Effects of Wind Farm Down-Regulation in the Offshore Wind Farm Alpha Ventus

Kretschmer, Matthias; Pettas, Vasilis; Cheng, Po‐Wen

doi:10.1115/iowtc2019-7554

Cited by 7 publications

(7 citation statements)

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“…Currently, downregulation (also referred to as curtailment or derating) is used commercially to follow reference power levels according to the grid system requirements. As previous numerical (Pettas et al, 2018) and in situ studies (Kretschmer et al, 2019) have shown, this process also reduces the structural loading. Moreover, turbine manufacturers already offer turbines with power-boosting capabilities to increase the energy production (e.g.…”

Section: Conceptual Analysis For a Single Turbineparticipant P1supporting

confidence: 52%

FarmConners market showcase results: wind farm flow control considering electricity prices

et al. 2022

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Abstract. The EU and UK have made ambitious commitments under the net-zero plans to decarbonise their economies by 2050. For this, offshore wind will play a major role, significantly contributing to a paradigm shift in the power generation and greater volatility of electricity prices. The operating strategy of wind farms should therefore move from power maximisation to profit maximisation which includes income from providing power system services and the reduction of maintenance costs. Wind farm flow control (WFFC) is a key enabler for this shift through mitigation of wake effects in the design and operation phases. The results of the FarmConners market showcases presented here are the first attempt to economically assess WFFC strategies with respect to electricity market prices. Here, we present a conceptual simulation study starting from individual turbine control and extend it to layouts with 10 and 32 turbines operated with WFFC based on the results of five participants. Each participant belonged to a different research group with their respective simulation environments, flow models and WFFC strategies. Via a comparative analysis of relative WFFC benefits estimated per participant, the implications of wind farm size, the applied control strategy and the overall model fidelity are discussed in zero-subsidy scenarios. For all the participants, it is seen that the income gain can differ significantly from the power gain depending on the electricity price under the same inflow, and a favourable control strategy for dominant wind directions can pay off even for low electricity prices. However, a strong correlation between income and power gain is also observed for the analysed high-electricity-price scenarios, underlining the need for additional modelling capabilities to carry out a more comprehensive value optimisation including lower prices and system requirements driven cases.

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Section: Conceptual Analysis For a Single Turbineparticipant P1supporting

confidence: 52%

FarmConners market showcase results: wind farm flow control considering electricity prices

et al. 2022

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“…For this purpose, the IEC 61400-1 recommends, inter alia., the Mann uniform shear spectral tensor model (Mann, 1994) or the Kaimal model (Kaimal et al, 1972). The turbulence spectral properties of a three-dimensional homogeneous wind field are described by the spectral velocity tensor ij (k) (Kristensen et al, 1989):…”

Section: Mann Turbulence Spectral Modelmentioning

confidence: 99%

Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

et al. 2021

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Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists of incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time series, which are derived by fitting a bivariate Gaussian shape function to lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the dynamic wake meandering (DWM) model, i.e., the target fields. The lidar-reconstructed wake fields are then input into aeroelastic simulations of the DTU 10 MW wind turbine for carrying out the load validation analysis. The power and load time series, predicted with lidar-reconstructed fields, exhibit a high correlation with the corresponding target simulations, thus reducing the statistical uncertainty (realization-to-realization) inherent to engineering wake models such as the DWM model. We quantify a reduction in power and loads' statistical uncertainty by a factor of between 1.2 and 5, depending on the wind turbine component, when using lidar-reconstructed fields compared to the DWM model results. Finally, we show that the number of lidar-scanned points in the inflow and the size of the lidar probe volume are critical aspects for the accuracy of the reconstructed wake fields, power, and load predictions.

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“…Load simulations are carried out using site-specific observations collected from the FINO1 meteorological mast installed at the German offshore wind farm Alpha Ventus. The wind farm is situated in the North Sea and about 45 km north of the island of Borkum (Kretschmer et al, 2019). Data were collected over a period of three years from 2011 to 2014 and details can be found in Kretschmer et al (2019).…”

Section: Site Conditionsmentioning

confidence: 99%

“…The wind farm is situated in the North Sea and about 45 km north of the island of Borkum (Kretschmer et al, 2019). Data were collected over a period of three years from 2011 to 2014 and details can be found in Kretschmer et al (2019). In the present work, we only make use of wind speeds and turbulence intensities measured from a 90 m sonic anemometer installed at the mast.…”

Section: Site Conditionsmentioning

confidence: 99%

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

Conti¹,

Pettas²,

Dimitrov³

et al. 2020

Preprint

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Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists in incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time-series, which are derived by fitting a bivariate Gaussian shape function on lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the Dynamic Wake Meandering (DWM) model. The lidar-reconstructed wake fields are input to aeroelastic simulations of the DTU 10 MW wind turbine and the resulting load predictions are compared with loads obtained with the target (no lidar-based) DWM simulated fields. The accuracy of load predictions is estimated across a variety of lidar beam configurations, probe volume sizes, and atmospheric turbulence conditions. The results indicate that the 10-min power and fatigue load statistics, predicted with lidar-reconstructed fields, are comparable with results obtained with the DWM simulations. Furthermore, the simulated power and load time-series exhibit a high level of correlation with the target observations, thus decreasing the statistical uncertainty (realization-to-realization) by a factor between 1.2 and 5, compared to results obtained with the baseline, which is DWM simulated fields with different random seeds. Finally, we show that the spatial resolutions of the lidar's scanning strategies as well as the size of the probe volume are critical aspects for the accuracy of the reconstructed wake fields and load predictions.

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Effects of Wind Farm Down-Regulation in the Offshore Wind Farm Alpha Ventus

Cited by 7 publications

References 0 publications

FarmConners market showcase results: wind farm flow control considering electricity prices

FarmConners market showcase results: wind farm flow control considering electricity prices

Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

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