Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-1029-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-1029-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jun 2023
Research article |
| 23 Jun 2023
| 23 Jun 2023
The eco-conscious wind turbine: design beyond purely economic metrics
Helena Canet
Wind Energy Institute, Technical University of Munich, 85748
Garching bei München, Germany
Adrien Guilloré
Wind Energy Institute, Technical University of Munich, 85748
Garching bei München, Germany
Wind Energy Institute, Technical University of Munich, 85748
Garching bei München, Germany
Related authors
Helena Canet, Stefan Loew, and Carlo L. Bottasso
Wind Energ. Sci., 6, 1325–1340, https://doi.org/10.5194/wes-6-1325-2021, https://doi.org/10.5194/wes-6-1325-2021, 2021
Short summary
Short summary
Lidar-assisted control (LAC) is used to redesign the rotor and tower of three turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save
mass, increase hub height, or extend lifetime. The first two strategies yield reductions in the cost of energy only for the tower of the largest machine, while more interesting benefits are obtained for lifetime extension.
Chengyu Wang, Filippo Campagnolo, Helena Canet, Daniel J. Barreiro, and Carlo L. Bottasso
Wind Energ. Sci., 6, 961–981, https://doi.org/10.5194/wes-6-961-2021, https://doi.org/10.5194/wes-6-961-2021, 2021
Short summary
Short summary
This paper quantifies the fidelity of the wakes generated by a small (1 m diameter) scaled wind turbine model operated in a large boundary layer wind tunnel. A detailed scaling analysis accompanied by large-eddy simulations shows that these wakes are very realistic scaled versions of the ones generated by the parent full-scale wind turbine in the field.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
Short summary
Short summary
The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Pietro Bortolotti, Helena Canet, Carlo L. Bottasso, and Jaikumar Loganathan
Wind Energ. Sci., 4, 397–406, https://doi.org/10.5194/wes-4-397-2019, https://doi.org/10.5194/wes-4-397-2019, 2019
Short summary
Short summary
The paper studies the effects of uncertainties in aeroservoelastic
wind turbine models. Uncertainties are associated with the wind
inflow characteristics and the blade surface state, and they are propagated
by means of two non-intrusive methods throughout the
aeroservoelastic model of a large conceptual offshore wind
turbine. Results are compared with a brute-force extensive Monte
Carlo sampling to assess the convergence characteristics of the
non-intrusive approaches.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1547–1575, https://doi.org/10.5194/wes-9-1547-2024, https://doi.org/10.5194/wes-9-1547-2024, 2024
Short summary
Short summary
We develop a new simple model to predict power losses incurred by a wind turbine when it yaws out of the wind. The model reveals the effects of a number of rotor design parameters and how the turbine is governed when it yaws. The model exhibits an excellent agreement with large eddy simulations and wind tunnel measurements. We showcase the capabilities of the model by deriving the power-optimal yaw strategy for a single turbine and for a cluster of wake-interacting turbines.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
Short summary
Short summary
A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
Jenna Iori, Carlo Luigi Bottasso, and Michael Kenneth McWilliam
Wind Energ. Sci., 9, 1289–1304, https://doi.org/10.5194/wes-9-1289-2024, https://doi.org/10.5194/wes-9-1289-2024, 2024
Short summary
Short summary
The controller of a wind turbine has an important role in regulating power production and avoiding structural failure. However, it is often designed after the rest of the turbine, and thus its potential is not fully exploited. An alternative is to design the structure and the controller simultaneously. This work develops a method to identify if a given turbine design can benefit from this new simultaneous design process. For example, a higher and cheaper turbine tower can be built this way.
Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
Short summary
Short summary
Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
Short summary
Short summary
The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
Short summary
Short summary
We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Emmanouil M. Nanos, Carlo L. Bottasso, Simone Tamaro, Dimitris I. Manolas, and Vasilis A. Riziotis
Wind Energ. Sci., 7, 1641–1660, https://doi.org/10.5194/wes-7-1641-2022, https://doi.org/10.5194/wes-7-1641-2022, 2022
Short summary
Short summary
A novel way of wind farm control is presented where the wake is deflected vertically to reduce interactions with downstream turbines. This is achieved by moving ballast in a floating offshore platform in order to pitch the support structure and thereby tilt the wind turbine rotor disk. The study considers the effects of this new form of wake control on the aerodynamics of the steering and wake-affected turbines, on the structure, and on the ballast motion system.
