APPENDIX K. ESTIMATION OF UTILITY AND ENVIRONMENTAL

RESULTS FROM NEMS-BT OUTPUT

TABLE OF CONTENTS

K.1 ALLOCATION OF INPUT ELECTRICIT Y SAVINGS BY CE NSUS DIVISION

AND END US E

.............................................................................................................. K-1

K.2 CHOICE OF IMPACT MULTIPLIERS

........................................................................ K-2

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 Census Division Used in NEMS-BT

.............. K-2

Table K.2.1 Comparison of Beverage Vending Machine Electricity Savings with Different Multipliers with Total Generation by Year

...................................... K-3

Table K.2.2 Final Implied Multipliers by Trial Standard Level–Beverage Vending

Machines

.......................................................................................................... K-4

LIST OF FIGURES

Figure K.3.1 Example of Regression Smoothi ng Fits for Fossil-Fuel Power Plants and Total Installed Capacity (30X case)

................................................................. K-5 Figure K.4.1 Response (Interpolating) Func tions for Selected Generation Outputs for

2025

.................................................................................................................. K-6 Figure K.5.1 Response (Interpolating) Functions for Carbon Dioxide for 2015, 2020, 2025, and 2030

................................................................................................. K-8

K-i BVM_FINAL_FR_TSD_AppendixK_08-14-09.docAPPENDIX K. ESTIMATION OF UTILITY AND

ENVIRONMENTAL RESULTS FROM NEMS-BT OUTPUT

The effects of proposed energy conservation standards for beverage vending machine equipment was analyzed using a variant of Na tional Energy Modeling System (NEMS) model, called NEMS-Building Technology (BT). The U. S. Department of Energy (DOE) Energy Information Administration (EIA) approves the name NEMS to describe only an official version of the model without any modification of the code or data. Because the analysis of beverage vending machine energy conservation standards en tails some minor code modifications and the

model is run under variations of DOE/EIA a ssumption policy scenarios, the name NEMS-BT

refers to the model as used here. Because the ma gnitude of national energy savings as a result of the proposed standards is very small relative to total electric genera tion, the DOE used an estimation method involving regression smoothing a nd 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 mode l (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 equipment are replaced with more efficient equipment meeting the energy conservation standards in today’s final rule.

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 environmental imp acts are national in scope, the NEMS-BT model operates at a census division level. Lack ing sales information for beverage vending machine equipment by census division, an alloca tion of savings was made on the basis of population. The shares used to distribute the sa vings across census divisions are shown in

Table K.1.1. The shares are held consta nt for the forecast period through 2030.

K-1 BVM_FINAL_FR_TSD_AppendixK_08-14-09.docTable 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 magnitudes 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 technologica lly feasible efficiency level or max tech) for both classes of beverage vending machine equipment considered in this rulemaking. Max tech is TSL 7 for Class A equipment and 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 therma l units (Btu) in site energy terms. Based upon a previous rulemaking that also affected elect ricity use for refrigeration (DOE 2008), NEMS-BT was run using the projected time series of savings that were multiplied by a series of factors that ranged from 10–60 (designated here with a suffi x X). Thus, for example, the 2030 beverage vending machine savings imposed upon the commer cial module in NEMS-BT in the 30X case

was 30  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). The numerical noise stems in part from a precise balancing between the demand and supply si des of NEMS that is not always achieved, given the iterative natu re of the solution process. Table K.2.1 compares the savings decrement applied to the model under various multipliers and how the magnitude of the savings compares with total electricity generation in the Energy Information Administration’s (EIA’s) Updated Annual Energy Outlook 2009 ( AEO2009 ) reference case. 1

The values of the multipliers used are sole ly dependent on the initial (and arbitrary) choice of Class A and Class B vending machines and a specific TSL for those machines. The NEMS-BT simulations were performed prior to th e final engineering anal ysis and selection of TSLs and so the 1X multiplier does not correspond to any specific TSL for in previous sections of the report. However, it was judged that the sa vings decrements (in TWh) tested in NEMS-BT (lower portion of Table K.2.1) we re in the relevant range of magnitudes to yield stable and robust predictions by the model and so additional runs using different multipliers were deemed unnecessary. For use in the predicting the impacts from the set of final TSLs, the savings decrement from any TSL was converted into an a ppropriate multiplier by scaling in proportion to

the values shown in the first row of Table K.2.1.

K-2 BVM_FINAL_FR_TSD_AppendixK_08-14-09.docTable K.2.1 Comparison of Beverage Vending Machine Electricity Savings with Different Multipliers with Total Generation by Year
Multiplier Electricity Savings 2015 2020 2025 2030 1X Savings, TWh 0.29 % of total generation 0.01 20X Savings, TWh 5.79 % of total generation 0.13 30X Savings, TWh 8.69 % of total generation 0.20 40X Savings, TWh 11.58 % of total generation 0.27 0.64

0.01

12.81

0.28

19.22

0.42

25.63

0.56 0.76

0.02

15.20

0.31

22.79

0.47

30.39

0.63 0.76

0.02

15.20

0.30

22.79

0.45

30.39

0.60

Based on this analysis, 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 2025, the total savings decrement applied to the model represen ts only about 0.6 percent of total electricity generation.

