Summary of Inputs for the

Technical Support Document:

Energy Efficiency Standards for Fluorescent Lamp Ballasts

- Life Cycle Cost

- National Energy Impacts

- Utility/Environmental Analysis

April 20, 1998

Prepared by U. S. Department of Energy,

Office of Codes and Standards

EE-RM-97-500 COMMENT 20D

Summary of Inputs for the 

Technical Support Document: 

I 

Energy Efficiency Standards for Fluorescent Lamp Ballasts 

- Life Cycle Cost 

- National Energy Impacts 

- UtilitylEnvironmental Analysis 

April 20, 1998 

Prepared by u.s. Department of Energy, 

Office of Codes and Standards 
INTRODUCTION

This document has been developed to provide interested parties with the inputs that the

Department of Energy will use to produce the life cycle cost, national energy impacts and

utility/environmental analysis sections of the Technical Support Document: Energy Efficiency

Standards for Fluorescent Lamp Ballasts.

The document contains a discussion and assumptions for those variables that have the greatest

impact on life cycle cost calculations. These are: annual lighting hours, ballast price, ballast

lifetime, electricity price and discount rate. The document presents the inputs and sensitivities

used in the July, 1997, Draft Report on Potential Impact of Possible Energy Efficiency Levels for

Fluorescent Lamp Ballasts, the inputs to be used for the technical Support Document and the

rationale for those inputs.

The document also presents comments and discussions regarding the national energy impacts and

the utility/environmental analyses.

LIFE CYCLE COST PARAMETERS

In this section the main inputs to the life cycle cost analysis are discussed. Assumptions for the

average and sensitivity analyses are described.

Annual Lighting Hours

1997 Report

The annual lighting hours were obtained from data supplied by Xenergy. The average values for

the commercial sector were 3600 hours for both 4 and 8 foot lamp ballasts. For 8 foot lamp

ballasts, 4000 hours was used for the industrial sector. For 8 foot high output (HO) lamp ballasts,

4000 hours was used for the commercial sector and 5000 hours for the industrial sector. Low and

high sensitivities were performed at 2500 and 8000 hours for both the commercial and industrial

sectors. For the probability-based sensitivity analysis, a distribution was obtained from the

Xenergy data (see Figure 4.5 of the draft ballast report, July, 1997).

Technical Support Document

DOE will use the same input values for the average and low and high life cycle cost analyses.

DOE will use the distribution in Figure 4.5 for the probability-based sensitivity analysis.

Rationale

Most of the comments on lighting hours concerned the probability-based sensitivity analysis.

These are discussed in the uncertainty/variability section below. No new information was provided

that would lead DOE to use another data base for estimation of annual lighting hours.

2

EE-RM-97-500 COMMENT 20D

INTRODUCTION 

This document has been developed to provide interested parties with the inputs that the 

Department of Energy will use to produce the life cycle cost, national energy impacts and 

utility/environmental analysis sections of the Technical Support Document: Energy Efficiency 

Standards for Fluorescent Lamp Ballasts. 

The document contains a discussion and assumptions for those variables that have the greatest 

impact on life cycle cost calculations. These are: annual lighting hours, ballast price, ballast 

lifetime, electricity price and discount rate. The document presents the inputs and sensitivities 

used in the July, 1997, Draft Report on Potential Impact of Possible Energy Efficiency Levels for 

Fluorescent Lamp Ballasts, the inputs to be used for the technical Support Document and the 

rationale for those inputs. 

The document also presents comments and discussions regarding the national energy impacts and 

the utility/environmental analyses. 

LIFE CYCLE COST PARAMETERS 

In this section the main inputs to the life cycle cost analysis are discussed. Assumptions for the 

average and sensitivity analyses are described. 

Annual Lighting Hours 

1997 Report 

The annual lighting hours were obtained from data supplied by Xenergy. The average values for 

the commercial sector were 3600 hours for both 4 and 8 foot lainp ballasts. For 8 foot lamp 

ballasts, 4000 hours was used for the industrial sector. For 8 foot high output (HO) lamp ballasts, 

4000 hours was used for the commercial sector and 5000 hours for the industrial sector. Low and 

high sensitivities were performed at 2500 and 8000 hours for both the commercial and industrial 

sectors. For the probability-based sensitivity analysis, a distribution was obtained from the 

Xenergy data (see Figure 4.5 of the draft ballast report, July, 1997). 

Technical Support DOCument 

DOE will use the same input values for the average and low and high life cycle cost analyses. 

DOE will use the distribution in Figure 4.5 for the probability-based sensitivity analysis. 

Rationale 

Most of the comments on lighting hours concerned the probability-based sensitivity analysis. 

These are discussed in the uncertainty/variability section below. No new information was provided 

that would lead DOE to use another data base for estimation of annual lighting hours. 

2 
Ballast Prices

1997 Repo

DOE obtained the ballasts prices from a survey that Gough and Associates (G&A) conducted for

LBNL. Responses were used to estimate 1996 "national average" end-user prices from three

sources: OEMs, electrical distributors, and lighting management companies (LMCs). Ballast

price sensitivities were not performed.

Technical Support Document In ut

DOE will use the same ballast prices as an input to the Technical Support Document. However,

the analysis will include a distribution, based on price surveys, of these ballast prices in the

probability based sensitivity analysis. Additionally, DOE will perform sensitivities at the high and

low prices obtained from the LBNL telephone survey.

Rationale

One commentor felt that the ballast prices used in this analysis more closely matched what they've

found in the marketplace than values used by LBNL in earlier versions of the analysis. However,

several other commentors raised questions about how the ballast end-user prices were estimated.

In particular, NEMA questioned the higher markup for magnetic ballasts relative to that for

electronic ballast.

The Gough and Associates (G&A) survey included luminaires: manufacturers and ballast

manufacturers, whose responses were used to derive the OEM prices, and LMCs, whose

responses were used to estimate LMC prices. G&A did not survey distributors. There is such a

large number of them that G&A felt it would have been difficult to get a representative sampling.

Distributor prices were instead estimated by applying a markup to OEM prices, as described

below. Markups were also used to estimate end-user prices from OEM prices, from distributor

prices, and from LMC prices.

All markups were obtained, either directly or indirectly, from the survey by G&A. Two of the

markups used came directly from the surveys. Ballast manufacturers were asked to estimate the

OEM-to-Distributor markups, and they estimated markups of 35 - 40% for magnetic and 10% for

electronic ballasts. LMCs were asked to estimate the contractor to end-user markup, which they

reported to be 13% for both magnetic and electronic ballasts. Additional markups were

calculated based on other quantities obtained from the survey, as follows. Both ballast

manufacturers and luminaire:manufacturers were asked to estimate ballast prices paid by OEMs.

G&A averaged the responses to get an average OEM price for each ballast type. LMCs were

asked to estimate average ballast prices paid by contractors, and the average of their responses

was taken to get an average contractor price. The 13% contractor to end-user markup was then

added to the contractor price to get an end user price. To get the OEM to end user markup,

G&A divided the end user price by the OEM price. LBNL averaged the markups for three

common magnetic ballast types (2F40T12, 2F96T12 and 2F96T12HO) to get an average OEM to

end user markup of 8#% for magnetic ballasts. LBNL also averaged the markups for three

3

EE-RM-97-500 COMMENT 20D

Ballast Prices 

1997 RIW0rt 

DOE obtained the ballasts prices from a survey that Gough and Associates (G&A) conducted for 

LBNL. Responses were used to estimate 1996 "national average" end-user prices from three 

sources: OEMs, electrical distributors, and lighting management companies (LMCs). Ballast 

price sensitivities were not performed. 

Technical Support Document Input 

DOE will use the same ballast prices as an input to the Technical Support Document. However, 

the analysis will include a distribution, based on price surveys, of these ballast prices in the 

probability based sensitivity analysis. Additionally, DOE will perform sensitivities at the high and 

low prices obtained from the LBNL telephone survey. 

Rationale 

One commentor felt that the ballast pnces used in this analysis more closely matched what they've 

found in the marketplace than values used by LBNL in earlier versions of the analysis. However, 

several other commentors raised questions about how the ballast end-user prices were estimated. 

In particular, NEMA questloned the higher markup for magnetic ballasts relative to that for 

electronic ballast. 

The Gough and Associates (G&A) survey included luminaire\:\manufacturers and ballast 

manufacturers, whose responses were used to derive the OEM prices, and LMCs, whose 

responses were used to estimate LMC prices. G&A did not survey distributors. There is such a 

large number of them that G&A felt it would have been difficult to get a representative sampling. 

Distributor prices were instead estimated by applying a markup to OEM prices, as described 

below. Markups were also used to estimate end-user prices from OEM prices, from distributor 

prices, and from LMC prices. 

All markups were obtained, either directly or indirectly, from the survey by G&A Two of the 

markups used came directly from the surveys. Ballast manufacturers were asked to estimate the 

OEM-to-Distributor markups, and they estimated markups of35 - 40% for magnetic and 10%for 

electronic ballasts. LMCs were asked to estimate the contractor to end-user markup, which they 

reported to be 13% for both magnetic and electronic ballasts. Additional markups were 

calculated based on other quantities obtained from the survey, as follows. Both ballast 

manufacturers and luminaire\\manufacturers were asked to estimate ballast prices paid by OEMs. 

G&A averaged the responses to get an average OEM price for each ballast type. LMCs were 

asked to estimate average ballast prices paid by contractors, and the average of their responses 

was taken to get an average contractor price. The 13% contractor to end-user markup was then 

added to the contractor price to get an end user price. To get the OEM to end user markup, 

G&A divided the end user price by the OEM price. LBNL averaged the markups for three 

common magnetic ballast types (2F40T12, 2F96T12 and 2F96T12HO) to get an average OEM to 

end user markup of 84,% for magnetic ballasts. LBNL also averaged the markups for three 

3 
common electronic ballast types (2F32T8, 2F96T12 and 2F96T12H0) to get an average OEM to

end user markup of 38% for electronic ballasts.

In order to get a better sense of whether the ballast prices obtained in the G&A survey were

reasonable, LBNL conducted an informal telephone survey during the first half of December,

1997. A simple average of the responses of 18 electrical distributors across the United States was

compared with the end user prices used in the draft report.

We will use the high and low prices and the distribution of prices obtained from the telephone

survey for both single-value and probability-based sensitivity analysis. The table of prices is

shown below. Distributors assumed the quote was for a good client ordering at least 100 ballasts

and that all ballasts were 120 Volt for two lamps.

4

EE-RM-97-500 COMMENT 20D

common electronic ballast types (2F32T8, 2F96T12 and 2F96T12HO) to get an average OEM to 

end user markup of 38% for electronic ballasts. 

In order to get a better sense of whether the ballast prices obtained in the G&A survey were 

reasonable, LBNL conducted an informal telephone survey during the first half of December, 

1997. A simple average of the responses of 18 electrical distributors across the United States was 

compared with the end user prices used in the draft report. 

We will use the high and low prices and the distribution of prices obtained from the telephone 

survey for both single-value and probability-based sensitivity analysis. The table of prices is 

shown below. Distributors assumed the quote was for a good client ordering at least 100 ballasts 

and that all ballasts were 120 Volt for two lamps. 

