D.J.N. Allaerts
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Our planet is warming up with potentially disastrous consequences. The main cause of this climate change is the increase of greenhouse gases in the atmosphere, which are mainly emitted by burning fossil fuels to generate energy. Therefore, fossil fuels need to be substituted to reduce emissions from the energy sector. Renewable energies offer an alternative with reduced emissions. Among these, wind and solar energy are growing the fastest. This thesis investigates how the wind energy supply can be increased by improving its operational efficiency.
There are several reasons why a wind turbine may not generate its maximum capacity, one of them being its placement. Turbines are often placed in farms, which allows the collective use of infrastructure and minimizes land usage. The downside is that the turbines influence one another: As a turbine extracts energy from the wind, an area with lowered wind speed develops downstream. This area is called wake, and other turbines affected by it will generate less energy.
The ways to address this problem can be split into pre- and post-construction measures. Pre-construction the wind farm layout can be optimized, and post-construction control strategies are needed to operate the wind farm optimally. These strategies fall under the term wind farm flow control and aim to manipulate the flow between the turbines to optimize the farm performance. A turbine’s wake can be altered by changing the turbine’s resistance to the flow or by misaligning the turbine with the wind direction. The former leads to a faster wake recovery, and the latter results in a redirection of the wake, also called wake-steering.
The current state-of-the-art of wind farm flow control is to utilize wake-steering in an open-loop control configuration. To this end, steady-state engineering models of the wake are used to optimize the farm set points offline. This is done for a selection of atmospheric conditions and the set points are stored in a lookup table (LuT). During operation, the flow conditions are used to look up the precomputed turbine set points. A problem with this approach arises as open-loop control assumes a perfect match between the model and the actual conditions in the field. There are reasons why this might not be the case: (i) There is an inevitable modeling error, which creates a mismatch between the model and the reality; (ii) conditions can arise that are offline not accounted for, e.g., time-varying atmospheric conditions or layout changes due to turbine downtime.
These problems can be addressed by closing the loop. In closed-loop control, measurements are used to continuously correct the model and to adapt it to the current state of the true wind farm. Optimal set points are then found based on the current model state. The control strategy can, therefore, react to new conditions. A challenge is that the optimization needs to happen online and requires a way to incorporate sensor data into the model. Previous work has designed closed-loop approaches using the same computationally cheap steady-state models that were previously used for open-loop control. This was achieved by adapting the parameters of the model based on the mismatch between the observed and predicted measurements, like power generated. A core assumption these models make is that the flow is in a never changing steady state. However, flow conditions do change, and the large spacing between turbines leads to minutes of delay between the control action the upstream turbine takes and the effect that the downstream turbine experiences. The question arises: What could be achieved using dynamic wake models instead of steady-state ones? These can incorporate wake dynamics, which could lead to better decision-making.
This thesis designs a closed-loop model-predictive wind farm flow control strategy based on a dynamic wake model to maximize the energy generated by a wind farm under time-varying conditions. The thesis is comprised of three building blocks: (i) The development of a dynamic wake model, (ii) the derivation of a sensor fusion strategy to identify the state of the flow field, (iii) the composition of a control strategy that uses the model to optimize the control set points. The building blocks are then connected to form the closed-loop control strategy.
The model building is based on the further development of an existing model, which utilizes a steady-state wake model and reintroduces flow dynamics. In the first step, the underlying wake model is substituted by a three-dimensional one, and the formulation is adapted to heterogeneous flow conditions. In the second step, the model is reformulated as a framework that links to an arbitrary wake model. This is done to profit from advancements in the steady-state model development and to significantly decrease the computational cost of the model. In the third step, the dynamic model is compared to a steady-state one in a set of high-fidelity wind farm simulations under time-varying conditions based on field measurements. The results show that the dynamic model does provide a better match with a simulated wind farm.
In the second part of the thesis, a state estimation methodology is introduced. To this end, an ensemble approach is adopted, where the multiple versions of the model are simulated in parallel. The correlation between the ensembles is then used to correct them based on the predicted and measured wind direction and power measurements of the turbines. A byproduct of the ensemble approach is that each estimated state also has an uncertainty based on how much the ensembles agree on its value.
The third part of this thesis investigates the control and optimization problem. This part focuses on the cost function formulation and the behavior it leads to. In a steady-state frame, the delays do not have to be taken into account, but in a dynamic formulation, they become a challenge. We, therefore, propose a cost-function formulation that synchronizes the control actions with their effect at the downstream turbines. This leads to a series of smaller optimization problems instead of one larger one.
The three building blocks of this thesis are then tested in a case study: The closed-loop controller is employed to maximize the energy of a ten-turbine wind farm under time-varying conditions. Both the farm layout and wind direction time series are based on field conditions. The controller generates an overall energy gain of up to 4% over the baseline using noise-free wind direction measurements.
This is on par with the steady-state approach. However, the closed-loop approach is found to be more robust to disturbed wind direction measurements - Where the performance of the steady-state approach decreases to 1.7% due to the sensor noise; the closed-loop approach still achieves a 2.5% gain.
The conclusion of the work presented in this thesis is thereby: Closed-loop wind farm flow control based on a dynamic engineering surrogate model leads to a more accurate and robust state estimation of the wind farm flow field but, given no preview, does not necessarily lead to a higher energy generation than what can be achieved with steady-state models. ...
There are several reasons why a wind turbine may not generate its maximum capacity, one of them being its placement. Turbines are often placed in farms, which allows the collective use of infrastructure and minimizes land usage. The downside is that the turbines influence one another: As a turbine extracts energy from the wind, an area with lowered wind speed develops downstream. This area is called wake, and other turbines affected by it will generate less energy.
The ways to address this problem can be split into pre- and post-construction measures. Pre-construction the wind farm layout can be optimized, and post-construction control strategies are needed to operate the wind farm optimally. These strategies fall under the term wind farm flow control and aim to manipulate the flow between the turbines to optimize the farm performance. A turbine’s wake can be altered by changing the turbine’s resistance to the flow or by misaligning the turbine with the wind direction. The former leads to a faster wake recovery, and the latter results in a redirection of the wake, also called wake-steering.
The current state-of-the-art of wind farm flow control is to utilize wake-steering in an open-loop control configuration. To this end, steady-state engineering models of the wake are used to optimize the farm set points offline. This is done for a selection of atmospheric conditions and the set points are stored in a lookup table (LuT). During operation, the flow conditions are used to look up the precomputed turbine set points. A problem with this approach arises as open-loop control assumes a perfect match between the model and the actual conditions in the field. There are reasons why this might not be the case: (i) There is an inevitable modeling error, which creates a mismatch between the model and the reality; (ii) conditions can arise that are offline not accounted for, e.g., time-varying atmospheric conditions or layout changes due to turbine downtime.
These problems can be addressed by closing the loop. In closed-loop control, measurements are used to continuously correct the model and to adapt it to the current state of the true wind farm. Optimal set points are then found based on the current model state. The control strategy can, therefore, react to new conditions. A challenge is that the optimization needs to happen online and requires a way to incorporate sensor data into the model. Previous work has designed closed-loop approaches using the same computationally cheap steady-state models that were previously used for open-loop control. This was achieved by adapting the parameters of the model based on the mismatch between the observed and predicted measurements, like power generated. A core assumption these models make is that the flow is in a never changing steady state. However, flow conditions do change, and the large spacing between turbines leads to minutes of delay between the control action the upstream turbine takes and the effect that the downstream turbine experiences. The question arises: What could be achieved using dynamic wake models instead of steady-state ones? These can incorporate wake dynamics, which could lead to better decision-making.
This thesis designs a closed-loop model-predictive wind farm flow control strategy based on a dynamic wake model to maximize the energy generated by a wind farm under time-varying conditions. The thesis is comprised of three building blocks: (i) The development of a dynamic wake model, (ii) the derivation of a sensor fusion strategy to identify the state of the flow field, (iii) the composition of a control strategy that uses the model to optimize the control set points. The building blocks are then connected to form the closed-loop control strategy.
