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• We present a novel integration for reducing CO 2 emissions in blast furnaces. • It combines top gas recycling, oxy-fuel regime, power to gas and biomass pyrolysis. • The integration was modelled using the extended operating line methodology. • The best performance was found for pyrolysis at 700 °C. • The CO 2 emissions were reduced by 58% (9.8 MJ/kg CO2 energy consumption) The paper introduces a novel approach for mitigating CO 2 emissions in blast furnaces by integrating top gas recycling, an oxy-fuel regime, power to gas, and biomass pyrolysis. Various case studies were conducted, involving the adjustment of pyrolysis temperatures (300 °C, 500 °C, 700 °C, and 900 °C) and varying the quantity of blast furnace gas directed to methanation for carbon recycling. Pinus radiata, abundant and cost-effective in Chile and Spain, was chosen as the biomass source. The integration was modeled using the extended operating line methodology and evaluated through 12 key performance indicators, such as flame temperature, coke consumption, CO 2 emissions, and specific primary energy consumption per unit of CO 2 avoided. Optimal performance was observed with pyrolysis at 700 °C and no blast furnace gas recycled through methanation. This configuration achieved a 58 % reduction in CO 2 emissions, with an energy consumption of 9.8 MJ/kg CO2 , and obviated the need for geological storage. Comparing this innovative proposal with other oxygen blast furnace approaches from the literature revealed a 13 percentage point improvement in CO 2 reduction over the second-best alternative. Additionally, the required electrolysis capacity, influencing capital expenditure, was 57 % lower, and energy consumption was reduced by 44 %. [ABSTRACT FROM AUTHOR]

• Advanced climate-resilient energy retrofit technology reduces the use of non-renewable energy in the building sector. • Renewable District Heating as an optimal technology in deep energy renovation strategies. • Sustainable cities and buildings are feasible by Centralised District Heating system powered by biomass. • Key Performance Indicators (KPIs), to achieve nZEB performances established in the European legislations. This paper presents the implementation of a biomass-fuelled District Heating System (DHS), as a part of a deep energy renovation exercise to achieve a climate-resilient campus with minimum carbon dioxide emissions. The case study is carried out for the University of Valladolid, an average-sized university in Spain, with a continental weather climate. Prior to renovation, the different building blocks had a wide-ranging level of fossil fuel consumption for space heating and domestic hot water ranging between 60 and 430 kWh/m2·year. The application of this centralised heating system allows to achieve the minimum threshold for near zero-energy buildings (nZEB) of 100–120 kWh/m2, in accordance with the Spanish Standards. These values correspond to the maximum European indicators for offices in continental weather conditions. Results of this comprehensive study show that 15 out of the 19 buildings reached the nZEB target, due to the proposed strategy. The overall carbon dioxide emissions have dropped by 92.69% as compared to the original fossil-fuel powered boiler, thus bringing carbon dioxide emissions down to 1.57 kgCO 2 /m2·y. Therefore, it is shown that deep energy renovation strategies through renewable energy DHS have the potential of achieving nZEB for universities in continental weather conditions. [ABSTRACT FROM AUTHOR]

• 2050 low-carbon economy pledge and Spanish photovoltaic investment plan. • Photovoltaic spatial allocations minimising unpredictable variability. • Impact of wind energy investments on the optimal photovoltaic spatial allocation. • Allocation of photovoltaic and wind investments minimising supply–demand mismatch. • Assessment of the current photovoltaic investments in Spain. After a period of stagnation that lasted until approximately 2018, Spain has established an ambitious investment program in photovoltaics, derived from the pledge to switch to a carbon-free energy system by 2050, the sustained cost decreases of the technology, and the excellent irradiance values over its territory. This paper implements an additional criterion for the spatial allocation of these investments based on minimising the energy variability generated because of weather intermittency. The analysis is based on hourly data for the years 2005–2020 (inclusive), in various locations homogeneously spread throughout Spain. Subtracting the deterministic daily and annual irradiance cycles to estimate the variability of the random component is discussed. Failing to do that yields significantly higher and distorted values for solar energy variability, resulting in low investment proportions when combined with other energies, like wind. The first results show that a straightforward, equally-weighted allocation of investments is suboptimal. It is also shown that investing in low-irradiance locations contributes to reducing overall variability. Secondly, the study analyses overall power variability minimisation conditional on a given wind investment weight. It is found that the impact is significant spatially, and that proportions of wind energy above 10% increase aggregated variability across all levels of aggregated power generated. Nevertheless, investment proportions in wind energy below 10% reduce the overall combined variability significantly due to the negative correlation between wind and solar irradiation. The third point addressed is the minimisation of the mismatches between renewable energy supply and aggregate electricity demand. The optimal proportion for solar and wind investments becomes close to 50% in this case. However, this proportion tilts towards solar investments if variability minimisation is also considered. Finally, the current spatial distribution of photovoltaic investments in Spain is analysed, and it is shown that there is room for improvement. It is also found that the current ratio between wind and photovoltaic energy may not be optimal, and that it would be advisable to increase proportionally more photovoltaic energy. [ABSTRACT FROM AUTHOR]