4 
Table 1. Dishibuter 71A!ast Prices

F40T12 IF40T12 F32T8 F96T12 I

F96T12

Corn ny state City EMAG El ronic Electronic Magnetic Electronic

CA San Francisco 9.50 1$ 15.751$

B GA Atlanta $ 9.951 NA

C PA Philadelphia 1$ 10.001$ 17.001$

NY Queens $ 10.001 NA 1$

CA

H

L

CA

MA

MA

MD

MI

CA

15.75

NA

NA I NA

NA

15.001$ 18.00

19.75' NA

Richmond 1$ 10.30 1$ 21.251$ 19.50 1 NA

$ 10.50' NA 1$ 17.501 NA

Pleasanton 1$ 10.501 NA 1 NA NA

Oakland $ 10.501$ 18.141$ 16.481 NA

Brighton 1$ 11.001 NA 1$ 17.001 NA

Brighton

Baltimore

Detroit

San Leandro

TX Houston

0 IIL

P TX

IL

Chicago

Houston

Chicago

CA (Oakland

[AVERAGES

Prices Listed in Draft Report

Percent difference

$ 11.001 NA 1$ 17.001 NA

$ 11.65 NA NA NA

$ 12.501$ 21.001 NA 1$ 23.50

$ 12.651$ 18.451 NA NA

NA

$ 32.00

NA

NA

NA

NA

NA

NA

NA

$ 12.951$ 22.951$ 21.951$ 17.95

$ 13.471 NA 1$ 22.301 NA

$ 14.401 NA 1$ 19.501$ 24.40

$ 15.001$ 19.001$ 19.001 NA

$ 15.261$ 23.101$ 21.701 NA

$ 11.731$ 19.631$ 18.651$ 20.12

$ 11.431$ 22.351$ 18. 601$ 19.90

2.6%1 -12.2%1 0.3% 1.1%

NA

NA

NA

NA

NA

$ 30.75

NA

NA

$ 31.38

$ 30.49

2.9%

VERAGES with 13% Mark-up Is 13.25 $ 22.18 $ 21.07 1$ 22.73 1 $ 35.45

The row titled AVERAGES, lists the average prices for the ballasts with no additional markup.

The average prices obtained for the energy efficient magnetic ballasts with either two 4-foot T12

lamps or two 8-foot lamps and the average prices for the electronic ballasts for either two 4-foot

T8 lamps or two 8-foot lamps were very close to those used in the draft Report (up to 3% higher)

whereas the average price for the T12 electronic ballast was lower (12.2%).

The last row of Table 1 shows what the average prices would be if a 13% mark up were applied.

This results in the electronic ballast for two 4-foot T12 lamps being only $0.17 less than the Draft

Report price while the prices for the other four would be 10% to 13% higher than those in the

Draft Report.

5

EE-RM-97-500 COMMENT 20D

Table 1 . Distributor Ballast Prices 

~ F40T12 F40T12 F32T8 F96T12 

A CA 0 $ 9.50 $ 15.75 

nie 

$ 

E 

15.75 NA F~ 8 GA $ 9.95 NA NA NA NA 

C PA ia $ 10.00 $ 17.00 $ 15.00 $ 18.00 $ 32.00 

D NY ns $ 10.00 NA $ 19.75 NA NA 

E CA Richmond $ 10.30 $ 21.25 $ 19.50 NA NA 

F NY IQueens $ 10.50 NA $ 17.50 NA NA 

G CA Pleasanton $ 10.50 NA NA NA NA 

H CA Oakland $ 10.50 $ 18.14 $ 16.48 NA NA 

I MA Brighton $ 11.00 NA $ 17.00 NA NA 

J MA Brighton $ 11.00 NA $ 17.00 NA NA 

K MD Baltimore $ 11.65 NA NA NA NA 

L MI Detroit $ 12.50 $ 21.00 NA $ 23.50 NA 

M CA San Leandro $ 12.65 $ 18.45 NA NA NA 

N TX Houston $ 12.95 $ 22.95 $ 21.95 $ 17.95 NA 

0 IL Chicago $ 13.47 NA $ 22.30 NA NA 

P TX Houston $ 14.40 NA $ 19.50 $ 24.40 $ 30.75 

Q IL Chicago $ 15.00 $ 19.00 $ 19.00 NA NA 

R CA Oakland $ 15.26 $ 23.10 $ 21.70 NA NA 

~VERAGES $ 11.73 $ 19.63 $ 18.65 $ 20.12 $ 31.38 

IPrices Listed in Draft Report $ 11.43 $ 22.35 $ 18.60 $ 19.90 $ 30.49 

iPercent difference 2.6% -12.2% 0.3% 1.1% 2.9% 

!AVERAGES with 13% Mark-up $ 13.25 $ 22.18 $ 21.07 $ 22.73 $ 35.45 

The row titled AVERAGES, lists the average prices for the ballasts with no additional markup. 

The average prices obtained for the energy efficient magnetic ballasts with either two 4-foot T12 

lamps or two 8-foot lamps and the average prices for the electronic ballasts for either two 4-foot 

T81amps or two 8-foot lamps were very close to those used in the draft Report (up to 3% higher) 

whereas the average price for the T12 electronic ballast was lower (12.2%). 

The last row of Table 1 shows what the average prices would be if a 13% mark up were applied . 

. This results in the electronic ballast for two 4-foot T121amps being only $0.17 less than the Draft 

Report price while the prices for the other four would be 10% to 13% higher than those in the 

Draft Report. ' 

5 
Ballast Lifetime

1997 Report

DOE used an average ballast lifetime of 45,000 hours for both magnetic and electronic ballasts.

A distribution of lifetimes was used in the probability based sensitivity analysis. For high and low

sensitivities, 60,000 and 33,000 hours per year were used.

Technical Support Document Input

DOE will use an average ballast lifetime of 50,000 hours for both magnetic and electronic ballasts.

A new distribution will be developed for the probability-based sensitivities based on the data in

Figure 1 below. For high and low sensitivities, DOE will use 33,000 and 80,000 hours per year.

Rationale

Ballast lifetime varies depending upon the properties (components and manufacturing process) of

a particular ballast and on how it is installed and used (including fixture type). Therefore, any

group of ballasts will have a range, of associated lifetimes. It is possible to consider a half-life

(time for 50% to fail) of that group and also an average lifetime for the group.

For the ballast analysis described in the July, 1997 report, 45,000 hours was used as a lifetime for

both magnetic and electronic ballasts. This was taken to be an average lifetime for use in the life

cycle cost analysis. We divided the 45,000 hour lifetime by annual operating hours to get ballast

service life in years. For example, for 4 foot commercial sector ballasts, we obtained 12.5 years

(45,000/3600). The value of 45,000 hrs came from the Illumination Engineers Society (IES) and

is also close to the value of 50,000 hours suggested by several interested parties at the March,

1997 ballast workshop in Washington D.C. It was also suggested by stakeholders that equal

lifetimes be used for magnetic and electronic ballasts.

When the 1997 report analysis was performed, there were not yet enough publicly available data

to accurately determine the average lifetimes and distributions of lifetimes of magnetic and

electronic ballasts. We also felt that there were not enough failure rate data available to perform a

theoretical reliability analysis for average ballast lifetime or to develop a frequency distribution of

ballast lifetimes. Due to this lack of data, we used the IES estimate of 45,000 hrs, which is close

to the 50,000 hours recommended by stakeholders. Additionally, the Advanced Lighting

Guidelines -1993 (page 4-9), published by the U.S. DOE, EPRI and California Energy

Commission concluded, based on a University of California field study, "that reliable electronic

high frequency ballasts can be produced with the quality control necessary to reach or exceed the.

ten to twelve year life span common with magnetic ballasts". This supports the use of an

electronic ballast lifetime at least as long as the magnetic ballast lifetime.

Based on recent data on early return rates for electronic ballasts, the ballast half-life could be as

high as a million hours were it not for non-electronic components such as capacitors. Assuming

that the capacitor is the limiting component with respect to lifetime, an estimate of 15 years half

6

EE-RM-97-500 COMMENT 20D

Ballast Lifetime 

1997 Report 

DOE used an average ballast lifetime of 45,000 hours for both magnetic and electronic ballasts .. 

A distribution oflifetimes was used in the probability based sensitivity analysis. For high and low 

sensitivities, 60,000 and 33,000 hours per year were used. 

Technical Support Document Input 

DOE will use an average ballast lifetime of 50,000 hours for both magnetic and electronic ballasts. 

A new distribution will be developed for the probability-based sensitivities based on the data in 

Figure 1 below. For high and low sensitivities, DOE will use 33,000 and 80,000 hours per year. 

Rationale 

Ballast lifetime varies depending upon the properties (components and manufacturing process) of 

a particular ballast and on how it is installed and used (including fixture type) . Therefore, any 

group of ballasts will have a range of associated lifetimes. It is possible to consider a half-life 

(time for 50% to fail) of that group and also an average lifetime for the group. 

For the ballast analysis described in the July, 1997 report, 45,000 hours was used as a lifetime for 

both magnetic and electronic ballasts. This was taken to be an average lifetime for use in the life 

cycle cost analysis. We divided the 45,000 hour lifetime by annual operating hours to get ballast 

service life in years. For example, for 4 foot commercial sector ballasts, we obtained 12.5 years 

(45,000/3600). The value of 45,000 hrs came from the Illumination Engineers Society (IES) and 

is also close to the value of 50,000 hours suggested by several interested parties at the March, 

1997 ballast workshop in Washington D.C. It was also suggested by stakeholders that equal 

lifetimes be used for magnetic and electronic ballasts. 

When the 1997 report analysis was performed, there were not yet enough publicly available data 

to accurately determine the average lifetimes and distributions of lifetimes of magnetic and 

electronic ballasts. We also felt that there were not enough failure rate data available to perform a 

theoretical reliability analysis for average ballast lifetime or to develop a frequency distribution of 

ballast lifetimes. Due to this lack of data, we used the IES estimate of 45,000 hrs, which is close 

to the 50,000 hours recommended by stakeholders. Additionally, the Advanced Lighting 

Guidelines -1993 (page 4-9), published by the U.S. DOE, EPRI and California Energy 

Commission concluded, based on a University of California field study, "that reliable electronic 

high frequency ballasts can be produced with the quality control necessary to reach or exceed the 

ten to twelve year life span common with magnetic ballasts". This supports the use of an 

electronic ballast lifetime at least as long as the magnetic ballast lifetime. 

Based on recent data on early return rates for electronic ballasts, the ballast half-life could be as 

high as a million hours were it not for non-electronic components such as capacitors. Assuming 

that the capacitor is the limiting component with respect to lifetime, an estimate of 15 years half 

6 
life has recently been arrived at for both magnetic and electronic ballasts'. The failure will be a

soft failure in that it will be seen as reduced light output, some flickering and reduced lamp life.

A recent memo to LBNL which discusses ballast failure modest led the Department to believe that

the distribution used in the July, 1997 report should be modified. When the ballast lifetime

distribution is revised, it should be kept in mind that manufacturer warranties for electronic and

magnetic ballasts are 5 and 3 years, respectively. Assuming that an end user will not incur any

cost for a failure during that period, the economic lifetime should have a threshold equal to that

warranty period. If the assumption that few failures are due to electronic components is correct,

then the distribution for electronic ballasts will show low probabilities for ballast lifetimes below

about 12 years. Figure 1 shows the estimated curves of failure rate for electronic and magnetic

ballasts versus time, taken from the memo cited above. The half life was estimated to be 15 years

for both types of ballast.

