Table B.I. Characterization and Summary of Commercial Electric Utility Sample

By Utility By Modeled Tariffs

Percent of US

in Modeled

Tariffs

Sample as

Percent of

Utility

Total Total Sampled Sampled

m a. Commercial Megawatt- Commercial Megawatt- MWh MWh

Utilit y

>,

iour Sale- I Cu ales Cust. Sales Cust. Sales

PG&E CA IOU 552,901 49,530,911 401,747 16,025,437 3.0% 1.7% 73% 32%

SoCalEdison CA IOU 481,417 51,410,408 419,163 23,057,727 3.1% 2.5% 87% 45%

Commonwealth Edison IL IOU 292,709 49,933,859 291,143 25,859,649 2.2% 2.8% 99% 52%

Virginia Power VA IOU 189,301 20,061,476 137,813 2,696,019 1.0% 0.3% 73% 13%

Detroit Edison MI IOU 177,088 17,997,214 166,003 7,613,394 1.2% 0.8% 94% 42%

Alabama Power AL IOU 174,602 11,330,312 154,077 7,917,907 1.1% 0.9% 88% 70%

Penn Power & Light PA IOU 149,221 20,475,206 143,849 8,781,171 1.1% 0.9% 96% 43%

Niagara Mohawk Power NY IOU 148,124 23,279,622 143,590 4,550,295 1.1% 0.5% 97% 20%

NSP (MN) MN IOU 135,183 5,009,755 110,807 4,461,852 0.8% 0.5% 82% 89%

Union Electric Co MO IOU 134,699 12,189,235 120,596 8,735,132 0.9% 0.9% 90% 72%

Appalachian Power VA IOU 110,740 16,390,313 78,554 674,926 0.6% 0.1% 71% 4%

Jersey Central P&L NJ IOU 106,157 10,510,309 104,922 5,358,036 0.8% 0.6% 99% 51%

Wisconsin Elec Power WI IOU 93,973 18,454,563 85,735 2,705,293 0.6% 0.3% 91% 15%

Boston Edison MA IOU 87,644 7,991,349 80,255 2,820,415 0.6% 0.3% 92% 35%

Ohio Power Company OH IOU 87,314 24,917,126 6,442 6,348,160 0.0% 0.7% 7% 25%

Central Power & Light TX IOU 85,311 12,844,712 74,107 2,099,439 0.5% 0.2% 87% 16%

Arizona Public Service AZ IOU 79,755 8,524,882 78,530 6,989,968 0.6% 0.8% 98% 82%

PEPCO MD IOU 70,909 15,307,001 53,620 1,317,800 0.4% 0.1% 76% 9%

Cleveland Electric Ilium OH IOU 64,907 5,883,328 63,161 2,765,339 0.5% 0.3% 97% 47%

Seattle City Light WA Muni 43,497 3,081,941 43,497 3,081,941 0.3% 0.3% 100% 100%

NSP (WI) WI IOU 31,236 866,425 27,449 831,739 0.2% 0.1% 88% 96%

Madison Gas & Electric WI IOU 16,220 1,727,832 14,815 685,233 0.1% 0.1% 91% 40%

Savannah Elec & Power GA IOU 14,100 1,156,078 708 517,052 0.0% 0.1% 5% 45%

Poudre Valley REA CO Co-o 2,774 377,974 2,101 96,424 0.0° 0.0° 76° 26°

Sample Total

US TOTAL

3,329,782 389,251,831

13,540,374 928,440,265

2,802,684 145,990,348 20.7% 15.7% 84% 38%

Because tariff-level information on sales is not as readily available for publicly and

cooperatively owned utilities as it is for investor-owned utilities that must report such information

annually to FERC, our sample contained only one municipal utility and one co-op. While the co-op

we included in our analysis had an average commercial price nearly the same as the average

commercial price of all of the co-ops in the country, the same was not true for the municipal utility

in our sample. As a sensitivity analysis, we later added several more municipal utilities, which

brought the weighted average electricity price of the municipal utilities in our sample closer to the

average commercial price of all of the municipal utilities in the country. We then re-ran the bill

calculator (which is described below) using the expanded set of tariffs. See the Results Section

below for the conclusions of this sensitivity analysis.

Electric utilities calculate customer bills using tariffs that define the type and amount of each

charge. These charges are generally levied on a monthly basis, and generally have three main

components: fixed charges, usage charges, and demand charges. We collected these tariffs for a

B-3

EE-RM-97-500 COMMENT 104

Table B.l. Characterization and Summary of Commercial Electric Utility Sample 

Utility 

PG&E 

SoCal Edison 

Commonwealth Edison 

Virginia Power 

Detroit Edison 

Alabama Power 

Penn Power & Light 

Niagara Mohawk Power 

NSP (MN) 

Union Electric Co 

Appalachian Power 

Jersey Central P&L 

Wisconsin Elec Power 

Boston Edison 

Ohio Power Company 

Central Power & Light 

Arizona Public Service 

PEPCO 

Cleveland Electric IIrum 

Seattle City Light 

NSP(WI) 

Madison Gas & Electric 

Savannah Elec & Power 

Poudre Vallev REA 

Sample Total 

US TOTAL 

CIl 

til 

Ui 

CA 

CA 

IL 

VA 

MI 

AL 

PA 

NY 

MN 

MO 

VA 

NJ 

WI 

MA 

OH 

TX 

AZ 

MD 

OH 

WA 

WI 

WI 

GA 

CO 

CIl 

Q. 

~ 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

Muni 

IOU 

IOU 

IOU 

Co-o 

By Utility 

Total Total 

Commercial Megawatt-

~ .. <:tnm .. r" 

552,901 49,530,911 

481,417 51,410,408 

292,709 49,933,859 

189,301 20,061,476 

177,088 17,997,214 

174,602 11,330,312 

149,221 20,475,206 

148,124 23,279,622 

135,183 5,009,755 

134,699 12,189,235 

110,740 16,390,313 

106,157 10,510,309 

93,973 18,454,563 

87,644 7,991,349 

87,314 24,917,126 

85,311 12,844,712 

79,755 8,524,882 

70,909 15,307,001 

64,907 5,883,328 

43,497 3,081,941 

31,236 866,425 

16,220 1,727,832 

14,100 1,156,078 

2774 377 974 

3,329,782 389,251,831 

13,540,374 928,440,265 

Percent of US Sample as 

By Modeled Tariffs in Modeled Percent of 

Tariffs Utility 

Sampled Sampled 

Commercial Megawatt- MWh MWh 

~ .. "tnm .. r" Cust. Sales Cust. Sales 

401,747 16,025,437 3.0% 1.7% 73% 32% 

419,163 23,057,727 3.1% 2.5% 87% 45% 

291,143 25,859,649 2.2% 2.8% 99% 52% 

137,813 2,696,019 1.0% 0.3% 73% 13% 

166.003 7,613,394 1.2% 0.8% 94% 42% 

154,077 7,917,907 1.1% 0.9% 88% 70% 

143,849 8,781,171 1.1% 0.9% 96% 43% 

143,590 4,550,295 1.1% 0.5% 97% 20% 

110,807 4,461,852 0.8% 0.5% 82% 89% 

120,596 8,735,132 0.9% 0.9% 90% 72% 

78,554 674,926 0.6% 0.1% 71% 4% 

104,922 5,358,036 0.8% 0.6% 99% 51% 

85,735 2,705,293 0.6% 0.3% 91% 15% 

80,255 2,820,415 0.6% 0.3% 92% 35% 

6,442 6,348,160 0.0% 0.7% 7% 25% 

74,107 2,099,439 0.5% 0.2% 87% 16% 

78,530 6,989,968 0.6% 0.8% 98% 82% 

53,620 1,317,800 0.4% 0.1% 76% 9% 

63,161 2,765,339 0.5% 0.3% 97% 47% 

43,497 3,081,941 0.3% 0.3% 100% 100% 

27,449 831,739 0.2% 0.1% 88% 96% 

14,815 685,233 0.1% 0.1% 91% 40% 

708 517,052 0.0% 0.1% 5% 45% 

2101 96424 0.0% 0.0% 76% 26% 

2,802,684 145,990,348 20.7% 15.7% 84% 38% 

Because tariff-level infonnation on sales is not as readily available for publicly and 

cooperatively owned utilities as it is for investor-owned utilities that must report such information 

annually to FERC, our sample contained only one municipal utility and one co-op. While the co-op 

we included in our analysis had an average commercial price nearly the same as the average 

commercial price of all of the co-ops in the country, the same was not true for the municipal utility 

in our sample. As a sensitivity analysis, we later added several more municipal utilities, which 

brought the weighted average electricity price of the municipal utilities in our sample closer to the 

average commercial price of all of the municipal utilities in the country. We then re-ran the bill 

calculator (which is described below) using the expanded set of tariffs. See the Results Section 

below for the conclusions of this sensitivity analysis. 

Electric utilities calculate customer bills using tariffs that define the type and amount of each 

charge. These charges are generally levied on a monthly basis, and generally have three main 

components: fixed charges, usage charges, and demand charges. We collected these tariffs for a 

B-3 

Table B.l. Characterization and Summary of Commercial Electric Utility Sample 

Utility 

PG&E 

SoCal Edison 

Commonwealth Edison 

Virginia Power 

Detroit Edison 

Alabama Power 

Penn Power & Light 

Niagara Mohawk Power 

NSP (MN) 

Union Electric Co 

Appalachian Power 

Jersey Central P&L 

Wisconsin Elec Power 

Boston Edison 

Ohio Power Company 

Central Power & Light 

Arizona Public Service 

PEPCO 

Cleveland Electric IIrum 

Seattle City Light 

NSP(WI) 

Madison Gas & Electric 

Savannah Elec & Power 

Poudre Vallev REA 

Sample Total 

US TOTAL 

CIl 

til 

Ui 

CA 

CA 

IL 

VA 

MI 

AL 

PA 

NY 

MN 

MO 

VA 

NJ 

WI 

MA 

OH 

TX 

AZ 

MD 

OH 

WA 

WI 

WI 

GA 

CO 

CIl 

Q. 

