• Energy Research

This paper presents a new method, based on the Smart Energy Systems concept. The aim is to increase the share of renewable energy penetration on islands. The method is applied to the island of Gran Canaria (Spain), considering the entire energy system of the island. Several smart renewable energy strategies are proposed following a cross-sectoral approach between the electricity, heating/cooling, desalination, transport and gas sectors. The different smart renewable energy strategies were applied in a series of steps, while looking for a transition from the current energy system to a nearly 100% renewable energy system. Based on the results, the study concludes that the suggested method is applicable for increasing renewable integration on islands and can potentially be used in helping energy planners to take decisions about priorities in development of the sector to improve such integration. The results indicate that, for the case of Gran Canaria, a 75.9% renewable energy system could be attained with technologies that can be implemented at present. Furthermore, it is shown that a nearly 100% renewable energy system in Gran Canaria is technically feasible and could be achieved if certain technologies acquire greater maturity. © 2018 Elsevier Ltd 443 421 2,048 5,537 Q1 Q1 SCIE

EnergyPLAN is an energy system analysis tool used worldwide for scientific analyses of national and regional energy planning strategies and alternatives. This work describes the MATLAB Toolbox for EnergyPLAN (MaT4EnergyPLAN), a set of functions developed to manage the EnergyPLAN software using MATLAB. The tool allows the user to take advantage of the energy system analysis capabilities of EnergyPLAN in combination with the computational advantages of MATLAB. This allows the user to easily manage EnergyPLAN files and analyse a large number of EnergyPLAN simulations. 11 0,313 0,863 Q3 Q4 SCIE

Abstract So-called Measure-Correlate-Predict (MCP) methods have been extensively proposed in renewable energy related literature to estimate the wind resources that represent the long-term conditions at a target site where a short-term wind data measurement campaign has been held. The main differences between the various MCP methods lie fundamentally in the type of relationship established between the wind data (speed and direction) recorded at the target site and the wind data recorded simultaneously at one or various nearby weather stations which serve as reference stations and for which long-term data series are also available. This paper reviews a wide range of MCP methods that have been proposed in the context of wind energy analysis, a number of which have been implemented in wind energy industry software applications. This review includes the initial methods first proposed in the 1940s which generally attempted only to estimate the long-term mean annual wind speed from a single reference station, and extends up to the most recent methods proposed in the present century based on automatic learning techniques which use several reference stations. In addition to offering a description of the linear, non-linear and probabilistic transfer functions used by the different algorithms, the hypotheses on which these functions are based and the data format with which the various methods work (time series or frequency distributions), this review will also cover limitations in the use of MCP methods, the uncertainty associated with them and the different reference data sources that have been studied. In this sense, the extensive collection of MCP methods which is brought together and reviewed in this paper, ranging from the simplest and easiest-to-use models to the most complicated computational ones which require specific user experience, comprises an extremely useful catalogue when it comes to choosing the best predictor method.

The present paper proposes the implementation of a new algorithm for the control of the speed regulators of Pelton wheel turbines, used in many of the pumped hydroelectric energy storage systems that operate in isolated electrical systems with high renewable energy participation. This algorithm differs substantially from the standard developments which use PID or PI governors in that, in addition to acting on the nozzle needles and deflectors, it incorporates a new inner-loop pressure stabilization circuit to improve frequency regulation and dampen the effects of the pressure waves that are generated when regulating needle position. The proposed algorithm has been implemented in the Gorona del Viento wind–hydro power plant, an installation which supplies the primary energy needs of the island of El Hierro (Canary Islands, Spain). Although, as well as its wind and hydro generation systems, the plant also has a diesel engine based generation system, the validation of the results of the study presented here focuses on situations in which frequency control is provided exclusively by the hydroelectric plant. It is shown that implementation of the proposed algorithm, which replaces the previous control system based on a classical PI governor, is able to damp the pressure wave that originates in the long penstock of the plant in the face of variations in non-dispatchable renewable generation, a situation which occurred with a high degree of relative frequency in the case study. The damper has enabled a substantial reduction in the cumulative time and the number of times that frequency exceeded different safety margins. Damper incorporation also reduced the number of under-frequency pump unit load shedding events by 93%.

Given the significant water-energy problems associated with many remote and arid areas of the planet, most studies, projects and developments of installations for the production of fresh water using desalination technologies powered by renewable energy sources have focussed on small-scale stand-alone systems. The most commonly used energy sources have been solar photovoltaic and wind and the most widely applied desalination technology that of reverse osmosis (RO). Most of the systems use batteries as a means of mass energy storage and the RO plants normally operate at constant pressure and flow rate. This paper presents a small-scale prototype SWRO (seawater reverse osmosis) desalination plant designed to continuously adapt its energy consumption to the variable power supplied by a wind turbine (WT), dispensing with mass energy storage in batteries and proposing the use of a supercapacitor bank as a dynamic regulation system. A description is given of the tests performed to date with the SWRO desalination plant connected to the conventional grid while controlling the number of pressure vessels that are connected/disconnected to/from the system and regulating their operating pressures and flow rates (within predetermined admissible limits) to maintain a constant permeate recovery rate and adapt the energy consumption of the plant to a widely varying simulated wind energy supply.One of the most important conclusions that can be drawn from the studies undertaken is the feasibility of adapting the consumption of the prototype of the SWRO desalination plant to widely varying WT-generated power. Despite using various time interval lengths in which it was assumed that the WT output power remained constant, a perfect fit was not obtained between the theoretical WT-generated power and the power consumed by the SWRO desalination plant, nor was it possible to maintain a constant permeate recovery rate at each instant. 239 222 2,912 5,746 Q1 Q1 SCIE

