Grey-box identification for photovoltaic power systems via particle-swarm algorithm

Amongst renewable generators, photovoltaics (PV) are becoming more popular as the appropriate low cost solution to meet increasing energy demands. However, the integration of renewable energy sources to the electricity grid possesses many challenges. The intermittency of these non-conventional sources often requires accurate forecast, planning and optimal management. Many attempts have been made to tackle these challenges; nonetheless, existing methods fail to accurately capture the underlying characteristics of the system. There exists scope to improve present PV yield forecasting models and methods. This paper explores the use of apriori knowledge of PV systems to build clear box models and identify uncertain parameters via heuristic algorithms. The model is further enhanced by incorporating black box models to account for unmodeled uncertainties in a novel grey-box forecasting and modeling of PV systems.