The Experts below are selected from a list of 2649 Experts worldwide ranked by ideXlab platform
Andrew Ning - One of the best experts on this subject based on the ideXlab platform.
-
Polynomial chaos to efficiently compute the Annual Energy Production in wind farm layout optimization
2018Co-Authors: Andres S Padron, Andrew P J Stanley, Juan J Alonso, Jared Thomas, Andrew NingAbstract: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.
-
polynomial chaos to efficiently compute the Annual Energy Production in wind farm layout optimization
Wind Energy Science Discussions, 2018Co-Authors: Andres S Padron, Andrew P J Stanley, Juan J Alonso, Jared Thomas, Andrew NingAbstract: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) is the expected power produced by the wind farm over a period of 1 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 on average by a factor of 5 the number of simulations required to accurately compute the AEP when compared to the rectangle rule for the different wind farm layouts considered. In the wind farm layout optimization problem, each optimization step requires an AEP computation. Thus, the ability to compute the AEP accurately with fewer simulations is beneficial as it reduces the cost to perform an optimization, which enables the use of more computationally expensive higher-fidelity models or the consideration of larger or multiple wind farm optimization problems. We perform a large suite of gradient-based optimizations to compare the optimal layouts obtained when computing the AEP with polynomial chaos based on regression and the rectangle rule. We consider three different starting layouts (Grid, Amalia, Random) and find that the optimization has many local optima and is sensitive to the starting layout of the turbines. We observe that starting from a good layout (Grid, Amalia) will, in general, find better optima than starting from a bad layout (Random) independent of the method used to compute the AEP. For both PC based on regression and the rectangle rule, we consider both a coarse ( ∼225 ) and a fine ( ∼625 ) number of simulations to compute the AEP. We find that for roughly one-third of the computational cost, the optimizations with the coarse PC based on regression result in optimized layouts that produce comparable AEP to the optimized layouts found with the fine rectangle rule. Furthermore, for the same computational cost, for the different cases considered, polynomial chaos finds optimal layouts with 0.4 % higher AEP on average than those found with the rectangle rule.
-
maximization of the Annual Energy Production of wind power plants by optimization of layout and yaw based wake control
Wind Energy, 2017Co-Authors: P M O Gebraad, Jared J Thomas, Andrew Ning, Paul Fleming, Katherine DykesAbstract:This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant Annual Energy Production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power Productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power Production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.
-
polynomial chaos for the computation of Annual Energy Production in wind farm layout optimization
Journal of Physics: Conference Series, 2016Co-Authors: Andres S Padron, Andrew P J Stanley, Jared J Thomas, Juan J Alonso, Andrew NingAbstract:Careful management of wake interference is essential to further improve Annual Energy Production (AEP) of wind farms. Wake effects can be minimized through optimization of turbine layout, wind farm control, and turbine design. Realistic wind farm optimization is challenging because it has numerous design degrees of freedom and must account for the stochastic nature of wind. In this paper we provide a framework for calculating AEP for any relevant uncertain (stochastic) variable of interest. We use Polynomial Chaos (PC) to efficiently quantify the effect of the stochastic variables—wind direction and wind speed—on the statistical outputs of interest (AEP) for wind farm layout optimization. When the stochastic variable includes the wind direction, polynomial chaos is one order of magnitude more accurate in computing the AEP when compared to commonly used simplistic integration techniques (rectangle rule), especially for non grid-like wind farm layouts. Furthermore, PC requires less simulations for the same accuracy. This allows for more efficient optimization and uncertainty quantification of wind farm Energy Production.
-
Maximization of the Annual Energy Production of wind power plants by optimization of layout and yaw‐based wake control
Wind Energy, 2016Co-Authors: P M O Gebraad, Jared J Thomas, Andrew Ning, Paul Fleming, Katherine DykesAbstract:This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant Annual Energy Production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power Productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power Production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.
Katherine Dykes - One of the best experts on this subject based on the ideXlab platform.
