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Abstract The main aim of this study was to analyse scientific and energy production, funding, collaboration among countries and most cited papers on the renewable energies topic through bibliometric and social network study of articles included in the Web of Science database. 12,167 papers were recovered from 2007 to 2016. Wind power is the energy from which a greater number of articles have been published (n = 3,930), followed by solar energy (n = 2,570) and ocean energy (n = 1,565). The United States leads the world production of articles (n = 2,320), followed by China (n = 1,629), the United Kingdom (n = 1007), Germany (n = 730) and Spain (n = 729). China is the leading country in financed works (80%), followed by South Korea (77%), Spain (61%) and the United States (59%). An important level of international collaboration has been identified, notably popping up the triangle made by United States, China and the European Union. Highlights • Scientific study based in the analysis of the energy production, funding and collaboration among countries. • Wind power is the energy from which a greater number of articles have been published. • The United States leads the world production of articles. • China is the leading country in financed works. • The triangle made by United States, China and the European Union have the most important level of collaboration. [ABSTRACT FROM AUTHOR]

The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed. [ABSTRACT FROM AUTHOR]

Solar cells lose efficiency as the operating temperature increases under normal operating conditions, resulting in a very significant power output reduction. This issue is most relevant in areas with the highest photovoltaic energy harvesting potential, where irradiance and ambient temperature are particularly high. A novel cooling system for commercial photovoltaic modules based on low-enthalpy geothermal cooling is proposed in this paper. Excess heat is evacuated from the solar module in a clean and efficient manner by a single-phase and close-loop cooling circuit that uses the underground as a natural heatsink. In addition, a thermoelectrical theoretical model is presented, that allows the optimization of the flowrate and the maximization of the extra power generation of the cooled module. A prototype was manufactured and tested in outdoor conditions in September 2021 in Spain. Test results are in good agreement with the theoretical model, which has been used for prediction of the performance of the system for a typical meteorological year in the region. A peak efficiency improvement of 10.7% is calculated for the month of July, with an optimal flowrate of 1.8 l/min per square meter of solar module. A yearly weighted average power gain of up to 4.9% is also calculated. [ABSTRACT FROM AUTHOR]

Fluctuations of the incoming solar irradiance impact the power generation from photovoltaic and concentrating solar thermal power plants. Accurate solar nowcasting becomes necessary to detect these sudden changes of generated power and to provide the desired information for optimal exploitation of solar systems. In the framework of the International Energy Agency's Photovoltaic Power Systems Program Task 16, a benchmarking exercise has been conducted relying on a bouquet of solar nowcasting methodologies by all-sky imagers (ASI). In this paper, four ASI systems nowcast the Global Horizontal Irradiance (GHI) with a time forecast ranging from 1 to 20 min during 28 days with variable cloud conditions spanning from September to November 2019 in southern Spain. All ASIs demonstrated their ability to accurately nowcast GHI, with RMSE ranging from 6.9% to 18.1%. Under cloudy conditions, all ASIs' nowcasts outperform the persistence models. Under clear skies, three ASIs are better than persistence. Discrepancies in the observed nowcasting performance become larger at increasing forecast horizons. The findings of this study highlight the feasibility of ASIs to reliably nowcast GHI at different sky conditions, time intervals and horizons. Such nowcasts can be used either to estimate solar power at distant times or detect sudden GHI fluctuations. • ASI-based GHI nowcasts outperform persistence model under cloudy conditions. • Under clear skies, three ASIs reveal more accurate nowcasts than persistence. • ASIs forecasts are more accurate from persistence at increasing forecast horizons. [ABSTRACT FROM AUTHOR]

This paper aims to carry out an integrated Life Cycle Assessment to evaluate the environmental performance of retrofitting an existing first-generation ethanol plant for sustainable aviation fuel production. Two scenarios are defined, considering: (i) the incorporation of an alcohol-to-jet conversion pathway into a current bioethanol plant in Spain to produce synthetic paraffinic kerosene with aromatics biofuel, and (ii) the operation of two different plants, namely, an existing bioethanol production facility and a new-build ATJ plant. Results indicate that the global warming impact associated with the retrofitting of the bioethanol facility is 44.15 gCO 2eq per MJ of final fuels produced. The main GHG impact is associated with the operation of both the cogeneration system (∼42 %) and the natural gas boilers (∼25 %) to cover the heat and power requirements of the bioethanol facility. Findings concerning the climate change impact of the new-build ATJ plant exhibit analogous behavior; the GHG impact is estimated at 44.53 gCO 2eq /MJ fuels. Future electricity supply mixes with high shares of renewables could lead to substantially lower GHG emissions (up to 61 %) in aviation fuel production, as compared to conventional jet fuels. The replacement of natural gas with sustainable alternatives could further decrease the global warming impact up to 97 %. [ABSTRACT FROM AUTHOR]

