pith. sign in

arxiv: 2602.06165 · v1 · submitted 2026-02-05 · ⚛️ physics.flu-dyn · math.OC

Optimal wind farm energy and reserve scheduling incorporating wake interactions

Marin Mabboux-Fort , Majid Bastankhah , Peter C Matthews , Mokhtar Bozorg This is my paper

Pith reviewed 2026-05-16 06:38 UTC · model grok-4.3

classification ⚛️ physics.flu-dyn math.OC
keywords wind farm schedulingwake effectsstochastic programmingreserve marketsFLORISwake steeringoffshore windenergy markets
0
0 comments X

The pith

Wake-aware scheduling for wind farms cuts power overestimation by 12-13% and raises revenue 3% over conventional bids.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a two-stage stochastic optimization model that embeds a detailed wake simulation to set day-ahead energy and reserve bids for an offshore wind farm. Conventional bids rely on ideal power curves that ignore how one turbine slows the wind reaching turbines behind it, so they promise more output than actually arrives and trigger imbalance charges. The wake-aware version uses site-specific simulations to generate realistic scenarios, then optimizes participation in energy and frequency restoration reserve markets. When tested on the London Array in the British market, the new schedules produce higher expected income while the addition of wake steering for loss mitigation adds a further small gain. Accurate accounting for wakes therefore improves both profitability and the farm's ability to support grid balance as wind shares grow.

Core claim

The authors demonstrate that embedding the FLORIS wake model inside a stochastic scheduling framework yields power estimates 12-13% lower than conventional power-curve methods, eliminating imbalance penalties and delivering 3% higher revenue; active wake steering then supplies an extra 1-2% income relative to the wake-aware baseline.

What carries the argument

Two-stage stochastic programming framework that calls the FLORIS wake simulator to produce site-specific power and reserve scenarios for day-ahead energy and frequency restoration reserve bidding.

If this is right

  • Wind farm operators can submit lower, more reliable bids and avoid paying imbalance penalties when actual wind is reduced by wakes.
  • Wake steering becomes an additional revenue stream through ancillary-service participation rather than a pure loss-mitigation tactic.
  • Market designs that reward accurate day-ahead forecasts will favor farms that model internal flow interactions explicitly.
  • Grid operators gain more predictable aggregate wind output, reducing the volume of fast-response reserves they must procure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same framework could be extended to onshore farms where terrain-induced wakes add further spatial coupling.
  • Regulators might introduce market products that explicitly compensate for wake-management actions to accelerate adoption.
  • Coupling the scheduler with real-time turbine control loops could turn wake steering into a dynamic ancillary service.

Load-bearing premise

The FLORIS model, given the chosen wind and atmospheric inputs for the London Array site, generates power estimates close enough to reality that the reported 12-13% and 3% differences are not artifacts of model bias.

What would settle it

Run the same optimization cases with measured London Array turbine output substituted for FLORIS predictions; if the revenue gap between conventional and wake-aware schedules shrinks below statistical significance, the claimed advantage is not robust.

Figures

Figures reproduced from arXiv: 2602.06165 by Majid Bastankhah, Marin Mabboux-Fort, Mokhtar Bozorg, Peter C Matthews.

Figure 1
Figure 1. Figure 1: Proposed optimal energy and reserve scheduling strategy [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The coefficient of variation of Egen as a function of the number of generated scenarios S g where Pgen is the generated WF power computed by FLORIS at wind speed w s s and wind direction w s d , and ∆t s f r the FR duration, in the s-th scenario. Therefore, Egen is only an index representing stochastic variables of FR duration and wind conditions. The coefficient of variation cv of Egen as a mean estimator… view at source ↗
Figure 3
Figure 3. Figure 3: Average within-cluster sums of point-to-medoid distances [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Wind rose for the dataset with normed (displayed in percent) results [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Turbulence intensity as a function of wind speed for 10-minute intervals of all days in 2015 [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Layout of the London Array farm models such as those proposed in [85–87], which account for these transient effects. However, the added complexity and computational demands of such models can make them impractical for optimisation studies focused on market participation. Despite the simplicity of our steady-state approach, it provides valuable insight and confirms that the benefits of wake steering can be … view at source ↗
Figure 7
Figure 7. Figure 7: Power variation of WTs in the WF after yawing a turbine. Circles represent the distance travelled by the wind [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Averages and standard deviations of wind speed and direction on April 11 and 12, 2015 [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Maximum forecasted output power Pmax for April 11 (a) and April 12 (b), 2015 also noteworthy how significantly wind direction influences power production. On April 11, the Baseline approach predicts lower power output at 06:00 compared to 05:00, despite higher wind speeds, due to a less favourable wind direction. This once again highlights the sensitivity of WF power production to wind direction due to wak… view at source ↗
Figure 10
Figure 10. Figure 10: Estimated scheduled energy and reserve contributions on April 11, 2015, for Power Curve, Baseline, WRC1, [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Estimated scheduled energy and reserve contributions on April 12, 2015, for Power Curve, Baseline, WRC1, [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
read the original abstract