Replacement R ates of Electronic and M a g netic B allasts

versus Time

1 00 ........................................................................................

40

C

2®

1 3 5 7 9 11 13 15 17 19 21 23 25 27

Years of Use at 3600 Hours Per Year

Figure l

t7aonatlc Ball

Electronic B&

' Memo from Tom Hagen, T&J Enterprises, to Isaac Turiel, LBNL dated December 17,1997.

2 op. Cit.

7

EE-RM-97-500 COMMENT 20D

life has recently been arrived at for both magnetic and electronic ballasts 1. The failure will be a 

soft failure in that it will be seen as reduced light output, some flickering and reduced lamp life. 

A recent memo to LBNL which discusses ballast failure modes2 led the Department to believe that 

the distribution used in the July, 1997 report should be modified. When the ballast lifetime 

distribution is revised, it should be kept in mind that manufacturer warranties for"electronic and 

magnetic ballasts are 5 and 3 years, respectively. Assuming that an end user will not incur any 

cost for a failure during that period, the economic lifetime should have a threshold equal to that 

warranty period. If the assumption that few failures are due to electronic components is correct, 

then the distribution for electronic ballasts will show low probabilities for ballast lifetimes below 

about 12 years. Figure 1 shows the estimated curves of failure rate for electronic and magnetic 

ballasts versus time, taken from the memo cited above. The half life was estimated to be 15 years 

for both types of ballast. 

Replacement Rates of Electronic and Magnetic Ballasts 

versus Time 

••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• ;.;; •••:111. !'III-iiIHII" 

O .. ~~ .. ~~~~~rr~~~~~~ 

1 3 I; 1 9 11 13 15 1119 21 23 25 27 

Y &llIIIi'S of' US&II at 3GDD !-Iou II'S P&IIr Y &liar 

Figure 1 

--+- Magnetic Ball s 

-iii-Electronic Bal a 

1 Memo from Tom Hagen, T&J Enterprises, to Isaac Turiel, LBNL dated December 17, 1997. 

2 Op. Cit. 

7 
Electricity prices

1997 Report

The assumed electricity prices (in 1995$) used for the sensitivity analysis are shown below, in

$/kWh. The average electricity prices come from the Energy Information Administration (EIA)

1997 Annual Energy Outlook. The low and high prices represent extremes that a small

percentage of end-users will experience and were used for the point sensitivity analysis.

Low Average Ii

Commercial 0.055 0.075 0.105

Industrial 0.03 0.048 0.065

For the probability-based sensitivity analysis, we used sales and revenue data from investor owned

and publicly owned electric utilities to obtain electricity price distributions for the commercial and

industrial sectors.

Technical Support Document Input

Average electricity prices will come from EIAs AEO 98. DOE will use distributions of prices

obtained from the most recent individual utility sales and revenue data for the probability based

sensitivity analyses. Extreme values from the distributions will be used for the point sensitivity

analyses.

Rationale

The AEO 98 is an update of the electricity prices. In the LCC analyses, the electricity prices will

change throughout the life of the ballast. We will use the average electricity prices because it is

not clear what the marginal cost is. ;'The marginal costs vary from utility to utility and from facility

to facility. Marginal costs depend upon both kWh energy and kW demand charges. These two

charges make it difficult to accurately approximate the marginal electricity costs. The fixed

charges are generally a small fraction of the overall electricity charge. DOE will also perform

sensitivity analyses to account for the variations in electricity prices.

Discount Rates

1997 Report

In the report, analyses were performed at 4%, 8% and 12% real discount rates.

Technical Support Document Input

DOE will perform analyses at 4%, 8% and 15% real discount rates.

Rationale

One commentor found the range of discount rates appropriate, and recommended that higher

rates not be used. Another stated that "the ROI used in the analysis should start from an absolute

8

EE-RM-97-500 COMMENT 20D

Electricity prices 

1997 Report 

The assumed electricity prices (in 1995$) used for the sensitivity analysis are shown below, in 

$/kWh. The average electricity prices come from the Energy Information Administration (EIA) 

1997 Annual Energy Outlook. The low and high prices represent extremes that a small 

percentage of end-users will experience and were used for the point sensitivity analysis. 

Low Ayerage High 

Commercial 

Industrial 

0.055 

0.03 

0.075 

0.048 

0.l05 

0.065 

For the probability-based sensitivity analysis, we used sales and revenue data from investor owned 

and publicly owned electric utilities to obtain electricity price distributions for the commercial and 

industrial sectors. 

Technical Support Document Input 

Average electricity prices will come from EIAs AEO 98. DOE will use distributions of prices 

obtained from the most recent individual utility sales and revenue data for the probability based 

sensitivity analyses. Extreme values from the distributions will be used for the point sensitivity 

analyses. 

Rationale 

The AEO 98 is an update of the electricity prices. In the LCC analyses, the electricity prices will 

change throughout the life of the ballast. We will use the average electricity prices because it is 

not clear what the marginal cost is.::The marginal costs vary from utility to utility and from facility 

to facility. Marginal costs depend upon both kWh energy and kW demand charges. These two 

charges make it difficult to accurately approximate the marginal electricity costs. The fixed . 

charges are generally a small fraction of the overall electricity charge. DOE will also perform 

sensitivity analyses to account for the variations in electricity prices. 

Discount Rates 

1997 Report 

In the report, analyses were performed at 4%,8% and 12% real discount rates. 

Technical Support Document Input 

DOE will perform analyses at 4%,8% and 15% real discount rates. 

Rationale 

One commentor found the range of discount rates appropriate, and recommended that higher 

rates not be used. Another stated that "the ROI used in the analysis should start from an absolute 

8 
minimum 15% real discount rate, with sensitivity analysis performed above this level and below

this level". A third recommended that life-cycle cost analyses be performed at discount rates of

16% and 28%. The third commentor said that the range of discount rates used in an

Interlaboratory Working Group3 report applied to industrial investments in energy efficiency was

from 12.5 to 20%. The draft report uses an 8% real discount rate with sensitivities at 4% and

12% real.

It should be noted that the Interlaboratory Working Group report used rates of 7% and 15% real

for discount rates in commercial (and residential) buildings. These rates were for "optimistic" and

"pessimistic" cases, respectively . For consistency with the upper band of this report , a 15% upper

level is being adopted. The higher values cited by the third commentor in the Interlaboratory

report were for the industrial sector and were not meant to be applied to commercial building

owners.

Other LCC Inputs

One commentor asked if U-tube lamps were included. U-tube lamps are driven by the same

ballasts as straight-tube lamps therefore we did not do separate LCC analyses for them. U-tube

systems were aggregated with straight-tube systems in the Xenergy system share data for use in

the national energy analysis. The same commentor suggested that F96T8 lamp ballasts be

included. Since F96T8 lamp/ballast systems have small market shares, the Department did not

select them as a product class. These systems were included in "Fluorescent Other" in the

national energy analysis.

One commentor asked that the faster lumen depreciation of T8s be taken into account . T8s have

slower lumen depreciation, not faster . The Lighting I pgrade Manual published by EPA's Green

Lights Program (EPA-430-B-95-009), February 1997 edition, Lighting Maintenance section, page

3, has a graph of lamp lumen depreciations. The four-foot T8 lamps have a slower and flatter

lamp lumen depreciation curve than do the four-foot T12 lamps. The same is true for the eightfoot T12 and T8 lamps. This effect has not been considered . in the LCC analysis, as it does not

impact lamp lifetime or relamping times , and therefore does not affect the result of the analysis.

Variability and Uncertainty

One commentor pointed out the difference between uncertainty and variability and suggested that

the analysis include estimates of uncertainty (e.g. standard deviation) for all inputs and outputs.

For those inputs which show large variability (e.g., electricity prices, or operating hours), we plan

to use the observed distribution of values in the analysis , wherever data permit. For those inputs

which are uncertain, we will estimate the uncertainty where practical. For example, the

3 Scenarios of U.S. Carbon Reductions, Interlaboratory Working Group on Energy -Efficient and Low Carbon

Technologies, LBNL-40533, September, 1997.

9

EE-RM-97-500 COMMENT 20D

minimum 15% real discount rate, with sensitivity analysis performed above this level and below 

this level". A third recommended that life-cycle cost analyses be performed at discount rates of 

16% and 28%. The third commentor said that the range of discount rates used in an 

Interlaboratory Working GrOUp3 report applied to industrial investments in energy efficiency was 

from 12.5 to 20%. The draft report uses an 8% real discount rate with sensitivities at 4% and 

12% real. 

It should be noted that the Interlaboratory Working Group report used rates of 7% and 15% real 

for discount rates in commerCial (and residential) buildings. These rates were for "optimistic" and 

"pessimistic" cases, respectively. For consistency with the upper band of this report, a 15% upper 

level is being adopted. The higher values cited by the third commentor in the Interlaboratory 

report were for the industrial sector and were not meant to be applied to commercial building 

owners. 

Other LCC Inputs 

One commentor asked if U-tube lamps were included. U-tube lamps are driven by the same 

ballasts as straight-tube lamps therefore we did not do separate Lee analyses for them. U-tube 

systems were aggregated with straight-tube systems in the Xenergy system share data for use in 

the national energy analysis. The same commentor suggested that F96T8 lamp ballasts be 

included. Since F96T8 lamplballast systems have small market shares, the Department did not 

select them as a product class. These systems were included in "Fluorescent Other" in the 

national energy analysis. 

One commentor asked that the faster lumen depreciation ofT8s be taken into account. T8s have 

slower lumen depreciation, not faster. The Lighting Upgrade Manual published by EPA's Green 

Lights Program (EPA-430-B-95-009), February 1997 edition, Lighting Maintenance section, page 

3, has a graph oflamp lumen depreciations. The four-foot T8 lamps have a slower and flatter 

lamp lumen depreciation curve than do the four-foot T12 lamps. The same is true for the eightfoot T12 and T8 lamps. This effeCt has not been considered. in the Lee analysis, as it does not 

impact lamp lifetime or relamping times, and therefore does not affect the result of the analysis. 

Variability and Uncertainty 

One commentor pointed out the difference between uncertainty and variability and suggested that 

the analysis include estimates of uncertainty (e.g. standard deviation) for all inputs and outputs. 

For those inputs which show large variability (e.g., electricity prices, or operating hours), we plan 

to use the observed distribution of values in the analysis, wherever data permit. For those inputs 

which are uncertain, we will estimate the uncertainty where practical. For example, the 

3 Scenarios of U.S. Carbon Reductions, Interlaboratory Working Group on Energy-Efficient and Low-Carbon 

Technologies, LBNL-40533, September, 1997. 

9 
distribution of ballast lifetimes in the field has not been measured . In such cases, we consulted

with knowledgeable experts to develop estimates of the uncertainty, and discussed those estimates

with stakeholders in workshops in order to obtain agreement on reasonable assumptions.

It is impossible to quantify all uncertainty analytically -- some things are just not completely

known. In addition, there are practical limits to the number of combinations of distributions

which can be analyzed within reasonable computing resources and time constraints. In order to

focus on the important issues and avoid unreasonable delays due to prolonged analysis , we plan to

conduct importance analysis to determine which variables, even if uncertain, have no significant

influence on the result. For example, previous analysis has shown that the labor rate charged to

change a lamp is unimportant, because this charge comprises such a small fraction of the total

ballast life cycle cost. In such a case , we plan to reduce the amount of attention paid to the

uncertainty in the unimportant variable.

