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

José F. Herbert-Acero, Oliver Probst, Pierre-Elouan Réthoré, Gunner Chr. Larsen and Krystel K. Castillo-Villar
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José F. Herbert-Acero: Chair for Wind Energy, Physics Department, Tecnológico de Monterrey, Eugenio Garza Sada 2501 Sur, 64849 Monterrey, N.L., Mexico
Oliver Probst: Chair for Wind Energy, Physics Department, Tecnológico de Monterrey, Eugenio Garza Sada 2501 Sur, 64849 Monterrey, N.L., Mexico
Pierre-Elouan Réthoré: Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Gunner Chr. Larsen: Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Krystel K. Castillo-Villar: Department of Mechanical Engineering, the University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA

Energies, 2014, vol. 7, issue 11, 1-87

Abstract: 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.

Keywords: wind farm design; micro-siting; wind farm layout; wind farm performance modeling; nonlinear optimization; metaheuristic optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (51)

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