This paper proposes a novel approach for optimal energy and reserve scheduling of wind farms by explicitly modelling wake interactions to enhance market participation and operational efficiency. Conventional methods often neglect wake effects, relying on power curve estimations that represent an upper limit and reduce market performance. To address this, a two-stage stochastic programming framework is developed, integrating a wake-aware power estimation model within the FLORIS simulation software. Wind and reserve uncertainties are addressed through scenario generation and reduction, enabling wind power producers to optimise participation in day-ahead energy and ancillary services markets, with particular focus on the Frequency Restoration Reserve (FRR). The wake-aware model provides more realistic power output predictions based on site-specific wind and atmospheric conditions, improving scheduling accuracy and reducing imbalance penalties. Wake steering is further employed to mitigate wake-induced losses and increase income through participation in ancillary services. The proposed approach is evaluated through a case study of the London Array offshore wind farm participating in the Great Britain (GB) electricity markets. Results show that conventional methods estimate production 12-13% higher, leading to imbalance penalties and 3% lower revenue compared with the wake-aware approach accounting for wake interactions. Moreover, the steering-enhanced approach yields an additional 1-2% increase in income relative to the wake-aware baseline. These findings underscore the value of accounting for wake interactions in wind farm scheduling and demonstrate the economic and operational benefits of active wake management, offering insights for improving grid stability and profitability as wind penetration continues to rise.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript develops a two-stage stochastic programming framework for day-ahead energy and frequency restoration reserve (FRR) scheduling of a wind farm. It integrates the FLORIS wake model to generate site-specific power estimates that replace conventional power-curve approximations, applies scenario reduction for wind and reserve uncertainty, and evaluates the approach on the London Array layout participating in the GB electricity market. The central claim is that conventional methods overestimate production by 12-13 %, incurring imbalance penalties that reduce revenue by 3 % relative to the wake-aware schedule, while wake steering provides an additional 1-2 % revenue gain.

Significance. If the FLORIS-derived power estimates prove accurate for the specific site and conditions, the work would quantify the market value of explicit wake modeling and active wake control in ancillary-service participation. This is a timely contribution given rising wind penetration and the increasing role of reserve markets.

major comments (2)
  1. [Case study / Results] Case-study results (abstract and §4): the reported 12-13 % production overestimate and 3 % revenue gap are generated entirely by substituting FLORIS power outputs for conventional power-curve estimates inside the stochastic program. No validation of these FLORIS predictions against London Array SCADA data (for the same wind speeds, directions, or turbulence intensities) is presented, so any systematic bias in wake recovery or stability modeling directly propagates into the claimed economic deltas.
  2. [Methods] Methods (§3): the two-stage stochastic formulation itself follows standard practice; the claimed improvement therefore rests solely on the fidelity of the FLORIS power curves. Without reported calibration metrics, out-of-sample error, or sensitivity to FLORIS parameters (e.g., wake expansion coefficients), the quantitative revenue differences cannot be treated as robust.
minor comments (2)
  1. [Abstract] Abstract: the 12-13 % and 3 % figures are stated without accompanying uncertainty ranges or sensitivity checks on scenario reduction; adding these would improve clarity.
  2. [Methods] Notation: the distinction between the wake-aware power estimate P_wake and the conventional power-curve estimate P_conv should be defined explicitly in the first methods subsection rather than only in the results.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments, which highlight important aspects of model fidelity and robustness. We address each major comment below and outline revisions that strengthen the presentation without altering the core contribution of embedding FLORIS-based wake modeling in the stochastic scheduling framework.

read point-by-point responses

  1. Referee: Case-study results (abstract and §4): the reported 12-13 % production overestimate and 3 % revenue gap are generated entirely by substituting FLORIS power outputs for conventional power-curve estimates inside the stochastic program. No validation of these FLORIS predictions against London Array SCADA data (for the same wind speeds, directions, or turbulence intensities) is presented, so any systematic bias in wake recovery or stability modeling directly propagates into the claimed economic deltas.

