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Cost Optimization of AC Microgrids in Grid-Connected and Isolated Modes Using a Population-Based Genetic Algorithm for Energy Management of Distributed Wind Turbines

Author

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  • Luis Fernando Grisales-Noreña

    (Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Talca, Curicó 3340000, Chile
    Grupo de Investigación en Alta Tensión—GRALTA, Escuela de Ingeniería Eléctrica y Electrónica, Universidad del Valle, Cali 760015, Colombia
    These authors contributed equally to this work.)

  • Héctor Pinto Vega

    (Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de Talca, Curicó 3340000, Chile
    These authors contributed equally to this work.)

  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromagnética, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
    These authors contributed equally to this work.)

  • Vanessa Botero-Gómez

    (Departamento de Electromecánica y Mecatrónica, Facultad de Ingenierías, Instituto Tecnológico Metropolitano, Medellín 050034, Colombia
    These authors contributed equally to this work.)

  • Daniel Sanin-Villa

    (Área de Industria, Materiales y Energía, Universidad EAFIT, Medellín 050022, Colombia
    These authors contributed equally to this work.)

Abstract

This research investigates the effectiveness of four metaheuristic algorithms, the Population-Based Genetic Algorithm, Particle Swarm Optimization, JAYA, and Generalized Normal Distribution Optimizer, for managing the energy production of wind-based distributed generators (DGs). The aim is to reduce operational costs in a 33-node microgrid (MG) operating under both connected and isolated configurations. The study seeks to identify the most efficient algorithm for minimizing operational expenses in distributed generation systems, specifically in terms of energy production and purchasing costs, as well as the maintenance costs of DGs. Due to limited statistical validation and unrealistic operational constraints in previous studies, we propose a novel framework that offers a robust, reproducible solution for optimizing the management of wind-based distributed generators in microgrids. Through 100 independent trials for each algorithm and configuration, rigorous statistical analyses are conducted, including ANOVA and Tukey’s post hoc test, to assess performance consistency and the significance of cost reduction outcomes across algorithms. The results indicate that the PGA demonstrates superior cost efficiency and stability, particularly in the connected MG configuration.

Suggested Citation

  • Luis Fernando Grisales-Noreña & Héctor Pinto Vega & Oscar Danilo Montoya & Vanessa Botero-Gómez & Daniel Sanin-Villa, 2025. "Cost Optimization of AC Microgrids in Grid-Connected and Isolated Modes Using a Population-Based Genetic Algorithm for Energy Management of Distributed Wind Turbines," Mathematics, MDPI, vol. 13(5), pages 1-23, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:704-:d:1597004
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    References listed on IDEAS

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    1. O., Yugeswar Reddy & J., Jithendranath & Chakraborty, Ajoy Kumar & Guerrero, Josep M., 2022. "Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties," Applied Energy, Elsevier, vol. 307(C).
    2. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    3. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal market-based operation of microgrid with the integration of wind turbines, energy storage system and demand response resources," Energy, Elsevier, vol. 239(PB).
    4. Mohammadjavad Mobarra & Miloud Rezkallah & Adrian Ilinca, 2022. "Variable Speed Diesel Generators: Performance and Characteristic Comparison," Energies, MDPI, vol. 15(2), pages 1-31, January.
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