The model building is based on the further development of an existing model, which utilizes a steady-state wake model and reintroduces flow dynamics. In the first step, the underlying wake model is substituted by a three-dimensional one, and the formulation is adapted to heterogeneous flow conditions. In the second step, the model is reformulated as a framework that links to an arbitrary wake model. This is done to profit from advancements in the steady-state model development and to significantly decrease the computational cost of the model. In the third step, the dynamic model is compared to a steady-state one in a set of high-fidelity wind farm simulations under time-varying conditions based on field measurements. The results show that the dynamic model does provide a better match with a simulated wind farm.
In the second part of the thesis, a state estimation methodology is introduced. To this end, an ensemble approach is adopted, where the multiple versions of the model are simulated in parallel. The correlation between the ensembles is then used to correct them based on the predicted and measured wind direction and power measurements of the turbines. A byproduct of the ensemble approach is that each estimated state also has an uncertainty based on how much the ensembles agree on its value.
The third part of this thesis investigates the control and optimization problem. This part focuses on the cost function formulation and the behavior it leads to. In a steady-state frame, the delays do not have to be taken into account, but in a dynamic formulation, they become a challenge. We, therefore, propose a cost-function formulation that synchronizes the control actions with their effect at the downstream turbines. This leads to a series of smaller optimization problems instead of one larger one.
The three building blocks of this thesis are then tested in a case study: The closed-loop controller is employed to maximize the energy of a ten-turbine wind farm under time-varying conditions. Both the farm layout and wind direction time series are based on field conditions. The controller generates an overall energy gain of up to 4% over the baseline using noise-free wind direction measurements.
This is on par with the steady-state approach. However, the closed-loop approach is found to be more robust to disturbed wind direction measurements - Where the performance of the steady-state approach decreases to 1.7% due to the sensor noise; the closed-loop approach still achieves a 2.5% gain.
The conclusion of the work presented in this thesis is thereby: Closed-loop wind farm flow control based on a dynamic engineering surrogate model leads to a more accurate and robust state estimation of the wind farm flow field but, given no preview, does not necessarily lead to a higher energy generation than what can be achieved with steady-state models. ...
Our planet is warming up with potentially disastrous consequences. The main cause of this climate change is the increase of greenhouse gases in the atmosphere, which are mainly emitted by burning fossil fuels to generate energy. Therefore, fossil fuels need to be substituted to reduce emissions from the energy sector. Renewable energies offer an alternative with reduced emissions. Among these, wind and solar energy are growing the fastest. This thesis investigates how the wind energy supply can be increased by improving its operational efficiency.
There are several reasons why a wind turbine may not generate its maximum capacity, one of them being its placement. Turbines are often placed in farms, which allows the collective use of infrastructure and minimizes land usage. The downside is that the turbines influence one another: As a turbine extracts energy from the wind, an area with lowered wind speed develops downstream. This area is called wake, and other turbines affected by it will generate less energy.
The ways to address this problem can be split into pre- and post-construction measures. Pre-construction the wind farm layout can be optimized, and post-construction control strategies are needed to operate the wind farm optimally. These strategies fall under the term wind farm flow control and aim to manipulate the flow between the turbines to optimize the farm performance. A turbine’s wake can be altered by changing the turbine’s resistance to the flow or by misaligning the turbine with the wind direction. The former leads to a faster wake recovery, and the latter results in a redirection of the wake, also called wake-steering.
The current state-of-the-art of wind farm flow control is to utilize wake-steering in an open-loop control configuration. To this end, steady-state engineering models of the wake are used to optimize the farm set points offline. This is done for a selection of atmospheric conditions and the set points are stored in a lookup table (LuT). During operation, the flow conditions are used to look up the precomputed turbine set points. A problem with this approach arises as open-loop control assumes a perfect match between the model and the actual conditions in the field. There are reasons why this might not be the case: (i) There is an inevitable modeling error, which creates a mismatch between the model and the reality; (ii) conditions can arise that are offline not accounted for, e.g., time-varying atmospheric conditions or layout changes due to turbine downtime.
These problems can be addressed by closing the loop. In closed-loop control, measurements are used to continuously correct the model and to adapt it to the current state of the true wind farm. Optimal set points are then found based on the current model state. The control strategy can, therefore, react to new conditions. A challenge is that the optimization needs to happen online and requires a way to incorporate sensor data into the model. Previous work has designed closed-loop approaches using the same computationally cheap steady-state models that were previously used for open-loop control. This was achieved by adapting the parameters of the model based on the mismatch between the observed and predicted measurements, like power generated. A core assumption these models make is that the flow is in a never changing steady state. However, flow conditions do change, and the large spacing between turbines leads to minutes of delay between the control action the upstream turbine takes and the effect that the downstream turbine experiences. The question arises: What could be achieved using dynamic wake models instead of steady-state ones? These can incorporate wake dynamics, which could lead to better decision-making.
This thesis designs a closed-loop model-predictive wind farm flow control strategy based on a dynamic wake model to maximize the energy generated by a wind farm under time-varying conditions. The thesis is comprised of three building blocks: (i) The development of a dynamic wake model, (ii) the derivation of a sensor fusion strategy to identify the state of the flow field, (iii) the composition of a control strategy that uses the model to optimize the control set points. The building blocks are then connected to form the closed-loop control strategy.
The model building is based on the further development of an existing model, which utilizes a steady-state wake model and reintroduces flow dynamics. In the first step, the underlying wake model is substituted by a three-dimensional one, and the formulation is adapted to heterogeneous flow conditions. In the second step, the model is reformulated as a framework that links to an arbitrary wake model. This is done to profit from advancements in the steady-state model development and to significantly decrease the computational cost of the model. In the third step, the dynamic model is compared to a steady-state one in a set of high-fidelity wind farm simulations under time-varying conditions based on field measurements. The results show that the dynamic model does provide a better match with a simulated wind farm.
In the second part of the thesis, a state estimation methodology is introduced. To this end, an ensemble approach is adopted, where the multiple versions of the model are simulated in parallel. The correlation between the ensembles is then used to correct them based on the predicted and measured wind direction and power measurements of the turbines. A byproduct of the ensemble approach is that each estimated state also has an uncertainty based on how much the ensembles agree on its value.
The third part of this thesis investigates the control and optimization problem. This part focuses on the cost function formulation and the behavior it leads to. In a steady-state frame, the delays do not have to be taken into account, but in a dynamic formulation, they become a challenge. We, therefore, propose a cost-function formulation that synchronizes the control actions with their effect at the downstream turbines. This leads to a series of smaller optimization problems instead of one larger one.
The three building blocks of this thesis are then tested in a case study: The closed-loop controller is employed to maximize the energy of a ten-turbine wind farm under time-varying conditions. Both the farm layout and wind direction time series are based on field conditions. The controller generates an overall energy gain of up to 4% over the baseline using noise-free wind direction measurements.
This is on par with the steady-state approach. However, the closed-loop approach is found to be more robust to disturbed wind direction measurements - Where the performance of the steady-state approach decreases to 1.7% due to the sensor noise; the closed-loop approach still achieves a 2.5% gain.
The conclusion of the work presented in this thesis is thereby: Closed-loop wind farm flow control based on a dynamic engineering surrogate model leads to a more accurate and robust state estimation of the wind farm flow field but, given no preview, does not necessarily lead to a higher energy generation than what can be achieved with steady-state models.
There are several reasons why a wind turbine may not generate its maximum capacity, one of them being its placement. Turbines are often placed in farms, which allows the collective use of infrastructure and minimizes land usage. The downside is that the turbines influence one another: As a turbine extracts energy from the wind, an area with lowered wind speed develops downstream. This area is called wake, and other turbines affected by it will generate less energy.
The ways to address this problem can be split into pre- and post-construction measures. Pre-construction the wind farm layout can be optimized, and post-construction control strategies are needed to operate the wind farm optimally. These strategies fall under the term wind farm flow control and aim to manipulate the flow between the turbines to optimize the farm performance. A turbine’s wake can be altered by changing the turbine’s resistance to the flow or by misaligning the turbine with the wind direction. The former leads to a faster wake recovery, and the latter results in a redirection of the wake, also called wake-steering.
The current state-of-the-art of wind farm flow control is to utilize wake-steering in an open-loop control configuration. To this end, steady-state engineering models of the wake are used to optimize the farm set points offline. This is done for a selection of atmospheric conditions and the set points are stored in a lookup table (LuT). During operation, the flow conditions are used to look up the precomputed turbine set points. A problem with this approach arises as open-loop control assumes a perfect match between the model and the actual conditions in the field. There are reasons why this might not be the case: (i) There is an inevitable modeling error, which creates a mismatch between the model and the reality; (ii) conditions can arise that are offline not accounted for, e.g., time-varying atmospheric conditions or layout changes due to turbine downtime.
