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.
Hadi Hoghooghi and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-102, https://doi.org/10.5194/wes-2026-102, 2026
Preprint under review for WES
Short summary
Short summary
Reliable wind turbine fatigue load estimates are key to assessing structural integrity and service life. This study integrates a simplified simulation model with machine learning to enhance predictions while protecting industrial design confidentiality. It achieves DEL errors under 5 % across varied conditions and performs well on simulated and field data, supporting improved monitoring, maintenance, and lifetime assessment.
Carlo Rosario Sucameli, Marta Bertelè, Robert Braunbehrens, Filippo Campagnolo, Simone Tamaro, Paul Hulsman, and Carlo Luigi Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-93, https://doi.org/10.5194/wes-2026-93, 2026
Preprint under review for WES
Short summary
Short summary
Wind turbines extract energy by slowing the air, so downstream turbines receive less wind and produce less power, a phenomenon called wake. Wind is not uniform: it varies in both speed and direction with height, also affecting downstream turbines. Using a year of measurements and simulations, we found that both effects influence the power production of downstream turbines by pushing the air sideways. We built improved models to capture these effects, enabling better wind farm control strategies.
Abhinav Anand and Carlo L. Bottasso
Wind Energ. Sci., 11, 1989–2008, https://doi.org/10.5194/wes-11-1989-2026, https://doi.org/10.5194/wes-11-1989-2026, 2026
Short summary
Short summary
We formulate a controller for wind turbines that has three main characteristics. First, it optimizes profit by balancing revenue from power generation with cost. Second, cost includes the effects of cyclic fatigue that, departing from most of the existing literature on control, is rigorously accounted for by an exact cycle counting on receding horizons. Third, it uses a model capable of learning and improving its performance based on measured or synthetic data.
Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-86, https://doi.org/10.5194/wes-2026-86, 2026
Preprint under review for WES
Short summary
Short summary
This paper presents an open-access wind-tunnel dataset of actuated, scaled wind turbines with time-resolved measurements of loads, actuator commands, inflow conditions and wakes. It covers multiple wake-control strategies (yaw steering, derating, Helix, dynamic yaw, Pulse, pitch control). Combined with numerical models, the dataset enables benchmarking, controller analysis, and validation of aeroelastic and wake models for wind-farm control research.
Simone Tamaro, Davide Bortolin, Filippo Campagnolo, Franz V. Mühle, and Carlo L. Bottasso
Wind Energ. Sci., 11, 1607–1630, https://doi.org/10.5194/wes-11-1607-2026, https://doi.org/10.5194/wes-11-1607-2026, 2026
Short summary
Short summary
This work presents the scaled experimental validation of an active power control (APC) algorithm that improves wind farm power-tracking accuracy during turbulent wind lulls. Tests in a large low-blockage wind tunnel, including dynamic wind-direction changes, show that the method outperforms three reference APC strategies, especially under high power demand, while keeping structural fatigue low.
Andre Thommessen, Abhinav Anand, Carlo L. Bottasso, and Christoph M. Hackl
Wind Energ. Sci., 11, 1399–1428, https://doi.org/10.5194/wes-11-1399-2026, https://doi.org/10.5194/wes-11-1399-2026, 2026
Short summary
Short summary
We present a method to forecast inertia that accounts for wake effects in a wind farm. The approach is based on mapping forecasted site conditions to each single wind turbine in the farm through a wake model. The resulting inflow conditions are used to predict the inertia that the wind farm can provide to the grid, taking the wind turbine control strategies and operational limits into account.
Marinos Manolesos, Sandrine Aubrun, Christian Bak, Paolo Bettini, Filippo Campagnolo, Alessandro Croce, Arslan Salim Dar, Michael Hölling, Stefan Ivanell, Konstantinos Kellaris, Miguel Alfonso Mendez, Pierluigi Montinari, Franz Mühle, George Papadakis, Fernardo Porté-Agel, Daniele Ragni, Sebastiano Randino, Andrea Sciacchitano, Lorenzo Schena, Gerard Schepers, Antonio Segalini, Wei Yu, and Carlo Luigi Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-51, https://doi.org/10.5194/wes-2026-51, 2026
Preprint under review for WES
Short summary
Short summary
As wind turbines grow to unprecedented sizes, ensuring their reliability requires advanced testing. We reviewed the current state of wind tunnel experiments, examining how scaled physical models are evaluated for wind flow, flexibility, and noise. We conclude that while laboratory testing remains essential, it is most powerful when integrated with computer simulations and real-world data. This combined approach will ultimately drive the design of more efficient and sustainable wind energy.