Final Implied Multipliers by TSL for A ll Beverage Vending Machine Equipment

Subsequent to the production of the NEMS si mulations 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 TSL) and the savings fr om the initial “refer ence” time series was represented as a scale factor. For example, the energy savings for the final version of the energy standard was2.06 TWh at TSL 5 for Class A equipmen t, as compared to the actual value used as

the 1X case in NEMS-BT of 2.59 TWh. Thus, the scale factor for TSL 5 is 2.06/2.59 = 0.796. The scale factor essentially adjusts the savings from all refrigeration equipment for a specific

TSL to be consistent with the reference series and, thus, be consistent with the interpolation procedure described above.

The final implied multipliers by TSL are pres ented in Table K.2.2. The first column in Table K.2.2 displays the scale fa ctors for each TSL. The scale factors can also be viewed as interpolating values relative to the set of NEMS-B T impact runs. Thus, for example, if an impact upon a particular variable from the “20X” case in the original set of simulations were interpolated to be a value of 4, then the impact for TSL 5 for all refrigeration equipment would be (1/20)  1.16  4 = 0.232. The remaining columns in Table K.2.2 show the implied multipliers from the 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 beverage vending machine equipment were 29.7, 44.5, and 59.4. 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).

K-3 BVM_FINAL_FR_TSD_AppendixK_08-14-09.docTable K.2.2 Final Implied Multipliers by Trial Standard Level–Beverage Vending

Machines
Equipment Type Scale NEMS-BT Implied Multipliers

Factor 20X 30X 40X Class A Equipment

TSL 1 0.041 484.6 726.9 969.2

TSL 2 0.197 101.3 152.0 202.6

TSL 3 0.437 45.8 68.7 91.5

TSL 4 0.674 29.7 44.5 59.4

TSL 5 0.796 25.1 37.7 50.2

TSL 6 0.878 22.8 34.2 45.6

TSL 7 1.068 18.7 28.1 37.5 Class B Equipment
TSL 1 0.017 1,199.9 1,799.9 2,399.8
TSL 2 0.025 808.6 1,212.8 1,617.1

TSL 3 0.124 161.0 241.6 322.1

TSL 4 0.142 140.8 211.2 281.6

TSL 5 0.382 52.3 78.5 104.6

TSL 6 0.426 47.0 70.5 93.9

K.3 REGRESSION SMOOTHING OF NEMS-BT OUTPUTS

Even at the selected multiplier levels shown in Table K.2.2, the simulations with NEMS-BT produces considerable 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 the AEO reference case and the energy efficiency standards case. The formulation of the smoothing regression was a simple quadratic function of time (in years) with the following form:

Y(T) = a 1  T + a 2  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 the year prior to th e year the standard goes into effect (2012). By forcing 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.

K-4 BVM_FINAL_FR_TSD_AppendixK_08-14-09.docFigure K.3.1 illustrates this procedure for the differences in total installed capacity and the three types of fossil fuel-fired generating plant capacity. The partic ular case shown is for beverage vending machine equipment at the “30X” level of savings (or 28.1X relative to TSL-7 for Class A beverage vending machine equipment) . NEMS-BT indicates a very smooth path of differences in total generation capacity as shown in the top left panel of the figure. (In this case, the regression smoothing would not be necessary.)

Total Generation Fossil Steam

2010 2015 2020 2025 2030 2010 2015 2020 2025 2030

0.5 0.8

0.6 0

NEMS-BT

Fitted NEMS-BT

Fitted 0.4

0.2Difference (GW)Difference (GW)Difference (GW)Difference (GW) -0.5

-1

-1.5

-2

-2.5 0

-0.2

-0.4

-0.6

-0.8

-1

Combined Cycle Combustion Turbine

2010 2015 2020 2025 2030 2010 2015 2020 2025 2030 1 5

0 4

NEMS-BT

Fitted NEMS-BT

Fitted -1

-2

-3

-4 3

2

1

0

-5 -1

-6 -2

Figure K.3.1 Example of Regression Smoothi ng Fits for Fossil-Fuel Power Plants and Total Installed Capacity (30X case)

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 fossil steam tec hnologies. In these cases, the model produces significant deviations from trend for up to 5 years throughout the forecast period. In this particular case, NEMS-BT appears to show some anomalous behavior in the last year or two of the simulation period, especially for fossil stea m. The regression smoothing approach provides a means of generating more plausible predictions in these instances. In some cases, a single particularly anomalous y ear was not used in regr ession fitting procedure.