-
maximization of the Annual Energy Production of wind power plants by optimization of layout and yaw based wake control
Wind Energy, 2017Co-Authors: P M O Gebraad, Jared J Thomas, Andrew Ning, Paul Fleming, Katherine DykesAbstract:This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant Annual Energy Production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power Productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power Production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.
-
Maximization of the Annual Energy Production of wind power plants by optimization of layout and yaw‐based wake control
Wind Energy, 2016Co-Authors: P M O Gebraad, Jared J Thomas, Andrew Ning, Paul Fleming, Katherine DykesAbstract:This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant Annual Energy Production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power Productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power Production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.
P M O Gebraad - One of the best experts on this subject based on the ideXlab platform.
-
maximization of the Annual Energy Production of wind power plants by optimization of layout and yaw based wake control
Wind Energy, 2017Co-Authors: P M O Gebraad, Jared J Thomas, Andrew Ning, Paul Fleming, Katherine DykesAbstract:This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant Annual Energy Production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power Productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power Production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.
-
Maximization of the Annual Energy Production of wind power plants by optimization of layout and yaw‐based wake control
Wind Energy, 2016Co-Authors: P M O Gebraad, Jared J Thomas, Andrew Ning, Paul Fleming, Katherine DykesAbstract:This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant Annual Energy Production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power Productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power Production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.
Jared J Thomas - One of the best experts on this subject based on the ideXlab platform.
-
maximization of the Annual Energy Production of wind power plants by optimization of layout and yaw based wake control
Wind Energy, 2017Co-Authors: P M O Gebraad, Jared J Thomas, Andrew Ning, Paul Fleming, Katherine DykesAbstract:This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant Annual Energy Production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power Productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power Production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.
-
polynomial chaos for the computation of Annual Energy Production in wind farm layout optimization
Journal of Physics: Conference Series, 2016Co-Authors: Andres S Padron, Andrew P J Stanley, Jared J Thomas, Juan J Alonso, Andrew NingAbstract:Careful management of wake interference is essential to further improve Annual Energy Production (AEP) of wind farms. Wake effects can be minimized through optimization of turbine layout, wind farm control, and turbine design. Realistic wind farm optimization is challenging because it has numerous design degrees of freedom and must account for the stochastic nature of wind. In this paper we provide a framework for calculating AEP for any relevant uncertain (stochastic) variable of interest. We use Polynomial Chaos (PC) to efficiently quantify the effect of the stochastic variables—wind direction and wind speed—on the statistical outputs of interest (AEP) for wind farm layout optimization. When the stochastic variable includes the wind direction, polynomial chaos is one order of magnitude more accurate in computing the AEP when compared to commonly used simplistic integration techniques (rectangle rule), especially for non grid-like wind farm layouts. Furthermore, PC requires less simulations for the same accuracy. This allows for more efficient optimization and uncertainty quantification of wind farm Energy Production.
-
Maximization of the Annual Energy Production of wind power plants by optimization of layout and yaw‐based wake control
Wind Energy, 2016Co-Authors: P M O Gebraad, Jared J Thomas, Andrew Ning, Paul Fleming, Katherine DykesAbstract:This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant Annual Energy Production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power Productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power Production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.
Mei Su - One of the best experts on this subject based on the ideXlab platform.
-
Annual Energy Production Estimation for Variable-Speed Wind Turbine at High-Altitude Site
Journal of Modern Power Systems and Clean Energy, 1Co-Authors: Dongran Song, Songyue Zheng, Sheng Yang, Jian Yang, Mi Dong, Mei SuAbstract:This letter presents a systematic approach to estimate the Annual Energy Production (AEP) of variable-speed wind turbines erected at high-altitude sites. Compared to the existing empirical-model based approaches, the proposed approach models the influence of the air density on the power Production while employing the theoretical power curve. Consequently, the proposed approach provides a precise estimation of AEP, which can serve as a foundation of the optimum turbinesite matching design at different-altitude sites.
