APPENDIX K. ESTIMATION OF UTILITY AND ENVIRONMENTAL

RESULTS FROM NEMS­BT OUTPUT

TABLE OF CONTENTS

K.1 Allocation of Input Electricity Savings by Census Division and End Use..................... K­1

K.2 Choice of Impact Multipliers.......................................................................................... K­2

K.3 Regression Smoothing of NEMS­BT Outputs................................................................ K­4

K.4 Estimation of Interpolating Functions from Savings Multipliers

................................... K­5

K.5 Environmental Impacts

................................................................................................... K­7

LIST OF TABLES

Table K.1.1 Shares of National Savings by Census Division Used in NEMS­BT.................... K­2

Table K.2.1 Comparison of Refrigeration Electricity Savings with Different

Multipliers with Total Generation

...................................................................... K­3

Table K.2.2 Final Implied Multipliers by Trial Standard Level

– All

Refrigeration Equipment..................................................................................... K­4

LIST OF FIGURES

Figure K.3.1 Example of Regression Smoothing Fits for Fossil­Fired and Total

Installed Capacity (5X case)

............................................................................... K­5

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

for 2025............................................................................................................... K­6

Figure K.5.1 Response (Interpolating) Functions for Carbon Dioxide for Years

2015, 2020, 2025 and 2030................................................................................. K­8

K­iAPPENDIX K. ESTIMATION OF UTILITY AND ENVIRONMENTAL

RESULTS FROM NEMS­BT OUTPUT

The effects of proposed energy conservation standards for commercial refrigeration

equipment was analyzed using a variant of National Energy Modeling System (NEMS) model,

called NEMS­BT. 1a Because the magnitude of national energy savings as a result of the

proposed standards is very small relative to total electric generation, DOE used an estimation

method involving regression smoothing and interpolation. This appendix describes the major

elements of this method.

To run a simulation in NEMS­BT model, the 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 10 of the TSD) for each trial

standard level (TSL). The electricity savings increase over time as various types of commercial

refrigeration equipment are replaced with more efficient equipment meeting the proposed

conservation standards.

The magnitude of the electricity decrement that would be required for NEMS­BT to

produce stable results out of the range of numerical noise is greater than the highest TSL under

consideration. Therefore, to estimate results for the TSLs evaluated here, a series of NEMS­BT

simulations is performed using higher values for the input electricity savings. These simulations

establish the relationship between the NEMS­BT output variables (electricity generation by fuel,

installed electricity generation capacity, and emissions) and the electricity savings inputs. For

each output variable, a regression model is estimated to represent an average response across the

series of simulations. Impacts are obtained using predicted values from the regression models

corresponding to the (smaller) levels of savings corresponding to TSLs.

K.1 Allocation of Input Electricity Savings by Census Division and End Use

Although the utility and environmental impacts are national in scope, the NEMS­BT

model operates at a census division level. Lacking sales information for commercial

refrigeration equipment by census division, an allocation of savings was made on the basis of

building populations in the Census of Retail Trade. The shares used to distribute the savings

across census divisions are shown in Table K.1.1. The shares are held constant for the forecast

period through 2030.

a DOE’s 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 analysis of PTAC and PTHP energy efficiency standard entails some

minor code modifications and the model is run under policy scenarios that are variations of DOE/EIA assumptions,

the name NEMS­BT 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.

K­1Table K.1.1 Shares of National Savings by Census Division Used in NEMS­BT

Census Division Share (%)

New England 6.4%

Middle Atlantic 14.9%

East North Central 14.4%

West North Central 6.4%

South Atlantic 19.3%

East South Central 4.9%

West South Central 9.9%

Mountain 6.3%

Pacific 17.5%

K.2 Choice of Impact Multipliers

To gain some understanding of the how NEMS­BT would respond to various magnitudes

of savings, a “reference” time series of savings was initially selected from preliminary version of

the NES spreadsheet model. This reference series was selected as the savings that would result

from the most stringent TSL (“max technologically feasible) for one specific type of

refrigeration equipment (equipment designated VCT.RC.L; Vertical Closed Transparent, Remote

Condensing, Low Temperature). The estimated savings from this particular equipment type in

2030 was 1.91 terrawatt­hours (TWh) or 6.5 Trillion Btu (in site energy terms). NEMS­BT was

run using the projected time series of savings pertaining to this case, as well as for higher

multipliers (designated here with a suffix X), including 5X, 10X, 15X, and 20X. (Thus, for

example, the 2030 refrigeration savings imposed upon the commercial module in NEMS­BT in

the 10X case was 19.1 TWh.)

It was judged on the basis of graphical output and comparison of trends among the output

variables, that the numerical noise from the model was overwhelming the impact of the savings

decrement for the smaller multiplier factors less than 5X. b 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 EIA’s Annual Energy Outlook (AEO2007)

reference case. 2c

b The numerical noise stems, in part, from 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 used here are solely dependent on the initial (and arbitrary)

choice of a particular refrigeration product and a specific trial standard level (TSL) for that product. The NEMS­BT

simulations were performed prior to the final selection of TSLs and so the 1X multiplier does not correspond to any

specific TSL for a product category in previous sections of the report. However, it was judged that the savings

decrements (in TWh) tested in NEMS­BT, 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 unnecessary. 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. This procedure is explained more fully below.