One commentor asked that a +/- range be given for the average annual operating hours. The

averages were from values in the Xenergy survey data We cannot provide a +/- range based on

the uncertainty of the operating hours in the Xenergy survey because Xenergy did not estimate

this. However, we can report the standard deviation of the average based on the variability of the

data. The average annual operating hours for 4-foot lamp ballasts in commercial facilities is 3600

hours/year and the standard deviation is 1500 hours/year. The average for 8-foot commercial

fixtures is also 3600 hours/year , with a standard deviation of 1600 hours/year and the 8-foot

industrial average is 4000 hours/year with a standard deviation of 1500 hours/year. The

commercial average for 8-foot HO is 4000 hours/year with a standard deviation of 1900

hours/year and the industrial 8-foot HO average is 5000 hours year with a standard deviation of

2000 hours/year.

Another commentor pointed out that, according to 1992 CBECS data, buildings representing a

significant fraction of floor space in several building types are reported to have building operating

hours of 60 hours/week (roughly 3000 hours/year) or less, and that "the assumption of 3600

hours as a minimum usage period for the analysis is not accurate". It is true that a significant

fraction of buildings are operated 3000 hours per year or less ; in the 1992 CBECS this fraction

was 69% of buildings, representing 55% of commercial floor space. However, there is also a

significant fraction operating for longer times, including 8% of all buildings in the 1992 CBECS,

representing 15% of commercial floor space , operating continuously (8760 hours/year). When

these values are averaged, the result is 4000 hours/year4. Although the average (not minimum)

value used in our analysis for lighting hours was 3600 hours/year, the commentor correctly notes

4 CBECS data reports the square feet of floor space in each of several operating hour ranges , e.g. 0-39

hours/week, 40-48 hourstweek, etc. For each of these ranges, we assumed that the average for that range is the

midpoint, e.g. that the average operating hours for buildings in the 40-48 hours /week range is 44 hours/week. We

weighted the average by square feet . As noted by the commentor, 1992 CBECS reports building operating hours, not

lighting operating hours. A 1992 EIA publication (Lighting in Commercial Buildings) used 1986 CBECS data to

calculate lighting operating hours. The average in this publication is 4200 hours/year for all lighting , and 4100

hours/year for fluorescent lighting.

10

EE-RM-97-500 COMMENT 20D

distribution of ballast lifetimes in the field has not been measured. In such cases, we consulted 

with knowledgeable experts to develop estimates of the uncertainty, and discussed those estimates 

with stakeholders in workshops in order to obtain agreement on reasonable assumptions. 

It is impossible to quantify all uncertainty analytically -- some things are just not completely 

known. In addition, there are practical limits to the number of combinations of distributions 

which can be analyzed within reasonable computing resources and time constraints. In order to 

focus on the important issues and avoid unreasonable delays due to prolonged analysis, we plan to 

conduct importance analysis to determine which variables, even if uncertain, have no significant 

influence on the result. For example, previous analysis has shown that the labor rate charged to 

change a lamp is unimportant, because this charge comprises such a small fraction of the total 

ballast life cycle cost. In such a case, we plan to reduce the amount of attention paid to the 

uncertainty in the unimportant variable. 

One commentor asked that a +/- range be given for the average annual operating hours. The 

averages were from values in the Xenergy survey data We cannot provide a +/- range based on 

the uncertainty of the operating hours in the Xenergy survey because Xenergy did not estimate 

this. However, we can report the standard deviation of the average based on the variability of the 

data. The average annual operating hours for 4-foot lamp ballasts in commercial facilities is 3600 

hours/year and the standard deviation is 1500 hours/year. The average for 8-foot commercial 

fixtures is also 3600 hours/year, with a standard deviation of 1600 hours/year and the 8-foot 

industrial average is 4000 hours/year with a standard deviation of 1500 hours/year. The 

commercial average for 8-foot HO is 4000 hours/year with a standard deviation of 1900 

hours/year and the industrial 8-foot HO average is 5000 hours year with a standard deviation of 

2000 hours/year. 

Another commentor pointed out that, according to 1992 CBECS data, buildings representing a 

significant fraction of floor space in several building types are reported to have building operating 

hours of 60 hours/week (roughly 3000 hours/year) or less, and that "the assumption of 3600 

hours as a minimum usage period for the analysis is not accurate". It is true that a significant 

fraction of buildings are operated 3000 hours per year or less; in the 1992 CBECS this fraction 

was 69% of buildings, representing 55% of commercial floor space. However, there is also a 

significant fraction operating for longer times, including 8% of all buildings in the 1992 CBECS, 

representing 15% of commercial floor space, operating continuously (8760 hours/year). When 

these values are averaged, the result is 4000 hours/year4

• Although the average (not minimum) 

value used in our analysis for lighting hours was 3600 hours/year, the commentor correctly notes 

4 CBECS data reports the square feet of floor space in each of several operating hour ranges, e.g. 0-39 

hours/week, 40-48 hourslweek, etc. For each of these ranges, we assumed that the average for that range is the 

midpoint, e.g. that the average operating hours for buildings in the 40-48 hours/week range is 44 hours/week. We 

weighted the average by square feet. As noted by the commentor, 1992 CBECS reports building operating hours, not 

lighting operating hours. A 1992 EIA publication (Lighting in Commercial Buildings) used 1986 CBECS data to 

calculate lighting operating hours. The average in this publication is 4200 hours/year for all lighting, and 4100 

hours/year for fluorescent lighting. 

10 
that for those end-users whose operating hours are less, the benefits of switching to more efficient

ballasts are also less. This is taken into account in the variability analysis, which examines the

LCC cost of ballast efficiency measures across the distribution of operating hours found in the

Xenergy data. For example, in the variability analysis we found that for 4-foot lamp ballasts,

replacing an energy efficient magnetic (EEM) ballast with a cathode. cutout (CC) ballast would

result in a net savings for 98% of all ballasts (those with operating hours greater than 900

hours/year), and replacing an EEM with an electronic ballast would result in savings for 97% of

ballasts (operating at 1100 hours/year or more).

One commentor asked if rooms darkened during occupancy were taken into account. We derived

the average value for lighting operating hours from survey data from energy audits performed by

Xenergy. These data do take into account rooms darkened during occupancy. One possible

explanation for our average lighting operating hours value being lower than the CBECS building

operating hours average is because our average does take into account rooms darkened during

occupancy, while the CBECS data do not.

Another commentor indicated that the variability analysis, which considered the full distribution of

U.S. commercial electricity prices, was an improvement over the prev ious practice of just using a

point estimate. However, some commenters still expressed concern that ballast efficiency

measures might adversely impact end-users whose electricity prices and/or operating hours are

low. It was also suggested that the distribution of U.S. commercial electricity prices and

commercial operating hours would not be representative of end-users affected by efficiency

measures, because end-users whose electricity prices or operating hours are high are more likely

to already be using higher efficiency ballasts, therefore those affected by new ballast efficiency

measures would disproportionately be those end-users with lower than average electricity prices

or operating hours.

To examine this possibility with respect to electricity price, electricity price data from participants

in a major voluntary lighting efficiency program, EPA's Green Lights program, were compared to

the distribution of U. S. commercial electricity rates for all consumers. As shown in Figure 2, the

distribution of electricity prices paid by participants in this voluntary efficiency program was not

found to be significantly different from the distribution of electricity rates for all commercial

electricity sales. The average U.S. commercial electricity rate is $0.078/kWh, and the average

rate paid by Green Lights customers is $0.079/k .5

5 The price distribution represents electricity prices paid by Green Lights project participants who installed

electronic ballasts that operate 4ft, 8ft, or gftHO ballasts. This represents approximately 10 million of the roughly 14

million ballasts installed. Because projects did not directly report electricity price, it was calculated from the reported

dollars saved divided by kWh saved by the project. Calculated electricity prices below $0.01/kWh or above $0.25/kWh

were assumed to result from reporting errors and were excluded. Only 2% of ballasts were installed in projects whose

calculated electricity prices fell outside of these bounds.

11

EE-RM-97-500 COMMENT 20D

that for those end-users whose operating hours are less, the benefits of switching to more efficient 

ballasts are also less. This is taken into account in the variability analysis, which examines the 

LCC cost of ballast efficiency measures across the distribution of operating hours found in the 

Xenergy data For example, in the variability analysis we found that for 4-foot lamp ballasts, 

replacing an energy efficient magnetic (EEM) ballast with a cathode. cutout (CC) ballast would 

result in a net savings for 98% of all ballasts (those with operating hours greater than 900 

hours/year), and replacing an EEM with an electronic ballast would result in savings for 97% of 

ballasts (operating at 1100 hours/year or more). 

One commentor asked if rooms darkened during occupancy were taken into account. We derived 

the average value for lighting operating hours from survey data from energy audits performed by 

Xenergy. These data do take into account rooms darkened during occupancy. One possible 

explanation for our average lighting operating hours value being lower than the CBECS building 

operating hours average is because our average does take into account rooms darkened during 

occupancy, while the CBECS data do not. 

Anothercommentor indicated that the variability analysis, which considered the full distribution of 

U.S. commercial electricity prices, was an improvement over the previous practice of just using a 

point estimate. However, some commentors still expressed concern that ballast efficiency . 

measures might adversely impact end-users whose electricity prices and/or operating hours are 

low. It was also suggested that the distribution of U.S. commercial electricity prices and 

commercial operating hours would not be representative of end-users affected by efficiency 

measures, because end-users whose electricity prices or operating hours are high are more likely 

to already be using higher efficiency ballasts, therefore those affected by new ballast efficiency 

measures would disproportionately be those end-users with lower than average electricity prices 

or operating hours. 

To examine this possibility with respect to electricity price, electricity price data from participants 

in a major voluntary lighting efficiency program, EPA's Green Lights program, were compared to 

the distribution of U. S. commercial electricity rates for all consumers. As shown in Figure 2, the 

distribution of electricity prices paid by participants in this voluntary efficiency program was not 

found to be significantly different from the distribution of electricity rates for all commercial 

electricity sales. The average U.S. commercial electricity rate is $0.078lkWh, and the average 

rate paid by Green Lights customers is $0.079/kWh.5 

5 The price distribution represents electricity prices paid by Green Lights project participants who installed 

electronic ballasts that operate 4ft, 8ft, or 8ftHO ballasts. This represents approximately 10 million of the roughly 14 

million ballasts installed. Because projects did not directly report electricity price, it was calculated from the reported 

dollars saved divided by kWh saved by the project Calculated electricity prices below $O.OlIkWh or above $O.25IkWh 

were assumed to result from reporting errors and were excluded. Only 2% of ballasts were installed in projects whose 

calculated electricity prices fell outside of these bounds. 

11 
Distribution of Electricity Prices for Green Lights Ballasts

vs. U.S. Commercial Electricity Sales

0.25

0.24

Comrnercial Electricity

....... Green Lights

L. 0.1 4

0.05+

O O O O O - -. r - N N N

C O O O O O O O O O O O O

®ectricity Price

Figure 2

It is more difficult to address the question of whether or not the distribution of lighting hours of

consumers who select more efficient ballasts is shifted to higher values because it is more difficult

to find a national distribution of lighting operating hours data for comparison. For example, the

sample of buildings used in the CBECS survey is selected to be representative; however, as noted

above, CBECS generally reports building operating hours, not lighting operating hours.