Energy demand prediction is an important problem whose solution is evaluated by policy makers in order to take key decisions affecting the economy of a country. A number of previous approaches to improve the quality of this estimation have been proposed in the last decade, the majority of them applying different machine learning techniques. In this paper, the performance of a robust hybrid approach, composed of a Variable Neighborhood Search algorithm and a new class of neural network called Extreme Learning Machine, is discussed. The Variable Neighborhood Search algorithm is focused on obtaining the most relevant features among the set of initial ones, by including an exponential prediction model. While previous approaches consider that the number of macroeconomic variables used for prediction is a parameter of the algorithm (i.e., it is fixed a priori), the proposed Variable Neighborhood Search method optimizes both: the number of variables and the best ones. After this first step of feature selection, an Extreme Learning Machine network is applied to obtain the final energy demand prediction. Experiments in a real case of energy demand estimation in Spain show the excellent performance of the proposed approach. In particular, the whole method obtains an estimation of the energy demand with an error lower than 2%, even when considering the crisis years, which are a real challenge. [ABSTRACT FROM AUTHOR]

Abstract: Demand response resources management is one of the most investigated solutions oriented to improve the efficiency in electricity markets. In this paper, the capability of customers to participate in short term markets is analyzed. An available methodology to analyze the daily and monthly energy consumptions of large customers is used to create energy offers and bids. This allows customers to participate in energy markets in order to buy, as first step, the usual electricity consumption and, additionally, to offer demand reductions in the short term electricity markets. Additionally, this paper shows the customer potential to participate in the Spanish electricity markets. [Copyright &y& Elsevier]

Abstract: A very flexible joint probability density function of wind speed and direction is presented in this paper for use in wind energy analysis. A method that enables angular–linear distributions to be obtained with specified marginal distributions has been used for this purpose. For the marginal distribution of wind speed we use a singly truncated from below Normal–Weibull mixture distribution. The marginal distribution of wind direction comprises a finite mixture of von Mises distributions. The proposed model is applied in this paper to wind direction and wind speed hourly data recorded at several weather stations located in the Canary Islands (Spain). The suitability of the distributions is judged from the coefficient of determination R 2. The conclusions reached are that the joint distribution proposed in this paper: (a) can represent unimodal, bimodal and bitangential wind speed frequency distributions, (b) takes into account the frequency of null winds, (c) represents the wind direction regimes in zones with several modes or prevailing wind directions, (d) takes into account the correlation between wind speeds and its directions. It can therefore be used in several tasks involved in the evaluation process of the wind resources available at a potential site. We also conclude that, in the case of the Canary Islands, the proposed model provides better fits in all the cases analysed than those obtained with the models used in the specialised literature on wind energy. [Copyright &y& Elsevier]

Abstract: A finite mixture model of continuous variable probability is used in this paper to represent the distribution of directional wind speed. The model is comprised of a finite mixture of von Mises (vM-pdf) distributions. The parameters of the model are estimated using the least squares method. The range of integration to compute the mean angle and the standard deviation of wind direction is adjusted to minimum variance requirements. The suitability of the distributions is judged from the coefficient of determination R 2. The model is applied in this paper to wind direction data recorded at several weather stations located in the Canary Islands (Spain). The conclusion reached is that the mixture distribution used in this paper provides a very flexible model for wind direction studies and can be applied in a widespread manner to represent the wind direction regimes in zones with several modes or prevailing wind directions. In the case of the Canary Islands, mixtures of two vM-pdfs provide better fits in all the cases analysed than those obtained with the models proposed in the specialised literature on wind energy. The conclusion is also drawn that when the number of components N of the mixture distribution increases, the value of R 2 increases. However, the variations in R 2 are not significant for values of N greater than six. [Copyright &y& Elsevier]