~ 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

Muni 

IOU 

IOU 

IOU 

Co-o 

By Utility 

Total Total 

Commercial Megawatt-

~ .. <:tnm .. r" 

552,901 49,530,911 

481,417 51,410,408 

292,709 49,933,859 

189,301 20,061,476 

177,088 17,997,214 

174,602 11,330,312 

149,221 20,475,206 

148,124 23,279,622 

135,183 5,009,755 

134,699 12,189,235 

110,740 16,390,313 

106,157 10,510,309 

93,973 18,454,563 

87,644 7,991,349 

87,314 24,917,126 

85,311 12,844,712 

79,755 8,524,882 

70,909 15,307,001 

64,907 5,883,328 

43,497 3,081,941 

31,236 866,425 

16,220 1,727,832 

14,100 1,156,078 

2774 377 974 

3,329,782 389,251,831 

13,540,374 928,440,265 

Percent of US Sample as 

By Modeled Tariffs in Modeled Percent of 

Tariffs Utility 

Sampled Sampled 

Commercial Megawatt- MWh MWh 

~ .. "tnm .. r" Cust. Sales Cust. Sales 

401,747 16,025,437 3.0% 1.7% 73% 32% 

419,163 23,057,727 3.1% 2.5% 87% 45% 

291,143 25,859,649 2.2% 2.8% 99% 52% 

137,813 2,696,019 1.0% 0.3% 73% 13% 

166.003 7,613,394 1.2% 0.8% 94% 42% 

154,077 7,917,907 1.1% 0.9% 88% 70% 

143,849 8,781,171 1.1% 0.9% 96% 43% 

143,590 4,550,295 1.1% 0.5% 97% 20% 

110,807 4,461,852 0.8% 0.5% 82% 89% 

120,596 8,735,132 0.9% 0.9% 90% 72% 

78,554 674,926 0.6% 0.1% 71% 4% 

104,922 5,358,036 0.8% 0.6% 99% 51% 

85,735 2,705,293 0.6% 0.3% 91% 15% 

80,255 2,820,415 0.6% 0.3% 92% 35% 

6,442 6,348,160 0.0% 0.7% 7% 25% 

74,107 2,099,439 0.5% 0.2% 87% 16% 

78,530 6,989,968 0.6% 0.8% 98% 82% 

53,620 1,317,800 0.4% 0.1% 76% 9% 

63,161 2,765,339 0.5% 0.3% 97% 47% 

43,497 3,081,941 0.3% 0.3% 100% 100% 

27,449 831,739 0.2% 0.1% 88% 96% 

14,815 685,233 0.1% 0.1% 91% 40% 

708 517,052 0.0% 0.1% 5% 45% 

2101 96424 0.0% 0.0% 76% 26% 

2,802,684 145,990,348 20.7% 15.7% 84% 38% 

Because tariff-level infonnation on sales is not as readily available for publicly and 

cooperatively owned utilities as it is for investor-owned utilities that must report such information 

annually to FERC, our sample contained only one municipal utility and one co-op. While the co-op 

we included in our analysis had an average commercial price nearly the same as the average 

commercial price of all of the co-ops in the country, the same was not true for the municipal utility 

in our sample. As a sensitivity analysis, we later added several more municipal utilities, which 

brought the weighted average electricity price of the municipal utilities in our sample closer to the 

average commercial price of all of the municipal utilities in the country. We then re-ran the bill 

calculator (which is described below) using the expanded set of tariffs. See the Results Section 

below for the conclusions of this sensitivity analysis. 

Electric utilities calculate customer bills using tariffs that define the type and amount of each 

charge. These charges are generally levied on a monthly basis, and generally have three main 

components: fixed charges, usage charges, and demand charges. We collected these tariffs for a 

B-3 
number of US electric utilities and modeled them for inclusion in CTAS calculations. CTAS

contains the specific commercial tariffs for the utilities shown in Table B.2.

Table B.2 Tariffs Used in the Commercial Analysis

Utility State Tariff Utility State Tariff

Alabama Power AL LPEM NSP WI Cg-5

Alabama Power AL LPM Niagara Mohawk Power Corp NY SC-2 Demand

Alabama Power AL LPS Niagara Mohawk Power Corp NY SC-2 Non-Demand

Appalachian Power Co VA SGS Ohio Power Co OH GS3-11

Arizona Public Service AZ E30 PEPCO DC GS

Arizona Public Service AZ E32 Pacific Gas & Electric CA Al

Boston Edison MA G-1 Pacific Gas & Electric CA AlO

Boston Edison MA G-2 Pennsylvania Power and Light PA GS-1

Central Power and Light TX 21 Pennsylvania Power and Light PA GS-3

Cleveland Electric Ilium. Co OH Gen Comm Poudre Valley CO B-97

Cleveland Electric Blum. Co OH Lg Comm Savannah Electric and Water GA GS-7

Commonwealth Edison Co IL Rate 6 non-TOU Seattle City Light WA Rate 31

Detroit Edison MI D3 Seattle City Light WA Rate 34

Jersey Central Power & Light NJ GS SoCal Edison CA GS-1

Madison G&E Wl Cg-l SoCal Edison CA GS-2

Madison G&E WI Cg-5 Union Electric Co MO No. 2

NSP MN AlO, D12, E13 Union Electric Co MO No. 3

NSP MN A14, D16, E15 Virginia Power Co VA GS-1

NSP WI Cg-2 Wisconsin Electric Power W1 Cg-I

NSP WI C.-_2 unmetered

B.1.3.2 Commercial Tariff Structure

Commercial tariffs can be complex. A commercial tariff usually includes the following types

of information:

Applicability / Availability - the type of customer served under this tariff.

Character of Service - the type of current, voltage, and phase (single or three-phase).

Monthly Rate - customer charge, energy charge, demand charge (if applicable), and extra

charges.

Definition of demand charges.

Rate periods (if time-of-use) and definition of seasons.

B-4

EE-RM-97-500 COMMENT 104

number of US electric utilities and modeled them for inclusion in CT AS calculations. CTAS 

contains the specific commercial tariffs for the utilities shown in Table B.2. 

T a bi e B 2 T . an 'ff S U se d' III th e C ommerCla , IA nalYSIS 

Utility State Tariff Utility State Tariff 

Alabama Power AL LPEM NSP WI Cg-5 

Alabama Power AL LPM Niagara Mohawk Power COIP NY SC-2 Demand 

Alabama Power AL LPS Niagara Mohawk Power COIP NY SC-2 Non-Demand 

Appalachian Power Co VA SGS Ohio Power Co OH GS3-11 

Arizona Public Service AZ E30 PEPCO DC GS 

Arizona Public Service AZ E32 Pacific Gas & Electric CA Al 

Boston Edison MA G-I Pacific Gas & Elecll"ic CA AIO 

Boston Edison MA G-2 Penllsylvania Power and Light PA GS-I 

Central Power and Light TX 21 Pennsylvania Power and Light PA GS-3 

Cleveland Electric lllum. Co OH GenComm Poudre Valley CO B-97 

Cleveland Electric lllum. Co OH LgComm Savannah Electric and Water GA GS-7 

Commonwealth Edison Co IL Rate 6 non-TOU Seattle City Light WA Rate 31 

Detroit Edison Ml D3 Seattle City Light WA Rate 34 

Jersey Cenhal Power & Light NJ GS SoCal Edison CA GS-I 

MadisonG&E WI Cg-I SoCal Edison CA GS-2 

MadisonG&E WI Cg-5 Union Electric Co MO No.2 

NSP MN AIO, Dl2, E13 Union Elech'ic Co MO No.3 

NSP MN A14, D16, EI5 Virginia Power Co VA GS-l 

NSP WI Cg-2 Wisconsin Elech'ic Power WI Cg-I 

NSP WI Cg-2 unmetered 

8.1.3.2 Commercial Tariff Structure 

Commercial tariffs can be complex. A commercial tariffusually includes the following types 

of information: 

• Applicability / Availability - the type of customer served under this tariff. 

• Character of Service - the type of current, voltage, and phase (single or three-phase). 

Monthly Rate - customer charge, energy charge, demand charge (if applicable), and extra 

charges. 

Definition of demand charges. 

Rate periods (if time-of-use) and definition of seasons. 

B-4 

number of US electric utilities and modeled them for inclusion in CT AS calculations. CTAS 

contains the specific commercial tariffs for the utilities shown in Table B.2. 

T a bi e B 2 T . an 'ff S U se d' III th e C ommerCla , IA nalYSIS 

Utility State Tariff Utility State Tariff 

Alabama Power AL LPEM NSP WI Cg-5 

Alabama Power AL LPM Niagara Mohawk Power COIP NY SC-2 Demand 

Alabama Power AL LPS Niagara Mohawk Power COIP NY SC-2 Non-Demand 

Appalachian Power Co VA SGS Ohio Power Co OH GS3-11 

Arizona Public Service AZ E30 PEPCO DC GS 

Arizona Public Service AZ E32 Pacific Gas & Electric CA Al 

Boston Edison MA G-I Pacific Gas & Elecll"ic CA AIO 

Boston Edison MA G-2 Penllsylvania Power and Light PA GS-I 

Central Power and Light TX 21 Pennsylvania Power and Light PA GS-3 

Cleveland Electric lllum. Co OH GenComm Poudre Valley CO B-97 

Cleveland Electric lllum. Co OH LgComm Savannah Electric and Water GA GS-7 

Commonwealth Edison Co IL Rate 6 non-TOU Seattle City Light WA Rate 31 

Detroit Edison Ml D3 Seattle City Light WA Rate 34 

Jersey Cenhal Power & Light NJ GS SoCal Edison CA GS-I 

MadisonG&E WI Cg-I SoCal Edison CA GS-2 

MadisonG&E WI Cg-5 Union Electric Co MO No.2 

NSP MN AIO, Dl2, E13 Union Elech'ic Co MO No.3 

NSP MN A14, D16, EI5 Virginia Power Co VA GS-l 

NSP WI Cg-2 Wisconsin Elech'ic Power WI Cg-I 

NSP WI Cg-2 unmetered 

8.1.3.2 Commercial Tariff Structure 

Commercial tariffs can be complex. A commercial tariffusually includes the following types 

of information: 

• Applicability / Availability - the type of customer served under this tariff. 