Furthermore, the most recent lighting hours data based on CBECS used 1986 survey data. As

another source, the distribution of lighting operating hours in the Xenergy data is compared to

that in the Green Lights data (as shown for 4-foot lamp ballasts in commercial buildings in Figure

3.) For 4-ft commercial ballasts, the Green Lights data is offset towards higher operating hours

compared to the Xenergy data, and has a much larger peak at 8760 hours/year. The average for

Xenergy data is 3600 hours/year vs. 4300 hours/year for the Green Lights data (rounded to the

nearest 100 hours/year).

12

EE-RM-97-500 COMMENT 20D

0.25 

0.2 

c 0.15 

0 

13 

I! 

II. 0.1 

0.05 

-- 0 -

..- C"') 

0 0 

0 0 

Distribution of Electricity Prices for Green Lights Ballasts 

vs. U.S. Commercial Electricity Sales 

It) t; m 0 0 

0 0 0 

.... C"') It) I"-

ci ci d .... 

o 

Electricity Price 

Figure 2 

..-

N 

o 

--Commercial Electricity 

- - - - - - . Green Ughts 

It is more difficult to address the question of whether or not the distribution of lighting hours of 

consumers who select more efficient ballasts is shifted to higher values because it is more difficult 

to fmd a national distribution of lighting operating hours data for comparison. For example, the 

sample of buildings used in the CBECS survey is selected to be representative; however, as noted 

above, CBECS generally reports building operating hours, not lighting operating hours. 

Furthermore, the most recent lighting hours data based on CBECS used 1986 survey data. As 

another source, the distribution of lighting operating hours in the Xenergy data is compared to 

that in the Green Lights data (as shown for 4-foot lamp ballasts in commercial buildings in Figure 

3.) For 4-ft commercial ballasts, the Green Lights data is offset towards higher operating hours 

compared to the Xenergy data, and has a much larger peak at 8760 hours/year. The average for 

Xenergy data is 3600 hours/year vs. 4300 hours/year for the Green Lights data (rounded to the 

nearest 100 hours/year). 

12 
Operating Hours Distribution, Green Lights vs. Xenergy

4FT Commercial

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0.00

N M V U) U) C9 r O

Annual Operating Hours

Xenergy

....... Green Lights

Figure 3

Both sets of lighting hours have uncertainties associated with them in that lighting hours are

estimated, not measured. For the Green Lights data, in some cases, more than 100 entries

(facilities) for a single company have identical (and rounded to the nearest 1000 hours) lighting

hours at locations across the country. That type of data entry raises doubts about the accuracy of

the annual lighting hours reported in the Green Lights data set. Therefore, it is difficult to

conclude whether the difference in average lighting hours between the Xenergy and Green Lights

data sets is proof that purchasersof electronic ballasts are biased towards longer lighting hours.

DOE has decided to retain the same average annual lighting hours and lighting hours distributions

used in the. July, 1997 report.

NATIONAL ENERGY IMPACTS

In the July 1997 Report,LENTL used Commend to project ballast sales and National Energy

Impacts. Commend will be used in the final report as well as a spreadsheet analysis to project

future ballast sales and National Energy Impacts. The spreadsheet uses a simplified approach to

estimating energy savings and economic parameters. The shipments forecast is an input to the

spreadsheet analysis but not to the COMMEND model.

Conversion Rate Scenarios

Several comments suggested that the current conversion rates band is too wide (Figure 4 shows

the rates used in the zero, constant, and high conversion rate scenarios. After 2000, rates in each

13

EE-RM-97-500 COMMENT 20D

Operating Hours Distribution, Green Lights "So Xenergy 

4FT Commercial 

0.18 

0.16 

0.14 

0.12 c 

0 0.10 tl 

t! 0.08 !!.. 

0.06 

0.04 

0.02 

0.00 0 (1; en 

CD $ 

.,.. 

" '. · . · . · , 

N 

gffi~810~ ,., ""'" 10 

Annual Operating Hours 

Figure 3 

· · · 

~ ~ ('I) I"- ~ 

. 

. 

--Xenergy 

••••••• Green Ughts 

Both sets of lighting hours have uncertainties associated with them in that lighting hours are 

estimated, not measured. For the Green Lights data, in some cases, more than 100 entries 

(facilities) for a single company have identical (and rounded to the nearest 1000 hours) lighting 

hours at locations across the country. That type of data entry raises doubts about the accuracy of 

the annual lighting hours reported in the Green Lights data set. Therefore, it is difficult to 

conclude whether the difference in average lighting hours between the Xenergy and Green Lights 

data sets is proof that purchasers of electronic ballasts are biased towards longer lighting hours. 

DOE has decided to retain the same average annual lighting hours and lighting hours distributions 

used in the July, 1997 report. 

NATIONAL ENERGY IMPACTS 

In the July 1997 Report, LBNL used Commend to project ballast sales and National Energy 

'impacts. Commend will be used in the final report as well as a spreadsheet analysis to project 

future ballast sales and National Energy Impacts. The spreadsheet uses a simplified approach to 

estimating energy savings and economic parameters. The shipments forecast is an input to the 

spreadsheet analysis but not to the COMMEND model. 

Conversion Rate Scenarios 

Several comments suggested that the current conversion rates band is too wide (Figure 4 shows 

the rates used in the zero, constant, and high conversion rate scenarios. After 2000, rates in each 

13 
scenario were held constant). It should be noted that the constant conversion rate was calibrated

against 1995 ballast sales data provided by NEMA. However, this represents the conversion rate

for the years 1992 to 1995 when there was high utility ISM rebate activity. The zero- and highconversion rates were chosen to bracket likely values. In other words, energy savings from

possible standards would fall within such a band. In light of comments, it is possible to further

narrow the band on conversion rates, when the next COMMEND analysis is performed.

Conversion Rate Scenarios

0) G) rn 0) 0) rn rn

Figure 4

8

-f--zero

conversion

constant

conversion

^- high

conversion

Preliminary results from an analysis of impacts of non-regulatory programs have just become

available, and will be incorporated in determining future conversion rates. However, there is no

easy and explicit relationship between voluntary programs and conversion rates. For example,

DSM programs may keep diminishing in the future, while conversion activities could continue,

though probably at a different level than before.

Projections of Ballast Sales

One commentor stated that the prediction that ballast sales will decline in the 1995-2005 period is

unlikely to be correct. They pointed out that if increased sales of 3- and 4-lamp electronic ballasts

replacing twice as many magnetic ballasts were going to cause a dip in sales, that such a dip

would have already occurred in the past seven years.

According to NEMA figures, sales of covered ballasts peaked in 1995, and declined by 6.5% in

1996. Thus, the predicted dip in sales may have already begun. The rise and then drop in ballast

sales can be attributed to two different factors. First, when the conversion from magnetic to

electronic ballasts occurs, it usually results in the early retirement of the magnetic ballasts (such as

in a retrofit project). Such retrofit activities tend to generate additional sales and thus contribute

to rising ballast sales. As the magnetic ballast stock is depleted through retrofit, the conversion

sales taper off, contributing to fewer sales in the future. Secondly, sales of electronic ballasts only

became significant in 1991, according to Bureau of Census figures. The downward effect on

14

EE-RM-97-500 COMMENT 20D

scenario were held constant). It should be noted that the constant conversion rate was calibrated 

against 1995 ballast sales data provided by NEMA. However, this represents the conversion rate 

for the years 1992 to 1995 when there was high utility DSM rebate activity. The zero- and highconversion rates were chosen to bracket likely values. In other words, energy savings from 

possible standards would fall within such a band. In light of comments, it is possible to further 

narrow the band on conversion rates, when the next COMMEND analysis is performed. 

20% 

15% 

10% 

5% 

0% (t) "'<t (J) (J) 

(J) (J) 

...... .... 

Conversion Rate Scenarios 

~ <0 

(J) 

(J) (J) 

"- co (J) (J) ~ (J) (J) (J) 

.... .... .... 

Figure 4 

8 

0 

N 

-+--zero 

conversion 

-m-- constant 

conversion 

-.-high 

conversion 

Preliminary results from an analysis of impacts of non-regulatory programs have just become 

available, and will be incorporated in determining future conversion rates. However, there is no 

easy and explicit relationship between voluntary programs and conversion rates. For example, 

DSM programs may keep diminishing in the future, while conversion activities could continue, 

though probably at a different level than before. 

Projections of Ballast Sales 

One commentor stated that the prediction that ballast sales will decline in the 1995-2005 period is 

unlikely to be correct. They pointed out that if increased sales of 3- and 4-lamp electronic ballasts 

replacing twice as many magnetic ballasts were going to cause a dip in sales, that such a dip 

would have already occurred in the past seven years. 

According to NEMA figures, sales of covered ballasts peaked in 1995, and declined by 6.5% in 

1996. Thus, the predicted dip in sales may have already begun. The rise and then drop in ballast 

sales can be attributed to two different factors. First, when the conversion from magnetic to 

electronic ballasts occurs, it usually results in the early retirement of the magnetic ballasts (such as 

in a retrofit project). Such retrofit activities tend to generate additional sales and thus contribute 

to rising ballast .sales. As the magnetic ballast stock is depleted through retrofit, the conversion 

sales taper off, contributing to fewer sales in the future. Secondly, sales of electronic ballasts only 

became significant in 1991, according to Bureau of Census figures. The downward effect on 

14 
ballast sales of one electronic ballast replacing two magnetic ballasts is gradually being felt, after

being balanced by the rising sales due to conversions in the earlier years.

Electricity Price Difference

One commentor noted that the electricity prices, used in the COMMEND analysis appear to be

different than those used in the LCC. The values used in the LCC analysis are for the year 2001.

The COMMEND analysis uses energy prices projected for the years 1992 - 2030, as shown in

Table 5.4 of the report. The prices to which the commenter refers, noted on page 5-3, are, for the

year 1992.

Lighting/HVAC Interactions

ACEEE noted that many published studies indicate that, on average in the U.S., additional energy

is saved due to lighting/HVAC interactions when lighting energy is reduced. ACEEE was

concerned that LBNL's analysis did not find large secondary savings due to the interactions.

To address this issue we reviewed some of the publications ACEEE mentioned in their review.

We compared our results with the material in E-Source6 and Rundquist et al.' We could not

locate the Eley Memo'. We can demonstrate that our findings are not that different than their

results.

The DOE-2 building model was used to calculate energy load changes due to installation of more

efficient lighting for each of the building types listed in Table 1, in several different climate zones.

CBECS data was used to determine the square footage of each building type found in each

climate zone. In the ballast analysis we then used C OMMEND to calculate changes in primary

energy consumption due to these changes in load. However, in response to requests for more

transparency, we have redone the calculation in a spreadsheet (which is available on request).

The load information was combined with the following additional information to calculate the

change in HVAC energy use due to a unit reduction in lighting energy : ( 1) market shares for the

different equipment types (e.g. oil-fired boiler, gas furnace, heat pump) by building type were

combined with (2) heating and cooling coincidence factors by building type; (3) equipment

efficiencies; and (4) distribution system losses.

We know that in hot climates, the use of more efficient lighting with smaller heat losses generates

large savings due to reduction in cooling energy use. However, in cold climates, the additional

energy needed for heating generally outweighs the cooling savings.