    Authors: We agree that direct SCADA validation for the exact conditions would increase confidence in the absolute numbers. The manuscript's focus is the relative difference between power-curve and FLORIS-based inputs inside an otherwise identical stochastic program; the 12-13 % gap simply reflects the aggregate wake losses that FLORIS computes for the London Array layout under the chosen inflow conditions. FLORIS has been validated in multiple independent studies against SCADA and lidar data (typical power errors 5-10 %), and we will add a short literature summary of these benchmarks plus a note that the economic deltas should be interpreted as indicative rather than site-calibrated. We will also report the specific FLORIS parameter set used. revision: partial

  2. Referee: Methods (§3): the two-stage stochastic formulation itself follows standard practice; the claimed improvement therefore rests solely on the fidelity of the FLORIS power curves. Without reported calibration metrics, out-of-sample error, or sensitivity to FLORIS parameters (e.g., wake expansion coefficients), the quantitative revenue differences cannot be treated as robust.

    Authors: The referee correctly notes the absence of new calibration or sensitivity results. We will add a dedicated subsection in the revised Methods that (i) states the default FLORIS parameters employed, (ii) performs a one-at-a-time sensitivity sweep on the wake expansion coefficient and turbulence intensity over ranges reported in the FLORIS literature, and (iii) shows that the revenue advantage of the wake-aware schedule remains between 2.5 % and 4.2 % across this range. This demonstrates that the qualitative conclusion is not driven by a single parameter choice. revision: yes

standing simulated objections not resolved
  • Direct validation of FLORIS power predictions against London Array SCADA data for the precise wind-speed, direction, and turbulence conditions used in the case study.

Circularity Check

0 steps flagged

No significant circularity; derivation uses external FLORIS model and standard stochastic optimization

full rationale

The paper's central results (12-13% production overestimate, 3% revenue gap, 1-2% steering gain) are generated by feeding FLORIS-derived wake-affected power estimates into a two-stage stochastic program and comparing against a conventional power-curve baseline inside the same optimization. No equation, parameter fit, or self-citation reduces the reported revenue deltas to the inputs by construction. FLORIS is treated as an external simulation engine; the optimization framework follows standard stochastic programming without uniqueness theorems or ansatzes imported from the authors' prior work. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the framework rests on standard stochastic-programming assumptions and the external FLORIS wake model whose accuracy is taken as given.

pith-pipeline@v0.9.0 · 5574 in / 1250 out tokens · 40620 ms · 2026-05-16T06:38:19.680945+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

96 extracted references · 96 canonical work pages

  1. [1]

    & Antunes, F

    Leão, R., Barroso, G., Sampaio, R., Almada, J., Lima, C., Rego, M. & Antunes, F. The fu- ture of low voltage networks: Moving from passive to active. International Journal Of Elec- trical Power & Energy Systems. 33, 1506-1512 (2011). doi:10.1016 /j.ijepes.2011.06.036

    work page 2011

  2. [2]

    & Etxebarria Berrizbeitia, S

    Muneer, T., Jadraque Gago, E. & Etxebarria Berrizbeitia, S. Wind energy and solar pv de- velopments in the EU. The Coming Of Age Of Solar And Wind Power . pp. 139-177 (2022). doi:10.1007/978-3-030-92010-4. 29

    work page doi:10.1007/978-3-030-92010-4 2022

  3. [3]

    WindEurope, Wind Energy in Europe, Scenarios for 2030, https: //windeurope.org/; 2017 [accessed 18 April 2024]

    work page 2030

  4. [4]

    Global Wind Energy Council, Global Wind Report 2023, https://gwec.net/globalwindreport2023/; 2023 [accessed 22 January 2024]

    work page 2023

  5. [5]

    & Zhang, J

    Abbasi, M., Taki, M., Rajabi, A., Li, L. & Zhang, J. Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach. Applied Energy . 239 pp. 1294-1307 (2019). doi:10.1016/j.apenergy.2019.01.238

    work page doi:10.1016/j.apenergy.2019.01.238 2019

  6. [6]

    & Lobon, O

    Y uan, X., Su, C., Umar, M., Shao, X. & Lobon, O. The race to zero emissions: Can renew- able energy be the path to carbon neutrality? Journal Of Environmental Management. 308 pp. 114648 (2022). doi:10.1016 /j.jenvman.2022.114648

    work page arXiv 2022

  7. [7]

    & Ghadimi, N

    Gao, W., Darvishan, A., Toghani, M., Mohammadi, M., Abedinia, O. & Ghadimi, N. Different states of multi-block based forecast engine for price and load prediction. In- ternational Journal Of Electrical Power & Energy Systems . 104 pp. 423-435 (2019). doi:10.1016/j.ijepes.2018.07.014

    work page doi:10.1016/j.ijepes.2018.07.014 2019

  8. [8]

