Jay Apt

Abstract:
A number of analyses, meta-analyses, and assessments, including those performed by the Intergovernmental Panel on Climate Change, the National Oceanic and Atmospheric Administration, the National Renewable Energy Laboratory, and the International Energy Agency, have concluded that deployment of a diverse portfolio of clean energy technologies makes a transition to a low-carbon-emission energy system both more feasible and less costly than other pathways. In contrast, Jacobson et al. [Jacobson MZ, Delucchi MA, Cameron MA, Frew BA (2015) Proc Natl Acad Sci USA 112(49):15060–15065] argue that it is feasible to provide “low-cost solutions to the grid reliability problem with 100% penetration of WWS [wind, water and solar power] across all energy sectors in the continental United States between 2050 and 2055”, with only electricity and hydrogen as energy carriers. In this paper, we evaluate that study and find significant shortcomings in the analysis. In particular, we point out that this work used invalid modeling tools, contained modeling errors, and made implausible and inadequately supported assumptions. Policy makers should treat with caution any visions of a rapid, reliable, and low-cost transition to entire energy systems that relies almost exclusively on wind, solar, and hydroelectric power.

Abstract:
Wind integration studies are an important tool for understanding the effects of increasing wind power deployment on grid reliability and system costs. This paper provides a detailed review of the statistical methods and results from 12 large-scale regional wind integration studies. In particular, we focus our review on the modeling methods and conclusions associated with estimating short-term balancing reserves (regulation and load-following). Several important observations proceed from this review. First, we found that many of the studies either explicitly or implicitly assume that wind power step-change data follow exponential probability distributions, such as the Gaussian distribution. To understand the importance of this issue we compared empirical wind power data to Gaussian data. The results illustrate that the Gaussian assumption significantly underestimates the frequency of very large changes in wind power, and thus may lead to an underestimation of undesirable reliability effects and of operating costs. Secondly, most of these studies make extensive use of wind speed data generated from mesoscale numerical weather prediction (NWP) models. We compared the wind speed data from NWP models with empirical data and found that the NWP data have substantially less power spectral energy, a measure of variability, at higher frequencies relative to the empirical wind data. To the extent that this difference results in reduced high-frequency variability in the simulated wind power plants, studies using this approach could underestimate the need for fast ramping balancing resources. On the other hand, the magnitude of this potential underestimation is uncertain, largely because the methods used for estimating balancing reserve requirements depend on a number of heuristics, several of which are discussed in this review. Finally, we compared the power systems modeling methods used in the studies and suggest potential areas where research and development can reduce uncertainty in future wind integration studies.

Abstract:
Using data from the North American Electric Reliability Council (NERC) for 1984–2006, we find several trends. We find that the frequency of large blackouts in the United States has not decreased over time, that there is a statistically significant increase in blackout frequency during peak hours of the day and during late summer and mid-winter months (although non-storm-related risk is nearly constant through the year) and that there is strong statistical support for the previously observed power-law statistical relationship between blackout size and frequency. We do not find that blackout sizes and blackout durations are significantly correlated. These trends hold even after controlling for increasing demand and population and after eliminating small events, for which the data may be skewed by spotty reporting. Trends in blackout occurrences, such as those observed in the North American data, have important implications for those who make investment and policy decisions in the electricity industry. We provide a number of examples that illustrate how these trends can inform benefit-cost analysis calculations. Also, following procedures used in natural disaster planning we use the observed statistical trends to calculate the size of the 100-year blackout, which for North America is 186,000 MW.