These problems can be addressed by closing the loop. In closed-loop control, measurements are used to continuously correct the model and to adapt it to the current state of the true wind farm. Optimal set points are then found based on the current model state. The control strategy can, therefore, react to new conditions. A challenge is that the optimization needs to happen online and requires a way to incorporate sensor data into the model. Previous work has designed closed-loop approaches using the same computationally cheap steady-state models that were previously used for open-loop control. This was achieved by adapting the parameters of the model based on the mismatch between the observed and predicted measurements, like power generated. A core assumption these models make is that the flow is in a never changing steady state. However, flow conditions do change, and the large spacing between turbines leads to minutes of delay between the control action the upstream turbine takes and the effect that the downstream turbine experiences. The question arises: What could be achieved using dynamic wake models instead of steady-state ones? These can incorporate wake dynamics, which could lead to better decision-making.
This thesis designs a closed-loop model-predictive wind farm flow control strategy based on a dynamic wake model to maximize the energy generated by a wind farm under time-varying conditions. The thesis is comprised of three building blocks: (i) The development of a dynamic wake model, (ii) the derivation of a sensor fusion strategy to identify the state of the flow field, (iii) the composition of a control strategy that uses the model to optimize the control set points. The building blocks are then connected to form the closed-loop control strategy.
The model building is based on the further development of an existing model, which utilizes a steady-state wake model and reintroduces flow dynamics. In the first step, the underlying wake model is substituted by a three-dimensional one, and the formulation is adapted to heterogeneous flow conditions. In the second step, the model is reformulated as a framework that links to an arbitrary wake model. This is done to profit from advancements in the steady-state model development and to significantly decrease the computational cost of the model. In the third step, the dynamic model is compared to a steady-state one in a set of high-fidelity wind farm simulations under time-varying conditions based on field measurements. The results show that the dynamic model does provide a better match with a simulated wind farm.
In the second part of the thesis, a state estimation methodology is introduced. To this end, an ensemble approach is adopted, where the multiple versions of the model are simulated in parallel. The correlation between the ensembles is then used to correct them based on the predicted and measured wind direction and power measurements of the turbines. A byproduct of the ensemble approach is that each estimated state also has an uncertainty based on how much the ensembles agree on its value.
The third part of this thesis investigates the control and optimization problem. This part focuses on the cost function formulation and the behavior it leads to. In a steady-state frame, the delays do not have to be taken into account, but in a dynamic formulation, they become a challenge. We, therefore, propose a cost-function formulation that synchronizes the control actions with their effect at the downstream turbines. This leads to a series of smaller optimization problems instead of one larger one.
The three building blocks of this thesis are then tested in a case study: The closed-loop controller is employed to maximize the energy of a ten-turbine wind farm under time-varying conditions. Both the farm layout and wind direction time series are based on field conditions. The controller generates an overall energy gain of up to 4% over the baseline using noise-free wind direction measurements.
This is on par with the steady-state approach. However, the closed-loop approach is found to be more robust to disturbed wind direction measurements - Where the performance of the steady-state approach decreases to 1.7% due to the sensor noise; the closed-loop approach still achieves a 2.5% gain.
The conclusion of the work presented in this thesis is thereby: Closed-loop wind farm flow control based on a dynamic engineering surrogate model leads to a more accurate and robust state estimation of the wind farm flow field but, given no preview, does not necessarily lead to a higher energy generation than what can be achieved with steady-state models.
Master thesis
(2023)
-
Ibrahim Ghazali, D.A.M. De Tavernier, D.J.N. Allaerts, D.A. von Terzi, S.P. Mulders
Operating in real-world conditions, modern large capacity wind turbines often experience off-design situations, enduring dynamic loads characterized by complex unsteady aerodynamics. Key among the challenges in predicting these dynamic loads is understanding the effects of wind shear and turbulence, both individually and in their complex interplay. This research aims to shed light on these phenomena, with an emphasis on their impacts on wind turbine fatigue loads and power production.
The research first provides an in-depth analysis of the influence of atmospheric stability on wind shear profile, aiming to extend the wind shear profile beyond the range of LiDAR measurements. Recognizing the limitations of existing power law and logarithmic law extrapolation methods, the study validates the use of multiple stability correction functions for accurate wind speed extrapolation. Subsequently, the research delves into the intricate effects of wind shear and turbulence on fatigue loads at the blade root of wind turbines, leveraging aeroelastic simulations. This research addresses the challenge of assessing wind turbine suitability for sites where one or several wind climate parameters surpass their design class values. It investigates the potential of the Response Surface Methodology (RSM) to estimate site-specific fatigue loads, a process that conventionally requires extensive aeroelastic simulations. This research also extends the scope to include the assessment of site-specific wind turbine power curves, validating the use of the Rotor Equivalent Wind Speed (REWS) and turbulence renormalization methods. Both methods show promise in estimating site-specific wind turbine power curves using a power curve measured under varying wind conditions.
In essence, this study emphasizes the significant impact of wind shear and turbulence on the performance and longevity of wind turbines. By shedding the light on potential improvements, this study hopes to contribute towards accurate power output and fatigue load assessments.
...
The research first provides an in-depth analysis of the influence of atmospheric stability on wind shear profile, aiming to extend the wind shear profile beyond the range of LiDAR measurements. Recognizing the limitations of existing power law and logarithmic law extrapolation methods, the study validates the use of multiple stability correction functions for accurate wind speed extrapolation. Subsequently, the research delves into the intricate effects of wind shear and turbulence on fatigue loads at the blade root of wind turbines, leveraging aeroelastic simulations. This research addresses the challenge of assessing wind turbine suitability for sites where one or several wind climate parameters surpass their design class values. It investigates the potential of the Response Surface Methodology (RSM) to estimate site-specific fatigue loads, a process that conventionally requires extensive aeroelastic simulations. This research also extends the scope to include the assessment of site-specific wind turbine power curves, validating the use of the Rotor Equivalent Wind Speed (REWS) and turbulence renormalization methods. Both methods show promise in estimating site-specific wind turbine power curves using a power curve measured under varying wind conditions.
In essence, this study emphasizes the significant impact of wind shear and turbulence on the performance and longevity of wind turbines. By shedding the light on potential improvements, this study hopes to contribute towards accurate power output and fatigue load assessments.
...
Operating in real-world conditions, modern large capacity wind turbines often experience off-design situations, enduring dynamic loads characterized by complex unsteady aerodynamics. Key among the challenges in predicting these dynamic loads is understanding the effects of wind shear and turbulence, both individually and in their complex interplay. This research aims to shed light on these phenomena, with an emphasis on their impacts on wind turbine fatigue loads and power production.
The research first provides an in-depth analysis of the influence of atmospheric stability on wind shear profile, aiming to extend the wind shear profile beyond the range of LiDAR measurements. Recognizing the limitations of existing power law and logarithmic law extrapolation methods, the study validates the use of multiple stability correction functions for accurate wind speed extrapolation. Subsequently, the research delves into the intricate effects of wind shear and turbulence on fatigue loads at the blade root of wind turbines, leveraging aeroelastic simulations. This research addresses the challenge of assessing wind turbine suitability for sites where one or several wind climate parameters surpass their design class values. It investigates the potential of the Response Surface Methodology (RSM) to estimate site-specific fatigue loads, a process that conventionally requires extensive aeroelastic simulations. This research also extends the scope to include the assessment of site-specific wind turbine power curves, validating the use of the Rotor Equivalent Wind Speed (REWS) and turbulence renormalization methods. Both methods show promise in estimating site-specific wind turbine power curves using a power curve measured under varying wind conditions.
In essence, this study emphasizes the significant impact of wind shear and turbulence on the performance and longevity of wind turbines. By shedding the light on potential improvements, this study hopes to contribute towards accurate power output and fatigue load assessments.
