Probabilistic Predictions of Aggregated Wind and Solar Power at Shagaya Farm in Kuwait

One way to mitigate the variability of wind and solar power generation is to install these plants in nearby locations and to have them paired with energy accumulation system. For example, in countries such as Kuwait having facilities like Shagaya with both PV panels and wind turbine allows having a continuous generation of renewable energy throughout the day. In fact, especially in desert areas winds associated with low-level jets permit wind power production during night-time when the solar generation is missing.

The National Center for Atmospheric Research (NCAR) has developed a system to generate wind and probabilistic solar predictions for the Shagaya facility located in a desert area in Kuwait. These predictions are based on the analog ensemble technique which post-processes the wind and solar irradiance predictions based on the Weather and Research Forecasting System (WRF) numerical model. The ensemble forecasts are made of twenty members and are generated independently for the wind and solar power. We present a method to pair the ensemble members from the two independent systems to obtain a unique ensemble prediction of the aggregated wind and solar generation.

The ensemble members provided by AnEn are statistically indistinguishable, and they are generated without space-time correlation. We apply the Schaake Shuffle (SS) technique, widely used for hydrological applications, to reorder the ensemble members and recover space-time variability of solar and wind power forecast time-series. In this technique, the ensemble members for a given forecast lead-time are ranked and matched with the rank of solar or wind power past observations at the same hours appropriately selected across the historical record. The ensembles are then reordered to match the original order of the selected historical data preserving the observed inter-plants correlation and the observed temporal autocorrelation. After the reordering through the SS, the paired solar and wind power corresponding members can be summed to build a unique ensemble of combined generation.

The combined solar-wind power ensemble predictions obtained by using the AnEn and the SS techniques are statistically consistent as verified by compiling rank histograms, spread/skill, and reliability diagrams. Also, a reliable quantification of the uncertainty of the total power production of the Shagaya plant is provided and each ensemble member preserves the temporal autocorrelation of the observations. Hence, the ensemble forecast can be used, for example, to optimize the charging/discharging cycles when an accumulation system is available.