Articles | Volume 9, issue 2
https://doi.org/10.5194/wes-9-321-2024
© Author(s) 2024. 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-9-321-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Feb 2024
Research article |
| 07 Feb 2024
| 07 Feb 2024
Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout
Rafael Valotta Rodrigues
CORRESPONDING AUTHOR
Wind and Energy Systems Department, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
now at: Electrical and Computer Engineering Department, University of Massachusetts Boston, 100 Morissey Blvd, Boston, MA 02125, United States of America
Mads Mølgaard Pedersen
Wind and Energy Systems Department, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Jens Peter Schøler
Wind and Energy Systems Department, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Julian Quick
Wind and Energy Systems Department, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Pierre-Elouan Réthoré
Wind and Energy Systems Department, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Related authors
Javier Criado Risco, Rafael Valotta Rodrigues, Mikkel Friis-Møller, Julian Quick, Mads Mølgaard Pedersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 585–600, https://doi.org/10.5194/wes-9-585-2024, https://doi.org/10.5194/wes-9-585-2024, 2024
Short summary
Short summary
Wind energy developers frequently have to face some spatial restrictions at the time of designing a new wind farm due to different reasons, such as the existence of protected natural areas around the wind farm location, fishing routes, and the presence of buildings. Wind farm design has to account for these restricted areas, but sometimes this is not straightforward to achieve. We have developed a methodology that allows for different inclusion and exclusion areas in the optimization framework.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
Short summary
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Thuy-Hai Nguyen, Julian Quick, Pierre-Elouan Réthoré, Jean-François Toubeau, Emmanuel De Jaeger, and François Vallée
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-131, https://doi.org/10.5194/wes-2024-131, 2024
Preprint under review for WES
Short summary
Short summary
Current offshore wind farms have been designed to maximize their production of electricity at all times, and not to keep some reserve power in case of unexpected events on the grid. We present a new formulation for designing wind farms to maximize revenues from both energy and reserve markets. It is applied on a real-life wind farm. We show that profits are expected to increase in a significant way for wind farms designed and operated for reserve, with less energy supplied.
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.
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-96, https://doi.org/10.5194/wes-2024-96, 2024
Preprint under review for WES
Short summary
Short summary
This research develops a new method for assessing Hybrid Power Plants (HPPs) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art Energy Management System (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.
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.
Javier Criado Risco, Rafael Valotta Rodrigues, Mikkel Friis-Møller, Julian Quick, Mads Mølgaard Pedersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 585–600, https://doi.org/10.5194/wes-9-585-2024, https://doi.org/10.5194/wes-9-585-2024, 2024
Short summary
Short summary
Wind energy developers frequently have to face some spatial restrictions at the time of designing a new wind farm due to different reasons, such as the existence of protected natural areas around the wind farm location, fishing routes, and the presence of buildings. Wind farm design has to account for these restricted areas, but sometimes this is not straightforward to achieve. We have developed a methodology that allows for different inclusion and exclusion areas in the optimization framework.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
Short summary
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
Short summary
Short summary
Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher-fidelity numerical models and validated with measurements of an existing wind farm cluster.
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.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, https://doi.org/10.5194/wes-7-1069-2022, 2022
Short summary
Short summary
Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Mads M. Pedersen and Gunner C. Larsen
Wind Energ. Sci., 5, 1551–1566, https://doi.org/10.5194/wes-5-1551-2020, https://doi.org/10.5194/wes-5-1551-2020, 2020
Short summary
Short summary
In this paper, the influence of optimal wind farm control and optimal wind farm layout is investigated in terms of power production. The capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm, Lillgrund. It shows that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.
