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An Overview of the Performance of PSO Algorithm in Renewable Energy Systems

In: Applying Particle Swarm Optimization

Author

Listed:
  • Omar Hazem Mohammed

    (Northern Technical University, Technical College of Mosul)

  • Mohammed Kharrich

    (Mohammed V University, Mohammadia School of Engineers)

Abstract

An increase in the penetration of renewable energy sources in the electrical production has been matched by the emergence of many and varied challenges and problems. Among the most important challenges is finding the smart technologies and algorithms which are capable of achieving efficient solutions. This chapter provides an expanded view of the uses of the particle swarm optimization (PSO) algorithm in the renewable energy systems field. Additionally, it describes how the algorithm can be developed to cope with problems related to renewable energies to achieve desired goals. The PSO algorithm was used to solve many problems in the renewable energy systems, such as in optimal hybrid power systems, optimal sizing, and optimal net present cost, among others, where the PSO algorithm showed its high adaptability in problem-solving. Further, many researchers proceeded with the study and development of the PSO algorithm. In contrast, other researchers tried to hybridize it with different algorithms to be more efficient and convenient to overcome some of the problems and challenges that they encountered. The renewable energy systems have several issues to discuss, such as the cost of investment, the feasible technical criteria, optimal control, and the ecological problems as well as the social effect. Overall, studies and research have proven that the PSO algorithm is one of the best algorithms used in the field of renewable energy. This is attributed to the algorithm’s simplicity, high efficiency, and effectiveness compared to other algorithms and optimization methods.

Suggested Citation

  • Omar Hazem Mohammed & Mohammed Kharrich, 2021. "An Overview of the Performance of PSO Algorithm in Renewable Energy Systems," International Series in Operations Research & Management Science, in: Burcu Adıgüzel Mercangöz (ed.), Applying Particle Swarm Optimization, edition 1, chapter 0, pages 307-320, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-70281-6_16
    DOI: 10.1007/978-3-030-70281-6_16
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    Cited by:

    1. Roldán-Blay, Carlos & Escrivá-Escrivá, Guillermo & Roldán-Porta, Carlos & Dasí-Crespo, Daniel, 2023. "Optimal sizing and design of renewable power plants in rural microgrids using multi-objective particle swarm optimization and branch and bound methods," Energy, Elsevier, vol. 284(C).
    2. Xing, Zongyi & Zhu, Junlin & Zhang, Zhenyu & Qin, Yong & Jia, Limin, 2022. "Energy consumption optimization of tramway operation based on improved PSO algorithm," Energy, Elsevier, vol. 258(C).

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