K-i APPENDIX K. ESTIMATION OF UTILITY AND ENVIRONMENTAL RESULTS FROM NEMS-BT OUTPUT

TABLE OF CONTENTS

K.1 Allocation of Input Electricity Savings by Census Division and End Use........................ K-1 K.2 Choice of Impact Multipliers............................................................................................. K-2 K.2.1 Final Implied Multipliers by TSL for All BVM Equipment.................................. K-3 K.3 Regression Smoothing of NEMS-BT Outputs................................................................... K-4 K.4 Estimation of Interpolating Functions from Savings Multipliers ...................................... K-6 K.5 Environmental Impacts ...................................................................................................... K-7

LIST OF TABLES

Table K.1.1 Shares of National Savings by Ce nsus Division Used in NEMS-BT .............. K-2 Table K.2.1 Comparison of BVM Electricity Savings with Different Multipliers with Total Generation .............................................................................................. K-3 Table K.2.2 Final Implied Multipliers by TSL–BVMs ....................................................... K-4

LIST OF FIGURES
Figure K.3.1 Example of Regression Smoothing F its for Fossil-Fired and Total Installed Capacity (30X case) .......................................................................... K-5 Figure K.4.1 Response (Interpolating) Functions fo r Selected Generation Outputs for 2025.................................................................................................................. K-6 Figure K.5.1 Response (Interpolating) Functions for Carbon Dioxide for Years 2015, 2020, 2025, and 2030. ...................................................................................... K-8K-iiAPPENDIX K. ESTIMATION OF UTILITY AND ENVIRONMENTAL RESULTS FROM NEMS-BT OUTPUT
The effects of proposed energy conservation standards for beverage vending machine (BVM) equipment was analyzed using a varian t of National Energy Modeling System (NEMS) model, called NEMS-BT. a Because the magnitude of national energy savings as a result of the proposed standards is very small relative to total electric generation, the U.S. Department of Energy (DOE) used an estimation method invo lving regression smoothing and interpolation. This appendix describes the major elements of this method.

To run a simulation in NEMS-BT model, th e refrigeration load in the commercial building module is reduced annually according to the electricity savings estimated by the National Energy Savings (NES) spreadsheet model (see chapter 11 of the TSD) for each trial standard level (TSL). The electric ity savings increase over time as various categories of beverage vending machine (BVM) equipment are replaced with more efficient equipment meeting the proposed conservation standards.
The magnitude of the electricity decrement that would be required for NEMS-BT to produce stable results out of the range of numerical noise is greater than the highest TSL under consideration. Therefore, to estimate results fo r the TSLs evaluated here, a series of NEMS-BT simulations is performed using higher values for the input electricity savi ngs. These simulations establish the relationship between the NEMS-BT out put variables (electricity generation by fuel, installed electricity generation capacity, and emissions) and the el ectricity savings inputs. For each output variable, a regression model is estimated to represen t an average response across the series of simulations. Impacts are obtained usi ng predicted values from the regression models corresponding to the (sma ller) levels of savings corresponding to TSLs.

K.1 ALLOCATION OF INPUT ELECTRICITY SAVINGS BY CENSUS DIVISION AND END USE

Although the utility and environm ental imp acts are national in scope, the NEMS-BT model operates at a census division level. L acking sales informati on for BVM equipment by census division, an allocation of savings was made on the basis of populati on. The shares used to distribute the savings across cen sus divisions are shown in Table K.1.1 . The shares are held constant for the forecast period through 2030.

a DOE’s Energy Information Administration (EIA) approves of the name NEMS to describe only an official version of the model without any modification of the code or data. Because the an alysis of BVM energy conservation standards entails some minor code modifications and the model is run under policy scenarios that are variations of DOE/EIA assumptions, the name NEMS-BT refers to the model as used here. (The Building Technologies [BT] program is one of the major program offices within DOE’ s Office of Energy Efficiency and Renewable Energy. The Building Technologies program is responsible for promulgating energy efficiency standards for the buildings sector mandated under the 2005 Energy Policy Act and earlier Federal legislation.)

