### 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 

### Summary ofInputs for the Technical SupportDocument: 

# I

Energy Efficiency Standards for Fluorescent Lamp Ballasts

- Life Cycle Cost

- National Energy Impacts

- UtilitylEnvironmental Analysis

April 20, 1998 

### Prepared by u.s. 

### Department ofEnergy, Office ofCodes 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 

### INTRODUCTION
This document has been developed to provide interested parties with the inputs that the Department ofEnergy will use to produce the life cycle cost, national energy impacts and utility/environmental analysis sections ofthe 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 inthe July, 1997, Draft Report on PotentialImpact ofPossible 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. 

### AnnualLightingHours
1997 Report Theannual 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 5000hours for the industrial sector. Low and high sensitivities were performed at 2500 and 8000 hours for both the commercial and industrial sectors. Forthe probability-based sensitivity analysis, a distribution was obtained from the Xenergy data (see Figure 4.5 ofthe 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 ofthe comments on lighting hours concerned theprobability-based sensitivity analysis. These are discussed inthe uncertainty/variability section below. Nonew information was provided that would lead DOE to use another database for estimation ofannual 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 BallastPrices
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, ofthese 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 commentorfelt 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 ofthe analysis. However, several other commentors raised questions about howthe 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 ofthem that G&A felt itwould have been difficult to get a representative sampling. Distributorprices 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 orindirectly, from the survey by G&A Two ofthe 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 oftheir responses was taken to get an average contractor price. The 13% contractorto end-user markup was then added to the contractorprice to get an end userprice. To getthe 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 common electronic ballast types (2F32T8, 2F96T12 and 2F96T12HO) to get an average OEM to end user markup of38% for electronic ballasts. In order to get a better sense ofwhether the ballast prices obtained inthe G&A survey were reasonable, LBNL conducted an informal telephone survey during the first halfof December, 1997. A simple average ofthe 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 ofprices obtained from the telephone survey for both single-value and probability-based sensitivity analysis. The table ofprices 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.



### 4Table 1. Dishibuter 71A!ast Prices F40T12

# I F40T12 F32T8 F96T12 

# I F96T12 Corn ny state City EMAG 

# El ronic Electronic Magnetic El

### ectronic

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 

# I s 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 

### Table 1 Distributor Ballast Prices 

# .

# ~ F40T12 F40T12 F32T8 F96T12

# F~ nie E A CA 0 $ 9.50 $ 15.75 $ 15.75 NA 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 ortwo 8-foot lamps and the average prices for the electronic ballasts for either two 4-foot T81amps ortwo 8-foot lamps were very close to those used inthe draft Report (up to 3%higher) whereas the average pricefor the T12 electronic ballast was lower (12.2%).
The last row ofTable 1 shows what the average prices would be ifa 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 otherfour would be 10% to 13%higherthan those inthe 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 BallastLifetime
1997 Report DOE used an average ballast lifetime of45,000 hours for both magnetic and electronic ballasts

.. A distribution oflifetimes was used in the probability based sensitivity analysis. Forhigh and low sensitivities, 60,000 and 33,000 hours peryear were used.
Technical Support Document Input DOE will use an average ballast lifetime of50,000 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. Forhigh and low sensitivities, DOE will use 33,000 and 80,000 hours peryear. Rationale Ballast lifetime varies depending upon the properties (components and manufacturing process) of a particularballast and onhow it is installed and used (including fixture type)

. Therefore, any group ofballasts will have a range ofassociated lifetimes. It is possible to consider a half-life (time for 50% to fail) ofthat 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 hourlifetime by annual operatinghours to get ballast service lifein years. Forexample, for 4 foot commercial sector ballasts, we obtained 12.5 years (45,000/3600). The value of45,000 hrs came from the Illumination Engineers Society (IES) and is also close to the value of50,000 hours suggested by several interested parties at the March, 1997 ballast workshop inWashington D.C. It was also suggested by stakeholders that equal lifetimes be used for magnetic and electronic ballasts.
Whenthe 1997 report analysis was performed, there were notyet enough publicly available data to accurately determine the average lifetimes and distributions oflifetimes ofmagnetic 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 orto develop a frequency distribution of ballast lifetimes. Dueto this lack ofdata, we used the IES estimate of45,000 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 ofCalifornia field study, "thatreliable electronic high frequency ballasts canbe produced with the quality control necessary to reach orexceed the ten to twelve yearlife span common with magnetic ballasts". This supports the use ofan electronic ballast lifetime at least as long as the magnetic ballast lifetime.
Based onrecent data on early return rates for electronic ballasts, the ballast half-life could be as high as a million hours were itnot 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 ag 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 

### 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. Ifthe 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 offailure rate for electronic and magnetic ballasts versus time, taken from the memo cited above. The halflife was estimated to be 15 

### years for both types ofballast.

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 TomHagen, T&J Enterprises, to Isaac Turiel, LBNL datedDecember 17, 1997.

