• Energy Research

Abstract The integration of solar photovoltaic (PV) into electricity networks introduces technical challenges due to varying PV output. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (MGs) with high penetration of solar PV generation. PV plants and optionally controllable distributed energy resources (DERs) in MGs can be operated in an optimized way based on nowcasting, which is also called very short-term solar irradiance forecasting up to 60 min ahead. This study presents an extensive literature review on nowcasting technologies along with their current and future possible applications in the control of MGs. Ramp rates control and scheduling of spinning reserves are found to be the most recognized applications of nowcasting in MGs. An online survey has been conducted to identify the limitations, benefits and challenges of deploying nowcasting in MGs. The survey outcomes show that the incorporation of nowcasting tools in MG operations is still limited, though the possibility of increasing solar PV penetration levels in MGs if nowcasting tools are incorporated is acknowledged. Additionally, recent nowcasting tools, such as sky camera-based tools, require further validation under various conditions for more widespread adaptation by power system operators.

Abstract The integration of solar photovoltaic (PV) into electricity networks introduces technical challenges due to varying PV output. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (MGs) with high penetration of solar PV generation. PV plants and optionally controllable distributed energy resources (DERs) in MGs can be operated in an optimized way based on nowcasting, which is also called very short-term solar irradiance forecasting up to 60 min ahead. This study presents an extensive literature review on nowcasting technologies along with their current and future possible applications in the control of MGs. Ramp rates control and scheduling of spinning reserves are found to be the most recognized applications of nowcasting in MGs. An online survey has been conducted to identify the limitations, benefits and challenges of deploying nowcasting in MGs. The survey outcomes show that the incorporation of nowcasting tools in MG operations is still limited, though the possibility of increasing solar PV penetration levels in MGs if nowcasting tools are incorporated is acknowledged. Additionally, recent nowcasting tools, such as sky camera-based tools, require further validation under various conditions for more widespread adaptation by power system operators.

Abstract The demand for accurate solar irradiance nowcast increases together with the rapidly growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly caused by clouds, have a significant impact on solar power plant dispatch and thus on electricity grids. All sky imager (ASI) based nowcasting systems, with a high temporal and spatial resolution, can provide irradiance nowcasts that can help to optimize CSP plant operation, solar power plant dispatch and grid operation. The radiative effect of clouds is highly variable and depends on micro- and macrophysical cloud properties. Frequently, nowcasting systems have to measure/estimate the radiative effect during complex multi-layer conditions with strong variations of the optical properties between individual clouds. We present a novel approach determining cloud transmittance from measurements or from correlations of transmittance with cloud height information. The cloud transmittance is measured by a pyrheliometer when shaded, as the ratio of shaded direct normal irradiance (DNI) and clear sky DNI. However, for most clouds, direct transmittance measurements are not available, as these clouds are not shading the used pyrheliometers. These clouds receive an estimated transmittance value based on (1) their height, (2) results of a probability analysis with historical cloud height and transmittance measurements as well as (3) recent transmittance measurements and their corresponding cloud height. Cloud heights are measured by a stereoscopic approach utilizing two ASIs. We discuss site dependencies of the presented transmittance estimation method and the potential integration of automatic cloud classification approaches. We validated the cloud transmittance estimation over two years (2016 and 2017) and compare the probabilistic cloud transmittance estimation approach with four simple approaches. The overall mean-absolute deviation (MAD) and root-mean-square deviation (RMSD) are 0.11 and 0.16 respectively for transmittance. The deviations are significantly lower for optically thick or thin clouds and larger for clouds with moderate transmittance between 0.18 and 0.585. Furthermore we validated the overall DNI forecast quality of the entire nowcasting system, using this transmittance estimation method, over the same data set with three spatially distributed pyrheliometers. Overall deviations of 13% and 21% are reached for the relative MAD and RMSD with a lead time of 10 min. The effects of the chosen data set on the validation results are demonstrated by means of the skill score.