Stefan Loew and Carlo L. Bottasso
Wind Energ. Sci., 7, 1605–1625, https://doi.org/10.5194/wes-7-1605-2022, https://doi.org/10.5194/wes-7-1605-2022, 2022
Short summary
Short summary
This publication presents methods to improve the awareness and control of material fatigue for wind turbines. This is achieved by enhancing a sophisticated control algorithm which utilizes wind prediction information from a laser measurement device. The simulation results indicate that the novel algorithm significantly improves the economic performance of a wind turbine. This benefit is particularly high for situations when the prediction quality is low or the prediction time frame is short.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
Short summary
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
Helena Canet, Stefan Loew, and Carlo L. Bottasso
Wind Energ. Sci., 6, 1325–1340, https://doi.org/10.5194/wes-6-1325-2021, https://doi.org/10.5194/wes-6-1325-2021, 2021
Short summary
Short summary
Lidar-assisted control (LAC) is used to redesign the rotor and tower of three turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save
mass, increase hub height, or extend lifetime. The first two strategies yield reductions in the cost of energy only for the tower of the largest machine, while more interesting benefits are obtained for lifetime extension.
Chengyu Wang, Filippo Campagnolo, Helena Canet, Daniel J. Barreiro, and Carlo L. Bottasso
Wind Energ. Sci., 6, 961–981, https://doi.org/10.5194/wes-6-961-2021, https://doi.org/10.5194/wes-6-961-2021, 2021
Short summary
Short summary
This paper quantifies the fidelity of the wakes generated by a small (1 m diameter) scaled wind turbine model operated in a large boundary layer wind tunnel. A detailed scaling analysis accompanied by large-eddy simulations shows that these wakes are very realistic scaled versions of the ones generated by the parent full-scale wind turbine in the field.
Marta Bertelè, Carlo L. Bottasso, and Johannes Schreiber
Wind Energ. Sci., 6, 759–775, https://doi.org/10.5194/wes-6-759-2021, https://doi.org/10.5194/wes-6-759-2021, 2021
Short summary
Short summary
A previously published wind sensing method is applied to an experimental dataset obtained from a 3.5 MW turbine and a nearby hub-tall met mast. The method uses blade load harmonics to estimate rotor-equivalent shears and wind directions at the rotor disk. Results indicate the good quality of the estimated shear, both in terms of 10 min averages and of resolved time histories, and a reasonable accuracy in the estimation of the yaw misalignment.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
Short summary
Short summary
The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Bart M. Doekemeijer, Stefan Kern, Sivateja Maturu, Stoyan Kanev, Bastian Salbert, Johannes Schreiber, Filippo Campagnolo, Carlo L. Bottasso, Simone Schuler, Friedrich Wilts, Thomas Neumann, Giancarlo Potenza, Fabio Calabretta, Federico Fioretti, and Jan-Willem van Wingerden
Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, https://doi.org/10.5194/wes-6-159-2021, 2021
Short summary
Short summary
This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 5, 1537–1550, https://doi.org/10.5194/wes-5-1537-2020, https://doi.org/10.5194/wes-5-1537-2020, 2020
Short summary
Short summary
A new method is described to identify the aerodynamic characteristics of blade airfoils directly from operational data of the turbine. Improving on a previously published approach, the present method is based on a new maximum likelihood formulation that includes errors both in the outputs and the inputs. The method is demonstrated on the identification of the polars of small-scale turbines for wind tunnel testing.
Filippo Campagnolo, Robin Weber, Johannes Schreiber, and Carlo L. Bottasso
Wind Energ. Sci., 5, 1273–1295, https://doi.org/10.5194/wes-5-1273-2020, https://doi.org/10.5194/wes-5-1273-2020, 2020
Short summary
Short summary
The performance of an open-loop wake-steering controller is investigated with a new wind tunnel experiment. Three scaled wind turbines are placed on a large turntable and exposed to a turbulent inflow, resulting in dynamically varying wake interactions. The study highlights the importance of using a robust formulation and plant flow models of appropriate fidelity and the existence of possible margins for improvement by the use of dynamic controllers.
Johannes Schreiber, Carlo L. Bottasso, and Marta Bertelè
Wind Energ. Sci., 5, 867–884, https://doi.org/10.5194/wes-5-867-2020, https://doi.org/10.5194/wes-5-867-2020, 2020
Short summary
Short summary
This paper validates a method to estimate the vertical wind shear and detect the presence and location of an impinging wake with field data. Shear and wake awareness have multiple uses, from turbine and farm control to monitoring and forecasting.
Results indicate a very good correlation between the estimated vertical shear and the one measured by a met mast and a remarkable ability to locate and track the motion of an impinging wake on an affected rotor.
Johannes Schreiber, Carlo L. Bottasso, Bastian Salbert, and Filippo Campagnolo
Wind Energ. Sci., 5, 647–673, https://doi.org/10.5194/wes-5-647-2020, https://doi.org/10.5194/wes-5-647-2020, 2020
Short summary
Short summary
The paper describes a new method that uses standard historical operational data and reconstructs the flow at the rotor disk of each turbine in a wind farm. The method is based on a baseline wind farm flow and wake model, augmented with error terms that are
learnedfrom operational data using an ad hoc system identification approach. Both wind tunnel experiments and real data from a wind farm at a complex terrain site are used to show the capabilities of the new method.