K­2Table K.2.1 Comparison of Refrigeration Electricity Savings with Different Multipliers

with Total Generation

2015 2020 2025 2030

1X Savings (TWh)

% of total generation 0.46

0.01% 1.16

0.02% 1.80

0.04% 1.91

0.04%

5X Savings (TWh)

% of total generation 2.30

0.05% 5.80

0.12% 9.00

0.18% 9.55

0.18%

10X Savings (TWh)

% of total generation 4.60

0.10% 11.60

0.25% 18.00

0.36% 19.10

0.35%

15X Savings (TWh)

% of total generation 6.90

0.16% 17.40

0.37% 27.00

0.54% 28.65

0.53%

20X Savings (TWh)

% of total generation 9.20

0.21% 23.20

0.49% 36.00

0.71% 38.20

0.71%

Based on this testing, a decision was made to use the multipliers in the range of 5X

through 15X. While these multipliers are large, they still do not yield large perturbations to the

base case simulation. For example, as Table K.1.1 indicates, even at 15X in the year 2025 the

total savings decrement applied to the model represents only about 0.5 percent of total electricity

generation.

Final Implied Multipliers by Trial Standard Level for All Refrigeration Equipment

Subsequent to the production of the NEMS simulations described in the previous section,

some revisions were made to the final set of energy savings estimates for the NOPR. In addition,

for purposes of reporting utility and environmental impacts for refrigeration, all fifteen types of

refrigeration equipment were aggregated. To adjust for these changes, the difference between

the final savings estimate (by trial standard level) and the savings from the initial “reference”

time series (VCT.RC.L at TSL­4) was represented as a scale factor. 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 first column in Table K.2.2 displays the scale factors for each TSL. d The remaining

columns display the implied multipliers from the three simulations performed with NEMS­BT

(using their designations of 5X, 10X, and 15X). For example, after combining all the

refrigeration equipment, the actual final multipliers used for TSL­3 were 2.7, 5.4, and 8.2.

Because the savings for TSL­1 through TSL­3 are smaller than those for TSL­4, the implied

multipliers are larger than those for TSL­4. (TSL­5, pertaining to approximately a three­year

payback for all equipment types is similar to TSL­3).

d The scale factors can also be viewed as interpolating values relative to the set of NEMS­BT impact runs. Thus, for

example, if an impact upon a particular variable from the “1X” case in the original set of simulations was

interpolated to be a value of, say, 0.10, then the impact for TSL­3 for all refrigeration equipment would be 1.839 x

0.10

= 0.1839.

K­3Table K.2.2 Final Implied Multipliers by Trial Standard Level

– All Refrigeration

Equipment

Scale

Factor Implied Multipliers

NEMSBT

("5X") NEMSBT

("10X") NEMSBT

("15X")

All Refrigeration Equipment

TSL

– 1 0.363

TSL

– 2 1.401

TSL

– 3 1.839

TSL

– 4 3.109

TSL

– 5 1.835 13.8 27.6

3.6 7.1

2.7 5.4

1.6 3.2

2.7 5.4 41.4

10.7

8.2

4.8

8.2

K.3 Regression Smoothing of NEMS­BT Outputs

Even at the selected multiplier levels shown in Table K.1.1, the simulations with

NEMS­BT produces considerable year­to­year variation that appears to be caused by numerical

noise from the model solution. This variation appears to affect the outputs related to specific

generation fuel and technologies more so than total 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 year­to­year variation that may be spurious results from the model

solution process.

To reduce the potential effect of numerical noise from NEMS­BT 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 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 2011, T

= 1 in 2012, etc.

a 1

, a 2 = estimated regression coefficients

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 the prior to the implementation of the standard (2011). By

forcing the impact to be zero in 2011, this procedure 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 differences in total installed capacity and

the three types of fossil fuel capacity. The particular case shown is for commercial refrigeration

equipment at the “10X” level of savings (or 5.4X relative TSL­3 for all commercial refrigeration

equipment—see Table K.2.2). Clearly, NEMS­BT 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.)

K­4For plants using specific fuels or technologies, the differences in particular years from a

smooth trend line are more substantial. The deviations from the fitted trend line are most

pronounced for combustion turbines. In this case, the model produces a significant negative

deviation from trend in the years between 2020 and 2025.

Nevertheless, over the entire forecast period, the long­term 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 short­term deviations from what appear to be long­term trends.