6

7

E-Source, 1994, Lrgntmg Technology Atlas, pp. 47-52.

Rundquist, R. et al., "Calculating Lighting and HVAC Interactions," ASHRAE Journal, Nov. 1993. 8 Memo to ASHRAE SPCC 90.1 it " Impact of Internal Gain on Cooling and Heating Energy."

5

EE-RM-97-500 COMMENT 20D

ballast sales of one electronic ballaSt replacing two magnetic ballasts is gradually being felt, after 

being balanced by the rising sales due to conversions in the earlier years. 

Electricity Price Difforence 

One commentor noted that the electricity prices used in the COMMEND analysis appear to be 

different than those used in the LCC. The values used in the LCC analysis are for the year 200l. 

The COMMEND analysis uses energy prices projected for the years 1992 - 2030, as shown in 

Table 5.4 of the report. The prices to whiGh the commentor refers, noted on page 5-3, arefor the 

year 1992. 

LightinglHVAC Interactions 

ACEEE noted that many published studies indicate that, on average in the U.S., additional energy 

is saved due to lighting/HV AC interactions when lighting energy is reduced. ACEEE was 

concerned that LBNL's analysis did not find large secondary savings due to the interactions. 

To address this issue we reviewed some of the publications ACEEE mentioned in their review. 

We compared our results with the material in E-Source6 and Rundquist et al. 7 We could not 

locate the Eley MemoS. We can demonstrate that our fmdings are not that different than their 

results. 

The DOE-2 building model was used to calculate energy load changes due to installation of more 

efficient lighting for each of the building types listed in Table 1, in several different climate zones. 

CBECS data was used to determine the square footage of each building type found in each 

climate zone. In the ballast analysis we then used COMMEND to calculate changes in primary 

energy consumption due to these changes in load. However, in response to requests for more 

transparency, we have redone the calculation in a spreadsheet (which is available on request). 

The load information was combined with the following additional information to calculate the 

change in HV AC energy use due to a unit reduction in lighting energy: (1) market shares for the 

different equipment types (e.g. oil-fired boiler, gas furnace, heat pump) by building type were 

combined with (2) heating and cooling coincidence factors by building type; (3) equipment 

efficiencies; and (4) distribution system losses. 

We know that in hot climates, the use of more efficient lighting with smaller heat losses generates 

large savings due to reduction in cooling energy use. However, in cold climates, the additional 

energy needed for heating generally outweighs the cooling savings. 

6 E-Source, 1994, Lighting Technology Atlas, pp. 47-52. 

7 Rundquist, R. et al., "Calculating Lighting and HV AC Interactions," ASHRAE Journal, Nov. 1993. 

8 Memo to ASHRAE SPeC 90.1 re "Impact of Intemal Gain on Cooling and Heating Energy." 

15 
The results in Table 2 show averages for each building type across several climate zones,

weighted by floor space of that building type found in each climate zone. It can be seen that

substantial net secondary savings can be attained in large offices, large hotels, and hospitals.

These building types constitute 23 percent of the floor area of the building types for which

calculations were performed. Schools, on the other hand, constitute close to 20 percent of the

same commercial floor area and lighting efficiency improvements generate large heating penalties

in this building type. This is because there is not much cooling and a large heating coincidence

between lighting and heating. The other building types show small net increases in energy

consumption due to installation of more efficient lighting.

When averaged, the net effect of lighting/HVAC interactions for the U.S. building stock covered

in our studies is very small (approximately zero). It should be noted that this calculation covers

about 76 percent of the commercial building area. The other 24 percent include assembly

buildings, parking garages, public order and safety buildings, building types that are not covered

by the conventional categories, and vacant buildings. We assumed that the average

lighting/HVAC interactions for these building types would be approximately equal to that found

in the 76 percent of buildings for which calculations were performed.'

E-source indicates that the bonus savings presented in their document are representative of typical

large office buildings. They present two sets of calculations. The first set is based on a previous

LBNL publication10 which we use as the basis of the more recent LBNL study". The second set is

based on Rundquist et al. (1993)2. For large offices, E-source indicates that cooling site energy

savings are in the range of 28 - 36 percent. As shown in Table 2, we calculate a 28% reduction in

cooling energy in large office buildings due to the installation of efficient lighting.

The numbers for heating penalties are not directly comparable because we present results in terms

of primary energy, while E-Source results are expressed as site energy. Nevertheless, E-source

indicates that the heating site energy penalty is 0 to 21 percent. We show an average heating

penalty of 16 percent for large office buildings in the U. S. in terms of primary energy.

It should be noted that the Rundquist study uses two generic commercial building types (small

and large). In the LBNL studies, 36 prototypical building models are simulated with distinct

prototypes for the types listed in Table 2.

9 The reason that the calculations were not performed for the other 24% of buildings is that there were not

enough buildings of those types in enough different climate zones in the CBECS data set. Although these building types

represent 24% of floor area, they are likely to represent a smaller percentage of building energy consumption.

10 Sezgen, A.O., and Y.J. Huang, 1994, " Lighting/HVAC interactions and their effects on energy

consumption and peak HVAC requirements in commercial buildings," ACEEE 1994 Summer Study on Energy

Efficiency) in Buildings.

1 Sezgen, 0., and J.G. Koomey, 1997, Interactions Between Lighting and Space Conditioning Energy Use in

U.S. Commercial Buildings. LBNL-39795, Lawrence Berkeley National Laboratory, Berkeley, California. (Review

Draft.)

16

EE-RM-97-500 COMMENT 20D

The results in Table 2 show averages for each building type across several climate zones, 

weighted by floor space of that building type found in each climate zone. It can be seen that 

substantial net secondary savings can be attained in large offices, large hotels, and hospitals. 

These building types constitute 23 percent of the floor area of the building types for which 

calculations were performed. Schools, on the other hand, constitute close to 20 percent of the 

same commercial floor area and lighting efficiency improvements generate large heating penalties 

in this building type. This is because there is not much cooling and a large heating coincidence 

between lighting and heating. The other building types show small net increases in energy 

consumption due to installation of more efficient lighting. 

When averaged, the net effect oflighting/HV AC·interactions for the U.S. building stock covered 

in our studies is very small (approximately zero). It should be noted that this calculation covers 

about 76 percent of the commercial building area. The other 24 percent include assembly 

buildings, parking garages, public order and safety buildings, building types that are not covered 

by the conventional categories, and vacant buildings. We assumed that the average 

lighting/HV AC interactions for these building types would be approximately equal to that found 

in the 76 percent of buildings for which calculations were performed.9 

E-source indicates that the bonus savings presented in their document are representative of typical 

large office buildings. They present two sets of calculations. The first set is based on a previous 

LBNL publication1o which we use as the basis of the more recent LBNL studyll. The second set is 

based on Rundquist et al. (1993f For large offices, E-source indicates that cooling site energy 

savings are in the range of 28 - 36 percent. As shown in Table 2, we calculate a 28% reduction in 

cooling energy in large office buildings due to the installation of efficient lighting. 

The numbers for heating penalties are not directly comparable because we present results in terms 

of primary energy, while E-Source results are expressed as site energy. Nevertheless, E-source 

indicates that the heating site energy penalty is 0 to 21 percent. We show an average heating 

penalty of 16 percent for large office buildings in the U.S. in terms of primary energy. 

It should be noted that the Rundquist study uses two generic commercial building types (small 

and large). In the LBNL studies, 36 prototypical building models are simulated with distinct 

prototypes for the types listed in Table 2. 

9 The reason that the .calculations were not perfonned for the other 24% of buildings is that there were not 

enough buildings of those types in enough different climate zones in the CBECS data set. Although these building types 

represent 24% of floor area, they are likely to represent a smaller percentage of building energy consumption. 

10 Sezgen, AO., and Y.l Huang, 1994, "LightinglHVAC interactions and their effects on energy 

consumption and peak HV AC requirements in commercial buildings," ACEEE 1994 Summer Study on Energy 

Efficienc\, in Buildings. 

1 Sezgen, 0., and lG. Koomey, 1997, Intemctions Between Lighting and Space Conditioning Energy Use in 

U.S. Commercial Buildings. LBNL-39795, Lawrence Berkeley National Labomtory, Berkeley, California. (Review 

Draft.) 

16 
Table 2. LightinglHVAC Interactions for the U.S. Building Stock

(Change in HVAC rime ene per one unit reduction in li^hting primary ene

Small

Office

Large

Office

Small

Retail

Large

Retail

Small

Hotel

Large

Hotel

Hospital Grocery School Restaurant Warehouse All

Buildings (2)

Floor Area Share

(within covered area)

7% 17% 14% 1 12% 2% 3% 3% 2% 17% 2% 19% 100%

Change in Heating

(kWh/kWh Li tiWXIJ

23% 16% 21% 1 30% 22% 17% 13% 36% 1 31% 28% 6% 20%

Change in Cooling

(kWh/kWh Lighting)(1

-23% -30% -14% -27% -21% -27% -33% -34% -18% -24% -5% -20%

Change in HVAC

(kWh/kWh LightiW

0% -14% 6% 3% 1% -11% -20% 2% 13% 4% 1% 0%

(1) all in primary energy terms: 1 kWh lighting electricity corresponds to 3.2 kWh primary energy.

(2) These numbers account for about 76% of the U. S . commercial floor area. The building types that are not accounted for are: U

assembly buildings (11% of commercial floor area), parking garage (2% of commercial area),

buildings related to public order and safety (1% of commercial area), building types

that are not covered by the conventional categories (3% of commercial area), and

vacant buildings (7% of commercial floor area).

17

EE-RM-97-500 COMMENT 20D

Table 2. LightinglHVAC Interactions for the U.S. Building Stock 

(Change in HV AC primary energy per one unit reduction in Ii, hUng prima~ enef2Y) 

Small Large Small Large Small Large ita! Gr I Restaurant 

Office Office Retail Retail Hotel Hotel 

Floor Area Share 7% 17% 14% 12% 2% 3% 3% 2% 17% 2% 

(within covered area) 

Change in Heating 23% 16% 21% 30% 22% 17% 13% 36% 31% 28% 

(kWhlkWh Lighting)(l) 

Change in Cooling -23% -30% -14% -27% -21% -27% -33% -34% -18% -24% 

(kWhlkWh LightingXI) 

Change in HVAC 0% -14% 6% 3% 1% -11% -20% 2% 13% 4% 

(kWhlkWh Lighting)(l) 

(1) all in primary energy terms: 1 kWh lighting electricity corresponds to 3.2 kWh primary energy. 

(2) These nwnbers account for abol,lt 76% of the U.S. commercial floor area. The building types that are not accounted for are: U 

assembly buildings (i 1 % of commercial floor area), parking garage (2% of commercial area), 

buildings related to public order and safety (1 % of commercial area), building types 

that are not covered by the conventional categories (3% of commercial area), and 

vacant buildings (7% of commercial floor area). 

17 

Warehouse All I~ • 

1<)010 100% 

6% 20% 

-5% -20% 

1% 0% 
Other COMMEND issues

One commentor recommended that commercial floor stock projections be updated with 1995

CBECS data. Building types used in COMMEND are similar to, but not exactly the same as,

those used in CBECS survey. Regional Economic Research (RER), under contract to EPRI to

support and upgrade COMMEND, conducts additional calibrations to update the COMMEND

floor stock file, based on CBECS results. RER has not yet developed an updated file with 1995

CBECS results. However, given that the floor space growth rate is on the order of 1% per year,

the results of the national energy analysis are unlikely to be significantly affected even if new floor

stock data are incorporated. Additionally, using 1992 as the base year of the forecast provides

valuable opportunities for LBNL to examine forecast results against empirical data for 1993

1995 to verify the accuracy of the forecasting model.

