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Multi-Objective Optimal Power Flow Solution Using a Non-Dominated Sorting Hybrid Fruit Fly-Based Artificial Bee Colony

Author

Listed:
  • Balasubbareddy Mallala

    (Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India)

  • Venkata Prasad Papana

    (Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India)

  • Ravindra Sangu

    (Vasireddy Venkatadri Institute of Technology, Guntur 522508, India)

  • Kowstubha Palle

    (Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India)

  • Venkata Krishna Reddy Chinthalacheruvu

    (Chaitanya Bharathi Institute of Technology, Hyderabad 500075, India)

Abstract

A new optimization technique is proposed for solving optimization problems having single and multiple objectives, with objective functions such as generation cost, loss, and severity value. This algorithm was developed to satisfy the constraints, such as OPF constraints, and practical constraints, such as ram rate limits. Single and multi-objective optimization problems were implemented with the proposed hybrid fruit fly-based artificial bee colony (HFABC) algorithm and the non-dominated sorting hybrid fruit fly-based artificial bee colony (NSHFABC) algorithm. HFABC is a hybrid model of the fruit fly and ABC algorithms. Selecting the user choice-based solution from the Pareto set by the proposed NSHFABC algorithm is performed by a fuzzy decision-based mechanism. The proposed HFABC method for single-objective optimization was analyzed using the Himmelblau test function, Booth’s test function, and IEEE 30 and IEEE 118 bus standard test systems. The proposed NSHFABC method for multi-objective optimization was analyzed using Schaffer1, Schaffer2, and Kursawe test functions, and the IEEE 30 bus test system. The obtained results of the proposed methods were compared with the existing literature.

Suggested Citation

  • Balasubbareddy Mallala & Venkata Prasad Papana & Ravindra Sangu & Kowstubha Palle & Venkata Krishna Reddy Chinthalacheruvu, 2022. "Multi-Objective Optimal Power Flow Solution Using a Non-Dominated Sorting Hybrid Fruit Fly-Based Artificial Bee Colony," Energies, MDPI, vol. 15(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4063-:d:829697
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    References listed on IDEAS

    as
    1. Nguyen, Thang Trung, 2019. "A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization," Energy, Elsevier, vol. 171(C), pages 218-240.
    2. Abdullah Khan & Hashim Hizam & Noor Izzri Abdul-Wahab & Mohammad Lutfi Othman, 2020. "Solution of Optimal Power Flow Using Non-Dominated Sorting Multi Objective Based Hybrid Firefly and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(16), pages 1-24, August.
    3. Wei, Jingdong & Zhang, Yao & Wang, Jianxue & Cao, Xiaoyu & Khan, Muhammad Armoghan, 2020. "Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method," Applied Energy, Elsevier, vol. 260(C).
    4. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.
    5. Senthilkumar Subramanian & Chandramohan Sankaralingam & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan & Kannadasan Raju & Lucian Mihet-Popa, 2021. "An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III," Sustainability, MDPI, vol. 13(1), pages 1-29, January.
    6. Mohammad Zohrul Islam & Noor Izzri Abdul Wahab & Veerapandiyan Veerasamy & Hashim Hizam & Nashiren Farzilah Mailah & Josep M. Guerrero & Mohamad Nasrun Mohd Nasir, 2020. "A Harris Hawks Optimization Based Single- and Multi-Objective Optimal Power Flow Considering Environmental Emission," Sustainability, MDPI, vol. 12(13), pages 1-25, June.
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    Cited by:

    1. Ebubekir Kaya, 2022. "A New Neural Network Training Algorithm Based on Artificial Bee Colony Algorithm for Nonlinear System Identification," Mathematics, MDPI, vol. 10(19), pages 1-27, September.

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