Multi-objective optimization in a boost converter for photovoltaic systems based on environmental data

Boost converters are widely used in photovoltaic systems for maximum power point tracking (MPPT). Their performance, however, depends significantly on the environmental conditions in which the dc-dc converter operate. This study aims to design a robust boost converter through a multi-objective optimization approach using the NSGA-II metaheuristic, seeking a single solution that minimizes sensitivity to climatic variations while maintaining satisfactory performance across different regions. To achieve this, a detailed model of both the photovoltaic panel and the boost converter—including parasitic elements—will be developed. Energy losses will be evaluated, and key parameters related to size, weight, and efficiency will be optimized under realistic climatic conditions. The methodology involves simulating the converter in closed loop alongside the photovoltaic panel using state-space modeling, with the goal of accurately determining the duty cycle at which the converter operates. This includes accounting for parasitic resistances and verifying operation at the maximum power point. Finally, the performance of one of the optimal converters will be evaluated using climate data from other cities.