In this second part of the paper, the new advances on thermoeconomic diagnosis presented in the part I are applied to the Escucha power plant, which is a 160 MW conventional coal fired power plant sited in Arago´n (Spain). As a result the validity of the methodology is proved and quantified.The methodology is validated using a specific simulator of the Escucha power plant cycle, mainly based on [ASME Power Division, Paper no. 62-WA-209, 1974] method. This simulator reproduces with high accuracy the cycle behavior for different operating conditions, either in design and in off design conditions. The error is lower than 1% in most of cases. The simulated results, i.e. temperatures, pressures, mass flow rates, power and so on, are considered as plant measured and validated values. In this way all measurement uncertainties are avoided.A complete thermoeconomic diagnosis is presented applying the Structural Theory of Thermoeconomics. The impact of the component inefficiencies on the fuel plant consumption, and the effect of a component inefficiency (intrinsic malfunction) on the rest of the plant components (induced malfunctions and dysfunctions), are analyzed and quantified. The methodology is validated quantifying its accuracy. [Copyright &y& Elsevier]

• We compare 14 models with various variables, functions and at most 3 coefficients. • The comparisons are based on data from 105 stations in 3 large areas of Spain. • With site-calibrated coefficients most models work accurately. • We study the site-calibrated coefficients as a function of geographical variables. • With general coefficients a simple homogeneous model is adequate in all 3 areas. Reliable data on the availability of solar energy is needed, as solar energy is an essential resource for sustainable development worldwide. However, ground-based radiometric stations are scarce, at least in large areas far from population and in developing countries, so there are difficulties in validating methods for estimating solar radiation. Indirect models mitigate the problem by providing radiation data from other meteorological variables, which can be measured with low-cost equipment and calibrated with data from secondary station networks. However, models' accuracy decreases if estimations are required far away from the calibration stations. It is hoped that modified models that include the influence of geographical and topographical variables can attenuate this drawback in data-scarce regions. This paper evaluates the accuracy and generality of 14 existing models of monthly global solar radiation based on temperature, which is a routinely measured variable. At first, models are locally calibrated at 105 stations in three large areas in Spain. Then, from the local coefficients of eight stations selected in each area, general equations are derived for the coefficients of each model as function of the ratio between elevation and distance to the sea. The predictions of these modified models, i.e., using coefficients derived from general equations, are compared both for the eight base stations and the remaining ones used for validation. In the comparisons, not only errors averaged in groups of stations are considered, but also local results. Several models perform well in some areas, but a simple homogeneous model is the only one whose indicators are good in all areas and hardly vary when using general coefficients derived from the data measured at all available stations. [ABSTRACT FROM AUTHOR]

Abstract: Energy supply concepts for zero energy residential building (ZERB) in Shanghai (humid) and Madrid (dry) are discussed in this paper. Simulation is employed as the main research method. Two typical housing models are designed according to the real occupancy condition, the life schedule, the thermostats settings, etc., for the two cities. An energy analysis considering the annual balance of input from the grid and output from renewable power systems is made. Indoor comfortable comparisons between the two models are presented to show optimal design strategies for HVAC under different weather conditions. Also performed is the analysis on the primary energy payback time and the CO2 equivalent saving in order to evaluate the performance of novel energy systems to verify the feasibility. [Copyright &y& Elsevier]

Abstract: In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. [ABSTRACT FROM AUTHOR]

Abstract: The need to obtain automatic filters to treat wind data is clear due to the huge amount of data available in any wind farm analysis. Automatic detection of performance deviations of wind turbines is required for maintenance purposes. In spite of some alarms being registered by the wind farm’s SCADA system, many times automatic use of real data use is not valid due to its low quality. In this paper, the problems to carry out the characterization of the turbines in a wind farm are stated and a new technique to filter the data is presented. The robust statistical technique of the Least Median of Squares (LMedS) combined with a random search is adapted to deal with this problem with promising results. A mathematical model representing the dependency of the wind turbine behaviour in function not only of the wind speed but also of other variables such as the direction of the wind is also proposed. The results achieved are tested with long term real data from wind farms in Spain. [Copyright &y& Elsevier]

• A novel asexual-reproduction evolutionary neural network is proposed. • Three kinds of generative adversarial networks are applied to augment the training data. • The wind data are decomposed by the ensemble empirical mode decomposition technology. • The proposed approach outperforms much better than other SIA-based models. Accurate forecasts of wind power generation are essential for the operation of wind farms. But for the newly developed stations, it is difficult to make accurate prediction because there are no sufficient historical data available. It will thus be interesting to explore new data augmentation and prediction modeling approach adaptive to such new-built wind farms. In this regard, a novel asexual-reproduction evolutionary neural network (ARENN) for short-term wind power prediction based on Wasserstein generative adversarial network with gradient penalty (WGANGP) and ensemble empirical mode decomposition (EEMD) is presented in this paper. To solve the dilemma that new-built wind farms lack sufficient wind power data, the WGANGP is first applied to generate realistic data with a similar distribution of real data to augment the training dataset, which is further decomposed into a series of more stable subsequences by the EEMD so as to reduce the prediction difficulty of the machine learning model. In this study, a novel ARENN prediction model is developed to make the short-term wind power prediction, in which an asexual-reproduction evolutionary approach is first proposed to optimize the neural network based on a set of different loss functions that facilitate the population of network parameters approximating to the global optimum along different error surfaces in the evolutionary process. The proposed approach is validated on the data collected from the wind farm located in Spain and the predicted results demonstrate the advantage of our proposed approach over other methods involved in this study. [ABSTRACT FROM AUTHOR]

