• Energy Research

The large-scale integration of non-dispatchable renewable energy must be carefully planned, especially in poorly robust electrical energy systems. Tenerife is the most populated of the islands in the Canary Archipelago (Spain) and has an isolated electrical system. It is envisaged for the horizon of 2040 that the island will have a high contribution of non-dispatchable renewable energies (fundamentally wind and solar photovoltaic). Tenerife also has exploitable high enthalpy geothermal resources. An energy planning model is implemented in this paper based on which different scenarios with high non-dispatchable renewable energy penetration in the island’s electrical system are simulated. Geothermal energy is also incorporated in the simulations as a dispatchable renewable energy source, with the power required sized according to the hypotheses of each scenario. The results are compared according to different technical and economic parameters. An evaluation is also made of the influence that the capacity of the electrical system to take on higher non-dispatchable renewable power and the presence of energy accumulation systems might have on the minimization of losses in the non-dispatchable renewable energy capacity factor. The results of the simulated scenarios show that geothermal energy could meet up to 28.8% of the island’s electrical energy demand, increasing the stability, flexibility and supply guarantee of system. A 100% renewable system could be possible through the incorporation of this energy source and the planning of energy accumulation systems. In the case study, the economic saving for Spain’s electrical system could exceed 400 €million per year. 14 1,981 11,1 Q1 Q1 SCIE 10,9

Given that the energy consumption of buildings equates to practically 40 % of the total energy demand of any electrical system, research studies on reducing such energy consumption are of paramount importance. This problem is accentuated in isolated weak electrical systems, like those found in many island territories, where there are significant limitations on the management of energy generation and demand. The objective of the present work is to identify the relationship between the energy consumption of residential housing and its thermal envelope characteristics within the framework of energy sustainability strategies. For this purpose, multiple solutions were compared based on combinations of values associated to different types of residential building, a wide range of thermal transmittance values based on the application of different materials and/or envelope designs, and different climate zones. It is concluded that the energy consumption of these buildings can be significantly reduced by optimizing the technical characteristics of the thermal envelope, adapting it the surrounding climate conditions. For the case study, the specific energy saving can be as high as 51.4 kWh/m2·year. Bearing in mind the housing stock in the study area, the total energy saving is equivalent to 9.17 % of its current energy demand. The results and conclusions obtained can be used in the development of planning strategies for new housing and/or the optimization of energy consumption in already existing housing stock. 1,397 SCIE 10,3 6,4 15 Q1

One of the main strategies to maximize renewable energy penetration in electrical systems is to optimize their dispatchable capacity. One of the benefits of geothermal energy, as a renewable energy source, is its high dispatchability. The isolated electrical energy system of Tenerife (one of the 8 islands of the Canary Archipelago) is highly dependent on fossil fuels. The advanced state of maturity of the geothermal exploration of Tenerife positions the island with a high degree of probability as one of the few regions in Spain with high-enthalpy geothermal resources. This paper reports on a study undertaken of the island’s geothermal potential which involved calculation of the heat stored in geothermal reservoirs and the identification of zones of interest for exploitation of this resource and its energy capacity. Among the results obtained, 3 areas of interest were identified for the exploitation of geothermal energy with a total installable electrical power of 117.8 MW, which would increase the renewable contribution to the island’s daily demand by over 20% as well as contribute to maximizing the flexibility and stability of the electrical energy supply in a scenario of high renewable energy penetration.

Due to the low dispatchability of wind power, the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the forecasting hour) chosen for the input layer parameters; and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecasting horizon, the greater the degree of improvement. 490 484 1,078 3,265 Q1 Q2 SCIE

The economic feasibility of a wind project is dependent on the wind regime since it relies on the power output of the turbines over the installation’s working life. Consequently, the interannual variability of wind speed at a potential wind energy conversion site is an issue of capital importance. Usually a wind data measurement campaign is limited to a period no longer than one year (i.e. short-term). Therefore, the process of decision-making for wind farm constructors must be based in this short-term data. Various methods have been proposed in the scientific literature for estimation of the long-term wind speed characteristics at such sites. These methods use simultaneous measurements of the wind speed at the site in question and at one or several nearby reference sites with a long history of wind data measurements. In this paper, long-term wind power densities which have been estimated through the use Artificial Neural Networks (ANNs), will be compared to those which have been calculated by means of the short-term wind data (i.e. considered to be representative of long-term wind performance). Mean hourly wind speeds and directions calculated in a 10 year period of time at six weather stations located on six different islands in the Canarian Archipelago (Spain) were used in this study. Among the different conclusions which this study revealed, we can highlight that the wind resource estimation based on ANNs is better than that dependant on short-term wind data. This is true when the correlation coefficient between the reference and candidate weather station is of 0.6.

