Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1399-2026
© Author(s) 2026. 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-11-1399-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Apr 2026
Research article |
| 27 Apr 2026
| 27 Apr 2026
Wind farm inertia forecasting accounting for wake losses, turbine-level control strategies, and operational constraints
Andre Thommessen
Laboratory for Mechatronic and Renewable Energy Systems (LMRES), Hochschule München (HM) University of Applied Sciences, 80335 Munich, Germany
Wind Energy Institute, Technical University of Munich (TUM), 85748 Garching, Munich, Germany
Abhinav Anand
Wind Energy Institute, Technical University of Munich (TUM), 85748 Garching, Munich, Germany
Wind Energy Institute, Technical University of Munich (TUM), 85748 Garching, Munich, Germany
Christoph M. Hackl
Laboratory for Mechatronic and Renewable Energy Systems (LMRES), Hochschule München (HM) University of Applied Sciences, 80335 Munich, Germany
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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
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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
Preprint under review for WES
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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
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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
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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
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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, Davide Bortolin, Filippo Campagnolo, Franz V. Muehle, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-254, https://doi.org/10.5194/wes-2025-254, 2025
Revised manuscript accepted for WES
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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.
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
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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 and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-101, https://doi.org/10.5194/wes-2025-101, 2025
Revised manuscript under review for WES
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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.
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
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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
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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
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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
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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
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Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
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
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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.
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
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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
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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
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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
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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
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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
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Lidar-assisted control (LAC) is used to redesign the rotor and tower of three turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save
mass, increase hub height, or extend lifetime. The first two strategies yield reductions in the cost of energy only for the tower of the largest machine, while more interesting benefits are obtained for lifetime extension.
Chengyu Wang, Filippo Campagnolo, Helena Canet, Daniel J. Barreiro, and Carlo L. Bottasso
Wind Energ. Sci., 6, 961–981, https://doi.org/10.5194/wes-6-961-2021, https://doi.org/10.5194/wes-6-961-2021, 2021
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This paper quantifies the fidelity of the wakes generated by a small (1 m diameter) scaled wind turbine model operated in a large boundary layer wind tunnel. A detailed scaling analysis accompanied by large-eddy simulations shows that these wakes are very realistic scaled versions of the ones generated by the parent full-scale wind turbine in the field.
Marta Bertelè, Carlo L. Bottasso, and Johannes Schreiber
Wind Energ. Sci., 6, 759–775, https://doi.org/10.5194/wes-6-759-2021, https://doi.org/10.5194/wes-6-759-2021, 2021
