IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v186y2019ics0360544219314732.html
   My bibliography  Save this article

An efficient fitness-based differential evolution algorithm and a constraint handling technique for dynamic economic emission dispatch

Author

Listed:
  • Shen, Xin
  • Zou, Dexuan
  • Duan, Na
  • Zhang, Qiang

Abstract

In this paper, an efficient fitness-based differential evolution (EFDE) algorithm and a constraint handling technique for dynamic economic emission dispatch (DEED) are proposed. In EFDE, there are three improvements compared to the standard differential evolution (DE) algorithm. First, an archive containing the current and previous population is established to provide more candidate solutions. Second, two mutation strategies are used to generate mutant individuals, where the population similarity is introduced to choose a suitable one between DE/rand/1 and DE/best/1. The fitness-based mutation operation is efficient to balance the exploration and exploitation ability of EFDE. Third, EFDE adopts a random-based mutation factor, and the crossover rate with the learning ability is developed to produce more excellent solutions. In addition, the infeasible solutions can be effectively avoided by the proposed repair technique. Four cases are selected to judge the performance of the proposed EFDE and constraint handling technique. For the fuel cost and emission minimizations of four DEED cases, a normalized approach (NA) is used to help EFDE to find the best compromise solutions in the evolution process. According to the simulation results, EFDE exhibits a huge advantage in comparison with the other approaches for the single-objective and multi-objective optimization problems.

Suggested Citation

  • Shen, Xin & Zou, Dexuan & Duan, Na & Zhang, Qiang, 2019. "An efficient fitness-based differential evolution algorithm and a constraint handling technique for dynamic economic emission dispatch," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219314732
    DOI: 10.1016/j.energy.2019.07.131
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219314732
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.07.131?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Wenqiang Yang & Yihang Zhang & Xinxin Zhu & Kunyan Li & Zhile Yang, 2024. "Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm," Energies, MDPI, vol. 17(6), pages 1-29, March.
    2. Liu, Zhi-Feng & Li, Ling-Ling & Liu, Yu-Wei & Liu, Jia-Qi & Li, Heng-Yi & Shen, Qiang, 2021. "Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach," Energy, Elsevier, vol. 235(C).
    3. P. S. Bhullar & J. S. Dhillon & R. K. Garg, 2024. "Crisscross Team Game Algorithm for Economic-Emission Power Dispatch Problem with Multiple Fuel Options," SN Operations Research Forum, Springer, vol. 5(2), pages 1-60, June.
    4. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun & Wu, Jiahui & Fan, Xiaochao & Xu, Qidan, 2020. "Dynamic environmental economic dispatch of hybrid renewable energy systems based on tradable green certificates," Energy, Elsevier, vol. 193(C).
    5. Shaheen, Abdullah M. & Ginidi, Ahmed R. & El-Sehiemy, Ragab A. & El-Fergany, Attia & Elsayed, Abdallah M., 2023. "Optimal parameters extraction of photovoltaic triple diode model using an enhanced artificial gorilla troops optimizer," Energy, Elsevier, vol. 283(C).
    6. Chen, Xu, 2020. "Novel dual-population adaptive differential evolution algorithm for large-scale multi-fuel economic dispatch with valve-point effects," Energy, Elsevier, vol. 203(C).
    7. Arunachalam Sundaram & Nasser S. Alkhaldi, 2024. "Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model," Energies, MDPI, vol. 17(4), pages 1-26, February.
    8. Mohamed H. Hassan & Salah Kamel & José Luís Domínguez-García & Mohamed F. El-Naggar, 2022. "MSSA-DEED: A Multi-Objective Salp Swarm Algorithm for Solving Dynamic Economic Emission Dispatch Problems," Sustainability, MDPI, vol. 14(15), pages 1-23, August.

    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:eee:energy:v:186:y:2019:i:c:s0360544219314732. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.