Solar PV technologies have been expanding rapidly, and the installed power has increased, especially in the last decade. Their inclusion in university buildings arises as a key factor for sustainable transition in cities, since they have an important potential as case studies for urban experimentation positive effects on students and other stakeholders. This paper proposes a method to determine the availability for installing solar PV in universities' rooftops, considering simulation and cartography data, together with a final experimental validation stage. Unavailable areas for each rooftop are determined, including obstacles, the inner use of the rooftop and shadowed areas. Finally, the method verifies the technical suitability for all the proposed scenarios and applies an economic-environmental multicriteria evaluation to select the most suitable one. The methodology is validated in the Universitat Politècnica de València Vera-Campus. In total, 88913 m2 of the rooftops are suitable for solar PV incorporation, representing 48 % of the total surface. 52 % of the solar PV installable capacity takes place in surfaces which can incorporate more than 100 kW, 20.6 % between 50 and 100 kW and 27.4 % less than 50 kW. Finally, the multicriteria evaluation proved that the scenario with all the possible installations is the most suitable one. [ABSTRACT FROM AUTHOR]

Heating up of photovoltaic modules during operation results in a significant power output reduction. This effect is especially relevant in regions with the highest photovoltaic potential, where irradiance and ambient temperature are particularly high. In this paper, a novel low enthalpy geothermal cooling system that improves the efficiency of a commercial solar module is described and experimentally validated. Two heat exchangers, one attached to the backside of the module, and another introduced inside a 15 m deep borehole, are connected in a closed circuit. A water-based coolant is pumped through the system, evacuating the excess heat from the module, and dissipating it underground in a clean and efficient manner, without additional water consumption after filling the system. The performance of the cooling system was experimentally tested by implementing it, for the first time, in a single-axis solar tracking isolated photovoltaic facility. Extensive tests were carried out in Spain, between September 2021 and August 2022 under a wide variety of environmental conditions. The efficiency improvement system has demonstrated its technical feasibility over a one-year period, showing a peak net efficiency improvement up to 13.4 %. Moreover, a positive net efficiency improvement has been demonstrated all year long, even during the winter months. [ABSTRACT FROM AUTHOR]

This paper proposes the first application of a split-range control technique on a concentrating solar collector to improve an absorption plant production. Solar absorption plants have solar power availability in phase with cooling demand under design conditions. Thus, it is a powerful cooling technology in the context of renewable energy and energy efficiency. These plants need control systems to cope with solar irradiance intermittency, reject irradiation disturbances, manage fossil fuels backup systems and dump closed-loop thermal-hydraulic oscillations. In this work, control techniques are proposed and simulated in an absorption plant in Spain. The plant consists of a concentrating Fresnel solar collector connected to an absorption chiller. The objectives are to operate with 100% renewable solar energy and avoid safety defocus events while reducing temperature oscillations and control actuators effort. Firstly, the current available plant controllers are defined, then two modifications are proposed. The first modification is a split-range controller capable of manipulating both flow and defocus of the Fresnel collector, the second modification is a PI controller to substitute the original chiller on-off controller. The results compare, through validated models, the different control systems and indicate that using both proposed controllers reduces 94% of the sum of actuators effort and 43% of the integral of absolute set-point tracking error compared to the plant's factory pre-set controllers. The suggested controllers increase 66% of energy production and 63% of exergy production. Besides, the split-range technique can be extended to any concentrating solar collector control. [Display omitted] [ABSTRACT FROM AUTHOR]

The paper aims to develop a decision tool for planning offshore wind farm locations, combining multi-criteria decision analysis and geographic information systems. This method considers several technological, environmental, social, and economic criteria. The criteria are used as evaluation factors and constraints to rank the identified potential locations through an Analytic Hierarchy Process. During the evaluation of the available areas, a pairwise comparison is used in which a group of experts determines the weights. Sensitivity and consensus analyses evaluate the results robustness. The methodology is developed for a case study in the Atlantic coastal areas of Portugal, Spain, and France. The results show the potential of this tool to support the planners in the designation of regions for floating wind farm development. [ABSTRACT FROM AUTHOR]

