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Energy Link Optimization in a Wireless Power Transfer Grid under Energy Autonomy Based on the Improved Genetic Algorithm

Author

Listed:
  • Zhihao Zhao

    (College of Automation, Chongqing University, Chongqing 400044, China)

  • Yue Sun

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing 400044, China
    College of Automation, Chongqing University, Chongqing 400044, China)

  • Aiguo Patrick Hu

    (Department of Engineering, The University of Auckland, Auckland 1142, New Zealand)

  • Xin Dai

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing 400044, China
    College of Automation, Chongqing University, Chongqing 400044, China)

  • Chunsen Tang

    (College of Automation, Chongqing University, Chongqing 400044, China)

Abstract

In this paper, an optimization method is proposed for the energy link in a wireless power transfer grid, which is a regional smart microgrid comprised of distributed devices equipped with wireless power transfer technology in a certain area. The relevant optimization model of the energy link is established by considering the wireless power transfer characteristics and the grid characteristics brought in by the device repeaters. Then, a concentration adaptive genetic algorithm (CAGA) is proposed to optimize the energy link. The algorithm avoided the unification trend by introducing the concentration mechanism and a new crossover method named forward order crossover, as well as the adaptive parameter mechanism, which are utilized together to keep the diversity of the optimization solution groups. The results show that CAGA is feasible and competitive for the energy link optimization in different situations. This proposed algorithm performs better than its counterparts in the global convergence ability and the algorithm robustness.

Suggested Citation

  • Zhihao Zhao & Yue Sun & Aiguo Patrick Hu & Xin Dai & Chunsen Tang, 2016. "Energy Link Optimization in a Wireless Power Transfer Grid under Energy Autonomy Based on the Improved Genetic Algorithm," Energies, MDPI, vol. 9(9), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:682-:d:76744
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    References listed on IDEAS

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    1. Gökan May & Bojan Stahl & Marco Taisch & Vittal Prabhu, 2015. "Multi-objective genetic algorithm for energy-efficient job shop scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7071-7089, December.
    2. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
    3. Vijith Vijayakumaran Nair & Jun Rim Choi, 2016. "An Efficiency Enhancement Technique for a Wireless Power Transmission System Based on a Multiple Coil Switching Technique," Energies, MDPI, vol. 9(3), pages 1-15, March.
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