Embedded nowcasting method using cloud speed persistence for a photovoltaic power plant

• Endogenous data is used to forecast PV power output.
• Cloud speed and ramp persistence are compared to persistence.
• Cloud speed persistence outperforms classical persistence by 16.2% at 20 s.

Accurate forecasting of the spatio-temporal variability of solar power is a critical enabler of economical grid-integration of large amounts of solar power. A new physically-based endogenous method to forecast power output and ramps a few minutes ahead is presented. This cloud speed persistence method consists of advecting the current distribution of power output across the plant using endogenous measurements of cloud motion vectors. The method was validated at a 48 MW photovoltaic power plant in south-western Nevada, USA. Excluding clear days and in terms of the percentage root mean squared error the new method outperformed persistence by 16.2% at 20 s, 10.6% at 60 s, and 4.0% at 120 s forecast horizon. Given plant dimensions (1807 × 539 m) and cloud motion vectors at the site, the method can be applied out to forecast horizons of 65 s, on average.