• Character of Service - the type of current, voltage, and phase (single or three-phase). 

Monthly Rate - customer charge, energy charge, demand charge (if applicable), and extra 

charges. 

Definition of demand charges. 

Rate periods (if time-of-use) and definition of seasons. 

B-4 
• Minimum monthly charge.

• Power factor adjustments and reactive power demand charges.

B.1.3.3 Commercial Tariff Spreadsheet Modeling .

Electric utilities typically have many commercial tariffs. Southern California Edison, for

instance, has over 100 commercial tariffs. Most of these tariffs target a very small number of

customers, or even individual customers, and thus have been ignored in CTAS. CTAS was designed

as an analysis tool to investigate marginal prices experienced by a range of customers over a range

of commercial tariffs throughout the country. It attempts to maximize coverage by running a

prototypical set of customers/buildings against as broad a set of tariffs as possible . Only the mostused two or three commercial tariffs for each utility have been included in CTAS. Taken together,

these tariffs covered 84 percent of the customers in our utility sample and 38 percent of the

commercial electricity sales by those sampled utilities.

Generally, the commercial tariff modeling effort focused on two common categories of

commercial tariffs: small general service and general service. We modeled the collected commercial

tariffs in spreadsheet form, excluding some tariffs and/or components that could not be applied to

buildings in MAISY'. For instance , if a tariff provided both single and three-phase service, only the

charges for single phase were modeled. Also, some tariffs charged different prices for primary,

secondary, and transmission voltage levels, but the buildings used in MAISY' only received service

at secondary levels. We ignored power factor and reactive demand charges, which are an adjustment

based on voltage and demand usage. We ignored other factors if the information could not be used

in MAISY® or was rarely applicable.

In addition to the parameters necessary to calculate customer bills, the spreadsheet also

contained the number of customers in a tariff, total number of customers in a commercial utility, the

megawatt-hours sold, kilowatt-hours sold per customer , and average price per kilowatt-hour. These

data were extracted from FERC Form 1 for IOU's and from comparable sources for municipal

utilities and co-ops. We used these tariff-level data on customers and delivered energy to weight the

resulting bill and marginal price calculations to represent a national distribution of marginal prices.

Our calculations did not use a tariff unless it was also possible to collect usage and delivered energy

data for that individual tariff (as opposed to being able to collect such information only for the entire

utility or a group of that utility 's tariffs). Industrial tariffs, as contrasted to commercial tariffs, have

not been included in CTAS . This is primarily because the MAISY'®' database does not include

industrial buildings.

B.1.4 Sample Buildings/Customers

The collection of buildings contained in CTAS came from MAISY®, a proprietary database

containing weighted, representative building collections for each state. We selected a subset of

buildings from MAISY' , the subsequent weighting of which was intended to create a "prototypical"

set of buildings that would exist in relatively the same proportions throughout the country, and would

thus be representative of customers existing within any of the modeled utility tariffs. Each sampled

B-5

EE-RM-97-500 COMMENT 104

• Minimum monthly charge. 

e Power factor adjustments and reactive power demand charges. 

B.1.3.3 Commercial Tariff Spreadsheet Modeling 

Electric utilities typically have many commercial tariffs. Southern California Edison, for 

instance, has over 100 commercial tariffs. Most of these tariffs target a very small number of 

customers, or_even individual customers, and thus have been ignored in CTAS. CTAS was designed 

as an analysis tool to investigate marginal prices experienced by a range of customers over a range 

of commercial tariffs throughout the country. It attempts to maximize coverage by running a 

prototypical set of customers/buildings against as broad a set of tariffs as possible. Only the mostused two or three commercial tariffs for each utility have been included in CTAS. Taken together, 

these tariffs covered 84 percent of the customers in our utility sample and 38 percent of the 

commercial electricity sales by those sampled utilities. 

Generally, the commercial tariff modeling effoli focused on two common categories of 

commercial tariffs: small general service and general service. We modeled the collected commercial 

tariffs in spreadsheet form~ excluding some tariffs and/or components that could not be applied to 

buildings in MAISy(Jj). For instance, if a tariff provided both single and three-phase service, only the 

charges for single phase were modeled. Also, some tariffs charged different prices for primary, 

secondary, and transmission voltage levels, but the buildings used in MAISy(Jj) only received service 

at secondary levels. We ignored power factor and reactive demand charges, which are an adjustment 

based on voltage and demand usage. We ignored other factors if the information could not be used 

in MAISy(Jj) or was rarely applicable. 

In addition to the parameters necessary to calculate customer bills, the spreadsheet also 

contained the number of customers in a tariff, total number of customers in a commercial utility, the 

megawatt-hours sold, kilowatt-hours sold per customer, and average price per kilowatt-hour. These 

data were extracted from FERC Fornl I for IOU's and from comparable sources for municipal 

utilities and co-ops. We used these tariff-level data on customers and delivered energy to weight the 

resulting bill and marginal price calculations to represent a national distribution of marginal prices. 

Our calculations did not use a tariff unless it was also possible to collect usage and delivered energy 

data for that individual tariff (as opposed to being able to collect such information only for the entire 

utility or a group of that utility's tariffs). Industrial tariffs, as contrasted to commercial tariffs, have 

not been included in CTAS. This is primarily because the MAISy(Jj) database does not include 

industrial buildings. 

8.1.4 Sample Buildings/Customers 

The collection of buildings contained in CTAS came from MAISy(Jj), a proprietary database 

containing weighted, representative building collections for each state. We selected a subset of 

buildings fromMAIS~, the subsequent weighting of which was intended to create a "prototypical" 

set of buildings that would exist in relatively the same proportions throughout the country, and would 

thus be representative of customers existing within any ofthe modeled utility tariffs. Each sampled 

B-5 

• Minimum monthly charge. 

e Power factor adjustments and reactive power demand charges. 

B.1.3.3 Commercial Tariff Spreadsheet Modeling 

Electric utilities typically have many commercial tariffs. Southern California Edison, for 

instance, has over 100 commercial tariffs. Most of these tariffs target a very small number of 

customers, or_even individual customers, and thus have been ignored in CTAS. CTAS was designed 

as an analysis tool to investigate marginal prices experienced by a range of customers over a range 

of commercial tariffs throughout the country. It attempts to maximize coverage by running a 

prototypical set of customers/buildings against as broad a set of tariffs as possible. Only the mostused two or three commercial tariffs for each utility have been included in CTAS. Taken together, 

these tariffs covered 84 percent of the customers in our utility sample and 38 percent of the 

commercial electricity sales by those sampled utilities. 

Generally, the commercial tariff modeling effoli focused on two common categories of 

commercial tariffs: small general service and general service. We modeled the collected commercial 

tariffs in spreadsheet form~ excluding some tariffs and/or components that could not be applied to 

buildings in MAISy(Jj). For instance, if a tariff provided both single and three-phase service, only the 

charges for single phase were modeled. Also, some tariffs charged different prices for primary, 

secondary, and transmission voltage levels, but the buildings used in MAISy(Jj) only received service 

at secondary levels. We ignored power factor and reactive demand charges, which are an adjustment 

based on voltage and demand usage. We ignored other factors if the information could not be used 

in MAISy(Jj) or was rarely applicable. 

In addition to the parameters necessary to calculate customer bills, the spreadsheet also 

contained the number of customers in a tariff, total number of customers in a commercial utility, the 

megawatt-hours sold, kilowatt-hours sold per customer, and average price per kilowatt-hour. These 

data were extracted from FERC Fornl I for IOU's and from comparable sources for municipal 

utilities and co-ops. We used these tariff-level data on customers and delivered energy to weight the 

resulting bill and marginal price calculations to represent a national distribution of marginal prices. 

Our calculations did not use a tariff unless it was also possible to collect usage and delivered energy 

data for that individual tariff (as opposed to being able to collect such information only for the entire 

utility or a group of that utility's tariffs). Industrial tariffs, as contrasted to commercial tariffs, have 

not been included in CTAS. This is primarily because the MAISy(Jj) database does not include 

industrial buildings. 

8.1.4 Sample Buildings/Customers 

The collection of buildings contained in CTAS came from MAISy(Jj), a proprietary database 

containing weighted, representative building collections for each state. We selected a subset of 

buildings fromMAIS~, the subsequent weighting of which was intended to create a "prototypical" 

set of buildings that would exist in relatively the same proportions throughout the country, and would 

thus be representative of customers existing within any ofthe modeled utility tariffs. Each sampled 

B-5 
building in MAISY' had a large number of associated characteristics, such as size, annual peak

demand, etc. Also included are hourly load profiles for an entire year. For each of seven states

(California, Washington, Colorado, Illinois, Texas, New York and Georgia) a subset of buildings

was selected for inclusion in CTAS. The hourly load profiles for these buildings were then extracted

from MAISY® and summed up over a month. Then these monthly loads and demand peaks were

inserted into CTAS. For each state, 189 buildings, with their MAISY® building weights, were

included, for a total of 1323 buildings. The 1323 buildings were apportioned to the main types of

commercial buildings used in CBECS 1995 as shown in Table B.3.3

Table B.3: Building Type s in the CTAS Sample

CBECS

Building Type

Percent of Total

Commercial

Buildings in US

Number of

Buildings in

Sample

Percent of

Buildings in

Sample

Education 7.6% 98 7.4%

Food Sales 3.5% 63 4.8%

Food Service 6.7% 91 6.9%

Health Care 2.5% 35 2.6%

Lodging 3.4% 42 3.2%

Mercantile and Service 30.9% 406 30.7%

Office 17.9% 231 17.5%

Public Assembly 7.6% 98 7.4%

Public Order & Safety 2.1% 28 2.1%

Religious 5.0% 63 4.8%

Warehouse & Storage 9.7% 126 9.5%

Other/Vacant 3.0% 42 3.2%

TOTAL 100.0% 1323 100.0%

Generally speaking, commercial customers were assigned to a particular tariff based on their

peak monthly and/or annual demand as measured in kilowatts over a short period of time, e.g. 15 or

30 minutes. We used this demand "window" to determine which of all the tariffs to apply to a

particular building in the CTAS collection. For instance the GS-2 tariff carried by Southern

California Edison was applied to a customer/building whose monthly peak demand is between 20

and 500 kW. This assignment by demand window means that a particular building had bills

calculated using some subset of all the modeled tariffs, typically one tariff per utility.

