish & Najib Rahman Sabory & Narayanan K & Paras Mandal, 2019. "A Recap of Voltage Stability Indices in the Past Three Decades," Energies, MDPI, vol. 12(8), pages 1-18, April.
    4. Ly Huu Pham & Minh Quan Duong & Van-Duc Phan & Thang Trung Nguyen & Hoang-Nam Nguyen, 2019. "A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem," Energies, MDPI, vol. 12(9), pages 1-25, May.

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