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Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem

Author

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  • Georgios Papazoglou

    (School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

  • Pandelis Biskas

    (School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

Abstract

Metaheuristic optimization techniques have successfully been used to solve the Optimal Power Flow (OPF) problem, addressing the shortcomings of mathematical optimization techniques. Two of the most popular metaheuristics are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The literature surrounding GA and PSO OPF is vast and not adequately organized. This work filled this gap by reviewing the most prominent works and analyzing the different traits of GA OPF works along seven axes, and of PSO OPF along four axes. Subsequently, cross-comparison between GA and PSO OPF works was undertaken, using the reported results of the reviewed works that use the IEEE 30-bus network to assess the performance and accuracy of each method. Where possible, the practices used in GA and PSO OPF were compared with literature suggestions from other domains. The cross-comparison aimed to act as a first step towards the standardization of GA and PSO OPF, as it can be used to draw preliminary conclusions regarding the tuning of hyper-parameters of GA and PSO OPF. The analysis of the cross-comparison results indicated that works using both GA and PSO OPF offer remarkable accuracy (with GA OPF having a slight edge) and that PSO OPF involves less computational burden.

Suggested Citation

  • Georgios Papazoglou & Pandelis Biskas, 2023. "Review and Comparison of Genetic Algorithm and Particle Swarm Optimization in the Optimal Power Flow Problem," Energies, MDPI, vol. 16(3), pages 1-25, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1152-:d:1042391
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    References listed on IDEAS

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    1. Stephen Frank & Steffen Rebennack, 2016. "An introduction to optimal power flow: Theory, formulation, and examples," IISE Transactions, Taylor & Francis Journals, vol. 48(12), pages 1172-1197, December.
    2. Georgios Papazoglou & Pandelis Biskas, 2022. "Review of Methodologies for the Assessment of Feasible Operating Regions at the TSO–DSO Interface," Energies, MDPI, vol. 15(14), pages 1-24, July.
    3. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    4. Aikaterini Forouli & Emmanouil A. Bakirtzis & Georgios Papazoglou & Konstantinos Oureilidis & Vasileios Gkountis & Luisa Candido & Eloi Delgado Ferrer & Pandelis Biskas, 2021. "Assessment of Demand Side Flexibility in European Electricity Markets: A Country Level Review," Energies, MDPI, vol. 14(8), pages 1-23, April.
    5. Marcel Sarstedt & Leonard Kluß & Johannes Gerster & Tobias Meldau & Lutz Hofmann, 2021. "Survey and Comparison of Optimization-Based Aggregation Methods for the Determination of the Flexibility Potentials at Vertical System Interconnections," Energies, MDPI, vol. 14(3), pages 1-27, January.
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