Joeri Alexis Frederik, Robin Weber, Stefano Cacciola, Filippo Campagnolo, Alessandro Croce, Carlo Bottasso, and Jan-Willem van Wingerden
Wind Energ. Sci., 5, 245–257, https://doi.org/10.5194/wes-5-245-2020, https://doi.org/10.5194/wes-5-245-2020, 2020
Short summary
Short summary
The interaction between wind turbines in a wind farm through their wakes is a widely studied research area. Until recently, research was focused on finding constant turbine inputs that optimize the performance of the wind farm. However, recent studies have shown that time-varying, dynamic inputs might be more beneficial. In this paper, the validity of this approach is further investigated by implementing it in scaled wind tunnel experiments and assessing load effects, showing promising results.
Johannes Schreiber, Amr Balbaa, and Carlo L. Bottasso
Wind Energ. Sci., 5, 237–244, https://doi.org/10.5194/wes-5-237-2020, https://doi.org/10.5194/wes-5-237-2020, 2020
Short summary
Short summary
An analytical wake model with a double-Gaussian velocity distribution is used to improve on a similar formulation by Keane et al (2016). The choice of a double-Gaussian shape function is motivated by the behavior of the near-wake region that is observed in numerical simulations and experimental measurements. The model is calibrated and validated using large eddy simulations replicating scaled wind turbine experiments, yielding improved results with respect to a classical single-Gaussian profile.
Pietro Bortolotti, Helena Canet, Carlo L. Bottasso, and Jaikumar Loganathan
Wind Energ. Sci., 4, 397–406, https://doi.org/10.5194/wes-4-397-2019, https://doi.org/10.5194/wes-4-397-2019, 2019
Short summary
Short summary
The paper studies the effects of uncertainties in aeroservoelastic
wind turbine models. Uncertainties are associated with the wind
inflow characteristics and the blade surface state, and they are propagated
by means of two non-intrusive methods throughout the
aeroservoelastic model of a large conceptual offshore wind
turbine. Results are compared with a brute-force extensive Monte
Carlo sampling to assess the convergence characteristics of the
non-intrusive approaches.
Pietro Bortolotti, Abhinav Kapila, and Carlo L. Bottasso
Wind Energ. Sci., 4, 115–125, https://doi.org/10.5194/wes-4-115-2019, https://doi.org/10.5194/wes-4-115-2019, 2019
Short summary
Short summary
The paper compares upwind and downwind three-bladed configurations
for a 10 MW wind turbine in terms of power and loads. For the
downwind case, the study also considers a load-aligned solution
with active coning. Results indicate that downwind solutions are
slightly more advantageous than upwind ones, although improvements
are small. Additionally, pre-alignment is difficult to achieve in
practice, and the active coning solution is associated with very
significant engineering challenges.
Jiangang Wang, Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 4, 71–88, https://doi.org/10.5194/wes-4-71-2019, https://doi.org/10.5194/wes-4-71-2019, 2019
Short summary
Short summary
This paper describes an LES approach for the simulation of wind
turbines and their wakes. The simulation model is used to
develop a complete digital copy of experiments performed with
scaled wind turbines in a boundary layer wind tunnel, including the
passive generation of a sheared turbulent flow. Numerical results
are compared with experimental measurements, with a good overall
matching between the two.
Marta Bertelè, Carlo L. Bottasso, and Stefano Cacciola
Wind Energ. Sci., 4, 89–97, https://doi.org/10.5194/wes-4-89-2019, https://doi.org/10.5194/wes-4-89-2019, 2019
Short summary
Short summary
This paper describes a new formulation for estimating the wind
inflow at the rotor disk, based on measurements of the blade loads.
The new method improves on previous formulations by exploiting the
rotational symmetry of the problem. Experimental results obtained
with an aeroelastically scaled model in a boundary layer wind
tunnel are used for validating the proposed approach.
Marta Bertelè, Carlo L. Bottasso, and Stefano Cacciola
Wind Energ. Sci., 3, 791–803, https://doi.org/10.5194/wes-3-791-2018, https://doi.org/10.5194/wes-3-791-2018, 2018
Short summary
Short summary
This work presents a new fully automated method to correct for
pitch misalignment imbalances of wind turbine rotors. The method
has minimal requirements, as it only assumes the availability of a
sensor of sufficient accuracy and bandwidth to detect the 1P
harmonic to the desired precision and the ability to command the
pitch setting of each blade independently from the others.
Extensive numerical simulations are used to demonstrate the new
procedure.