• Fiscal policies can be potential tools to boost renewables. • Electricity self-generation is implemented and evaluated. • Removal taxes to renewables achieves price competitiveness. The sustainability of the environment implies a dire need to redesign electricity systems. The aim of this paper is to evaluate the role of alternative fiscal incentives to promote renewable electricity. To this end, a disaggregated, electricity-related Computable General Equilibrium model is developed, taking the current structure of the (renewable and non-renewable) electricity sector into account. The Computable General Equilibrium model developed presents at least two advantages: the changes are analyzed from a global perspective (including impacts on prices), and second, rebound effects are accounted for. The fiscal measures examine three scenarios of change in line with the decarbonization processes of economies. Specifically, the scenarios assess: (1) a 100% decrease in renewable electricity taxes; (2) a 23% increase in taxes paid by the non-renewable electricity sector; and, (3) both together. In addition, these fiscal measures are evaluated assuming improvements in electricity self-generation. The case study is the Spanish economy for 2016. The Computable General Equilibrium model is calibrated on a symmetric input–output table, constructed from supply and use tables. These tables disaggregate the energy and electricity sectors. Results reveal the role and strength of fiscal policy in boosting the production of electricity generation technologies using green energy resources, and reducing the production of electricity generated from brown sources. However, results suggest that these effects are limited to the energy chain sectors, with insignificant effects in the whole economy. This study also provides better information and guidance to politicians and decision-makers who wish to promote renewables, and the need to have a clear understanding of the influence price competitiveness has on the economy. Moreover, the results of this study have implications for rebound effects when electricity self-generation is implemented. [ABSTRACT FROM AUTHOR]

Abstract: The changes experienced by electricity markets in recent years have created the necessity for more accurate forecast tools of electricity prices, both for producers and consumers. Many methodologies have been applied to this aim, but in the view of the authors, state space models are not yet fully exploited. The present paper proposes a univariate dynamic harmonic regression model set up in a state space framework for forecasting prices in these markets. The advantages of the approach are threefold. Firstly, a fast automatic identification and estimation procedure is proposed based on the frequency domain. Secondly, the recursive algorithms applied offer adaptive predictions that compare favourably with respect to other techniques. Finally, since the method is based on unobserved components models, explicit information about trend, seasonal and irregular behaviours of the series can be extracted. This information is of great value to the electricity companies’ managers in order to improve their strategies, i.e. it provides management innovations. The good forecast performance and the rapid adaptability of the model to changes in the data are illustrated with actual prices taken from the PJM interconnection in the US and for the Spanish market for the year 2002. [Copyright &y& Elsevier]

• The model estimates the biomass required to produce the syngas in a BFBGP. • The proposed FF-PSO algorithm showed to solve model dynamic Power Generation systems. • The FF-PSO model proposed for the ANN-based model has the best performance. Agriculture and forestry crop residues represent more than half of the world's residual biomass; these residues turn into synthesis gas (syngas) and are used for power generation. Including Syngas Gensets into hybrid renewable microgrids for electricity generation is an interesting alternative, especially for rural communities where forest and agricultural waste are abundant. However, energy demand is not constant throughout the day. The variations in the energy demand provoke changes in both gasification plant efficiency and biomass consumption. This paper presents an Artificial Neural Network (ANN) based model hybridized with a Particle Swarm Optimization (PSO) algorithm for a Biomass Gasification Plant (BGP) that allows estimating the amount of biomass needed to produce the required syngas to meet the energy demand. The proposed model is compared with two traditional models of ANNs: Feed Forward Back Propagation (FF-BP) and Cascade Forward Propagation (CF-P). ANNs are trained in MATLAB software using a set of historical real data from a BGP located in the Distributed Energy Resources Laboratory of the Universitat Politècnica de València in Spain. The model performance is validated using the Mean Squared Error (MSE) and linear regression analysis. The results show that the proposed model performs 23.2% better in terms of MSE than de other models. The tunning parameters of the optimal PSO algorithm for this application were found. Finally, the model was validated to predict the necessary biomass and syngas to cover the energy demand. [ABSTRACT FROM AUTHOR]