Given the temporal variability of wind, the intermittent operation of off-grid wind-powered seawater reverse osmosis (SWRO) desalination plants has been proposed. However, it has also been suggested that intermittency may damage plant membranes. This paper presents the results and analysis of testing a 100 m3/day rated capacity SWRO plant (two pressure vessels, 1:1 arrangement with 5 and 2 membrane elements [Hydranautics 6-SWC4-LD] per pressure vessel). For over 4 months (totaling 3,136 h), the SWRO plant was operated intermittently—1 h on and 1 h off—24 h each day, resulting in 1,568 start-stop cycles. The evolution of various parameters was analyzed using machine learning techniques, hypothesis testing, and effect size analysis. At the end of the test period, membrane autopsies were conducted on the first and last membranes of the system.The results show that cumulative intermittent operating hours significantly influenced the normalized permeate flow (NPF increased slightly by 0.0065 m3/h), normalized salt rejection (NSR increased by 0.12 %), normalized salt permeate concentration (NSPC decreased by 4.5 mg/L), and normalized pressure differential (NPD showed no significant change). Statistical analysis indicated that the statistically significant differences between the means of these normalized parameters at the start and end of the test are primarily the result of changes in operating parameters—such as feed water temperature (decreased by 1.2 °C) and conductivity (decreased by 527 mg/L)—rather than membrane degradation. According to the autopsies, only slight fouling was detected and no telescoping effect was observed. The integrity tests were negative, except for the methylene blue test, which detected some abrasion marks on the polyamide layer due to displacement of the feed channel spacers.These results provide useful information about the behavior of wind-powered SWRO systems with a high number of starts and stops, suggesting that frequent intermittent operation does not significantly damage SWRO membranes over the tested period.

Recent studies in the field of renewable energies, and specifically in wind resource prediction, have shown growing interest in proposals for Measure-Correlate-Predict (MCP) methods which simultaneously use data recorded at various reference weather stations. In this context, the use of a high number of reference stations may result in overspecification with its associated negative effects. These include, amongst others, an increase in the estimation error and/or overfitting which could be detrimental to the generalisation capacity of the model when handling new data (prediction).This paper analyses the benefits of feature selection for use with Artificial Neural Network (ANN) techniques with a multilayer perceptron (MLP) structure when the ANNs are used as MCP methods to predict mean hourly wind speeds at a target site. The features considered in this study were the mean hourly wind speeds and directions recorded in 2003 and 2004 at five weather stations in the Canary Archipelago (Spain).The two feature selection techniques considered in the analysis were the Correlation Feature Selection (CFS), which is a correlation-based filter approach (FA), and an MLP-based wrapper approach (WA). The metrics used to compare the results were the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the index of agreement (IoA).Evaluation of the mean errors obtained in the 10-fold cross-validation tests for the year used to represent the short-term wind data period resulted in several conclusions. These included, notably, that the WA gave lower mean errors than the FA in 100% of the cases analysed independently of the metric employed. However, the FA resulted in a significant reduction in computational load and considerable enhancement of model interpretability. When very good correlation coefficients were obtained between the target and reference stations, no significant statistical difference was observed at 5% level between the three models (FA, WA and the models constructed with all the variables) in most of the cases analysed. 507 490 2,912 5,746 Q1 Q1 SCIE

A new method is presented for the design and operation of wind and wave-energy-powered modular seawater reverse osmosis (SWRO) desalination plants. The SWRO modules are designed to achieve discrete adaptation of their power consumption to the estimated power output of the renewable energy technologies. The mean, mode and median power output values are proposed as statistical parameters to select the size of the single-stage SWRO modules. The aim is to select the system configurations which provide the minimum specific product water cost, the highest annual freshwater production, and the most efficient exploitation of the energy resource. A statistical inference analysis determined the existence of significant differences between their results. A discussion is conducted on the optimal systems obtained with both renewable technologies, and an economic sensitivity analysis of the variables employed is performed. The method is applied to a case study in Gran Canaria island (Spain), using local climate data measurements to assess the energy resource. Results show that the median was the best statistical parameter for the design of the wind-powered desalination system and the mode for the wave-based system. In the case study, the wind-powered system obtained better results despite a lower installed power than the wave-powered system. 15 1,794 9,501 Q1 Q1 SCIE

A method, which involves genetic algorithms, is presented for the optimal sizing of a system comprising a medium-scale modular seawater reverse osmosis desalination plant powered exclusively by off-grid wind energy. The system uses a water storage reservoir that allows coverage of a particular hourly freshwater demand. The use of massive energy storage devices is discarded, although flywheels are used as a dynamic regulation subsystem as well as an uninterrupted power device to supply energy to the control subsystem. The method considers the interannual variation of wind energy, for which it uses machine learning techniques, and introduces randomness in the daily freshwater demand profile. The control strategy is based on ensuring that the energy consumption of the desalination modules remains in synchrony with wind generation throughout the system’s useful life, either operating under constant pressure and flow conditions or varying these parameters within an acceptable range. The proposed method is applied to a case study, aiming to cover a freshwater demand of 1825 × 103 m3/year, which is equivalent to the water production of a desalination plant with a 5000 m3/day capacity. As the proposed method evaluates the influence of diverse economic and technical parameters, it constitutes a useful tool in the design and implementation of such systems. The results obtained with the optimal system of the case study are compared with those obtained on the basis of a configuration that uses backup batteries to ensure continuous operation. It is shown that the variable operating strategy provides the optimal economic system. 23 3,035 9,746 Q1 Q1 SCIE