En este trabajo se presenta y se analiza una primera aproximación de un algoritmo cuyo objetivo final es optimizar técnica y económicamente la gestión de los excedentes energéticos de una instalación de autoconsumo renovable. Para ello, y como variables adicionales de control, se han considerado el coste de la energía comprada al sistema por la instalación, así como el precio del pool energético en la venta de los excedentes. El algoritmo se ha implementado en LabVIEW y simulado en MATLAB, lo que ha permitido obtener unos resultados preliminares que facilitan la tarea de mejora del algoritmo para futuros trabajos y versiones de este.

The scarcity of water resources on the island of Gran Canaria (Canary Islands, Spain) is such that 88% of the water supply for human consumption comes from seawater desalination plants. This type of process has a high specific energy consumption. Gran Canaria has an isolated electrical system of low robustness. In this paper, a geothermal plant is designed and integrated into a system that already has non-dispatchable renewable generation (wind and photovoltaic) in order to meet, based on a self-consumption regime, the energy demand of a high-capacity desalination plant. The aim is for the diversified renewable system to improve the stability and management of renewable electrical energy generation. Geothermal plant production can adapt to the energy balance between demand and non-dispatchable renewable generation. The geothermal plant’s design is based on an organic Rankine cycle and its resulting power is 4.16 MW. Its integration in the renewable generation system significantly improves the contribution of renewables in meeting the desalination plant’s energy demand and therefore reducing its dependency on the island’s electrical system. The mean cost of electrical energy generation with the diversified renewable system is 57.37 EUR/MWh, considerably lower than the mean cost of conventional generation on Gran Canaria of 153.9 EUR/MWh.

In the work presented in this paper Artificial Neural Networks (ANNs) were used to estimate the long-term wind speeds at a candidate site. The specific costs of the wind energy were subsequently determined on the basis of the knowledge of these wind speeds. The results were compared with those obtained with a linear Measure-Correlate-Predict (MCP) method. The mean hourly wind speeds and directions recorded over a 10 year period at six weather stations located on different islands in the Canary Archipelago (Spain) were used as a case study. The power-wind speed curves for five wind turbines of different rated power were also used. The mean absolute percentage error (MAPE), Pearson's correlation coefficient and the Index of Agreement (IoA) between measured and estimated data were used to evaluate the errors made with the different metrics analysed.Amongst the conclusions that can be drawn from the study it can be stated that, in all the cases analysed, the MAPE of the specific cost of the energy obtained using ANNs was lower than that obtained with the linear MCP method that was employed. In some cases reductions in the error of up to 26.5% were achieved with the ANNs with respect to the method used for purposes of comparison. However, when the correlation coefficients between the short-term data series of the candidate and reference sites were relatively low, the MAPEs generated by all the analysed metrics were higher when the estimation models were used than when the data for the short-term period at the candidate site was taken as being representative of the long-term wind performance at that site. (C) 2011 Elsevier Ltd. All rights reserved. 3881 3869 2,473 5,106 Q1 Q1 SCIE

Energy consumption in buildings plays a significant role in the global energy demand. The European Union has promoted different regulatory directives in the framework of energy efficiency to develop the construction of buildings with nearly zero energy consumption. The main objective of this paper is to simulate how the design characteristics of different factors of the window elements of buildings (frame, glass, and shading systems) located in a hot climate region affect their cooling primary energy consumption. For this purpose, a comparative analysis is carried out with multiple simulations of different types of single-family residential dwellings using the EnergyPlus energy model. From the results obtained, it can be deduced that, compared to the standard design configuration, the primary energy consumption for cooling of the buildings studied can be reduced by up to 12.7% and 29.5% by modifying the design characteristics of the frame–glass assembly or the shading system of the window openings, respectively. The conclusions drawn from this study can serve as a reference in normative and regulatory documents affecting the building sector for the establishment of minimum requirements for certain characteristics of the constructive design of buildings.