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.

10arenewables integration10aRT-00110awind power1 aCoughlin, Katie1 aEto, Joseph, H uhttps://gridintegration.lbl.gov/publications/analysis-wind-power-and-load-data02172nas a2200313 4500008003900000245014600039210006900185260001200254300000800266520114100274653002301415653002701438653001101465653003401476100001901510700001801529700001801547700001901565700001801584700001701602700001601619700001601635700002301651700002501674700001901699700002001718700003301738856008701771 2010 d00aUse of Frequency Response Metrics to Assess the Planning and Operating Requirements for Reliable Integration of Variable Renewable Generation0 aUse of Frequency Response Metrics to Assess the Planning and Ope c12/2010 a1413 aThis report presents a systematic approach to identifying metrics that are useful for operating and planning a reliable system with increased amounts of variable renewable generation which builds on existing industry practices for frequency control after unexpected loss of a large amount of generation. The report introduces a set of metrics or tools for measuring the adequacy of frequency response within an interconnection. Based on the concept of the frequency nadir, these metrics take advantage of new information gathering and processing capabilities that system operators are developing for wide-area situational awareness. Primary frequency response is the leading metric that will be used by this report to assess the adequacy of primary frequency control reserves necessary to ensure reliable operation. It measures what is needed to arrest frequency decline (i.e., to establish a frequency nadir) at a frequency higher than the highest set point for under-frequency load shedding within an interconnection. These metrics can be used to guide the reliable operation of an interconnection under changing circumstances.

10afrequency response10arenewables integration10aRT-00110avariable renewable generation1 aEto, Joseph, H1 aUndrill, John1 aMackin, Peter1 aDaschmans, Ron1 aWilliams, Ben1 aHaney, Brian1 aHunt, Randy1 aEllis, Jeff1 aIllian, Howard, F.1 aMartinez, Carlos, A.1 aO'Malley, Mark1 aCoughlin, Katie1 aLaCommare, Kristina, Hamachi uhttps://gridintegration.lbl.gov/publications/use-frequency-response-metrics-assess02151nas a2200253 4500008003900000245007900039210006900118260001200187300001200199490000700211520125100218653005401469653002601523653002001549653006001569653003601629653002301665653003501688100002001723700002201743700002501765700002001790856008701810 2009 d00aStatistical analysis of baseline load models for non-residential buildings0 aStatistical analysis of baseline load models for nonresidential c04/2009 a374-3810 v413 aPolicymakers are encouraging the development of standardized and consistent methods to quantify the electric load impacts of demand response programs. For load impacts, an essential part of the analysis is the estimation of the baseline load profile. In this paper, we present a statistical evaluation of the performance of several different models used to calculate baselines for commercial buildings participating in a demand response program in California. In our approach, we use the model to estimate baseline loads for a large set of proxy event days for which the actual load data are also available. Measures of the accuracy and bias of different models, the importance of weather effects, and the effect of applying morning adjustment factors (which use data from the day of the event to adjust the estimated baseline) are presented. Our results suggest that (1) the accuracy of baseline load models can be improved substantially by applying a morning adjustment, (2) the characterization of building loads by variability and weather sensitivity is a useful indicator of which types of baseline models will perform well, and (3) models that incorporate temperature either improve the accuracy of the model fit or do not change it.

10aautomated demand response pilots & implementation10abaseline load profile10ademand response10ademand response and distributed energy resources center10ademand response research center10aimpacts estimation10apilot studies & implementation1 aCoughlin, Katie1 aPiette, Mary, Ann1 aGoldman, Charles, A.1 aKiliccote, Sila uhttps://www.sciencedirect.com/science/article/abs/pii/S0378778808002375?via%3Dihub02186nas a2200157 4500008004100000245012400041210006900165520153700234653005401771653003501825100002001860700002201880700002501902700002001927856008101947 2008 eng d00aEstimating Demand Response Load Impacts: Evaluation of Baseline Load Models for Non-Residential Buildings in California0 aEstimating Demand Response Load Impacts Evaluation of Baseline L3 aBoth Federal and California state policymakers are increasingly interested in developing more standardized and consistent approaches to estimate and verify the load impacts of demand response programs and dynamic pricing tariffs. This study describes a statistical analysis of the performance of different models used to calculate the baseline electric load for commercial buildings participating in a demand-response (DR) program, with emphasis on the importance of weather effects. During a DR event, a variety of adjustments may be made to building operation, with the goal of reducing the building peak electric load. In order to determine the actual peak load reduction, an estimate of what the load would have been on the day of the event without any DR actions is needed. This *baseline load profile* (BLP) is key to accurately assessing the load impacts from event-based DR programs and may also impact payment settlements for certain types of DR programs. We tested seven baseline models on a sample of 33 buildings located in California. These models can be loosely categorized into two groups: (1) *averaging methods*, which use some linear combination of hourly load values from previous days to predict the load on the event, and (2) *explicit weather models*, which use a formula based on local hourly temperature to predict the load. The models were tested both with and without *morning adjustments*, which use data from the day of the event to adjust the estimated BLP up or down.

This report describes a new Berkeley Lab approach for modeling the likely peak electricity load reductions from proposed energy efficiency programs in the National Energy Modeling System (NEMS). This method is presented in the context of the commercial unitary air conditioning (CUAC) energy efficiency standards. A previous report investigating the residential central air conditioning (RCAC) load shapes in NEMS revealed that the peak reduction results were lower than expected. This effect was believed to be due in part to the presence of the squelch, a program algorithm designed to ensure changes in the system load over time are consistent with the input historic trend. The squelch applies a system load-scaling factor that scales any differences between the end-use bottom-up and system loads to maintain consistency with historic trends. To obtain more accurate peak reduction estimates, a new approach for modeling the impact of peaky end uses in NEMS-BT has been developed. The new approach decrements the system load directly, reducing the impact of the squelch on the final results. This report also discusses a number of additional factors, in particular non-coincidence between end-use loads and system loads as represented within NEMS, and their impacts on the peak reductions calculated by NEMS.

Using Berkeley Lab's new double-decrement approach reduces the conservation load factor (CLF) on an input load decrement from 25% down to 19% for a SEER 13 CUAC trial standard level, as seen in NEMS-BT output. About 4 GW more in peak capacity reduction results from this new approach as compared to Berkeley Lab's traditional end-use decrement approach, which relied solely on lowering end use energy consumption. The new method has been fully implemented and tested in the Annual Energy Outlook 2003 (AEO2003) version of NEMS and will routinely be applied to future versions. This capability is now available for use in future end use efficiency or other policy analysis that requires accurate representation of time varying load reductions

1 aLaCommare, Kristina, Hamachi1 aGumerman, Etan1 aMarnay, Chris1 aChan, Peter, T.1 aCoughlin, Katie uhttps://gridintegration.lbl.gov/publications/new-approach-modeling-peak-utility