The research first provides an in-depth analysis of the influence of atmospheric stability on wind shear profile, aiming to extend the wind shear profile beyond the range of LiDAR measurements. Recognizing the limitations of existing power law and logarithmic law extrapolation methods, the study validates the use of multiple stability correction functions for accurate wind speed extrapolation. Subsequently, the research delves into the intricate effects of wind shear and turbulence on fatigue loads at the blade root of wind turbines, leveraging aeroelastic simulations. This research addresses the challenge of assessing wind turbine suitability for sites where one or several wind climate parameters surpass their design class values. It investigates the potential of the Response Surface Methodology (RSM) to estimate site-specific fatigue loads, a process that conventionally requires extensive aeroelastic simulations. This research also extends the scope to include the assessment of site-specific wind turbine power curves, validating the use of the Rotor Equivalent Wind Speed (REWS) and turbulence renormalization methods. Both methods show promise in estimating site-specific wind turbine power curves using a power curve measured under varying wind conditions.
In essence, this study emphasizes the significant impact of wind shear and turbulence on the performance and longevity of wind turbines. By shedding the light on potential improvements, this study hopes to contribute towards accurate power output and fatigue load assessments.
Master thesis
(2023)
-
Y.X.F. Birnie-Scott, D.J.N. Allaerts, Paul van der Laan, Mads Christian Baungaard, Mikkel Kiilerich Østerlund
The recurred idea of developing multi-rotor wind turbines has led to the need of more accurate surrogate wake models which allow for a fast annual energy production (AEP) calculation and further understanding of the aerodynamic power losses of multi-rotor wind turbines.
The present thesis develops a surrogate wake model of a multi-rotor-two turbine validated against computational fluid dynamics (CFD) simulations of type RANS-AD. The outcome is a superposition model of an analytical representation of the wake which base function coefficients are stored in look-up tables as a function of the wind inflow conditions affecting the turbine. The derived surrogate model is able to predict the overall wind farm efficiency with more than 90% accuracy while compared to RANS-AD models.
Towards the end of the thesis, a comparison between a single-rotor wind farm of 18 V29 turbines and a multi-rotor wind farm composed by nine 2R-V29 turbines (hypothetical turbine) is evaluated through RANS-AD simulations within the same wind-farm area. The energy ouput showed to be highly dependent on the wind-farm geometry, and the wind direction average suggest that 5% more energy yield is obtained from the multi-rotor-farm for velocities below rated speed. ...
The present thesis develops a surrogate wake model of a multi-rotor-two turbine validated against computational fluid dynamics (CFD) simulations of type RANS-AD. The outcome is a superposition model of an analytical representation of the wake which base function coefficients are stored in look-up tables as a function of the wind inflow conditions affecting the turbine. The derived surrogate model is able to predict the overall wind farm efficiency with more than 90% accuracy while compared to RANS-AD models.
Towards the end of the thesis, a comparison between a single-rotor wind farm of 18 V29 turbines and a multi-rotor wind farm composed by nine 2R-V29 turbines (hypothetical turbine) is evaluated through RANS-AD simulations within the same wind-farm area. The energy ouput showed to be highly dependent on the wind-farm geometry, and the wind direction average suggest that 5% more energy yield is obtained from the multi-rotor-farm for velocities below rated speed. ...
The recurred idea of developing multi-rotor wind turbines has led to the need of more accurate surrogate wake models which allow for a fast annual energy production (AEP) calculation and further understanding of the aerodynamic power losses of multi-rotor wind turbines.
The present thesis develops a surrogate wake model of a multi-rotor-two turbine validated against computational fluid dynamics (CFD) simulations of type RANS-AD. The outcome is a superposition model of an analytical representation of the wake which base function coefficients are stored in look-up tables as a function of the wind inflow conditions affecting the turbine. The derived surrogate model is able to predict the overall wind farm efficiency with more than 90% accuracy while compared to RANS-AD models.
Towards the end of the thesis, a comparison between a single-rotor wind farm of 18 V29 turbines and a multi-rotor wind farm composed by nine 2R-V29 turbines (hypothetical turbine) is evaluated through RANS-AD simulations within the same wind-farm area. The energy ouput showed to be highly dependent on the wind-farm geometry, and the wind direction average suggest that 5% more energy yield is obtained from the multi-rotor-farm for velocities below rated speed.
The present thesis develops a surrogate wake model of a multi-rotor-two turbine validated against computational fluid dynamics (CFD) simulations of type RANS-AD. The outcome is a superposition model of an analytical representation of the wake which base function coefficients are stored in look-up tables as a function of the wind inflow conditions affecting the turbine. The derived surrogate model is able to predict the overall wind farm efficiency with more than 90% accuracy while compared to RANS-AD models.
Towards the end of the thesis, a comparison between a single-rotor wind farm of 18 V29 turbines and a multi-rotor wind farm composed by nine 2R-V29 turbines (hypothetical turbine) is evaluated through RANS-AD simulations within the same wind-farm area. The energy ouput showed to be highly dependent on the wind-farm geometry, and the wind direction average suggest that 5% more energy yield is obtained from the multi-rotor-farm for velocities below rated speed.
When designing an airborne wind energy system, it is necessary to be able to estimate the traction force that the kite produces as a function of its flight trajectory. Being a flexible structure, the geometry of a soft kite depends on its aerodynamic loading and vice versa, which forms a complex fluid-structure interaction (FSI) problem.
Currently, kite design is usually done on an experimental basis since no model meets the requirements of being both accurate and fast.
In this project, an FSI methodology is developed to study the steady-state aerodynamic performance of leading-edge inflatable (LEI) kites by coupling two fast and simple models.
On the structural part, the deformations are calculated with a particle system model, based on the assumption that the shape of the kite can be modelled using a wireframe wing model represented by the bridle line attachment points, whose coordinate changes are modelled using a bridle
line system model and canopy billowing relations.
On the aerodynamic side, the load distribution is calculated with a 3D nonlinear vortex step method, coupled with 2D polars obtained with a correlation model derived from Reynolds averaged Navier-Stokes (RANS) analysis, to account for viscous effects and flow separation. Furthermore, with the 2D correlation model it is possible to consider changes in the thickness and the camber of each section. Based on 2D thin airfoil theory, the three-quarter chord point is used to determine the magnitude of the forces, and the one-quarter chord point is used to determine the direction of these forces.
Moreover, the model developed for LEI kites can consider canopy billowing and variations in kite and airfoil geometry while proving robust and inexpensive.
This model has been validated with several geometries and a RANS analysis of the LEI kite, showing great accuracy for pre-stall angles of attack.
The coupling of these two models results in a fast aeroelastic model of LEI kites capable of predicting the steady-state deformations and aerodynamic forces on the kite for the range of actuation settings and inflow conditions expected during a normal pumping cycle. Furthermore, the results show that the deformations follow the same trends as the results from the photogrammetry analysis and that, by taking into account the deformations that the kite undergoes, the aerodynamic forces more closely resemble experimental data. ...
Currently, kite design is usually done on an experimental basis since no model meets the requirements of being both accurate and fast.
In this project, an FSI methodology is developed to study the steady-state aerodynamic performance of leading-edge inflatable (LEI) kites by coupling two fast and simple models.
On the structural part, the deformations are calculated with a particle system model, based on the assumption that the shape of the kite can be modelled using a wireframe wing model represented by the bridle line attachment points, whose coordinate changes are modelled using a bridle
line system model and canopy billowing relations.
On the aerodynamic side, the load distribution is calculated with a 3D nonlinear vortex step method, coupled with 2D polars obtained with a correlation model derived from Reynolds averaged Navier-Stokes (RANS) analysis, to account for viscous effects and flow separation. Furthermore, with the 2D correlation model it is possible to consider changes in the thickness and the camber of each section. Based on 2D thin airfoil theory, the three-quarter chord point is used to determine the magnitude of the forces, and the one-quarter chord point is used to determine the direction of these forces.
Moreover, the model developed for LEI kites can consider canopy billowing and variations in kite and airfoil geometry while proving robust and inexpensive.
This model has been validated with several geometries and a RANS analysis of the LEI kite, showing great accuracy for pre-stall angles of attack.
The coupling of these two models results in a fast aeroelastic model of LEI kites capable of predicting the steady-state deformations and aerodynamic forces on the kite for the range of actuation settings and inflow conditions expected during a normal pumping cycle. Furthermore, the results show that the deformations follow the same trends as the results from the photogrammetry analysis and that, by taking into account the deformations that the kite undergoes, the aerodynamic forces more closely resemble experimental data. ...
