• Energy Research

Abstract The vast solar resource available in Algeria and the high temperatures recorded in this country make the solar thermal integration of gas-fired power plants (ISCC) an attractive method to increase yield by means of solar energy. A feasibility study of potential solar thermal hybridization with parabolic trough technology was performed in 21 Algerian open cycle gas turbines and combined cycle gas turbines, which represent about 97% of electricity generation. An yield increase of 24.9 GW h/year and CO2 emission savings of 9.91 kton/year were determined to be feasible with solar field sizes ranging from 30 to 37 loops in combined cycle centrals. In the case of open cycle gas turbines, a solar potential of 1085.7 GW h/year and CO2 emission savings of 652.1 kton/year were recorded with solar shares in the range of 3–4%. The ISCC production model is provided herein as complementary material for future replications of this study (freely available without restrictions in R language) at http://www.github.com/EDMANSolar/AlgeriaSolar .

Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the Bias-based Quality Control (BQC), that detects low-magnitude defects by analyzing the stability of the deviations between several independent radiation databases and measurements. In this study, we extend the validation of the BQC by analyzing the quality of all publicly available Spanish radiometric networks measuring global horizontal irradiance (9 networks, 732 stations). Similarly to our previous validation, the BQC found many defects such as shading, soiling, or calibration issues not detected by classical QC methods. The results questioned the quality of SIAR, Euskalmet, MeteoGalica, and SOS Rioja, as all of them presented defects in more than 40% of their stations. Those studies based on these networks should be interpreted cautiously. In contrast, the number of defects was below a 5% in BSRN, AEMET, MeteoNavarra, Meteocat, and SIAR Rioja, though the presence of defects in networks such as AEMET highlights the importance of QC even when using a priori reliable stations.

Abstract Exactly how to estimate solar resources in areas without pyranometers is of great concern for solar energy planners and developers. This study addresses the mapping of daily global irradiation by combining geostatistical interpolation techniques with support vector regression machines. The support vector regression machines training process incorporated commonly measured meteorological variables (temperatures, rainfall, humidity and wind speed) to estimate solar irradiation and was performed with data of 35 pyranometers over continental Spain. Genetic algorithms were used to simultaneously perform feature selection and model parameter optimization in the calibration process. The model was then used to estimate solar irradiation in a massive set of exogenous stations, 365 sites without irradiation sensors, so as to overcome the lack of pyranometers. Then, different spatial techniques for interpolation, fed with both measured and estimated irradiation values, were evaluated and compared, which led to the conclusion that ordinary kriging demonstrated the best performance. Training and interpolation mean absolute errors were as low as 1.81 MJ / m 2 day and 1.74 MJ / m 2 day , respectively. Errors improved significantly as compared to interpolation without exogenous stations and others referred in the bibliography for the same region. This study presents an innovative methodology for estimating solar irradiation, which is especially promising since it may be implemented broadly across other regions and countries under similar circumstances.

Abstract The major sources of uncertainty in short-term assessment of global horizontal radiation (G) are the pyranometer type and their operation conditions for measurements, whereas the modeling approach and the geographic location are critical for estimations. The influence of all these factors in the uncertainty of the data has rarely been compared. Conversely, solar radiation data users are increasingly demanding more accurate uncertainty estimations. Here we compare the annual bias and uncertainty of all the mentioned factors using 732 weather stations located in Spain, two satellite-based products and three reanalyses. The largest uncertainties were associated to operational errors such as shading (bias = −8.0%) or soiling (bias = −9.4%), which occurred frequently in low-quality monitoring networks but are rarely detected because they pass conventional QC tests. Uncertainty in estimations greatly changed from reanalysis to satellite-based products, ranging from the gross accuracy of ERA-Interim ( + 6.1 - 6.7 + 18.8 %) to the high quality and spatial homogeneity of SARAH-1 ( + 1.4 - 5.3 + 5.6 %). Finally, photodiodes from the Spanish agricultural network SIAR showed an uncertainty of - 5 . 4 + 6 . 9 %, which is far greater than that of secondary standards ( ± 1.5%) and similar to SARAH-1. This is probably caused by the presence of undetectable operational errors and the use of uncorrected photodiodes. Photodiode measurements from low-quality monitoring networks such as SIAR should be used with caution, because the chances of adding extra uncertainties due to poor maintenance or inadequate calibration considerably increase.