Jiangang Wang, Chengyu Wang, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2018-47, https://doi.org/10.5194/wes-2018-47, 2018
Revised manuscript has not been submitted
Short summary
Short summary
This paper describes a Scale Adaptive Simulation (SAS) approach for
the numerical simulation of wind turbines and their wakes. The SAS
formulation is found to be about one order of magnitude faster than
a classical LES approach. The simulation models are compared to
each other and with experimental measurements obtained with scaled
wind turbines in a boundary layer wind tunnel.
Marta Bertelè, Carlo L. Bottasso, Stefano Cacciola, Fabiano Daher Adegas, and Sara Delport
Wind Energ. Sci., 2, 615–640, https://doi.org/10.5194/wes-2-615-2017, https://doi.org/10.5194/wes-2-615-2017, 2017
Short summary
Short summary
The rotor of a wind turbine is used to determine some important parameters of the wind, including the direction of the wind vector relative to the rotor disk and horizontal and vertical shears. The method works by using measurements provided by existing onboard load sensors. The observed wind characteristics can be used to implement advanced features in smart wind turbine and wind farm controllers.
Carlo L. Bottasso, Alessandro Croce, Federico Gualdoni, Pierluigi Montinari, and Carlo E. D. Riboldi
Wind Energ. Sci., 1, 297–310, https://doi.org/10.5194/wes-1-297-2016, https://doi.org/10.5194/wes-1-297-2016, 2016
Short summary
Short summary
The paper discusses different concepts for reducing loads on wind turbines using movable blade tips. Passive and semi-passive tip solutions move freely in response to air load fluctuations, while in the active case an actuator drives the tip motion in response to load measurements. The various solutions are compared with a standard blade and with each other in terms of their ability to reduce both fatigue and extreme loads.
Riccardo Riva, Stefano Cacciola, and Carlo Luigi Bottasso
Wind Energ. Sci., 1, 177–203, https://doi.org/10.5194/wes-1-177-2016, https://doi.org/10.5194/wes-1-177-2016, 2016
Short summary
Short summary
This paper presents a method to assess the stability of a wind turbine. The proposed approach uses the recorded time history of the system response and fits to it a periodic reduced-order model that can handle stochastic disturbances. Stability is computed by using Floquet theory on the reduced-order model. Since the method only uses response data, it is applicable to any simulation model as well as to experimental test data. The method is compared to the well-known operational modal analysis.
Pietro Bortolotti, Carlo L. Bottasso, and Alessandro Croce
Wind Energ. Sci., 1, 71–88, https://doi.org/10.5194/wes-1-71-2016, https://doi.org/10.5194/wes-1-71-2016, 2016
Short summary
Short summary
The paper presents a new method to conduct the holistic optimization of a wind turbine. The proposed approach allows one to define the rotor radius and tower height, while simultaneously performing the detailed sizing of rotor and tower. For the rotor, the procedures perform simultaneously the design both from the aerodynamic and structural points of view. The overall optimization seeks a minimum for the cost of energy, while accounting for a wide range of user-defined design constraints.
G. A. M. van Kuik, J. Peinke, R. Nijssen, D. Lekou, J. Mann, J. N. Sørensen, C. Ferreira, J. W. van Wingerden, D. Schlipf, P. Gebraad, H. Polinder, A. Abrahamsen, G. J. W. van Bussel, J. D. Sørensen, P. Tavner, C. L. Bottasso, M. Muskulus, D. Matha, H. J. Lindeboom, S. Degraer, O. Kramer, S. Lehnhoff, M. Sonnenschein, P. E. Sørensen, R. W. Künneke, P. E. Morthorst, and K. Skytte
Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, https://doi.org/10.5194/wes-1-1-2016, 2016
Related subject area
Thematic area: Wind technologies | Topic: Systems engineering
Designing wind turbines for profitability in the day-ahead market
Aerodynamic effects of leading-edge erosion in wind farm flow modeling
Control co-design optimization of floating offshore wind turbines with tuned liquid multi-column dampers
Knowledge engineering for wind energy
HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries
Drivers for optimum sizing of wind turbines for offshore wind farms
A comparison of eight optimization methods applied to a wind farm layout optimization problem
Optimization of wind farm operation with a noise constraint
Flutter behavior of highly flexible blades for two- and three-bladed wind turbines
Mihir Kishore Mehta, Michiel Zaaijer, and Dominic von Terzi
Wind Energ. Sci., 9, 2283–2300, https://doi.org/10.5194/wes-9-2283-2024, https://doi.org/10.5194/wes-9-2283-2024, 2024
Short summary
Short summary
In a subsidy-free era, there is a need to optimize wind turbines for maximizing farm revenue instead of minimizing cost of energy. A wind-farm-level modeling framework with a simplified market model is used to optimize the turbine size for maximum profitability. The results show that the optimum size is driven mainly by the choice of the economic metric and the market price scenario, with a design optimized for the cost of energy already performing well w.r.t. most profitability-based metrics
Jens Visbech, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 1811–1826, https://doi.org/10.5194/wes-9-1811-2024, https://doi.org/10.5194/wes-9-1811-2024, 2024
Short summary
Short summary
Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Wei Yu, Sheng Tao Zhou, Frank Lemmer, and Po Wen Cheng
Wind Energ. Sci., 9, 1053–1068, https://doi.org/10.5194/wes-9-1053-2024, https://doi.org/10.5194/wes-9-1053-2024, 2024
Short summary
Short summary
Integrating a tuned liquid multi-column damping (TLMCD) into a floating offshore wind turbine (FOWT) is challenging. The synergy between the TLMCD, the turbine controller, and substructure dynamics affects the FOWT's performance and cost. A control co-design optimization framework is developed to optimize the substructure, the TLMCD, and the blade pitch controller simultaneously. The results show that the optimization can significantly enhance FOWT system performance.
Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber
Wind Energ. Sci., 9, 883–917, https://doi.org/10.5194/wes-9-883-2024, https://doi.org/10.5194/wes-9-883-2024, 2024
Short summary
Short summary
This paper delves into the crucial task of transforming raw data into actionable knowledge which can be used by advanced artificial intelligence systems – a challenge that spans various domains, industries, and scientific fields amid their digital transformation journey. This article underscores the significance of cross-industry collaboration and learning, drawing insights from sectors leading in digitalisation, and provides strategic guidance for further development in this area.
Juan Pablo Murcia Leon, Hajar Habbou, Mikkel Friis-Møller, Megha Gupta, Rujie Zhu, and Kaushik Das
Wind Energ. Sci., 9, 759–776, https://doi.org/10.5194/wes-9-759-2024, https://doi.org/10.5194/wes-9-759-2024, 2024
Short summary
Short summary
A methodology for an early design of hybrid power plants (wind, solar, PV, and Li-ion battery storage) consisting of a nested optimization that sizes the components and internal operation optimization. Traditional designs that minimize the levelized cost of energy give worse business cases and do not include storage. Optimal operation balances the increasing revenues and faster battery degradation. Battery degradation and replacement costs are needed to estimate the viability of hybrid projects.
Mihir Mehta, Michiel Zaaijer, and Dominic von Terzi
Wind Energ. Sci., 9, 141–163, https://doi.org/10.5194/wes-9-141-2024, https://doi.org/10.5194/wes-9-141-2024, 2024
Short summary
Short summary
Turbines are becoming larger. However, it is important to understand the key drivers of turbine design and explore the possibility of a global optimum, beyond which further upscaling might not reduce the cost of energy. This study explores, for a typical farm, the entire turbine design space with respect to rated power and rotor diameter. The results show a global optimum that is subject to various modeling uncertainties, farm design conditions, and policies with respect to wind farm tendering.
Jared J. Thomas, Nicholas F. Baker, Paul Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John Jasa, Christopher Bay, Federico Tilli, David Bieniek, Nick Robinson, Andrew P. J. Stanley, Wesley Holt, and Andrew Ning
Wind Energ. Sci., 8, 865–891, https://doi.org/10.5194/wes-8-865-2023, https://doi.org/10.5194/wes-8-865-2023, 2023
Short summary
Short summary
This work compares eight optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. Optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely spaced and roughly on a grid in the interior.
Camilla Marie Nyborg, Andreas Fischer, Pierre-Elouan Réthoré, and Ju Feng
Wind Energ. Sci., 8, 255–276, https://doi.org/10.5194/wes-8-255-2023, https://doi.org/10.5194/wes-8-255-2023, 2023
Short summary
Short summary
Our article presents a way of optimizing the wind farm operation by keeping the emitted noise level below a defined limit while maximizing the power output. This is done by switching between noise reducing operational modes. The method has been developed by using two different noise models, one more advanced than the other, to study the advantages of each model. Furthermore, the optimization method is applied to different wind farm cases.
Mayank Chetan, Shulong Yao, and D. Todd Griffith
Wind Energ. Sci., 7, 1731–1751, https://doi.org/10.5194/wes-7-1731-2022, https://doi.org/10.5194/wes-7-1731-2022, 2022
Short summary
Short summary
Though large wind turbines are appealing to reduce costs, larger blades are prone to aero-elastic instabilities due to their long, slender, highly flexible nature. New rotor concepts are emerging including two-bladed rotors and downwind configurations. We introduce a comprehensive evaluation of flutter behavior including classical flutter and edgewise vibration for large-scale two-bladed rotors. The study aims to provide designers with insights to mitigate flutter in future designs.
