IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2539-d1090815.html
   My bibliography  Save this article

Capacity Optimization of Independent Microgrid with Electric Vehicles Based on Improved Pelican Optimization Algorithm

Author

Listed:
  • Jiyong Li

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Ran Chen

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Chengye Liu

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Xiaoshuai Xu

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

  • Yasai Wang

    (School of Electrical Engineering, Guangxi University, Nanning 530004, China)

Abstract

In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based on the load and energy storage characteristics of electric vehicles, the classification of electric vehicles was proposed, and their mathematical models were established. The idea of robust optimization was adopted to construct the uncertain scenario set. Considering the incentive demand response, a two-layer power capacity optimization model of a microgrid was constructed. The improved pelican optimization algorithm (IPOA) was proposed as the two-layer model. In view of the slow convergence rate of the pelican optimization algorithm (POA) and its tendency to fall into the local optimum, methods such as elite reverse learning were proposed to generate the initial population, set disturbance inhibitors, and introduce Lévy flight to improve the initial population of the algorithm and enhance its global search ability. Finally, an independent microgrid was used as an example to verify the effectiveness of the proposed model and the improved algorithm. Considering that the total power capacity optimization cost of the microgrid after addition of electric vehicles was reduced by CNY 139,600, the total power capacity optimization cost of the microgrid after IOPA optimization was reduced by CNY 49,600 compared with that after POA optimization.

Suggested Citation

  • Jiyong Li & Ran Chen & Chengye Liu & Xiaoshuai Xu & Yasai Wang, 2023. "Capacity Optimization of Independent Microgrid with Electric Vehicles Based on Improved Pelican Optimization Algorithm," Energies, MDPI, vol. 16(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2539-:d:1090815
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2539/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/6/2539/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, Siyu & Han, Yang & Yang, Ping & Mahmoud, Karar & Lehtonen, Matti & Darwish, Mohamed M.F. & Zalhaf, Amr S., 2022. "An optimal network constraint-based joint expansion planning model for modern distribution networks with multi-types intermittent RERs," Renewable Energy, Elsevier, vol. 194(C), pages 137-151.
    2. Liying Wang & Luyao Zhang & Weiguo Zhao & Xiyuan Liu, 2022. "Parameter Identification of a Governing System in a Pumped Storage Unit Based on an Improved Artificial Hummingbird Algorithm," Energies, MDPI, vol. 15(19), pages 1-23, September.
    3. Anderson, Benjamin & Rane, Jayaraj & Khan, Rabia, 2023. "Distributed wind-hybrid microgrids with autonomous controls and forecasting," Applied Energy, Elsevier, vol. 333(C).
    4. Farhad Zishan & Saeedeh Mansouri & Farzad Abdollahpour & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya, 2023. "Allocation of Renewable Energy Resources in Distribution Systems While considering the Uncertainty of Wind and Solar Resources via the Multi-Objective Salp Swarm Algorithm," Energies, MDPI, vol. 16(1), pages 1-17, January.
    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. Qiang Zhou & Jianmei Zhang & Pengfei Gao & Ruixiao Zhang & Lijuan Liu & Sheng Wang & Lin Cheng & Wei Wang & Shiyou Yang, 2023. "Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    2. Zhou, Siyu & Han, Yang & Zalhaf, Amr S. & Chen, Shuheng & Zhou, Te & Yang, Ping & Elboshy, Bahaa, 2023. "A novel multi-objective scheduling model for grid-connected hydro-wind-PV-battery complementary system under extreme weather: A case study of Sichuan, China," Renewable Energy, Elsevier, vol. 212(C), pages 818-833.
    3. Lu Liu & Yun Zeng, 2023. "Intelligent ISSA-Based Non-Singular Terminal Sliding-Mode Control of DC–DC Boost Converter Feeding a Constant Power Load System," Energies, MDPI, vol. 16(13), pages 1-23, June.
    4. Farhad Zishan & Lilia Tightiz & Joon Yoo & Nima Shafaghatian, 2023. "Sustainability of the Permanent Magnet Synchronous Generator Wind Turbine Control Strategy in On-Grid Operating Modes," Energies, MDPI, vol. 16(10), pages 1-18, May.
    5. Ahmed Ismail M. Ali & Zuhair Muhammed Alaas & Mahmoud A. Sayed & Abdulaziz Almalaq & Anouar Farah & Mohamed A. Mohamed, 2022. "An Efficient MPPT Technique-Based Single-Stage Incremental Conductance for Integrated PV Systems Considering Flyback Central-Type PV Inverter," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    6. Chen, Xianqing & Dong, Wei & Yang, Lingfang & Yang, Qiang, 2023. "Scenario-based robust capacity planning of regional integrated energy systems considering carbon emissions," Renewable Energy, Elsevier, vol. 207(C), pages 359-375.

    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:16:y:2023:i:6:p:2539-:d:1090815. 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.