One commentor questioned some of the values used for system shares in new construction. They

found the shares of electronic ballasts to be too low, CFLs too low, and mercury vapor too high

(they felt the latter should be zero). New construction shares were estimated from Lighting

Research Institute surveys of ballast manufacturers, OEMs, LMCs, and lamp manufacturers

conducted in 1995, as well as review of Bureau of Census data. The figures shown in Appendix

B, Tables B.9 through B. 11, represent estimated shares for the year 1993. LBNL also estimated

system shares for the year 2000 based on the same surveys, and these shares were used in the

national energy analysis projections. For the year 2000, electronic ballasts in new buildings were

estimated to account for 88% of all 4-foot ballasts, 59% of 8-foot ballasts and 59% of 8-foot HO

ballasts in new construction (vs. 45%, 16%, and 25% respectively in 1993). For CFL shares, very

few data are available; the shares in 1993 commercial and industrial buildings were estimated from

Bureau of Census lamp data. The CFL shares have minimal effect on the national energy analysis

results. Table B.4 contains errors for the warehouse shares; MV shares are actually zero, as for

all other building types. These shares have minimal impact on the national energy analysis results

for fluorescent ballast impacts.

Some commentors found that the COMMEND model is not adequately described. Additional

documentation can be obtained from EPRI's contractor, RER, on the COMMEND model

structure and its application. We would also be happy to forward this documentation to the

commentor on request.

Non-Regulatory Programs

Commentors suggested that the impacts of voluntary efficiency programs should be more

adequately taken into account. One commentor observed that although the dollar amount spent

on DSM programs has declined in recent years, the numbers of ballasts installed due to DSM

programs may still have remained the same or even increased, since the price differential between

magnetic and electronic ballasts has gone down. As explained below, our preliminary analysis

suggests that this is not the case, and that the number of electronic ballasts installed due to utility

18

EE-RM-97-500 COMMENT 20D

Other COMMEND issues 

One commentor recommended that commercial floor stock projections be updated with 1995 

CBECS data. Building types used in COMMEND are similar to, but not exactly the same as, 

those used in CBECS survey. Regional Economic Research (RER), under contract to EPRI to 

support and upgrade COMMEND, conducts additional calibrations to update the COMMEND 

floor stock file, based on CBECS results. RER has not yet developed an updated file with 1995 

CBECS results. However, given that the floor space growth rate is on the order of 1 % per year, 

the results of the national energy analysis are unlikely to be significantly affected even if new floor 

stock data are incorporated. Additionally, using 1992 as the base year of the forecast provides 

valuable opportunities for LBNL to examine forecast results against empirical data for 1993 -

1995 to verify the accuracy of the forecasting model. 

One commentor questioned some of the values used for system shares in new construction. They 

found the shares of electronic ballasts to be too low, CFLs too low, and mercury vapor too high 

(they felt the latter should be zero). New construction shares were estimated from Lighting 

Research Institute surveys of ballast manufacturers, OEMs, LMCs, and lamp manufacturers 

conducted in 1995, as well as review of Bureau of Census data. The figures shown in Appendix 

B, Tables B.9 through B.ll, represent estimated shares for the year 1993. LBNL also estimated 

system shares for the year 2000 based on the same surveys, and these shares were used in the 

national energy analysis projections. For the year 2000, electronic ballasts in new buildings were 

estimated to account for 88% of all 4-foot ballasts, 59% of 8-foot ballasts and 59% of 8-foot HO 

ballasts in new construction (vs. 45%, 16%, and 25% respectively in 1993). For CFL shares, very 

few data are available; the·shares in 1993 commercial and industrial buildings were estimated from 

Bureau of Census lamp data. The CFL shares have minimal effect on the national energy analysis 

results. Table B.4 contains errors for the warehouse shares; MV shares are actually zero, as for 

all other building types. These shares have minimal impact on the national energy analysis results 

for fluorescent ballast impacts. 

Some commentors found that the COMMEND model is not adequately described. Additional 

documentation can be obtained from EPRI's contractor, RER, on the COMMEND model 

structure and its application. We would also be happy to forward this documentation to the 

commentor on request. 

Non-Regulatory Programs 

Commentors suggested that the impacts of voluntary efficiency programs should be more 

adequately taken into account. One commentor observed that although the dollar amount spent 

on DSM programs has declined in recent years, the numbers of ballasts installed due to DSM 

programs may still have remained the same or even increased, since the price differential between 

magnetic and electronic ballasts has gone down. As explained below, our preliminary analysis 

suggests that this is not the case, and that the number of electronic ballasts installed due to utility 

18 
DSM programs has indeed declined in recent years.

Since the release of the draft ballast analysis , LBNL has undertaken a more detailed analysis of

non-regulatory programs, studying utility DSM program impacts and ASH /IES building

code impacts as well as those of EPA and FEMP programs. Here we summarize the results of

our preliminary analysis of DSM progr ams. We have collected data from 9 utilities12 to study

their programs offering rebates on electronic fluorescent ballasts. The data were complete for 6

of the utilities but not complete for the other 3. The six utilities included in the complete data set

represent about 15% to 20% of national energy efficiency spending , a major component of ISM

spending, for the years 1990 to 199713. The nine utilities account for 25-35% of national

expenditures.

We took the following steps to estimate the number of ballasts nationwide to which a utility

rebate applied. First, for each year we divided the total number of dollars the utilities in our

sample spent on ballast rebates by the total number of ballasts to which these rebates applied, to

get the average dollar spent per ballast in our sample. For example, in 1994 the six utilities in our

sample spent a total of about $36 million on about 3.5 million ballasts, for an average expenditure

of approximately $10 per ballast.

Next we calculated the fraction of energy efficiency spending that was used for ballast rebates by

the 6 utilities in our sample. In 1994 these utilities spent a total of about $280 million on all

energy efficiency programs14, so ballast rebates represented 13% ($36 million/ $280 million) of

the total.

We then estimated the amount spent by all utilities nationwide on ballast rebates by assuming that

the fraction of energy efficiency spending used for ballast rebates nationwide was equal to the

fraction used by utilities in our sample (13% in 1994). Again using 1994 as an example, utilities

spent about $1.6 billion in that year on all energy efficiency programs. We therefore estimated

12 Eight of the utilities included in the sample are: Central Maine Power Co., Long Island Lighting Co.,

Pacific Gas & Electric, Rochester Gas & Electric, Southern California Edison, Consolidated Edison, Baltimore Gas and

Electric and San Diego Gas & Electric. The ninth utility wished to remain anonymous. Data was requested from 32

utilities, including those with the 15 largest DSM programs, but many were unable or unwilling to provide information

on electronic ballast rebate measures.

13 1997 estimates were based on projections using at least 6 months of data because data for the full year were

not yet available. Also, the 1997 estimates were based on only four utilities, as two utilities were not able to provide

data for the year; one of these was unable to provide information for 1996 as well, These smaller samples reduce

confidence in estimates for those years.

14 Data on utility spending on energy efficiency programs for the individual utilities in the sample and for the

nation as a whole were taken from Table 22 and Table 23 in the DOE-ETA's annual reports for 1993, 1994, 1995 and

1996 in "U.S. Electric Utility Demand-Side Management " (DOE/EIA-0589 (93), DOE/EIA-0589 (94), DOE/EIA0589 (95), DOEJEIA-0589 (96)) These annual reports are based on information provided in Form EIA-861, "Annual

Electric Utility Report." Data for 1990, 1991, 1992 were based on this same form, but were provided directly by the

EIA as annual reports were not published for those years. Utilities themselves provided specific data on electronic

ballast rebate measures.

19

EE-RM-97-500 COMMENT 20D

DSM programs has indeed declined in recent years. 

Since the release of the draft ballast analysis, LBNL has undertaken a more detailed analysis of 

non-regulatory programs, studying utility DSM program impacts and ASHRAEIIES building 

code impacts as well as those of EP A and FEMP programs. Here we summarize the results of 

our preliminary analysis ofDSM programs. We have collected data from 9 utilities 12 to study 

their programs offering rebates on electronic fluorescent ballasts. The data were complete for 6 

of the utilities but not complete for the other 3. The six utilities included in the complete data set 

represent about 15% to 20% of national energy efficiency spending, a major component ofDSM 

spending, for the years 1990 to 199713. The nine utilities account for 25-35% of national 

expenditures. 

We took the following steps to estimate the number of ballasts nationwide to which a utility 

rebate applied. First, for each year we divided the total number of dollars the utilities in our 

sample spent on ballast rebates by the total number of ballasts to which these rebates applied, to 

get the average dollar spent per ballast in our sample. For example, in 1994 the six utilities in our 

sample spent a total of about $36 million on about 3.5 million ballasts, for an average expenditure 

of approximately $10 per ballast. 

Next we calculated the fraction of energy efficiency spending that was used for ballast rebates by 

the 6 utilities in our sample. In 1994 these utilities spent a total of about $280 million on all 

energy efficiency programs1

\ so ballast rebates represented 13% ($36 million! $280 million) of 

the total. 

We then estimated the amount spent by all utilities nationwide oil ballast rebates by assuming that 

the fraction of energy efficiency spending used for 'ballast rebates nationwide was equal to the 

fraction used by utilities in our sample (13% in 1994). Again using 1994 as an example, utilities 

spent about $1.6 billion in that year on all energy efficiency programs. We therefore estimated 

12 Eight of the utilities included in the sample are: Central Maine Power Co., Long Island Lighting Co., 

Pacific Gas & Electric, Rochester Gas & Electric, Southern California Edison, Consolidated Edison, Baltimore Gas and 

Electric arid San Diego Gas & Electric. The ninth utility wished to remain arlonymous. Datawas requested from 32 

utilities, including those with the 15 largest DSM programs, but marlY were unable or unwilling to provide information 

on electronic ballast rebate measures. 

13 1997 estimates were based on proj~ctions using at least 6 months of data because data for the full year were 

not yet available. Also, the 1997 estimates were based on only four utilities, as two utilities were not able to provide 

data for the year; one of these was unable to provide information for 1996 as well. These smaller samples reduce 

confidence in estimates for those years. 

14 Data on utility spending on energy efficiency programs for the individual utilities in the sample and for the 

nation as a whole were taken from Table 22 and Table 23 in the DOE-ElA's annual reports for 1993, 1994, 1995 and 

1996 in "U .S. Electric Utility Demand-Side Management." (DOFJElA-0589 (93), DOEJEIA-0589 (94), DOFJEIA0589 (95), DOFJElA-0589 (96)) These annual reports are based on information provided in Form EIA-861, "Annual 

Electric Utility Report." Data for 1990,1991,1992 were based on this same form, but were provided directly by the 

EIA as annual reports were not published for those years. Utilities themselves provided specific data on electronic 

ballast rebate measures. 

19 
that about $200 million (13% of $1.6 billion), was spent by utilities nationwide on ballast rebates

that year.

