Articles | Volume 10, issue 8
https://doi.org/10.5194/wes-10-1661-2025
© Author(s) 2025. 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-10-1661-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Offshore wind farm layout optimization accounting for participation in secondary reserve markets
Thuy-Hai Nguyen
CORRESPONDING AUTHOR
Electrical Power Engineering Unit, University of Mons, Boulevard Dolez 31, 7000 Mons, Belgium
Julian Quick
Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Pierre-Elouan Réthoré
Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
Jean-François Toubeau
Electrical Power Engineering Unit, University of Mons, Boulevard Dolez 31, 7000 Mons, Belgium
Emmanuel De Jaeger
Department of Mechatronics, Electrical Energy and Dynamic Systems, University of Louvain, 2 Place du Levant, 1348 Louvain-la-Neuve, Belgium
François Vallée
Electrical Power Engineering Unit, University of Mons, Boulevard Dolez 31, 7000 Mons, Belgium
Related authors
No articles found.
Julian Quick, Edward Hart, Marcus Binder Nilsen, Rasmus Sode Lund, Jaime Liew, Piinshin Huang, Pierre-Elouan Rethore, Jonathan Keller, Wooyong Song, and Yi Guo
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-63, https://doi.org/10.5194/wes-2025-63, 2025
Revised manuscript under review for WES
Short summary
Short summary
Wind turbine main bearings often fail prematurely, creating costly maintenance challenges. This study examined how wake effects – where upstream turbines create disturbed airflow that impacts downstream turbines – affect bearing lifespans. Using computer simulations, we found that wake effects reduce bearing life by 16% on average. The direction of wake impact matters significantly due to interactions between wind forces and gravity, informing better wind turbine and farm farm design strategies.
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Wind Energ. Sci., 10, 559–578, https://doi.org/10.5194/wes-10-559-2025, https://doi.org/10.5194/wes-10-559-2025, 2025
Short summary
Short summary
This research develops a new method for assessing hybrid power plant (HPP) 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.
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.
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.
Rafael Valotta Rodrigues, Mads Mølgaard Pedersen, Jens Peter Schøler, Julian Quick, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 321–341, https://doi.org/10.5194/wes-9-321-2024, https://doi.org/10.5194/wes-9-321-2024, 2024
Short summary
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.
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.
Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding
Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
Short summary
Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
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.
Julian Quick, Ryan N. King, Garrett Barter, and Peter E. Hamlington
Wind Energ. Sci., 7, 1941–1955, https://doi.org/10.5194/wes-7-1941-2022, https://doi.org/10.5194/wes-7-1941-2022, 2022
Short summary
Short summary
Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of alignment with the incoming wind, thereby steering wakes away from downstream turbines. Trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present an optimization approach for efficiently exploring the trade-offs between power and loading during wake steering.
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.