EE-RM-97-500 COMMENT 104

building in MAISY® had a large number of associated characteristics, such as size, annual peak 

demand, etc. Also included are hourly load profiles for an entire year. For each of seven states 

(California, Washington, Colorado, Illinois, Texas, New York and Georgia) a subset of buildings 

was selected for inclusion in CTAS. The hourly load profiles for these buildings were then extracted 

from MAISY® and summed up over a month. Then these monthly loads and demand peaks were 

inserted into CTAS. F or each state, 189 buildings, with their MAISYrw building weights, were 

included, for a total of 1323 buildings. The 1323 buildings were apportioned to the main types of 

commercial buildings used in CBECS 1995 as shown in Table B.3.3 

Table B.3: Building Types III the CTAS Sampe 

CBECS 

Building Type 

Education 

Food Sales 

Food Service 

Health Care 

Lodging 

Mercantile and Service 

Office 

Public Assembly 

Public Order & Safety 

Religious 

Warehouse & Storage 

OtherN acant 

I TOTAL 

Percent of Total 

Commercial 

Buildings in US 

7.6% 

3.5% 

6.7% 

2.5% 

3.4% 

30.9% 

17.9% 

7.6% 

2.1% 

5.0% 

9.7% 

3.0% 

100.0% 

Number of 

Buildings in 

Sample 

98 

63 

91 

35 

42 

406 

231 

98 

28 

63 

126 

42 

1323 

Percent of 

Buildings in 

Sample 

7.4% 

4.8% 

6.9% 

2.6% 

3.2% 

30.7% 

17.5% 

7.4% 

2.1% 

4.8% 

9.5% 

3.2% 

100.0% 

Generally speaking, commercial customers were assigned to a particular tariffbased on their 

peak monthly and/or annual demand as measured in kilowatts over a short period oftime, e.g. 15 or 

30 minutes. We used this demand "window" to determine which of all the tariffs to apply to a 

particular building in the CTAS collection. For instance the GS-2 tariff carried by Southern 

California Edison was applied to a customer/building whose monthly peak demand is between 20 

and 500 kW. This assignment by demand window means that a particular building had bills 

calculated using some subset of all the modeled tariffs, typically one tariff per utility. 

B-6 

building in MAISY® had a large number of associated characteristics, such as size, annual peak 

demand, etc. Also included are hourly load profiles for an entire year. For each of seven states 

(California, Washington, Colorado, Illinois, Texas, New York and Georgia) a subset of buildings 

was selected for inclusion in CTAS. The hourly load profiles for these buildings were then extracted 

from MAISY® and summed up over a month. Then these monthly loads and demand peaks were 

inserted into CTAS. F or each state, 189 buildings, with their MAISYrw building weights, were 

included, for a total of 1323 buildings. The 1323 buildings were apportioned to the main types of 

commercial buildings used in CBECS 1995 as shown in Table B.3.3 

Table B.3: Building Types in the CTAS Sample 

CBECS 

Building Type 

Education 

Food Sales 

Food Service 

Health Care 

Lodging 

Mercantile and Service 

Office 

Public Assembly 

Public Order & Safety 

Religious 

Warehouse & Storage 

OtherN acant 

I TOTAL 

Percent of Total 

Commercial 

Buildings in US 

7.6% 

3.5% 

6.7% 

2.5% 

3.4% 

30.9% 

17.9% 

7.6% 

2.1% 

5.0% 

9.7% 

3.0% 

100.0% 

Number of 

Buildings in 

Sample 

98 

63 

91 

35 

42 

406 

231 

98 

28 

63 

126 

42 

1323 

Percent of 

Buildings in 

Sample 

7.4% 

4.8% 

6.9% 

2.6% 

3.2% 

30.7% 

17.5% 

7.4% 

2.1% 

4.8% 

9.5% 

3.2% 

100.0% 

Generally speaking, commercial customers were assigned to a particular tariffbased on their 

peak monthly and/or annual demand as measured in kilowatts over a short period oftime, e.g. 15 or 

30 minutes. We used this demand "window" to determine which of all the tariffs to apply to a 

particular building in the CTAS collection. For instance the GS-2 tariff carried by Southern 

California Edison was applied to a customer/building whose monthly peak demand is between 20 

and 500 kW. This assignment by demand window means that a particular building had bills 

calculated using some subset of all the modeled tariffs, typically one tariff per utility. 

B-6 
B.1.5 Commercial Tariffs Spreadsheet (CTAS)

B.1.5.1 CTAS Calculations

CTAS does its work in batch mode; that is, a run is executed and output is produced,

automatically weighted by building type, without any further user intervention. A run consists of

the following steps, nested and repeated as explained:

• Each customer/building is considered in turn.

• For each building a selection is made of applicable tariffs, based on that building's peak

annual demand.

• For each tariff a calculation of a monthly bill in $/month is performed.

• A second calculation with the same month, tariff and building is performed with

decremented monthly energy use and peak demand to obtain a change in the building's

bill representing ballast energy and demand savings under a ballast standard. The

assumed energy use decrement is 5 percent; the demand decrement is 80 percent of 5

percent, that is, 4 percent (see below for derivation of these percentages).

• A monthly marginal price in $/kWh for this month-tariff-building is computed by dividing

the difference between the two bills by the difference between the two energy

consumption levels, e.g., change in $/change in kWh

• An average price in $/kWh is computed by dividing the original, undecremented bill by

the original energy use, e.g. bill $/total kWh.

• The percentage difference between the average and marginal price is calculated by

subtracting the average price from the marginal price and dividing by the marginal price.

• These calculations are repeated for the 12 months of a calendar year.

• Annual values are obtained for average price, marginal price, and percent difference by

summing each month's values over the year and dividing by 12.

• The process is repeated for each applicable tariff for the particular customer/building.

• The next building is considered and the complete process repeated, until all

customer/buildings have been processed.

B.1.5.2 CTAS Output

The output from a CTAS run is written to a comma-separated-values, or "csv" file, with

a record for each building-tariff match. This file, containing the fields shown below, is loaded into

a FoxPro table where additional processing occurs to produce various charts and distributions.

Field Name Field Description

ACCTNO MAISY' customer / building ID number

STATE State from which sample customer was drawn

ANPKKW Building annual peak demand in kW measured over 1 hour

B-7

EE-RM-97-500 COMMENT 104

B.1.S Commercial Tariffs Spreadsheet (CT AS) 

B.1.S.1 CTAS Calculations 

CTAS does its work in batch mode; that is, a run is executed and output is produced, 

automatically weighted by building type, without any further user intervention. A run consists of 

the following steps, nested and repeated as explained: 

• Each customerlbuilding is considered in turn. 

• For each building a selection is made of applicable tariffs, based on that building's peak 

annual demand. 

• For each tariff a calculation of a monthly bill in $/month is performed. 

• A second calculation with the same month, tariff and building is performed with 

decremented monthly energy use and peak demand to obtain a change in the building's 

bill representing ballast energy and demand savings under a ballast standard. The 

assumed energy use decrement is 5 percent; the demand decrement is 80 percent of 5 

percent, that is, 4 percent (see below for derivation of these percentages). 

• A monthly marginal price in $/kWh for this month-tariff-building is computed by dividing 

the difference between the two bills by the difference between the two energy 

consumption levels, e.g., change in $/change in kWh 

• An average price in $/kWh is computed by dividing the original, undecremented bill by 

the original energy use, e.g. bill $/total kWh. 

• The percentage difference between the average and marginal price is calculated by 

subtracting the average price from the marginal price and dividing by the marginal price. 

• These calculations are repeated for the 12 months of a calendar year. 

• Annual values are obtained for average price, marginal price, and percent difference by 

summing each month's values over the year and dividing by 12. 

• The process is repeated for each applicable tariff for the particular customerlbuilding. 

• The next building is considered and the complete process repeated, until all 

customer/buildings have been processed. 

B.1.S.2 CTAS Output 

The output from a CTAS run is written to a comma-separated-values, or "csv" file, with 

a record for each building-tariff match. This file, containing the fields shown below, is loaded into 

a FoxPro table where additional processing occurs to produce various charts and distributions. 

Field Name 

ACCTNO 

STATE 

ANPKKW 

Field Description 

MA1SY® customer / building 1D number 

State from which sample customer was drawn 

Building annual peak demand in kW measured over 1 hour 

B-7 

B.1.S Commercial Tariffs Spreadsheet (CT AS) 

B.1.S.1 CTAS Calculations 

CTAS does its work in batch mode; that is, a run is executed and output is produced, 

automatically weighted by building type, without any further user intervention. A run consists of 

the following steps, nested and repeated as explained: 

• Each customerlbuilding is considered in turn. 

• For each building a selection is made of applicable tariffs, based on that building's peak 

annual demand. 

• For each tariff a calculation of a monthly bill in $/month is performed. 

• A second calculation with the same month, tariff and building is performed with 

decremented monthly energy use and peak demand to obtain a change in the building's 

bill representing ballast energy and demand savings under a ballast standard. The 

assumed energy use decrement is 5 percent; the demand decrement is 80 percent of 5 

percent, that is, 4 percent (see below for derivation of these percentages). 

• A monthly marginal price in $/kWh for this month-tariff-building is computed by dividing 

the difference between the two bills by the difference between the two energy 

consumption levels, e.g., change in $/change in kWh 

• An average price in $/kWh is computed by dividing the original, undecremented bill by 

the original energy use, e.g. bill $/total kWh. 

• The percentage difference between the average and marginal price is calculated by 

subtracting the average price from the marginal price and dividing by the marginal price. 

• These calculations are repeated for the 12 months of a calendar year. 

• Annual values are obtained for average price, marginal price, and percent difference by 

summing each month's values over the year and dividing by 12. 

