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Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer

Author

Listed:
  • Hassan M. H. Farh

    (Faculty of Construction and Environment, Department of Building and Real Estate, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

  • Abdullrahman A. Al-Shamma’a

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abdullah M. Al-Shaalan

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abdulaziz Alkuhayli

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abdullah M. Noman

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Tarek Kandil

    (Department of Electrical and Computer Engineering, College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460, USA)

Abstract

In this study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains a diesel generator, photovoltaics (PV), a wind turbine (WT), and batteries as a storage system. The proposed HRES aims to electrify a remote region in northern Saudi Arabia based on annualized system cost (ASC) minimization and power system reliability enhancement. To differentiate and evaluate the performance, the BO was compared to four recent metaheuristic algorithms, called big-bang–big-crunch (BBBC), crow search (CS), the genetic algorithm (GA), and the butterfly optimization algorithm (BOA), to find the optimal design for the proposed off-grid HRES in terms of optimal and worst solutions captured, mean, convergence rate, and standard deviation. The obtained results reveal the efficacy of BO compared to the other four metaheuristic algorithms where it achieved the optimal solution of the proposed off-grid HRES with the lowest ASC (USD 149,977.2), quick convergence time, and fewer oscillations, followed by BOA (USD 150,236.4). Both the BBBC and GA algorithms failed to capture the global solution and had high convergence time. In addition, they had high standard deviation, which revealed that their solutions were more dispersed with obvious oscillations. These simulation results proved the supremacy of BO in comparison to the other four metaheuristic algorithms.

Suggested Citation

  • Hassan M. H. Farh & Abdullrahman A. Al-Shamma’a & Abdullah M. Al-Shaalan & Abdulaziz Alkuhayli & Abdullah M. Noman & Tarek Kandil, 2022. "Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer," Sustainability, MDPI, vol. 14(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1533-:d:736741
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    References listed on IDEAS

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    Cited by:

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    2. Fernando García-Muñoz & Miguel Alfaro & Guillermo Fuertes & Manuel Vargas, 2022. "DC Optimal Power Flow Model to Assess the Irradiance Effect on the Sizing and Profitability of the PV-Battery System," Energies, MDPI, vol. 15(12), pages 1-16, June.
    3. Adefarati, T. & Bansal, R.C. & Naidoo, R. & Onaolapo, K.A. & Bettayeb, M. & Olulope, P.K. & Sobowale, A.A., 2024. "Design and techno-economic assessment of a standalone photovoltaic-diesel-battery hybrid energy system for electrification of rural areas: A step towards sustainable development," Renewable Energy, Elsevier, vol. 227(C).
    4. Elena Sosnina & Andrey Dar’enkov & Andrey Kurkin & Ivan Lipuzhin & Andrey Mamonov, 2022. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption," Energies, MDPI, vol. 16(1), pages 1-38, December.

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