• Energy Research

Water is a scarce resource in the Mediterranean region where adverse climatic conditions promoting water shortages tend to increase with climate change. Under water scarcity conditions and high atmosphere evaporative demand, the risks of decreased water quality, and land salinization are major threats to the sustainability of irrigated agriculture in this region. The assessment of the quality of irrigation water is increasingly important to ensure the maintenance of long-term salt balance at a crop, farm, and regional scale. This study is focused on the spatial and temporal variability of water quality for irrigation in the Alqueva reservoir (Southern Portugal). The assessment was performed every 2 months during a distinctive drought year (2017) and included inorganic ions (Na+, Ca2+, Mg2+, K+, NH4+, Cl-, F-, SO42-, NO3-, and NO2-), pH, and electric conductivity (ECW) of water. Sodium adsorption ratio (SAR) was calculated, and potential soil permeability problems were estimated. The assessment showed significantly higher values of the physicochemical parameters in the most upstream sites, located near tributaries inflows, and an upward trend in ion concentrations throughout the year, with significantly higher concentrations of Na+, Mg2+, Cl-, and SO42, registered through May to November, reflecting the severe drought felt in the summer, autumn, and winter. The evaluation of water quality for irrigation indicated a slight to moderate risk of reduced infiltration rates, which should be considered whenever sprinkler irrigation is used, mainly in fine-textured soils, which are prevalent in the irrigated area. The multivariate statistical approach, using principal component analysis and factor analysis, identified two principal components related to salinity and nutrient concentrations. The cluster analysis revealed three groups of similarity between samples pointing to a more time- than space-controlled pattern. Overall, the temporal dynamics of the water physicochemical parameters could indicate that an abnormal annual distribution of precipitation and temperature may distort seasonal differences. To prevent water and soil degradation, a more frequent assessment of the water quality should be considered, allowing for the selection of appropriate soil and water management measures in irrigated areas.

Abstract The mesoscale Weather Research and Forecasting (WRF) Model is used over the Iberian Peninsula to generate 60 years (1950–2010) of climate data, at 5 km resolution, in order to evaluate and characterize the incident shortwave downward radiation at the surface (SW ↓), in present climate. The simulated values of SW ↓ in the period 2000–2009 were compared with data measured in Spanish and Portuguese meteorological stations before and a statistical BIAS correction was applied using data from Clouds and the Earth's Radiant Energy System (CERES), on board four different satellites. The spatial and temporal comparison between WRF results and observations show a good agreement for the analyzed period, although the model overestimates observations. This overestimation has a mean normalized bias of about 7% after BIAS correction (or 17% for original WRF output). Additionally, the present simulation was confronted against another previously validated WRF simulation performed with different resolution and set of parametrizations, showing comparable results. WRF adequately reproduces the observational features of SW ↓ with correlation coefficients above 0.8 in annual and seasonal basis. 60 years of simulated SW ↓ over the Iberian Peninsula were produced, which showed annual mean values that range from 130 W/m 2 , in the northern regions, to a maximum of around 230 W/m 2 in the southeast of the Iberian Peninsula (IP). SW ↓ over IP shows a positive gradient from north to south and from west to east, with local effects influenced by topography and distance to the coast. The analysis of the simulated cloud fraction indicates that clear sky days are found in > 30% of the period at the southern area of IP, particularly in the Algarve (Portugal) and Andalusia (Spain), and this value increases significantly in the summer season for values above 80%.

Accurate operational solar irradiance forecasts are crucial for better decision making by solar energy system operators due to the variability of resource and energy demand. Although numerical weather prediction (NWP) models can forecast solar radiation variables, they often have significant errors, particularly in the direct normal irradiance (DNI), which is especially affected by the type and concentration of aerosols and clouds. This paper presents a method based on artificial neural networks (ANN) for generating operational DNI forecasts using weather and aerosol forecasts from the European Center for Medium-range Weather Forecasts (ECMWF) and the Copernicus Atmospheric Monitoring Service (CAMS), respectively. Two ANN models were designed: one uses as input the predicted weather and aerosol variables for a given instant, while the other uses a period of the improved DNI forecasts before the forecasted instant. The models were developed using observations for the location of Évora, Portugal, resulting in 10 min DNI forecasts that for day 1 of forecast horizon showed an improvement over the downscaled original forecasts regarding R2, MAE and RMSE of 0.0646, 21.1 W/m2 and 27.9 W/m2, respectively. The model was also evaluated for different timesteps and locations in southern Portugal, providing good agreement with experimental data.