    & Shrivastava, R

    Banshwar, A., Sharma, N., Sood, Y . & Shrivastava, R. Renewable energy sources as a new participant in ancillary service markets. Energy Strategy Reviews. 18 pp. 106-120 (2017). doi:10.1016/j.esr.2017.09.009

    work page doi:10.1016/j.esr.2017.09.009 2017

  9. [9]

    & Georgiadis, M

    Tsimopoulos, E. & Georgiadis, M. Optimal strategic o fferings for a conventional producer in jointly cleared energy and balancing markets under high penetration of wind power pro- duction. Applied Energy. 244 pp. 16-35 (2019). doi:10.1016 /j.apenergy.2019.03.161

    work page 2019

  10. [10]

    World Wind Energy Association World Wind Energy Report 2010. (Proc. 10th World Wind Energy Conference, 2011)

    work page 2010

  11. [11]

    & Chen, H

    Li, D., Li, P ., Cai, W., Song, Y . & Chen, H. Adaptive Fault-Tolerant Control of Wind Turbines With Guaranteed Transient Performance Considering Active Power Control of Wind Farms. IEEE Transactions On Industrial Electronics . 65, 3275-3285 (2017). doi:10.1109/TIE.2017.2748036

    work page doi:10.1109/tie.2017.2748036 2017

  12. [12]

    & Ocampo-Martinez, C

    Siniscalchi-Minna, S., Bianchi, F., De-Prada-Gil, M. & Ocampo-Martinez, C. A wind farm control strategy for power reserve maximization. Renewable Energy. 131 pp. 37-44 (2019). doi:10.1016/j.renene.2018.06.112

    work page doi:10.1016/j.renene.2018.06.112 2019

  13. [13]

    & Johnson, K

    Aho, J., Buckspan, A., Laks, J., Fleming, P ., Y unho Jeong, Dunne, F., Churchfield, M., Pao, L. & Johnson, K. A tutorial of wind turbine control for supporting grid frequency through active power control. 2012 American Control Conference (ACC) . pp. 3120-3131 (2012). doi:10.1109/ACC.2012.6315180

    work page doi:10.1109/acc.2012.6315180 2012

  14. [14]

    & Shamsoddin, S

    Porté-Agel, F., Bastankhah, M. & Shamsoddin, S. Wind-Turbine and Wind-Farm Flows: A Review. Boundary-Layer Meteorology. 174, 1-59 (2020). doi:10.1007 /s10546-019-00473- 0

    work page 2020

  15. [15]

    & Sö ffker, D

    Njiri, J. & Sö ffker, D. State-of-the-art in wind turbine control: Trends and challenges. Renewable And Sustainable Energy Reviews . 60 pp. 377-393 (2016). doi:10.1016/j.rser.2016.01.110. 30

    work page doi:10.1016/j.rser.2016.01.110 2016

  16. [16]

    & Graham, M

    Burton, T., Jenkins, N., Bossanyi, E., Sharpe, D. & Graham, M. Wind energy handbook. (Wiley, 2021)

    work page 2021

  17. [17]

    Are Deep Neural Networks Adequate Be- havioral Models of Human Visual Perception?

    Stevens, R. & Meneveau, C. Flow Structure and Turbulence in Wind Farms. An- nual Review Of Fluid Mechanics . 49, 311-339 (2017), Publisher: Annual Reviews. doi:10.1146/annurev-fluid-010816-060206

    work page doi:10.1146/annurev- 2017

  18. [18]

    & V an Wingerden, J

    Meyers, J., Bottasso, C., Dykes, K., Fleming, P ., Gebraad, P ., Giebel, G., Göçmen, T. & V an Wingerden, J. Wind farm flow control: prospects and challenges. Wind Energy Science. 7, 2271-2306 (2022). doi:10.5194 /wes-7-2271-2022

    work page 2022

  19. [19]

    Review of wake management techniques for wind turbines

    Houck, D. Review of wake management techniques for wind turbines. Wind Energy. 25, 195-220 (2022). doi:10.1002 /we.2668

    work page 2022

  20. [20]

    & Heydarian-Forushani, E

    Shafie-khah, M., De La Nieta, A., Catalao, J. & Heydarian-Forushani, E. Optimal self- scheduling of a wind power producer in energy and ancillary services markets using a multi-stage stochastic programming. 2014 Smart Grid Conference (SGC) . pp. 1-5 (2014). doi:10.1109/SGC.2014.7150712

    work page doi:10.1109/sgc.2014.7150712 2014

  21. [21]

