Wind Plant Ramping Behavior

Ela, Erik; Kemper, Jason J.

doi:10.2172/969723

Cited by 15 publications

(10 citation statements)

References 2 publications

(3 reference statements)

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“…An important operational question is whether one can develop a method to create short-term, real-time forecasts of ramp size and duration (Ela et al 2009, GE Energy 2008, CAISO 2007. Since a short-term forecast will be based on some combination of the same-day wind conditions and general statistics, it is inherently a problem of evaluating correlations between the wind deltas at different time scales.…”

Section: Discussionmentioning

confidence: 99%

“…If a ramp of a given size persists for a long time, the system operator may have to line up additional reserves to keep the system in balance, In a study of data for Minnesota, Ela and Kemper (2009) distinguish between ramping events which persist for on the order of an hour or more and lead to a substantial change in the MW provided by wind, and "false ramps" where the same ramp rate occurs but with a duration of 15 minutes or less. An important statistical question is whether the beginning of a true ramp event can be reliably distinguished from a "false ramp"; this will clearly require a precise analysis of subtle temporal patterns in the data.…”

Section: Statistical Metricsmentioning

confidence: 99%

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Analysis of Wind Power and Load Data at Multiple Time Scales

Coughlin¹,

Eto²

2010

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In this study we develop and apply new methods of data analysis for high resolution wind power and system load time series, to improve our understanding of how to characterize highly variable wind power output and the correlations between wind power and load. These methods are applied to wind and load data from the ERCOT region, and wind power output from the PJM and NYISO areas. We use a wavelet transform to apply mathematically well-defined operations of smoothing and differencing to the time series data. This approach produces a set of time series of the changes in wind power and load (or "deltas"), over a range of times scales from a few seconds to approximately one hour. A number of statistical measures of these time series are calculated. We present sample distributions, and devise a method for fitting the empirical distribution shape in the tails. We also evaluate the degree of serial correlation, and linear correlation between wind and load. Our examination of the data shows clearly that the deltas do not follow a Gaussian shape; the distribution is exponential near the center and appears to follow a power law for larger fluctuations. Gaussian distributions are frequently used in modeling studies. These are likely to over-estimate the probability of small to moderate deviations. This in turn may lead to an over-estimation of the additional reserve requirement (hence the cost) for high penetration of wind. The Gaussian assumption provides no meaningful information about the real likelihood of large fluctuations. The possibility of a power law distribution is interesting because it suggests that the distribution shape for of wind power fluctuations may become independent of system size for large enough systems.

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Section: Discussionmentioning

confidence: 99%

Section: Statistical Metricsmentioning

confidence: 99%

Analysis of Wind Power and Load Data at Multiple Time Scales

Coughlin¹,

Eto²

2010

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show abstract

“…Even so, nacelle position relative to true north and wind direction are not typically part of the turbine system -i.e., they were never collected. 6 At a minimum, turbine generation, nacelle wind speed, and turbine availability status or equivalent were collected. In the case of turbine availability status, Xcel Energy translated numerous equivalent codes into a binary variable indicating the availability of the turbine.…”

Section: Turbine Datamentioning

confidence: 99%

“…A difficult issue for wind power forecasting is consistently and accurately predicting wind power ramps (Ela and Kemper 2009). Although the core wind/power forecasting system described above has skill foreseeing power ramps generated from large-scale weather events (e.g., cold fronts), there is a need to fine-tune this capability to accurately predict the time, magnitude, and duration of intermediate and smaller-scale events including thunderstorm outflows.…”

Section: Wind Ramp Predictionmentioning

confidence: 99%