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Optimal reactive power dispatch of permanent magnet synchronous generator-based wind farm considering levelised production cost minimisation

Author

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  • Li, Jian
  • Wang, Ni
  • Zhou, Dao
  • Hu, Weihao
  • Huang, Qi
  • Chen, Zhe
  • Blaabjerg, Frede

Abstract

As wind power penetration increases, large wind farms (WFs) need to provide reactive power according to modern grid codes. Permanent magnet synchronous generator-based wind turbines (WTs) can generate reactive power, by assigning the appropriate reactive power to each WT to meet the reactive power requirements of the grid. This is a more economical method than setting up additional reactive power compensation equipment. This study proposes an optimal reactive power dispatch strategy for minimising a levelised production cost, and is implemented in two ways: minimising the power loss of a WF, and maximising the lifetime of WTs. The reactive power references of each WT are chosen as the optimisation variables, and a particle swarm optimisation algorithm is adopted to solve the optimisation problem. The proposed and traditional reactive power dispatch strategies are demonstrated and compared on a WF with 25 WTs to validate the effectiveness of the proposed approach.

Suggested Citation

  • Li, Jian & Wang, Ni & Zhou, Dao & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2020. "Optimal reactive power dispatch of permanent magnet synchronous generator-based wind farm considering levelised production cost minimisation," Renewable Energy, Elsevier, vol. 145(C), pages 1-12.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:1-12
    DOI: 10.1016/j.renene.2019.06.014
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    Cited by:

    1. Wang, Ni & Li, Jian & Yu, Xiang & Zhou, Dao & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2020. "Optimal active and reactive power cooperative dispatch strategy of wind farm considering levelised production cost minimisation," Renewable Energy, Elsevier, vol. 148(C), pages 113-123.
    2. Lenin Kanagasabai, 2022. "Tangible power loss lessening by hybridized beautiful demoiselle-enriched particle swarm and pyramid optimization algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 450-468, February.
    3. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system," Renewable Energy, Elsevier, vol. 147(P1), pages 1418-1431.
    4. Liao, Hao & Hu, Weihao & Wu, Xiawei & Wang, Ni & Liu, Zhou & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Active power dispatch optimization for offshore wind farms considering fatigue distribution," Renewable Energy, Elsevier, vol. 151(C), pages 1173-1185.
    5. Shojaei, Amir Hossein & Ghadimi, Ali Asghar & Miveh, Mohammad Reza & Gandoman, Foad H. & Ahmadi, Abdollah, 2021. "Multiobjective reactive power planning considering the uncertainties of wind farms and loads using Information Gap Decision Theory," Renewable Energy, Elsevier, vol. 163(C), pages 1427-1443.

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