Solar radiation estimates with clear sky models require estimations of aerosol data. The low spatial resolution of current aerosol datasets, with their remarkable drift from measured data, poses a problem in solar resource estimation. This paper proposes a new downscaling methodology by combining support vector machines for regression (SVR) and kriging with external drift, with data from the MACC reanalysis datasets and temperature and rainfall measurements from 213 meteorological stations in continental Spain. The SVR technique was proven efficient in aerosol variable modeling. The Linke turbidity factor (TL) and the aerosol optical depth at 550 nm (AOD 550) estimated with SVR generated significantly lower errors in AERONET positions than MACC reanalysis estimates. The TL was estimated with relative mean absolute error (rMAE) of 10.2% (compared with AERONET), against the MACC rMAE of 18.5%. A similar behavior was seen with AOD 550, estimated with rMAE of 8.6% (compared with AERONET), against the MACC rMAE of 65.6%. Kriging using MACC data as an external drift was found useful in generating high resolution maps (0.05° × 0.05°) of both aerosol variables. We created high resolution maps of aerosol variables in continental Spain for the year 2008. The proposed methodology was proven to be a valuable tool to create high resolution maps of aerosol variables (TL and AOD 550). This methodology shows meaningful improvements when compared with estimated available databases and therefore, leads to more accurate solar resource estimations. This methodology could also be applied to the prediction of other atmospheric variables, whose datasets are of low resolution.

Traditionally, the accuracy of solar power forecasts has been measured in terms of classic metrics, such as root mean square error (RMSE) or mean absolute error (MAE), and it is widely accepted that the smaller the error, the greater the economic benefits. Nevertheless, this is not as straightforward as it may seem, because market conditions must be studied first. Relationships between magnitudes of deviations between forecast and actual production and market penalties that apply at each moment are crucial. In this study, we analyze various day-ahead production forecasts for a 1.86 MW photovoltaic plant considering different techniques and sets of inputs. A nRMSE of 22.54% was obtained for a Support Vector Regression model trained by numerical weather predictions (NWP). This model produced the most benefits. An annual forecasting value of 4788 with respect to a persistence model was obtained for trading in the Iberian (Spain and Portugal) day-ahead electricity market. Annual value added by the NWP service totaled 2801 and room for improvement regarding NWP variables rose to 3877. As a general trend, it was found that smaller errors (RMSE) generated higher incomes. For each 1 kW h improvement in RMSE, the annual value of forecasting increased 22.32. Nevertheless, some models that gave larger errors than others also brought greater benefits. Thus, market conditions must be considered to accurately evaluate model economic performance. © 2017 Elsevier Ltd

Abstract In this study, we have performed an analysis of potential irradiation increase across Europe, derived from moving photovoltaic (PV) modules to a horizontal position during cloudy and overcast situations. The motivation of research is that PV technology has expanded outside countries endowed with high solar irradiation and it is nowadays present in high-latitude regions and areas with frequent cloud cover. Unlike concentrating solar technologies, PV panels can make use of both beam and diffuse irradiance. During cloudy or overcast situations, most of solar irradiance comes from the diffuse component, which approaches an isotropic behavior under those circumstances. Then, a PV panel facing the zenith would receive more irradiation than a PV panel following the Sun’s path. In our approach, we have used data from several Baseline Surface Radiation Network stations. Results showed a yearly potential irradiation increase of 3.01% with respect to a reference single-axis tracking system (SATS) in the northern-most station. Values for other stations were correlated to climate. Thus, the sunniest station would increase its annual irradiation by 0.16% compared to a reference SATS. Daily irradiation increases of up to 19.91% were registered. Two predictive models were created to develop a SATS, which could determine in advance the optimum position of PV panels. Model 1, which was based on the persistence of irradiance, was designed to work real time in order to increase production. Yearly gains of up to 2.51% of irradiation were registered. However, Model 2 was developed to update production forecasts in the intra-day electricity markets so that PV plant owners can maximize their revenues. It is based on irradiance predictions from a numerical weather prediction service and underperformed current SATS.

Nowadays, solar resource estimation via clear-sky models is widely accepted when correctly validated with on ground records. In the past, different approaches have been proposed in order to determine clear-sky periods of solar radiation on-ground records: visual inspection of registers, discretization via a threshold value of clear sky index, and correlation with estimated clear sky solar irradiation. However, due to the fact that the process must be automated and the need for universality, the search for clear-sky conditions presents a challenging feat. This study proposes a new algorithm based on the persistent value of the Linke turbidity in conjunction with a transitory filter. The determinant of the correlation matrix of estimated clear-sky solar irradiance and measured irradiance is calculated to distinguish between days under clear-sky conditions and cloudy or overcast days. The method was compared and proved superior against a review of other 10 commonly used techniques at 21 sites of the Baseline Surface Radiation Network, which includes diverse climates and terrain.