Downscaling of global solar irradiation in R

A methodology for downscaling solar irradiation from satellite-derived databases is described using R software. Different packages such as raster, parallel, solaR, gstat, sp and rasterVis are considered in this study for improving solar resource estimation in areas with complex topography, in which downscaling is a very useful tool for reducing inherent deviations in satellite-derived irradiation databases, which lack of high global spatial resolution. A topographical analysis of horizon blocking and sky-view is developed with a digital elevation model to determine what fraction of hourly solar irradiation reaches the Earth's surface. Eventually, kriging with external drift is applied for a better estimation of solar irradiation throughout the region analyzed. This methodology has been implemented as an example within the region of La Rioja in northern Spain, and the mean absolute error found is a striking 25.5% lower than with the original database

Meteorological data for RES-E integration studies: State of the art review

The ongoing growth of RES-E requires power system modellers to adapt both methodologies and datasets, in particular time series for electricity generation from wind and PV. Meteorological models are increasingly used for this purpose. This report provides on overview on the methodologies available and the approaches pursued by recent RES-E integration studies. Based on this review, recommendations for best practice are identified.JRC.F.6-Energy Technology Policy Outloo

Spatial Downscaling of 2-Meter Air Temperature Using Operational Forecast Data

We present a method for enhancing the spatial resolution of 2 meter temperature (T2m) estimates. The method is based on operational forecast data supplied by the European Centre for Medium Range Weather Forecast. From the hourly and monthly average temperatures a vertical gradient is determined by linear fitting to the temperature data in larger areas of 1x1° or 2x2°. Validation against data from more than 8000 meteorological stations worldwide shows that the estimates of annual average temperature at these points becomes significantly more accurate when applying the vertical gradients to correct the local temperature estimates to the elevation of the stations. When the elevation difference between forecast and station is larger than 300m, the overall mean absolute deviation of the individual stations bias values decreases from 3.44°C to 1.02°C and the root mean square deviation decreases from 4.11°C to 1.42°C. The gradients have also been applied to the ERA-Interim reanalysis data and the validation results are similar. The vertical temperature gradients will be useful for studies in many fields, including renewable energy and the study of energy performance of buildings.JRC.F.7-Renewables and Energy Efficienc

Towards Climate Resilient and Energy Efficient Buildings: A Comparative Study on Energy Related Components, Adaptation Strategies, and Whole Building Performance

L'abstract è presente nell'allegato / the abstract is in the attachmen

Impact of climate change on solar irradiation and variability over the Iberian Peninsula using regional climate models

As solar energy will be an increasingly important renewable energy source in the future years, the study of how climate change affects both temporal and spatial variability is very important. In this paper, we study future changes of the solar radiation resource in the Iberian Peninsula (IP) through a set of simulations from ESCENA project until mid-century. The evaluation of the simulations against observations indicates contrasting biases for the different regional climate models (RCMs) in terms of solar irradiation amount and its interannual variability. We propose a diagnostic for the quality of solar energy resource, in which the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) in the IP is captured by the RCMs in general terms. The analysis of scenarios indicates a future increase in solar irradiation, although not all scenarios agree in the geographical distribution of this increase. In most projections, a shift is projected from the category with optimal resource quality towards the category with high irradiation/high variability, pointing to a certain quality loss in the solar resource. This result is not general, as a few scenarios show an opposite result. The exceptions are not linked to a particular GCM or emissions scenario. Finally, results from a first approximation to the issue of the ability of solar energy to cover power demand peaks in summer show important differences between regions of the IP. The spatially-averaged correlation of solar irradiation and summer surface temperatures for the whole IP is rather high, which is a positive result as the strong interconnections of the power grid within the IP could allow a distribution of solar power surpluses in certain regions for such high-temperature episodes

Renewable and resilient power systems under future climate variability

The concern about the consequences of carbon-intensive activities across all socio-economic sectors is accelerating the path towards renewables-based power systems. However, larger renewable energy penetration allied with unknown future demand adds vulnerability and uncertainty to the design of power systems. This work assesses the impact of climate variability and energy demand in renewables-based power systems. An hourly-based modelling tool is used to simulate the power system for Portugal in 2050. A multiyear model calibration is proposed, enabling a more reliable simulation. Regarding climate, two representative concentration pathways (RCP4.5 and RCP8.5), totaling 473 climate realizations, are tested. Five electricity demand-flexibility scenarios are tested for each activity sector, assuming diverging levels for electricity demand, storage and demand-side management. The impacts of climate variability on supply and demand are simultaneously analyzed and quantified. Energy demand plays a crucial role in the power system. Results show that residential demand may increase between 4 and 60%, which are used to define scenarios. The cross-border interconnection needs quadruplicate from low to high demand, while the renewable generation share decreases 16 p.p. Climate variability, depending on the scenario, leads to changes in residential demand between -8 to +5% around its median, while renewables generation share might oscillate between -15 and +15 p.p. Cross-border interconnection energy trading needs may vary by a factor of two due to climate variability, from -62 to +226% around its median. Fully renewables-based power systems are especially vulnerable to climate. The system power capacity required under a climatic median year varies 3-fold according to demand-flexibility scenarios. For that same system to be resilient under unfavorable years, it is required an increase of up to 200-fold in storage or doubling of cross-border interconnection. A power system designed for unfavorable years requires 54% more installed capacity. Hence, future climate variability will be critical in the power systems’ operation, thus pivotal to evaluate and consider in its planning