Forecasting wind ramps: can long-range lidar increase accuracy?

Würth, Ines; Ellinghaus, S; Wigger, Maayen; Niemeier, M J; Clifton, Andrew; Cheng, Po Wen

doi:10.1088/1742-6596/1102/1/012013

Cited by 12 publications

(19 citation statements)

References 5 publications

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“…In this work, we developed a 5 min ahead power forecast. In order for remote-sensing-based forecasts to be useful for power grid balancing and electricity trading, the forecast horizon needs to be extended further (Würth et al, 2019). The accuracy of the lidar-based forecasts is expected to decrease with increasing lead times; however, Würth et al (2018) found the accuracy of the state-of-the-art persistence to decrease faster.…”

Section: Forecast Horizon and Scanning Trajectorymentioning

confidence: 99%

“…The simplest statistical model for even shorter lead times is persistence, which assumes the future value will be equal to the current one. Persistence is often referred to as a benchmark in very short-term forecasting (Würth et al, 2019). Other statistical models such as ARMA (autoregressive moving average) take a higher number of past values and past forecasting errors into account (Torres et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…While Doppler radars are capable of measuring in distances of up to 32 km (Nygaard and Newcombe, 2018) with high temporal and spatial resolution and provide volumetric 2D wind field information in the case of a dual set-up (Hirth et al, 2017), such devices are also rather expensive, comparatively large and thus not easily deployable at far-offshore sites (Würth et al, 2019). Studies also indicated their reduced data availability in comparison to lidars, especially during clear-air situations (Vignaroli et al, 2017;Hirth et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Theuer

Dooren

Bremen³

et al. 2020

Wind Energ. Sci.

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Abstract. Decreasing gate closure times on the electricity stock exchange market and the rising share of renewables in today's energy system causes an increasing demand for very short-term power forecasts. While the potential of dual-Doppler radar data for that purpose was recently shown, the utilization of single-Doppler lidar measurements needs to be explored further to make remote-sensing-based very short-term forecasts more feasible for offshore sites. The aim of this work was to develop a lidar-based forecasting methodology, which addresses a lidar's comparatively low scanning speed. We developed a lidar-based forecast methodology using horizontal plan position indicator (PPI) lidar scans. It comprises a filtering methodology to recover data at far ranges, a wind field reconstruction, a time synchronization to account for time shifts within the lidar scans and a wind speed extrapolation to hub height. Applying the methodology to seven free-flow turbines in the offshore wind farm Global Tech I revealed the model's ability to outperform the benchmark persistence during unstable stratification, in terms of deterministic as well as probabilistic scores. The performance during stable and neutral situations was significantly lower, which we attribute mainly to errors in the extrapolation of wind speed to hub height.

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Section: Forecast Horizon and Scanning Trajectorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Theuer

Dooren

Bremen³

et al. 2020

Wind Energ. Sci.

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“…The lidar data from ParkCast is filtered with a range filter which is originally applied in image processing but adapted for lidar data filtering for the long-range lidar by Würth et al (2018). In comparison to the CNR filter, the range filter removes less valid data points while ensuring the filter quality.…”

Section: Determination Of Wind Evolution Model Parametersmentioning

confidence: 99%

Parameterization of Wind Evolution using Lidar

Chen¹,

Schlipf²,

Cheng³

2020

Preprint

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Abstract. Wind evolution refers to the change of the turbulence structure of the eddies over time while the eddies are advected by the main flow over space. With the development of the lidar-assisted wind turbine control, modelling of the wind evolution becomes an interesting topic, because the control system should only react to the changes in the wind field which can be predicted accurately over the distance to avoid harmful and unnecessary control action. This paper aims to achieve a parameterization model for the wind evolution model to predict the wind evolution model parameters according to the wind field conditions. For this purpose, a two-parameter wind evolution model suggested in literature was applied to model the wind evolution and the wind evolution was estimated using lidar data. A statistical analysis was done to reveal the characteristics of wind evolution model parameters. Gaussian process regression was applied to achieve the parameterization model. The results have proven the applicability of Gaussian process regression model to predict the wind evolution model parameters with sufficient accuracy.

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“…Each data point in the time series corresponds to a 10-minute average. Reproduced without modifications from Würth et al [12].…”

Section: Intra-hour Variability Of Wind Power Generationmentioning

confidence: 99%

Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36

et al. 2019

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The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

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Forecasting wind ramps: can long-range lidar increase accuracy?

Cited by 12 publications

References 5 publications

Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Minute-scale power forecast of offshore wind turbines using long-range single-Doppler lidar measurements

Parameterization of Wind Evolution using Lidar

Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36

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