Maarten Paul van der Laan, Søren Juhl Andersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 4, 645–651, https://doi.org/10.5194/wes-4-645-2019, https://doi.org/10.5194/wes-4-645-2019, 2019
Short summary
Short summary
Wind farm layouts are designed by simple engineering wake models, which are fast to compute but also include a high uncertainty. Higher-fidelity models, such as Reynolds-averaged Navier–Stokes, can be used to verify optimized wind farm layouts, although the computational costs are high due to the large number of cases that are needed to calculate the annual energy production. This article presents a new wind turbine control method to speed up the high-fidelity simulations by a factor of 2–3.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Gunner Christian Larsen
Wind Energ. Sci., 4, 303–323, https://doi.org/10.5194/wes-4-303-2019, https://doi.org/10.5194/wes-4-303-2019, 2019
Short summary
Short summary
In this paper, detailed inflow information extracted from measurements is used to improve the accuracy of simulated wind turbine fatigue loads. Inflow information from nearby met masts is utilised as well as information from a blade-mounted flow sensor in combination with a method to compensate for the disturbance to the flow caused by the presence of the wind turbine.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Søren Juhl Andersen
Wind Energ. Sci., 3, 121–138, https://doi.org/10.5194/wes-3-121-2018, https://doi.org/10.5194/wes-3-121-2018, 2018
Short summary
Short summary
The wind speed measured by a flow sensor mounted on the blade of a wind turbine is disturbed by the turbine. This paper presents a method to obtain the free turbulence inflow by compensating for this disturbance.
The method is tested using numerical simulations and can be used to extract inflow information for accurate aeroelastic load simulations.
Mads M. Pedersen, Torben J. Larsen, Helge Aa. Madsen, and Gunner Chr. Larsen
Wind Energ. Sci., 2, 547–567, https://doi.org/10.5194/wes-2-547-2017, https://doi.org/10.5194/wes-2-547-2017, 2017
Short summary
Short summary
This paper presents an alternative method to evaluate power performance and loads on wind turbines using a blade-mounted flow sensor. A high correlation is found between the wind speed measured at the blades and the power/loads, and simulations indicate that it is possible to reduce the time required for power and load assessment considerably. This result, however, cannot be confirmed from the full-scale measurement study due to practical circumstances.
Dalibor Cavar, Pierre-Elouan Réthoré, Andreas Bechmann, Niels N. Sørensen, Benjamin Martinez, Frederik Zahle, Jacob Berg, and Mark C. Kelly
Wind Energ. Sci., 1, 55–70, https://doi.org/10.5194/wes-1-55-2016, https://doi.org/10.5194/wes-1-55-2016, 2016
Short summary
Short summary
Feasibility of a freely available CFD tool, OpenFOAM, in calculating flows of general relevance to the wind industry is investigated by comparing several aspects of its performance to a well-established in-house EllipSys3D solver. The comparison is focused on CFD solver demands regarding grid generation process and computational time.
The quality and accuracy of the achieved results are investigated by conducting the computations using identical/similar solver parameters and numerical setups..
Related subject area
Thematic area: Wind technologies | Topic: Design concepts and methods for plants, turbines, and components
One-to-one aeroservoelastic validation of operational loads and performance of a 2.8 MW wind turbine model in OpenFAST
Identification of electro-mechanical interactions in wind turbines
Identification of operational deflection shapes of a wind turbine gearbox using fiber-optic strain sensors on a serial production end-of-line test bench
A sensitivity-based estimation method for investigating control co-design relevance
Validation of aeroelastic dynamic model of active trailing edge flap system tested on a 4.3 MW wind turbine
Effect of Blade Inclination Angle for Straight Bladed Vertical Axis Wind Turbines
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
Gradient-based wind farm layout optimization with inclusion and exclusion zones
A novel techno-economical layout optimization tool for floating wind farm design
Hybrid-Lambda: a low-specific-rating rotor concept for offshore wind turbines
Nonlinear vibration characteristics of virtual mass systems for wind turbine blade fatigue testing
Extreme wind turbine response extrapolation with the Gaussian mixture model
The effect of site-specific wind conditions and individual pitch control on wear of blade bearings
A neighborhood search integer programming approach for wind farm layout optimization
Enabling control co-design of the next generation of wind power plants
Offshore wind farm optimisation: a comparison of performance between regular and irregular wind turbine layouts
A data-driven reduced-order model for rotor optimization
Grand challenges in the design, manufacture, and operation of future wind turbine systems
Computational fluid dynamics (CFD) modeling of actual eroded wind turbine blades
Grand Challenges: wind energy research needs for a global energy transition
Current status and grand challenges for small wind turbine technology
CFD-based curved tip shape design for wind turbine blades
Impacts of wind field characteristics and non-steady deterministic wind events on time-varying main-bearing loads
Kenneth Brown, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024, https://doi.org/10.5194/wes-9-1791-2024, 2024
Short summary
Short summary
This paper presents a study of the popular wind turbine design tool OpenFAST. We compare simulation results to measurements obtained from a 2.8 MW land-based wind turbine. Measured wind conditions were used to generate turbulent flow fields through several techniques. We show that successful validation of the tool is not strongly dependent on the inflow generation technique used for mean quantities of interest. The type of inflow assimilation method has a larger effect on fatigue quantities.
