Cuckoo search for wind farm optimization with auxiliary infrastructure

Wind Energy ◽  
2018 ◽  
Vol 21 (10) ◽  
pp. 855-875 ◽  
Author(s):  
Svetlana Afanasyeva ◽  
Jussi Saari ◽  
Olli Pyrhönen ◽  
Jarmo Partanen
Keyword(s):  
Wind Farm ◽  
Cuckoo Search ◽  
Wind Farm Optimization

scholarly journals Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization

2018 ◽  
Author(s):  
Andrés Santiago Padrón ◽  
Jared Thomas ◽  
Andrew P. J. Stanley ◽  
Juan J. Alonso ◽  
Andrew Ning
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Energy Production ◽  
Wind Farm ◽  
Polynomial Chaos ◽  
Optimization Problems ◽  
Probability Distributions ◽  
Regression Result ◽  
Computationally Efficient ◽  
Wind Farm Optimization

Abstract. In this paper, we develop computationally-efficient techniques to calculate statistics used in wind farm optimization with the goal of enabling the use of higher-fidelity models and larger wind farm optimization problems. We apply these techniques to maximize the Annual Energy Production (AEP) of a wind farm by optimizing the position of the individual wind turbines. The AEP (a statistic itself) is the expected power produced by the wind farm over a period of one year subject to uncertainties in the wind conditions (wind direction and wind speed) that are described with empirically-determined probability distributions. To compute the AEP of the wind farm, we use a wake model to simulate the power at different input conditions composed of wind direction and wind speed pairs. We use polynomial chaos (PC), an uncertainty quantification method, to construct a polynomial approximation of the power over the entire stochastic space and to efficiently (using as few simulations as possible) compute the expected power (AEP). We explore both regression and quadrature approaches to compute the PC coefficients. PC based on regression is significantly more efficient than the rectangle rule (the method most commonly used to compute the expected power). With PC based on regression, we have reduced by as much as an order of magnitude the number of simulations required to accurately compute the AEP, thus enabling the use of more expensive, higher-fidelity models or larger wind farm optimizations. We perform a large suite of gradient-based optimizations with different initial turbine locations and with different numbers of samples to compute the AEP. The optimizations with PC based on regression result in optimized layouts that produce the same AEP as the optimized layouts found with the rectangle rule but using only one-third of the samples. Furthermore, for the same number of samples, the AEP of the optimal layouts found with PC is 1 % higher than the AEP of the layouts found with the rectangle rule.

scholarly journals Analytical model for the power–yaw sensitivity of wind turbines operating in full wake

2020 ◽  
Vol 5 (1) ◽  
pp. 427-437 ◽  
Author(s):  
Jaime Liew ◽  
Albert M. Urbán ◽  
Søren Juhl Andersen
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Power Production ◽  
Wake Flow ◽  
High Fidelity ◽  
Combined Effects ◽  
Wind Farm Optimization ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations ◽  
Blade Element

Abstract. Wind turbines are designed to align themselves with the incoming wind direction. However, turbines often experience unintentional yaw misalignment, which can significantly reduce the power production. The unintentional yaw misalignment increases for turbines operating in the wake of upstream turbines. Here, the combined effects of wakes and yaw misalignment are investigated, with a focus on the resulting reduction in power production. A model is developed, which considers the trajectory of each turbine blade element as it passes through the wake inflow in order to determine a power–yaw loss exponent. The simple model is verified using the HAWC2 aeroelastic code, where wake flow fields have been generated using both medium- and high-fidelity computational fluid dynamics simulations. It is demonstrated that the spatial variation in the incoming wind field, due to the presence of wakes, plays a significant role in the power loss due to yaw misalignment. Results show that disregarding these effects on the power–yaw loss exponent can yield a 3.5 % overestimation in the power production of a turbine misaligned by 30∘. The presented analysis and model is relevant to low-fidelity wind farm optimization tools, which aim to capture the combined effects of wakes and yaw misalignment as well as the uncertainty on power output.

Variable-sized wind turbines are a possibility for wind farm optimization

Wind Energy ◽  
2013 ◽  
Vol 17 (10) ◽  
pp. 1483-1494 ◽  
Author(s):  
Leonardo P. Chamorro ◽  
N. Tobin ◽  
R.E.A Arndt ◽  
F. Sotiropoulos
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farm Optimization
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