Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm

Mosetti, Giovanni; Poloni, Carlo; Diviacco, B.

doi:10.1016/0167-6105(94)90080-9

Cited by 676 publications

(560 citation statements)

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“…En trabajos presentados anteriormente (Mosetti et al, 1994); (Gradya, et al, 2004) A continuación, se hace un repaso de las heurísticas y las metaheurísticas y, se muestran algunos resultados obtenidos en el problema de optimización de diseño de parques eólicos.…”

Section: Uso De Técnicas Heurísticas Y Metahurísticas Para El Diseño unclassified

“…Las causas de esto son la complejidad matemática y la numerosa cantidad de variables y restricciones (Mosetti, et al, 1994); (Gradya, et al, 2004); (Bilbao & Alba, 2009);(Gallego, et al, 2015).…”

Section: Heurísticasunclassified

“…En trabajos presentados anteriormente Mosetti et al (1994); Gradya et al (2004); Bilbao & Alba (2009) ;Eroglu & Ulusam (2012); Saavedra-Moreno et al (2011) se ha demostrado la ventaja de técnicas metaheurísticas frente a el uso de técnicas empíricas y métodos aleatorios de posicionamiento. Entre las principales metaheurísticas aplicadas en el problema de optimización en el diseño de parques eólicos se encuentran los Algoritmos Genéticos (AG), la Optimización por Enjambre de Partículas (OEP) y la Optimización por Colonia de Hormigas (OCH).…”

Section: Introductionunclassified

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Optimización heurística en el diseño de parques eólicos

Cantor¹,

Rivera²

2016

Ingeniería y Región

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La optimización del costo de la energía por medio de la ubicación de turbinas en el diseño de parques eólicos es un problema que en las últimas dos décadas ha tomado importancia y que para resolverlo se han aplicado metaheurísticas de alto nivel. En el presente artículo, se detalla un modelo de parque eólico y se destaca la importancia del diseño óptimo de parques eólicos proponiendo la mejor ubicación de las turbinas en un layout. El layout hace referencia a la discretización del terreno de búsqueda en una cuadricula por medio de un arreglo de coordenadas cartesianas para su posterior codificación en una cadena binaria: un uno representa una turbina a instalar en la posición indicada y un cero indica lo contrario. De esta manera, en este artículo se muestran las diferentes metaheurísticas aplicadas para resolver este problema y como estas han tenido mejores resultados que técnicas empíricas utilizadas tradicionalmente. Por otro lado, se introduce una metaheurística de alto nivel basada en la caza de la larva de la hormiga león (Ant Lion Optimizer) y se muestra una técnica derivada de este trabajo que utiliza variables binarias (Binary Ant Lion Optimizer). Por último, se introduce un problema de diseño de parque eólico. Este problema utiliza un evaluador del costo de la energía que cuenta con modelos de la distribución del viento, del efecto estela y de la función de costos. Este problema se estudió para determinar el menor costo por medio de heurísticas de bajo nivel y por medio de metaheurísticas; se encontró que el mejor resultado se daba al utilizar el algoritmo de variables binarias con heurísticas de bajo nivel.

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Section: Uso De Técnicas Heurísticas Y Metahurísticas Para El Diseño unclassified

Section: Heurísticasunclassified

Section: Introductionunclassified

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Optimización heurística en el diseño de parques eólicos

Cantor¹,

Rivera²

2016

Ingeniería y Región

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“…The optimal spacing between columns was suggested to be 3R-6R [4] according to actual operational experience. In order to determine a method for optimizing turbine spacing on wind farms with different wind conditions and regional boundaries, genetic algorithms have been employed, using the ratio of total investment to the power generation efficiency as the target parameter [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Electro-Geometric Model for Lightning Shielding of Multiple Wind Turbines

et al. 2017

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Wind turbine blades being struck by lightning is one of the most urgent problems facing wind farms. In order to reduce the probability of lightning accidents on wind farms, this paper presents a new electro-geometric model for multiple turbines. In this new model, based on the physical model of lightning leader development, the striking distance range of the blade tip receptor is calculated, taking into account the influence of the charged particles around the blade. Lightning shielding amongst multiple turbines is provided in combination with the traditional electro-geometric model, and a criterion formula is obtained for mutual shielding for multiple turbines. The influence of environmental factors, such as temperature, atmospheric pressure, air humidity, and altitude, on lightning shielding on large-scale wind farms is also analyzed by studying the lightning shielding distance between wind turbines. The calculation shows that the larger the relative air density and the absolute humidity, and the lower the altitude, and the larger the lightning shielding distance between wind turbines. The method proposed in this paper provides a theoretical basis for the lightning protection on wind farms under different environmental conditions.

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“…Today, the design of a wind farm is typically based on an optimization of the power output only, whereas the load aspect is treated in a rudimentary manner only, in the sense that the wind turbines are required only to comply with the design codes. Wind farm layout optimization is a relatively new research topic, with some of the first articles on the subject includes Mosetti et al in 1994 1 and Beyer et al in 1996. 2 Since then, the topic has become gradually more popular to reach around 10 journal and conference articles produced every year (see 3 and 4 for a more exhaustive review).…”

Section: Introductionmentioning

confidence: 99%

TOPFARM: Multi‐fidelity optimization of wind farms

et al. 2013

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A wind farm layout optimization framework based on a multi-fidelity optimization approach is applied to the offshore test case of Middelgrunden, Denmark as well as to the onshore test case of Stag Holt -Coldham wind farm, UK. While aesthetic considerations have heavily influenced the famous curved design of the Middelgrunden wind farm, this work focuses on demonstrating a method that optimizes the profit of wind farms over their lifetime based on a balance of the energy production income, the electrical grid costs, the foundations cost, and the cost of wake turbulence induced fatigue degradation of different wind turbine components. A multi-fidelity concept is adapted, which uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU-FP6 TOP-FARM project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production (AEP), a foundations cost model depending on the water depth and an electrical grid cost function dictated by cable length. The second level calculates the AEP and adds a wake-induced fatigue degradation cost function on the basis of the interpolation in a database of simulations performed for various wind speeds and wake setups with the aero-elastic code HAWC2 and the dynamic wake meandering model. The third level, not considered in this present paper, includes directly the HAWC2 and the dynamic wake meandering model in the optimization loop in order to estimate both the fatigue costs and the AEP. The novelty of this work is the implementation of the multi-fidelity approach in the context of wind farm optimization, the inclusion of the fatigue degradation costs in the optimization framework, and its application on the optimal performance as seen through an economical perspective.

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Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm

Cited by 676 publications

References 1 publication

Optimización heurística en el diseño de parques eólicos

Optimización heurística en el diseño de parques eólicos

An Electro-Geometric Model for Lightning Shielding of Multiple Wind Turbines

TOPFARM: Multi‐fidelity optimization of wind farms

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