Cited articles
Al-Behadili, S. H. and El-Osta, W.: Life Cycle Assessment of Dernah (Libya) wind farm, Renew. Energy, 83, 1227–1233, https://doi.org/10.1016/j.renene.2015.05.041,
2015. a, b, c
Aldersey-Williams, J. and Rubert, T.: A theoretical justification and
critical assessment, Energy Policy, 124, 169-179, https://doi.org/10.1016/j.enpol.2018.10.004, 2019. a
Beauson, J., Laurent, A., and Pagh Jensen, J.: The complex end-of-life of wind turbine blades: A review of the European context, Renewable and Sustain. Energ. Rev., 155, 111847, https://doi.org/10.1016/j.rser.2021.111847, 2022. a, b, c, d
Beiter, P., Cooperman, A., Lantz, E., Stehly, T., Shields, M., Wiser, R., Telsnig, T., Kitzing, L., Berkhout, V., and Kikuchi, Y.: Wind power costs driven by innovation and experience with further reductions on the horizon, WIREs Energ. Environ., 10, e398, https://doi.org/10.1002/wene.398, 2021. a
Boeing, F. and Regett, A.: Hourly CO2 Emission Factors and Marginal Costs of Energy Carriers in Future Multi-Energy Systems, Energies, 12, 2260,
https://doi.org/10.3390/en12122260, 2019. a, b
Bortolotti, P., Bottasso, C. L., and Croce, A.: Combined preliminary-detailed design of wind turbines, Wind Energ. Sci., 1, 71–88, https://doi.org/10.5194/wes-1-71-2016, 2016. a, b, c
Bortolotti, P., Berry, D., Murray, R., Gaertner, E., Jenne, D., Damiani, R., Barter, G. E., and Dykes, K.: A Detailed Wind Turbine Blade Cost Model, NREL Report TP-5000-73585, NREL, Golden, CO, USA, https://doi.org/10.2172/1529217, 2019. a
Canet, H., Guilloré, A., and Bottasso, C. L.: Figures: The eco-conscious wind turbine: design beyond purely economic metrics, Zenodo [data set], https://doi.org/10.5281/zenodo.8067416, 2023. a
Demir, N. and Taskin, A.: Life cycle assessment of wind turbines in Pinarbasi-Kayseri, J. Clean. Product., 54, 253–263,
https://doi.org/10.1016/j.jclepro.2013.04.016, 2013. a, b, c, d
Deutsche Bundesnetzagentur: SMARD Strommarktdaten platform,
https://www.smard.de/home (last access: 3 March 2020), 2020. a
Deutsche WindGuard: Wissenschaftlicher Bericht: Vorbereitung und Begleitung bei der Erstellung eines Erfahrungsbericht gemäß § 97 Erneuerbare-Energien Gesetz, Teilvorhaben II e): Wind an Land, Technical Report, https://www.erneuerbare-energien.de/EE/Redaktion/DE/Downloads/bmwi_de/deutsche-windguard-vorbereitung-begleitung-erfahrungsbericht
(last access: 24 November 2021), 2019. a, b, c, d
Duffy, A., Hand, M., Wiser, R., Lantz, E., Dalla Riva, A., Berkhout, V., Stenkvist, M., Weir, D., and Lacal-Arántegui, R.: Land-based wind energy cost trends in Germany, Denmark, Ireland, Norway, Sweden and the United States, Appl.Energy, 277, 114777, https://doi.org/10.1016/j.apenergy.2020.114777, 2020. a, b, c
Enercon: E-115 EP-3,
https://www.enercon.de/produkte/ep-3/e-115-ep3/, last access: 24 November 2021. a
Fingersh, L., Hand, M., and Laxson, A.: Wind Turbine Design Cost and Scaling Model, NREL Report NREL/TP-500-40566, NREL,
https://www.nrel.gov/docs/fy07osti/40566.pdf (last access: 24 November 2021), 2006. a
Gillingham, K. and Stock, J. H.: The Cost of Reducing Greenhouse Gas Emissions, J. Econ. Perspect., 32, 53–72, https://doi.org/10.1257/jep.32.4.53, 2018. a
Guilloré, A., Canet, H., and Bottasso, C. L.: Life-Cycle Environmental Impact of Wind Turbines: What are the Possible Improvement Pathways?, J. Phys.: Conf. Ser., 2265, 042033, https://doi.org/10.1088/1742-6596/2265/4/042033, 2022 a, b
Hauschild, M. Z., Rosenbaum, R. K., and Olsen, S. I.: Life Cycle Assessment, Theory and Practice, Springer, https://doi.org/10.1007/978-3-319-56475-3, 2018. a, b, c
Hawkes, A. D.: Estimating marginal CO2 emissions rates for national electricity systems, Energy Policy, 38, 5977–5987,
https://doi.org/10.1016/j.enpol.2010.05.053, 2014. a, b
Hill, C. and Norton, A.: LCA database of environmental impacts to inform material selection process, Damage Controlled Composite Materials (DACOMAT) Deliverable 6.1, https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5c240650f&appId=PPGMS
(last access: 24 November 2021), 2018. a
