This paper presents novel analyses of high-resolution wind power and electric system load time series data. We use a discrete wavelet transform to resolve the data into independent time series of step changes (deltas) at different time scales, and present a variety of statistical metrics as a function of the time scale. We show that the probability distribution for wind power deltas is not Gaussian, has an exponential shape near the center and is well fit by a power-law in the tails. We provide a physical interpretation for the observed power-law behavior, and discuss the potential significance for modeling studies, prediction of extreme events, and the extrapolation of statistical characteristics to higher wind penetration levels. Several metrics are presented to quantify the degree of auto-correlation in the wind data, and of the correlations between wind and load. We show that the shape of the autocorrelation function is the same at different time scales, a property of self-similar statistical processes that is consistent with the observed power-law behavior.

%B Renewable Energy %I Elsevier %V 68 %P 494-504 %8 08/2014 %N August 2014 %2 LBNL-1003916 %R 10.1016/j.renene.2014.02.011 %0 Report %D 2010 %T Analysis of Wind Power and Load Data at Multiple Time Scales %A Katie Coughlin %A Joseph H Eto %K renewables integration %K RT-001 %K wind power %XIn 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.

%I LBNL %C Berkeley %8 12/2010 %2 LBNL-4147E %0 Report %D 2010 %T Use of Frequency Response Metrics to Assess the Planning and Operating Requirements for Reliable Integration of Variable Renewable Generation %A Joseph H Eto %A John Undrill %A Peter Mackin %A Ron Daschmans %A Ben Williams %A Brian Haney %A Randy Hunt %A Jeff Ellis %A Howard F. Illian %A Carlos A. Martinez %A Mark O'Malley %A Katie Coughlin %A Kristina Hamachi LaCommare %K frequency response %K renewables integration %K RT-001 %K variable renewable generation %XThis report presents a systematic approach to identifying metrics that are useful for operating and planning a reliable system with increased amounts of variable renewable generation which builds on existing industry practices for frequency control after unexpected loss of a large amount of generation. The report introduces a set of metrics or tools for measuring the adequacy of frequency response within an interconnection. Based on the concept of the frequency nadir, these metrics take advantage of new information gathering and processing capabilities that system operators are developing for wide-area situational awareness. Primary frequency response is the leading metric that will be used by this report to assess the adequacy of primary frequency control reserves necessary to ensure reliable operation. It measures what is needed to arrest frequency decline (i.e., to establish a frequency nadir) at a frequency higher than the highest set point for under-frequency load shedding within an interconnection. These metrics can be used to guide the reliable operation of an interconnection under changing circumstances.

%P 141 %8 12/2010 %2 LBNL-4142E