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Optimizing energy costs and reliability: A multi-objective framework with learning-enhanced manta ray foraging for hybrid PV/battery systems

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  • Alghamdi, Ali S.

Abstract

This paper presents a multi-objective framework for designing a hybrid photovoltaic energy and battery storage system (PV/Battery) with the aim of minimizing the cost of electricity, loss of energy expectation, and loss of load expectation. Focusing on meeting the energy demand of a commercial complex in Al-Jubail industrial city, Saudi Arabia, utilizing real-world data, the proposed framework employs the multi-objective improved manta ray foraging optimization algorithm in conjunction with fuzzy decision-making. To enhance algorithm performance and prevent premature convergence, a Learning-based Hunting Movement Strategy is incorporated. Results highlight the superior performance of Case 3, achieving an optimal balance between cost and reliability. The obtained values for cost of electricity, loss of energy expectation, and loss of load expectation are 0.2255 $/kWh, 170.67 kWh/yr, and 14 h/yr, respectively. This study emphasizes the strengthened capability of the algorithm, augmented through the Learning-based Hunting Movement Strategy, establishing its superiority over established multi-objective methods with a higher percentage of dominant solutions.

Suggested Citation

  • Alghamdi, Ali S., 2024. "Optimizing energy costs and reliability: A multi-objective framework with learning-enhanced manta ray foraging for hybrid PV/battery systems," Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:energy:v:291:y:2024:i:c:s0360544224001178
    DOI: 10.1016/j.energy.2024.130346
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    Cited by:

    1. Toopshekan, Ashkan & Ahmadi, Esmaeil & Abedian, Ali & Vaziri Rad, Mohammad Amin, 2024. "Techno-economic analysis, optimization, and dispatch strategy development for renewable energy systems equipped with Internet of Things technology," Energy, Elsevier, vol. 296(C).

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