This paper analyses the capacity credits (CCs) of renewable photovoltaic (PV), concentrated solar power (CSP) and wind technologies in the Spanish power system. This system has steadily increased the share of renewables, reaching a penetration level of over 30%. The predictions made by ENTSO-e suggest that this level will increase to 50% by 2030. Therefore, different scenarios are studied in this paper to investigate the evolution of renewable integration and assess the corresponding contributions to reliability. The assessment is performed using a sequential Monte Carlo (SMC) method considering the seasonality of renewable generation and the uncertainties related to renewable sources, failure issues and the maintenance of thermal-based units. The baseline for SMC is provided by historical annual time series of irradiance and wind power data from the Spanish system. In the solar case, these time series are transformed into power time series with models of CSP and PV generation. The former includes different thermal storage strategies. For wind generation, a moving block bootstrap (MBB) technique is used to generate new wind power time series. The CC is assessed based on the equivalent firm capacity (EFC) using standard reliability metrics, namely, the loss of load expectation (LOLE). The results highlight the low contribution of renewables to power system adequacy when the Spanish power system has a high share of renewable generation. In addition, the results are compared with those of similar studies. • The contribution of renewables to reliability in future scenarios is low. • Conventional generation has a key role to ensure reliability in future power systems. • Different storage strategies in CSP plants have a slight effect on reliability. • Hydro power in Spain contributes to increase the capacity credit of solar technologies. • Definitions of Capacity credit and Capacity value yield very different results. [ABSTRACT FROM AUTHOR]

Extreme Wind Speed events (EWS) are responsible for the worst damages caused by wind in wind farms. An accurate estimation of the frequency and intensity of EWS is essential to avoid wind turbine damage and to minimize cut-out events in these facilities. In this paper we discuss how generative Data Augmentation (DA) techniques improve the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in EWS prediction problems. These problems are usually tackled as classification tasks, which are highly unbalanced due to the small number of EWS events in wind farms. Different versions of Variational AutoEncoders (VAE) are proposed and analysed in this work (VAEs, Conditional VAEs (CVAEs) and Class-Informed VAEs (CI-VAE)) as generative DA techniques to balance the data in EWS problems, leading to better performance of the prediction systems. The proposed generative DA techniques have been compared against traditional DA algorithms in a real problem of EWS prediction in Spain, considering ERA5 reanalysis data as predictive variables. The results showed that the CI-VAE with a Convolutional Neural Network approach obtained the best results, with values of Precision 0.62, Recall 0.74 and F1 score 0.67, improving up to 4% the results of the method without data augmentation techniques. [Display omitted] • Generative data augmentation techniques can improve extreme wind prediction. • Variational Autoencoders modifications are used to generate synthetic wind extreme samples. • Experiments on real wind speed data from a Spanish wind farm are presented. • Comparisons with traditional data augmentation techniques show prediction improvements. [ABSTRACT FROM AUTHOR]

Finding an optimal bidding strategy for a wind farm in the electricity market is not straightforward due to the wind variability. This issue is becoming more relevant as renewable plants are more exposed to market signals. Considering the characteristics of the European markets, operators must submit the energy bidding between 12 and 36 h ahead the actual delivery time. This bidding can be adjusted later in any session of the intraday markets, but sometimes this is not enough to reduce significantly the deviation risk. This paper presents a practical application of a method to analytically calculate the optimal bidding of a wind power plant, based on the maximisation of the income function. The results show that, given the characteristics of the deviation markets in Spain, the optimal bidding strategy depends essentially on the deviation of the system. The proposed technique has been tested and validated in a real application by considering actual data for energy production and forecasts for an operating wind farm in Spain, as well as real market deviations and prices provided by the Spanish system and market operators; analysing the advantages of the proposed optimal bidding strategy over the most plausible option, based on bidding the forecasted energy. [ABSTRACT FROM AUTHOR]

Agriculture is one of the most energy intensive activities within the European Union. The common absence of electric infrastructure in these rural settings has led to the need for in situ generation in many cases. The existing excess pressures in large pressurised irrigation networks makes the generation of micro hydropower here a possible alternative renewable energy source. Pump-as-turbines have been proposed as a cost-effective technology for such purposes, taking advantage of the excess pressure in pipe networks to produce energy. This paper presents a methodology for the design of a plant for micro hydropower generation in an agricultural farm and outlines the predicted benefits of the installation. These predicted benefits were also compared with the measured performance of the plant for the 2019 irrigation season. The main aim of the plant was to replace a diesel generator that supplied energy to the farm. The excess pressure found in the pipe network varied between 0m and almost 60m. The nominal power of the pump-as-turbine was selected to supply the maximum energy requirements of the farm. The predicted operation time of the plant was estimated at up to 3199 h concentrated between April and September. An annual savings of approximately €2950 and 11 t eCO 2 were also estimated. The measured results showed an actual operation time of 2443 h between May and September, as in April the monitoring system was not operational. For this operation time, the savings were €2258 and 8.4 t eCO 2. Considering the April theoretical irrigation time obtained, the savings raised up to approximately €2434 and 9.1 teCO 2. The return on investment of the installation was computed to be recovered in less than ten years. • Real pilot PAT installation operating during an entire irrigation season. • Electricity was brought to remote places with no access to grid connection. • Diesel energy source was fully replaced by micro hydropower technology. • Solar and micro hydro hybrid solution was used for self-consumption. • More than 9 tCO 2 and €2400 were saved during the 2019 irrigation season. [ABSTRACT FROM AUTHOR]