Nicola Bodini, Patrick Moriarty, Regis Thedin, Paula Doubrawa, Cristina Archer, Myra Blaylock, Carlo Bottasso, Bruno Carmo, Lawrence Cheung, Camille Dubreuil, Rogier Floors, Thomas Herges, Daniel Houck, Ali Kanjari, Colleen M. Kaul, Christopher Kelley, Ru LI, Julie K. Lundquist, Desirae Major, Anh Kiet Nguyen, Mike Optis, Luan R. C. Parada, Alfredo Peña, Julian Quick, David Ricarte, William C. Radünz, Raj K. Rai, Oscar Garcia Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-34, https://doi.org/10.5194/wes-2026-34, 2026
Revised manuscript under review for WES
Short summary
Short summary
Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more efficient wind farms.
Andreas Vad, Adrien Guilloré, Abhinav Anand, Vasilis Pettas, Anik H. Shah, Ion Lizarraga-Saenz, Maria Aparicio-Sanchez, Irene Eguinoa, Nicolau Conti Gost, Iasonas Tsaklis, Ariane Frère, Koen W. Hermans, Joseph K. Kamau, Nikolay Dimitrov, Tuhfe Göçmen, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-45, https://doi.org/10.5194/wes-2026-45, 2026
Preprint under review for WES
Short summary
Short summary
A modular, computationally efficient framework for wind‑farm response modeling is presented. It combines an engineering wake model with surrogate models trained on extensive aeroelastic simulations generated using a novel method for synthetic waked and clean inflows. The wind‑farm‑agnostic framework supports multiple turbine types and layouts, enabling accurate, low‑cost predictions for design, operation, and control.
Carlo L. Bottasso, Sandrine Aubrun, Nicolaos A. Cutululis, Julia Gottschall, Athanasios Kolios, Jakob Mann, and Paul Veers
Wind Energ. Sci., 11, 347–348, https://doi.org/10.5194/wes-11-347-2026, https://doi.org/10.5194/wes-11-347-2026, 2026
Short summary
Short summary
This editorial celebrates the 10th anniversary of Wind Energy Science, reflecting on a decade of rapid scientific progress and the journal’s role in advancing fundamental, interdisciplinary research. It highlights key developments in wind energy, the importance of open science and academia–industry collaboration, and emerging challenges such as data sharing and artificial intelligence. Above all, it honors the research community that has shaped the journal and looks ahead to the next decade.
Hadi Hoghooghi and Carlo L. Bottasso
Wind Energ. Sci., 11, 373–393, https://doi.org/10.5194/wes-11-373-2026, https://doi.org/10.5194/wes-11-373-2026, 2026
Short summary
Short summary
We formulate and demonstrate a new digital shadow (i.e., a virtual copy) for wind turbines. The digital shadow is designed in order to be capable of mirroring the response of the machine even in complex inflow conditions. Results from field measurements illustrate the ability of the shadow to estimate loads with good accuracy, even with minimal tuning.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 10, 2705–2728, https://doi.org/10.5194/wes-10-2705-2025, https://doi.org/10.5194/wes-10-2705-2025, 2025
Short summary
Short summary
We proposed a new method for active power control that uniquely combines induction control with wake steering to maximize power tracking margins. Our methodology results in significantly improved robustness against wind fluctuations and fatigue loading when compared to the state of the art.
Abhinav Anand, Robert Braunbehrens, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-67, https://doi.org/10.5194/wes-2025-67, 2025
Revised manuscript under review for WES
Short summary
Short summary
We present a new method for wind farm control, based on the optimization of an economic cost function that accounts for revenue from power production and cost due to operation and maintenance. The new formulation also includes constraints to ensure a desired lifetime duration. The application to relevant scenarios shows consistently improved profit when compared to alternative formulations from the recent literature.
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.
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