When designing an airborne wind energy system, it is necessary to be able to estimate the traction force that the kite produces as a function of its flight trajectory. Being a flexible structure, the geometry of a soft kite depends on its aerodynamic loading and vice versa, which forms a complex fluid-structure interaction (FSI) problem.
Currently, kite design is usually done on an experimental basis since no model meets the requirements of being both accurate and fast.
In this project, an FSI methodology is developed to study the steady-state aerodynamic performance of leading-edge inflatable (LEI) kites by coupling two fast and simple models.
On the structural part, the deformations are calculated with a particle system model, based on the assumption that the shape of the kite can be modelled using a wireframe wing model represented by the bridle line attachment points, whose coordinate changes are modelled using a bridle
line system model and canopy billowing relations.
On the aerodynamic side, the load distribution is calculated with a 3D nonlinear vortex step method, coupled with 2D polars obtained with a correlation model derived from Reynolds averaged Navier-Stokes (RANS) analysis, to account for viscous effects and flow separation. Furthermore, with the 2D correlation model it is possible to consider changes in the thickness and the camber of each section. Based on 2D thin airfoil theory, the three-quarter chord point is used to determine the magnitude of the forces, and the one-quarter chord point is used to determine the direction of these forces.
Moreover, the model developed for LEI kites can consider canopy billowing and variations in kite and airfoil geometry while proving robust and inexpensive.
This model has been validated with several geometries and a RANS analysis of the LEI kite, showing great accuracy for pre-stall angles of attack.
The coupling of these two models results in a fast aeroelastic model of LEI kites capable of predicting the steady-state deformations and aerodynamic forces on the kite for the range of actuation settings and inflow conditions expected during a normal pumping cycle. Furthermore, the results show that the deformations follow the same trends as the results from the photogrammetry analysis and that, by taking into account the deformations that the kite undergoes, the aerodynamic forces more closely resemble experimental data.
Currently, kite design is usually done on an experimental basis since no model meets the requirements of being both accurate and fast.
In this project, an FSI methodology is developed to study the steady-state aerodynamic performance of leading-edge inflatable (LEI) kites by coupling two fast and simple models.
On the structural part, the deformations are calculated with a particle system model, based on the assumption that the shape of the kite can be modelled using a wireframe wing model represented by the bridle line attachment points, whose coordinate changes are modelled using a bridle
line system model and canopy billowing relations.
On the aerodynamic side, the load distribution is calculated with a 3D nonlinear vortex step method, coupled with 2D polars obtained with a correlation model derived from Reynolds averaged Navier-Stokes (RANS) analysis, to account for viscous effects and flow separation. Furthermore, with the 2D correlation model it is possible to consider changes in the thickness and the camber of each section. Based on 2D thin airfoil theory, the three-quarter chord point is used to determine the magnitude of the forces, and the one-quarter chord point is used to determine the direction of these forces.
Moreover, the model developed for LEI kites can consider canopy billowing and variations in kite and airfoil geometry while proving robust and inexpensive.
This model has been validated with several geometries and a RANS analysis of the LEI kite, showing great accuracy for pre-stall angles of attack.
The coupling of these two models results in a fast aeroelastic model of LEI kites capable of predicting the steady-state deformations and aerodynamic forces on the kite for the range of actuation settings and inflow conditions expected during a normal pumping cycle. Furthermore, the results show that the deformations follow the same trends as the results from the photogrammetry analysis and that, by taking into account the deformations that the kite undergoes, the aerodynamic forces more closely resemble experimental data.
Master thesis
(2022)
-
J.J.J. Kokkedee, W.A.A.M. Bierbooms, D.J.N. Allaerts, R. Schmehl, A.H. van Zuijlen
With an ever increasing demand for sustainable energy, limitations of current sustainable technologies are studied widely. In wind farms, the so-called wake effect provides the biggest limitation on wind farm total power output. Using wind from the unaffected boundary layer to re-energize the wind flow in the wake provides a method of limiting this wake effect. In this study, kites are introduced to steer the wind flow of the unaffected boundary layer into the wake through a downwash velocity. RANS (Reynolds-averaged Navier-Stokes) simulations are performed in Computational Fluid Dynamics (CFD) software OpenFOAM of the atmospheric boundary layer (1), a small four-turbine wind farm (2) and a wind farm with static kites between the turbines (3). The turbines are modelled through the actuator disc approach, and kites are introduced through the more complex actuator line method. Results of the athmospheric boundary layer (ABL) and wind farm simulations correspond well with literature. Through extensive kite parameter studies, an optimal layout of kites in the wind farm is presented yielding a wind farm efficiency increase of 2.3 %, which increases over 5% for even larger kites. Kite size and the kite’s downstream location show to impact the re-energising levels of the wake flow the most. The kites generate a downwash wake instead of a single downwash velocity, a finding that should further be studied in future research.
...
With an ever increasing demand for sustainable energy, limitations of current sustainable technologies are studied widely. In wind farms, the so-called wake effect provides the biggest limitation on wind farm total power output. Using wind from the unaffected boundary layer to re-energize the wind flow in the wake provides a method of limiting this wake effect. In this study, kites are introduced to steer the wind flow of the unaffected boundary layer into the wake through a downwash velocity. RANS (Reynolds-averaged Navier-Stokes) simulations are performed in Computational Fluid Dynamics (CFD) software OpenFOAM of the atmospheric boundary layer (1), a small four-turbine wind farm (2) and a wind farm with static kites between the turbines (3). The turbines are modelled through the actuator disc approach, and kites are introduced through the more complex actuator line method. Results of the athmospheric boundary layer (ABL) and wind farm simulations correspond well with literature. Through extensive kite parameter studies, an optimal layout of kites in the wind farm is presented yielding a wind farm efficiency increase of 2.3 %, which increases over 5% for even larger kites. Kite size and the kite’s downstream location show to impact the re-energising levels of the wake flow the most. The kites generate a downwash wake instead of a single downwash velocity, a finding that should further be studied in future research.
Wind farm interactions with the surrounding airflow leads to a reduction in velocity greater than the linear sum of single turbine inductions and is known as global or upstream blockage. The mechanisms and magnitude of global blockage effect are not yet fully understood. Models to simulate upstream blockage to improve efficiency estimates and better understand global blockage have thus far not been refined. The aim of this research is to investigate the sensitivity of upstream blockage to the numerical configuration of CFD simulations to improve model accuracy and understanding of global blockage.
RANS simulations are executed under steady state conditions with a k − ϵ turbulence model. A neutral stability, pressure-driven atmospheric boundary layer is modelled with fully developed uni-directional flow. Wind farms are modelled as actuator discs with 5 rows of turbines on flat terrain. Streamwise and spanwise spacing is set to 7 turbine diameters (D) and 5D respectively. Global blockage is measured against the induction of a single turbine at 2.5D upstream. Variables investigated include the domain height, lateral extent, inlet and outlet distances from the wind farm.
Domain heights ranging from 5D to 25D are investigated for change in magnitude and scale of upstream blockage for a laterally infinite wind farm. A clear trend of increasing blockage with domain height is observed. At domain heights of less than 15D, upstream velocity is increased by a maximum of 0.18% (5D). Larger domain heights produce a maximum velocity reduction of 0.23% (25D). The shape of upstream blockage is independent of domain height.
A finite wind farm of 5 columns and lateral extents ranging from 2.5D to 20D on each side are utilized to investigate the impact on blockage. Wider domains of 5D to 20D display increasing blockage with width, while a domain of 2.5D exhibits behaviour similar to a laterally infinite wind farm. Blockage ranging from 0.13% (10D) to 0.31% (20D) reduction in velocity is shown to be highest at the center column of turbines and decreases toward the outer columns.
Inlet and outlet distances ranging from 15D to 100D are modelled. Upstream blockage for inlet distances of 50D to 100D produce consistent upstream blockage magnitude and extent of 0.22% and 30D respectively. Shorter inlet distances result in decreased upstream blockage with a minimum of 0.12% (15D). The shape of blockage remains consistent through all inlet ranges. Outlet distance have no identifiable impact on upstream blockage magnitude and extent.