2 Op. Cit. 7Electricity 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 

### Electricityprices 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 ofend-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
Forthe probability-based sensitivity analysis, we used sales and revenue datafrom 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 ofprices obtained from the most recent individual utility sales and revenue datafor 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 ofthe electricity prices. Inthe LCC analyses, the electricity prices will change throughoutthe life ofthe 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 ofthe overall electricity charge. DOE will also perform sensitivity analyses to account for the variations in electricity prices.



### DiscountRates 1997 Report In the report, analyses were performed at4%,8% and 12% real discount rates. Technical Support Document Input DOE will perform analyses at4%,8% and 15%real discount rates. Rationale One commentor found the range ofdiscount rates appropriate, and recommended that higher rates not be used. Another stated that "theROI used inthe 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 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 commentorsaid that the range ofdiscount 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. Itshould be noted that the Interlaboratory Working Group report used rates of7% 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 ofthis report, a 15%upper level is being adopted. Thehigher 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 LCCInputs
One commentor asked ifU-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 intheXenergy system share data for use in the national energy analysis. The same commentor suggested thatF96T8 lamp ballasts be included. Since F96T8 lamplballast systems have small market shares, theDepartment did not select them as a product class. These systems were included in "Fluorescent Other" in the national energy analysis.
One commentor asked thatthe faster lumen depreciation ofT8s be taken into account. T8s have slowerlumen depreciation, notfaster. 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 orrelamping times, and therefore does not affect the result ofthe analysis. 

### Variability and Uncertainty
One commentor pointed out the difference between uncertainty and variability and suggested that the analysis include estimates ofuncertainty (e.g. standard deviation) for all inputs and outputs.
Forthose inputs which show large variability (e.g., electricity prices, oroperating hours), weplan to use the observed distribution ofvalues inthe analysis, wherever datapermit. For those inputs which are uncertain, we will estimate the uncertainty where practical. For example, the

3 Scenarios ofU.S. CarbonReductions, Interlaboratory Working Group onEnergy-EfficientandLow-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 hourst

week, 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 distribution ofballast lifetimes in the field has not been measured. In such cases, we consulted with knowledgeable experts to develop estimates ofthe uncertainty, and discussed those estimates with stakeholders in workshops in order to obtain agreement on reasonable assumptions. Itis impossible to quantify all uncertainty analytically

-- some things arejustnot completely known. In addition, there are practical limits to the number ofcombinations ofdistributions which canbe analyzed within reasonable computing resources and time constraints. In orderto focus onthe important issues and avoid unreasonable delays due to prolonged analysis, we plan to conduct importance analysis to determine which variables, even ifuncertain, have no significant influence onthe 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 ofthe total ballast life cycle cost. In such a case, we plan to reduce the amount ofattention 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 ofthe operating hours in the Xenergy survey because Xenergy did not estimate this. However, we can report the standard deviation ofthe average based on the 

### variability ofthe 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 3600hours/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 outthat, according to 1992 CBECS data, buildings representing 

### a significant fraction offloor space in several building types are reported to have building operating hours of 60 hours/week (roughly 3000 hours/year) orless, and that "the assumption of3600 hours as a minimum usage period for the analysis is not accurate". Itis true that a significant fraction ofbuildings are operated 3000 hours per year or less; in the 1992 CBECS this fraction was 69% ofbuildings, representing 55% ofcommercial floor space. However, there is also a significant fraction operating for longertimes, including 8% ofall buildings in the 1992 CBECS, representing 15% ofcommercial floor space, operating continuously (8760 hours/year). When these values are averaged, the result is 4000 hours/year 4• Although the average (not minimum) value used in our analysis for lighting hours was 3600 hours/year, the commentorcorrectly notes

4 CBECS datareports the square feet offloor space ineachofseveral operating hourranges, e.g. 0-39 hours/week, 40-48 hourslweek, etc. For eachofthese ranges, we assumed thatthe average forthatrange is the midpoint, e.g. thatthe average operating hours for buildings in the 40-48 hours/weekrange is 44 hours/week. We weighted the average by square feet. Asnoted by the commentor, 1992 CBECS reports building operating hours, not lighting operating hours. A 1992 EIApublication (Lighting in Commercial Buildings) used 1986 CBECS data to calculate lightingoperating hours. The average inthis publicationis 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 