This paper presents a model for optimizing the life cycle economic and environmental impacts of a hybrid renewable energy and battery storage system – as energy supply technologies (EST) for off-grid farms. Micro-hydropower and photovoltaics represent the primary renewable energy (RE) sources, with batteries and a diesel generator considered as possible back-up systems. A modified Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed and applied to a case study farm in Southern Spain and provided different EST solutions to optimize a design based on economic (life cycle costing or LCC) and environmental (life cycle assessment or LCA) constraints. The results highlighted that cases with a strong dependence on batteries could achieve minimum costs and lower impacts for climate change and energy resources (non-renewable fuels). In contrast, the optimal system for minimizing the consumption of material resources (metals/minerals) resulted in higher fuel use, which was up to 25% more expensive. The optimal EST solutions used up to 37% of the total energy generated; however, the results were sensitive to energy production-demand distributions. This methodology can help identify the most cost-effective solutions with the lower environmental impacts for decarbonizing the agricultural sector. [Display omitted] [ABSTRACT FROM AUTHOR]

The practical feasibility to install solar updraft tower (SUT) for power production require huge capital investment and difficulties in installing tall giant chimney structure. The chimney is the most expensive element of SUT and typically covers one-fourth of the total SUT cost. Previous finding has shown that a divergent chimney shows significant increase in power output than a conventional design for the same height. This paper deals with the unexplored scientific question: for how much height of divergent chimney can be dispensed with for the same or higher performance of a conventional design? Using an experimentally validated numerical model, we systematically investigated height reduction of chimney (and hence substantial cost) of a high performing divergent chimney whose power output is identical with a long unstable costly conventional cylindrical chimney. The conventional cylindrical design was chosen as the conventional SUT prototype installed at Manzanares, Spain in the year 1980. Results show that it is possible to reduce the vertical limit by about 80% of the height of conventional cylindrical chimney. Further, the cost analysis estimated for each SUT designs and the optimum divergent chimney SUT holds payback period 40% less than the conventional SUT. The proposed designs would be useful for floating chimney as well made of fabric material with light density air filled to keep short chimney height upright vertically to resolve the instability issues with the conventional tall chimney structure during high wind speed environmental conditions The present investigation would assist scientific community to develop more advance clean energy-based power generation systems to achieve net-zero carbon emissions targets. [Display omitted] [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) can play a significant role in economic growth. This article examines the development of the wind energy market in the EU. The applicable regulations, directives, and legal acts are reviewed, and the State's role in the development of the renewable energy market is discussed. The analyses were carried out based on the available literature and Eurostat data. The authors of the paper used basic statistics to summarize the changes in wind energy and make a prognosis. The data analysis uses the Autoregressive Integrated Moving Average Method (ARIMA). The survey found that the highest average wind cumulative installed capacities were in Germany (31026 MW), Spain (18602 MW), United Kingdom (7894 MW) and France (6108 MW). Our analysis indicates that the biggest average annual installed wind capacity was in Germany (2952.6 MW), Spain (1224.5 MW) and the United Kingdom (1363.8 MW). These same countries achieved the highest average wind gross electricity production in the years 2004–17: Germany (50.9 TWh), Spain (39.3 TWh) and United Kingdom (18.9 TWh). The study finds that wind cumulative installed capacity will increase in most countries of the EU. • The European Union onshore wind energy market seems to be stable. • Wind power plays an important role as a source of energy supply. • The most important wind gross electricity producers in the EU are: Germany and Spain. • The highest increase of wind cumulative installed capacity in 2022 will be in Croatia. [ABSTRACT FROM AUTHOR]

