Articles | Volume 8, issue 5
https://doi.org/10.5194/wes-8-865-2023
© 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-8-865-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jun 2023
Research article |
| 01 Jun 2023
| 01 Jun 2023
A comparison of eight optimization methods applied to a wind farm layout optimization problem
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
National Renewable Energy Laboratory, Golden, CO 80401, USA
Nicholas F. Baker
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
Paul Malisani
Applied Mathematics Department, IFP Energies nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France
Erik Quaeghebeur
Uncertainty in AI Group, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
Sebastian Sanchez Perez-Moreno
RWE Renewables GmbH, 20354 Hamburg, Germany
John Jasa
National Renewable Energy Laboratory, Golden, CO 80401, USA
Christopher Bay
National Renewable Energy Laboratory, Golden, CO 80401, USA
Federico Tilli
TU Delft, 2628 CD Delft, Netherlands
David Bieniek
RWE Renewables GmbH, 20354 Hamburg, Germany
Nick Robinson
UL Renewables, British Columbia, Kelowna, Canada
Andrew P. J. Stanley
National Renewable Energy Laboratory, Golden, CO 80401, USA
Wesley Holt
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
Andrew Ning
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA
National Renewable Energy Laboratory, Golden, CO 80401, USA
Related authors
Jared J. Thomas, Christopher J. Bay, Andrew P. J. Stanley, and Andrew Ning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-4, https://doi.org/10.5194/wes-2022-4, 2022
Revised manuscript not accepted
Short summary
Short summary
We wanted to determine if and how optimization algorithms may be exploiting inaccuracies in the simple models used for wind farm layout optimization. Comparing optimization results from a simple model to large-eddy simulations showed that even a simple model provides enough information for optimizers to find good layouts. However, varying the number of wind directions in the optimization showed that the wind resource discretization can negatively impact the optimization results.
Paul Malisani, Tristan Bartement, and Pauline Bozonnet
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-118, https://doi.org/10.5194/wes-2024-118, 2024
Preprint under review for WES
Short summary
Short summary
Maritime authorities often impose turbine alignment constraints on developers to secure the navigation of boats within the wind farm, potentially creating substantial wake losses. The proposed contribution is a new Wind Farm Layout Optimization (WFLO) algorithm that considers these constraints. The proposed method optimizes the grid-alignment parameters and the turbines' location on this grid. We illustrate the algorithm's performances on a challenging example.
Regis Thedin, Garrett Barter, Jason Jonkman, Rafael Mudafort, Christopher J. Bay, Kelsey Shaler, and Jasper Kreeft
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-6, https://doi.org/10.5194/wes-2024-6, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This work investigates asymmetries in terms of power performance and fatigue loading on a 5-turbine wind farm subject to wake steering strategies. Both the yaw misalignment angle and the wind direction were varied from negative to positive. We highlight conditions in which fatigue loading is lower while still maintenance good power gains and show that partial wake is the source of the asymmetries observed. We provide recommendations in terms of yaw misalignment angles for a given wind direction.
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.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
Short summary
Short summary
This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
Marco Mangano, Sicheng He, Yingqian Liao, Denis-Gabriel Caprace, Andrew Ning, and Joaquim R. R. A. Martins
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-10, https://doi.org/10.5194/wes-2023-10, 2023
Revised manuscript not accepted
Short summary
Short summary
High-fidelity MDO enables more effective system design than conventional approaches. MDO can shorten the wind turbine design cycle and reduce the cost of energy. We present a first-of-its-kind high-fidelity aerostructural optimization study of a turbine rotor using a coupled CFD-CSM solver. We simultaneously improve the rotor aerodynamic efficiency and reduce the mass of a rotor of a 10 MW wind turbine using 100+ design variables. We discuss the results with unprecedented detail.
