Abstract
The worldwide growing development of PV capacity requires an accurate forecast for a safer and cheaper PV grid penetration. Solar energy variability mainly depends on cloud cover evolution. Thus, relationships between weather variables and forecast uncertainties may be quantified to optimize forecast use. An intraday solar energy forecast algorithm using satellite images is fully described and validated over three years in the Paris (France) area. For all tested horizons (up to 6 h), the method shows a positive forecast skill score compared to persistence (up to 15%) and numerical weather predictions (between 20% and 40%). Different variables, such as the clear-sky index (Kc), solar zenith angle (SZA), surrounding cloud pattern observed by satellites and northern Atlantic weather regimes have been tested as predictors for this forecast method. Results highlighted an increasing absolute error with a decreasing SZA and Kc. Root mean square error (RMSE) is significantly affected by the mean and the standard deviation of the observed Kc in a 10 km surrounding area. The highest (respectively, lowest) errors occur at the Atlantic Ridge (respectively, Scandinavian Blocking) regime. The differences of relative RMSE between these two regimes are from 8% to 10% in summer and from 18% to 30% depending on the time horizon. These results can help solar energy users to anticipate—at the forecast start time and up to several days in advance—the uncertainties of the intraday forecast. The results can be used as inputs for other solar energy forecast methods.
Highlights
Since the year 2000, the cost of PV modules generation has significantly decreased, increasing the competitiveness of PV power against electricity generated from fossil fuels [1]
The global horizontal irradiance (GHI) forecasts for the single pixel colocated at Palaiseau are compared with the GHI measured by the Baseline Surface Radiation Network (BSRN) station
The relative Root mean square error (RMSE) is more regularly sensitive to the Kc obs and is impacted by the lowest Sun elevations. As this metric is weighted with the mean of the observation values, the Sun position effect is strongly attenuated and the errors appear larger for low GHI values
Summary
Since the year 2000, the cost of PV modules generation has significantly decreased, increasing the competitiveness of PV power against electricity generated from fossil fuels [1]. Financial incentives for the use of low-carbon energy have been established all over the world by various institutions. The total PV capacity installed in the world has been multiplied by 14.4, from 40.2 GW in 2010 to 580.2 GW in 2019. The total capacity of all renewable energies doubled from 1226.6 to 2536 GW [2]. The dramatic increase in PV production raises the issue of its massive penetration into the grid. The injection of any power source in the grid must be balanced at each instant by an equal consumption to ensure the safe and stable operation of the grid at a constant frequency
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