This paper analyses the influence of the variation of meteorological and operational parameters on estimation of the power output of a dispatchable wind farm (WF). The active power set-points (APSPs), established to regulate the wind farm power output (WFPO), are one of the analysed parameters. The WF considered as case study is integrated in the Gorona del Viento wind-hydro power plant (El Hierro-Canary Islands-Spain), which supplies the primary energy demand of the island. Statistical inference between the errors generated by different WFPO estimation models, each fed with different input features, is performed. These models are based on supervised machine learning (ML) regression algorithms, namely support vector regression, random forest, and a combination of the strengths of these two base learning algorithms constructed using stacked regression ensemble techniques. From the results obtained, the following conclusions are drawn: a) There is a marked difference between the errors obtained with the model that only considers wind speed and direction and that which additionally incorporates the APSP parameter, showing the importance of considering this particular parameter; b) the model which incorporates air density and turbulence intensity in addition to the APSP improves the values of all the metrics, independently of the ML technique employed. • The active power set-point (APSP) parameter is very important in WFPO estimation. • Air density, turbulence intensity and APSP parameters improve WFPO estimation. • Eight models are compared for wind farm power output (WFPO) estimation. • Three machine learning techniques implemented in the models are analysed. • Non-parametric statistical inference is used to compare the models. [ABSTRACT FROM AUTHOR]

Volatility and sharp increases in the price of electricity are serious economic problems in the primary sector because they affect modernization investments for irrigation systems in Spain. This paper presents a new virtual power plant (VPP) model that integrates all available full-scale distributed renewable generation technologies. The proposed VPP operates as a single plant in the wholesale electricity market and aims to maximize profit from its operation to meet demand. Two levels of renewable energy integration in the VPP were considered: first, a wind farm and six hydroelectric power plants that inject the generated electricity directly to the distribution network, and second, on-site photovoltaic plants associated with each of the electricity supply points in the system that are designed to prioritize self-consumption. The proposed technical-economic dispatch model was developed as a mixed-integer optimization problem that determines the hourly operation of distributed large-scale renewable generation plants and on-site generation plants. The model was applied to real data from an irrigation system comprising a number of water pumping stations in Aragon (Spain). The results of the VPP model demonstrate the importance of the technical and economic management of all production facilities to significantly reduce grid dependence and final electricity costs. • An optimal technical-economic dispatch model of a Virtual Power Plant is proposed. • Large and small-scale distributed renewable generation are combined in the model. • The mathematical problem is solved by mixed-integer linear programming. • The real application to the operation of an irrigation system is presented. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the variation of the transmittance of the receiver glass envelope as a function of the incidence angle and we measure its impact on the annual optical efficiency of a LFR plant using ray-tracing techniques. For this purpose, we draw up a detailed model of the LFR collector installed on the roof of the School of Engineering of the University of Seville, Spain. We also calculate the optical efficiency with and without a secondary reflector and with constant or variable transmittance receiver glass envelope properties. We run simulations using a clear-sky annual 1-min synthetic data set as input and calculate an average annual optical efficiency using efficiency matrices and Incidence Angle Modifiers (IAM) obtained from ray-tracing simulations. We find that the effect of the variation of the receiver glass envelope optical properties, as a function of the incidence angle, reduces the annual optical efficiency by 2.5%when the LFR plant has a basic secondary reflector and by 0.7% when there is no secondary reflector, according to the results obtained when using constant optical properties. We also evaluate the performance of the system with an optimised secondary reflector design. • State the importance of taking into account the angular dependency of the receiver glass envelope. • Quantify the impact of estimating LFR optical efficiency with variable receiver glass envelope transmissivity. • Evaluate the role of two parabolic secondary reflectors on the production of a LFR plant. • Compare the efficiency matrixes and IAM methods in the optical efficiency calculations. [ABSTRACT FROM AUTHOR]

In recent years, pump-as-turbines (PATs) have been highlighted for their potential benefits as an application of micro-hydropower (MHP) in water distribution networks. However, PATs come with disadvantages of relatively low peak efficiencies, which can be reduced further with large flow fluctuations. MHP and PATs in particular applied in irrigation networks is a relatively new area of research focus for these devices, and one that poses significant opportunities for energy saving as well as significant challenges due to variations in flow rate. This paper discusses the validation of a statistical methodology to estimate the flow and head variability in a network, and to select PATs whose best efficiency point (BEP) returns the lowest payback period. A comparison between the predicted and actual occurrence probabilities for different flow rates was carried out at nine potential points for MHP installation identified within a real network in Southwestern Spain. For the flow occurrence probability, the coefficient of determination (R2) of 0.804. A total of 281.0 MWh were obtained from the flow prediction and PAT selection methodology, in contrast to 230.5 MWh using the actual measured data. An overall difference of 0.2% was obtained when both PATs were simulated under actual conditions. • The validation of a methodology for flow prediction and pump as turbine selection was carried out. • An average coefficient of determination of 0.804 was obtained for flow occurrence probability. • Difference of 22% on the energy recovery potential with predicted and actual conditions. • Difference of 0.2% was found when predicted pump as turbines were tested under real conditions. • In comparison to alternative approaches, this methodology represents a 7.4 and 38% improvement in accuracy. [ABSTRACT FROM AUTHOR]