• The process is repeated for each applicable tariff for the particular customerlbuilding. 

• The next building is considered and the complete process repeated, until all 

customer/buildings have been processed. 

B.1.S.2 CTAS Output 

The output from a CTAS run is written to a comma-separated-values, or "csv" file, with 

a record for each building-tariff match. This file, containing the fields shown below, is loaded into 

a FoxPro table where additional processing occurs to produce various charts and distributions. 

Field Name 

ACCTNO 

STATE 

ANPKKW 

Field Description 

MA1SY® customer / building 1D number 

State from which sample customer was drawn 

Building annual peak demand in kW measured over 1 hour 

B-7 
TARFTYP

ARFINDX

TARPTR

UTILCO

TARFNAME

NCUS

DELVDMWH

BLDGWT

BLDGOPHR

ANAVGRAT

ANMR1.

ANE 1

AVGBILL

AVGBILL 1

AVGKWH

AVGKWHI

Tariff group index defined by demand "window"

Index used in program

Index used in program

Electric utility's name, e.g. Consolidated Edison

Name of particular tariff, e.g. GS-1

Number of customers billed under that tariff

Annual megawatt-hours delivered by utility to customers under

this tariff

Calculated weight of building, derived in part from MAISY'

weight

Weekly hours of operation of building

Annual average electricity price

Annual average electricity marginal price

Annual percent difference between marginal and average

electricity price

Annual average bill, defined as average of 12 monthly bills

Annual average decremented bill, defined as average of 12

monthly average bills

Annual average energy use, defined as average of 12 monthly

energy usage levels

Annual average decremented energy use, defined as average of 12

monthly decremented energy usage levels

B.1.5.3 Demand Decrement Due to Standards - The Role of Lighting Coincidence and

Diversity

In the process of marginal price estimation, it was necessary to account for demand charges

in the calculation of some commercial sector bills. Demand charges depend upon the peak building

demand (kW) each month. For each customer or building analyzed, the monthly energy use (kWh)

and the monthly demand (kW) were based on actual annual kWh and actual summer and winter peak

kW and summer and winter average peak kW. Since a change from energy-efficient magnetic

(EEM) ballasts meeting the current standard to electronic ballasts for four-foot T12 systems would

save about 15 percent of lighting energy used by fluorescent systems and since about 30-35 percent

of building energy use is for lighting, about 5 percent of building energy use will be saved if EEM

ballasts are converted to electronic ballasts. We assumed that the 5 percent reduction occurs every

month.

It was also necessary to estimate the reduction in peak demand if building energy use is

reduced by 5 percent on average. Some analysts prefer to discuss this issue in terms of diversity and

coincidence. Coincidence is the lighting kW during the building peak divided by the lighting peak

kW. In this situation, we expected the coincidence factor to be close to 1.0 since whatever the

maximum number of systems that operate during the lighting peak, a very similar number of systems

B-8

EE-RM-97-500 COMMENT 104

TARFTYP 

ARFINDX 

TARPTR 

UTILCO 

TARFNAME 

NCUS 

DELVDMWH 

BLDGWT 

BLDGOPHR 

ANAVGRAT 

ANMRI. 

ANEI 

AVGBILL 

AVGBILLl 

AVGKWH 

AVGKWHI 

Tariff group index defined by demand "window" 

Index used in program 

Index used in program 

Electric utility's name, e.g. Consolidated Edison 

Name ofpatiicular tariff, e.g. GS-l 

Number of customers billed under that tariff 

Annual megawatt-hours delivered by utility to customers under 

this tariff 

Calculated weight of building, derived in part from MAISY® 

weight 

Weekly hours of operation of building 

Annual average electricity price 

Annual average electricity marginal price 

Annual percent difference between marginal and average 

electricity price 

Annual average bill, defined as average of 12 monthly bills 

Annual average decremented bill, defined as average of 12 

monthly average bills 

Annual average energy use, defined as average of 12 monthly 

energy usage levels 

Annual average decremented energy use, defined as average of 12 

monthly decremented energy usage levels 

B.1.S.3 Demand Decrement Due to Standards - The Role of Lighting Coincidence and 

Diversity 

In the process of marginal price estimation, it was necessary to account for demand charges 

in the calculation of some commercial sector bills. Demand charges depend upon the peak building 

demand (kW) each month. For each customer or building analyzed, the monthly energy use (kWh) 

and the monthly demand (kW) were based on actual annual kWh and actual summer and winter peak 

kW and summer and winter average peak kW. Since a change from energy-efficient magnetic 

(EEM) ballasts meeting the current standard to electronic ballasts for four-foot T12 systems would 

save about 15 percent oflighting energy used by fluorescent systems and since about 30-35 percent 

of building energy use is for lighting, about 5 percent of building energy use will be saved if EEM 

ballasts are converted to electronic ballasts. We assumed that the 5 percent reduction occurs every 

month. 

It was also necessary to estimate the reduction in peak demand if building energy use is 

reduced by 5 percent on average. Some analysts prefer to discuss this issue in terms of diversity and 

coincidence. Coincidence is the lighting kW during the building peak divided by the lighting peak 

kW. In this situation, we expected the coincidence factor to be close to 1.0 since whatever the 

maximum number of systems that operate during the lighting peak, a very similar number of systems 

B-8 

TARFTYP 

ARFINDX 

TARPTR 

UTILCO 

TARFNAME 

NCUS 

DELVDMWH 

BLDGWT 

BLDGOPHR 

ANAVGRAT 

ANMRI. 

ANEI 

AVGBILL 

AVGBILLl 

AVGKWH 

AVGKWHI 

Tariff group index defined by demand "window" 

Index used in program 

Index used in program 

Electric utility's name, e.g. Consolidated Edison 

Name ofpatiicular tariff, e.g. GS-l 

Number of customers billed under that tariff 

Annual megawatt-hours delivered by utility to customers under 

this tariff 

Calculated weight of building, derived in part from MAISY® 

weight 

Weekly hours of operation of building 

Annual average electricity price 

Annual average electricity marginal price 

Annual percent difference between marginal and average 

electricity price 

Annual average bill, defined as average of 12 monthly bills 

Annual average decremented bill, defined as average of 12 

monthly average bills 

Annual average energy use, defined as average of 12 monthly 

energy usage levels 

Annual average decremented energy use, defined as average of 12 

monthly decremented energy usage levels 

B.1.S.3 Demand Decrement Due to Standards - The Role of Lighting Coincidence and 

Diversity 

In the process of marginal price estimation, it was necessary to account for demand charges 

in the calculation of some commercial sector bills. Demand charges depend upon the peak building 

demand (kW) each month. For each customer or building analyzed, the monthly energy use (kWh) 

and the monthly demand (kW) were based on actual annual kWh and actual summer and winter peak 

kW and summer and winter average peak kW. Since a change from energy-efficient magnetic 

(EEM) ballasts meeting the current standard to electronic ballasts for four-foot T12 systems would 

save about 15 percent oflighting energy used by fluorescent systems and since about 30-35 percent 

of building energy use is for lighting, about 5 percent of building energy use will be saved if EEM 

ballasts are converted to electronic ballasts. We assumed that the 5 percent reduction occurs every 

month. 

It was also necessary to estimate the reduction in peak demand if building energy use is 

reduced by 5 percent on average. Some analysts prefer to discuss this issue in terms of diversity and 

coincidence. Coincidence is the lighting kW during the building peak divided by the lighting peak 

kW. In this situation, we expected the coincidence factor to be close to 1.0 since whatever the 

maximum number of systems that operate during the lighting peak, a very similar number of systems 

B-8 
can be expected to operate during the building peak. Edison Electric Institute (EEI) suggested using

0.9 for the default coincidence factor.'

Coincidence factor (C) = lighting kW during building peak/lighting peak kW

The diversity factor is a measure of the percent of the installed kW for lighting used during

the lighting peak. Diversity was already accounted for because the actual building energy usage

(which we have from the MAISY' database) already excluded those systems not in use during some

parts ofthe day. We were instead concerned with lighting average kW because that was, in essence,

reduced by 5 percent in the bill calculator when monthly energy use was reduced by 5 percent. We

assumed that the lighting hours would not change when more efficient ballasts were installed.

Diversity (D) = lighting peak kW/lighting kW installed

Although some analysts might multiply coincidence (C) times diversity (D), that would not

yield the desired ratio of lighting kW during the building peak to lighting average kW.

In relative terms, we assumed demand reduction to be 80 percent of energy savings. We

chose 80 percent because we believe that coincidence is the main factor we need to consider and the

only two data points we had available averaged to 0.80. These two data points were EEI's

suggestion of 0.90 as a default value and data from Massachusetts Electric (also submitted by EEI)

indicating an annual average coincidence factor of approximately 0.70.5 For our case of 5% energy

savings, we assumed that the demand reduction would be 4% of peak kW. This means that if 500

identical fixtures are operating on average, that only 400 (or 80%) are operating during the building

peak demand period . We use the 80% value relative to the average value because the monthly

lighting energy use can be considered to be equal to the average kW times the monthly lighting hours

and we are reducing monthly lighting energy use by a fixed percentage.

B.1.5.4 Weighting Method

The sample of commercial buildings consisted of 1323 buildings from seven states with each

building supplying multiple observations to the final data set, one for each applicable tariff. Each

observation in the final data set represented a unique combination of a building and an applicable

tariff, for a total of 29,133 observations.

The composition of our sample posed a dual weighting problem: distributing weights across

buildings and across tariffs. Our weighting approach solved these problems sequentially. For each

building a weight was derived from its original MAISY' building weight and scaled up to the

national level. This adjusted building weight was then apportioned across all the tariffs that were

applied to the building. A composite weighting factor for each building/tariff pair combined both

of these weighting factors. These weights were based on energy, average monthly kWh usage at the

building level and annual MWh sales for each tariff.

