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A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms

Author

Listed:
  • Saket Gupta

    (Electrical Engineering Department, Delhi Technological University, Delhi 110042, India)

  • Narendra Kumar

    (Electrical Engineering Department, Delhi Technological University, Delhi 110042, India)

  • Laxmi Srivastava

    (Electrical Engineering Department, Madhav Institute of Technology & Science, Gwalior 474005, India)

  • Hasmat Malik

    (Berkeley Education Alliance for Research in Singapore (BEARS), University Town, NUS Campus, Singapore 138602, Singapore)

  • Amjad Anvari-Moghaddam

    (Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark)

  • Fausto Pedro García Márquez

    (Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain)

Abstract

This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.

Suggested Citation

  • Saket Gupta & Narendra Kumar & Laxmi Srivastava & Hasmat Malik & Amjad Anvari-Moghaddam & Fausto Pedro García Márquez, 2021. "A Robust Optimization Approach for Optimal Power Flow Solutions Using Rao Algorithms," Energies, MDPI, vol. 14(17), pages 1-28, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5449-:d:627177
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    References listed on IDEAS

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    1. Warid Warid & Hashim Hizam & Norman Mariun & Noor Izzri Abdul-Wahab, 2016. "Optimal Power Flow Using the Jaya Algorithm," Energies, MDPI, vol. 9(9), pages 1-18, August.
    2. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
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    Cited by:

    1. Mohammed Hamouda Ali & Ali M. El-Rifaie & Ahmed A. F. Youssef & Vladimir N. Tulsky & Mohamed A. Tolba, 2023. "Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm," Energies, MDPI, vol. 16(2), pages 1-29, January.
    2. Basim ALBaaj & Orhan Kaplan, 2025. "Enhanced COVID-19 Optimization Algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid," Energies, MDPI, vol. 18(3), pages 1-29, January.
    3. Jamal, Raheela & Zhang, Junzhe & Men, Baohui & Khan, Noor Habib & Ebeed, Mohamed & Jamal, Tanzeela & Mohamed, Emad A., 2024. "Chaotic-quasi-oppositional-phasor based multi populations gorilla troop optimizer for optimal power flow solution," Energy, Elsevier, vol. 301(C).

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