Cloud Shadows in Satellite-based Solar Irradiance Estimation: Improved Correction using EUMETSAT's Cloud Top Height Data

The estimation of solar surface irradiance at high spatio-temporal resolution from geo-stationary satellite images is a well-established technique, for example by using the Heliosat method. The method has widely reduced the need for expensive ground measurements, especially in remote regions. However, the location of cloud shadows at the ground is difficult to determine and thus a significant source of errors when either the distance from the sub-satellite point or the cloud top height (CTH) increases. Although several methods have been proposed in the literature to reduce these errors, it is still an issue. We present a novel approach to correct the cloud shadow location based on the satellite-cloud-sun geometry using the CTH maps from the EUMETSAT data archive. It uses satellite viewing angles and solar position angles to determine the correct cloud shadow location for each cloudy pixel. The method is tested on cloud index (CI) maps for the months of July, August and September 2018 derived by applying the Heliosat method on the 0.6 um visible channel images from Meteosat-8 located at 41.5°E. Convective clouds with large CTHs are frequently observed over the Indian subcontinent in these three months due to the Indian summer monsoon. The global horizontal solar irradiance (GHI) obtained from the corrected CI image is validated at two BSRN stations. The normalized root mean square error (nRMSE) is reduced from 23.2% to 20.9% for the Gurgaon station and from 15.4% to 13.9% at Tiruvallur. In general, correcting the cloud shadow location on CI map improved the accuracy of the estimated GHI. Nonetheless, the method is sensitive to the accuracy of the CTH dataset and individual cases were found for which the correction reduced the accuracy

Impact of tropical convective conditions on solar irradiance forecasting based on cloud motion vectors

Intra-day forecasts of global horizontal solar irradiance (GHI) are widely produced by displacing existing clouds on a geo-stationary satellite image to their future locations with cloud motion vectors (CMVs) derived from preceding images. The CMV estimation methods assume rigid cloud bodies with advective motion, which performs reasonably well in mid-latitudes but can be strained for tropical and sub-tropical climatic zones during prolonged periods of seasonal convection. We study the impact of the South Asian monsoon time convection on the accuracy of CMV based forecasts by analysing 2 years of forecasts from three commonly used CMV methods—Block-match, Farnebäck (Optical flow) and TV-L1 (Optical flow). Forecasted cloud index (CI) maps of the entire image section are validated against analysis CI maps for the period 2018–2019 for forecast lead times from 0 to 5.5 h. Site-level GHI forecasts are validated against ground measured data from two Baseline Surface Radiation Network stations—Gurgaon (GUR) and Tiruvallur (TIR), located in hot semi-arid and tropical savanna climatic zones respectively. The inter-seasonal variation of forecast accuracy is prominent and a clear link is found between the increase in convection, represented by a decrease in outgoing longwave radiation (OLR), and the decrease in forecast accuracy. The GUR site shows the highest forecast error in the southwest monsoon period and exhibits a steep rise of forecast error with the increase in convection. The highest forecast error occurs in the northeast monsoon period of December in TIR. The impact of convection on the number of erroneous time blocks of predicted photovoltaic production is also studied. Our results provide insights into the contribution of convection to errors in CMV based forecasts and shows that OLR can be used as a feature in future forecasting methods to consider the impact of convection on forecast accuracy

DYNAMOS - Dynamik systemischer Effekte durch die Einspeisung erneuerbarer Energien: Hochfrequente Fluktuationen und deren Auswirkung auf den Abruf marktorientierter Systemdienstleistung

Dynamik systemischer Effekte durch die Einspeisung erneuerbarer Energien: Hochfrequente Fluktuationen und deren Auswirkung auf den Abruf marktorientierter Systemdienstleistung - Schlussbericht DLR-Institut für Vernetzte Energiesysteme - Projekt: DYNAMO

Short-term solar forecasting based on sky images to enable higher PV generation in remote electricity networks