Michael J. LoCascio, Christopher J. Bay, Majid Bastankhah, Garrett E. Barter, Paul A. Fleming, and Luis A. Martínez-Tossas
Wind Energ. Sci., 7, 1137–1151, https://doi.org/10.5194/wes-7-1137-2022, https://doi.org/10.5194/wes-7-1137-2022, 2022
Short summary
Short summary
This work introduces the FLOW Estimation and Rose Superposition (FLOWERS) wind turbine wake model. This model analytically integrates the wake over wind directions to provide a time-averaged flow field. This new formulation is used to perform layout optimization. The FLOWERS model provides a smooth flow field over an entire wind plant at fraction of the computational cost of the standard numerical integration approach.
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022, https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
Short summary
In wind plants, turbines can be yawed to steer their wakes away from downstream turbines and achieve an increase in plant power. The yaw angles become expensive to solve for in large farms. This paper presents a new method to solve for the optimal turbine yaw angles in a wind plant. The yaw angles are defined as Boolean variables – each turbine is either yawed or nonyawed. With this formulation, most of the gains from wake steering can be reached with a large reduction in computational expense.
Jared J. Thomas, Christopher J. Bay, Andrew P. J. Stanley, and Andrew Ning
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-4, https://doi.org/10.5194/wes-2022-4, 2022
Revised manuscript not accepted
Short summary
Short summary
We wanted to determine if and how optimization algorithms may be exploiting inaccuracies in the simple models used for wind farm layout optimization. Comparing optimization results from a simple model to large-eddy simulations showed that even a simple model provides enough information for optimizers to find good layouts. However, varying the number of wind directions in the optimization showed that the wind resource discretization can negatively impact the optimization results.
Andrew P. J. Stanley, Jennifer King, Christopher Bay, and Andrew Ning
Wind Energ. Sci., 7, 433–454, https://doi.org/10.5194/wes-7-433-2022, https://doi.org/10.5194/wes-7-433-2022, 2022
Short summary
Short summary
In this paper, we present a computationally inexpensive model to calculate wind turbine blade fatigue caused by waking and partial waking. The model accounts for steady state on the blade, as well as wind turbulence. The model is fast enough to be used in wind farm layout optimization, which has not been possible with more expensive fatigue models in the past. The methods introduced in this paper will allow for farms with increased energy production that maintain turbine structural reliability.
Andrew P. J. Stanley, Owen Roberts, Jennifer King, and Christopher J. Bay
Wind Energ. Sci., 6, 1143–1167, https://doi.org/10.5194/wes-6-1143-2021, https://doi.org/10.5194/wes-6-1143-2021, 2021
Short summary
Short summary
Wind farm layout optimization is an essential part of wind farm design. In this paper, we present different methods to determine the number of turbines in a wind farm, as well as their placement. Also in this paper we explore the effect that the objective function has on the wind farm design and found that wind farm layout is highly sensitive to the objective. The optimal number of turbines can vary greatly, from 15 to 54 for the cases in this paper, depending on the metric that is optimized.
Erik Quaeghebeur, René Bos, and Michiel B. Zaaijer
Wind Energ. Sci., 6, 815–839, https://doi.org/10.5194/wes-6-815-2021, https://doi.org/10.5194/wes-6-815-2021, 2021
Short summary
Short summary
We present a technique to support the optimal layout (placement) of wind turbines in a wind farm. It efficiently determines good directions and distances for moving turbines. An improved layout reduces production losses and so makes the farm project economically more attractive. Compared to most existing techniques, our approach requires less time. This allows wind farm designers to explore more alternatives and provides the flexibility to adapt the layout to site-specific requirements.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Jennifer King, Paul Fleming, Ryan King, Luis A. Martínez-Tossas, Christopher J. Bay, Rafael Mudafort, and Eric Simley
Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, https://doi.org/10.5194/wes-6-701-2021, 2021
Short summary
Short summary
This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
Luis A. Martínez-Tossas, Jennifer King, Eliot Quon, Christopher J. Bay, Rafael Mudafort, Nicholas Hamilton, Michael F. Howland, and Paul A. Fleming
Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, https://doi.org/10.5194/wes-6-555-2021, 2021
Short summary
Short summary
In this paper a three-dimensional steady-state solver for flow through a wind farm is developed and validated. The computational cost of the solver is on the order of seconds for large wind farms. The model is validated using high-fidelity simulations and SCADA.
Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, https://doi.org/10.5194/wes-5-945-2020, 2020
Short summary
Short summary
This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
Erik Quaeghebeur and Michiel B. Zaaijer
Wind Energ. Sci., 5, 285–308, https://doi.org/10.5194/wes-5-285-2020, https://doi.org/10.5194/wes-5-285-2020, 2020
Short summary
Short summary
Meteorological and oceanic datasets are fundamental to the modeling of offshore wind farms. Data quality issues in one such dataset led us to conduct a study to establish whether such issues are more generally present in these datasets. The answer is yes and users should be aware of this. We therefore also investigated how such issues can be avoided. The result is a set of techniques and recommendations for dataset producers, leading to substantial quality improvements with limited extra effort.
Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, and Michiel B. Zaaijer
Wind Energ. Sci., 5, 259–284, https://doi.org/10.5194/wes-5-259-2020, https://doi.org/10.5194/wes-5-259-2020, 2020
Short summary
Short summary
The design and management of an offshore wind farm involve expertise in many disciplines. It is hard for a single person to maintain the overview needed. Therefore, we have created WESgraph, a knowledge base for the wind farm domain, implemented as a graph database. It stores descriptions of the multitude of domain concepts and their various interconnections. It allows users to explore the domain and search for relationships within and across disciplines, enabling various applications.
Andrew P. J. Stanley and Andrew Ning
Wind Energ. Sci., 4, 663–676, https://doi.org/10.5194/wes-4-663-2019, https://doi.org/10.5194/wes-4-663-2019, 2019
Short summary
Short summary
When designing a wind farm, one crucial step is finding the correct location or optimizing the location of the wind turbines to maximize power production. In the past, optimizing the turbine layout of large wind farms has been difficult because of the large number of interacting variables. In this paper, we present the boundary-grid parameterization method, which defines the layout of any wind farm with only five variables, allowing people to study and design wind farms regardless of the size.
Daniel S. Zalkind, Gavin K. Ananda, Mayank Chetan, Dana P. Martin, Christopher J. Bay, Kathryn E. Johnson, Eric Loth, D. Todd Griffith, Michael S. Selig, and Lucy Y. Pao
Wind Energ. Sci., 4, 595–618, https://doi.org/10.5194/wes-4-595-2019, https://doi.org/10.5194/wes-4-595-2019, 2019
Short summary
Short summary
We present a model that both (1) reduces the computational effort involved in analyzing design trade-offs and (2) provides a qualitative understanding of the root cause of fatigue and extreme structural loads for wind turbine components from the blades to the tower base. We use this model in conjunction with design loads from high-fidelity simulations to analyze and compare the trade-offs between power capture and structural loading for large rotor concepts.
Jennifer Annoni, Christopher Bay, Kathryn Johnson, Emiliano Dall'Anese, Eliot Quon, Travis Kemper, and Paul Fleming
Wind Energ. Sci., 4, 355–368, https://doi.org/10.5194/wes-4-355-2019, https://doi.org/10.5194/wes-4-355-2019, 2019
Short summary
Short summary
Typically, turbines do not share information with nearby turbines in a wind farm. Relying on a single turbine sensor on the back of a turbine nacelle can lead to large errors in yaw misalignment or excessive yawing due to noisy sensor measurements. The wind farm consensus control approach in this paper shows the benefits of sharing information between nearby turbines by computing a robust estimate of the wind direction using noisy sensor information from these neighboring turbines.