K-1Table K.1.1 Shares of National Savings by Census Division Used in NEMS-BT

Census Division Share (%) New England 11.2% Middle Atlantic 14.1% East North Central 16.0% West North Central 6.8% South Atlantic 18.4% East South Central 6.0% West South Central 11.2% Mountain 6.5% Pacific 16.0%

K.2 CHOICE OF IMPACT MULTIPLIERS

To gain some understanding of the how NE MS-BT would respond to various ma gnitudes of savings, a “reference” time series of savings wa s initially selected from a preliminary version of the NES spreadsheet model. This reference seri es was selected as the savings that would result from the most stringent TSL (maximum technol ogically feasible efficiency level) for both classes of beverage vending machine equipment c onsidered in this rulemaking (TSL 7 for Class A equipment plus TSL 6 for Class B equipment) . The preliminary estimated savings from these two equipment types in 2030 was 0.76 terawatt-hours (TWh) or 2.59 trillion British thermal units (Btu) (in site energy terms). Based upon a previous rulemaking that also affected electricity use for refrigeration (DOE 2008), NEMS-BT was run usi ng the projected time series of savings that were multiplied by a series of factors that ranged from 10 thr ough 60 (designated here with a suffix X). (Thus, for example, the 2030 BVM sa vings imposed upon the commercial module in NEMS-BT in the 30X case was 30 x 2.59 TWh = 22.8 TWh.).
Based on the graphical output and a comparison of trends among the output variables, the numerical noise from the model appeared to overw helm the impact of the savings decrement for the smaller multiplier factors ( i.e. , less than 20X). b Table K.2.1 compares the savings decrement applied to the m odel under various multipliers and how the magnitude of the savings compares with total electricity generation in the Ener gy Information Administration’s (EIA’s) Annual Energy Outlook (AEO 2008) reference case. 1 c

b The numerical noise stems, in part, fr om the fact that a precise balancing between the demand and supply sides of NEMS is not always achieved, given the iterative nature of the solution process.

c The reader should note that the values of the multipliers us ed here are solely dependent on the initial (and arbitrary) choice of class A and class B vending machine products and a specific trial sta ndard level (TSL) for those products. The NEMS-BT simulations were performed prior to the final engineering analysis and selection of TSLs and so the 1X multiplier does not correspond to any specific TS L for a product category in prev ious sections of the report. However, it was judged that the savings decrements (in TWh) tested in NEMS-BT, shown in lower portion of Table K.2.1, were in the relevant range of magnitudes to yield stable and robust predictions by the model and so additional runs using different multipliers were deemed u nnecessary. For use in the predicting the impacts from the set of final TSLs, the savings decrement from any TSL was converted into an appropriate multiplier by scaling in proportion to the values shown in the first row of Table K.2.1.

K-2Table K.2.1 Comparison of BVM Electricity Savings with Differen t Multipliers with Total Generation
Multiplier 2015 2020 2025 2030 Savings (TWh) 0.29 0.64 0.76 0.76 1X % of total generation 0.01% 0.01% 0.02% 0.01% Savings (TWh) 5.79 12.81 15.20 15.20 20X % of total generation 0.13% 0.27% 0.31% 0.29% Savings (TWh) 8.69 19.22 22.79 22.79 30X % of total generation 0.19% 0.41% 0.46% 0.44% Savings (TWh) 11.58 25.63 30.39 30.39 40X % of total generation 0.26% 0.54% 0.61% 0.58%
Based on this testing, a decision was made to use the multipliers in the range of 20X through 40X. While these multipliers are greater than 1, they still do not yield large perturbations to the base case simulation. For example, as Ta ble K.2.1 indicates, even at 40X in the year 2025, the total savings decrement applied to the model represents only about 0.6 percent of total electricity generation.