Inaccurate wind forecasts create negative technical and economic effects on power systems such as wind curtailments, increase of operating reserves or economic penalties that wind owners have to face. In the Spanish market, wind energy units deviating against the system incur in significant extra costs. Taking advantage of the geographical dispersion of wind farms, wind aggregation poses a solution to imperfect predicting techniques reducing wind portfolio differences between predicted and real production. In the Spanish electric power system, we measure in this paper the efficacy of aggregating wind promoters in reducing deviations and costs. For this purpose, an empirical method using measures of real productions and schedules is proposed, taking advantage of an optimization model that builds a wind portfolio with the minimum wind deviation, and defining key performance indexes to compute the benefits of the aggregation. To obtain significant and reliable values, the whole year of 2016 is assessed using 67 wind farms representing 7% of the total wind power installed in the Spanish peninsula at the end of 2016. It will be shown that aggregation is able to reduce a maximum of 65% of individual wind deviations which translates in a reduction of no more than 58% of individual wind deviation extra-costs. In addition, this limit of maximum extra-costs reduction represents 2% of loss of income in the daily energy market. • Optimization model that builds a wind portfolio with the minimum wind deviations. • Definition of Key Performance Indexes to measure the efficacy of wind aggregation. • Application case to the Spanish power system: 7% wind installed capacity in year 2016. • Aggregation in Spain reduces a maximum of 65% of individual wind deviations. [ABSTRACT FROM AUTHOR]

In 2017, renewable energy accounted for 19.5% of the total energy used for heating and cooling in the European Union. This paper analyses the technical and economic feasibility of using mine water from flooded underground coal mines in Spain to provide renewable thermal energy to buildings located in surrounding areas. High efficiency heat pump system is proposed to provide sustainable energy for district heating and cooling, leading to a reduction of greenhouse gas emissions to the atmosphere. The results obtained show that 20 MW of thermal energy is available for heat recovery from mine waters, compared to 4 MW of electrical power committed for pumping from inside the mines and heat pump consumption. The economic model that has been developed indicates that the feasibility of the geothermal plants depends on the amount of thermal energy demanded, the efficiency of the system and the distance from the abandoned mines to potential users. A mine water geothermal plant with 10 MW of power provides energy for about 1,700 households at a distance of 2 km with an investment cost of 3.25 M€. Regarding the reduction of CO 2 emissions, the emission factor is reduced to 0.048 kgCO 2 kWh−1 using this approach. • The geothermal potential has been analyzed in three closed mines in NW Spain. • The geothermal use of water from a mine allows heating and cooling nearby buildings. • The feasibility depends on the system efficiency and the distance to buildings. • A geothermal plant with 10 MW of power could provide energy for 1,700 households. • 10 MW geothermal plant reaches an IRR of 12% for a distance to buildings of 2 km. [ABSTRACT FROM AUTHOR]

This paper discusses the techno-economic potential of solar thermal calciner technology in the cement industry. On the basis of a solar calciner test rig built at the German Aerospace Center (DLR), a solar cement plant is designed and the heliostat field is calculated. The energy balance in the solar calciner is analyzed and different scenarios are investigated. The achievable CO 2 avoidance rate for solar cement plants for the considered scenarios lies between 14 and 17%. CO 2 avoidance costs are 118 EUR/t in a conservative base case and can be as low as 74 EUR/t depending on the chosen direct normal irradiation (DNI), reactor efficiency and solar multiple. A strong impact of the reactor efficiency on the costs was shown. Increasing the reactor efficiency by 15% points reduces the avoidance costs by 26%. Additionally, the CO 2 emission reduction potential is calculated for Spain through 2050. It was found that for solar calciners, replacing the fossil fuel in the conventional calciner, emission reductions in the Spanish cement industry range between 2 and 7% by 2050. Implementation of a controlled sequestration of the CO 2 in the solar calciner shows a big impact and emission reductions from 8 to 28% can be achieved. Image 1 • Implementation of solar calcination into Spanish cement industry analyzed. • Already 39% of cement plants are in regions with enough solar radiation. • Performing the step of calcination with solar energy would reduce emissions by 2–7%. • Sequestration of CO 2 in the calciner has big impact, reduction by 8–28% possible. [ABSTRACT FROM AUTHOR]