Abstract The demand for accurate solar irradiance nowcast increases together with the rapidly growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly caused by clouds, have a significant impact on solar power plant dispatch and thus on electricity grids. All sky imager (ASI) based nowcasting systems, with a high temporal and spatial resolution, can provide irradiance nowcasts that can help to optimize CSP plant operation, solar power plant dispatch and grid operation. The radiative effect of clouds is highly variable and depends on micro- and macrophysical cloud properties. Frequently, nowcasting systems have to measure/estimate the radiative effect during complex multi-layer conditions with strong variations of the optical properties between individual clouds. We present a novel approach determining cloud transmittance from measurements or from correlations of transmittance with cloud height information. The cloud transmittance is measured by a pyrheliometer when shaded, as the ratio of shaded direct normal irradiance (DNI) and clear sky DNI. However, for most clouds, direct transmittance measurements are not available, as these clouds are not shading the used pyrheliometers. These clouds receive an estimated transmittance value based on (1) their height, (2) results of a probability analysis with historical cloud height and transmittance measurements as well as (3) recent transmittance measurements and their corresponding cloud height. Cloud heights are measured by a stereoscopic approach utilizing two ASIs. We discuss site dependencies of the presented transmittance estimation method and the potential integration of automatic cloud classification approaches. We validated the cloud transmittance estimation over two years (2016 and 2017) and compare the probabilistic cloud transmittance estimation approach with four simple approaches. The overall mean-absolute deviation (MAD) and root-mean-square deviation (RMSD) are 0.11 and 0.16 respectively for transmittance. The deviations are significantly lower for optically thick or thin clouds and larger for clouds with moderate transmittance between 0.18 and 0.585. Furthermore we validated the overall DNI forecast quality of the entire nowcasting system, using this transmittance estimation method, over the same data set with three spatially distributed pyrheliometers. Overall deviations of 13% and 21% are reached for the relative MAD and RMSD with a lead time of 10 min. The effects of the chosen data set on the validation results are demonstrated by means of the skill score.

Abstract All-sky imager based systems can be used to measure a number of cloud properties. Configurations consisting of two all-sky imagers can be used to derive cloud heights for weather stations, aviation and nowcasting of solar irradiance. One key question for such systems is the optimal distance between the all-sky imagers. This problem has not been studied conclusively in the literature. To the best of our knowledge, no previous in-field study of the optimal camera distance was performed. Also, comprehensive modeling is lacking. Here, we address this question with an in-field study on 93 days using 7 camera distances between 494 m and 2562 m and one specific cloud height estimation approach. We model the findings and draw conclusions for various configurations with different algorithmic methods and camera hardware. The camera distance is found to have a major impact on the accuracy of cloud height determinations. For the used 3 megapixel cameras, cloud heights up to 12,000 m and the used algorithmic approaches, an optimal camera distance of approximately 1500 m is determined. Optimal camera distances can be reduced to less than 1000 m if higher camera resolutions (e.g. 6 megapixel) are deployed. A step-by-step guide to determine the optimal camera distance is provided.

Abstract All-sky imager based systems can be used to measure a number of cloud properties. Configurations consisting of two all-sky imagers can be used to derive cloud heights for weather stations, aviation and nowcasting of solar irradiance. One key question for such systems is the optimal distance between the all-sky imagers. This problem has not been studied conclusively in the literature. To the best of our knowledge, no previous in-field study of the optimal camera distance was performed. Also, comprehensive modeling is lacking. Here, we address this question with an in-field study on 93 days using 7 camera distances between 494 m and 2562 m and one specific cloud height estimation approach. We model the findings and draw conclusions for various configurations with different algorithmic methods and camera hardware. The camera distance is found to have a major impact on the accuracy of cloud height determinations. For the used 3 megapixel cameras, cloud heights up to 12,000 m and the used algorithmic approaches, an optimal camera distance of approximately 1500 m is determined. Optimal camera distances can be reduced to less than 1000 m if higher camera resolutions (e.g. 6 megapixel) are deployed. A step-by-step guide to determine the optimal camera distance is provided.

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.