Changes to the numerical configuration show a clear correlation of increased blockage with cross sectional area of the domain. Constraining the domain in the vertical and lateral directions constricts flow resulting in reduced blockage. Blockage becomes independent of inlet distances at values of 50D and higher. Outlet distance has no identifiable impact on upstream blockage. Choosing a numerical configuration with adequately sized domain boundaries is pertinent in producing realistic upstream blockage. ...
RANS simulations are executed under steady state conditions with a k − ϵ turbulence model. A neutral stability, pressure-driven atmospheric boundary layer is modelled with fully developed uni-directional flow. Wind farms are modelled as actuator discs with 5 rows of turbines on flat terrain. Streamwise and spanwise spacing is set to 7 turbine diameters (D) and 5D respectively. Global blockage is measured against the induction of a single turbine at 2.5D upstream. Variables investigated include the domain height, lateral extent, inlet and outlet distances from the wind farm.
Domain heights ranging from 5D to 25D are investigated for change in magnitude and scale of upstream blockage for a laterally infinite wind farm. A clear trend of increasing blockage with domain height is observed. At domain heights of less than 15D, upstream velocity is increased by a maximum of 0.18% (5D). Larger domain heights produce a maximum velocity reduction of 0.23% (25D). The shape of upstream blockage is independent of domain height.
A finite wind farm of 5 columns and lateral extents ranging from 2.5D to 20D on each side are utilized to investigate the impact on blockage. Wider domains of 5D to 20D display increasing blockage with width, while a domain of 2.5D exhibits behaviour similar to a laterally infinite wind farm. Blockage ranging from 0.13% (10D) to 0.31% (20D) reduction in velocity is shown to be highest at the center column of turbines and decreases toward the outer columns.
Inlet and outlet distances ranging from 15D to 100D are modelled. Upstream blockage for inlet distances of 50D to 100D produce consistent upstream blockage magnitude and extent of 0.22% and 30D respectively. Shorter inlet distances result in decreased upstream blockage with a minimum of 0.12% (15D). The shape of blockage remains consistent through all inlet ranges. Outlet distance have no identifiable impact on upstream blockage magnitude and extent.
Changes to the numerical configuration show a clear correlation of increased blockage with cross sectional area of the domain. Constraining the domain in the vertical and lateral directions constricts flow resulting in reduced blockage. Blockage becomes independent of inlet distances at values of 50D and higher. Outlet distance has no identifiable impact on upstream blockage. Choosing a numerical configuration with adequately sized domain boundaries is pertinent in producing realistic upstream blockage. ...
Wind farm interactions with the surrounding airflow leads to a reduction in velocity greater than the linear sum of single turbine inductions and is known as global or upstream blockage. The mechanisms and magnitude of global blockage effect are not yet fully understood. Models to simulate upstream blockage to improve efficiency estimates and better understand global blockage have thus far not been refined. The aim of this research is to investigate the sensitivity of upstream blockage to the numerical configuration of CFD simulations to improve model accuracy and understanding of global blockage.
RANS simulations are executed under steady state conditions with a k − ϵ turbulence model. A neutral stability, pressure-driven atmospheric boundary layer is modelled with fully developed uni-directional flow. Wind farms are modelled as actuator discs with 5 rows of turbines on flat terrain. Streamwise and spanwise spacing is set to 7 turbine diameters (D) and 5D respectively. Global blockage is measured against the induction of a single turbine at 2.5D upstream. Variables investigated include the domain height, lateral extent, inlet and outlet distances from the wind farm.
Domain heights ranging from 5D to 25D are investigated for change in magnitude and scale of upstream blockage for a laterally infinite wind farm. A clear trend of increasing blockage with domain height is observed. At domain heights of less than 15D, upstream velocity is increased by a maximum of 0.18% (5D). Larger domain heights produce a maximum velocity reduction of 0.23% (25D). The shape of upstream blockage is independent of domain height.
A finite wind farm of 5 columns and lateral extents ranging from 2.5D to 20D on each side are utilized to investigate the impact on blockage. Wider domains of 5D to 20D display increasing blockage with width, while a domain of 2.5D exhibits behaviour similar to a laterally infinite wind farm. Blockage ranging from 0.13% (10D) to 0.31% (20D) reduction in velocity is shown to be highest at the center column of turbines and decreases toward the outer columns.
Inlet and outlet distances ranging from 15D to 100D are modelled. Upstream blockage for inlet distances of 50D to 100D produce consistent upstream blockage magnitude and extent of 0.22% and 30D respectively. Shorter inlet distances result in decreased upstream blockage with a minimum of 0.12% (15D). The shape of blockage remains consistent through all inlet ranges. Outlet distance have no identifiable impact on upstream blockage magnitude and extent.
Changes to the numerical configuration show a clear correlation of increased blockage with cross sectional area of the domain. Constraining the domain in the vertical and lateral directions constricts flow resulting in reduced blockage. Blockage becomes independent of inlet distances at values of 50D and higher. Outlet distance has no identifiable impact on upstream blockage. Choosing a numerical configuration with adequately sized domain boundaries is pertinent in producing realistic upstream blockage.
RANS simulations are executed under steady state conditions with a k − ϵ turbulence model. A neutral stability, pressure-driven atmospheric boundary layer is modelled with fully developed uni-directional flow. Wind farms are modelled as actuator discs with 5 rows of turbines on flat terrain. Streamwise and spanwise spacing is set to 7 turbine diameters (D) and 5D respectively. Global blockage is measured against the induction of a single turbine at 2.5D upstream. Variables investigated include the domain height, lateral extent, inlet and outlet distances from the wind farm.
Domain heights ranging from 5D to 25D are investigated for change in magnitude and scale of upstream blockage for a laterally infinite wind farm. A clear trend of increasing blockage with domain height is observed. At domain heights of less than 15D, upstream velocity is increased by a maximum of 0.18% (5D). Larger domain heights produce a maximum velocity reduction of 0.23% (25D). The shape of upstream blockage is independent of domain height.
A finite wind farm of 5 columns and lateral extents ranging from 2.5D to 20D on each side are utilized to investigate the impact on blockage. Wider domains of 5D to 20D display increasing blockage with width, while a domain of 2.5D exhibits behaviour similar to a laterally infinite wind farm. Blockage ranging from 0.13% (10D) to 0.31% (20D) reduction in velocity is shown to be highest at the center column of turbines and decreases toward the outer columns.
Inlet and outlet distances ranging from 15D to 100D are modelled. Upstream blockage for inlet distances of 50D to 100D produce consistent upstream blockage magnitude and extent of 0.22% and 30D respectively. Shorter inlet distances result in decreased upstream blockage with a minimum of 0.12% (15D). The shape of blockage remains consistent through all inlet ranges. Outlet distance have no identifiable impact on upstream blockage magnitude and extent.
Changes to the numerical configuration show a clear correlation of increased blockage with cross sectional area of the domain. Constraining the domain in the vertical and lateral directions constricts flow resulting in reduced blockage. Blockage becomes independent of inlet distances at values of 50D and higher. Outlet distance has no identifiable impact on upstream blockage. Choosing a numerical configuration with adequately sized domain boundaries is pertinent in producing realistic upstream blockage.
Measurement campaigns and CFD simulations have recently identified a large-scale flow phenomenon called wind-farm flow blockage. This is found to bear a significant and far-reaching reduction in wind speed upstream of a wind farm. The wind farm blockage is attributed to the cumulative induction effects of multiple wind turbines placed in series. Wind-farm flow blockage has important consequences on energy production because it reduces the available kinetic energy in the incoming wind flow. In turn, this causes leading wind turbines in a wind farm to produce less energy than they each would in isolation. To date, the physics of this global blockage effect is not entirely understood, and they are therefore an active research topic. Due to the increasing demand for wind energy, reducing annual energy production (AEP) uncertainties and power production bias seems to be a challenge for wind energy researchers. Understanding wind farm blockage in complex terrain becomes crucial to account for uncertainties and power production bias.
This thesis set out to perform Reynolds-Averaged Navier-Stokes (RANS) simulations to assess the impact of wind-farm flow blockage in complex terrain using the open-source software OpenFOAM. A laterally infinite row of turbines is simulated on top of a 2-D hill defined by the mathematical curve ’Witch of Agnesi’. The set of simulations is performed for varying atmospheric conditions: truly neutral and stable free atmospheric conditions. Thermal stratification imposed under stable conditions is of particular interest due to the excitation of atmospheric gravity waves (AGWs) by the turbine array and the topology. The velocity fields due to the presence of the turbine array on top of the hill are compared to the ones without. The resulting flow reduction is then compared to the cases without the hill in order to assess the impact of complex terrain on wind farm blockage. A series of sensitivity analyses are performed for varying inter-array spacing and hill size variations in order to further the understanding of wind farm blockage.