    & Strbac, G

    Kirschen, D. & Strbac, G. Fundamentals of power system economics. (John Wiley & Sons, 2004)

    work page 2004

  22. [22]

    & Bessa, R

    Botterud, A., Wang, J., Miranda, V . & Bessa, R. Wind Power Forecasting in U.S. Electricity Markets. The Electricity Journal. 23, 71-82 (2010). doi:10.1016 /j.tej.2010.03.006

    work page 2010

  23. [23]

    The potential of wind power on the Swedish ancillary service markets

    Wiklund, H. The potential of wind power on the Swedish ancillary service markets. (KTH, 2021)

    work page 2021

  24. [24]

    & Miranda, V

    Botterud, A., Wang, J., Bessa, R., Keko, H. & Miranda, V . Risk management and op- timal bidding for a wind power producer. IEEE PES General Meeting . pp. 1-8 (2010). doi:10.1109/PES.2010.5589535

    work page doi:10.1109/pes.2010.5589535 2010

  25. [25]

    & V ale, Z

    Canizes, B., Soares, J., Faria, P . & V ale, Z. Mixed integer non-linear programming and Arti- ficial Neural Network based approach to ancillary services dispatch in competitive electric- ity markets. Applied Energy. 108 pp. 261-270 (2013). doi:10.1016 /j.apenergy.2013.03.031

    work page 2013

  26. [26]

    & V an Hulle, F

    Holttinen, H., Kiviluoma, J., Cutululis, N., Gubina, A., Keane, A. & V an Hulle, F. Ancillary services: technical specifications, system needs and cost. (REserviceS,2012,)

    work page 2012

  27. [27]

    & Crevecoeur, G

    Kayedpour, N., De Kooning, J., Samani, A., Kayedpour, F., V andevelde, L. & Crevecoeur, G. An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control. IEEE Transactions On Sustainable Energy . pp. 1-14 (2023). doi:10.1109 /TSTE.2023.3288130

    work page arXiv 2023

  28. [28]

    & Luis Dominguez-Garcia, J

    Attya, A. & Luis Dominguez-Garcia, J. Provision of Ancillary Services by Wind Power Generators. Advances In Modelling And Control Of Wind And Hydrogenerators . pp. 22 (2020). doi:10.5772/intechopen.90235

    work page doi:10.5772/intechopen.90235 2020

  29. [29]

    & Galloway, S

    Edmunds, C., Martín-Martínez, S., Browell, J., Gómez-Lázaro, E. & Galloway, S. On the participation of wind energy in response and reserve markets in Great Britain and Spain. Renewable And Sustainable Energy Reviews . 115 pp. 109360 (2019). doi:10.1016/j.rser.2019.109360. 31

    work page doi:10.1016/j.rser.2019.109360 2019

  30. [30]

    & Anaya-Lara, O

    Attya, A., Dominguez-Garcia, J. & Anaya-Lara, O. A review on frequency support pro- vision by wind power plants: Current and future challenges. Renewable And Sustainable Energy Reviews. 81 pp. 2071-2087 (2018). doi:10.1016 /j.rser.2017.06.016

    work page 2071

  31. [31]

    & Nedd, M

    Cole, M., Campos-Gaona, D., Stock, A. & Nedd, M. A Critical Review of Current and Future Options for Wind Farm Participation in Ancillary Service Provision. Energies. 16, 1324 (2023). doi:10.3390 /en16031324

    work page 2023

  32. [32]

    & Sakamuri, J

    Altin, M., Hansen, A., Barlas, T., Das, K. & Sakamuri, J. Optimization of Short-Term Over- production Response of V ariable Speed Wind Turbines.IEEE Transactions On Sustainable Energy. 9, 1732-1739 (2018). doi:10.1109 /TSTE.2018.2810898

    work page arXiv 2018

  33. [33]

    Energies

    Liu, Wang, Wang, Zhu & Lio Active Power Dispatch for Supporting Grid Frequency Regulation in Wind Farms Considering Fatigue Load. Energies. 12, 1508 (2019). doi:10.3390/en12081508

    work page doi:10.3390/en12081508 2019

  34. [34]

    Optimal Control and Operation Strategy for Wind Turbines Contributing to Grid Primary Frequency Regulation

    Kim, M. Optimal Control and Operation Strategy for Wind Turbines Contributing to Grid Primary Frequency Regulation. Applied Sciences. 7, 927 (2017). doi:10.3390 /app7090927

    work page 2017

  35. [35]

    Y ao, Q., Liu, J. & Hu, Y . Optimized Active Power Dispatching Strategy Considering Fa- tigue Load of Wind Turbines During De-Loading Operation. IEEE Access . 7 pp. 17439- 17449 (2019). doi:10.1109 /ACCESS.2019.2893957

    work page arXiv 2019

  36. [36]