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
Short summary
Short summary
Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
Andrés Santiago Padrón, Jared Thomas, Andrew P. J. Stanley, Juan J. Alonso, and Andrew Ning
Wind Energ. Sci., 4, 211–231, https://doi.org/10.5194/wes-4-211-2019, https://doi.org/10.5194/wes-4-211-2019, 2019
Short summary
Short summary
We propose the use of a new method to efficiently compute the annual energy production (AEP) of a wind farm by properly handling the uncertainties in the wind direction and wind speed. We apply the new ideas to the layout optimization of a large wind farm. We show significant computational savings by reducing the number of simulations required to accurately compute and optimize the AEP of different wind farms.
Christopher J. Bay, Jennifer King, Paul Fleming, Rafael Mudafort, and Luis A. Martínez-Tossas
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-19, https://doi.org/10.5194/wes-2019-19, 2019
Preprint withdrawn
Short summary
Short summary
This work details a new low-fidelity wake model to be used in determining operational strategies for wind turbines. With the additional physics that this model captures, optimizations have found new control strategies that provide greater increases in performance than previously determined, and these performance increases have been confirmed in high-fidelity simulations. As such, this model can be used in the design and optimization of future wind farms and operational schemes.
Andrew P. J. Stanley and Andrew Ning
Wind Energ. Sci., 4, 99–114, https://doi.org/10.5194/wes-4-99-2019, https://doi.org/10.5194/wes-4-99-2019, 2019
Short summary
Short summary
We show that optimizing wind turbine design and wind turbine layout at the same time is superior to doing so sequentially. This coupled optimization can reduce the cost of energy by 2–5 % compared to sequential optimization. We also demonstrate that wind farms with closely spaced wind turbines can greatly benefit from different turbine designs throughout the farm. Heterogeneous turbine design can reduce the cost of energy by up to 10 % compared to farms with all identical turbines.
Andrew Ning
Wind Energ. Sci., 1, 327–340, https://doi.org/10.5194/wes-1-327-2016, https://doi.org/10.5194/wes-1-327-2016, 2016
Short summary
Short summary
Wind turbines rarely operate in isolation but rather in close proximity within wind farms. Currently analysis methods are designed for analyzing turbines in isolation, or within a waked region. Actuator cylinder theory is extended to handle multiple vertical axis wind turbines in close proximity. We find good agreement in power predictions as compared to available higher-fidelity simulation data. The corresponding code may be useful for conceptual design and has been fully open-sourced.
Related subject area
Thematic area: Wind technologies | Topic: Systems engineering
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
Designing wind turbines for profitability in the day-ahead markets
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
The eco-conscious wind turbine: design beyond purely economic metrics
Optimization of wind farm operation with a noise constraint
Flutter behavior of highly flexible blades for two- and three-bladed wind turbines
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.
Mihir Kishore Mehta, Michiel Zaaijer, and Dominic von Terzi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-43, https://doi.org/10.5194/wes-2024-43, 2024
Revised manuscript accepted for WES
Short summary
Short summary
In a subsidy-free era, there is a need to optimize turbines to maximize the revenue of the farm instead of minimizing the LCoE. A wind farm-level modeling framework with a simplified market model to optimize the size of wind turbines to maximize revenue-based metrics like IRR/NPV. The results show that the optimum turbine size is driven mainly by the choice of the economic metric and the market price scenario, with an LCoE-optimized design already performing well w.r.t. metrics like IRR.
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.
Helena Canet, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci., 8, 1029–1047, https://doi.org/10.5194/wes-8-1029-2023, https://doi.org/10.5194/wes-8-1029-2023, 2023
Short summary
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.