Fiona Dominique Lüdecke, Martin Schmid, and Po Wen Cheng
Wind Energ. Sci., 9, 1527–1545, https://doi.org/10.5194/wes-9-1527-2024, https://doi.org/10.5194/wes-9-1527-2024, 2024
Short summary
Short summary
Large direct-drive wind turbines, with a multi-megawatt power rating, face design challenges. Moving towards a more system-oriented design approach could potentially reduce mass and costs. Exploiting the full design space, though, may invoke interaction mechanisms, which have been neglected in the past. Based on coupled simulations, this work derives a better understanding of the electro-mechanical interaction mechanisms and identifies potential for design relevance.
Unai Gutierrez Santiago, Aemilius van Vondelen, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-83, https://doi.org/10.5194/wes-2024-83, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Knowing the loads applied to wind turbine gearboxes throughout their service life is becoming increasingly important. Operational deflection shapes identified from fiber-optic strain measurements have enabled the estimation of the gearbox input torque. This allows for future improvements in assessing the remaining useful life. Additionally, tracking the operational deflection shapes over time could enhance condition monitoring in planetary gear stages.
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.
Andrea Gamberini, Thanasis Barlas, Alejandro Gomez Gonzalez, and Helge Aagaard Madsen
Wind Energ. Sci., 9, 1229–1249, https://doi.org/10.5194/wes-9-1229-2024, https://doi.org/10.5194/wes-9-1229-2024, 2024
Short summary
Short summary
Movable surfaces on wind turbine (WT) blades, called active flaps, can reduce the cost of wind energy. However, they still need extensive testing. This study shows that the computer model used to design a WT with flaps aligns well with measurements obtained from a 3month test on a commercial WT featuring a prototype flap. Particularly during flap actuation, there were minimal differences between simulated and measured data. These findings assure the reliability of WT designs incorporating flaps.
Laurence Boyd Morgan, Abbas Kazemi Amiri, William Leithead, and James Carroll
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-42, https://doi.org/10.5194/wes-2024-42, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This paper presents a systematic study into the effect of blade inclination angle, chord distribution, and blade length on vertical axis wind turbine performance. It is shown that for rotors of identical power production, both blade volume and rotor torque can be significantly reduced through the use of aerodynamically optimised inclined rotor blades. This demonstrates the potential of V-Rotors to reduce the cost of energy for offshore wind when compared to H-Rotors.
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 697–719, https://doi.org/10.5194/wes-9-697-2024, https://doi.org/10.5194/wes-9-697-2024, 2024
Short summary
Short summary
Wind farms at sea are becoming more densely clustered, which means that next to individual wind turbines interfering with each other in a single wind farm also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and are also highly sensitive to the spacing between the wind farms.
Javier Criado Risco, Rafael Valotta Rodrigues, Mikkel Friis-Møller, Julian Quick, Mads Mølgaard Pedersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 585–600, https://doi.org/10.5194/wes-9-585-2024, https://doi.org/10.5194/wes-9-585-2024, 2024
Short summary
Short summary
Wind energy developers frequently have to face some spatial restrictions at the time of designing a new wind farm due to different reasons, such as the existence of protected natural areas around the wind farm location, fishing routes, and the presence of buildings. Wind farm design has to account for these restricted areas, but sometimes this is not straightforward to achieve. We have developed a methodology that allows for different inclusion and exclusion areas in the optimization framework.
