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Optimizing Rural MG’s Performance: A Scenario-Based Approach Using an Improved Multi-Objective Crow Search Algorithm Considering Uncertainty

Author

Listed:
  • Mohammad Hossein Taabodi

    (Department of Electrical Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran)

  • Taher Niknam

    (Department of Electrical Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran)

  • Seyed Mohammad Sharifhosseini

    (Department of Electrical Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran)

  • Habib Asadi Aghajari

    (Department of Electrical Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran)

  • Seyyed Mohammad Bornapour

    (Electrical Engineering Department, Yasouj University, Yasouj 7493475918, Iran)

  • Ehsan Sheybani

    (School of Information Systems and Management, Muma College of Business, University of South Florida, Tampa, FL 33620, USA)

  • Giti Javidi

    (School of Information Systems and Management, Muma College of Business, University of South Florida, Tampa, FL 33620, USA)

Abstract

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs’ coordination. In this paper, two distinct models for RMGs are presented. The first model includes an islanded rural microgrid (IRMG) and the second model consists of three RMGs that are interconnected with one another and linked to the distribution network. The proposed models take into account the uncertainty in load, photovoltaics (PVs), and wind turbines (WTs) with consideration of their correlation by using a scenario-based technique. Three objective functions are defined for optimization: minimizing operational costs including maintenance and fuel expenses, reducing voltage deviation to maintain power quality, and decreasing pollution emissions from fuel cells and microturbines. A new optimization method, namely the Improved Multi-Objective Crow Search Algorithm (IMOCSA), is proposed to solve the problem models. IMOCSA enhances the standard Crow Search Algorithm through three key improvements: an adaptive chaotic awareness probability to better balance exploration and exploitation, a mutation mechanism applied to the solution repository to prevent premature convergence, and a K-means clustering method to control repository size and increase algorithmic efficiency. Since the proposed problem is a multi-objective non-linear optimization problem with conflicting objectives, the idea of the Pareto front is used to find a group of optimal solutions. To assess the effectiveness and efficiency of the proposed models, they are implemented in two different case studies and the analysis and results are illustrated.

Suggested Citation

  • Mohammad Hossein Taabodi & Taher Niknam & Seyed Mohammad Sharifhosseini & Habib Asadi Aghajari & Seyyed Mohammad Bornapour & Ehsan Sheybani & Giti Javidi, 2025. "Optimizing Rural MG’s Performance: A Scenario-Based Approach Using an Improved Multi-Objective Crow Search Algorithm Considering Uncertainty," Energies, MDPI, vol. 18(2), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:294-:d:1564618
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    References listed on IDEAS

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