Studies have shown that the unpredictability and variability of wind power is reduced in systems with large numbers of geographically diverse wind plants. These effects are caused by the decreased correlation of power output between wind plants as their separation and diversity in terrain increases. One way that system operators have increased geographic diversity is by enlarging balancing areas through the physical or administrative connection of adjacent systems. This strategy can be extended from the regional level to the transcontinental level. As such, it is important to study the correlation and statistical characteristics of aggregate wind power between large, distant systems. This paper analyzes multi‐year historical data from four North American system operators—Bonneville Power Administration, Electric Reliability Council of Texas, Midwest Independent Transmission System Operator and PJM—to see how effective transcontinental interconnection of systems is at enabling wind plant integration. The effects of separation and timescale on correlations of instantaneous and hourly variations are analyzed. The analysis is complemented by a study of a hypothetical transcontinental connection of the systems across yearly, monthly, daily and hourly timescales. The results show that correlations between large systems exhibit similar characteristics as the correlations between individual wind plants, but are somewhat larger in magnitude. The transcontinental system exhibits a close to normal distribution of power output and decreased variability, but there is still appreciable and statistically significant correlation at the longer timescales driven by seasonal and diurnal forcing, as well as synoptic weather systems. Copyright © 2013 John Wiley & Sons, Ltd.

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When modeling wind power from several sources, consideration of the dependency structure of the sources is of critical importance. Failure to appropriately account for the dependency structure can lead to unrealistic models, which may result in erroneous conclusions from wind integration studies and other analyses. The dependency structure is fully described by the multivariate joint distribution function of the wind power. However, few-if any-explicit joint distribution models of wind power exist. Instead, copulas can be used to create joint distribution functions, provided that the selected copula family reasonably approximates the dependency structure. Unfortunately, there is little guidance on which copula family should be used to model wind power. The purpose of this paper is to investigate which copula families are best suited to model wind power dependency structures. Bivariate copulas are considered in particular. The paper focuses on power from wind plants-collections of wind turbines with a common interconnection point-but the methodology can be generally extended to consider power from individual wind turbines or even aggregate wind power from entire systems. Twelve Archimedean and elliptical copulas are evaluated using hourly data from 500 wind plant pairs in the National Renewable Energy Laboratory's Eastern Dataset. The evaluation is based on 2 and Cramér-von Mises statistics. Application guidelines recommending which copula family to use are developed. It is shown that a default assumption of Gaussian dependence is not justified and that the use of Gumbel copulas can result in improved models. An illustrative example shows the application of the guidelines to model dependence of wind power sources in Monte Carlo simulations.

The proliferation of off-grid photovoltaic (PV) systems is rapidly increasing in the least developed countries. The sizing of system componentsprimarily PV panels and batteries-is critically influenced by the expected daily load. However, accurately estimating incipient electrical load of rural consumers is fraught with challenges. Load estimation error is propagated through the design phase, potentially resulting in a system that is unduly expensive or fails to meet reliability targets. This article investigates the effects of daily load estimation error on system design, cost and reliability. Load and insolation data from seven off-grid systems in Malawi were collected. The systems were redesigned using three different intuitive design approaches considering different levels of load estimation error, ranging from ± 90% of the actual measured load. The cost of each design is estimated from in-country prices. The reliability of each design is determined from an hourly simulation using the measured data. The results show that PV array and battery sizing scale proportionately with load estimation error and that the cost of load over-estimation is approximately US$1.92 to US$6.02 per watthour, whereas underestimation can precipitously degrade reliability. A cost-versus-reliability analysis shows that for the Malawi systems, on average 46% of the PV and battery costs are used to improve the simulated hourly