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
Arnoud, A., Guvenen, F., and Kleineberg, T.: Benchmarking Global Optimizers,
Working Paper 26340, National Bureau of Economic Research,
https://doi.org/10.3386/w26340, 2019. a
Baker, N. F., Thomas, J. J., Stanley, A. P. J., and Ning, A.: IEA Task 37
Wind Farm Layout Optimization Case Studies, Zenodo [code and data set], https://doi.org/10.5281/zenodo.5809681, 2021. a, b, c, d
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind
turbine wakes in yawed conditions, J. Fluid Mech., 806,
506–541, https://doi.org/10.1017/jfm.2016.595, 2016. a
Belegundu, A. D. and Chandrupatla, T. R.: Optimization Concepts and
Applications in Engineering, 2nd Edn., Cambridge University Press, ISBN 13:978-0521878463, 2011. a
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B.: Julia: A fresh
approach to numerical computing, SIAM Rev., 59, 65–98,
https://doi.org/10.1137/141000671, 2017. a
Bortolotti, P., Dykes, K., Merz, K., Sethuraman, L., and Zahle, F.: IEA Wind
Task 37 on System Engineering in Wind Energy, WP2 – Reference Wind Turbines,
Tech. rep., National Renewable Energy Laboratory (NREL), Golden, CO, USA, https://doi.org/10.2172/1529216, 2018. a
Dykes, K. L., Zahle, F., Merz, K., McWilliam, M., and Bortolotti, P.: IEA Wind Task 37: Systems Modeling Framework and Ontology for Wind Turbines and
Plants, https://www.osti.gov/biblio/1375625 (last access: 29 May 2023), 2017. a
Eurostat: Electricity prices by type of user, Eurostat [data set],
https://ec.europa.eu/eurostat/databrowser/view/ten00117/default/table?lang=en
(last access: 29 May 2023), 2021. a
Feng, J. and Shen, W. Z.: Solving the wind farm layout optimization problem
using random search algorithm, Renew. Energ., 78, 182–192,
https://doi.org/10.1016/j.renene.2015.01.005, 2015. a, b, c
Fleming, P., Ning, A., Gebraad, P., and Dykes, K.: Wind Plant System
Engineering through Optimization of Layout and Yaw Control, Wind Energy, 19, 329–344, https://doi.org/10.1002/we.1836, 2015. a
Gebraad, P., Thomas, J. J., Ning, A., Fleming, P., and Dykes, K.: Maximization
of the Annual Energy Production of Wind Power Plants by Optimization of
Layout and Yaw-Based Wake Control, Wind Energy, 20, 97–107,
https://doi.org/10.1002/we.1993, 2017. a
Gill, P., Murray, W., and Saudners, M.: SNOPT: an SQP algorithm for
large-scale constrained optimization, SIAM Rev., 47, 99–131, 2005. a
Gill, P. E., Murray, W., and Saunders, M. A.: SNOPT: An SQP algorithm for
large-scale constrained optimization, SIAM Journal of Optimization, 12,
979–1006, https://doi.org/10.1137/S1052623499350013, 2002. a
Grady, S., Hussaini, M., and Abdullah, M.: Placement of wind turbines using
genetic algorithms, Renew. Energ, 30, 259–270,
https://doi.org/10.1016/j.renene.2004.05.007, 2005. a, b
Gray, J. S., Hearn, T. A., Moore, K. T., Hwang, J., Martins, J., and Ning, A.: Automatic Evaluation of Multidisciplinary Derivatives Using a Graph-Based Problem Formulation in OpenMDAO, in: 15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 16–20 June 2014, Atlanta, GA,
https://doi.org/10.2514/6.2014-2042, 2014. a
Gray, J. S., Hwang, J. T., Martins, J. R. R. A., Moore, K. T., and Naylor,
B. A.: OpenMDAO: An Open-Source Framework for Multidisciplinary Design,
Analysis, and Optimization, Struct. Multidiscip. O.,
59, 1075–1104, https://doi.org/10.1007/s00158-019-02211-z, 2019. a
