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The Economic Optimization of a Grid-Connected Hybrid Renewable System with an Electromagnetic Frequency Regulator Using a Genetic Algorithm

Author

Listed:
  • Aziz Oloroun-Shola Bissiriou

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
    These authors contributed equally to this work.)

  • Joale de Carvalho Pereira

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
    These authors contributed equally to this work.)

  • Ednardo Pereira da Rocha

    (Department of Engineering and Technology, Federal Rural University of Semi-Arid (DET-UFERSA), Mossoró 59625-900, Brazil
    These authors contributed equally to this work.)

  • Ricardo Ferreira Pinheiro

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
    These authors contributed equally to this work.)

  • Elmer Rolando Llanos Villarreal

    (Department of Natural Sciences, Mathematics, and Statistics, Federal Rural University of Semi-Arid (DCME-UFERSA), Mossoró 59625-900, Brazil
    These authors contributed equally to this work.)

  • Andrés Ortiz Salazar

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (DCA-UFRN), Natal 59072-970, Brazil
    Centre for Renewable Energy and Rational Use of Energy (CER-UNI), National University of Engineering, Tupac Amaru Avenue 210, 25, Lima 15333, Peru
    These authors contributed equally to this work.)

Abstract

This paper presents a comprehensive economic optimization of a grid-connected hybrid renewable energy system (HRES) enhanced with an electromagnetic frequency regulator (EFR) to improve frequency stability and provide clean and continuous electricity to the Macau City Campus while reducing dependence on fossil sources. The system includes photovoltaic (PV) arrays, wind turbines, battery storage, EFR, and a backup diesel generator. A genetic algorithm (GA) is employed to optimally size these components with the objective of maximizing the net present value (NPV) over the system’s lifetime. The GA implementation was validated on standard benchmark functions to ensure correctness and was finely tuned for robust convergence. Comprehensive sensitivity analyses of key parameters (discount rate, component costs, resource availability, etc.) were performed to assess solution robustness. The optimized design ( PV ≈ 35 kWp , WT = 30 kW , ESS ≈ 200 kWh , and EFR = 30 kW ) achieves a highly positive net present value of BRL 1.86 M in 2015 values (BRL 3.11 M in 2025) and discounted payback in approximately 9 years. A comparative assessment with the 2015 baseline project revealed up to a 10.1% enhancement in the net present value, underscoring the economic advantages of the optimized design. These results confirm the system’s strong economic viability and environmental benefits, providing a valuable guideline for future grid-connected hybrid energy systems.

Suggested Citation

  • Aziz Oloroun-Shola Bissiriou & Joale de Carvalho Pereira & Ednardo Pereira da Rocha & Ricardo Ferreira Pinheiro & Elmer Rolando Llanos Villarreal & Andrés Ortiz Salazar, 2025. "The Economic Optimization of a Grid-Connected Hybrid Renewable System with an Electromagnetic Frequency Regulator Using a Genetic Algorithm," Energies, MDPI, vol. 18(16), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4404-:d:1727308
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    References listed on IDEAS

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