B-9

EE-RM-97-500 COMMENT 104

can be expected to operate during the building peak. Edison Electric Institute (EEl) suggested using 

0.9 for the default coincidence factor.4 

Coincidence factor (C) = lighting kW during building peak/lighting peak kW 

The diversity factor is a measure ofthe percent of the installed kW for lighting used during 

the lighting peak. Diversity was already accounted for because the actual building energy usage 

(which we have from the MAISY® database) already excluded those systems not in use during some 

parts of the day. We were instead concerned with lighting average kW because that was, in essence, 

reduced by 5 percent in the bill calculator when monthly energy use was reduced by 5 percent. We 

assumed that the lighting hours would not change when more efficient ballasts were installed. 

Diversity (D) = lighting peak kW/lighting kW installed 

Although some analysts might multiply coincidence (C) times diversity (D), that would not 

yield the desired ratio of lighting kW during the building peak to lighting average kW. 

In relative terms, we assumed demand reduction to be SO percent of energy savings. We 

chose SO percent because we believe that coincidence is the main factor we need to consider and the 

only two data points we had available averaged to O.SO. These two data points were EEl's 

suggestion of 0.90 as a default value and data from Massachusetts Electric (also submitted by EEl) 

indicating an annual average coincidence factor of approximately 0.70.5 For our case of 5% energy 

savings, we assumed that the demand reduction would be 4% of peak kW. This means that if 500 

identical fixtures are operating on average, that only 400 (or SO%) are operating during the building 

peak demand period. We use the SO% value relative to the average value because the monthly 

lighting energy use can be considered to be equal to the average kW times the monthly lighting hours 

and we are reducing monthly lighting energy use by a fixed percentage. 

B.1.S.4 Weighting Method 

The sample of commercial buildings consisted of 1323 buildings from seven states with each 

building supplying multiple observations to the final data set, one for each applicable tariff. Each 

observation in the final data set represented a unique combination of a building and an applicable 

tariff, for a total of 29,133 observations. 

The composition of our sample posed a dual weighting problem: distributing weights across 

buildings and across tariffs. Our weighting approach solved these problems sequentially. For each 

building a weight was derived from its original MAISY® building weight and scaled up to the 

national level. This adjusted building weight was then apportioned across all the tariffs that were 

applied to the building. A composite weighting factor for each building/tariff pair combined both 

of these weighting factors. These weights were based on energy, average monthly kWh usage at the 

building level and annual MWh sales for each tariff. 

B-9 

can be expected to operate during the building peak. Edison Electric Institute (EEl) suggested using 

0.9 for the default coincidence factor.4 

Coincidence factor (C) = lighting kW during building peak/lighting peak kW 

The diversity factor is a measure ofthe percent of the installed kW for lighting used during 

the lighting peak. Diversity was already accounted for because the actual building energy usage 

(which we have from the MAISY® database) already excluded those systems not in use during some 

parts of the day. We were instead concerned with lighting average kW because that was, in essence, 

reduced by 5 percent in the bill calculator when monthly energy use was reduced by 5 percent. We 

assumed that the lighting hours would not change when more efficient ballasts were installed. 

Diversity (D) = lighting peak kW/lighting kW installed 

Although some analysts might multiply coincidence (C) times diversity (D), that would not 

yield the desired ratio of lighting kW during the building peak to lighting average kW. 

In relative terms, we assumed demand reduction to be SO percent of energy savings. We 

chose SO percent because we believe that coincidence is the main factor we need to consider and the 

only two data points we had available averaged to O.SO. These two data points were EEl's 

suggestion of 0.90 as a default value and data from Massachusetts Electric (also submitted by EEl) 

indicating an annual average coincidence factor of approximately 0.70.5 For our case of 5% energy 

savings, we assumed that the demand reduction would be 4% of peak kW. This means that if 500 

identical fixtures are operating on average, that only 400 (or SO%) are operating during the building 

peak demand period. We use the SO% value relative to the average value because the monthly 

lighting energy use can be considered to be equal to the average kW times the monthly lighting hours 

and we are reducing monthly lighting energy use by a fixed percentage. 

B.1.S.4 Weighting Method 

The sample of commercial buildings consisted of 1323 buildings from seven states with each 

building supplying multiple observations to the final data set, one for each applicable tariff. Each 

observation in the final data set represented a unique combination of a building and an applicable 

tariff, for a total of 29,133 observations. 

The composition of our sample posed a dual weighting problem: distributing weights across 

buildings and across tariffs. Our weighting approach solved these problems sequentially. For each 

building a weight was derived from its original MAISY® building weight and scaled up to the 

national level. This adjusted building weight was then apportioned across all the tariffs that were 

applied to the building. A composite weighting factor for each building/tariff pair combined both 

of these weighting factors. These weights were based on energy, average monthly kWh usage at the 

building level and annual MWh sales for each tariff. 

B-9 
B.1.6 RESULTS

Figure 1 below displays the results of the commercial analysis by showing the distribution

of the differences we derived between marginal electricity prices and average electricity prices.

(These are the outputs from the marginal electricity price analysis; see chapter 4, Figure B.3, for the

distribution of inputs to the LCC analysis). Electricity marginal prices range from 85 percent below

to 51 percent above the average price for the same customer. At the consumption-weighted mean

of the differences, electricity marginal prices are 5.2 percent lower than average prices.` In our LCC

analysis of commercial sector ballasts, we used this distribution of differences along with a

distribution of average commercial sector electricity prices (which we obtained from EIA data on

over 3000 utilities serving commercial customers for the year 1997) to generate a distribution of

marginal electricity prices.

In the LCC analysis of industrial sector ballasts, we used this same distribution of differences

along with a distribution of average industrial sector electricity prices (from EIA data on over 2000

utilities serving industrial customers for the year 1997) to generate a distribution of marginal

electricity prices:

As a result of the sensitivity analysis mentioned earlier (where we included additional

municipal utility tariffs), we found a similar distribution of weighted amounts by which average

prices exceeded marginal prices, the mean of which was -5.6 percent. We also performed a

sensitivity analysis to determine the impact of altering the percentage energy savings and demand

reduction. For a 10 percent energy savings and 5 percent demand reduction, we calculated an

average percent difference from the average price of -5.6 percent, compared to the base case of -5.2

percent.

Table B.4 shows the time series of marginal electricity prices used for the NES analysis,

adjusted from the AEO Reference Case forecast. A factor was used to calculate the marginal price

from the average and marginal prices as follows. The average price for the commercial sector was

7.620/kWh (1997$), which came from the weighted binned distribution of average prices from EIA

1997 Electric Utilities. Note that it differed slightly from the 1997 price reported by AEO 99. For

the industrial sector, the average price was 4.550/kWh (1997$) from a similar distribution from

utilities serving the industrial sector. The adjustment factor was the ratio of the weighted average

of the modeled marginal price of $0.05815 to the weighted average of the average prices of

$0.06119 from the marginal rates analysis (see section B.1 above). This factor reduced the average

prices noted above for each sector by approximately 5 percent.

The individual amounts by which average prices exceeded marginal prices had to be binned to prepare inputs for the

Crystal Ball LCC analysis. The weighted mean of the binned distribution was -5.7 percent.

B-10

EE-RM-97-500 COMMENT 104

B.1.6 RESULTS 

Figure 1 below displays the results ofthe commercial analysis by showing the distribution 

of the differences we derived between marginal electricity prices and average electricity prices. 

(These are the outputs from the marginal electricity price analysis; see chapter 4, Figure B.3, for the 

distribution of inputs to the Lee analysis). Elecuicity marginal prices range from 85 percent below 

to 51 percent above the average price for the same customer. At the consumption-weighted mean 

ofthe differences, electricity marginal prices are 5.2 percent lower than average prices.c In our Lee 

analysis of commercial sector ballasts, we used this distribution of differences along with a 

distribution of average commercial sector elecu'icity prices (which we obtained from EIA data on 

over 3000 utilities serving commercial customers for the year 1997) to generate a distribution of 

marginal electricity prices. 

In the Lee analysis of industrial sector ballasts, we used this same distribution of differences 

along with a distribution of average industrial sector electricity prices (from EIA data on over 2000 

utilities serving industrial customers for the year 1997) to generate a distribution of marginal 

electricity prices. 

As a result of the sensitivity analysis mentioned earlier (where we included additional 

municipal utility tariffs), we found a similar distribution of weighted amounts by which average 

prices exceeded marginal prices, the mean of which was -5.6 percent. We also performed a 

sensitivity analysis to determine the impact of altering the percentage energy savings and demand 

reduction. F or a 10 percent energy savings and 5 percent demand reduction, we calculated an 

average percent difference from the average price of -5.6 percent, compared to the base case of -5.2 

percent. 

Table BA shows the time series of marginal electricity prices used for the NES analysis, 

adjusted from the AEO Reference ease forecast. A factor was used to calculate the marginal price 

from the average and marginal prices as follows. The average price for the commercial sector was 

7.62¢/kWh (1997$), which came from the weighted binned distribution of average prices fromEIA 

1997 Electric Utilities. Note that it differed slightly from the 1997 price reported by AEO 99. For 

the industrial sector, the average price was 4.55¢/kWh (1997$) from a similar distribution from 

utilities serving the industrial sector. The adjustment factor was the ratio of the weighted average 

of the modeled marginal price of $0.05815 to the weighted average of the average prices of 

$0.06119 from the marginal rates analysis (see section B.l above). This factor reduced the average 

prices noted above for each sector by approximately 5 percent. 

C The individual amounts by which average prices exceeded marginal prices had to be binned to prepare inputs for the 

Crystal Ball LCC analysis. The weighted mean of the binned distribution was -5.7 percent. 

B-I0 

B.1.6 RESULTS 

Figure 1 below displays the results ofthe commercial analysis by showing the distribution 

of the differences we derived between marginal electricity prices and average electricity prices. 

(These are the outputs from the marginal electricity price analysis; see chapter 4, Figure B.3, for the 

distribution of inputs to the Lee analysis). Elecuicity marginal prices range from 85 percent below 

to 51 percent above the average price for the same customer. At the consumption-weighted mean 

ofthe differences, electricity marginal prices are 5.2 percent lower than average prices.c In our Lee 

analysis of commercial sector ballasts, we used this distribution of differences along with a 

distribution of average commercial sector elecu'icity prices (which we obtained from EIA data on 

over 3000 utilities serving commercial customers for the year 1997) to generate a distribution of 

marginal electricity prices. 

