JM
Johan Meyers
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Mesoscale modelling of North Sea wind resources with COSMO-CLM
Model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses
Journal article
(2024)
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Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, Nicole P. M. van Lipzig
As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses.
...
As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller, with a tendency to install larger turbines at high capacity densities. It is, however, not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that for a typical capacity density and for SW winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 54 % and 30 % depending on the proximity, size and number of the upwind farms. The efficiency losses due to intra- and inter-farm wakes become larger with increasing capacity density as the layout-integrated, annual capacity factor varies between 51.8 % and 38.2 % over the considered range of 3.5 to 10 MW km−2. Also, the simulated efficiency of the wind farm layout is greatly impacted by switching from 5 MW turbines to next-generation, 15 MW turbines, as the annual energy production increases by over 27 % at the same capacity density. In conclusion, our results show that the wake losses in future wind farm clusters are highly sensitive to the inter-farm distances and the capacity densities of the individual wind farms and that the evolution of turbine technology plays a crucial role in offsetting these wake losses.
Journal article
(2024)
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Koen Devesse, Sebastiano Stipa, Joshua Brinkerhoff, Dries Allaerts, Johan Meyers
As offshore wind farms grow in size, the blockage effect associated with the atmospheric gravity waves they trigger is expected to become more important. To model this, recent research has produced an Atmospheric Perturbation Model (APM), which simulates the mesoscale flow in the atmospheric boundary layer at a low computational cost compared to traditional methods. However, as a simplified reduced-order model, it can not resolve individual turbine wakes, and has to be coupled to an engineering wake model to predict farm power output. Over the years, three coupling methods have been developed, and been combined into the open-source framework WAYVE. This paper compares them, discussing both their theoretical validity and their performance. For the latter, we validate the velocities and power outputs predicted by WAYVE against 27 LES simulations. We find that the velocity matching (VM) and the pressure-based (PB) methods perform the best. Of these two, the VM method is more consistent with the APM output, while the PB method has a significantly lower computational cost.
...
As offshore wind farms grow in size, the blockage effect associated with the atmospheric gravity waves they trigger is expected to become more important. To model this, recent research has produced an Atmospheric Perturbation Model (APM), which simulates the mesoscale flow in the atmospheric boundary layer at a low computational cost compared to traditional methods. However, as a simplified reduced-order model, it can not resolve individual turbine wakes, and has to be coupled to an engineering wake model to predict farm power output. Over the years, three coupling methods have been developed, and been combined into the open-source framework WAYVE. This paper compares them, discussing both their theoretical validity and their performance. For the latter, we validate the velocities and power outputs predicted by WAYVE against 27 LES simulations. We find that the velocity matching (VM) and the pressure-based (PB) methods perform the best. Of these two, the VM method is more consistent with the APM output, while the PB method has a significantly lower computational cost.
Michiel Kenis is a PhD researcher at the Energy Systems Integration & Modeling Group at the University of Leuven with a doctoral mandate from the Flemish Institute for Technological Research (VITO). He was a visiting researcher at the Massachusetts Institute of Technology. His research focuses on cross-border electricity markets. He holds a MSc in energy engineering and a MSc in policy economics, both from the University of Leuven. Luca Lanzilao completed his MSc degree in mathematical engineering from Politecnico di Torino in 2018. Currently, he is pursuing a PhD at KU Leuven. His research focuses on studying the response of the atmospheric boundary layer to wind farm forcing, with particular emphasis on meso-scale phenomena, such as gravity waves. Kenneth Bruninx received a MSc degree in energy engineering in 2011, a MSc in management, and a PhD degree in mechanical engineering in 2016, all from the University of Leuven (KU Leuven), Belgium. Currently, he is an assistant professor at the Faculty of Technology, Policy, and Management of TU Delft, Netherlands and a research fellow at the Department of Mechanical Engineering, KU Leuven, Belgium. His research interests include market design, policies, and regulation for integrated energy systems. Johan Meyers is a professor of mechanical engineering at KU Leuven since 2009. His research focuses on the simulation of turbulent flows and the atmospheric boundary layer with applications in wind energy. In 2012, he obtained an ERC grant on wind farm control and has been involved in various European projects on wind energy since. He served as the vice president of the European Academy of Wind Energy from 2017 to 2019 and as its president from 2019 to end of 2021. He has been active as an associate editor for Computers & Fluids and is currently an associate editor for Wind Energy Science. Erik Delarue received MSc and PhD degrees in mechanical engineering from the University of Leuven, Belgium, in 2005 and 2009, respectively. He is currently an associate professor with the University of Leuven, TME Branch (energy conversion) and active with EnergyVille. His research focus and expertise are on quantitative tools, supporting an efficient operation of, and transition toward, a low-carbon energy system (mathematical modeling of energy systems). Applications relate to flexibility through energy systems integration, market design, and energy policies.
