Optimizing Hybrid Renewable Systems for Critical Loads in Andean Medical Centers Using Metaheuristics

Abstract

The electrification of rural medical centers in high Andean areas represents a critical challenge for equitable development due to limited access to reliable energy. Hybrid Renewable Energy Systems (HRESs), which combine solar photovoltaic generation, Battery Energy Storage Systems (BESSs), and backup diesel generators, are emerging as viable solutions to ensure the supply of critical loads. However, their effective implementation requires optimal sizing methodologies that consider multiple technical and economic constraints and objectives. In this study, an optimization model based on metaheuristic algorithms is developed, specifically, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), to identify optimal configurations of an HRES applied to a remote medical center in the Peruvian Andes. The results show that GA achieved the lowest Life Cycle Cost (LCC), with a high share of renewable energy (64.04%) and zero Energy Not Supplied (ENS) defined as the amount of load demand not met by the system, significantly outperforming PSO and ACO. GA was also found to offer greater stability and operational robustness. These findings confirm the effectiveness of metaheuristic methods for designing efficient and resilient energy solutions adapted to isolated rural contexts.