### that for those end-users whose operating hours are less, the benefits ofswitching to more efficient ballasts are also less. This is taken into account in the variability analysis, which examines the LCC cost ofballast efficiency measures across the distribution ofoperating hours found inthe 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% ofall 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 ifrooms darkened during occupancy were taken into account. We derived the average value for lighting operating hours from survey datafrom 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 lowerthan 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 thatthe variability analysis, which considered the full distribution of U.S. commercial electricity prices, was an improvement overthe previous practice of justusing 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 ofU.S. commercial electricity prices and commercial operating hours would not be representative ofend-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 ofU. S. commercial electricity rates for all consumers. As 

### shown in Figure 2, the distribution ofelectricity prices paid by participants in this voluntary efficiency program was not found to be significantly different from the distribution ofelectricity 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 distributionrepresents electricity prices paid by GreenLights projectparticipants who installed electronic ballaststhatoperate 4ft, 8ft, or 8ftHO ballasts. Thisrepresents approximately 10 million oftheroughly 14 millionballasts installed. Becauseprojects did not directly report electricity price, it wascalculated from the reported dollars saved divided by kWh savedby the project Calculatedelectricity prices below $O.OlIkWhorabove $O.25IkWh were assumed toresultfrom reporting errors and were excluded. Only 2% ofballasts were installed inprojects whose calculated electricity prices fell outside ofthese bounds. 

### 11Distribution 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 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

Itis more difficult to address the question ofwhether or not the distribution oflighting hours of consumers who select more efficient ballasts is shifted to higher values because it is more difficult to fmd a national distribution oflighting operating hours data for comparison. For example, the sample ofbuildings used inthe 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 oflighting operating hours in the Xenergy datais compared to that in the Green Lights data (as shown for 4-foot lamp ballasts in commercial buildings inFigure 3.) For 4-ft commercial ballasts, the Green Lights datais offsettowards higher operatinghours 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).



### 12Operating 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 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 

### $
.,.. " '. 

# ·. ·

# . · ,

## gffi~810~ N ,., ""'" 

# 10 Annual Operating Hours



### Figure 3 

# · · 

# ·**~ ~**  ('I)

### ~ I"- 

# . . --Xenergy
••••••• Green Ughts

Both sets oflighting hours have uncertainties associated with them in that lighting hours are estimated, not measured. Forthe 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. Thattype ofdata entry raises doubts about the accuracy of the annual lighting hours reported inthe Green Lights data set. Therefore, it is difficult to conclude whetherthe difference in average lightinghours between the Xenergy and Green Lights data sets is proofthat purchasers ofelectronic ballasts are biased towards longer lighting hours. DOEhas decided to retainthe same average annual lighting hours and lightinghours distributions used in the July, 1997 report. 

### NATIONAL ENERGY IMPACTS
In the July 1997 Report, LBNL used Commend to projectballast sales and National Energy 'impacts. Commend will beused inthe 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 tothe spreadsheet analysis but notto 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

13scenario 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 ratefor 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 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. Inlight ofcomments, itis 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 ofimpacts ofnon-regulatory programs havejustbecome 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 BallastSales
One commentor stated that the prediction that ballast sales will decline inthe 1995-2005 period is unlikely to be correct. They pointed out that ifincreased sales of3- 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 ofcovered 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 inballast 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 ofthe 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 inthe future. Secondly, sales ofelectronic ballasts only became significant in 1991, accordingto Bureau ofCensus 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 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 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 

### ballast sales ofone electronic ballaStreplacing two magnetic ballasts is gradually being felt, after being balanced by the rising sales due to conversions in the earlieryears. ElectricityPrice Difforence 

### One commentor noted that the electricity pricesused 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 ofthe report. The prices to whiGh the commentorrefers, noted on page 5-3, areforthe year 1992. LightinglHVACInteractions 