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Michiel Kenis is a PhD researcher at the Energy Systems Integration & Modeling Group at the University of Leuven with a doctoral mandate from the Flemish Institute for Technological Research (VITO). He was a visiting researcher at the Massachusetts Institute of Technology. His research focuses on cross-border electricity markets. He holds a MSc in energy engineering and a MSc in policy economics, both from the University of Leuven. Luca Lanzilao completed his MSc degree in mathematical engineering from Politecnico di Torino in 2018. Currently, he is pursuing a PhD at KU Leuven. His research focuses on studying the response of the atmospheric boundary layer to wind farm forcing, with particular emphasis on meso-scale phenomena, such as gravity waves. Kenneth Bruninx received a MSc degree in energy engineering in 2011, a MSc in management, and a PhD degree in mechanical engineering in 2016, all from the University of Leuven (KU Leuven), Belgium. Currently, he is an assistant professor at the Faculty of Technology, Policy, and Management of TU Delft, Netherlands and a research fellow at the Department of Mechanical Engineering, KU Leuven, Belgium. His research interests include market design, policies, and regulation for integrated energy systems. Johan Meyers is a professor of mechanical engineering at KU Leuven since 2009. His research focuses on the simulation of turbulent flows and the atmospheric boundary layer with applications in wind energy. In 2012, he obtained an ERC grant on wind farm control and has been involved in various European projects on wind energy since. He served as the vice president of the European Academy of Wind Energy from 2017 to 2019 and as its president from 2019 to end of 2021. He has been active as an associate editor for Computers & Fluids and is currently an associate editor for Wind Energy Science. Erik Delarue received MSc and PhD degrees in mechanical engineering from the University of Leuven, Belgium, in 2005 and 2009, respectively. He is currently an associate professor with the University of Leuven, TME Branch (energy conversion) and active with EnergyVille. His research focus and expertise are on quantitative tools, supporting an efficient operation of, and transition toward, a low-carbon energy system (mathematical modeling of energy systems). Applications relate to flexibility through energy systems integration, market design, and energy policies.
Review
(2022)
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Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Gocmen, J.W. van Wingerden
Wind farm control has been a topic of research for more than two decades. It has been identified as a core component of grand challenges in wind energy science to support accelerated wind energy deployment and to transition to a clean and sustainable energy system for the 21st century. The prospect of collective control of wind turbines in an array, to increase energy extraction, reduce structural loads, improve the balance of systems, reduce operation and maintenance costs, etc. has inspired many researchers over the years to propose innovative ideas and solutions. However, practical demonstration and commercialization of some of the more advanced concepts has been limited by a wide range of challenges, which include the complex physics of turbulent flows in wind farms and the atmosphere, uncertainties related to predicting structural load and failure statistics, and the highly multi-disciplinary nature of the overall design optimization problem, among others. In the current work, we aim at providing a comprehensive overview of the state of the art and outstanding challenges, thus identifying the key research areas that could further enable commercial uptake and success of wind farm control solutions. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight in control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design (co-design).
...
Wind farm control has been a topic of research for more than two decades. It has been identified as a core component of grand challenges in wind energy science to support accelerated wind energy deployment and to transition to a clean and sustainable energy system for the 21st century. The prospect of collective control of wind turbines in an array, to increase energy extraction, reduce structural loads, improve the balance of systems, reduce operation and maintenance costs, etc. has inspired many researchers over the years to propose innovative ideas and solutions. However, practical demonstration and commercialization of some of the more advanced concepts has been limited by a wide range of challenges, which include the complex physics of turbulent flows in wind farms and the atmosphere, uncertainties related to predicting structural load and failure statistics, and the highly multi-disciplinary nature of the overall design optimization problem, among others. In the current work, we aim at providing a comprehensive overview of the state of the art and outstanding challenges, thus identifying the key research areas that could further enable commercial uptake and success of wind farm control solutions. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight in control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design (co-design).