Hirth, L.: Integration costs and the value of wind power. Thoughts on a valuation framework for variable renewable electricity sources, USAEE Working Paper, USAEE, 12–150 https://doi.org/10.2139/ssrn.2187632, 2012. a
Hirth, L. and Müller, S.: System-friendly wind power: How advanced wind turbine design can increase the economic value of electricity generated through wind power, Energ. Econ., 56, 51–63, https://doi.org/10.1016/j.eneco.2016.02.016, 2016. a, b, c, d
IPCC: International Panel on Climate Change: Climate Change 2007: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J., and Hanson, C. E., Cambridge University Press, Cambridge, UK, ISBN 978052188010-7, 2007. a, b, c, d
IRENA – International Renewable Energy Agency: Renewable Power Generation Costs in 2020, Abhu Dhabi, ISBN 978-92-9260-348-9, 2021. a
Jansen, M., Staffell, I., Kitzing, L., Quoilin, S., Wiggelinkhuizen, E., Bulder, B., Riepin, I., and Müsgens, F.: Offshore wind competitiveness in mature markets without subsidy, Nat. Energy, 5, 614–622,
https://doi.org/10.1038/s41560-020-0661-2, 2020. a
Joskow, P.: Comparing the costs of intermittent and dispatchable electricity generation technologies, Am. Econ. Rev. Pap. Proc., 100, 238–241,
https://doi.org/10.1257/aer.101.3.238, 2011. a, b
Kikstra, J. S., Waidelich, P., Rising, J., Yumahev, D., Hope, C., and Brierley, C. M.: The social cost of carbon dioxide under climate-economy feedbacks and temperature variability, Environ. Res. Lett., 16, 094037,
https://doi.org/10.1088/1748-9326/ac1d0b, 2021. a, b
Koffi, B., Cerutti, A. K., Duerr, M., Iancu, A., Kona, A., and Janssens-Maenhout, G.: Covenant of Mayors for Climate and Energy: Default emission factors for local emission inventories, Technical Report EU 28718 EN, Publications Office of the European Union, Luxembourg,
https://doi.org/10.2760/290197, 2017. a
Lantz, E., Wiser, R., and Hand, M.: IEA Wind Task 26 – The Past and Future Cost of Wind Energy, technical report NREL/TP-6A20-53510, NREL, Golden, CO, USA, https://www.nrel.gov/docs/fy12osti/53510.pdf (last access: 24 November 2021), 2012. a
Lantz, E., Dalla Riva, A., Hethey, J., and Vitina, A.: Impacts of Wind Turbine Technology on the System Value of Wind in Europe, technical report NREL/TP-6A20-70337, NREL, Golden, CO, USA, https://doi.org/10.2172/1437346, 2017. a, b, c
Mathworks: MATLAB 2019a, Fmincon, The MathWorks, Inc., Natick, Massachusetts, USA, https://www.mathworks.com/help/optim/ug/fmincon.html (last access: 10 December 2021), 2019. a
Myhre, G., Shindell, G. Breon, F.-M., Collins, W., Fuglestvedt, J., Huang, F., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and Natural Radiative Forcing, in: book section 8, Cambridge University Press, 659–740, https://doi.org/10.1017/CBO9781107415324.018, 2013. a, b
National Academies of Sciences, Engineering and Medicine: Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide, The National Academies Press, Washington DC, https://doi.org/10.17226/24651, 2017. a, b
Pfaffel, S., Faulstich, S., and Rohrig, K.:Performance and Reliability of Wind Turbines: A Review, Energies, 10, 1904, https://doi.org/10.3390/en10111904, 2017. a
Ramirez-Tejeda, K., Turcotte, D., and Pike, S.: Unsustainable Wind Turbine Blade Disposal Practices in the United States: A Case for Policy Intervention and Technological Innovation, New Solut., 26, 581–598, https://doi.org/10.1177/1048291116676098, 2017. a
Ricke, K., Drouet, L., Caldeira, K., and Tavoni, M.: Country-level social cost of carbon, Nat. Clim. Change, 8, 895–900, https://doi.org/10.1038/s41558-018-0282-y, 2018. a, b
Roser, M.: Why did renewables become so cheap so fast?, Our World in Data,
https://ourworldindata.org/cheap-renewables-growth, last access: 29 December 2021. a
Schleisner, L.: Life cycle assessment of a wind farm and related externalities, Renew. Energy, 20, 279–288, https://doi.org/10.1016/S0960-1481(99)00123-8, 2000. a
Seckinger, N. and Radgen, P.: Dynamic Prospective Average and Marginal GHG Emission Factors – Scenario-Based Method for the German Power System until 2050, Energies, 14, 2527, https://doi.org/10.3390/en14092527, 2021. a, b
Seshadri, A.: NSGA-II: A multi-objective optimization algorithm, MATLAB Central File Exchange,