The integration of a high share of photovoltaic (PV) power generation in remote electricity networks is often limited by the networks’ capabilities to accommodate PV power fluctuations caused by passing clouds. Increasing the share of PV penetration in such networks is accompanied by an increased effort to achieve integration. In the absence of solar forecasting, sufficient spinning reserve must always be provided to cover unforeseen reductions. The expected ramp rates are magnified in small and centralised PV systems and can be in the order of a few seconds. In this study, we investigate the use of a low-cost sky camera for very short-term solar forecasting. Almost 2 months of sky camera data have been recorded in Perth, Western Australia and processed for to provide high-resolution irradiance forecasts based on visible sky images. For performance validation, the capability to provide reliable forecasts under constant clear sky conditions is investigated. During these times, PV generation is expected to be high and reliable, which provides an opportunity to reduce the online spinning reserve often enabling power station operation with one less operating diesel generation. For networks with disconnected diesel generators, we assume that clouds that could reduce the PV generation output have to be predicted at least 2 min before their arrival to have enough time for a diesel generator to start and synchronize with the grid. Therefore, we define an irradiance threshold discriminating between the persistent state of constant clear sky (stays clear) and the non-persistent state (cloud shading event) based on a 2–5 min time horizon. In a binary evaluation, we achieve an overall accuracy of 97% correct forecasts and low 3% false alarms of cloud events indicating a high potential for fuel savings. Focusing on the rare (2% of the time) but more critical non-persistent conditions, we found 8 out of 84 cloud events have not been predicted in advance. Reasons for erroneous forecasts and suggestions for model improvements are provided

Improving the satellite retrieval of surface solar irradiance during an eclipse

Solar eclipse causes high magnitude fluctuations in the Surface Solar Irradiance (SSI) for a short duration and consequently reduces the output of solar PV systems. Grid operators try to estimate the impending loss in PV power generation prior to the occurrence of an eclipse in order to schedule conventional generators for compensating the loss. The worldwide installed capacity of grid connected solar PV systems is expected to steeply rise in the coming decade as a result of the various policy initiatives aimed to tackle the climate change. In future electric supply networks with a high penetration of solar PV systems, such large ramps in generation could impact the stability of the network. Although a solar eclipse is a purely deterministic phenomenon, it’s impact on the satellite retrieval of Surface Solar Irradiance (SSI) is complicated due to the possibility of cloud presence in the regions affected by the eclipse. The extraterrestrial solar irradiance is reduced by the moon during an eclipse. On the one hand this causes clouds to appear darker and they get assigned lower reflectance values than they should have in reality. This leads to predicting higher values for the solar irradiance under these clouds than expected. On the other hand, the eclipse also reduces the clear sky irradiance reaching the earth surface. We developed a method to make corrections for both of these effects on the High Resolution Visible (HRV) channel images from Meteosat-11 The results are validated against ground measurements of irradiance provided by BSRN, IEA-PVPS, DTN and the National Weather Services networks. The validation is performed for sites with locations across Europe and for the last two eclipses

Analyzing Spatial Variations of Cloud Attenuation by a Network of All-Sky Imagers

All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation’s local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network’s estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m2, GHI 98 W/m2) compared to the ASI pair (RMSD for DNI 173 W/m2, GHI 119 W/m2 and radiometer alone (RMSD for DNI 213 W/m2), GHI 140 W/m2). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions

Measurement of diffuse and plane of array irradiance by a combination of a pyranometer and an all-sky imager

Accurate, robust and cost-efficient measurements of diffuse horizontal irradiance (DHI) and global tilted irradiance (GTI) are of great interest for solar energy applications. However, the available measurement techniques exhibit at least one of these shortcomings: restriction of GTI measurement to a single plane, intensive maintenance, high acquisition cost or increased deviations, especially at new measurement sites. To avoid these shortcomings, we suggest a comparably inexpensive and robust setup of a thermopile pyranometer and an all-sky imager (ASI) for measurement of DHI and GTI. The pyranometer measures global horizontal irradiance (GHI) and our method consecutively estimates diffuse sky radiance, DHI, direct normal irradiance (DNI) and GTI, by merging information from the combined setup. The system is developed and validated at two sites in Spain and Germany. Measurement of GTI is benchmarked for seven planes over GTI derived by transposition based on DHI and DNI from a tracker setup with a pyrheliometer and shaded thermopile pyranometer. Our results indicate that the measurement system can be applied at both sites. The proposed method avoids time-consuming radiometric calibrations of the camera by the combination of both sensors and a self-calibration. The measurement system is promising in particular for measurement of GTI. For 10-min average GTI, our approach yields an rRMSD of 1.6...4.8% for planes with tilts in the range of 20°...61°. Thus, at both sites and for all planes, it outperforms the tracker-based transposition yielding 2.3...6.5%. DHI is measured significantly more accurately than reported in previous works using an ASI alone

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

Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India

Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result