Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution

Abstract

Accurate forecasts of the power production of distributed photovoltaic (PV) systems are essential to support grid operation and enable a high PV penetration rate in the electricity grid. In this study, we analyse the performance of 12 different models that forecast the day-ahead power production in agreement with market conditions. These models include regression, support vector regression, ensemble learning, deep learning and physical based techniques. In addition, we examine the effect of aggregating multiple PV systems with a varying inter-system distance on the forecast model performance. The models are evaluated both on their technical and economic performance. From a technical perspective, the results show a positive effect from both an increasing inter-system distance and a larger sized PV fleet on the model performance, which was not the case for the economic assessment. Furthermore, the ensemble and deep learning models perform better than the alternatives from a technical point of view. For the economic assessment, the results indicate the superiority of the physical based model, followed by the deep learning models. Lastly, our findings show the importance of considering the user's objective when assessing solar power forecast models.