Amalia Ida Hietanen, Thor Heine Snedker, Katherine Dykes, and Ilmas Bayati
Wind Energ. Sci., 9, 417–438, https://doi.org/10.5194/wes-9-417-2024, https://doi.org/10.5194/wes-9-417-2024, 2024
Short summary
Short summary
The layout of a floating offshore wind farm was optimized to maximize the relative net present value (NPV). By modeling power generation, losses, inter-array cables, anchors and operational costs, an increase of EUR 34.5 million in relative NPV compared to grid-based layouts was achieved. A sensitivity analysis was conducted to examine the impact of economic factors, providing valuable insights. This study contributes to enhancing the efficiency and cost-effectiveness of floating wind farms.
Daniel Ribnitzky, Frederik Berger, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 9, 359–383, https://doi.org/10.5194/wes-9-359-2024, https://doi.org/10.5194/wes-9-359-2024, 2024
Short summary
Short summary
This paper provides an innovative blade design methodology for offshore wind turbines with very large rotors compared to their rated power, which are tailored for an increased power feed-in at low wind speeds. Rather than designing the blade for a single optimized operational point, we include the application of peak shaving in the design process and introduce a design for two tip speed ratios. We describe how enlargement of the rotor diameter can be realized to improve the value of wind power.
Aiguo Zhou, Jinlei Shi, Tao Dong, Yi Ma, and Zhenhui Weng
Wind Energ. Sci., 9, 49–64, https://doi.org/10.5194/wes-9-49-2024, https://doi.org/10.5194/wes-9-49-2024, 2024
Short summary
Short summary
This paper explores the nonlinear influence of the virtual mass mechanism on the test system in blade biaxial tests. The blade theory and simulation model are established to reveal the nonlinear amplitude–frequency characteristics of the blade-virtual-mass system. Increasing the amplitude of the blade or decreasing the seesaw length will lower the resonance frequency and load of the system. The virtual mass also affects the blade biaxial trajectory.
Xiaodong Zhang and Nikolay Dimitrov
Wind Energ. Sci., 8, 1613–1623, https://doi.org/10.5194/wes-8-1613-2023, https://doi.org/10.5194/wes-8-1613-2023, 2023
Short summary
Short summary
Wind turbine extreme response estimation based on statistical extrapolation necessitates using a small number of simulations to calculate a low exceedance probability. This is a challenging task especially if we require small prediction error. We propose the use of a Gaussian mixture model as it is capable of estimating a low exceedance probability with minor bias error, even with limited simulation data, having flexibility in modeling the distributions of varying response variables.
Arne Bartschat, Karsten Behnke, and Matthias Stammler
Wind Energ. Sci., 8, 1495–1510, https://doi.org/10.5194/wes-8-1495-2023, https://doi.org/10.5194/wes-8-1495-2023, 2023
Short summary
Short summary
Blade bearings are among the most stressed and challenging components of a wind turbine. Experimental investigations using different test rigs and real-size blade bearings have been able to show that rather short time intervals of only several hours of turbine operation can cause wear damage on the raceways of blade bearings. The proposed methods can be used to assess wear-critical operation conditions and to validate control strategies as well as lubricants for the application.
Juan-Andrés Pérez-Rúa, Mathias Stolpe, and Nicolaos Antonio Cutululis
Wind Energ. Sci., 8, 1453–1473, https://doi.org/10.5194/wes-8-1453-2023, https://doi.org/10.5194/wes-8-1453-2023, 2023
Short summary
Short summary
With the challenges of ensuring secure energy supplies and meeting climate targets, wind energy is on course to become the cornerstone of decarbonized energy systems. This work proposes a new method to optimize wind farms by means of smartly placing wind turbines within a given project area, leading to more green-energy generation. This method performs satisfactorily compared to state-of-the-art approaches in terms of the resultant annual energy production and other high-level metrics.
Andrew P. J. Stanley, Christopher J. Bay, and Paul Fleming
Wind Energ. Sci., 8, 1341–1350, https://doi.org/10.5194/wes-8-1341-2023, https://doi.org/10.5194/wes-8-1341-2023, 2023
Short summary
Short summary
Better wind farms can be built by simultaneously optimizing turbine locations and control, which is currently impossible or extremely challenging because of the size of the problem. The authors present a method to determine optimal wind farm control as a function of the turbine locations, which enables turbine layout and control to be optimized together by drastically reducing the size of the problem. In an example, a wind farm's performance improves by 0.8 % when optimized with the new method.