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A previously published wind sensing method is applied to an experimental dataset obtained from a 3.5 MW turbine and a nearby hub-tall met mast. The method uses blade load harmonics to estimate rotor-equivalent shears and wind directions at the rotor disk. Results indicate the good quality of the estimated shear, both in terms of 10 min averages and of resolved time histories, and a reasonable accuracy in the estimation of the yaw misalignment.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
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The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Cited articles
AEMC: System Security Market Frameworks Review, Final report, https://www.aemc.gov.au/sites/default/files/content/f510069a-791b-4e4d-8bc0-9e6a216be7a2/System-Security-Market-Frameworks-Review-Final-Report.pdf (last access: 11 April 2026), 2017. a, b
Aho, J., Pao, L., Fleming, P., and Ela, E.: Controlling wind turbines for secondary frequency regulation: An analysis of AGC capabilities under new performance based compensation policy, in: Proc. 13th Wind Integration Workshop, https://www.osti.gov/biblio/1172284 (last access: 16 April 2026), 2014. a, b
Anand, A., Petzschmann, J., Strecker, K., Braunbehrens, R., Kaifel, A., and Bottasso, C. L.: Profit-optimal data-driven operation of a hybrid power plant participating in energy markets, J. Phys. Conf. Ser., 2767, 092069, https://doi.org/10.1088/1742-6596/2767/9/092069, 2024. a
Bao, W. B. W., Ding, L. D. L., Yin, S. Y. S., Wang, K. W. K., and Terzija, V.: Active rotor speed protection for DFIG synthetic inertia control, in: Proc. MedPower 2016, Institution of Engineering and Technology, https://doi.org/10.1049/cp.2016.1074, 2016. a
Bundesnetzagentur: Erläuterungsdokument zum Entwurf eines Konzeptes für die Spezifikationen und technischen Anforderungen der transparenten, diskriminierungsfreien und marktgestützten Beschaffung der nicht frequenzgebundenen Systemdienstleistung (nfSDL) “Trägheit der lokalen Netzstabilität”, Ruling Chamber 6, https://www.bundesnetzagentur.de/DE/Beschlusskammern/1_GZ/BK6-GZ/2023/BK6-23-010/BK6-23-010_zweite_konsultation.html?nn=877610 (last access: 11 April 2026), 2024. a
Courtier, P., Freydier, C., Geleyn, J.-F., Rabier, F., and Rochas, M.: The Arpege project at Meteo France, Seminar on Numerical Methods in Atmospheric Models, 9–13 September 1991, II, https://www.ecmwf.int/en/elibrary/74049-arpege-project-meteo-france (last access: 16 April 2026), 1991. a
Dirscherl, C. and Hackl, C. M.: Dynamic power flow in wind turbine systems with doubly-fed induction generator, in: Proc. IEEE ENERGYCON 2016, Leuven, Belgium, IEEE, 1–6, https://doi.org/10.1109/ENERGYCON.2016.7514104, 2016. a, b
Duckwitz, D.: Power System Inertia, PhD thesis, Universität Kassel, Fachbereich Elektrotechnik/Informatik, https://doi.org/10.17170/kobra-20190510451, 2019. a, b
ENTSO-E: Frequency Stability Evaluation Criteria for the Synchronous Zone of Continental Europe, ENTSO-E RG-CE System Protection & Dynamics Sub Group, https://eepublicdownloads.entsoe.eu/clean-documents/SOC%20documents/RGCE_SPD_frequency_stability_criteria_v10.pdf (last access: 11 April 2026), 2016. a
ENTSO-E: Future System Inertia, https://eepublicdownloads.entsoe.eu/clean-documents/Publications/SOC/Nordic/Nordic_report_Future_System_Inertia.pdf (last access: 11 April 2026), 2017. a
ENTSO-E: Inertia and rate of change of frequency, https://eepublicdownloads.azureedge.net/clean-documents/SOC documents/Inertia and RoCoF_v17_clean.pdf (last access: 11 April 2026), 2020. a
ENTSO-E: Frequency stability in long-term scenarios and relevant requirements, ENTSO-E Project Inertia Team, https://eepublicdownloads.azureedge.net/clean-documents/Publications/ENTSO-E general publications/211203_Long_term_frequency_stability_scenarios_for_publication.pdf (last access: 11 April 2026), 2021. a, b, c, d, e
Hackl, C. M., Jané-Soneira, P., Pfeifer, M., Schechner, K., and Hohmann, S.: Full- and Reduced-Order State-Space Modeling of Wind Turbine Systems with Permanent Magnet Synchronous Generator, Energies, 11, 1809, https://doi.org/10.3390/en11071809, 2018. a
Höhn, S., Rauscher, F., Deiml, G., and Nuschke, M.: Provision of instantaneous reserve from offshore wind farms: From system need to technical realization, in: VDE ETG Transformation der Stromversorgung – Netzregelung und Systemstabilität, VDE VERLAG GMBH, 106–115, ISBN 978-3-8007-6293-4, 2024. a, b, c, d, e, f
Hu, J., Yan, Z., Xu, X., and Chen, S.: Inertia Market: Mechanism Design and Its Impact on Generation Mix, J. Mod. Power Syst. Cle., 11, 744–756, https://doi.org/10.35833/MPCE.2022.000511, 2023. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, International Conference on Learning Representations (ICLR), arXiv [preprint], https://doi.org/10.48550/arXiv.1412.6980, 2014. a
