Optimization of wind farm turbines layout using an evolutive algorithm

González, Javier Serrano; Gonzalez-Rodriguez, Angel G.; Mora, José Castro; Santos, J.R.; Payán, Manuel Burgos

doi:10.1016/j.renene.2010.01.010

Cited by 298 publications

(129 citation statements)

References 16 publications

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“…The relatively cheap computational cost of such models allows gradient-free methods to be used in the optimization process. A number of layout optimization studies have utilized gradient-free approaches to minimize wake losses (Kusiak and Song, 2010), maximize net present value (González et al, 2010), and minimize noise propagation (Kwong et al, 2012). Other optimization approaches include particle swarm optimizations that determine turbine layouts and rotor diameters (Chowdhury et al, 2012), extended pattern searches for multimodal layout optimization (Du Pont and Cagan, 2012), and even game-theoretic methods (Mar-den et al, 2013).…”

Section: Approaches To Wind Plant Optimizationmentioning

confidence: 99%

Optimization of wind plant layouts using an adjoint approach

et al. 2017

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Abstract. Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier-Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utilityscale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flow physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.

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Section: Approaches To Wind Plant Optimizationmentioning

confidence: 99%

Optimization of wind plant layouts using an adjoint approach

et al. 2017

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“…Each term included in Equation (14), with the exception of the interest rate, is a function of the wind farm layout. The corresponding mathematical expressions for each variable, as proposed by Gonzá lez et al [135,278,280], will be discussed in Section 3.2.6.…”

Section: Net Present Value (Npv)mentioning

confidence: 99%

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

et al. 2014

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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. OPEN ACCESSEnergies 2014, 7 6931

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“…There are very few other methods that also account for aspects of turbine selection in tandem with turbine placement; one other example includes the method presented by Chen et al [10], which considers differing hub-heights as an optimization parameter in maximizing wind farm power output. Other powerful wind farm layout optimization methods include those developed in [11][12][13][14][15][16][17][18][19]. The majority of these methods, while providing important and diverse capabilities in the context of turbine micro-siting for a single wind farm, do not simultaneously focus turbine type selection during the layout optimization process, as is required in the explorations presented in this paper.…”

Section: A Temporally-and Spatially-varying Energy Resourcementioning

confidence: 99%

“…The growth of the wake downwind of a turbine, also accounting for wake merging scenarios, is determined using the wake growth model proposed by [27]. The corresponding energy deficit downwind of a turbine is determined using the velocity deficit model developed by [28], which is widely used in wind farm power generation estimation [14,15,[29][30][31]. The UWFLO power generation model also accounts for the possibility of a turbine being 'partially' in the wake of another turbine located upwind.…”

Section: Approach To Determine Optimal Turbine Choicesmentioning

confidence: 99%

“…The parameter P M represents the total wind power potential in GW of the target market region. According to Equation (15), the PEMS of a commercial turbine (S i ) is conveyed in terms of the total gigawatts of likely installation of that turbine in the target market. As estimated by NREL [49], a total wind power potential value of 10,459 GW at an 80 m height for the contiguous United States (which excludes Hawaii and Alaska) is used in this paper.…”

Section: Development Of a Market Suitability Metric For Wind Turbinesmentioning

confidence: 99%

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Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

et al. 2016

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Abstract:The suitability of turbine configurations to different wind resources has been traditionally restricted to considering turbines operating as standalone entities. In this paper, a framework is thus developed to investigate turbine suitability in terms of the minimum cost of energy offered when operating as a group of optimally-micro-sited turbines. The four major steps include: (i) characterizing the geographical variation of wind regimes in the onshore U.S. market; (ii) determining the best performing turbines for different wind regimes through wind farm layout optimization; (iii) developing a metric to quantify the expected market suitability of available turbine configurations; and (iv) exploring the best tradeoffs between the cost and capacity factor yielded by these turbines. One hundred thirty one types of commercial turbines offered by major global manufacturers in 2012 are considered for selection. It is found that, in general, higher rated power turbines with medium tower heights are the most favored. Interestingly, further analysis showed that "rotor diameter/hub height" ratios greater than 1.1 are the least attractive for any of the wind classes. It is also observed that although the "cost-capacity factor" tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar.

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Optimization of wind farm turbines layout using an evolutive algorithm

Cited by 298 publications

References 16 publications

Optimization of wind plant layouts using an adjoint approach

Optimization of wind plant layouts using an adjoint approach

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

Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

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