The results obtained in this study show that the magnitude of wind farm blockage is amplified due to the presence of the hill. Additionally, the excitation of AGWs is seen to have a major impact on the wind farm blockage due to alterations caused to the pressure field. The impact of blockage is seen to be dominant up to at least 10-15 turbine diameters upstream of the turbine array under truly neutral conditions. While the effects are more pronounced and much more dominant under stable free atmosphere conditions. All the stable free atmosphere cases simulated show a reduction ranging from 1-4% at different upstream locations while neutral cases show slightly lower yet non-negligible reduction due to blockage.
This study ultimately concludes that the existing ’wakes-only’ approach for estimating energy losses still has a significant power production bias. Therefore accounting for the blockage effects in the farm upstream is also equally important and must be analysed before commissioning a wind farm. ...
This thesis set out to perform Reynolds-Averaged Navier-Stokes (RANS) simulations to assess the impact of wind-farm flow blockage in complex terrain using the open-source software OpenFOAM. A laterally infinite row of turbines is simulated on top of a 2-D hill defined by the mathematical curve ’Witch of Agnesi’. The set of simulations is performed for varying atmospheric conditions: truly neutral and stable free atmospheric conditions. Thermal stratification imposed under stable conditions is of particular interest due to the excitation of atmospheric gravity waves (AGWs) by the turbine array and the topology. The velocity fields due to the presence of the turbine array on top of the hill are compared to the ones without. The resulting flow reduction is then compared to the cases without the hill in order to assess the impact of complex terrain on wind farm blockage. A series of sensitivity analyses are performed for varying inter-array spacing and hill size variations in order to further the understanding of wind farm blockage.
The results obtained in this study show that the magnitude of wind farm blockage is amplified due to the presence of the hill. Additionally, the excitation of AGWs is seen to have a major impact on the wind farm blockage due to alterations caused to the pressure field. The impact of blockage is seen to be dominant up to at least 10-15 turbine diameters upstream of the turbine array under truly neutral conditions. While the effects are more pronounced and much more dominant under stable free atmosphere conditions. All the stable free atmosphere cases simulated show a reduction ranging from 1-4% at different upstream locations while neutral cases show slightly lower yet non-negligible reduction due to blockage.
This study ultimately concludes that the existing ’wakes-only’ approach for estimating energy losses still has a significant power production bias. Therefore accounting for the blockage effects in the farm upstream is also equally important and must be analysed before commissioning a wind farm. ...
Measurement campaigns and CFD simulations have recently identified a large-scale flow phenomenon called wind-farm flow blockage. This is found to bear a significant and far-reaching reduction in wind speed upstream of a wind farm. The wind farm blockage is attributed to the cumulative induction effects of multiple wind turbines placed in series. Wind-farm flow blockage has important consequences on energy production because it reduces the available kinetic energy in the incoming wind flow. In turn, this causes leading wind turbines in a wind farm to produce less energy than they each would in isolation. To date, the physics of this global blockage effect is not entirely understood, and they are therefore an active research topic. Due to the increasing demand for wind energy, reducing annual energy production (AEP) uncertainties and power production bias seems to be a challenge for wind energy researchers. Understanding wind farm blockage in complex terrain becomes crucial to account for uncertainties and power production bias.
This thesis set out to perform Reynolds-Averaged Navier-Stokes (RANS) simulations to assess the impact of wind-farm flow blockage in complex terrain using the open-source software OpenFOAM. A laterally infinite row of turbines is simulated on top of a 2-D hill defined by the mathematical curve ’Witch of Agnesi’. The set of simulations is performed for varying atmospheric conditions: truly neutral and stable free atmospheric conditions. Thermal stratification imposed under stable conditions is of particular interest due to the excitation of atmospheric gravity waves (AGWs) by the turbine array and the topology. The velocity fields due to the presence of the turbine array on top of the hill are compared to the ones without. The resulting flow reduction is then compared to the cases without the hill in order to assess the impact of complex terrain on wind farm blockage. A series of sensitivity analyses are performed for varying inter-array spacing and hill size variations in order to further the understanding of wind farm blockage.
The results obtained in this study show that the magnitude of wind farm blockage is amplified due to the presence of the hill. Additionally, the excitation of AGWs is seen to have a major impact on the wind farm blockage due to alterations caused to the pressure field. The impact of blockage is seen to be dominant up to at least 10-15 turbine diameters upstream of the turbine array under truly neutral conditions. While the effects are more pronounced and much more dominant under stable free atmosphere conditions. All the stable free atmosphere cases simulated show a reduction ranging from 1-4% at different upstream locations while neutral cases show slightly lower yet non-negligible reduction due to blockage.
This study ultimately concludes that the existing ’wakes-only’ approach for estimating energy losses still has a significant power production bias. Therefore accounting for the blockage effects in the farm upstream is also equally important and must be analysed before commissioning a wind farm.
This thesis set out to perform Reynolds-Averaged Navier-Stokes (RANS) simulations to assess the impact of wind-farm flow blockage in complex terrain using the open-source software OpenFOAM. A laterally infinite row of turbines is simulated on top of a 2-D hill defined by the mathematical curve ’Witch of Agnesi’. The set of simulations is performed for varying atmospheric conditions: truly neutral and stable free atmospheric conditions. Thermal stratification imposed under stable conditions is of particular interest due to the excitation of atmospheric gravity waves (AGWs) by the turbine array and the topology. The velocity fields due to the presence of the turbine array on top of the hill are compared to the ones without. The resulting flow reduction is then compared to the cases without the hill in order to assess the impact of complex terrain on wind farm blockage. A series of sensitivity analyses are performed for varying inter-array spacing and hill size variations in order to further the understanding of wind farm blockage.
The results obtained in this study show that the magnitude of wind farm blockage is amplified due to the presence of the hill. Additionally, the excitation of AGWs is seen to have a major impact on the wind farm blockage due to alterations caused to the pressure field. The impact of blockage is seen to be dominant up to at least 10-15 turbine diameters upstream of the turbine array under truly neutral conditions. While the effects are more pronounced and much more dominant under stable free atmosphere conditions. All the stable free atmosphere cases simulated show a reduction ranging from 1-4% at different upstream locations while neutral cases show slightly lower yet non-negligible reduction due to blockage.
This study ultimately concludes that the existing ’wakes-only’ approach for estimating energy losses still has a significant power production bias. Therefore accounting for the blockage effects in the farm upstream is also equally important and must be analysed before commissioning a wind farm.
The power production of downstream wind turbines in a wind farm is significantly impacted by wake effects of upstream turbines. Improving the layout optimization process could reduce these wake losses and therefore result in more efficient wind farms. The wakes are also affected by atmospheric stability conditions, as stable conditions lead to a reduced wake recovery, and unstable conditions lead to an improved wake recovery. The topic of atmospheric stability has quickly gained more attention in wind energy research over the recent years. However, there is still little research done that focuses specifically on the effects of atmospheric stability on the wind farm layout optimization process.
This thesis aims to determine the effects of atmospheric stability on the optimal layout of a wind farm and to quantify the potential benefits of considering stability conditions in the layout optimization process. A stability-dependent Jensen wake model is developed, using a stability-dependent wake decay coefficient based on the non-dimensional Obukhov length. The developed model is implemented in FLORIS, a wake modeling utility for Python, to calculate the wind field and the annual energy production (AEP). A simplistic layout optimization method is used, in which the positioning of wind turbines relative to each other is fixed and only the orientation of the entire wind farm is varied. To quantify the potential benefits of considering stability conditions, the layout optimization is done twice for each analyzed case: once using the determined stability conditions and once using the assumption of neutral stability conditions. The resulting benefit of considering stability effects is expressed as the potential AEP gain.