Lee, J., Muljadi, E., Sorensen, P., and Kang, Y. C.: Releasable Kinetic Energy-Based Inertial Control of a DFIG Wind Power Plant, IEEE T. Sustain. Energ., 7, 279–288, https://doi.org/10.1109/tste.2015.2493165, 2016. a, b, c, d
Matevosyan, J.: Inertia monitoring, Tech. rep., https://t.e2ma.net/click/4pvdmf/4109f19/ss9kbw (last access: 11 April 2026), 2022. a
MATLAB: Choosing a Simulation Mode, https://www.mathworks.com/help/simulink/ug/choosing-a-simulation-mode.html (last access: 11 April 2026), 2025a. a
MATLAB: Solver-Based Nonlinear Optimization, https://www.mathworks.com/help/optim/solver-based-nonlinear-optimization.html?s_tid=CRUX_lftnav (last access: 11 April 2026), 2025b. a, b, c
MATLAB: parfor: Execute for-loop iterations in parallel on workers, https://www.mathworks.com/help/parallel-computing/parfor.html (last access: 11 April 2026), 2025c. a
Meseguer Urban, A., de Battista, M., Svendsen, H. G., D'Arco, S., and Sanchez, S.: Advanced integrated supervisory and wind turbine control for optimal operation of large Wind Power Plants: Controller adaptation for varying conditions and ancillary services (Deliverable no. D3.2), https://www.totalcontrolproject.eu/dissemination-activities/public-deliverables (last access: 11 April 2026), 2019. a, b, c, d, e
Nguyen, T.-T., Vu, T., Paudyal, S., Blaabjerg, F., and Vu, T. L.: Grid-Forming Inverter-based Wind Turbine Generators: Comprehensive Review, Comparative Analysis, and Recommendations, arXiv [preprint], https://doi.org/10.48550/arxiv.2203.02105, 2022. a
NREL: FLORIS, Version 3.2, GitHub, https://github.com/NREL/floris (last access: 11 April 2026), 2022. a
Ramtharan, G., Jenkins, N., and Ekanayake, J.: Frequency support from doubly fed induction generator wind turbines, IET Renew. Power Gen., 1, 3–9, https://doi.org/10.1049/iet-rpg:20060019, 2007. a
Rodriguez-Amenedo, J. L., Gomez, S. A., Martinez, J. C., and Alonso-Martinez, J.: Black-Start Capability of DFIG Wind Turbines Through a Grid-Forming Control Based on the Rotor Flux Orientation, IEEE Access, 9, 142910–142924, https://doi.org/10.1109/access.2021.3120478, 2021. a, b
Roscoe, A., Knueppel, T., Silva, R. D., Brogan, P., Gutierrez, I., Elliott, D., and Campion, J.-C. P.: Response of a grid forming wind farm to system events, and the impact of external and internal damping, IET Renew. Power Gen., 14, 3908–3917, https://doi.org/10.1049/iet-rpg.2020.0638, 2020. a, b, c, d, e, f
Schöll, C., Rauscher, F., Massmann, J., Küchler, S., Singer, R., Ernst, R., and Rogalla, S.: Testing of Grid-Forming Converters to analyze the State of the Art, in: VDE ETG Transformation der Stromversorgung – Netzregelung und Systemstabilität, VDE VERLAG GMBH, 62–67, ISBN 978-3-8007-6293-4, 2024. a
Sehnke, F., Osendorfer, C., Rückstiess, T., Graves, A., Peters, J., and Schmidhuber, J.: Parameter-exploring policy gradients, Neural Networks, 23, 551–559, https://doi.org/10.1016/j.neunet.2009.12.004, 2010. a
Shah, S. and Gevorgian, V.: Control, Operation, and Stability Characteristics of Grid-Forming Type 3 Wind Turbines, in: 19th Wind Integration Workshop, https://www.nrel.gov/docs/fy21osti/78158.pdf (last access: 11 April 2026), 2020. a
Soltani, M. N., Knudsen, T., Svenstrup, M., Wisniewski, R., Brath, P., Ortega, R., and Johnson, K.: Estimation of Rotor Effective Wind Speed: A Comparison, IEEE T. Contr. Syst. T., 21, 1155–1167, https://doi.org/10.1109/TCST.2013.2260751, 2013. a
Stanley, A. P., Roberts, O., Lopez, A., Williams, T., and Barker, A.: Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density, Energy Reports, 8, 3507–3525, https://doi.org/10.1016/j.egyr.2022.02.226, 2022. a
Taul, M. G., Wang, X., Davari, P., and Blaabjerg, F.: Current Limiting Control With Enhanced Dynamics of Grid-Forming Converters During Fault Conditions, IEEE J. Em. Sel. Top. P., 8, 1062–1073, https://doi.org/10.1109/JESTPE.2019.2931477, 2020. a, b
Thommessen, A., Anand, A., Bottasso, C. L., and Hackl, C. M.: Figures WES Journal article: Wind farm inertia forecasting accounting for wake losses, turbine-level control strategies, and operational constraints, Zenodo [data set], https://doi.org/10.5281/zenodo.15176372, 2026. a
VDE: Technische Anforderungen an Netzbildende Eigenschaften inklusive der Bereitstellung von Momentanreserve: Anforderungen für Netzbildende Einheiten (Version 1.0), https://www.vde.com/de/fnn/aktuelles/netzbildende-eigenschaften (last access: 11 April 2026), 2024a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non–hydrostatic) modelling framework of DWD and MPI–M: Description of the non–hydrostatic dynamical core, Q. J. Roy. Meteor. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a
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.
We present a method to forecast inertia that accounts for wake effects in a wind farm. The...
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