Comparing Spatio-Temporal Models for Aggregate PV Power Nowcasting

The photovoltaic (PV) power fluctuations caused by passing clouds have become a major concern for grid operators. Consequently, utilities are requiring proper treatments to limit the intermittent PV generation. On this point, solar nowcasting provides a remedy by enabling the transition from reactive control to proactive, which often offers remarkable reliability to PV systems. Sensor networks that utilize spatio-temporal models are considered promising for solar nowcasting. However, current studies on sensor network nowcasting have dedicated much to point nowcasts, where the geographic smoothing effect that occurs in aggregate PV systems is generally left out. In this context, this paper presents a comparing study on spatio-temporal models for aggregate PV power nowcasting. Through empirical studies, the forecast skill of spatio-temporal models is found to decrease for a larger PV aggregation. In addition, the spatio-temporal regression shows competitive performance in various scenarios, yielding a priority for practical use.