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.

K-5 BVM_FINAL_FR_TSD_AppendixK_08-14-09.doc0 10 20 30 40 K.4 ESTIMATION OF INTERPOLATING FUNCTIONS FROM SAVINGS

MULTIPLIERS

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. , coal-fired electricity generation), the predicted values fr om 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 explanatory variab le. The y-intercept of this regression is forced through the origin. This restriction ensures that the predicted value of a NE MS-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 (slope) 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 against the total reduction in generation capacity for the year 2030. 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 2030 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.

-30 -25 -20 -15 -10

-5 0 0 10 20 30 40 50Difference (TWh)

Savings Multiplier Total Generation

-14 -12 -10 -8 -6 -4 -2 0 0 10 20 30 40 50Difference (TWh)

Savings Multiplier Natural GasDifference (TWh) -16 -14 -12 -10

-8 -6 -4 -2 0 0 10 20 30 40 50Difference(TWh)

Saving Multiplier Coal

Renewables

2

1

0

-1

-2

-3

-4

-5

-6
Savings Multiplier 50

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

K-6 BVM_FINAL_FR_TSD_AppendixK_08-14-09.docWithin this range of savings, the graph demonstrat es that the impact on total capacity predicted by the model is proportional to the magnitude of the refrigeration electricity savings.

For the other generation types shown in Figur e K.4.1, the responses deviate from strict proportionality. For example, for renewables, the response at the 20X is proportionately greater than either the 30X or 40X levels. The approach essentially produces a weighted average of the responses across these various savings 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.2.2. Thus, for example, the scale factor associated with TSL 4 for Class A equipment is 0.674. For total electricity generation in 2030, the estimated response coefficient is -0.260 ( e.g ., the slope of regression line in the top left panel of Figure K.4.1). Thus, the estimated impact on total generation in 2030 from this specific TSL would be 0.674  -0.260 =

-0.269 TWh.

K.5 ENVIRONMENTAL IMPACTS

The general approach for estimating the impact s on the utility sector was used to develop an environmental assessment in terms of change s in emissions. Annual values of the emissions for oxides of nitrogen (NO

x ), mercury (Hg), and carbon dioxide (CO 2 ) were extracted from the relevant tables produced by NEMS. Similar to examples shown in Figure K.3.1, the projections of emissions 2012–2030 were smoothed using the qua dratic function in time discussed above. Response functions were then estimat ed at 5-year intervals between 2015–2030.

Figure K.5.1 shows the estimated response (or in terpolating) functions for carbon dioxide emissions. There is very little disparity in the magnitudes of carbon dioxide emissions across the three multipliers selected. Howeve r, the 30X case shows a somewhat greater response than the 20X and 40X cases in 2030. But the regression-base d average takes all thr ee 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 2015–2030 reflects the increasing reduction in em issions over selected years. There is little change in the slopes from 2025–2030, reflecting a dist inct slowdown in the rate of increase of savings. By the middle part of the next decade, most beverage vending machine equipment will have already been replaced by more efficient equipment mandated by today’s standards. Moreover, reduction in nuclear a nd renewable generation prompted by today’s

standards have the effect of re tarding further reductions in CO 2 emissions toward the end of the period.

K-7 BVM_FINAL_FR_TSD_AppendixK_08-14-09.doc-30 -25 -20 -15 -10

-5 0 0 10 20 30 40 50Difference (MMT-CE)

Savings Multiplier Carbon Dioxide Emissions (2015)

-30 -25 -20 -15 -10

-5 0 0 10 20 30 40 50Difference (MMT-CE)

Savings Multiplier Carbon Dioxide Emissions (2020)

-30 -25 -20 -15 -10

-5 0 0 10 20 30 40 50Difference (MMT-CE)

Savings Multiplier Carbon Dioxide Emissions (2025)

-30 -25 -20 -15 -10

-5 0 0 10 20 30 40 50Difference (MMT-CE)

Savings Multiplier Carbon Dioxide Emissions (2030)

Figure K.5.1 Response (Interpolating) Functions for Carbon Dioxide for 2015, 2020, 2025,

and 2030

K-8 BVM_FINAL_FR_TSD_AppendixK_08-14-09.docREFERENCES

1. U.S. Department of Energy

– Energy Information Administration. An Updated Annual Energy Outlook 2009 Reference Case Reflecting Provisions of the American Recovery and Reinvestment Act and Recent Change s in the Economic Outlook . April 2009. Washington,

D.C. SR/OIAF/2009-03.

2. U.S. Department of Energy. 2008. Tec hnical Support Document for Commercial Refrigeration Equipment.

K-9 BVM_FINAL_FR_TSD_AppendixK_08-14-09.doc