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Reactive Power Optimization for Distribution Network Based on Distributed Random Gradient-Free Algorithm

Author

Listed:
  • Jun Xie

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Chunxiang Liang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Yichen Xiao

    (College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

Abstract

The increasing penetration of distributed energy resources in distribution systems has brought a number of network management and operational challenges; reactive power variation has been identified as one of the dominant effects. Enormous growth in a variety of controllable devices that have complex control requirements are integrated in distribution networks. The operation modes of traditional centralized control are difficult to tackle these problems with central controller. When considering the non-linear multi-objective functions with discrete and continuous optimization variables, the proposed random gradient-free algorithm is employed to the optimal operation of controllable devices for reactive power optimization. This paper presents a distributed reactive power optimization algorithm that can obtain the global optimum solution based on random gradient-free algorithm for distribution network without requiring a central coordinator. By utilizing local measurements and local communications among capacitor banks and distributed generators (DGs), the proposed reactive power control strategy can realize the overall network voltage optimization and power loss minimization simultaneously. Simulation studies on the modified IEEE-69 bus distribution systems demonstrate the effectiveness and superiority of the proposed reactive power optimization strategy.

Suggested Citation

  • Jun Xie & Chunxiang Liang & Yichen Xiao, 2018. "Reactive Power Optimization for Distribution Network Based on Distributed Random Gradient-Free Algorithm," Energies, MDPI, vol. 11(3), pages 1-13, March.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:534-:d:134204
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    Citations

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    Cited by:

    1. Junyong Wu & Chen Shi & Meiyang Shao & Ran An & Xiaowen Zhu & Xing Huang & Rong Cai, 2019. "Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network," Energies, MDPI, vol. 12(17), pages 1-24, August.
    2. Linan Qu & Shujie Zhang & Hsiung-Cheng Lin & Ning Chen & Lingling Li, 2020. "Multiobjective Reactive Power Optimization of Renewable Energy Power Plants Based on Time-and-Space Grouping Method," Energies, MDPI, vol. 13(14), pages 1-15, July.

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