Maaike Sickler, Bart Ummels, Michiel Zaaijer, Roland Schmehl, and Katherine Dykes
Wind Energ. Sci., 8, 1225–1233, https://doi.org/10.5194/wes-8-1225-2023, https://doi.org/10.5194/wes-8-1225-2023, 2023
Short summary
Short summary
This paper investigates the effect of wind farm layout on the performance of offshore wind farms. A regular farm layout is compared to optimised irregular layouts. The irregular layouts have higher annual energy production, and the power production is less sensitive to wind direction. However, turbine towers require thicker walls to counteract increased fatigue due to increased turbulence levels in the farm. The study shows that layout optimisation can be used to maintain high-yield performance.
Nicholas Peters, Christopher Silva, and John Ekaterinaris
Wind Energ. Sci., 8, 1201–1223, https://doi.org/10.5194/wes-8-1201-2023, https://doi.org/10.5194/wes-8-1201-2023, 2023
Short summary
Short summary
Wind turbines have increasingly been leveraged as a viable approach for obtaining renewable energy. As such, it is essential that engineers have a high-fidelity, low-cost approach to modeling rotor load distributions. In this study, such an approach is proposed. This modeling approach was shown to make high-fidelity predictions at a low computational cost for rotor distributed-pressure loads as rotor geometry varied, allowing for an optimization of the rotor to be completed.
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.
Kisorthman Vimalakanthan, Harald van der Mijle Meijer, Iana Bakhmet, and Gerard Schepers
Wind Energ. Sci., 8, 41–69, https://doi.org/10.5194/wes-8-41-2023, https://doi.org/10.5194/wes-8-41-2023, 2023
Short summary
Short summary
Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge is required to optimize blade maintenance. Providing accurate modeling tools is therefore essential. This novel study assesses CFD approaches for modeling high-resolution scanned LE surfaces from an actual blade with LEE damages.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Alessandro Bianchini, Galih Bangga, Ian Baring-Gould, Alessandro Croce, José Ignacio Cruz, Rick Damiani, Gareth Erfort, Carlos Simao Ferreira, David Infield, Christian Navid Nayeri, George Pechlivanoglou, Mark Runacres, Gerard Schepers, Brent Summerville, David Wood, and Alice Orrell
Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, https://doi.org/10.5194/wes-7-2003-2022, 2022
Short summary
Short summary
The paper is part of the Grand Challenges Papers for Wind Energy. It provides a status of small wind turbine technology in terms of technical maturity, diffusion, and cost. Then, five grand challenges that are thought to be key to fostering the development of the technology are proposed. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Improvement areas are highlighted, within which 10 key enabling actions are finally proposed to the wind community.
Mads H. Aa. Madsen, Frederik Zahle, Sergio González Horcas, Thanasis K. Barlas, and Niels N. Sørensen
Wind Energ. Sci., 7, 1471–1501, https://doi.org/10.5194/wes-7-1471-2022, https://doi.org/10.5194/wes-7-1471-2022, 2022
Short summary
Short summary
This work presents a shape optimization framework based on computational fluid dynamics. The design framework is used to optimize wind turbine blade tips for maximum power increase while avoiding that extra loading is incurred. The final results are shown to align well with related literature. The resulting tip shape could be mounted on already installed wind turbines as a sleeve-like solution or be conceived as part of a modular blade with tips designed for site-specific conditions.
Edward Hart, Adam Stock, George Elderfield, Robin Elliott, James Brasseur, Jonathan Keller, Yi Guo, and Wooyong Song
Wind Energ. Sci., 7, 1209–1226, https://doi.org/10.5194/wes-7-1209-2022, https://doi.org/10.5194/wes-7-1209-2022, 2022
Short summary
Short summary
We consider characteristics and drivers of loads experienced by wind turbine main bearings using simplified models of hub and main-bearing configurations. Influences of deterministic wind characteristics are investigated for 5, 7.5, and 10 MW turbine models. Load response to gusts and wind direction changes are also considered. Cubic load scaling is observed, veer is identified as an important driver of load fluctuations, and strong links between control and main-bearing load response are shown.