https://www.mathworks.com/matlabcentral/fileexchange/10429-nsga-ii-a-multi-objective-optimization-algorithm,
last access: 14 September 2020. a
Silver-Evans, K., Azevedeo, I. L., and Morgan, M. G.: Marginal Emissions for the U.S. Electricity, Environ. Sci. Technol., 46, 4742–4748, https://doi.org/10.1021/es300145v, 2012. a, b, c
Simpson, J., Loth, E., and Dykes, K.: Cost of Valued Energy for design of renewable energy systems, Renew. Energy, 153, 290–300,
https://doi.org/10.1016/j.renene.2020.01.131, 2020. a, b
Song, Y., Youn, J., and Gutowski, T.: Life-cycle energy analysis of fiber-reinforced composites, Composites Pt. A, 40, 1257–1265, 2009. a
Swisher, P., Murcia Leon, J. P., Gea Bermúdez, J., Koivisto, M. J., Madsen, H. A., and Münster, M.: Competitiveness of a low specific power, low cut-out wind speed wind turbine in North and Central Europe towards 2050, Appl. Energy, 306, 118043, https://doi.org/10.1016/j.apenergy.2021.118043, 2022. a, b, c
Tremeac, B. and Meunier, F.: Life cycle analysis of 4.5 MW and 250 W wind turbines, Renew. Sustain. Energ. Rev., 13, 2104–2110, https://doi.org/10.1016/j.rser.2009.01.001, 2009. a, b, c, d
Ueckerdt, F., Hirth, L., Luderer, G., and Edenhofer, O.: System LCOE: What are the costs of variable renewables?, Energy, 63, 61–75,
https://doi.org/10.1016/j.energy.2013.10.072, 2013. a, b
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O., Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki, V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P., Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J., Robertson, A., Sanz Rodrigo, J., Sempreviva, A. M., Smith, J. C., Tuohy, A., and Wiser, R.: Grand challenges in the science of wind energy, Science, 366,
6464, https://doi.org/10.1126/science.aau2027, 2019. a
Vestas: Life Cycle Assessment of Electricity Production from a V90-2.0 MW Gridstreamer Wind Plant, Technical report, Aahrus, Denmark, https://www.vestas.com/content/dam/vestas-com/global/en/sustainability/reports-and-ratings/lcas/LCA_V902MW_version1.pdf.coredownload.inline.pdf (last access: 23 June 2023), 2011. a, b, c, d, e, f, g, h, i
Vestas: Life Cycle Assessment of Electricity Production from an onshore V100-2.6 MW Wind Plant, Technical report, Aahrus, Denmark, https://www.vestas.com/content/dam/vestas-com/global/en/sustainability/reports-and-ratings/lcas/LCA_V1002_6MW_version_1_1.pdf.coredownload.inline.pdf
(last access: 10 December 2021), 2013a. a, b, c, d, e, f, g, h, i
Vestas: Life Cycle Assessment of Electricity Production from an onshore V90-3.0 MW Wind Plant, Technical report, Aahrus, Denmark, https://www.vestas.com/content/dam/vestas-com/global/en/sustainability/reports-and-ratings/lcas/LCA_V903MW_version_1_1.pdf.coredownload.inline.pdf
(last access: 10 December 2021), 2013b. a, b, c, d, e, f, g, h, i
Wolf, M. A., Pant, R., Chomkhamsri, K., Sala, S., and Pennington, D.: The International Reference Life Cycle Data System (ILCD) Handbook – Towards more sustainable production and consumption for a resource-efficient Europe, Technical report, Joint Research Center, European Commission, ISBN 978-92-79-21640-4, 2012. a, b
Zampori, L. and Pant, R.: Suggestions for updating the Product Environmental Footprint (PEF) method, EUR 29682 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-76-00654-1, https://doi.org/10.2760/424613, 2019. a
Short summary
We propose a new approach to design that aims at optimal trade-offs between economic and environmental goals. New environmental metrics are defined, which quantify impacts in terms of CO2-equivalent emissions produced by the turbine over its entire life cycle. For some typical onshore installations in Germany, results indicate that a 1 % increase in the cost of energy can buy about a 5 % decrease in environmental impacts: a small loss for the individual can lead to larger gains for society.
We propose a new approach to design that aims at optimal trade-offs between economic and...
Altmetrics
Final-revised paper
Preprint
Similar articles
Enabling efficient sizing of hybrid power...
Assaad et al.
Designing wind turbines for profitability...
Mehta et al.
Aerodynamic effects of leading-edge...
Visbech et al.