Validation of an all-sky imager based nowcasting system for industrial PV plants

Because of the cloud-induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward-facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial-sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all-sky cameras (“WobaS-4cam”). The WobaS-4cam system is operational at 2 solar energy research centers and at a commercial 50-MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS-4cam system is considered to be at least state of the art

Enhancement and Validation of Cloud Detection Methods - Comparison between a Threshold Based and a Clear Sky Library Technique for Cloud Detection

Short-term forecasting (0-30 minutes) of solar irradiance is of great signi�cance for the optimal operation and power prediction of grid-connected photovoltaic (PV) and concentrating solar power (CSP) power plants. One method to provide such nowcasting, uses all sky cameras. The resulting images are of higher resolution than what can be obtained from satellites and the upwards pointing nature of the camera makes it easy to capture low-lying clouds. An accurate cloud detection will improve the accuracy of short-term forecasting of solar irradiance. Cloud detection algorithms based on ground based imagery have been developed and employed in the last years. The Clear Sky Library (CSL) method uses the red to blue ratio (RBR) from clear sky days throughout the year, as a reference to detect clouds in the image. However, it has shown, that changes in the atmospheric aerosol concentration, causes variations in the clear sky RBR. As a consequence, the �xed threshold techniques, as well as the CSL method are frequently unable to detect thin clouds in an atmosphere with a high aerosol content. In this thesis an enhanced CSL method is presented, taken account of the atmospheric condition by introducing a turbidity factor (Linke Turbidity (TL)). In literature there exists several cloud detection methods. To date, there is no satisfactory single parameter for the performance of cloud detection algorithms, which makes it difficult to compare di�erent methods. To be able to measure their performance and to see how di�erent methods compare, a validation method is developed. The validation method is applied to the enhanced CSL algorithm and the previous algorithm (based on a xed threshold technique). Comparing both validation results showed, that the CSL method outperforms the previous algorithm by yielding an improvement in the overall accuracy of at least 20 %. The best improvement could be achieved for overcast scenarios. Reasons for shortcomings in accuracy and the performance are discussed, and ideas to further improve the cloud detection algorithm, especially in problematic cases, are investigated