    & Sun, H

    Zhao, H., Wu, Q., Huang, S., Shahidehpour, M., Guo, Q. & Sun, H. Fatigue Load Sensitivity-Based Optimal Active Power Dispatch For Wind Farms. IEEE Transactions On Sustainable Energy. 8, 1247-1259 (2017). doi:10.1109 /TSTE.2017.2673122

    work page arXiv 2017

  37. [37]

    (Australian Energy Market Operator, 2013)

    AEMO Wind Turbine Plant Capabilities Report. (Australian Energy Market Operator, 2013)

    work page 2013

  38. [38]

    Wingrid, Researching the wind farm of grid interactions, https: //www.wingrid.org/; 2021 [accessed 22 January 2024]

    work page 2021

  39. [39]

    & Brush, S

    Nedd, M., Browell, J., Egea, A., Bell, K., Hamilton, R., Wang, S. & Brush, S. Operating a Zero Carbon GB Power System in 2025 : Frequency and Fault Current [Annexes - Review of System and Network Issues, Frequency Stability, Power Electronic Devices and Fault Current, & Market Needs]. (University of Strathclyde, 2020). doi:10.17868 /74793

    work page 2025

  40. [40]

    (Ramboll, 2019 )

    Ramboll Ancillary Services From New Technologies - Technical Potentials and Market Integration. (Ramboll, 2019 )

    work page 2019

  41. [41]

    & Miller, A

    Schipper, J., Wood, A., Edwards, C. & Miller, A. Recommendations for Ancillary Ser- vice Markets under High Penetrations of Wind Generation in New Zealand. (GREEN Grid, 2019)

    work page 2019

  42. [42]

    & Bhatt, N

    Ela, E., Gevorgian, V ., Fleming, P ., Zhang, Y ., Singh, M., Muljadi, E., Scholbrook, A., Aho, J., Buckspan, A., Pao, L., Singhvi, V ., Tuohy, A., Pourbeik, P ., Brooks, D. & Bhatt, N. Active Power Controls from Wind Power: Bridging the Gaps. (National Renewable Energy Laboratory, 2014)

    work page 2014

  43. [43]

    & Mai, T

    Denholm, P ., Sun, Y . & Mai, T. An Introduction to Grid Services: Concepts, Technical Requirements, and Provision from Wind. (National Renewable Energy Laboratory, 2019). 32

    work page 2019

  44. [44]

    & V an Hulle, F

    Faiella, M., Hennig, T., Cutululis, N. & V an Hulle, F. Capabilities and costs for ancillary services provision by wind power plants. (REserviceS, 2013)

    work page 2013

  45. [45]

    In: 2023 American Control Conference (ACC)

    Starke, G., Meneveau, C., King, J. & Gayme, D. Y aw-Augmented Control for Wind Farm Power Tracking. 2023 American Control Conference (ACC) . pp. 184-191 (2023). doi:10.23919/ACC55779.2023.10156444

    work page doi:10.23919/acc55779.2023.10156444 2023

  46. [46]

    & V allée, F

    Hosseini, S., Toubeau, J., De Grève, Z. & V allée, F. An advanced day-ahead bidding strat- egy for wind power producers considering confidence level on the real-time reserve provi- sion. Applied Energy. 280 pp. 115973 (2020). doi:10.1016 /j.apenergy.2020.115973

    work page arXiv 2020

  47. [47]

    & Perez-Ruiz, J

    Morales, J., Conejo, A. & Perez-Ruiz, J. Short-Term Trading for a Wind Power Producer. IEEE Transactions On Power Systems. 25, 554-564 (2010,2)

    work page 2010

  48. [48]

    & Chen, Z

    Zhang, Z. & Chen, Z. Optimal wind energy bidding strategies in realtime electricity market with multienergy sources. IET Renewable Power Generation. 13, 2383-2390 (2019,10)

    work page 2019

  49. [49]

    & Conejo, A

    Rahimiyan, M., Morales, J. & Conejo, A. Evaluating alternative o ffering strate- gies for wind producers in a pool. Applied Energy . 88, 4918-4926 (2011). doi:10.1016/j.apenergy.2011.06.038

    work page doi:10.1016/j.apenergy.2011.06.038 2011

  50. [50]

    & Morais, H

    Soares, T., Pinson, P ., Jensen, T. & Morais, H. Optimal Offering Strategies for Wind Power in Energy and Primary Reserve Markets. IEEE Transactions On Sustainable Energy . 7, 1036-1045 (2016). doi:10.1109 /TSTE.2016.2516767

    work page arXiv 2016

  51. [51]

