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An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

Author

Listed:
  • Xuanhu He

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing JiaoTong University, Beijing 100044, China)

  • Wei Wang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing JiaoTong University, Beijing 100044, China
    These authors contributed equally to this work.)

  • Jiuchun Jiang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing JiaoTong University, Beijing 100044, China
    These authors contributed equally to this work.)

  • Lijie Xu

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing JiaoTong University, Beijing 100044, China
    These authors contributed equally to this work.)

Abstract

Optimal power flow (OPF) objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC) algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

Suggested Citation

  • Xuanhu He & Wei Wang & Jiuchun Jiang & Lijie Xu, 2015. "An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow," Energies, MDPI, vol. 8(4), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:4:p:2412-2437:d:47372
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    References listed on IDEAS

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    8. Gonggui Chen & Xingting Yi & Zhizhong Zhang & Hangtian Lei, 2018. "Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach," Energies, MDPI, vol. 11(12), pages 1-18, December.
    9. Daw Saleh Sasi Mohammed & Muhammad Murtadha Othman & Ahmed Elbarsha, 2020. "A Modified Artificial Bee Colony for Probabilistic Peak Shaving Technique in Generators Operation Planning: Optimal Cost–Benefit Analysis," Energies, MDPI, vol. 13(12), pages 1-23, June.
    10. Hatem Diab & Mahmoud Abdelsalam & Alaa Abdelbary, 2021. "A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques," Sustainability, MDPI, vol. 13(9), pages 1-25, April.
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    12. Sherif S. M. Ghoneim & Mohamed F. Kotb & Hany M. Hasanien & Mosleh M. Alharthi & Attia A. El-Fergany, 2021. "Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis," Sustainability, MDPI, vol. 13(14), pages 1-15, July.
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