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.

This paper presents an exploratory study of the possible physical impacts of climate change on the electric power system, and how these impacts could be incorporated into resource planning in the Western United States. While many aspects of climate change and energy have been discussed in the literature, there has not yet been a systematic review of the relationship between specific physical effects and the quantitative analyses that are commonly used in planning studies. The core of the problem is to understand how the electric system is vulnerable to physical weather risk, and how to make use of information from climate models to characterize the way these risks may evolve over time, including a treatment of uncertainty. In this paper, to provide the necessary technical background in climate science, we present an overview of the basic physics of climate and explain some of the methodologies used in climate modeling studies, particularly the importance of emissions scenarios. We also provide a brief survey of recent climate-related studies relevant to electric system planning in the Western US. To define the institutional context, we discuss the core elements of the resource and reliability planning processes used currently by utilities and by the Western Electricity Coordinating Council. To illustrate more precisely how climate-related risk could be incorporated into modeling exercises, we discuss three idealized examples. Overall, we argue that existing methods of analysis can and should be extended to encompass the uncertainties related to future climate. While the focus here is on risk related to physical impacts, the same principles apply to a consideration of how future climate change policy decisions might impact the design and functioning of the electric grid. We conclude with some suggestions and recommendations on how to begin developing this approach within the existing electric system planning framework for the West.