Finally, we estimated the number of ballasts nationwide to which a rebate applied by assuming the

average dollars spent per ballast for all utilities would be equal to the average spent by utilities in

our sample ($10/ballast in 1994). Hence, in 1994 we estimated that about 20 million electronic

ballasts ($200 million divided by $10/ballast) were rebated. This is about 75% of the 26 million

electronic ballasts reported by NEMA to be sold in that year15. It should be noted that the NEMA

data do not include imported ballasts.

Table 3 below shows the estimates of rebated electronic ballasts obtained from analysis of the six

and nine utility data sets. Some additional assumptions were made in order to use the data for the

three additional utilities, they are described in the full report on utility DSM rebates for ballasts16.

For comparison, we have added the NEMA shipments data for electronic ballasts to the table. The

analysis of DSM data using either data set indicates that the number of rebated ballasts has

declined since 1995. The fraction of electronic ballast sales that were influenced by rebates is

significant for all years using either data set. It should be noted that there is appreciable uncertainty

in the estimates of number of rebated ballasts. One way of seeing that uncertainty is to look at the

difference between the estimates of rebated ballasts obtained from the two data sets. The

estimation technique assumes that the average rebate value and the fraction of energy efficiency

spending on rebates calculated for each sample are the same as those for the United States as a

whole. A larger data set would help to reduce the uncertainty in these estimates.

Table 3 Estimates of Rebated Ballasts (Millions) from Utility DSM Programs

1992 1993 1994 995 1996 1997

6 Utilities 4.58 10.8 19.4 16.1 10.5 6.34

9 Utilities 7.02 9.36 12.8 14.4 7.1 NA

NEMA

Shipments*

NA 22.9 26.0 33.1 30.3 35.0`"

*NEMA shipments data for electronic ballasts.

** Estimated by NEMA from 6 months data.

UTILITY ANALYSIS

One commentor pointed out that projections for oil and gas generation after 1995 are available

from GRI, EPRI, and EIA, and could be used in the analysis. Most analyses take EIA data, such

as electric utility fuel prices as a starting point. The important result for estimating the effect of

15 NEMA provided sales figures for electronic and magnetic ballasts for 1993 through 1997.

16 Report on Electronic Ballast DSM Rebate Research, Lawrence Berkeley National Laboratory, April, 1998.

20

EE-RM-97-500 COMMENT 20D

that about $200 million (13% of $1.6 billion), was spent by utilities nationwide on ballast rebates 

that year. 

Finally, we estimated the number of ballasts nationwide to which a rebate applied by assuming the 

average dollars spent per ballast for all utilities would be equal to the average spent by utilities in 

our sample ($lOlballast in 1994). Hence, in 1994 we estimated that about 20 million electronic 

ballasts ($200 million divided by $10lballast) were rebated. This is about 75% of the 26 million 

electronic ballasts reported by NEMA to be sold in that year15 . It should be noted that the NEMA 

data do not include imported ballasts. 

Table 3 below shows the estimates of rebated electronic ballasts obtained from analysis of the six 

arid nine utility data sets. Some additional assumptions were made in order to use the data for the 

three additional utilities, they are described in the full report on utility DSM rebates for ballasts16. 

For comparison, we have added the NEMA shipments data for electronic ballasts to the table. The 

analysis of DSM data using either data set indicates that the number of rebated ballasts has 

declined since 1995. The fraction of electronic ballast sales that were influenced by rebates is 

significant for all years using either data set. It should be noted that there is appreciable uncertainty 

in the estimates of number of rebated ballasts. One way of seeing that uncertainty is to look at the 

difference between the estimates of rebated ballasts obtained from the two data sets. The 

estimation technique assumes that the average rebate value and the fraction of energy efficiency 

spending on rebates calculated for each sample are the same as those for the United States as a 

whole. A larger data set would help to reduce the uncertainty in these estimates. 

Table 3 Estimates of Rebated Ballasts (Millions) from Utility DSM Pro~ rams 

1992 1993 1994 

6 Utilities . 4.58 10.8 19.4 

9 Utilities 7.02 9.36 12.8 

NEMA NA 22.9 26.0 

Shipments· 

*NEMA shipments data for electromc ballasts. 

** Estimated by NEMA from 6 months data. 

UTILITY ANALYSIS 

1995 1996 1997 

16.1 10.5 6.34 

14.4 7.1 NA 

33.1 30.3 35.0·· 

One commentor pointed out that projections for oil and gas generation after 1995 are available 

from GRI, EPRI, and EIA, and could be used in the analysis. Most analyses take EIA data, such 

as electric utility fuel prices as a starting point. The important result for estimating the effect of 

15 NEMA provided sales figures for electronic and magnetic ballasts for 1993 through 1997. 

16 Report on Electronic Ballast DSM Rebate Research, Lawrence Berkeley National Laboratory, April, 1998. 

20 
standards on utility costs is not the overall fuel mix, but the marginal effect on fuel consumption

and power plant construction.

This commentor also stated that it was not clear how the Conservation Load Factor (CLF) was

calculated, and asked if it was calculated on a regional level first and then aggregated, or at the

national level only.

The CLF is equal to the annual average load savings from a conservation measure, divided by the

reduction in electricity demand at the time of the utility system peak demand. The quantity CLF

times 8,760 (hours per year) is equal to the amount of annual energy savings required to reduce a

unit of peak demand. For example, a CLF of 0.5 implies that 4,380 kWh (0.5 x 8,760) of energy

savings results in a 1 kW reduction of peak demand.

The conservation load factor used in this analysis was taken from a previous LBNL study on the

impact of lighting policies on electricity consumption. 17 In that study, the peak demand savings

were averaged over the hours 9 am to 5 pm on the peak summer day. The summer peak day was

chosen because for all North American Electric Reliability Council (NERC) regions in the U.S., the

adjusted reserve margin was, at the time the study was conducted (1992), projected to be lower in

the summer than the winter for the following ten years. The CLF calculation was performed at the

national level.

This same commentor suggested that because SO2 and NO. emissions have declined over the past

several years, marginal emissions due to energy savings will be lower than average emissions.

Emissions of S02 are unlikely to be affected by any policy, such as efficiency standards because

emissions are capped by legislation. However, the reduced demand for electricity due to standards

will also reduce the demand for SO2 emission allowances. This reduction in SO2 emissions

allowance demand is reported in the analysis. The actual reduction in NOx emissions will be

determined by which marginal thermal generation is reduced through lower electricity sales. Most

new capacity is likely to be both efficient and clean, and therefore operate at low marginal cost

high in the dispatch order (i.e. utilities will dispatch the newer, cleaner sources before going to the

older, more expensive sources). Generation from these new resources is therefore unlikely to be

reduced by a reduction in electricity sales. On the contrary, it is likely that the displaced generation

will be from older, dirtier plants low in the dispatch order. We did not estimate the importance of

this effect. We felt that using average emissions was a reasonable assumption which, if anything,

would somewhat underestimate the actual emissions reduction. Low or zero emission generation,

such as hydro, nuclear, and wind typically have low marginal costs and therefore would also be

high in the dispatch order and can be safely excluded from consideration.

- Several commentors concurred that the ASEUM model's methodology may be outdated in an era

of deregulated utilities which are unlikely to remain vertically integrated. It is true that the electric

17 Atkinson, et. aL (1992) Analysis of Federal Policy Options for Improving U . S. Lighting Ener Efficiency:

Commercial and Residential Buildings. Lawrence Berkeley National Laboratory, Berkeley, CA. LBL-31469.

21

EE-RM-97-500 COMMENT 20D

standards on utility costs is not the overall fuel mix, but the marginal effect on fuel consumption 

and power plant construction. 

This commentor also stated that it was not clear how the Conservation Load Factor (CLF) was 

calculated, and asked if it was calculated on a regional level first and then aggregated, or at the 

national level only. 

The CLF is equal to the annual average load savings from a conservation measure, divided by the 

reduction in electricity demand at the time of the utility system peak demand. The quantity CLF 

times 8,760 (hours per year) is equal to the amount of annual energy savings required to red.uce a 

unit of peak demand. For example, a CLF of 0.5 implies that 4,380 kWh (0.5 x 8,760) of energy 

savings results in al kW reduction of peak demand. 

The conservation load factor used in this analysis was taken from a previous LBNL study on the 

impact of lighting policies on electricity consumption. 17 In that study, the peak demand savings 

were averaged over the hours 9 am to 5 pm on the peak summer day. The summer peak day was 

chosen because for all North American Electric Reliability Council (NERC) regions in the U.S., the 

adjusted reserve margin was, at the time the study was conducted (1992), projected to be lower in 

the summer than the winter for the following ten years. The CLF calculation was performed at the 

national level. 

This same commentor suggested that because 802 and NOx emissions have declined over the past 

several years, marginal emissions due to energy savings will be lower than average emissions. 

Emissions of 802 are unlikely to be affected by any policy, such as efficiency standards because 

emissions are capped by legislation. However, the reduced demand for electricity due to standards 

will also reduce the demand for 802 emission allowances. This reduction in 802 emissions 

allowance demand is reported in the analysis. The actual reduction in NOx emissions will be 

determined by which marginal thertnal generation is reduced through lower electricity sales. Most 

new capacity is likely to be both efficient and clean, and therefore operate at low marginal cost 

high in the dispatch order (i.e. utilities will dispatch the newer, cleaner sources before going to the 

older, more expensive sources). Generation from these new resources is therefore unlikely to be 

reduced by a reduction in electricity sales. On the contrary, it is likely that the displaced generation 

will be from older, dirtier plants low in the dispatch order. We did not estimate the importance of 

this effect. We felt that using average emissions was a reasonable assumption which, if anything, 

would somewhat underestimate the actual emissions reduction. Low or zero emission generation, 

such as hydro, nuclear, and wind typically have low marginal costs and therefore would also be 

high in the dispatch order and can be safely excluded from consideration. 

-Several commentors.concurred that the ASEUM model's methodology may be outdated in an era 

of deregulated utilities which are unlikely to remain vertically integrated. It is true that the electric 

17 Atkinson, et. aL (1992) Analysis of Federal Policy Options for Improving u.s. Lighting Energy Efficiency: 

Commercial and Residential Buildings. Lawrence Berkeley National Laboratory, Berkeley, CA LBL-31469. 

21 
utility industry is undergoing a radical restructuring and the assumptions of cost recovery

underlying ASEUM are becoming dated. We agree that other methodologies may need to be

developed to carry out the utility analysis, the DOE/EIA NEMS (National Energy Modeling

System) is being considered for this purpose.

Other

One commentor felt that not enough results were shown for the industrial sector. The industrial

sector was analyzed in separate COMMEND runs, using electricity pri ce projections, floor stock

projections, and equipment shares distinct to the industrial sector . The Technical Support

Document will show expanded results from the industrial sector.

22

EE-RM-97-500 COMMENT 20D

utility industry is undergoing a radical restructuring and the assumptions of cost recovery 

underlying ASEUM are becoming dated. We agree that other methodologies may need to be 

developed to carry out the utility analysis, the DOEIEIA NEMS (National Energy Modeling 

System) is being considered for this purpose. 

Other 

One commentor felt that not enough results were shown for the industrial sector. The industrial 

sector was analyzed in separate COMMEND runs, using electricity price projections, floor stock 

projections, and equipment shares distinct to the industrial sector. The Technical Support 

Document will show expanded results from the industrial sector. 

22