There is a growing interest on how climate change may affect the energy sector, including changes in wind energy generation. This paper builds on existing research adding an economic component that considers how climate change can affect operating margins and investment values in specific wind farms in Spain. A projection of wind speed was carried out using an ensemble of three climate models, two scenarios (RCP 4.5 and 8.5) and two time periods (2018–2041 and 2042–2065) per plant. Using historical power curves, the changes in wind speed were converted to production output. The results show variations in production of up to 8% and changes in operating margins up to 10%. Seasonal generation may fluctuate as well, with an increase in summer and decrease in winter. An investment analysis was also conducted to consider how climate change may influence future developments in the sector. • Climate change will affect wind speed and therefore wind energy production. • In four wind farms in Spain we project variations in wind speed of up to ± 5%. • This will imply changes in production (±8%) and in operative margins (±10%). • The regulatory framework is more relevant in comparison. [ABSTRACT FROM AUTHOR]

Hydrokinetic energy has been mainly studied in areas where the principal driver of the current is the tide. However, in certain areas river discharges play also a principal role. The exploitation of the hydrokinetic resource in such areas has its own peculiarities, dictated by the combined influence of the two driving agents. The objective of this paper is to investigate the exploitation of hydrokinetic energy in the Miño Estuary, the largest estuary in NW Spain and N Portugal, with a focus on the site-specific performance of hydrokinetic energy converters (HECs) and its intra-annual variability. A state-of-the-art hydrodynamics numerical model is implemented and successfully validated based on field data. A third-generation HEC—to be more specific, the new Smart Freestream Turbine (SFT)—is considered, and its performance at the location with the greatest potential is assessed by means of: (i) site-specific efficiency, (ii) availability factor, and (iii) capacity factor. We find that, whereas the site-specific efficiency does not vary significantly, the availability and capacity factors do experience substantial intra-annual (seasonal) variability. In summer and autumn, river discharges are low, and the tide dominates the hydrokinetic resource. In contrast, during winter and spring, the river discharges significantly contribute to the resource, leading to a considerable increase in the availability and capacity factors. More generally, the results imply that in areas subject to combined fluvial and tidal influences the performance of HECs may depart significantly from that in tide-dominated areas, and this departure must be carefully weighed in assessing a project. • Hydrokinetic resource under influence of tide and river discharges is investigated. • River discharges induce a pronounced seasonal variability in the hydrokinetic resource. • The hydrokinetic resource is controlled to a large extent by the hydrological regime. • River discharges lead to a considerable increase in the performance during winter. [ABSTRACT FROM AUTHOR]

Spain must reduce its energy consumption by 23% and achieve 100% renewable energy in electricity generation by 2030. This paper presents the current energy scenario en Spain, and the outlooks for different renewable options, with special focus on photovoltaic (PV) solar energy. In 2012, Spain was the number two European country in terms of installed rewnewable energy power. Solar PV technology has the potential to meet Spain's future energy demand and its associated environmental challenges. This paper gives a review of solar energy economy at global scale for both PV and thermal power technologies. The Spanish energy scenario shows actual trends and progress made by solar power. Economic concepts of levelised cost of electricity and grid parity are presented. The financial analysis shows that PV electricity achieves grid parity at a plant profitability rate up to 7.26%. [ABSTRACT FROM AUTHOR]

Abstract: The most widespread support system for renewable-energy-sourced electricity (RES-E) is the feed-in tariff (FIT). FITs can be divided into fixed-FIT and premium systems. The latter type may also include a cap and floor option, turning it into a variable premium system. The cap value (upper limit) aims to avoid windfall profits for the RES-E investor if the electricity price rises quickly, while the floor value (lower limit) ensures minimum revenue, reducing the risk caused by low electricity prices. This paper examines the performance of the variable premium system as established in Spain (hourly-based) and investigates the possibility of alternative novel designs. For this purpose, an analysis was undertaken of the cap & floor system established in Spain based on real market data over a three year period and its performance compared to the fixed premium system (no cap & floor limitations) and two novel cap & floor designs (monthly- and yearly-based). Two other novel alternatives were also considered: daily- and weekly-based. It is shown that the hourly-based cap & floor system did not result in a reduction of the overall RES-E revenue compared to the fixed premium system over the three year period, though revenue was more evenly distributed from year-to-year. The paper concludes that variable premium systems should be based on monthly or yearly cap & floor mechanism as opposed to an hourly one. [Copyright &y& Elsevier]