Journal article
(2022)
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Paul Veers, Katherine Dykes, S. Basu, Alessandro Bianchini, Peter Green, Lena Kitzing, Branko Kosović, Johan Meyers, Mark O’Malley, More authors...
Wind energy is anticipated to play a central role in enabling a rapid transition from fossil fuels to a system based largely on renewable power. For wind power to fulfill its expected role as the backbone – providing nearly half of the electrical energy – of a renewable-based, carbon-neutral energy system, critical challenges around design, manufacture, and deployment of land and offshore technologies must be addressed. During the past 3 years, the wind research community has invested significant effort toward understanding the nature and implications of these challenges and identifying associated gaps. The outcomes of these efforts are summarized in a series of 10 articles, some under review by Wind Energy Science (WES) and others planned for submission during the coming months. This letter explains the genesis, significance, and impacts of these efforts.
...
Wind energy is anticipated to play a central role in enabling a rapid transition from fossil fuels to a system based largely on renewable power. For wind power to fulfill its expected role as the backbone – providing nearly half of the electrical energy – of a renewable-based, carbon-neutral energy system, critical challenges around design, manufacture, and deployment of land and offshore technologies must be addressed. During the past 3 years, the wind research community has invested significant effort toward understanding the nature and implications of these challenges and identifying associated gaps. The outcomes of these efforts are summarized in a series of 10 articles, some under review by Wind Energy Science (WES) and others planned for submission during the coming months. This letter explains the genesis, significance, and impacts of these efforts.
Journal article
(2020)
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Tuhfe Göcmen, Konstanze Kölle, Jan Willem Van Wingerden, Gregor Giebel, Soren Juhl Andersen, Irene Eguinoa, Thomas Duc, Filippo Campagnolo, Mikel Iribas-Latour, David Astrain, Carlo Bottasso, Johan Meyers
Careful validation of the modelling and control actions is of vital importance to build confidence in the value of coordinated wind farm control (WFC). The efficiency of flow models applied to WFC should be evaluated to provide reliable assessment of the performance of WFC. In order to achieve that, FarmConners launches a common benchmark for code comparison to demonstrate the potential benefits of WFC, such as increased power production and mitigation of loads. The benchmark builds on available data sets from previous and ongoing campaigns: synthetic data from high-fidelity simulations, measurements from wind tunnel experiments, and field data from a real wind farm. The participating WFC models are first to be calibrated or trained using normal operation periods. For the blind test, both the axial induction and wake steering control approaches are included in the dataset and to be evaluated through the designed test cases. Three main test cases are specified, addressing the impact of WFC on single full wake, single partial wake, and multiple wake. The WFC model outcomes will be compared during the blind test phase, through power gain and wake loss reduction as well as alleviation of wake-added turbulence intensity and structural loads. The probabilistic validation will be based on the median and quartiles of the observations and WFC model predictions. Every benchmark participant will be involved in the final publication, where the comparison of different tools will be performed using the defined test cases. Instructions on how to participate are also provided on farmconners.readthedocs.io.
...
Careful validation of the modelling and control actions is of vital importance to build confidence in the value of coordinated wind farm control (WFC). The efficiency of flow models applied to WFC should be evaluated to provide reliable assessment of the performance of WFC. In order to achieve that, FarmConners launches a common benchmark for code comparison to demonstrate the potential benefits of WFC, such as increased power production and mitigation of loads. The benchmark builds on available data sets from previous and ongoing campaigns: synthetic data from high-fidelity simulations, measurements from wind tunnel experiments, and field data from a real wind farm. The participating WFC models are first to be calibrated or trained using normal operation periods. For the blind test, both the axial induction and wake steering control approaches are included in the dataset and to be evaluated through the designed test cases. Three main test cases are specified, addressing the impact of WFC on single full wake, single partial wake, and multiple wake. The WFC model outcomes will be compared during the blind test phase, through power gain and wake loss reduction as well as alleviation of wake-added turbulence intensity and structural loads. The probabilistic validation will be based on the median and quartiles of the observations and WFC model predictions. Every benchmark participant will be involved in the final publication, where the comparison of different tools will be performed using the defined test cases. Instructions on how to participate are also provided on farmconners.readthedocs.io.
