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Multi-Objective Energy Management in Microgrids: Improved Honey Badger Algorithm with Fuzzy Decision-Making and Battery Aging Considerations

Author

Listed:
  • Mohana Alanazi

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Abdulaziz Alanazi

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia)

  • Zulfiqar Ali Memon

    (Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates)

  • Ahmed Bilal Awan

    (Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates)

  • Mohamed Deriche

    (Artificial Intelligence Research Centre, College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates)

Abstract

A multi-objective energy management and scheduling strategy for a microgrid comprising wind turbines, solar cells, fuel cells, microturbines, batteries, and loads is proposed in this work. The plan uses a fuzzy decision-making technique to reduce pollution emissions, battery storage aging costs, and operating expenses. To be more precise, we applied an improved honey badger algorithm (IHBA) to find the best choice variables, such as the size of energy resources and storage, by combining fuzzy decision-making with the Pareto solution set and a chaotic sequence. We used the IHBA to perform single- and multi-objective optimization simulations for the microgrid’s energy management, and we compared the results with those of the conventional HBA and particle swarm optimization (PSO). The results showed that the multi-objective method improved both goals by resulting in a compromise between them. On the other hand, the single-objective strategy makes one goal stronger and the other weaker. Apart from that, the IHBA performed better than the conventional HBA and PSO, which also lowers the cost. The suggested approach beat the alternative tactics in terms of savings and effectively reached the ideal solution based on the Pareto set by utilizing fuzzy decision-making and the IHBA. Furthermore, compared with the scenario without this cost, the results indicated that integrating battery aging costs resulted in an increase of 7.44% in operational expenses and 3.57% in pollution emissions costs.

Suggested Citation

  • Mohana Alanazi & Abdulaziz Alanazi & Zulfiqar Ali Memon & Ahmed Bilal Awan & Mohamed Deriche, 2024. "Multi-Objective Energy Management in Microgrids: Improved Honey Badger Algorithm with Fuzzy Decision-Making and Battery Aging Considerations," Energies, MDPI, vol. 17(17), pages 1-31, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4373-:d:1469017
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    References listed on IDEAS

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    1. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    2. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    3. Ferahtia, Seydali & Rezk, Hegazy & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal techno-economic energy management strategy for building’s microgrids based bald eagle search optimization algorithm," Applied Energy, Elsevier, vol. 306(PB).
    4. Dey, Bishwajit & Misra, Srikant & Garcia Marquez, Fausto Pedro, 2023. "Microgrid system energy management with demand response program for clean and economical operation," Applied Energy, Elsevier, vol. 334(C).
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