### 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 ofthe 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 ofmore efficient lighting for each ofthe building types listed in Table 1, in several different climate zones. CBECS data was used to determine the square footage ofeach 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 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 ofmore efficient lighting with smallerheat 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., "CalculatingLighting and HVAC Interactions," ASHRAE Journal, Nov. 1993. 8 Memo to ASHRAE SPeC 90.1 re "Impact ofIntemal GainonCooling andHeating 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% offloor 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 Theresults in Table 2 show averages for each building type across several climate zones, weighted by floor space ofthat building type found in each climate zone. Itcan be seen that substantial net secondary savings can be attained in large offices, large hotels, and hospitals. These building types constitute 23 percent ofthe floor area ofthe building types for which calculations were performed. Schools, onthe other hand, constitute close to 20 percent ofthe same commercial floor area and lighting efficiency improvements generate large heating penalties in this building type. This is because there is notmuch cooling and a large heating coincidence between lighting and heating. The other building types show small net increases in energy consumption due to installation ofmore efficient lighting.
When averaged, the net effect oflighting/HVAC·interactions for the U.S. building stock covered in our studies is very small (approximately zero). Itshould be noted that this calculation covers about 76 percent ofthe 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 ofbuildings for which calculations were performed. 9
E-source indicates that the bonus savings presented in their document are representative oftypical large office buildings. They present two sets ofcalculations. The first set is based on a previous LBNL publication1o which we use as the basis ofthe 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 inthe range of28

- 36 percent. As shown 

### inTable 2, we calculate a 28% reduction in cooling energy in large office buildings due to the installation ofefficient lighting.
Thenumbers for heating penalties are not directly comparable because we present results interms ofprimary 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 inthe U.S. in terms ofprimary energy. It should be noted that the Rundquist study uses two generic commercial building types (small and large). Inthe LBNL studies, 36prototypical building models are simulated with distinct prototypes for the types listed in Table 2.

9 The reasonthatthe .calculations were notperfonned for the other 24% ofbuildings is thatthere were not enough buildings ofthose types inenough different climate zones inthe CBECS data set. Although these building types represent 24% offloor area, they are likely to represent a smallerpercentage ofbuilding energy consumption. 10 Sezgen, AO., and Y.l Huang, 1994, "LightinglHVAC interactions and theireffects onenergy consumption and peak HVAC requirements incommercial buildings," ACEEE 1994 Summer Study onEnergy Efficienc\, inBuildings. 1 Sezgen, 0., and lG. Koomey, 1997, Intemctions BetweenLighting and Space Conditioning Energy Use in U.S. CommercialBuildings. 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 Table 2. LightinglHVAC Interactions for the U.S. Building Stock (Changein HV AC primary energy peroneunit 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 inHeating 23% 16% 21% 30% 22% 17% 13% 36% 31% 28%

(kWhlkWhLighting)(l) Change inCooling -23% -30% -14% -27% -21% -27% -33% -34% -18% -24%

(kWhlkWhLightingXI) Change inHVAC 0% -14% 6% 3% 1% -11% -20% 2% 13% 4%

(kWhlkWhLighting)(l)

(1) all inprimary energy terms: 1kWhlighting electricity corresponds to 3.2 kWhprimary energy. (2) Thesenwnbers account for abol,lt 76% ofthe U.S. commercial floor area. The building types that are notaccounted for are: U assembly buildings (i 1 % ofcommercial floor area), parking garage (2% ofcommercial area), buildings relatedtopublic orderand safety (1% ofcommercial area), building types thatarenotcovered by the conventional categories (3% ofcommercial area), and vacantbuildings (7%ofcommercial 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 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 ofthe national energy analysis are unlikely to be significantly affected even ifnew floor stock data are incorporated. Additionally, using 1992 as the base year ofthe forecast provides valuable opportunities for LBNL to examine forecast results against empirical data for 1993

- 1995 to verify the accuracy ofthe forecasting model. One commentor questioned some ofthe values used for system shares in new construction. They found the shares ofelectronic 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 ofballast manufacturers, OEMs, LMCs, and lamp manufacturers conducted in 1995, as well as review ofBureau ofCensus data. The figures shown in Appendix B, Tables B.9through 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. Forthe year 2000, electronic ballasts in new buildings were estimated to account for 88% ofall 4-foot ballasts, 59% of8-foot ballasts and 59% of8-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 ofCensus 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-RegulatoryPrograms 

### Commentors suggested that the impacts ofvoluntary 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 ofballasts 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 ofelectronic 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 programs. 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 and1996 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 

### DSM programs has indeed declined in recent years. Since the release ofthe 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 ofEPA and FEMP programs. Here we summarize the results of our preliminary analysis ofDSMprograms. We have collected data from 9 utilities 12 

### to study their programs offering rebates on electronic fluorescent ballasts. The data were complete for 6 ofthe utilities but not complete for the other 3. The sixutilities included in the complete data set represent about 15% to 20% ofnational energy efficiency spending, amajor component ofDSM spending, for the years 1990 to 199713. The nine utilities account for 25-35% ofnational expenditures. We took the following steps to estimate the number ofballasts nationwide to which a utility rebate applied. First, for each year we divided the total number ofdollars the utilities in our sample spent on ballastrebates by the total number ofballasts to which these rebates applied, to get the average dollar spent perballast in our sample. For example, in 1994 the six utilities in our sample spent atotal ofabout $36 million on about 3.5 million ballasts, for an average expenditure ofapproximately $10 per ballast. Next we calculated the fraction ofenergy efficiency spending that was used for ballastrebates by 