In the Lee analysis of industrial sector ballasts, we used this same distribution of differences 

along with a distribution of average industrial sector electricity prices (from EIA data on over 2000 

utilities serving industrial customers for the year 1997) to generate a distribution of marginal 

electricity prices. 

As a result of the sensitivity analysis mentioned earlier (where we included additional 

municipal utility tariffs), we found a similar distribution of weighted amounts by which average 

prices exceeded marginal prices, the mean of which was -5.6 percent. We also performed a 

sensitivity analysis to determine the impact of altering the percentage energy savings and demand 

reduction. F or a 10 percent energy savings and 5 percent demand reduction, we calculated an 

average percent difference from the average price of -5.6 percent, compared to the base case of -5.2 

percent. 

Table BA shows the time series of marginal electricity prices used for the NES analysis, 

adjusted from the AEO Reference ease forecast. A factor was used to calculate the marginal price 

from the average and marginal prices as follows. The average price for the commercial sector was 

7.62¢/kWh (1997$), which came from the weighted binned distribution of average prices fromEIA 

1997 Electric Utilities. Note that it differed slightly from the 1997 price reported by AEO 99. For 

the industrial sector, the average price was 4.55¢/kWh (1997$) from a similar distribution from 

utilities serving the industrial sector. The adjustment factor was the ratio of the weighted average 

of the modeled marginal price of $0.05815 to the weighted average of the average prices of 

$0.06119 from the marginal rates analysis (see section B.l above). This factor reduced the average 

prices noted above for each sector by approximately 5 percent. 

C The individual amounts by which average prices exceeded marginal prices had to be binned to prepare inputs for the 

Crystal Ball LCC analysis. The weighted mean of the binned distribution was -5.7 percent. 

B-I0 
Table B.4 Marginal Prices, Commercial and Industrial Sectors

Commercial (cent/kWh) Industrial ('cent/kWh)

1997 7.24 4.32

1998 7.08 4.21

1999 6.97 4.20

2000 6.84 4.16

2005 6.53 4.00

2010 6.26 3.79

2015 5.92 3.54

2020 5.71 3.41

2025 5.71 3.41

2030 5.71 3.41

Figure B.1

Commercial Electricity Marginal Prices

Differences from Annual Average

Margiiial < Average Marginal. > Average

25% .............................................................

10%

5%

0%

Y t

y+ f k

vbyg ; i

0

00 00 00 00 o0 a0 0 0 0 0 0 0 0 00 0

O LO O LU O Ln O LO O LU O LO O

M N -1 -1 N M

% Difference from Average

Mean = -5.2 percent Median = -3.3 percent

Minimum = -85 percent Maximum = +51 percent

B-11

EE-RM-97-500 COMMENT 104

T a bi e B4 , M ' I P , argma rIces, c ommerCla 'I an dId n us t'lS na ec t ors 

25% 

tI) 

CIl 20% .... 

m 

(f) 

~ 15% 

(J 

.... 

.... 

(J 10% 

CIl 

--W 

...... 

o 5% 

'#. 

1997 

1998 

1999 

2000 

2Q05 

2010 

2015 

2020 

2025 

2030 

Commercial (cent/kWh) Industrial (cent/kWh) 

7.24 

7.08 

6.97 

6.84 

6.53 

6.26 

5.92 

5.71 

5.71 

5.71 

Figure B.l 

Commercial Electricity Marginal Prices 

Differences from Annual Average 

4.32 

4.21 

4.20 

4.16 

4.00 

3.79 

3.54 

3.41 

3.41 

3.41 

Marginal < Average Marginal> Average 

!J 

~1 : 

I 

~ 

% Difference from Average 

Mean = -5.2 percent 

Minimum = -85 percent 

B-ll 

Median = -3.3 percent 

Maximum = +51 percent 

I 

T a bi e B4 , M ' I P , argma rIces, c ommerCla 'I an dId n us t'lS na ec t ors 

25% 

tI) 

CIl 20% .... 

m 

(f) 

~ 15% 

(J 

.... 

.... 

(J 10% 

CIl 

--W 

...... 

o 5% 

'#. 

1997 

1998 

1999 

2000 

2Q05 

2010 

2015 

2020 

2025 

2030 

Commercial (cent/kWh) Industrial (cent/kWh) 

7.24 

7.08 

6.97 

6.84 

6.53 

6.26 

5.92 

5.71 

5.71 

5.71 

Figure B.l 

Commercial Electricity Marginal Prices 

Differences from Annual Average 

4.32 

4.21 

4.20 

4.16 

4.00 

3.79 

3.54 

3.41 

3.41 

3.41 

Marginal < Average Marginal> Average 

~------------------------~&~--------------------------~ !"I 

I II 

<f?-

l!l 

~ 

I 

~ 

<f?-

o ~ o 

l!l 

% Difference from Average 

Mean = -5.2 percent 

Minimum = -85 percent 

B-ll 

Median = -3.3 percent 

Maximum = +51 percent 

<f?-

o 

N 

~ o 

l!l 

N 
B.2 NES SPREADSHEET DOCUMENTATION

The following is the documentation for the NES spreadsheet model used for the national

energy savings analysis. See Chapter 5 for details on assumptions and scenarios for the ballast

analysis. The NES model (NES_v4.xls) is found on DOE's website

http://www. eren. doe. gov/buildings/co des_standards/applbrf/ballast.htnil.

B.2.1 Overview

The NES spreadsheet performs calculations of national energy savings based on user inputs

similar to those for the LCC spreadsheet (see Appendix A). These are used along with forecasts of

magnetic ballast shipments and a social discount rate to perform projections for converting from

magnetic ballasts to more efficient ballast options: electronic ballasts and cathode cutout ballasts.

The national energy savings, energy cost savings, equipment costs, and net present value for four

major ballast classes (1 F40T l2, 2F40T12, 2F96T12, and 2F96T 12HO) are forecasted from the start

year through 2030.

The NES spreadsheet operates in Excel 97 or Excel 7 (Windows 95). When the spreadsheet

is opened, always select "Enable Macros" in the Excel pop-up window.

The defaults listed in each section below have been set to model Scenario 1 for the two

ballast trial standards levels (electronic and cathode cutout). The user makes selections to model the

other Scenarios, as described in the instructions in section B.2.3.9 below.

B.2.2 Model Worksheets

The workbook NES_v4.xls includes the following worksheets:

RESULTS contains the summary table of energy savings, benefits (energy cost savings),

equipment costs, and NPV for the four ballast classes and the national totals.

The screen also shows the current inputs for fuel price projection , weights,

delay period, and shipments.

Annual contains annual energy savings (for the Utility, Environmental and Indirect

Employment Analyses and annual benefits and costs (for the Indirect

Employment Analysis).

Engineering contains inputs for the calculations of energy savings and change in equipment

costs for each ballast class.

B-12

EE-RM-97-500 COMMENT 104

B.2 NES SPREADSHEET DOCUMENTATION 

The following is the documentation for the NES spreadsheet model used for the national 

energy savings analysis. See Chapter 5 for details on assumptions and scenarios for the ballast 

analysis. .The NES model (NES_v4.xls) is found on DOE's website 

http://www . eren. doe. gov /buildings/codes _ standards/ app lbrf/ballast.html. 

B.2.1 Overview 

The NES spreadsheet perfol111S calculations of national energy savings based on user inputs 

similar to those for the LCC spreadsheet (see Appendix A). These are used along with forecasts of 

magnetic ballast shipments and a social discount rate to perform projections for converting from 

magnetic ballasts to more efficient ballast options: electronic ballasts and cathode cutout ballasts. 

The national energy savings, energy cost savings, equipment costs, and net present value for four 

major ballast classes (lF40T12, 2F40T12, 2F96T12, and 2F96T12HO) are forecasted from the start 

year through 2030. 

The NES spreadsheet operates in Excel 97 or Excel 7 (Windows 95). When the spreadsheet 

is opened, always select "Enable Macros" in the Excel pop-up window. 

The defaults listed in each section below have been set to model Scenario 1 for the two 

ballast trial standards levels (electronic and cathode cutout). The user makes selections to model the 

other Scenarios, as described in the instructions in section B.2.3.9 below. 

B.2.2 Model Worksheets 

The workbook NES _ v4.xls includes the following worksheets: 

RESULTS 

Annual 

Engineering 

contains the summary tab Ie of energy savings, benefits (energy cost savings), 

equipment costs, and NPV for the four ballast classes and the national totals. 

The screen also shows the current inputs for fuel price projection, weights, 

delay period, and shipments. 

contains annual energy savings (for the Utility, Environmental and Indirect 

Employment Analyses and annual benefits and costs (for the Indirect 

Employment Analysis). 

contains inputs for the calculations of energy savings and change in equipment 

costs for each ballast class. 

B-12 

B.2 NES SPREADSHEET DOCUMENTATION 

The following is the documentation for the NES spreadsheet model used for the national 

energy savings analysis. See Chapter 5 for details on assumptions and scenarios for the ballast 

analysis. .The NES model (NES_v4.xls) is found on DOE's website 

http://www . eren. doe. gov /buildings/codes _ standards/ app lbrf/ballast.html. 

B.2.1 Overview 

The NES spreadsheet perfol111S calculations of national energy savings based on user inputs 

similar to those for the LCC spreadsheet (see Appendix A). These are used along with forecasts of 

magnetic ballast shipments and a social discount rate to perform projections for converting from 

magnetic ballasts to more efficient ballast options: electronic ballasts and cathode cutout ballasts. 

The national energy savings, energy cost savings, equipment costs, and net present value for four 

major ballast classes (lF40T12, 2F40T12, 2F96T12, and 2F96T12HO) are forecasted from the start 

year through 2030. 

The NES spreadsheet operates in Excel 97 or Excel 7 (Windows 95). When the spreadsheet 

is opened, always select "Enable Macros" in the Excel pop-up window. 

The defaults listed in each section below have been set to model Scenario 1 for the two 

ballast trial standards levels (electronic and cathode cutout). The user makes selections to model the 

other Scenarios, as described in the instructions in section B.2.3.9 below. 

