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In this work we propose a method to explore the impact of aeronautical easements in the design of urban wind farms near airports. We provide a comprehensive review of airport easements, their classes, shapes and regulations, along with how and to what extent the different types of easements may affect the prospective installation of wind energy generation facilities near airports. We consider not only the physical easements, called Obstacle Limitation Surfaces (Approach Surface and others), but also the electromagnetic or radio easements associated to Communications, Navigation and Surveillance systems (radars and other air navigation aids). As a practical application, we study different cases of potential deployments of both isolated wind turbines and large wind farms, both around and/or inside four different airports in Spain. We give quantitative guidelines that could be used as a reference in the design process, easing the quick approval by the corresponding national air safety authority.

Wind power ramp events are sudden increases or decreases of wind speed within a short period of time. Their prediction is nowadays one of the most important research trends in wind energy production because they can potentially damage wind turbines, causing an increase in wind farms management costs. In this paper, 6-h and 24-h binary (ramp/non-ramp) prediction based on reservoir computing methodology is proposed. This forecasting may be used to avoid damages in the turbines. Reservoir computing models are used because they are able to exploit the temporal structure of data. We focus on echo state networks, which are one of the most successfully applied reservoir computing models. The variables considered include past values of the ramp function and a set of meteorological variables, obtained from reanalysis data. Simulations of the system are performed in data from three wind farms located in Spain. The results show that our algorithm proposal is able to correctly predict about 60% of ramp events in both 6-h and 24-h prediction cases and 75% of the non-ramp events in the next 24-h case. These results are compared against state of the art models, obtaining in all cases significant improvements in favour of the proposed algorithm.

This paper proposes a novel hybrid approach for feature selection in two different relevant problems for marine energy applications: significant wave height (Hm0) and wave energy flux (P) prediction. Specifically, a hybrid Grouping Genetic Algorithm – Extreme Learning Machine approach (GGA-ELM) is proposed, in such a way that the GGA searches for several subsets of features, and the ELM provides the fitness of the algorithm, by means of its accuracy on Hm0 or P prediction. Since the GGA was specifically created for problems involving a number of groups, the proposed algorithm may be used to evolve different groups of features in parallel, which may improve the performance of the predictions obtained. After the feature selection process with the GGA-ELM, the final results are given by an ELM and also by a Support Vector Machine, both working on the best GGA groups obtained. The performance of the proposed system has been tested in a real problem of Hm0 and P prediction at the Western coast of the USA, obtaining good results.

In this paper we tackle the optimal Discharge Scheduling of Energy Storage systems Problem (DSESP) in MicroGrids, considering renewable generation, and applying hyper-heuristic (HH) algorithms. The problem consists of, given the generation and load profiles in the MicroGrid, obtaining the optimal discharge scheduling of the Energy Storage System (ESS) that minimizes the consumption from the utility grid. HHs are a novel methodology in optimization problems that constructs a solution to a given problem by means of the application of basic heuristics, evolved using a global search algorithm. This methodology can be easily adapted to solve the DSESP, in this case by using an evolutionary algorithm as global approach. In this paper we detail the adaptations performed to a HH to tackle the DSESP, mainly in the encoding of solutions, and new evolutionary operators that improve the evolution of good solutions to the problem. The performance of the proposed approach has been evaluated in a real Microgrid, with different scenarios of generation and load profiles, obtaining around 5% reduction of the energy consumption from the utility grid.

