Estimación de costos de generación en energía fotovoltaica utilizando el algoritmo heurístico de optimización de enjambre de partículas evolutivas diferenciales

Abstract

The objective of this research is to optimize generation costs in photovoltaic systems using the DEEPSO heuristic algorithm (Differential Evolutionary Particle Swarm Optimization). The variability of solar energy complicates its efficient prediction and planning. Results from manual calculations were compared with those obtained using DEEPSO in 50 kW, 100 kW, 150 kW, 250 kW, and 300 kW systems, adjusting the quantity and type of solar panels to optimize resources and space. The results showed that DEEPSO significantly reduced the Levelized Cost of Energy (LCOE), achieving a reduction of up to 24%, maintaining low and stable costs. The conclusions confirm that DEEPSO is an efficient tool for cost optimization in photovoltaic systems. In addition to reducing the LCOE, DEEPSO simplified the design and planning process, eliminating the need for multiple manual iterations. For example, the LCOE was reduced from 5.95 ¢/kWh to 4.53 ¢/kWh in 50 kW systems.