### the 6utilities in our sample. In 

### 1994 these utilities spent a total ofabout $280 million on all energy efficiency programs1\ 

### so ballastrebates represented 13% ($36 million! $280 million) of the total. We then estimated the amount spentby all utilities nationwide oil ballastrebates by assuming that the fraction ofenergy 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 Eightofthe utilities included inthe sample are: CentralMaine PowerCo.,LongIslandLighting Co., Pacific Gas & Electric, Rochester Gas & Electric, Southern California Edison, Consolidated Edison, Baltimore Gas and Electric arid San Diego Gas & Electric. The ninthutility wished toremain arlonymous. Datawasrequested from 32 utilities, including those with the 15 largestDSMprograms, butmarlY were unable orunwilling toprovide information onelectronic ballastrebate measures. 13 1997 estimates were based on**proj~ctions** using atleast 6 months ofdatabecause data for the full year were notyet available. Also, the 1997 estimates were based ononly four utilities, as two utilities were notable toprovide data for the year; one ofthese wasunable toprovide informationfor 1996 as well. These smaller samples reduce confidence inestimates for those years. 14 Data onutility spending onenergy efficiency programs for the individual utilities inthe sample and for the nationas a whole were taken from Table 22 and Table 23 intheDOE-ElA's annual reports for 1993, 1994, 1995 and 1996 in "U.S. ElectricUtility Demand-SideManagement." (DOFJElA-0589 (93), DOEJEIA-0589 (94), DOFJEIA0589 (95), DOFJElA-0589 (96)) These annual reports are based oninformationprovided inForm EIA-861, "Annual Electric Utility Report." Datafor 1990,1991,1992were based onthis same form, butwere provided directly by the EIAas annual reports were notpublished for those years. Utilities themselves provided specific data onelectronic ballastrebate 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 

### that about $200 million (13% of$1.6 billion), was spent by utilities nationwide on ballast rebates that year. Finally, we estimated the number ofballasts nationwide to which a rebate applied by assuming the average dollars spent per ballast for all utilities would be equal to the average spent by utilities in our sample ($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% ofthe 26 million electronic ballasts reported by NEMA to be sold in that year15

. 

### It 

### should be noted thatthe NEMA data do not include imported ballasts. Table 3 below shows the estimates ofrebated electronic ballasts obtained from analysis of the six arid nine utility data sets. Some additional assumptions were made in orderto use the datafor the three additional utilities, they are described in the full report on utility DSM rebates for ballasts 16

. 

### For comparison, we have added the NEMA shipments data for electronic ballasts to the table. The analysis ofDSM data using either data set indicates that the number ofrebated ballasts has declined since 1995. The fraction ofelectronic ballast sales that were influenced by rebates is significant for all years using either data set. It should be noted thatthere is appreciable uncertainty in the estimates ofnumber ofrebated ballasts. One way ofseeing that uncertainty is to look at the difference between the estimates ofrebated ballasts obtained from the two data sets. The estimation technique assumes that the average rebate value and the fraction ofenergy 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 ofRebated 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.
UTILITYANALYSIS 

### 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 onElectronic BallastDSMRebate 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 

### standards on utility costs is not the overall fuel mix, butthe 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 ifit 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 ofthe utility systempeak demand. The quantity CLF times 8,760 (hours peryear) is equal to the amount ofannual energy savings required to red.uce a unit ofpeak demand. For example, a CLF of0.5 implies that 4,380 kWh (0.5 x 8,760) ofenergy savings results in al kW reduction ofpeak demand. The conservation load factor used in this analysis was taken from a previous LBNL study on the impact oflighting 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 overthe past several years, marginal emissions due to energy savings will be lower than average emissions. Emissions of802 

### 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, itis 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

ofderegulated utilities which are unlikely to remain vertically integrated. It

### is true that the electric

17 Atkinson, et. aL (1992)Analysis ofFederalPolicy Options forImproving 

### u.s. LightingEnergy Efficiency: Commercial andResidential 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 utility industry is undergoing a radical restructuring and the assumptions ofcost recovery underlying ASEUM are becoming dated. We agree that other methodologies may need to be developed to carry out the utility analysis, the DOEIEIANEMS (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 tothe industrial sector. The Technical Support Document will show expanded results from the industrial sector.



### 22