Cited articles
Brijs, T., De Vos, K., De Jonghe, C., and Belmans, R.: Statistical analysis of negative prices in European balancing markets, Renew. Energ., 80, 53–60, https://doi.org/10.1016/j.renene.2015.01.059, 2015. a
Cañas-Carretón, M. and Carrión, M.: Generation capacity expansion considering reserve provision by wind power units, IEEE T. Power Syst., 35, 4564–4573, https://doi.org/10.1109/TPWRS.2020.2994173, 2020. a
Chitsazan, M. A., Fadali, M. S., and Trzynadlowski, A. M.: Wind speed and wind direction forecasting using echo state network with nonlinear functions, Renew. Energ., 131, 879–889, https://doi.org/10.1016/j.renene.2018.07.060, 2019. a
ECMWF: Intercomparison of operational wave forecasting systems against in-situ observations: data from BoM, DMI, DWD, ECCC, ECMWF, JMA, LOPS, METEOAM, METFR, METNO, NCEP, PRTOS, SHNSM, UKMO, Tech. rep., WMO Lead Centre for Wave Forecast Verification LC-WFV and European Centre for Medium-Range Weather Forecasts ECMWF, http://confluence.ecmwf.int/download/attachments/116958928/LCWFV_10ff_report_00_DJF2024.pdf (last access: August 2025), 2024. a
Elia: Terms and Conditions for balancing service providers for automatic Frequency Restoration Reserve (aFRR), Tech. rep., Elia, https://www.elia.be/en/electricity-market-and-system/system-services/keeping-the-balance/afrr (last access: August 2025), 2022. a
Elia: Elia Open Data Portal, https://opendata.elia.be/pages/home/, last access: April 2025. a
Energinet: Outlook for ancillary services 2023–2040, Tech. rep., Energinet, https://en.energinet.dk/media/jbglyjdf/outlook-for-ancillary-services-2023-2040.pdf (last access: August 2025), 2023. a
ENTSO-E: ENTSO-E Transparency Platform, https://transparency.entsoe.eu/, last access: April 2025. 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
González, J. S., Rodriguez, A. G. G., Mora, J. C., Santos, J. R., and Payan, M. B.: Optimization of wind farm turbines layout using an evolutive algorithm, Renew. Energ., 35, 1671–1681, https://doi.org/10.1016/j.renene.2010.01.010, 2010. a
Gonzalez, J. S., Payan, M. B., and Riquelme-Santos, J. M.: Optimization of Wind Farm Turbine Layout Including Decision Making Under Risk, IEEE Syst. J., 6, 94–102, https://doi.org/10.1109/JSYST.2011.2163007, 2012. a
Hou, P., Hu, W., Soltani, M., and Chen, Z.: Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm, IEEE T. Sustain. Energ., 6, 1272–1282, https://doi.org/10.1109/TSTE.2015.2429912, 2015. a
Kayedpour, N., Samani, A. E., De Kooning, J. D. M., Vandevelde, L., and Crevecoeur, G.: Model Predictive Control With a Cascaded Hammerstein Neural Network of a Wind Turbine Providing Frequency Containment Reserve, IEEE T. Energy Conver., 37, 198–209, https://doi.org/10.1109/TEC.2021.3093010, 2022. a
Kayedpour, N., Kooning, J. D. M. D., Samani, A. E., Kayedpour, F., Vandevelde, L., and Crevecoeur, G.: An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control, IEEE T. Sustain. Energ., 15, 276–289, https://doi.org/10.1109/TSTE.2023.3288130, 2024. a
Lago, J., Marcjasz, G., De Schutter, B., and Weron, R.: Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark, Appl. Energ., 293, 116983, https://doi.org/10.1016/j.apenergy.2021.116983, 2021. a
Liang, J., Grijalva, S., and Harley, R. G.: Increased wind revenue and system security by trading wind power in energy and regulation reserve markets, IEEE T. Sustain. Energ., 2, 340–347, https://doi.org/10.1109/TSTE.2011.2111468, 2011. a
Long, H., Li, P., and Gu, W.: A data-driven evolutionary algorithm for wind farm layout optimization, Energy, 208, 118310, https://doi.org/10.1016/j.energy.2020.118310, 2020. a
Nguyen, T.-H.: ThuyHnguyen/WINDFLOWER: First release (v1.0.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.13946931, 2024. a
Park, J. and Law, K. H.: Layout optimization for maximizing wind farm power production using sequential convex programming, Appl. Energ., 151, 320–334, https://doi.org/10.1016/j.apenergy.2015.03.139, 2015. a