K.2.1 Final Implied Multipliers by TSL for All BVM Equipment

Subsequent to the production of the NEMS si mu lations described earl ier, some revisions were made to the final set of energy savings esti mates for the final rule. In addition, for purposes of reporting utility and environm ental impacts for beverage vending machines, class A and class B equipment are distinguished. To adjust for th ese changes, the difference between the final savings estimate (by trial standard level) and the savings from th e initial “reference” time series was represented as a scale factor. (For example, the energy savings for the final version of the energy standard was 5.28 TWh at TSL-5, as compared to the actual value used as the 1X case in NEMS-BT of 5.99 TWh. Thus, the scale factor for TSL-5 is 5.28/5.99 = 0.881). The scale factor essentially adjusts the savings from all refrig eration equipment for a specific TSL to be consistent with the reference series and, thus, be consistent with the interpolation procedure described above.

The first column in Table K.2.2 displays the scale factors for each TSL. d The remaining columns display the implied multipliers from th e three simulations performed with NEMS-BT (using their designations of 20X, 30X, and 40X). For example, the actual final multipliers used for TSL-4 for class A BVM equipment were 2.8, 5.7, and 8.5. Because the savings for TSL-1 through TSL-3 are smaller than those for TSL-4, th e implied multipliers are larger than those for TSL-4 (and vice versa for TSL-5).

d The scale factors can also be viewed as interpolating values relative to the set of NEMS-BT impact runs. Thus, for example, if an impact upon a particular variable from the “2X” case in the original set of simulations was interpolated to be a value of, say, 0.10, then the impact for TSL-4 for all refrigeration equipment would be 1⁄2 x 0.703 x 0.10 = 0.035.

K-3Table K.2.2 Final Implied Multipliers by TSL–BVMs
Implied Multipliers

Scale Factor NEMSBT ("20X") NEMSBT ("30X") NEMSBT ("40X") Class A Equipment TSL – 1 0.022 890.4 1,335.6 1,780.7 TSL – 2 0.100 200.3 300.5 400.7 TSL – 3 0.225 89.0 133.6 178.1 TSL – 4 0.352 56.8 85.2 113.7 TSL – 5 0.415 48.2 72.3 96.4 TSL – 6 0.457 43.8 65.7 87.6 TSL – 7 0.553 36.1 54.2 72.3 Class B Equipment TSL – 1 0.007 2,819.1 4,228.6 5,638.1 TSL – 2 0.011 1,882.7 2,824.1 3,765.4 TSL – 3 0.053 380.3 570.4 760.6 TSL – 4 0.060 332.3 498.5 664.6 TSL – 5 0.163 123.1 184.6 246.1 TSL – 6 0.181 110.6 165.9 221.1

K.3 REGRESSION SMOOTHING OF NEMS-BT OUTPUTS

Even at the selec ted multiplier levels shown in Table K.2.2 , the simulations with NEMS-BT produces considerab le year -to-year variation th at appears to be caused by numerical noise from the model solution. This variation app ears to affect the out puts related to specific generation fuel and technologies more so than to tal generation or total installed capacity. For the utility impact assessment, it is more important to identify distinct trends in the utility sector adjustment process rather than y ear-to-year variation that may be spurious results from the model solution process.
To reduce the potential effect of nume rical noise from NEMS-BT simulation, a smoothing regression was estimated for each of the model output variables. The output variables are measured as the differences between th e Annual Energy Outlook (A EO) reference case and the energy efficiency standards case. The formul ation of the smoothing regression was a simple quadratic function of time (in years) with the following form:
Y(T) = a 1 x T + a 2 x T 2 + u Eq. K.1

Where: Y = Output variable for time period T (difference from reference case), T

= Time in years, T = 0 in 2012, T = 1 in 2013, etc.

a 1 , a 2 = estimated regression coefficients, and

u = random disturbance term.
The regression specification is such that the fitted (predicted) value of the variable is forced through the origin in th e year prior to the implementati on of the standard (2012). By