Cited articles
Abdulrahman, M. and Wood, D.: Investigating the Power-COE trade-off for wind farm layout optimization considering commercial turbine selection and hub height variation, Renew. Energ., 102, 267–278, 2017. a
Allen, J., King, R., and Barter, G.: Wind farm simulation and layout optimization in complex terrain, J. Phys., 1452, 012066, https://doi.org/10.1088/1742-6596/1452/1/012066, 2020. a
Baker, N. F., Stanley, A. P., Thomas, J. J., Ning, A., and Dykes, K.: Best practices for wake model and optimization algorithm selection in wind farm layout optimization, in: AIAA Scitech 2019 forum, p. 0540, https://doi.org/10.2514/6.2019-0540, 2019. a, b, c, d
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energ., 70, 116–123, 2014. a
Bortolotti, P., Dykes, K., Merz, K., and Zahle, F.: IEA Wind Task 37 on systems engineering in wind energy, WP2-Reference Wind Turbines: IEA Wind Task, 37, https://doi.org/10.2172/1529216, 2019. a
Brogna, R., Feng, J., Sørensen, J. N., Shen, W. Z., and Porté-Agel, F.: A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain, Appl. Energ., 259, 114189, https://doi.org/10.1016/j.apenergy.2019.114189, 2020. a, b
Ciavarra, A. W., Rodrigues, R. V., Dykes, K., and Réthoré, P.-E.: Wind farm optimization with multiple hub heights using gradient-based methods, J. Phys. Conf. Ser., 2265, 022012, https://doi.org/10.1088/1742-6596/2265/2/022012, 2022. a
Criado Risco, J., Valotta Rodrigues, R., Friis-Møller, M., Quick, J., Mølgaard Pedersen, M., and Réthoré, P.-E.: Gradient-based Wind Farm Layout Optimization With Inclusion And Exclusion Zones, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-5, in review, 2023. a
Croonenbroeck, C. and Hennecke, D.: A comparison of optimizers in a unified standard for optimization on wind farm layout optimization, Energy, 216, 119244, https://doi.org/10.1016/j.energy.2020.119244, 2021. a, b, c
Feng, J. and Shen, W. Z.: Wind farm power production in the changing wind: Robustness quantification and layout optimization, Energ. Convers. Manage., 148, 905–914, 2017b. a
Fischereit, J., Schaldemose Hansen, K., Larsén, X. G., van der Laan, M. P., Réthoré, P.-E., and Murcia Leon, J. P.: Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models, Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, 2022. a
Gao, X., Yang, H., Lin, L., and Koo, P.: Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore, J. Wind Eng. Ind. Aerod., 139, 89–99, 2015. a
González, J. S., Payán, M. B., and Santos, J. M. R.: Optimal design of neighbouring offshore wind farms: A co-evolutionary approach, Appl. Energ., 209, 140–152, 2018. a
Guirguis, D., Romero, D. A., and Amon, C. H.: Gradient-based multidisciplinary design of wind farms with continuous-variable formulations, Appl. Energ., 197, 279–291, 2017. a
GWEC: GLOBAL WIND ENERGY COUNCIL, Global Wind Report, https://gwec.net/global-wind-report-2022/ (last access: 5 February 2024), 2022. a
IEA Wind Task 37: IEA Wind Task 37: WFLOCS announcement, https://github.com/IEAWindTask37/iea37-wflo-casestudies/blob/master/cs1-2/iea37-wflocs-announcement.pdf (last access: 25 April 2023), 2018. a
King, R. N., Dykes, K., Graf, P., and Hamlington, P. E.: Optimization of wind plant layouts using an adjoint approach, Wind Energ. Sci., 2, 115–131, https://doi.org/10.5194/wes-2-115-2017, 2017. a
Kirchner-Bossi, N. and Porté-Agel, F.: Realistic wind farm layout optimization through genetic algorithms using a Gaussian wake model, Energies, 11, 3268, https://doi.org/10.3390/en11123268, 2018. a
Liu, D. C. and Nocedal, J.: On the limited memory BFGS method for large scale optimization, Math. Programm., 45, 503–528, 1989. a
Maclaurin, D., Duvenaud, D., and Adams, R. P.: Autograd: Effortless gradients in numpy, in: ICML 2015 AutoML workshop, vol. 238, https://github.com/HIPS/autograd (last access: 29 January 2024), 2015. a