Abstract This paper proposes a corrective algorithm for improving the accuracy of global horizontal irradiation (GHI) forecasts obtained from the numerical weather prediction (NWP) model of the European Centre for Medium-range Weather Forecast (ECMWF). Firstly, GHI forecasts were compared with experimental values from two ground-based stations located at the south of Portugal (Evora and Sines), and the influence of Sun-Earth geometry and atmospheric variables on the differences between predictions and measurements was analysed in order to identify the most relevant parameters. These differences are shown to be correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative air humidity and total water column. Since the ECMWF model directly or indirectly provides all these variables, it is possible to estimate the bias of the predicted GHI as a function of forecast data taking as reference the measurements, which means that an algorithm based on correlations of such parameters can be used to correct forecasts in an operational time horizon. With that goal, an artificial neural network (ANN) based algorithm was developed in this work to improve GHI predictions, including also as input the global solar irradiation predicted by a reference clear sky model. The internal structure of the ANN was optimised, and a spatial and temporal downscaling procedure was also developed to obtain half-hour irradiation values. This algorithm was tested against the original ECMWF forecasts and a persistence model for four locations with different orography and climate as well as for various sky conditions (overcast, partly cloudy and clear sky), showing that it successfully improves the model predictions. Higher values of a Global Performance Index (GPI) based on seven statistical indicators and Forecast Score (FS) were found for the algorithm simulations, e.g. respectively 1.066 and 0.348 when considering all the locations and cloud cover conditions, while for the original ECMWF predictions the GPI is −1.874 and the FS is 0.282. This algorithm is useful if integrated into energy management tools of solar energy systems, namely low/medium temperature solar thermal and photovoltaic systems.

Abstract Day-ahead forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS), the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF), are used to simulate a concentrating solar power (CSP) plant through the System Advisor Model (SAM) to assess the potential value of the IFS in the electricity market. Although DNI forecasting from the IFS still demands advances towards cloud and aerosol representation, present results show substantial improvements with the new operational radiative scheme ecRad (cycle 43R3). A relative difference of approximately 0.12% for the total annual energy availability is found between forecasts and local measurements, while approximately 10.6% is obtained for the previous version. Results of electric energy injection to the grid from a simulated linear focus parabolic-trough system shows correlations coefficients of approximately 0.87 between hourly values of electric energy based on forecasted and measured DNI, while 0.92 are obtained for the daily values. In the context of control strategy, four operational strategies are given for different weather scenarios to handle the energy management of a CSP plant, including the effect of thermal energy storage capacity. Charge and discharge operational strategies are applied accordingly to the predicted energy availability.

Solar photovoltaic power plants typically consist of rows of solar panels, where the accurate estimation of solar irradiance on inclined surfaces significantly impacts energy generation. Existing practices often only account for the first row, neglecting shading from subsequent rows. In this work, ten transposition models were assessed against experimental data and a transposition model for inner rows was developed and validated. The developed model incorporates view factors and direct and circumsolar irradiances shading from adjacent rows, significantly improving global tilted irradiance (GTI) estimates. This model was validated against one-minute observations recorded between 14 April and 1 June 2022, at Évora, Portugal (38.5306, −8.0112) resulting in values of mean bias error (MBE) and root-mean-squared error (RMSE) of −12.9 W/m2 and 76.8 W/m2, respectively, which represent an improvement of 368.3 W/m2 in the MBE of GTI estimations compared to the best-performing transposition model for the first row. The proposed model was also evaluated in an operational forecast setting where corrected forecasts of direct and diffuse irradiance (0 to 72 h ahead) were used as inputs, resulting in an MBE and RMSE of −33.6 W/m2 and 169.7 W/m2, respectively. These findings underscore the potential of the developed model to enhance solar energy forecasting accuracy and operational algorithms’ efficiency and robustness.

Short-term forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS) and the global numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF) were used in the simulation of a solar power tower, through the System Advisor Model (SAM). Recent results demonstrated that DNI forecasts have been enhanced, having the potential to be a suitable tool for plant operators that allows achieving higher energy efficiency in the management of concentrating solar power (CSP) plants, particularly during periods of direct solar radiation intermittency. The main objective of this work was to assert the predictive value of the IFS forecasts, regarding operation outputs from a simulated central receiver system. Considering a 365-day period, the present results showed an hourly correlation of ≈0.78 between the electric energy injected into the grid based on forecasted and measured data, while a higher correlation was found for the daily values (≈0.89). Operational strategies based on the forecasted results were proposed for plant operators regarding the three different weather scenarios. Although there were still deviations due to the cloud and aerosol representation, the IFS forecasts showed a high potential to be used for supporting informed energy dispatch decisions in the operation of central receiver units.