IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5695917.html
   My bibliography  Save this article

Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

Author

Listed:
  • Qian Zhang
  • Jinjin Ding
  • Weixiang Shen
  • Jinhui Ma
  • Guoli Li

Abstract

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

Suggested Citation

  • Qian Zhang & Jinjin Ding & Weixiang Shen & Jinhui Ma & Guoli Li, 2020. "Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, March.
    • Handle: RePEc:hin:jnlmpe:5695917
    DOI: 10.1155/2020/5695917
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/5695917.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2020/5695917.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2020/5695917?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ramakrishna S. S. Nuvvula & Devaraj Elangovan & Kishore Srinivasa Teegala & Rajvikram Madurai Elavarasan & Md. Rabiul Islam & Ravikiran Inapakurthi, 2021. "Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO," Energies, MDPI, vol. 14(17), pages 1-23, August.
    2. Liya Yue & Pei Hu & Shu-Chuan Chu & Jeng-Shyang Pan, 2023. "Multi-Objective Gray Wolf Optimizer with Cost-Sensitive Feature Selection for Predicting Students’ Academic Performance in College English," Mathematics, MDPI, vol. 11(15), pages 1-16, August.

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:5695917. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.