The results of the first cases looked promising, showing potential AEP gains of 7.4%, 5.6%, and 9.2%. However, these cases consist of unrealistic wind conditions and were mostly intended to study the effects of different stability conditions on the resulting optimal layout. For example, it is found that it is more beneficial to reduce wake overlap for stable conditions than for unstable conditions, which results in stable wind directions playing a dominant role in the optimization process. Cases with semi-realistic wind conditions showed significantly lower potential AEP gains of 0.1% and 0.7%. Finally, a real case based on meteorological data from an offshore site in the Netherlands resulted in a potential AEP gain of 0.0%.
It is concluded that the benefits of considering stability effects in the layout optimization process are likely to be insignificant. In most cases, the layout optimization under neutral stability conditions already optimizes for wind directions with stable conditions, as stable conditions tend to be more frequent in wind directions with high wind speeds. It is expected that there can be a small potential AEP gain in cases with unusual stability distributions that differ significantly from the described trend. However, even in such cases it is likely that the benefits are still very small.
...
This thesis aims to determine the effects of atmospheric stability on the optimal layout of a wind farm and to quantify the potential benefits of considering stability conditions in the layout optimization process. A stability-dependent Jensen wake model is developed, using a stability-dependent wake decay coefficient based on the non-dimensional Obukhov length. The developed model is implemented in FLORIS, a wake modeling utility for Python, to calculate the wind field and the annual energy production (AEP). A simplistic layout optimization method is used, in which the positioning of wind turbines relative to each other is fixed and only the orientation of the entire wind farm is varied. To quantify the potential benefits of considering stability conditions, the layout optimization is done twice for each analyzed case: once using the determined stability conditions and once using the assumption of neutral stability conditions. The resulting benefit of considering stability effects is expressed as the potential AEP gain.
The results of the first cases looked promising, showing potential AEP gains of 7.4%, 5.6%, and 9.2%. However, these cases consist of unrealistic wind conditions and were mostly intended to study the effects of different stability conditions on the resulting optimal layout. For example, it is found that it is more beneficial to reduce wake overlap for stable conditions than for unstable conditions, which results in stable wind directions playing a dominant role in the optimization process. Cases with semi-realistic wind conditions showed significantly lower potential AEP gains of 0.1% and 0.7%. Finally, a real case based on meteorological data from an offshore site in the Netherlands resulted in a potential AEP gain of 0.0%.
It is concluded that the benefits of considering stability effects in the layout optimization process are likely to be insignificant. In most cases, the layout optimization under neutral stability conditions already optimizes for wind directions with stable conditions, as stable conditions tend to be more frequent in wind directions with high wind speeds. It is expected that there can be a small potential AEP gain in cases with unusual stability distributions that differ significantly from the described trend. However, even in such cases it is likely that the benefits are still very small.
...
The power production of downstream wind turbines in a wind farm is significantly impacted by wake effects of upstream turbines. Improving the layout optimization process could reduce these wake losses and therefore result in more efficient wind farms. The wakes are also affected by atmospheric stability conditions, as stable conditions lead to a reduced wake recovery, and unstable conditions lead to an improved wake recovery. The topic of atmospheric stability has quickly gained more attention in wind energy research over the recent years. However, there is still little research done that focuses specifically on the effects of atmospheric stability on the wind farm layout optimization process.
This thesis aims to determine the effects of atmospheric stability on the optimal layout of a wind farm and to quantify the potential benefits of considering stability conditions in the layout optimization process. A stability-dependent Jensen wake model is developed, using a stability-dependent wake decay coefficient based on the non-dimensional Obukhov length. The developed model is implemented in FLORIS, a wake modeling utility for Python, to calculate the wind field and the annual energy production (AEP). A simplistic layout optimization method is used, in which the positioning of wind turbines relative to each other is fixed and only the orientation of the entire wind farm is varied. To quantify the potential benefits of considering stability conditions, the layout optimization is done twice for each analyzed case: once using the determined stability conditions and once using the assumption of neutral stability conditions. The resulting benefit of considering stability effects is expressed as the potential AEP gain.
The results of the first cases looked promising, showing potential AEP gains of 7.4%, 5.6%, and 9.2%. However, these cases consist of unrealistic wind conditions and were mostly intended to study the effects of different stability conditions on the resulting optimal layout. For example, it is found that it is more beneficial to reduce wake overlap for stable conditions than for unstable conditions, which results in stable wind directions playing a dominant role in the optimization process. Cases with semi-realistic wind conditions showed significantly lower potential AEP gains of 0.1% and 0.7%. Finally, a real case based on meteorological data from an offshore site in the Netherlands resulted in a potential AEP gain of 0.0%.
It is concluded that the benefits of considering stability effects in the layout optimization process are likely to be insignificant. In most cases, the layout optimization under neutral stability conditions already optimizes for wind directions with stable conditions, as stable conditions tend to be more frequent in wind directions with high wind speeds. It is expected that there can be a small potential AEP gain in cases with unusual stability distributions that differ significantly from the described trend. However, even in such cases it is likely that the benefits are still very small.
This thesis aims to determine the effects of atmospheric stability on the optimal layout of a wind farm and to quantify the potential benefits of considering stability conditions in the layout optimization process. A stability-dependent Jensen wake model is developed, using a stability-dependent wake decay coefficient based on the non-dimensional Obukhov length. The developed model is implemented in FLORIS, a wake modeling utility for Python, to calculate the wind field and the annual energy production (AEP). A simplistic layout optimization method is used, in which the positioning of wind turbines relative to each other is fixed and only the orientation of the entire wind farm is varied. To quantify the potential benefits of considering stability conditions, the layout optimization is done twice for each analyzed case: once using the determined stability conditions and once using the assumption of neutral stability conditions. The resulting benefit of considering stability effects is expressed as the potential AEP gain.
The results of the first cases looked promising, showing potential AEP gains of 7.4%, 5.6%, and 9.2%. However, these cases consist of unrealistic wind conditions and were mostly intended to study the effects of different stability conditions on the resulting optimal layout. For example, it is found that it is more beneficial to reduce wake overlap for stable conditions than for unstable conditions, which results in stable wind directions playing a dominant role in the optimization process. Cases with semi-realistic wind conditions showed significantly lower potential AEP gains of 0.1% and 0.7%. Finally, a real case based on meteorological data from an offshore site in the Netherlands resulted in a potential AEP gain of 0.0%.
It is concluded that the benefits of considering stability effects in the layout optimization process are likely to be insignificant. In most cases, the layout optimization under neutral stability conditions already optimizes for wind directions with stable conditions, as stable conditions tend to be more frequent in wind directions with high wind speeds. It is expected that there can be a small potential AEP gain in cases with unusual stability distributions that differ significantly from the described trend. However, even in such cases it is likely that the benefits are still very small.
Bachelor thesis
(2021)
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W. Brachmi, P. Deval, Guido Insinger, G. Kandiyoor, A. Korkmaz, Jakub Łabor, M. Prashar, A.K. Shokolarov, J.M. van Spronsen, E. Vretoudakis, M. Pini, D.J.N. Allaerts, M.T. Bieber
”To provide a novel remotely controlled aircraft for in-situ and remote sensing atmospheric
measurements at high altitudes designed for researching and monitoring climate change.”
“To design a costeffective unmanned subsonic aircraft for atmospheric measurements
at altitudes exceeding 25 km using sustainable fuels”. ...
measurements at high altitudes designed for researching and monitoring climate change.”
“To design a costeffective unmanned subsonic aircraft for atmospheric measurements
at altitudes exceeding 25 km using sustainable fuels”. ...
”To provide a novel remotely controlled aircraft for in-situ and remote sensing atmospheric
measurements at high altitudes designed for researching and monitoring climate change.”
“To design a costeffective unmanned subsonic aircraft for atmospheric measurements
at altitudes exceeding 25 km using sustainable fuels”.
measurements at high altitudes designed for researching and monitoring climate change.”
“To design a costeffective unmanned subsonic aircraft for atmospheric measurements
at altitudes exceeding 25 km using sustainable fuels”.
This thesis research is set up to increase the knowledge on the influence of atmospheric stability on the Global Blockage Effect (GBE) of offshore wind farm flows, thereby aiming to improve the accuracy of energy yield calculations and predictions of offshore wind farms.
...
This thesis research is set up to increase the knowledge on the influence of atmospheric stability on the Global Blockage Effect (GBE) of offshore wind farm flows, thereby aiming to improve the accuracy of energy yield calculations and predictions of offshore wind farms.