Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes

Pichault, Mathieu; Vincent, Claire Louise; Skidmore, Grant; Monty, Jason

doi:10.5194/wes-6-131-2021

Cited by 10 publications

(17 citation statements)

References 37 publications

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“…As persistence inherently performs well over stationary wind regimes, lower %Imp P with easterlies was expected. Note the prevalence of westerly ramps was associated with mesoscale and synoptic-scale frontal systems characteristic of southeast Australia [70]. The considerations above underline the potentially critical role of terrain roughness, topography and synoptic-scale meteorology, adding another layer of complexity to short-term forecasting.…”

Section: Resultsmentioning

confidence: 97%

“…We further evaluated the performance of the forecasts independently during wind power ramps, i.e., when large forecast errors were likely to occur. The ramp identification approach followed that of a previous study at the site focusing on ramp characterisation [70], where ramps were identified as the 1% strongest variations of the wind power time series based on continuous wavelet analysis. Further details about the ramp characterisation can be found in [70].…”

Section: Model Evaluationmentioning

confidence: 99%

“…The ramp identification approach followed that of a previous study at the site focusing on ramp characterisation [70], where ramps were identified as the 1% strongest variations of the wind power time series based on continuous wavelet analysis. Further details about the ramp characterisation can be found in [70]. We used three binary ramp detection statistics, namely the forecast accuracy (FA), ramp capture (RC) and critical success index (CSI), defined as follows [7]:…”

Section: Model Evaluationmentioning

confidence: 99%

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Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR

et al. 2021

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It remains unclear to what extent remote sensing instruments can effectively improve the accuracy of short-term wind power forecasts. This work seeks to address this issue by developing and testing two novel forecasting methodologies, based on measurements from a state-of-the-art long-range scanning Doppler LiDAR. Both approaches aim to predict the total power generated at the wind farm scale with a five minute lead time and use successive low-elevation sector scans as input. The first approach is physically based and adapts the solar short-term forecasting approach referred to as “smart-persistence” to wind power forecasting. The second approaches the same short-term forecasting problem using convolutional neural networks. The two methods were tested over a 72 day assessment period at a large wind farm site in Victoria, Australia, and a novel adaptive scanning strategy was implemented to retrieve high-resolution LiDAR measurements. Forecast performances during ramp events and under various stability conditions are presented. Results showed that both LiDAR-based forecasts outperformed the persistence and ARIMA benchmarks in terms of mean absolute error and root-mean-squared error. This study is therefore a proof-of-concept demonstrating the potential offered by remote sensing instruments for short-term wind power forecasting applications.

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Section: Resultsmentioning

confidence: 97%

Section: Model Evaluationmentioning

confidence: 99%

Section: Model Evaluationmentioning

confidence: 99%

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Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR

et al. 2021

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“…In summary, the occurrence of wind power ramps seems to be associated with modest wind speeds when wind turbines are operating at below rated power in the steepest part of the power curve. Another factor which could affect the prevalence of ramp events is the passage of fronts [40] which is not picked up from the pressure fields alone although the individual charts in Figure 5 shows that such activity is frequently present for several of the most commonly occurring patterns. Alternatively, the transition between particular patterns may play a role and this is explored in the next section.…”

Section: Wind Power Rampsmentioning

confidence: 99%

Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps

2021

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Large-scale weather patterns and their variability can influence both the amount of wind power production and its temporal variation, i.e., wind power ramps. In this study, we use a self-organizing map to cluster hourly sea level pressure into a discrete number of weather patterns. The dependency of wind power production and wind power ramps on these weather patterns is studied for the Belgian offshore wind farm fleet. A newly developed wavelet-surrogate ramp-detection algorithm is used for the identification of wind power ramps. It was observed that low-pressure systems, southwesterly and northeasterly wind flows are often associated with high levels of wind power production. Regarding wind power ramps, the type of transition between weather patterns was shown to determine whether ramp up or ramp down events would occur. Ramp up events tend to occur due to the transition from a high-pressure to a low-pressure system, or the weakening of the intensity of a deep low-pressure system. The reverse is associated with ramp down events.

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“…Intermittent and abrupt changes in wind speed pose a risk to grid stability, especially during periods of low generation when there is limited availability of reserve power [3]. Curtailment of wind ramps represents a loss of potential profits and a possible increase in energy prices [4].…”

Section: Introductionmentioning

confidence: 99%

Wind-Ramp Predictability

Pereyra-Castro

Caetano

2022

Atmosphere

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The intermittent nature of wind resources is challenging for their integration into the electrical system. The identification of weather systems and the accurate forecast of wind ramps can improve wind-energy management. In this study, extreme wind ramps were characterized at four different geographical sites in terms of duration, persistence, and weather system. Mid-latitude systems are the main cause of wind ramps in Mexico during winter. The associated ramps last around 3 h, but intense winds are sustained for up to 40 h. Storms cause extreme wind ramps in summer due to the downdraft contribution to the wind gust. Those events last about 1 to 3 h. Dynamic downscaling is computationally costly, and statistical techniques can improve wind forecasting. Evaluation of the North American Mesoscale Forecast System (NAM) operational model to simulate wind ramps and two bias-correction methods (simple bias and quantile mapping) was done for two selected sites. The statistical adjustment reduces the excess of no-ramps (≤|0.5| m/s) predicted by NAM compared to observed wind ramps. According to the contingency table-derived indices, the wind-ramp distribution correction with simple bias method or quantile mapping method improves the prediction of positive and negative ramps.

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Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes

Cited by 10 publications

References 37 publications

Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR

Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR

Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps

Wind-Ramp Predictability

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