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

Shree Om Bade, Olusegun Stanley Tomomewo (), Ajan Meenakshisundaram (), Maharshi Dey, Moones Alamooti and Nabil Halwany
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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

Clean Technol., 2025, vol. 7, issue 1, 1-31

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.

Keywords: hybrid renewable energy system; multi-criteria approach; optimal sizing; PSO; GA; GAPSO (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2025
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