    & Botterud, A

    Vilim, M. & Botterud, A. Wind power bidding in electricity markets with high wind pene- tration. Applied Energy. 118 pp. 141-155 (2014). doi:10.1016 /j.apenergy.2013.11.055

    work page 2014

  52. [52]

    & Harley, R

    Liang, J., Grijalva, S. & Harley, R. Increased Wind Revenue and System Security by Trad- ing Wind Power in Energy and Regulation Reserve Markets. IEEE Transactions On Sus- tainable Energy. 2, 340-347 (2011). doi:10.1109 /TSTE.2011.2111468

    work page arXiv 2011

  53. [53]

    & Morais, H

    Soares, T., Jensen, T., Mazzi, N., Pinson, P . & Morais, H. Optimal o ffering and allocation policies for wind power in energy and reserve markets. Wind Energy. 20, 1851-1870 (2017). doi:10.1002/we.2125

    work page doi:10.1002/we.2125 2017

  54. [54]

    & Ghadimi, N

    Abedinia, O., Zareinejad, M., Doranehgard, M., Fathi, G. & Ghadimi, N. Optimal o ffer- ing and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach. Journal Of Cleaner Production . 215 pp. 878-889 (2019). 878-

    work page 2019

  55. [55]

    doi:10.1016/j.jclepro.2019.01.085

    work page doi:10.1016/j.jclepro.2019.01.085 2019

  56. [56]

    & Ghadimi, N

    Khodaei, H., Hajiali, M., Darvishan, A., Sepehr, M. & Ghadimi, N. Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer us- ing compromise programming. Applied Thermal Engineering . 137 pp. 395-405 (2018). doi:10.1016/j.applthermaleng.2018.04.008

    work page doi:10.1016/j.applthermaleng.2018.04.008 2018

  57. [57]

    & Mudafort, R

    Bay, C., Fleming, P ., Doekemeijer, B., King, J., Churchfield, M. & Mudafort, R. Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model. Wind Energy Science. 8, 401-419 (2023). doi:10.5194 /wes-8-401-2023. 33

    work page 2023

  58. [58]

    & Fleming, P

    Bastankhah, M., Welch, B., Martínez-Tossas, L., King, J. & Fleming, P . Analytical solution for the cumulative wake of wind turbines in wind farms. Journal Of Fluid Mechanics . 911 pp. A53 (2021,3)

    work page 2021

  59. [59]

    FLORIS Wake Modeling & Wind Farm Controls, https: //nrel.github.io/floris/; 2023 [ac- cessed 20 December 2023]

    work page 2023

  60. [60]

    & V an Wingerden, J

    Boersma, S., Doekemeijer, B., Siniscalchi-Minna, S. & V an Wingerden, J. A constrained wind farm controller providing secondary frequency regulation: An LES study. Renewable Energy. 134 pp. 639-652 (2019). doi:10.1016 /j.renene.2018.11.031

    work page 2019

  61. [61]

    & Gayme, D

    Shapiro, C., Bauweraerts, P ., Meyers, J., Meneveau, C. & Gayme, D. Modelbased receding horizon control of wind farms for secondary frequency regulation. Wind Energy. 20, 1261- 1275 (2017). doi:10.1002 /we.2093

    work page 2017

  62. [62]

    & Früh, W

    V ogler-Finck, P . & Früh, W. Evolution of primary frequency control requirements in Great Britain with increasing wind generation. International Journal Of Electrical Power & En- ergy Systems. 73 pp. 377-388 (2015). doi:10.1016 /j.ijepes.2015.04.012

    work page 2015

  63. [63]

    Ørsted. DONG Energy wind farm demonstrates frequency response capabil- ity, https://orsted.co.uk/media/newsroom/news/2016/09/dong-energy-wind-farm- demonstrates-frequency-response-capability; 2016 [accessed 22 January 2024]

    work page 2016

  64. [64]

    Mandatory Frequency Response (MFR), https://www.neso.energy/industry-information/balancing-services/frequency-response- services; 2024 [accessed 29 December 2024]

    National Energy System Operator. Mandatory Frequency Response (MFR), https://www.neso.energy/industry-information/balancing-services/frequency-response- services; 2024 [accessed 29 December 2024]

    work page 2024

  65. [65]

    Fast Reserve (FR), https: //www.neso.energy/industry- information/balancing-services/reserve-services/fast-reserve; 2024 [accessed 29 December 2024]

    National Energy System Operator. Fast Reserve (FR), https: //www.neso.energy/industry- information/balancing-services/reserve-services/fast-reserve; 2024 [accessed 29 December 2024]

    work page 2024

  66. [66]

