Evaluación de dos nuevos algoritmos en el diseño de granjas eólicas

En los últimos años el crecimiento en el consumo de energía eléctrica ha sido exorbitante, lo cual ha generado la necesidad de utilizar un recurso prometedor como el viento para extraer dicha energía. La distribución de turbinas de viento dentro de una granja eólica, con el objeto de optimizar la energía capturada, es un problema complejo de resolver. En este artículo se intenta solucionar este problema abordándolo de dos formas distintas: una es la adaptación del algoritmo GWO para vectores booleanos y la otra, DonQuijote, es un método nuevo que incluye el uso de la Evolución Diferencial y surge del analisis del problema. Para mostrar la eficiencia de los métodos se comparan con un Algoritmo Genético, tan estudiado en el área. La mejor propuesta participó en la competencia WFLO de la GECCO 2015.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Evaluación de dos nuevos algoritmos en el diseño de granjas eólicas

En los últimos años el crecimiento en el consumo de energía eléctrica ha sido exorbitante, lo cual ha generado la necesidad de utilizar un recurso prometedor como el viento para extraer dicha energía. La distribución de turbinas de viento dentro de una granja eólica, con el objeto de optimizar la energía capturada, es un problema complejo de resolver. En este artículo se intenta solucionar este problema abordándolo de dos formas distintas: una es la adaptación del algoritmo GWO para vectores booleanos y la otra, DonQuijote, es un método nuevo que incluye el uso de la Evolución Diferencial y surge del analisis del problema. Para mostrar la eficiencia de los métodos se comparan con un Algoritmo Genético, tan estudiado en el área. La mejor propuesta participó en la competencia WFLO de la GECCO 2015.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Evaluación de dos nuevos algoritmos en el diseño de granjas eólicas

En los últimos años el crecimiento en el consumo de energía eléctrica ha sido exorbitante, lo cual ha generado la necesidad de utilizar un recurso prometedor como el viento para extraer dicha energía. La distribución de turbinas de viento dentro de una granja eólica, con el objeto de optimizar la energía capturada, es un problema complejo de resolver. En este artículo se intenta solucionar este problema abordándolo de dos formas distintas: una es la adaptación del algoritmo GWO para vectores booleanos y la otra, DonQuijote, es un método nuevo que incluye el uso de la Evolución Diferencial y surge del analisis del problema. Para mostrar la eficiencia de los métodos se comparan con un Algoritmo Genético, tan estudiado en el área. La mejor propuesta participó en la competencia WFLO de la GECCO 2015.XVI Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Optimizing energy output and layout costs for large wind farms using particle swarm optimization

The design of a wind farm involves several complex optimization problems. We consider the multi-objective optimization problem of maximizing the energy output under the consideration of wake effects and minimizing the cost of the turbines and land area used for the wind farm. We present an efficient particle swarm optimization algorithm that computes a set of trade-off solutions for the given task. Our algorithm can be easily integrated into the layout process for developing wind farms and gives designers new insights into the trade-off between energy output and land area.Kalyan Veeramachaneni, Markus Wagner, Una-May O’Reilly and Frank Neuman

Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization

Wind is one of the major sources of clean and renewable energy, and global wind energy has been experiencing a steady annual growth rate of more than 20% over the past decade. In the U.S. energy market, although wind energy is one of the fastest increasing sources of electricity generation (by annual installed capacity addition), and is expected to play an important role in the future energy demographics of this country, it has also been plagued by project underperformance and concept-to-installation delays. There are various factors affecting the quality of a wind energy project, and most of these factors are strongly coupled in their influence on the socio-economic, production, and environmental objectives of a wind energy project. To develop wind farms that are profitable, reliable, and meet community acceptance, it is critical to accomplish balance between these objectives, and therefore a clean understanding of how different design and natural factors jointly impact these objectives is much needed. In this research, a Multi-objective Wind Farm Design (MOWFD) methodology is developed, which analyzes and integrates the impact of various factors on the conceptual design of wind farms. This methodology contributes three major advancements to the wind farm design paradigm: (I) provides a new understanding of the impact of key factors on the wind farm performance under the use of different wake models; (II) explores the crucial tradeoffs between energy production, cost of energy, and the quantitative role of land usage in wind farm layout optimization (WFLO); and (III) makes novel advancements on mixed-discrete particle swarm optimization algorithm through a multi-domain diversity preservation concept, to solve complex multi-objective optimization (MOO) problems. A comprehensive sensitivity analysis of the wind farm power generation is performed to understand and compare the impact of land configuration, installed capacity decisions, incoming wind speed, and ambient turbulence on the performance of conventional array layouts and optimized wind farm layouts. For array-like wind farms, the relative importance of each factor was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models. In contrast, for optimized wind farm layouts, the choice of wake models was observed to have no significant impact on the sensitivity indices. The MOWFD methodology is designed to explore the tradeoffs between the concerned performance objectives and simultaneously optimize the location of turbines, the type of turbines, and the land usage. More importantly, it facilitates WFLO without prescribed conditions (e.g., fixed wind farm boundaries and number of turbines), thereby allowing a more flexible exploration of the feasible layout solutions than is possible with other existing WFLO methodologies. In addition, a novel parameterization of the Pareto is performed to quantitatively explore how the best tradeoffs between energy production and land usage vary with the installed capacity decisions. The key to the various complex MO-WFLOs performed here is the unique set of capabilities offered by the new Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm, developed, tested and extensively used in this dissertation. The MO-MDPSO algorithm is capable of dealing with a plethora of problem complexities, namely: multiple highly nonlinear objectives, constraints, high design space dimensionality, and a mixture of continuous and discrete design variables. Prior to applying MO-MDPSO to effectively solve complex WFLO problems, this new algorithm was tested on a large and diverse suite of popular benchmark problems; the convergence and Pareto coverage offered by this algorithm was found to be competitive with some of the most popular MOO algorithms (e.g., GAs). The unique potential of the MO-MDPSO algorithm is further established through application to the following complex practical engineering problems: (I) a disc brake design problem, (II) a multi-objective wind farm layout optimization problem, simultaneously optimizing the location of turbines, the selection of turbine types, and the site orientation, and (III) simultaneously minimizing land usage and maximizing capacity factors under varying land plot availability

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI