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Multi-Criteria Optimization of a Hybrid Renewable Energy System Using Particle Swarm Optimization for Optimal Sizing and Performance Evaluation

Author

Listed:
  • Shree Om Bade

    (Department of Energy Engineering, University of North Dakota, Grand Forks, ND 58202, USA)

  • Olusegun Stanley Tomomewo

    (Department of Energy Engineering, University of North Dakota, Grand Forks, ND 58202, USA)

  • Ajan Meenakshisundaram

    (Department of Energy Engineering, University of North Dakota, Grand Forks, ND 58202, USA)

  • Maharshi Dey

    (UbiQD Inc., Los Alamos, NM 87544, USA)

  • Moones Alamooti

    (Department of Energy Engineering, University of North Dakota, Grand Forks, ND 58202, USA)

  • Nabil Halwany

    (Huawei Technologies, 2450 Copenhagen, Denmark)

Abstract

The major challenges in designing a Hybrid Renewable Energy System (HRES) include selecting appropriate renewable energy sources and storage systems, accurately sizing each component, and defining suitable optimization criteria. This study addresses these challenges by employing Particle Swarm Optimization (PSO) within a multi-criteria optimization framework to design an HRES in Kern County, USA. The proposed system integrates wind turbines (WTS), photovoltaic (PV) panels, Biomass Gasifiers (BMGs), batteries, electrolyzers (ELs), and fuel cells (FCs), aiming to minimize Annual System Cost (ASC), minimize Loss of Power Supply Probability (LPSP), and maximize renewable energy fraction (REF). Results demonstrate that the PSO-optimized system achieves an ASC of USD6,336,303, an LPSP of 0.01%, and a REF of 90.01%, all of which are reached after 25 iterations. When compared to the Genetic Algorithm (GA) and hybrid GA-PSO, PSO improved cost-effectiveness by 3.4% over GA and reduced ASC by 1.09% compared to GAPSO. In terms of REF, PSO outperformed GA by 1.22% and GAPSO by 0.99%. The PSO-optimized configuration includes WT (4669 kW), solar PV (10,623 kW), BMG (2174 kW), battery (8000 kWh), FC (2305 kW), and EL (6806 kW). Sensitivity analysis highlights the flexibility of the optimization framework under varying weight distributions. These results highlight the dependability, cost-effectiveness, and sustainability for the proposed system, offering valuable insights for policymakers and practitioners transitioning to renewable energy systems.

Suggested Citation

  • Shree Om Bade & Olusegun Stanley Tomomewo & Ajan Meenakshisundaram & Maharshi Dey & Moones Alamooti & Nabil Halwany, 2025. "Multi-Criteria Optimization of a Hybrid Renewable Energy System Using Particle Swarm Optimization for Optimal Sizing and Performance Evaluation," Clean Technol., MDPI, vol. 7(1), pages 1-31, March.
    • Handle: RePEc:gam:jcltec:v:7:y:2025:i:1:p:23-:d:1607878
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    References listed on IDEAS

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    1. Kelvin Nkalo Ukoima & Ogbonnaya Inya Okoro & Patrick Ifeanyi Obi & Udochukwu Bola Akuru & Innocent Ewean Davidson, 2024. "Optimal Sizing, Energy Balance, Load Management and Performance Analysis of a Hybrid Renewable Energy System," Energies, MDPI, vol. 17(21), pages 1-21, October.
    2. Sun, Hongyue & Ebadi, Abdol Ghaffar & Toughani, Mohsen & Nowdeh, Saber Arabi & Naderipour, Amirreza & Abdullah, Aldrin, 2022. "Designing framework of hybrid photovoltaic-biowaste energy system with hydrogen storage considering economic and technical indices using whale optimization algorithm," Energy, Elsevier, vol. 238(PA).
    3. Yousef Alhumaid & Khalid Khan & Fahad Alismail & Muhammad Khalid, 2021. "Multi-Input Nonlinear Programming Based Deterministic Optimization Framework for Evaluating Microgrids with Optimal Renewable-Storage Energy Mix," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    4. Eriksson, E.L.V. & Gray, E.MacA., 2019. "Optimization of renewable hybrid energy systems – A multi-objective approach," Renewable Energy, Elsevier, vol. 133(C), pages 971-999.
    5. Ahmed M. Nassef & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Ahmad Baroutaji, 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
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    Cited by:

    1. Shree Om Bade & Olusegun Stanley Tomomewo & Michael Mann & Johannes Van der Watt & Hossein Salehfar, 2025. "Optimal Sizing and Techno-Economic Evaluation of a Utility-Scale Wind–Solar–Battery Hybrid Plant Considering Weather Uncertainties, as Well as Policy and Economic Incentives, Using Multi-Objective Opt," Energies, MDPI, vol. 18(13), pages 1-39, July.
    2. Sudip Chowdhury & Aashish Kumar Bohre & Akshay Kumar Saha, 2025. "Meta-Heuristic Optimization for Hybrid Renewable Energy System in Durgapur: Performance Comparison of GWO, TLBO, and MOPSO," Sustainability, MDPI, vol. 17(15), pages 1-28, July.
    3. Shree Om Bade & Hossein Salehfar & Olusegun Stanley Tomomewo & Johannes Van der Watt & Michael Mann, 2025. "Evaluating the Economic Feasibility of Utility-Scale Hybrid Power Plants Under Divergent Policy Environments: A Multi-Objective Approach," Energies, MDPI, vol. 18(17), pages 1-24, August.
    4. Mehrdad Ghahramani & Daryoush Habibi & Seyyedmorteza Ghamari & Hamid Soleimani & Asma Aziz, 2025. "Renewable-Based Isolated Power Systems: A Review of Scalability, Reliability, and Uncertainty Modeling," Clean Technol., MDPI, vol. 7(3), pages 1-37, September.
    5. Yamilet González Cusa & José Hidalgo Suárez & Jorge Laureano Moya Rodríguez & Tulio Hernández Ramírez & Silvio A. B. Vieira de Melo & Ednildo Andrade Torres, 2025. "Optimal Sizing of an Off-Grid Hybrid Energy System with Metaheuristics and Meteorological Forecasting Based on Wavelet Transform and Long Short-Term Memory Networks," Energies, MDPI, vol. 18(20), pages 1-34, October.

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