Total Capacity

­3 ­2 ­1 0 1 2010 2015 2020 2025 2030Difference(GW)

NEMS­BT

Fitted Fossil Steam

­4 ­3 ­2 ­1 0 1 2010 2015 2020 2025 2030Difference(GW)

NEMS­BT

Fitted

Combined Cycle

­0.8 ­0.6 ­0.4 ­0.2

0 0.2 0.4 0.6 2010 2015 2020 2025 2030Difference(GW)

NEMS­BT

Fitted Combustion Turbine

­1 ­0.5

0 0.5

1 1.5

2 2010 2015 2020 2025 2030Difference(GW) NEMS­BT

Fitted

Figure K.3.1 Example of Regression Smoothing Fits for Fossil­Fired and Total Installed

Capacity (5X case)

K.4 Estimation of Interpolating Functions from Savings Multipliers

The development of utility and environmental impacts focused on specific years from the

NEMS­BT simulations: 2015, 2020, 2025 and 2030. For each output variable (e.g., coal­fired

electricity generation), the predicted values from the trend regression were extracted for each

savings multiplier level (5X, 10X, and 15X). 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 variable. The y­intercept of this regression is forced through the

origin. This restriction ensures that the predicted value of a NEMS­BT output variable is zero

when the savings multiplier is zero. Predicted values for non­zero multipliers are calculated by

taking the estimated regression coefficient (slope) times the value of the savings multiplier.

K­5Figure K.4.1 shows several examples of this estimation approach. The magnitude of the

energy savings multiplier is plotted on the x­axis against the total reduction in generation

capacity for the year 2025. The results shown are based on the three simulations selected for

commercial refrigeration equipment (5X, 10X, and 15X as described above). The fitted 2025

values from the smoothing regression of the NEMS­BT output are shown in the large solid

(black) diamonds. In the case of total generation capacity, the linear regression line is closely

aligned to the three points corresponding to the 5X, 10X, and 15X savings multipliers.

Total Generation

­30 ­25 ­20 ­15 ­10 ­5 0 0 5 10 15 20

Savings MultiplierDifference(TWh) Coal

­20 ­15 ­10 ­5 0 0 5 10 15 20

Savings MultiplierDifference(TWh)

Natural Gas

­5 ­4 ­3 ­2 ­1 0 1 0 5 10 15 20

Savings MultiplierDifference(TWh) Nuclear

­7 ­6 ­5 ­4 ­3 ­2 ­1 0 0 5 10 15 20

Savings MultiplierDifference(TWh)

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

2025

Within this range of savings, the graph demonstrates 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 Figure K.4.1, the responses deviate from strict

proportionality. For example, for natural gas, the response at the 15X is proportionately greater

than either the 5X or 10X levels. The approach taken here essentially produces a weighted

average of the responses across these various savings multipliers.

The predicted value of a NEMS­BT output variable for a given standards level is

interpolated along the regression line estimated for that variable. The savings from a

particular standards case is first converted into a savings multiplier using the relationships shown

in Table K.2.1. Thus, for example, if Trial Standard Level 1 were to yield a decrement of

0.654 TWh in 2025 for total refrigeration electricity use, the interpolated savings multiplier

would be calculated as 0.363 (= 0.654/1.80, from the first row in Table K.2.1). For total

generation, the estimated response coefficient is ­1.74 (e.g., the slope of regression line in the top

K­6left panel Figure K.4.1). Thus, the estimated impact on total generation in 2025 from this

specific trial standard level would be 0.363 x ­1.74

= ­0.63 TWh.

K.5 Environmental Impacts

The general approach for estimating the impacts 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 extracted 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 five­year intervals between 2015 and 2030.

Figure K.5.1 shows the estimated response (or interpolating) functions for carbon dioxide

emissions. Up through 2020, there is some disparity in the magnitudes of carbon dioxide

emissions across the three multipliers selected, with the 10X case showing the smallest response.

However, taking all three cases into account, there is a strong indication of a reduction in carbon

dioxide emissions. By 2025, total carbon emissions are approximately proportional to the level

of the saving decrement (for the savings decrements shown in Table K.2.1 corresponding to 5X,

10X, and 15X).

The scale of the y­axis is held constant for each of the plots and so the clockwise rotation

of the slopes across the years 2015 through 2030 reflects the increasing reduction in emissions

over selected years. There is little change in the slopes between 2025 and 2030. By 2030, the

impact of the conservation standards on nuclear generation is more pronounced. The decline

(relative to the reference case) in electricity generation from nuclear plants tends to offset the

reductions in carbon dioxide emissions from other (fossil fuel) sources.

K­72015 2020

0 5 10 15 20 0 5 10 15 20Difference(MMT­CO2)Difference(MMT­CO2) 0

­2

­4

­6

­8

­10

­12

­14Difference(MMT­CO2)Difference(MMT­CO2) 0

­2

­4

­6

­8

­10

­12

­14

Savings Multiplier Savings Multiplier

2025 2030

0 5 10 15 20 0 5 10 15 20

0

­2

­4

­6

­8

­10

­12

­14 0

­2

­4

­6

­8

­10

­12

­14

Savings Multiplier Savings Multiplier

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

2020, 2025 and 2030.

K­8REFERENCES

1. U.S. Department of Energy. Energy Information Administration. National Energy Modeling

System: An Overview 2003. (DOE/EIA­0581(2003), March 2003.

http://tonto.eia.doe.gov/reports/reports_kindD.asp?type=model%20documentation.

2. U.S. Department of Energy/Energy Information Administration (DOE/EIA), Annual Energy

Outlook 2007

, February 2007. Washington, D.C. DOE/EIA­0383(2007).

K­9