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: As a result of state renewable portfolio standards and federal tax credits, there is growing interest and investment in renewable sources of electricity in the United States and worldwide. Wind and solar energy are the fastest growing renewable sources of electric energy with U.S. wind power capacity increasing from 8.7 GW in 2005 to 33.5 GW 2009 and solar increasing from 211 MW to 603 MW over the same period [EIA:2010]. However wind and solar power plants are intermittent and variable: that is, they do not produce power at all times of day; and even when power is being produced, output can change rapidly. Biomass, geothermal and hydroelectric energy sources do not suffer from intermittency and variability to the same extent, however growth of these sources has been limited. The U.S. electric system, which was developed throughout the 20th century, was designed around power plants that are primarily intended to deliver constant power. In order to enable a ten-fold increase in the percentage of intermittent and variable resources from the present 2%, as envisioned in a number of state renewable portfolio standards, electricity systems require significant changes in technology, operating policies, and infrastructure. To understand this need, numerous government, academic, and electricity industry organizations have studied the challenges and opportunities for integrating wind, and to a lesser extent solar, resources into electricity infrastructures. This paper summarizes the conclusions from these studies and highlights a number of areas where additional research is needed to facilitate good decision-making regarding the increase of renewable power integration. Our review covers two DOE-sponsored national studies [DOE:2008], six regional studies covering Texas [ERCOT:2008], New York [NYSERDA:2005], Minnesota [MN:2006], California [CEC:2010], the South-central U.S. [SPP:2010], the Eastern U.S. [NREL:2010], four European reports [EWIS:2010, EWEA:2009, CEER:2009, EPRI:2010], and several academic reports. This paper provides an overview of the results from industry studies (Section 1), a brief discussion of related academic publications in this area (Section 2) and a more detailed analysis of several of the industry reports (Section 3).
Abstract: Using data from two large US wind interconnection studies and two grid-scale wind power plants, this paper provides evidence that mesoscale meteorological models under-predict the variability in wind speeds, but for large wind farms the power production data have more similar statistics. Specifically, the mesoscale models under-predict the high-frequency variability in wind speeds, as measured by the power spectral density and the probability of large changes in wind speeds. However, these differences only appear to translate into an under-prediction of power production variability when modeling small wind plants (less than 10 square miles in area), where the effect of geographic diversity is minimal. When modeling larger wind plants, the filtering of the power output due to geographic diversity roughly offsets the filtering effect of the mesoscale model on predicted wind speeds. The exception to this is that the simulated data consistently under-predict the probability of very large wind ramping events, such as a 50% change in power output over an hour. The results show some evidence that methods aimed at correcting the reduced variability may result in too much high-frequency variability. We conclude that while meteorological models are important for large-scale wind integration studies, caution is needed for analyses that could be sensitive to the probability of large ramping events and high-frequency variability.