• Energy Research

Abstract Solar global irradiation is barely recorded in isolated rural areas around the world. Traditionally, solar resource estimation has been performed using parametric-empirical models based on the relationship of solar irradiation with other atmospheric and commonly measured variables, such as temperatures, rainfall, and sunshine duration, achieving a relatively high level of certainty. Considerable improvement in soft-computing techniques, which have been applied extensively in many research fields, has lead to improvements in solar global irradiation modeling, although most of these techniques lack spatial generalization. This new methodology proposes support vector machines for regression with optimized variable selection via genetic algorithms to generate non-locally dependent and accurate models. A case of study in Spain has demonstrated the value of this methodology. It achieved a striking reduction in the mean absolute error (MAE) – 41.4% and 19.9% – as compared to classic parametric models; Bristow & Campbell and Antonanzas-Torres et al., respectively.

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 including the use of local measurements. This methodology has been implemented as an example within the region of La Rioja in northern Spain. The mean absolute error found using the methodology proposed is 91.92 kW h/m2 vs. 172.62 kW h/m2 using the original satellite-derived database (a striking 46.75% lower). The code is freely available without restrictions for future replications or variations of the study at https://github.com/EDMANSolar/downscaling.

The current electrification status in West African countries presents rural electrification rates below 40%, national grid losses above 39% with frequent disruptions, and electricity prices averaging $0.35/kWh, up to national values of $0.66/kWh. With this, off-grid systems have gained great attention during the last decade as energy solutions; especially solar home systems (SHS) and mini grids. Nowadays, 385 mini grids with a power of near 30 MW are operating in West Africa, with 95% based on PV. Since 2019, result-based tenders with international aid funding—more effective than previous competitive tenders—seek to install at least 317 new mini grids in Togo, 250 in Nigeria, 100 in Burkina Faso, and two in Mali. Besides, the market for mini-grid energy access start-ups grew from $19 million in 2013 to $339 million in 2018. Despite this recent development in West Africa, research and data for mini grids in this region is scarce, and it is mostly approached from the technological side, with a striking lack of information regarding the social impact. This work tries to describe the present status of research and current operating installations, as well as the main challenges for future development of off grid mini grids in West Africa, which pose as the missing link between SHS and grid extension.

Abstract This work presents a kind of smart baseline models for solar irradiation forecasting, as models are only fed with meteorological records and solar-computed values, easy-to-obtain inputs that facilitate their implementation worldwide. Global horizontal irradiation (GHI) is predicted for horizons of 1 h in a site of Southeast Spain. Two types of approaches are undertaken: fixed models, trained just once with a global database, and moving models, where the training database is updated based on the features of the testing sample. The approaches are implemented with two machine learning algorithms, support vector regression (SVR) and random forest (RFs), along with the classic linear regression and kNN. Besides, genetic algorithms (GAs) are used to automate the training process of fixed models, a task traditionally performed based on the experience or the researcher. Significant improvements were obtained over the basic persistence methods with both approaches. In the case of moving models, results proved that the best approach to update the calibration set was by computing the Euclidean distance in the principal components space. Results of both approaches were comparable in terms of MAE and forecast skill ( s ), though slightly superior predictions were obtained with the moving SVR, with a forecast skill ranging from 8% to 23% and a testing MAE ranging from 49 to 64 W / m 2 for the different states of cloudiness. Anyway, both approaches are valid baselines to compare new forecasting models fed with more difficult-to-obtain features, supplementing the classic but naive persistence models.

Abstract Variability of solar resource poses difficulties in grid management as solar penetration rates rise continuously. Thus, the task of solar power forecasting becomes crucial to ensure grid stability and to enable an optimal unit commitment and economical dispatch. Several forecast horizons can be identified, spanning from a few seconds to days or weeks ahead, as well as spatial horizons, from single site to regional forecasts. New techniques and approaches arise worldwide each year to improve accuracy of models with the ultimate goal of reducing uncertainty in the predictions. This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends. Firstly, the motivation to achieve an accurate forecast is presented with the analysis of the economic implications it may have. It is followed by a summary of the main techniques used to issue the predictions. Then, the benefits of point/regional forecasts and deterministic/probabilistic forecasts are discussed. It has been observed that most recent papers highlight the importance of probabilistic predictions and they incorporate an economic assessment of the impact of the accuracy of the forecasts on the grid. Later on, a classification of authors according to forecast horizons and origin of inputs is presented, which represents the most up-to-date compilation of solar power forecasting studies. Finally, all the different metrics used by the researchers have been collected and some remarks for enabling a fair comparison among studies have been stated.

