IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i9p682-d76744.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/9/682/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/9/9/682/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Avilés A., Camilo & Oliva H., Sebastian & Watts, David, 2019. "Single-dwelling and community renewable microgrids: Optimal sizing and energy management for new business models," Applied Energy, Elsevier, vol. 254(C).
    2. Tao Ren & Yan Zhang & Shuenn-Ren Cheng & Chin-Chia Wu & Meng Zhang & Bo-yu Chang & Xin-yue Wang & Peng Zhao, 2020. "Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates," Mathematics, MDPI, vol. 8(8), pages 1-25, July.
    3. Beck, Fabian G. & Biel, Konstantin & Glock, Christoph H., 2019. "Integration of energy aspects into the economic lot scheduling problem," International Journal of Production Economics, Elsevier, vol. 209(C), pages 399-410.
    4. Thomas Sachs & Anna Gründler & Milos Rusic & Gilbert Fridgen, 2019. "Framing Microgrid Design from a Business and Information Systems Engineering Perspective," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(6), pages 729-744, December.
    5. Riccardo Iacobucci & Benjamin McLellan & Tetsuo Tezuka, 2018. "The Synergies of Shared Autonomous Electric Vehicles with Renewable Energy in a Virtual Power Plant and Microgrid," Energies, MDPI, vol. 11(8), pages 1-20, August.
    6. Khatereh Ghasvarian Jahromi & Davood Gharavian & Hamid Reza Mahdiani, 2023. "Wind power prediction based on wind speed forecast using hidden Markov model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 101-123, January.
    7. Zhenya Ji & Xueliang Huang & Changfu Xu & Houtao Sun, 2016. "Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach," Energies, MDPI, vol. 9(11), pages 1-18, November.
    8. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    9. Izadbakhsh, Maziar & Gandomkar, Majid & Rezvani, Alireza & Ahmadi, Abdollah, 2015. "Short-term resource scheduling of a renewable energy based micro grid," Renewable Energy, Elsevier, vol. 75(C), pages 598-606.
    10. e Silva, Danilo P. & Félix Salles, José L. & Fardin, Jussara F. & Rocha Pereira, Maxsuel M., 2020. "Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data," Applied Energy, Elsevier, vol. 278(C).
    11. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    12. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    13. Kia, M. & Shafiekhani, M. & Arasteh, H. & Hashemi, S.M. & Shafie-khah, M. & Catalão, J.P.S., 2020. "Short-term operation of microgrids with thermal and electrical loads under different uncertainties using information gap decision theory," Energy, Elsevier, vol. 208(C).
    14. Ali Hadi Abdulwahid & Shaorong Wang, 2016. "A Novel Approach for Microgrid Protection Based upon Combined ANFIS and Hilbert Space-Based Power Setting," Energies, MDPI, vol. 9(12), pages 1-25, December.
    15. Mohamed Els. S. Abdelwareth & Dedet Candra Riawan & Chow Chompoo-inwai, 2023. "Optimum Generated Power for a Hybrid DG/PV/Battery Radial Network Using Meta-Heuristic Algorithms Based DG Allocation," Sustainability, MDPI, vol. 15(13), pages 1-25, July.
    16. Umeozor, Evar Chinedu & Trifkovic, Milana, 2016. "Operational scheduling of microgrids via parametric programming," Applied Energy, Elsevier, vol. 180(C), pages 672-681.
    17. Xu Zhang & Hua Zhang & Jin Yao, 2020. "Multi-Objective Optimization of Integrated Process Planning and Scheduling Considering Energy Savings," Energies, MDPI, vol. 13(23), pages 1-31, November.
    18. Reza Ahmadi Kordkheili & Seyyed Ali Pourmousavi & Mehdi Savaghebi & Josep M. Guerrero & Mohammad Hashem Nehrir, 2016. "Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks," Energies, MDPI, vol. 9(1), pages 1-17, January.
    19. Matjaz Rozman & Michael Fernando & Bamidele Adebisi & Khaled M. Rabie & Tim Collins & Rupak Kharel & Augustine Ikpehai, 2017. "A New Technique for Reducing Size of a WPT System Using Two-Loop Strongly-Resonant Inductors," Energies, MDPI, vol. 10(10), pages 1-18, October.
    20. Govind Joshi & Salman Mohagheghi, 2021. "Optimal Operation of Combined Energy and Water Systems for Community Resilience against Natural Disasters," Energies, MDPI, vol. 14(19), pages 1-19, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:682-:d:76744. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.