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<p>The integration of variable renewable energy (VRE) into power systems requires optimal capacity planning to ensure cost-effective and reliable operation. While metaheuristic algorithms are widely applied, there is limited rigorous benchmarking comparing the performance of leading single-objective and multi-objective algorithms within a unified stochastic framework for the hybridization of renewable energy technologies. To bridge this gap, this study develops a stochastic optimization framework and conducts a comprehensive evaluation of six metaheuristics: Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD) and Generalized Differential Evolution 3 (GDE3) for multi-objective optimization; and Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) for single-objective optimization. The multi-objective approaches aimed to maximize total energy output and minimize energy production cost, while the single-objective methods focused on minimizing the levelized cost of electricity (LCOE). A case study for the hybridization of a small hydropower plant with Solar PV was conducted. The results show that NSGA-II delivered the lowest LCOE of 6.54 US ₵ per kWh with a system capacity of 16.33&#xa0;MW and a capacity factor of 42.74%. DE outperformed other single-objective methods, offering the lowest mean LCOE of 8.96 US ₵ per kWh, a system capacity of 19.37&#xa0;MW, and a capacity factor of 49.31%. The proposed framework provides a robust tool for system designers and policymakers to bolster sustainable and economically viable deployment of VRE systems which is central to a clean energy transition.</p>

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Stochastic optimization framework for capacity planning of hybrid solar PV–small hydropower systems using metaheuristic algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Edward B. Ssekulima,
  • Amir H. Etemadi

摘要

The integration of variable renewable energy (VRE) into power systems requires optimal capacity planning to ensure cost-effective and reliable operation. While metaheuristic algorithms are widely applied, there is limited rigorous benchmarking comparing the performance of leading single-objective and multi-objective algorithms within a unified stochastic framework for the hybridization of renewable energy technologies. To bridge this gap, this study develops a stochastic optimization framework and conducts a comprehensive evaluation of six metaheuristics: Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD) and Generalized Differential Evolution 3 (GDE3) for multi-objective optimization; and Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) for single-objective optimization. The multi-objective approaches aimed to maximize total energy output and minimize energy production cost, while the single-objective methods focused on minimizing the levelized cost of electricity (LCOE). A case study for the hybridization of a small hydropower plant with Solar PV was conducted. The results show that NSGA-II delivered the lowest LCOE of 6.54 US ₵ per kWh with a system capacity of 16.33 MW and a capacity factor of 42.74%. DE outperformed other single-objective methods, offering the lowest mean LCOE of 8.96 US ₵ per kWh, a system capacity of 19.37 MW, and a capacity factor of 49.31%. The proposed framework provides a robust tool for system designers and policymakers to bolster sustainable and economically viable deployment of VRE systems which is central to a clean energy transition.