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Annual Energy Production

The Experts below are selected from a list of 2649 Experts worldwide ranked by ideXlab platform

Andrew Ning – 1st expert on this subject based on the ideXlab platform

  • Polynomial chaos to efficiently compute the Annual Energy Production in wind farm layout optimization
    , 2018
    Co-Authors: Andres S Padron, Andrew P J Stanley, Juan J Alonso, Jared Thomas, Andrew Ning

    Abstract:

    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, 2018
    Co-Authors: Andres S Padron, Andrew P J Stanley, Juan J Alonso, Jared Thomas, Andrew Ning

    Abstract:

    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, 2017
    Co-Authors: P M O Gebraad, Andrew Ning, Jared J Thomas, Paul Fleming, Katherine Dykes

    Abstract:

    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 – 2nd expert 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, 2017
    Co-Authors: P M O Gebraad, Andrew Ning, Jared J Thomas, Paul Fleming, Katherine Dykes

    Abstract:

    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, 2016
    Co-Authors: P M O Gebraad, Andrew Ning, Jared J Thomas, Paul Fleming, Katherine Dykes

    Abstract:

    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 – 3rd expert 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, 2017
    Co-Authors: P M O Gebraad, Andrew Ning, Jared J Thomas, Paul Fleming, Katherine Dykes

    Abstract:

    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, 2016
    Co-Authors: P M O Gebraad, Andrew Ning, Jared J Thomas, Paul Fleming, Katherine Dykes

    Abstract:

    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.