This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established methodology that, contrary to other soft-computing approaches, has been under-explored in problems of wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs algorithms, and we show that they are able obtain excellent results in real problems of very short-term wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear regression approaches, different types of neural networks and support vector regression algorithms in this problem.We also show that RTs have a very small computation time, that allows the retraining of the algorithms whenever new wind speed data are collected from the measuring towers. Ministerio de Ciencia y Tecnología ECO2010-22065-C03-02 Ministerio de Ciencia y Tecnología TIN2011-28956-C02 Junta de Andalucía P12-TIC-1728 Universidad Pablo de Olavide APPB813097

CRO and HS for wind speed prediction This paper introduces a new hybrid bio-inspired solver which combines elements from the recently proposed Coral Reefs Optimization (CRO) algorithm with operators from the Harmony Search (HS) approach, which gives rise to the coined CRO-HS optimization technique. Specifically, this novel bio-inspired optimizer is utilized in the context of short-term wind speed prediction as a means to obtain the best set of meteorological variables to be input to a neural Extreme Learning Machine (ELM) network. The paper elaborates on the main characteristics of the proposed scheme and discusses its performance when predicting the wind speed based on the measures of two meteorological towers located in USA and Spain. The good results obtained in these experiments when compared to naïve versions of the CRO and HS algorithms are promising and pave the way towards the utilization of the derived hybrid solver in other optimization problems arising from diverse disciplines. This research work has been partially supported by Iberdrola, as well as by the Spanish Ministry of Economy and Competitiveness under project grant ECO2010-22065-C03-02.

This paper tackles the optimization of a stand-alone hybrid photovoltaic-batteries-hydrogen (PV-hydrogen) system, using an evolutionary algorithm. Specifically, a stand alone power system for feeding a remote telecommunications facility is studied. The considered system is specifically designed to cover the power necessities of remote, isolated telecommunications facilities, so it must be able to work in an unattended way during a long time period. On the other hand, if maintenance visits are scheduled, it is intuitive that the cost of the stand alone system could be reduced. Thus, two different optimization problems have been considered in this work. The first one consists in the obtention of the optimal number, distribution (two different arrays of batteries must be fed) and disposition (slope and azimuth) of the PV panels in the facility, for the case of autonomous operation of the telecommunication system during at least two years. The second problem considered consists of scheduling a maintenance visit per year, where a technician is able to reconfigure the system. In this case, the problem consists of obtaining the optimal number, distribution, disposition of the PV panels, and also the time of the year where the maintenance visit should take place. An evolutionary algorithm, able to tackle both problems with very few changes, is described in this paper. The proposed evolutionary algorithm has been analyzed in a simulation of a real PV-hydrogen system sited at National Spanish Institute for Aerospace Technology (INTA), at Torrejon de Ardoz, Madrid, Spain. The well-known software TRNSYS has been used in order to simulate the behavior of this PV-hydrogen system. Several simulations of the system recreating different weather conditions of three Spanish cities (Madrid, Barcelona and La Coruna) have been carried out, and a comparative analysis of the results obtained by the evolutionary algorithm has been done. The results obtained in the first problem tackled showed that in Madrid the system was able to work in an unattended way during 23 months with 6 PV panels, whereas adding one extra panel, the system was able to work in an unattended way for more than 24 months. In the case of Barcelona and La Coruna solutions with 6 PV panels provide 21 and 20 months of unattended work of the system. In the second problem tackled, we have obtained an important reduction in the number of PV panels needed for obtaining an unattended work of the system between two maintenance visits.

This paper presents a novel algorithm for wind farm design and layout optimization: the Coral Reefs Optimization algorithm (CRO). The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction. The CRO is fully described and detailed in this paper, and then applied to the design of a real offshore wind farm in northern Europe. It is shown that the CRO outperforms the results of alternative algorithms in this problem, such as Evolutionary Approaches, Differential Evolution or Harmony Search algorithms.

In this paper a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms is proposed. A realistic model for the wind farm is considered in the optimization process, which includes orography, shape of the wind farm, simulation of the wind speed and direction, and costs of installation, connection and road construction among wind turbines. Regarding the solution of the problem, this paper introduces a greedy heuristic algorithm which is able to obtain a reasonable initial solution for the problem. This heuristic is then used to seed the initial population of the evolutionary algorithm, improving its performance. It is shown that the proposed seeded evolutionary approach is able to obtain very good solutions to this problem, which maximize the economical benefit which can be obtained from the wind farm.

Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine.

This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points.