Griewank, A. and Walther, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Society for Industrial and Applied
Mathematics, 2nd Edn., ISBN 978-0-89871-659-7, eISBN 978-0-89871-776-1, https://doi.org/10.1137/1.9780898717761, 2008. a
Ha, D.: A Visual Guide to Evolution Strategies, blog.otoro.net,
https://blog.otoro.net/2017/10/29/visual-evolution-strategies/ (last access: 29 May 2023), 2017. a
Hansen, N., Akimoto, Y., and Baudis, P.: CMA-ES/pycma on Github, Zenodo [code], https://doi.org/10.5281/zenodo.2559634, 2019. a
Herbert-Acero, J., Probst, O., Rethore, P., Larsen, G., and Castillo-Villar,
K.: A Review of Methodological Approaches for the Design and Optimization of
Wind Farms, Energies, 7, 6930–7016, https://doi.org/10.3390/en7116930, 2014. a, b, c, d
IEA: IEA Wind Task 37, https://iea-wind.org/task37/ (last access: 29 May 2023), 2021. a
Katic, I., Højstrup, J., and Jensen, N.: A Simple Model for Cluster
Efficiency, in: European Wind Energy Association Conference and Exhibition,
European Wind Energy Association, Rome–Italy, 1986. a
Koziel, S. and Yang, X.-S. (Eds.): Computational Optimization, Methods and
Algorithms, Studies in Computational Intelligence, Springer, Berlin,
Heidelberg, ISBN 978-3-642-20858-4, https://doi.org/10.1007/978-3-642-20859-1, 2011. a
Kunakote, T., Sabangban, N., Kumar, S., Tejani, G. G., Panagant, N., Pholdee,
N., Bureerat, S., and Yildiz, A. R.: Comparative Performance of Twelve
Metaheuristics for Wind Farm Layout Optimisation, Arch. Comput. Meth. E., 29, 717–730, https://doi.org/10.1007/s11831-021-09586-7, 2022. a, b, c
Lambe, A. B. and Martins, J. R. R. A.: Extensions to the Design Structure
Matrix for the Description of Multidisciplinary Design, Analysis, and
Optimization Processes, Struct. Multid. O., 46,
273–284, https://doi.org/10.1007/s00158-012-0763-y, 2012. a
Martins, J. R. R. A. and Hwang, J. T.: Review and Unification of Methods for
Computing Derivatives of Multidisciplinary Computational Models, AIAA
J., 51, 2582–2599, https://doi.org/10.2514/1.J052184, 2013. a
Martins, J. R. R. A. and Ning, A.: Engineering Design Optimization, Cambridge
University Press, https://doi.org/10.1017/9781108980647, 2021. a
MathWorks: MATLAB Direct search function,
https://de.mathworks.com/help/gads/patternsearch.html (last access:
3 July 2020), 2020. a
Mobahi, H. and Fisher III, J.: A Theoretical Analysis of Optimization by
Gaussian Continuation, in: Vol. 29, Proceedings of the AAAI Conference on Artificial Intelligence, https://doi.org/10.1609/aaai.v29i1.9356, 2015. a, b
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,
https://doi.org/10.1016/0167-6105(94)90080-9, 1994. a
Niayifar, A. and Porté-Agel, F.: Analytical Modeling of Wind Farms: A New
Approach for Power Prediction, Energies, 9, 741, https://doi.org/10.3390/en9090741,
2016. a
Ning, A.: Sparse Nonlinear Optimization Wrapper (SNOW), GitHub [code], https://github.com/byuflowlab/SNOW.jl (last access: 29 May 2023), 2021. a
Quaeghebeur, E.: equaeghe/wflopg: Initial release, Zenodo [code],
https://doi.org/10.5281/zenodo.4072253, 2020. a
Quaeghebeur, E., Bos, R., and Zaaijer, M. B.: Wind farm layout optimization using pseudo-gradients, Wind Energ. Sci., 6, 815–839, https://doi.org/10.5194/wes-6-815-2021, 2021. a, b, c
Réthoré, P.-E., Fuglsang, P., Larsen, G. C., Buhl, T., Larsen, T. J.,
and Aagaard Madsen, H.: TOPFARM: Multi-fidelity optimization of wind farms,
Wind Energy, 17, 1797–1816, 2014. a