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 .

Abstract Bifacial photovoltaic (PV) modules and cells can transform solar radiation into electricity from both front and rear sides, unlike traditional solar technologies which can only generate power through the front face. This ability has shown to increase electric output with various levels of increment, depending on parameters such as distance to the ground, distance between modules, and albedo. This power gain characteristic versatility has attracted the industry, gaining both commercial and research interest. Measuring soiling effects in bifacial modules is an important milestone for the technology, since it is an important source of efficiency loss, thus relevant to the industry when evaluating and designing bifacial systems. In this work soiling rates are measured for bifacial minimodules and compared with traditional monofacial minimodules. The experiment was carried out for a period of two months in Santiago, Chile, measuring short circuit current of the minimodules along with the irradiance in the module plane and albedo. Also, a methodology is proposed to distinguish between soiling in the front and rear sides of bifacial modules, with which a mixed integer lineal problem is designed to obtain optimum cleaning frequency under different strategies and conditions for a period of three years, from 2014 to 2016. It is observed that soiling rate in the monofacial minimodule is 0.301%/day, meanwhile a rate of 0.236%/day was measured for the bifacial module. Also, a rate of 0.0394%/day was calculated for the rear side of the bifacial module, roughly 8.8 times smaller than the front side rate. Finally, several optimizations and simulations where performed to see the effects of soiling rate, albedo, rain, cleaning costs and strategy in cleaning frequency of both the front and rear sides of a bifacial PV system.

Abstract Accurate information on global solar radiation is essential to design and operate the systems that are based on solar energy. However, global solar radiation measurement is very rare while the measurements of other meteorological parameters such as air temperature, relative humidity, sunshine duration and precipitation are common in meteorological stations all around the world. Therefore, modelling global solar radiation is an important issue to fill the gaps in database and to estimate global solar radiation in places where global solar radiation measurement is not available. There are many different approaches in the literature for modelling global solar radiation. Two new methodologies are presented in this paper to develop parametric models for estimation of daily global solar radiation based on sunshine duration and relative humidity as well as a review of fourteen different already exist parametric models which are based on air temperature, maximum temperature, minimum temperature, precipitation, sunshine duration and relative humidity. The proposed models improve the estimation results of the other fourteen models with average mean absolute error (MAE) of 0.947 MJ/m 2 for Adana station, 1.086 MJ/m 2 for Goksun station, 1.074 MJ/m 2 for Tarsus station and 1.060 MJ/m 2 for whole study area. Hence, the proposed models which significantly approximate to measurements from pyranometers can be useful for the modelling global solar radiation in Eastern Mediterranean Region.

Solar home systems (SHS) represent one of the most promising technologies for a rapid and independent electrification in those areas of Sub-Saharan Africa (SSA) without access to electricity. This study addressed the environmental impact of SHS in SSA through updated life cycle inventories and five impact categories: greenhouse gases (GHG) emissions, fossil fuels, metal and water depletion and human toxicity. Sixteen scenarios were considered, including manufacturing, transportation, recycling and user-related variables, such as the installation site, adequacy of SHS user operation and battery lifespan. The results showed that lead-acid batteries were the largest contributor to environmental impact among the SHS components, accounting for up to 36–76% of the environmental impact indicators. Apart from the components, user training for SHS operation, with the goal of maximizing usable energy and battery lifetime, proved to be critical to achieve improvements in the energy payback time and GHG emissions, which (under scenarios of high solar resources) can reach the range of 5.3–7.1 years and 0.14–0.18 kgCO2 eq/kWh, respectively. In addition, SHS GHG emission factors were benchmarked with those of other electrification approaches, such as national grids, 100% PV and hybrid PV-diesel off-grid mini grids and off-grid diesel generators. SHS achieved GHG emission factor values equivalent to PV-based mini grids in most scenarios and was strikingly lower compared to SSA national grids and diesel generators.