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Stochastic Optimization Framework for the Capacity Planning of Hybrid Solar PV–Small Hydropower Systems Using Metaheuristic Algorithms

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Variable Renewable Energy (VRE) grid integration 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 an integrated stochastic framework that facilitates the hybridization of renewable energy technologies. To address this challenge, this study develops a stochastic optimization framework and conducts a comprehensive evaluation of six metaheuristics

Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD), Generalized Differential Evolution 3 (GDE3) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) were used for multi-objective optimization while Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) for single-objective optimization. The multi-objective approaches aimed to minimize energy production cost while maximizing the total generated energy output. 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 is a robust tool for system planners, project developers, regulators and policymakers to bolster sustainable and economically viable deployment of VRE systems which is central to a clean energy transition.

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