Revels, J., Lubin, M., and Papamarkou, T.: Forward-Mode Automatic
Differentiation in Julia, arXiv [preprint],
https://doi.org/10.48550/arXiv.1607.07892, 2016. a
Rios, L. M. and Sahinidis, N. V.: Derivative-free optimization: a review of
algorithms and comparison of software implementations, J. Global
Optim., 56, 1247–1293, https://doi.org/10.1007/s10898-012-9951-y, 2013. a
Serrano González, J., Trigo García, Á. L., Burgos Payán,
M., Riquelme Santos, J., and González Rodríguez, Á. G.:
Optimal wind-turbine micro-siting of offshore wind farms: A grid-like layout
approach, Appl. Energ., 200, 28–38,
https://doi.org/10.1016/j.apenergy.2017.05.071, 2017. 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, b, c, d
Thomas, J.: jaredthomas68/thomas2022-8-opt-algs-wflop: Initial release (submission), Zenodo [code and data set], https://doi.org/10.5281/zenodo.7125349, 2022a. a
Thomas, J., Gebraad, P., and Ning, A.: Improving the FLORIS Wind Plant Model for Compatibility with Gradient-Based Optimization, Wind Engineering41, 313–329, https://doi.org/10.1177/0309524X17722000, 2017. a
Thomas, J. J. and Ning, A.: A Method for Reducing Multi-Modality in the Wind
Farm Layout Optimization Problem, J. Phys.-Conf. Ser., 1037, 042012,
https://doi.org/10.1088/1742-6596/1037/4/042012, 2018. a
Thomas, J. J., Annoni, J., Fleming, P., and Ning, A.: Comparison of Wind Farm
Layout Optimization Results Using a Simple Wake Model and Gradient-Based
Optimization to Large-Eddy Simulations, in: AIAA Scitech 2019 Forum,
7–11 January 2019, San Diego, California, https://doi.org/10.2514/6.2019-0538, 2019. a
Thomas, J., Stanley, P. J., Lee, E., Holt, W., Baker, N. F., and Ning, A.: jaredthomas68/FLOWFarm.jl: For 8 algorithms WFLOP paper (thomas2022-8-algs-wflop), Zenodo [code], https://doi.org/10.5281/zenodo.7125827, 2022b.
a
Tilli, F.: Greedy wind farm layout optimization using pre-averaged losses,
Master's thesis, Delft University of Technology,
http://resolver.tudelft.nl/uuid:4b118ae1-536d-4e0b-a30b-d88ba818c918 (last access: 11 May 2023), 2019. a
Turner, S., Romero, D., Zhang, P., Amon, C., and Chan, T.: A new mathematical
programming approach to optimize wind farm layouts, Renew. Energ., 63,
674–680, https://doi.org/10.1016/j.renene.2013.10.023, 2014. a, b
Wasan, M. T.: Stochastic Approximation, in: Cambridge Tracts in Mathematics, Series Number 58, Cambridge University Press, ISBN 13:9780521073684,
ISBN 10:0521073685, 1969. a
Wolpert, D. and Macready, W.: No free lunch theorems for optimization, IEEE
T. Evolut. Comput., 1, 67–82,
https://doi.org/10.1109/4235.585893, 1997. 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, Journal of Open Source Software, 5, 2564, https://doi.org/10.21105/joss.02564, 2020. a
Yang, Q., Li, H., Li, T., and Zhou, X.: Wind farm layout optimization for
levelized cost of energy minimization with combined analytical wake model and
hybrid optimization strategy, Energ. Convers. Manageme., 248,
114778, https://doi.org/10.1016/j.enconman.2021.114778, 2021. a
Zergane, S., Smaili, A., and Masson, C.: Optimization of wind turbine placement
in a wind farm using a new pseudo-random number generation method, Renew.
Energ, 125, 166–171, https://doi.org/10.1016/j.renene.2018.02.082,
2018. a
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.
This work compares eight optimization algorithms (including gradient-based, gradient-free, and...
Altmetrics
Final-revised paper
Preprint