K-4forcing the impact to be zero in 2011, this pro cedure generates estimated impacts that typically could be expected to change in a systematic fashion beginning in 2012.
Figure K.3.1 illustrates this procedure for the diffe rences in to tal installed capacity and the three types of fossil fuel capacity. The partic ular case shown is for be verage vending machine equipment at the “30X” level of savings (or 54.2X relative to TSL-7 for Class A beverage vending machine equipment—see Table K.2.2 ). Clearly, NEMS-BT indicates a very smooth path of differences in total generation capacity as sho wn in the top left panel of the figure. (In this case, the regression smoothing would not be necessary.)
For plants using specific fuels or technologies , the differences in pa rticular years from a smooth trend line are more substantial. The deviations from the fitted trend line are most pronounced for facilities using combined cycle and combustion turbine technologies. In these cases, the model produces significan t deviations from trend for periods of up to four years throughout the forecast period. e

Nevertheless, over the entire for ecast period, the long-term trends in the capacity of all of these types of plants are apparent from the graphs. The approach in this analysis is to abstract these short-term deviations from what appear to be long-term trends.

Total Generation

-3 -2.5

-2 -1.5

-1 -0.5

0 0.5

2010 2015 2020 2025 2030Difference (GW)

NEMS-BT Fitted Fossil Steam

-2.5

-2 -1.5

-1 -0.5

0 0.5

2010 2015 2020 2025 2030Difference (GW)

NEMS-BT Fitted

Combined Cycle

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1

0 0.1 0.2 0.3 0.4

2010 2015 2020 2025 2030Difference (GW)

NEMS-BT Fitted Combustion Turbine

-0.2 -0.1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

2010 2015 2020 2025 2030Difference (GW) NEMS-BT Fitted

Figure K.3.1 Example of Regre ssion Smoothing Fits for Foss il-Fired and Total Installed Capacity (30X case)

e In this particular case, NEMS-BT appears to show some anomalous behavior in the last several years of the simulation period, especially for combined cycle and co mbustion turbines. The regression smoothing approach provides a means of generating more plausible predictions in these instances. In some cases , the last year or two of the projection was not used in regression fitting procedure.

K-5K.4 ESTIMATION OF INTERPOLATING FUNCTIONS FROM SAVINGS MULTIPLIE RS

The development of utility and environmental impacts focused on specific years from the NEMS-BT simulations: 2015, 2020, 2025, and 2030. For each output variable ( e.g. , coalfired electricity generation), the predicted valu es from the trend regression were extracted for each savings multiplier level (20X, 30X, and 40X). A subsequent linear regression was performed using these values for the dependent variable and the values of the saving multiplier as the independent or ex planatory variable. The y-intercept of this regression is forced through the origin. This restriction ensures that the pred icted value of a NEMS-BT output variable is zero when the savings multiplier is zero. Predicted values for non-zero multipliers are calculated by taking the estimated regression coefficient (slo pe) times the value of the savings multiplier.
Figure K.4.1 shows several examples of this estimation approach. The magnitude of the energy savings multiplier is plotted on the x- axis aga inst the total reduction in generation capacity for the year 2025. The results shown ar e based on the three simulations selected for beverage vending machine equipment (20X, 30X, and 40X as described above). The fitted 2025 values from the smoothing regression of the NEMS-BT output are show n in the large solid (black) diamonds. In the case of total generation capacity, the lin ear regression line is closely aligned to the three points corresponding to the 20X, 30X, and 40X savings multipliers.

