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

Abstract

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.