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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.

Cloud Motion Vector (CMV) estimation from consecutive satellite images is widely used commercially for providing hours-ahead intraday forecasts of solar irradiance and PV power production. The modelling assumptions in these methods are generally satisfied for advective motion which is common in mid-latitudes, but strained for tropical meteorological conditions dominated by convective clouds. The region under analysis in this study encompasses both tropical and sub-tropical climatic zones and is affected by seasonal strong convection, i.e., the South Asian Monsoon. The purpose of this study is to benchmark the monthly forecast error of three commonly used CMV estimation techniques - Block-match, Farnebäck (Optical flow) and TV-L1 (Optical flow), for analysing their performance on a seasonal basis. The main focus of this work is the analysis of the limitations of image processing based Block-match and Optical flow techniques in predicting irradiance during the Monsoon period, which presents frequent convective formation and dissipation. Forecasted Cloud Index (CI) maps are validated against reference analysis CI maps for the period 2018-2019 for forecast lead times from 0 to 5.5 hours ahead using the Peak Signal to Noise Ratio (PSNR) metric for estimating the accuracy. Persistence of analysis cloud index maps are used as the reference worst case scenario forecast. Site-level forecasts of irradiance for the same period are validated against ground measured irradiance from two BSRN stations - Gurgaon and Tiruvallur, located in Northern and Southern India respectively. From the Winter period in March to the Monsoon period in August, there is a marked reduction of the 30 minutes ahead forecast accuracy by 3 dB in terms of Peak Signal to Noise Ratio at the image-wide level. This can be observed for all the three methods and the worst-case persistence scenario. Both optical flow methods outperform Block-match by 0.5 dB for the entire period of analysis. The Gurgaon BSRN site is affected by Summer Monsoon and shows an increase in nRMSE by a factor of 3 for all the methods. This station shows a seasonal pattern of forecast error closely matching the image-wide forecast accuracy. The forecast error for the Tiruvallur BSRN station on the other hand reaches its peak in December (Data for October and November are absent), due to its location in the Winter Monsoon climatic zone. Again, the nRMSE for all methods increase by a factor of almost 3 from March to December. The inter-method difference in accuracy is not significant and a seasonal difference (20% nRMSE) dominates. This highlights the shortcomings of image processing techniques in extrapolating future cloud locations under convective situations, where there is rapid change in cloud content between consecutive images.

Solar energy plays a crucial role in helping cities to decentralize energy production and thus decarbonize the energy mix. Reliable resource assessments are needed to support the deployment of solar power systems, especially in cities of developing countries where large solar potential remains untapped. The aim of this work is to assess the potential of rooftop solar photovoltaic (PV) in three populated cities in Ecuador's mainland (Quito, Guayaquil and Cuenca) and in the Galapagos Islands. The assessment involves (i) the estimation of the available rooftop area based on geographic information system data, (ii) the calculation of energy yield based on hourly satellite-derived irradiance and meteorological data, and (iii) the economic feasibility assessment in terms of levelized cost of electricity (LCOE) compared to representative electricity tariffs. In addition, a sensitivity analysis is carried out to assess the variability of the estimated technical and economic potential with respect to changes in the input parameters. The results reveal a total available rooftop area of about 144 km2, mainly concentrated in urban parishes. The estimated energy yield is 16.94 ± 3.38 TWh/a, which could cover almost twice the annual energy consumption in 2019 of the study areas. The economic assessment shows that the LCOE ranges between 7.65 and 21.12 USD cents/kWh. However, the comparison of LCOE against representative residential tariff suggests that rooftop PV technology is not cost-competitive under most of the financial scenarios. The findings from this study will be of interest for local authorities and other decision makers to design policies and financing strategies to increase the penetration of rooftop PV and thus exploiting the large potential assessed in the study areas. The described methodology can be used for assessing the potential in other regions of Ecuador and thereby support the diversification and decarbonization of the energy mix in the country.

Remote sensing 14(22), 5685 (2022). doi:10.3390/rs14225685 special issue: "Special Issue "Remote Sensing for Smart Renewable Cities" / Special Issue Editor: Dr. Panagiotis Kosmopoulos, Guest Editor", Published by MDPI, Basel

The rapid growth of solar power generation and the variable nature of the solar resource pose challenges for our electricity grids. Forecasting future changes in the irradiance might help to cost-efficiently manage this variability both for photovoltaic and concentration solar plants as well as grids with high solar penetrations. So far, for shortest-term forecasts with lead times of a few minutes, all-sky imager based nowcasting systems are used. However, due to the complexity of dynamically changing 3d cloud shapes as well as certain geometrical effects such as self-occlusion or near-horizon saturation, all-sky imager based nowcasting systems exhibit inherent weaknesses. Here, we present a novel system to generate shortest-term solar forecasts, which is located at Plataforma Solar de Almería in southern Spain. This approach is based on downward-facing cameras (shadow cameras), taking images of the ground. From these images, spatially resolved irradiance maps are derived. By tracking cloud shadows, future irradiances are predicted. A demonstration system is achieved, which provides shortest-term forecasts for the next 2 min. To the best of our knowledge, this is the first time such a system is developed. We benchmark several possible algorithmic approaches on 16 days and compare the deviations to a state-of-the-art all-sky imager based nowcasting system on 22 days. The root-mean-squared deviation (RMSD) of this shadow camera based nowcasting system for direct normal irradiance (DNI) and 1‑min temporal averages is 15.6 % for lead times of 2 min (MAD, DNI: 9.6 %). In comparison to an all-sky imager system, this is an improvement as the all-sky imager system only reaches 22.0 % RMSD and 14.8 % MAD (both DNI). This demonstrates the feasibility and attractiveness in terms of accuracy of the proposed concept.

Cloud base height (CBH) is an important parameter for many applications such as aviation, climatology or solar irradiance nowcasting (forecasting for the next seconds to hours ahead). The latter application is of increasing importance to operate distribution grids as well as photovoltaic power plants, energy storage systems and flexible consumers. To nowcast solar irradiance, systems based on all-sky-imagers (ASIs), cameras monitoring the entire sky dome above their point of installation, have been demonstrated. Accurate knowledge of CBH is required to nowcast the spatial distribution of solar irradiance around the ASI's location at a resolution down to 5 m. Two ASIs located at a distance of usually less than 6 km can be combined into an ASI-pair to measure CBH. However, the accuracy of such systems is limited. We present and validate a method to measure CBH using a network of ASIs to enhance accuracy. To the best of our knowledge, this is the first method to measure CBH by a network of ASIs which is demonstrated experimentally. In this study, the deviations of 42 ASI-pairs are studied in comparison to a ceilometer and characterized by camera distance. The ASI-pairs are formed from seven ASIs and feature camera distances of 0.8...5.7 km. Each of the 21 ASI-tuples formed from seven ASIs yields two independent ASI-pairs as the ASI used as main and auxiliary camera respectively is swapped. Deviations found are compiled into conditional probabilities telling how probable it is to receive a certain reading of CBH from an ASI-pair given that true CBH takes on some specific value. Based on such statistical knowledge, in the inference the likeliest actual CBH is estimated from the readings of all 42 ASI-pairs. Based on the validation results, ASI-pairs with small camera distance (especially if