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Several detailed technical investigations of grid ancillary service impacts of wind power plants in the United States have recently been performed.These studies were applied to Xcel Energy (in Minnesota) and PacifiCorp and the Bonneville Power Administration (both in the northwestern United States). Although the approaches vary, three utility time frames appear to be most at issue: regulation, load following and unit commitment. This article describes and compares the analytic frameworks from recent analysis and discusses the implications and cost estimates of wind integration. The findings of these studies indicate that relatively large-scale wind generation will have an impact on power system operation and costs, but these impacts and costs are relatively low at penetration rates that are expected over the next several years. PrefaceWith the continuing decline in the as-delivered cost of wind energy and the continuing spread of favourable renewable policies at the state and federal level, many US utilities are taking a serious look at wind power. Foremost in these examinations are concerns associated with accommodating the variable nature of power production in the interconnected grid system. At first blush the uncontrollability of output presents a formidable obstacle, often resulting in high estimates of ancillary service costs or assumptions that wind capacity must be 'backed up' with large amounts of dispatchable conventional technology such as natural gas-fired combustion turbines. However, these cursory examinations often overlook key factors such as:• the stochastic nature of grid systems, which must routinely contend with varying and uncertain demand and unexpected transmission and generation outages; • the ability to forecast wind power output in both hourly and day-ahead time frames; • actual wind farm power output characteristics, including multiple-generator smoothing (intra-and inter-site) and new generator and wind farm interface abilities; • large-scale geographic diversity resulting in smoothing of aggregate power output;• the evolution of US competitive wholesale markets, including near-real-time operations and unscheduled deviation practices.Recently, several more detailed technical investigations of grid ancillary service impacts have been performed. This article will summarize the issues of grid integration, approaches and results of recent studies, and implications for future work.Recent studies relevant to costs of grid integration of wind energy have been performed for the following utilities: Xcel Energy (in Minnesota) and Pacificorp and the Bonneville Power Administration (both in the northwestern United States). In addition, market rules from the Mid-Atlantic region (Pennsylvania, New Jersey and Maryland, or PJM, power pool) have been examined for cost impacts. Although the approaches vary, three utility time frames appear to be most at issue: regulation, load following and unit commitment. Market-based and integrated provider, cost-based approaches to evaluation have been examined....

The variability inherent in wind power production will require increased flexibility in the power system, when a significant amount of load is covered with wind power. Standard deviation (σ) of variability in load and net load (load net of wind) has been used when estimating the effect of wind power on the short term reserves of the power system. This method is straightforward and easy to use when data on wind power and load exist. In this paper, the use of standard deviation as a measure of reserve requirement is studied. The confidence level given by ±3–6 times σ is compared to other means of deriving the extra reserve requirements over different operating time scales. Also taking into account the total variability of load and wind generation and only the unpredicted part of the variability of load and wind is compared. Using an exceedence level can provide an alternative approach to confidence level by standard deviation that provides the same level of risk. The results from US indicate that the number of σ that result in 99% exceedence in load following time scale is between 2.3–2.5 and the number of σ for 99.7% exceedence is 3.4. For regulation time scale the number of σ for 99.7 % exceedence is 5.6. The results from the Nordic countries indicate that the number of σ should be increased by 67–100% if better load predictability is taken into account (combining wind variability with load forecast errors).