Pedersen, M. M., Meyer Forsting, A., van der Laan, P., Riva, R., Alcayaga Romàn, L. A., Criado Risco, J., Friis-Møller, M., Quick, J., Schøler Christiansen, J. P., Valotta Rodrigues, R., Olsen, B. T., and Réthoré, P.-E.: PyWake 2.5.0: An open-source wind farm simulation tool, https://doi.org/10.5281/zenodo.6806136, Zenodo [code], 2023. a
Perroy, E., Lucas, D., and Debusschere, V.: Provision of Frequency Containment Reserve Through Large Industrial End-Users Pooling, IEEE T. Smart Grid, 11, 26–36, https://doi.org/10.1109/TSG.2019.2916623, 2020. 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, b
Riva, R., Liew, J. Y., Friis-Møller, M., Dimitrov, N. K., Barlas, E., Réthoré, P.-E., and Pedersen, M. M.: Welcome to TOPFARM, DTU Wind Energy [code], https://gitlab.windenergy.dtu.dk/TOPFARM/TopFarm2 (last access: August 2025), 2024. a
Sickler, M., Ummels, B., Zaaijer, M., Schmehl, R., and Dykes, K.: Offshore wind farm optimisation: a comparison of performance between regular and irregular wind turbine layouts, Wind Energ. Sci., 8, 1225–1233, https://doi.org/10.5194/wes-8-1225-2023, 2023. a
Soares, T., Jensen, T. V., Mazzi, N., Pinson, P., and Morais, H.: Optimal offering and allocation policies for wind power in energy and reserve markets, Wind Energy, 20, 1851–1870, https://doi.org/10.1002/we.2125, 2017. a, b
Stanley, A. P. J., Roberts, O., King, J., and Bay, C. J.: Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant, Wind Energ. Sci., 6, 1143–1167, https://doi.org/10.5194/wes-6-1143-2021, 2021. a
Sumetha-Aksorn, P., Dykes, K., and Yilmaz, O. C.: Assessing the economical impact of innovations for offshore wind farms through a holistic modelling approach, J. Phys. Conf. Ser., 2265, 042036, https://doi.org/10.1088/1742-6596/2265/4/042036, 2022. a
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
Topham, E. and McMillan, D.: Sustainable decommissioning of an offshore wind farm, Renew. Energ., 102, 470–480, https://doi.org/10.1016/j.renene.2016.10.066, 2017. a
Toubeau, J.-F., Ponsart, C., Stevens, C., De Grève, Z., and Vallée, F.: Sizing of underwater gravity storage with solid weights participating in electricity markets, Int. T. Electr. Energy, 30, e12549, https://doi.org/10.1002/2050-7038.12549, 2020. a
Valotta Rodrigues, R., Pedersen, M. M., Schøler, J. P., Quick, J., and Réthoré, P.-E.: Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout, Wind Energ. Sci., 9, 321–341, https://doi.org/10.5194/wes-9-321-2024, 2024. a
Van Binsbergen, D., Daems, P.-J., Verstraeten, T., Nejad, A., and Helsen, J.: Performance comparison of analytical wake models calibrated on a large offshore wind cluster, J. Phys. Conf. Ser., 2767, 092059, https://doi.org/10.1088/1742-6596/2767/9/092059, 2024. a
Wang, P., Zareipour, H., and Rosehart, W. D.: Descriptive models for reserve and regulation prices in competitive electricity markets, IEEE T. Smart Grid, 5, 471–479, https://doi.org/10.1109/TSG.2013.2279890, 2013. a
Windvision, Enercon, Eneco, and Elia: Delivery of downward aFRR by wind farms, Tech. rep., Elia, https://www.elia.be/-/media/project/elia/elia-site/electricity-market-and-system---document-library/balancing---balancing-services-and-bsp/2015/2015-study-report-delivery-of-downward-afrr-by-wind-farms.pdf (last access: August 2025), 2015. a
Short summary
Current offshore wind farms have been designed to maximize their production of electricity at all times and not to keep a reserve of 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. We apply it to a real-life wind farm and show that profits are expected to increase in a significant way for wind farms designed and operated for reserve, with less energy supplied.
Current offshore wind farms have been designed to maximize their production of electricity at...
Altmetrics
Final-revised paper
Preprint