Mittal, P., Kulkarni, K., and Mitra, K.: A novel hybrid optimization methodology to optimize the total number and placement of wind turbines, Renew. Energ., 86, 133–147, 2016. a
Mittal, P., Mitra, K., and Kulkarni, K.: Optimizing the number and locations of turbines in a wind farm addressing energy-noise trade-off: A hybrid approach, Energ. Convers. Manage., 132, 147–160, 2017. a
Mosetti, G., Poloni, C., and Diviacco, B.: Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm, J. Wind Eng. Ind. Aerod., 51, 105–116, 1994. a
Ning, A., Dykes, K., and Quick, J.: Systems engineering and optimization of wind turbines and power plants, Institution of Engineering and Technology, vol. 2, 235–292, ISBN 9781785615238, https://doi.org/10.1049/pbpo125g_ch7, 2019. a
Nyborg, C. M., Fischer, A., Réthoré, P.-E., and Feng, J.: Optimization of wind farm operation with a noise constraint, Wind Energ. Sci., 8, 255–276, https://doi.org/10.5194/wes-8-255-2023, 2023. a
Padrón, A. S., Thomas, J., Stanley, A. P. J., Alonso, J. J., and Ning, A.: Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization, Wind Energ. Sci., 4, 211–231, https://doi.org/10.5194/wes-4-211-2019, 2019. a
Pedersen, M. M. and Larsen, G. C.: Integrated wind farm layout and control optimization, Wind Energ. Sci., 5, 1551–1566, https://doi.org/10.5194/wes-5-1551-2020, 2020. a
Pedersen, M. M., Forsting, A. M., Riva, R., Romàn, L. A. A., Risco, J. C., Friis-Møller, M., Rodrigues, R. V., Quick, J., Christiansen, J. P. S., and Réthoré, P.-E.: PyWake 2.4.0: An open-source wind farm simulation tool, https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake (31 January 2024), 2022. a
Pedersen, M. M., van der Laan, P., Friis-Møller, M., Forsting, A. M., Riva, R., Romàn, L. A. A., Risco, J. C., Quick, J., Christiansen, J. P. S., Olsen, B. T., Rodrigues, R. V., and Réthoré, P.-E.: DTUWindEnergy/PyWake: PyWake, Zenodo [code], https://doi.org/10.5281/zenodo.6806136, 2023. a
Pérez, B., Mínguez, R., and Guanche, R.: Offshore wind farm layout optimization using mathematical programming techniques, Renew. Energ., 53, 389–399, 2013. a
Perez, R. E., Jansen, P. W., and Martins, J. R.: pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization, Struct. Multidisc. O., 45, 101–118, 2012. a
Pérez-Rúa, J.-A. and Cutululis, N. A.: A framework for simultaneous design of wind turbines and cable layout in offshore wind, Wind Energ. Sci., 7, 925–942, https://doi.org/10.5194/wes-7-925-2022, 2022. a
Pillai, A. C., Chick, J., Khorasanchi, M., Barbouchi, S., and Johanning, L.: Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm, Ocean Eng., 139, 287–297, 2017. a
Pookpunt, S. and Ongsakul, W.: Design of optimal wind farm configuration using a binary particle swarm optimization at Huasai district, Southern Thailand, Energ. Convers. Manage.t, 108, 160–180, 2016. a
Powell, M. J.: An efficient method for finding the minimum of a function of several variables without calculating derivatives, Comput. J., 7, 155–162, 1964. a
Quick, J., Rethore, P.-E., Mølgaard Pedersen, M., Rodrigues, R. V., and Friis-Møller, M.: Stochastic gradient descent for wind farm optimization, Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, 2023. a
Réthoré, P.-E., Fuglsang, P., Larsen, G. C., Buhl, T., Larsen, T. J., and Madsen, H. A.: TOPFARM: Multi-fidelity optimization of wind farms, Wind Energ., 17, 1797–1816, 2014. a
Rodrigues, R. V.: Data Used for Article: Speeding up large wind farms layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout, Zenodo [data set], https://doi.org/10.5281/zenodo.10402450, 2023. a
Rodrigues, R. V., Friis-Møller, M., Dykes, K., Pollini, N., and Jensen, M.: A surrogate model of offshore wind farm annual energy production to support financial valuation, J. Phys. Conf. Ser., 2265, 022003, https://doi.org/10.1088/1742-6596/2265/2/022003, 2022. a