Conference paper
(2019)
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Sebastian Rapp, Roland Schmehl, Espen Oland, Sture Schmidt, Thomas Haas, Johan Meyers
Airborne wind energy is an emerging technology that uses tethered unmanned aerial vehicles for harvesting wind energy at altitudes higher than conventional towered wind turbines. To make the technology competitive to other renewable energy technologies an automatic control system is required that allows autonomously operating the system throughout all phases of flight. In this study a modular control system is presented, adapting the underlying kinematic and dynamic framework from conventional aerospace terminology and applying this to tethered crosswind flight with varying tether length. The high level control strategy in form of a state machine as well as the cascaded flight control structure consisting of path-following guidance and control, attitude and rate loop is presented along with the winch controller. The present work is a first step towards a methodology for the systematic development of reliable and high-performance control solutions for airborne wind energy systems. Models for the airborne system, ground station, as well as the tether connecting the ground system with the airframe will be presented. Results from a simulation study in a realistic wind field will be used to demonstrate the feasibility of the proposed concept and to identify particularly challenging situations in the operational envelope.
...
Airborne wind energy is an emerging technology that uses tethered unmanned aerial vehicles for harvesting wind energy at altitudes higher than conventional towered wind turbines. To make the technology competitive to other renewable energy technologies an automatic control system is required that allows autonomously operating the system throughout all phases of flight. In this study a modular control system is presented, adapting the underlying kinematic and dynamic framework from conventional aerospace terminology and applying this to tethered crosswind flight with varying tether length. The high level control strategy in form of a state machine as well as the cascaded flight control structure consisting of path-following guidance and control, attitude and rate loop is presented along with the winch controller. The present work is a first step towards a methodology for the systematic development of reliable and high-performance control solutions for airborne wind energy systems. Models for the airborne system, ground station, as well as the tether connecting the ground system with the airframe will be presented. Results from a simulation study in a realistic wind field will be used to demonstrate the feasibility of the proposed concept and to identify particularly challenging situations in the operational envelope.
Wind turbines are often sited together in wind
farms as it is economically advantageous. Controlling the flow within
wind farms to reduce the fatigue loads, maximize energy production and
provide ancillary services is a challenging control problem due to the
underlying time-varying non-linear wake dynamics. In this paper, we
present a control-oriented dynamical wind farm model called the
WindFarmSimulator (WFSim) that can be used in closed-loop wind farm
control algorithms. The three-dimensional Navier–Stokes equations were
the starting point for deriving the control-oriented dynamic wind farm
model. Then, in order to reduce computational complexity, terms
involving the vertical dimension were either neglected or estimated in
order to partially compensate for neglecting the vertical dimension.
Sparsity of and structure in the system matrices make this model
relatively computationally inexpensive. We showed that by taking the
vertical dimension partially into account, the estimation of flow data
generated with a high-fidelity wind farm model is improved relative to
when the vertical dimension is completely neglected in WFSim. Moreover,
we showed that, for the study cases considered in this work, WFSim is
potentially fast enough to be used in an online closed-loop control
framework including model parameter updates. Finally we showed that the
proposed wind farm model is able to estimate flow and power signals
generated by two different 3-D high-fidelity wind farm models.
...
Wind turbines are often sited together in wind farms as it is economically advantageous. Controlling the flow within wind farms to reduce the fatigue loads, maximize energy production and provide ancillary services is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, we present a control-oriented dynamical wind farm model called the WindFarmSimulator (WFSim) that can be used in closed-loop wind farm control algorithms. The three-dimensional Navier–Stokes equations were the starting point for deriving the control-oriented dynamic wind farm model. Then, in order to reduce computational complexity, terms involving the vertical dimension were either neglected or estimated in order to partially compensate for neglecting the vertical dimension. Sparsity of and structure in the system matrices make this model relatively computationally inexpensive. We showed that by taking the vertical dimension partially into account, the estimation of flow data generated with a high-fidelity wind farm model is improved relative to when the vertical dimension is completely neglected in WFSim. Moreover, we showed that, for the study cases considered in this work, WFSim is potentially fast enough to be used in an online closed-loop control framework including model parameter updates. Finally we showed that the proposed wind farm model is able to estimate flow and power signals generated by two different 3-D high-fidelity wind farm models.