The repowering of already installed wind farms is considered one of the most promising and cost-effective short-term strategies to scale-up wind capacity. In this study, we apply Markowitz's mean-variance (MV) portfolio optimization theory to explore alternative repowering actions in Spain. The efficient portfolios – a direct outcome of the MV optimization – offer optimal repowering alternatives to current wind farm generation mixes. They deliver the highest possible average power output (yield) for a given level of supply risk. Different repowering scenarios are considered in this paper that range from a full restructuring of the existing wind generation mix to restricting by certain amounts the percentage of down-/uprating of each reference region. Results show that, depending on the configuration of the MV portfolio optimization problem, hourly fluctuations in the aggregate power supply can be reduced as much as 12–31%, while retaining the current level of energy productivity. In addition, for the level of energy supply risk experienced with the existing portfolio of Spanish wind farms; we can derive more efficient mixes that boost-up productivity by 16–55%. This work aims at providing valuable insight for energy policy-making in the direction of optimally repowering future renewable generation. [ABSTRACT FROM AUTHOR]

Optimal siting of wind farms based on a pre-assessment of the spatiotemporal variability of wind resources is considered a suitable method for reducing fluctuations in the delivered output. In this study, we explore the potential for balancing wind energy generation in the Iberian Peninsula using Principal Component Analysis (PCA). This technique permits the discovery of possibly new promising locations for wind power harvesting and an evaluation of the existing wind farm network in terms of reliability in energy generation. Data input to the PCA consists of hourly wind capacity factor in a 5-km spatial resolution grid covering the entire peninsula. These data are derived from an equivalent wind farm power curve fed by modeled wind speed data from 80 m above ground level. PCA reveals three significant balancing patterns prevailing over the IP, where half of the currently operating wind farms in Spain are placed. Hence, among the many constituents of the existing wind farm network, these spots offer the best opportunity for stable power supply. The paper concludes by making proposals on an optimum wind capacity allocation based on the idea of equally distributing installed power between positive/negative dipoles emerging from balancing principal components. [ABSTRACT FROM AUTHOR]

This paper develops a semi-empirical model to calculate direct solar shortwave irradiance on a horizontal surface for any cloudiness condition. The model is based on radiative transfer simulations combined with empirical relationships; it makes use of the global solar irradiance, experimentally measured in most of the radiometric stations, and the Cloud Modification Factor (CMF). A dataset of irradiance from eight Spanish locations is used to tune the model parameters and a part of the data is used to validate the model. By using long-term series of experimental global irradiance data, the corresponding long-term direct irradiances are reconstructed for each location using the proposed model. An evaluation of the model performance is carried out for different solar zenith angles. Following, monthly averages of the hourly data were calculated from the reconstructed values. A high correlation is found when compared with the corresponding measured values. To make use of the obtained results, a characterization of the typical climatic values of the solar direct irradiance in Spain is carried out. The results provide representative values of solar direct radiation and show the availability of this solar resource in the studied areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: An analysis of scientific publications on solar energy was conducted to determine whether public interest in the subject is mirrored by more intense research in the area. To this end, the research published by Spain and Germany, the two EU countries with the highest installed photovoltaic capacity, was analyzed based on Web of Science data. The results show that: solar output has risen substantially; solar research has a greater impact (measured in terms of citations) than publications on other renewables such as wind power; scientific production on solar energy is high in Germany and Spain, which together account for 51% of the total in the EU-27; the pattern of topics researched in the two countries is very similar; and their international collaboration is more intense than the world average and higher than in countries such as the USA, China or Japan. Collaboration between them is likewise intense. The main conclusion is the divergence in Germany and Spain between solar energy demand/output growth, being exponential, and the growth of research papers on the subject, which is linear. [Copyright &y& Elsevier]

Abstract: This paper provides an economic valuation of RES-E promoted by the Spanish feed-in system. First, supported RES-E is evaluated in terms of CO2 emissions avoided when they are introduced in the Spanish electricity market instead of other potential polluting energy sources. And secondly, these positive environmental externalities of supported RES-E are compared with the funds they received from the Spanish feed-in system, in order to estimate the economic balance of this support system. The estimate for 2011 shows that approximately 10% of premiums paid to promote RES-E could be explained, from an economic point of view, by the monetary value of CO2 emissions avoided by not using alternative energy sources, as coal and combined cycle. Furthermore, the economic evaluation of National Energy Commission (CNE [Spanish acronym]) proposal that links the energy sources responsible for CO2 emissions to finance the support system of RES-E, confirms that the monetary value of CO2 emissions avoided when using promoted RES-E is nearly 70% of the revenues from the sale of allowances allocated to polluting technologies in the last National Plan of Emission Allowances in 2011. Promotion of RES-E over the use of fossil energy sources, must take into account not only the costs of their implementation, but also the overall balance of their use. [Copyright &y& Elsevier]