Rodrigues, S., Bauer, P., and Bosman, P. A.: Multi-objective optimization of wind farm layouts–Complexity, constraint handling and scalability, Renew. Sustain. Energ. Rev., 65, 587–609, 2016. a
Rodrigues, S. F., Pinto, R. T., Soleimanzadeh, M., Bosman, P. A., and Bauer, P.: Wake losses optimization of offshore wind farms with moveable floating wind turbines, Energ. Convers. Manage., 89, 933–941, 2015. a
Stanley, A. P. J. and Ning, A.: Massive simplification of the wind farm layout optimization problem, Wind Energ. Sci., 4, 663–676, https://doi.org/10.5194/wes-4-663-2019, 2019. a
Stanley, A. P., Ning, A., and Dykes, K.: Optimization of turbine design in wind farms with multiple hub heights, using exact analytic gradients and structural constraints, Wind Energ., 22, 605–619, 2019. a
Technical University of Denmark: Sophia HPC Cluster, Research Computing at DTU, https://doi.org/10.57940/FAFC-6M81, 2019.
Thomas, J. J., Bay, C. J., Stanley, A. P. J., and Ning, A.: Gradient-Based Wind Farm Layout Optimization Results Compared with Large-Eddy Simulations, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-4, 2022. a, b, c
Thomas, J. J., Baker, N. F., Malisani, P., Quaeghebeur, E., Sanchez Perez-Moreno, S., Jasa, J., Bay, C., Tilli, F., Bieniek, D., Robinson, N., Stanley, A. P. J., Holt, W., and Ning, A.: A comparison of eight optimization methods applied to a wind farm layout optimization problem, Wind Energ. Sci., 8, 865–891, https://doi.org/10.5194/wes-8-865-2023, 2023. a
Tingey, E. B. and Ning, A.: Trading off sound pressure level and average power production for wind farm layout optimization, Renew. Energ., 114, 547–555, 2017. a
van Dijk, M. T., van Wingerden, J.-W., Ashuri, T., and Li, Y.: Wind farm multi-objective wake redirection for optimizing power production and loads, Energy, 121, 561–569, 2017. a
Veeramachaneni, K., Wagner, M., O'Reilly, U.-M., and Neumann, F.: Optimizing energy output and layout costs for large wind farms using particle swarm optimization, in: 2012 IEEE Congress on Evolutionary Computation, IEEE, 1–7, https://doi.org/10.1109/cec.2012.6253002, 2012. a
Virtanen, P., Gommers, P., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, R., Kern, R., Larson, E., Carey, C. J., Polat, I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., and van Mulbregt, P.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Meth., 17, 261–272, 2020. a
Wan, C., Wang, J., Yang, G., Gu, H., and Zhang, X.: Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy, Renew. Energ., 48, 276–286, 2012. a
Wright, S. and Nocedal, J.: Numerical optimization, Springer Science, 35, https://doi.org/10.1007/b98874, 1999. a
Wu, N., Kenway, G., Mader, C. A., Jasa, J., and Martins, J. R.: pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems, J. Open Source Softw., 5, 2564, https://doi.org/10.21105/joss.02564, 2020. a
Yang, K. and Deng, X.: Layout optimization for renovation of operational offshore wind farm based on machine learning wake model, J. Wind Eng. Ind. Aerod., 232, 105280, https://doi.org/10.1016/j.jweia.2022.105280, 2023. a
Short summary
The use of wind energy has been growing over the last few decades, and further increase is predicted. As the wind energy industry is starting to consider larger wind farms, the existing numerical methods for analysis of small and medium wind farms need to be improved. In this article, we have explored different strategies to tackle the problem in a feasible and timely way. The final product is a set of recommendations when carrying out trade-off analysis on large wind farms.
The use of wind energy has been growing over the last few decades, and further increase is...
Altmetrics
Final-revised paper
Preprint