Differential Evolution-Based Optimization of Hybrid PV–Wind Energy Using Reanalysis Data

Hybrid photovoltaic (PV) systems augmented by wind-induced energy contributions can improve energy reliability under variable atmospheric conditions. However, their performance remains highly sensitive to site-specific weather patterns, panel orientation, and system parameter selection. This study presents a computational optimization framework based on Differential Evolution (DE) to enhance the combined energy output of a hybrid PV–wind system using high-resolution reanalysis data. Hourly solar irradiance from NASA POWER and near-surface wind components from ERA5 were processed through a unified data ingestion and preprocessing pipeline supporting GRIB and NetCDF formats to evaluate seasonal and annual energy production. The optimization jointly adjusted PV tilt angle, effective PV area scaling, and a wind energy scaling parameter to maximize total energy yield. Case studies for San Antonio (TX), Denver (CO), and Albuquerque (NM) demonstrate seasonal energy gains of 36–57% and annual improvements of 36.9–56.2% relative to baseline fixed-parameter configurations. The results indicate that evolutionary optimization combined with reanalysis-driven energy modeling provides a robust and scalable approach for improving hybrid renewable energy performance across diverse climatic regions.