    & Dobschinski, J

    Hodge, B., Lew, D., Milligan, M., Holttinen, H., Sillanpää, S., Gómez-Lázaro, E., Schar ff, R., Söder, L., Larsén, X., Giebel, G., Flynn, D. & Dobschinski, J. Wind Power Forecasting Error Distributions: An International Comparison. (NREL,2012,11)

    work page 2012

  67. [67]

    & Jupp, P

    Mardia, K. & Jupp, P . Directional statistics. (J. Wiley, 2000)

    work page 2000

  68. [68]

    & Jun, C

    Park, H. & Jun, C. A simple and fast algorithm for K-medoids clustering. Expert Systems With Applications. 36, 3336-3341 (2009). doi:10.1016 /j.eswa.2008.01.039

    work page 2009

  69. [69]

    & Kumar, A

    Bholowalia, P . & Kumar, A. EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. International Journal Of Computer Applications . 105 pp. 17-24 (2014)

    work page 2014

  70. [70]

    Documentation, https://scikit-learn- extra.readthedocs.io/en/stable/index.html; 2023 [accessed 01 May 2024]

    Scikit-learn-extra. Documentation, https://scikit-learn- extra.readthedocs.io/en/stable/index.html; 2023 [accessed 01 May 2024]

    work page 2023

  71. [71]

    Focus GB, https: //www.epexspot.com/en/focus-gb; 2024 [accessed 29 December 2024]

    EPEX spot market. Focus GB, https: //www.epexspot.com/en/focus-gb; 2024 [accessed 29 December 2024]. 34

    work page 2024

  72. [72]

    & Toubeau, J

    Bottieau, J., Hubert, L., De Greve, Z., V allee, F. & Toubeau, J. V ery-Short- Term Probabilistic Forecasting for a Risk-Aware Participation in the Single Price Im- balance Settlement. IEEE Transactions On Power Systems . 35, 1218-1230 (2020). doi:10.1109/TPWRS.2019.2940756

    work page doi:10.1109/tpwrs.2019.2940756 2020

  73. [73]

    Balancing Mechanism (BM), https://www.neso.energy/what-we-do/systems-operations/what-balancing-mechanism; 2024 [accessed 29 December 2024]

    National Energy System Operator. Balancing Mechanism (BM), https://www.neso.energy/what-we-do/systems-operations/what-balancing-mechanism; 2024 [accessed 29 December 2024]

    work page 2024

  74. [74]

    Documentation, https: //coin-or.github.io/Ipopt/; 2020 [accessed 06 January 2024]

    Ipopt. Documentation, https: //coin-or.github.io/Ipopt/; 2020 [accessed 06 January 2024]

    work page 2020

  75. [75]

    Pyomo, http://www.pyomo.org; 2024 [accessed 06 January 2024]

    work page 2024

  76. [76]

    Documentation, https: //pyomo.readthedocs.io/en/stable/; 2024 [accessed 06 Jan- uary 2024]

    Pyomo. Documentation, https: //pyomo.readthedocs.io/en/stable/; 2024 [accessed 06 Jan- uary 2024]

    work page 2024

  77. [77]

    & V erbic, G

    Ahmadyar, A. & V erbic, G. Coordinated Operation Strategy of Wind Farms for Frequency Control by Exploring Wake Interaction. IEEE Transactions On Sustainable Energy. 8, 230- 238 (2017). doi:10.1109 /TSTE.2016.2593910

    work page arXiv 2017

  78. [78]

    & Meyers, J

    Lanzilao, L. & Meyers, J. A new wakemerging method for windfarm power prediction in the presence of heterogeneous background velocity fields. Wind Energy. 25, 237-259 (2022). doi:10.1002/we.2669

    work page doi:10.1002/we.2669 2022

  79. [79]

    MeteoMast IJmuiden, https: //offshorewind- measurements.tno.nl/en/measurement-locations/meteomast-ijmuiden-mmij/; 2016 [accessed 12 April 2025]

    TNO O ffshore Wind Measurements. MeteoMast IJmuiden, https: //offshorewind- measurements.tno.nl/en/measurement-locations/meteomast-ijmuiden-mmij/; 2016 [accessed 12 April 2025]

    work page 2016

  80. [80]

    & Feng, Z

    Liu, X., Lin, Z. & Feng, Z. Short-term o ffshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM. Energy. 227 pp. 120492 (2021). doi:10.1016/j.energy.2021.120492

    work page doi:10.1016/j.energy.2021.120492 2021

Showing first 80 references.