Total Generation

-35 -30 -25 -20 -15 -10

-5 0 0 10 20 30 40 50

Savings MultiplierDifference (TWh) Coal

-20 -15 -10

-5 0 0 10 20 30 40 50

Saving MultiplierDifference(TWh)

Natural Gas

-10 -8 -6 -4 -2 0 0 10 20 30 40 50

Savings MultiplierDifference (TWh) Renewables

-7 -6 -5 -4 -3 -2 -1 0 0 10 20 30 40 50

Savings MultiplierDifference (TWh)

Figure K.4.1 Response (Interpolating) Functions for Selected Generation Outputs for 2025

K-6Within this range of savings, the graph de monstrates the impact on total capacity predicted by the model is proporti onal to the magnitude of the re frigeration electricity savings.
For the other generation types shown in Figure K.4.1 , the responses deviate from strict proportionality. For example, for coal, the response at the 30X is proportiona tely less than either the 20X or 40X levels. The approach taken here essentially produces a weighted average of the responses across these various sav ings multipliers.
The predicted value of a NE MS-BT output variable for a given standards level is interpolated along the regression line estimated for that variable. The computation of savings from a particular standards case first takes into account the scale fact ors shown in the first column in Table K.2.1 . Thus, for example, the scale factor associa ted with Trial Standard Level 4 for Class A equipment is 0.352. For total electr icity generation in 2025, the estimated response coefficient is -0.765 ( e.g ., the slope of regression lin e in the top left panel Figure K.4.1 ). Thus, the estimated im pact on total generation in 2025 from this specific trial standard level would be 0.352 x -765 = -0.269 TWh.

K.5 ENVIRONMENTAL IMPACTS

The general approach for estimating the im pact s on the utility sector was also used to develop an environmental assessment in terms of changes in emissions . Annual values of the emissions for NO x , mercury, and carbon dioxide were ex tracted from the relevant tables produced by NEMS.
Similar to examples shown in Figure K.3.1 , the projections of emissions from 2012 through 2030 were smoothed using the quadratic function in time discussed above. Response functions were then estimated at 5-year intervals between 2015 and 2030.
Figure K.5.1 shows the estimated response (or inter polating) functions for carbon dioxide emissions. There is very little disparity in the m agnitudes of carbon dioxide emissions across the three multipliers selected. Howeve r, the 30X case shows a somewhat smaller response than the 20X and 40X cases. However, the regression-base d average takes all th ree cases into account, leading to strong indication of a re duction in carbon dioxide emissions.
The scale of the y-axis is held constant for each of the plots and so the clockwise rotation of the slopes across the years 2015 through 2030 re flects the increasing reduction in emissions over selected years. There is little change in the slopes between 2025 and 2030, reflecting a distinct slowdown in the rate of increase of savings. By the middl e part of the next decade, most beverage vending machine equipment will have already been replaced by more efficient equipment mandated by the standards. Moreover, reduction in nuclear a nd renewable generation prompted by the conservation standard have the effect of retarding further reductions in carbon dioxide emissions toward the end of the period.

K-7Carbon Dioxide Emissions (2015)

-30 -25 -20 -15 -10 -5 0 0 10 20 30 40 50

Savings MultiplierDifference (MMT-CE) Carbon Dioxide Emissions (2020)

-30 -25 -20 -15 -10 -5 0 0 10 20 30 40 50

Savings MultiplierDifference (MMT-CE)

Carbon Dioxide Emissions (2025)

-30 -25 -20 -15 -10 -5 0 0 10 20 30 40 50

Savings MultiplierDifference (MMT-CE) Carbon Dioxide Emissions (2030)

-30 -25 -20 -15 -10 -5 0 0 10 20 30 40 50

Savings MultiplierDifference (MMT-CE)

Figure K.5.1 Response (Interpolating) Func tions for Carbon Dioxide for Years 2015, 2020, 2025, and 2030.

K-8K-9 REFERENCES

1. U.S. Department of Energy/Energy In formation Administration (DOE/EIA), Annual Energy Outlook 2008 , June 2008. Washington, D.C. DOE/EIA-0383(2008).
2. U.S. Department of Energy. 2008. T echnical Support Document for Commercial Refrigeration Equipment.