Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms

Saavedra-Moreno, B.; Salcedo‐Sanz, Sancho; Paniagua-Tineo, A.; Prieto, Luís; Portilla-Figueras, Antonio

doi:10.1016/j.renene.2011.04.018

Cited by 125 publications

(59 citation statements)

References 14 publications

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“…The procedure is repeated m times or until a turbine violates the proximity constraint. In this way, it is guaranteed that feasible wind farm layouts are generated, similarly to previous works [130][131][132].…”

Section: Random Wind Farm Layoutsmentioning

confidence: 86%

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

et al. 2016

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Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.

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Section: Random Wind Farm Layoutsmentioning

confidence: 86%

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

et al. 2016

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“…Las metaheurísticas (Mosetti, et al, 1994); (Gradya, et al, 2004); (Bilbao & Alba, 2009); (Gallego, et al, 2015); (Saavedra-Moreno, et al, 2011); (Eroglu & Ulusam, 2012) son el conjunto de estrategias que aplican procedimientos heurísticos de alto rendimiento para resolver problemas complejos. El prefijo 'meta' implica que una metaheurística va más allá de una heurística en la búsqueda de soluciones de buena calidad.…”

Section: Metaheurísticasunclassified

“…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

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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“…The results demonstrated that the free placement of turbines, which are fixed in number, is superior compared to other applied scenarios. Saavedra-Moreno et al [6] suggested a greedy evolutionary algorithm considering factors such as orography, wind velocity, installment cost, road construction between wind turbines and shape of wind farm, for maximizing the profit function. Rahbari et al [4] applied a hybrid evolutionary approach, GA-QAP, to incorporate experts preferences into realistic designs of wind farm layouts taking into account prohibited installation areas.…”

Section: Placement Of Wind Turbinesmentioning

confidence: 99%

Developing Realistic Designs for Wind Farms: Incorporation of an Imperialist Competitive Algorithm

Raahemifar

Rosen

Rahbari

et al. 2014

Proceedings of the 4th World Sustainability Forum

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Abstract:The optimal positioning of wind turbines plays an important role in acquiring the anticipated output power from wind farms. This paper addresses challenges related to typical restriction assumptions for turbine arrangements in wind farms with a candidate selection approach. A hybrid quadratic assignment problem-imperialist competitive algorithm (ICA-QAP) method with an initial candidate points' selection (ICPS) approach is applied to two case studies. This hybrid algorithm is used to obtain optimal layout designs in terms of maximum efficiency. The current study incorporates previously utilized indicators from the literature for wind farms, such as wake effects, turbine hub height, rotor diameter, and power losses, and proposes additional criteria such as load-bearing capacity of soil and its restrictions. This is done to make the method applicable for realistic cases, and to incorporate the comments of expert designers into the design process and results. The results indicate that the achieved optimal layout design provides superior performance of the farms compared to that provided in previous similar studies. An efficiency improvement of about OPEN ACCESS 2 5% is attained for the first considered case. In addition, a combination of Dijkstra's Minimum Spanning and K-means approaches is applied to reduce the length of cables in the farm by 664 m in the second studied case.

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Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms

Cited by 125 publications

References 14 publications

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

Optimización heurística en el diseño de parques eólicos

Developing Realistic Designs for Wind Farms: Incorporation of an Imperialist Competitive Algorithm

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