B.2.2 Model Worksheets 

The workbook NES _ v4.xls includes the following worksheets: 

RESULTS 

Annual 

Engineering 

contains the summary tab Ie of energy savings, benefits (energy cost savings), 

equipment costs, and NPV for the four ballast classes and the national totals. 

The screen also shows the current inputs for fuel price projection, weights, 

delay period, and shipments. 

contains annual energy savings (for the Utility, Environmental and Indirect 

Employment Analyses and annual benefits and costs (for the Indirect 

Employment Analysis). 

contains inputs for the calculations of energy savings and change in equipment 

costs for each ballast class. 

B-12 
Com Elec Price contains various projections of commercial sector average and marginal

electricity prices.

Ind Elec Price contains various projections of industrial sector average and marginal

electricity prices.

1F40T12 through 2F96T12H0

contain the forecast calculations for the four separate ballast classes.

flee Conv contains conversion factors from kWh to Btus derived from EIA's Annual

Energy Outlook 1999. The results are used to convert the energy savings

results from site TWh to source Quads.

Shipments contains the summary of the shipments forecasts for magnetic ballasts and the

electronic and cathode cutout ballasts that replace them under the standards.

Ballast Retire Schedule contains ballast retirement schedules for the four ballast classes.

Lamp Repl Schedule contains lamp equipment and replacement costs, to account for lamp

replacements over the lifetime of each set of annual ballast shipments. The

equipment costs are different for T12 and T8 lamps. The equipment and

labor costs are different for lamps used with cathode cutout ballasts vs.

magnetic and electronic ballasts (since their lifetime is shortened by CC

ballasts and they are replaced more frequently over the lifetime of the

ballast).

Default Engr contains the default engineering and price data.

Setup is used as an interface between user inputs and the rest of the worksheets.

This sheet also contains the marginal electricity price calculations, calculations

of weights based on user inputs for percentage conversion to electronic and

cathode cutout ballasts and percentage T12 and T8 electronic, temporary

shipments calculations for the Shipments worksheet, and electricity prices for

the graphs in the Fuel Price Projection menu.

B.2.3 Spreadsheet Operation

The user interacts with the spreadsheet by clicking choices or entering data using the

graphical interface. The entry point to the user interface is the LIST INPUTS menu. The LIST

INPUTS menu can be invoked by clicking the [LIST INPUTS] button on the RESULTS worksheet

or by hitting <CRTL>L from any worksheet.

From the LIST INPUTS menu, the user can make selections as well as enter the input

screens for selecting (1) Electricity Price Projection, (2) Effective Date of Ballast Standards and

B-13

EE-RM-97-500 COMMENT 104

Com Elec Price 

Ind Elec Price 

contains various projections of commercial sector average and marginal 

electricity prices. 

contains various projections of industrial sector average and marginal 

electricity prices. 

IF40T12 through 2F96T12HO 

Elec Cony 

Shipments 

contain the forecast calculations for the four separate ballast classes. 

contains conversion factors from kWh to Btus derived from EIA's Annual 

Energy Outlook 1999. The results are used to convert the energy savings 

results from site TWh to source Quads. 

contains the summary of the shipments forecasts for magnetic ballasts and the 

electronic and cathode cutout ballasts that replace them under the standards. 

Ballast Retire Schedule contains ballast retirement schedules for the four ballast classes. 

Lamp Repl Schedule contains lamp equipment and replacement costs, to account for lamp 

replacements over the lifetime of each set of annual ballast shipments. The 

equipment costs are different for TI2 and T8 lamps. The equipment and 

labor costs are different for lamps used with cathode cutout ballasts vs. 

magnetic and electronic ballasts (since their lifetime is shortened by CC 

ballasts and they are replaced more frequently over the lifetime of the 

ballast). 

Default Engr 

Setup 

contains the default engineering and price data. 

is used as an interface between user inputs and the rest of the worksheets. 

This sheet also contains the marginal electricity price calculations, calculations 

of weights based on user inputs for percentage conversion to electronic and 

cathode cutout ballasts and percentage TI2 and T8 electronic, temporary 

shipments calculations for the Shipments worksheet, and electricity prices for 

the graphs in the Fuel Price Projection menu. 

B.2.3 Spreadsheet Operation 

The user interacts with the spreadsheet by clicking choices or entering data using the 

graphical interface. The entry point to the user interface is the LIST INPUTS menu. The LIST 

INPUTS menu can be invoked by clicking the [LIST INPUTS] button on the RESULTS worksheet 

or by hitting <CRTL>L from any worksheet. 

From the LIST INPUTS menu, the user can make selections as well as enter the input 

screens for selecting (1) Electricity Price Projection, (2) Effective Date of Ballast Standards and 

B-13 

Com Elec Price 

Ind Elec Price 

contains various projections of commercial sector average and marginal 

electricity prices. 

contains various projections of industrial sector average and marginal 

electricity prices. 

IF40T12 through 2F96T12HO 

Elec Cony 

Shipments 

contain the forecast calculations for the four separate ballast classes. 

contains conversion factors from kWh to Btus derived from EIA's Annual 

Energy Outlook 1999. The results are used to convert the energy savings 

results from site TWh to source Quads. 

contains the summary of the shipments forecasts for magnetic ballasts and the 

electronic and cathode cutout ballasts that replace them under the standards. 

Ballast Retire Schedule contains ballast retirement schedules for the four ballast classes. 

Lamp Repl Schedule contains lamp equipment and replacement costs, to account for lamp 

replacements over the lifetime of each set of annual ballast shipments. The 

equipment costs are different for TI2 and T8 lamps. The equipment and 

labor costs are different for lamps used with cathode cutout ballasts vs. 

magnetic and electronic ballasts (since their lifetime is shortened by CC 

ballasts and they are replaced more frequently over the lifetime of the 

ballast). 

Default Engr 

Setup 

contains the default engineering and price data. 

is used as an interface between user inputs and the rest of the worksheets. 

This sheet also contains the marginal electricity price calculations, calculations 

of weights based on user inputs for percentage conversion to electronic and 

cathode cutout ballasts and percentage TI2 and T8 electronic, temporary 

shipments calculations for the Shipments worksheet, and electricity prices for 

the graphs in the Fuel Price Projection menu. 

B.2.3 Spreadsheet Operation 

The user interacts with the spreadsheet by clicking choices or entering data using the 

graphical interface. The entry point to the user interface is the LIST INPUTS menu. The LIST 

INPUTS menu can be invoked by clicking the [LIST INPUTS] button on the RESULTS worksheet 

or by hitting <CRTL>L from any worksheet. 

From the LIST INPUTS menu, the user can make selections as well as enter the input 

screens for selecting (1) Electricity Price Projection, (2) Effective Date of Ballast Standards and 

B-13 
Delay Period, (3) Discount Rate and Discount Year, (4) Shipments Forecast, ( 5) Ballast

Assumptions, and (6) Ballast Weights by clicking the corresponding button.

When inside any one of the six input screens, the user can switch over to any other screen

or back to the LIST INPUTS menu by clicking the corresponding button at the bottom.

After making the selections, the user can start the calculation by clicking the

[CALCULATE] button or by hitting <ALT>C. In case the user wants to disregard the selections

that have been made, the setup session can be canceled by clicking the [CANCEL] button in the

LIST INPUTS menu, or the X at the top right of any screen. Note that the RESULTS screen will

still reflect any changes made prior to the last calculation.

In the Ballast Assumptions screen, to return to the default input values, click on the [Load

Defaults] button. If new results were previously calculated, another calculation must be done (using

[CALCULATE] or <ALT>C) in order to produce the default results. This button resets only the

Ballast Assumption and Weights inputs; it does not affect changed inputs from other menus in

LIST INPUTS.

The user may view a summary of the inputs for fuel price projection, weights, and shipments

in the upper right of the RESULTS screen; the summary is updated after each calculation. In

Weights Assumptions, % Converted to Electronic, the first period is listed as "skipped" if there is

no delay period.

IMPORTANT NOTE: Input changes should always be made in the LIST INPUTS menu.

The user may view the impacts on the other worksheets, but editing those sheets could cause

calculation errors, especially if formulae are overwritten.

The exception is the Default Engr worksheet; here the user may change the default

engineering values. These values will then be loaded when Load Defaults is selected. (Results will

then be different than those from the original NES_v.4 model.)

Fuel Price Projection Menu

The user selects one of the five available electricity forecasts for 1999. The selected forecast

trend is used for projecting future marginal electricity prices, which are shown in graphic and table

form for the commercial and industrial sectors. Selecting < Show Comparison> produces.a graph

of the average prices for the five forecasts (the trend line is similar to that of the marginal prices).

The default is the AEO 1999 Reference Case projection.

Marginal prices for both the commercial and industrial sectors are used in the NPV

calculations. The NES model and analysis uses a single point value for each year (rather than a

distribution of marginal prices as used for the LCC analysis). The marginal prices used in this

analysis, calculated from the AEO Reference Case forecast, are shown in Table B.4 above.

B-14

EE-RM-97-500 COMMENT 104

Delay Period, (3) Discount Rate and Discount Year, (4) Shipments Forecast, (5) Ballast 

Assumptions, and (6) Ballast Weights by clicking the cOtTesponding button. 

When inside anyone of the six input screens, the user can switch over to any other screen 

or back to the LIST INPUTS menu by clicking the cOlTesponding button at the bottom. 

After making the selections, the user can start the calculation by clicking the 

[CALCULATE] button or by hitting <AL T>C. In case the user wants to disregard the selections 

that have been made, the setup session can be canceled by clicking the [CANCEL] button in the 

LIST INPUTS menu, or the X at the top right of any screen. Note that the RESULTS screen will 

still reflect any changes made prior to the last calculation. 

In the Ballast Assumptions screen, to return to the default input values, click on the [Load 

Defaults] button. If new results were previously calculated, another calculation must be done (using 

[CALCULA TE] or <AL T>C) in order to produce the default results. This button resets only the 

Ballast Assumption and Weights inputs; it does not affect changed inputs from other menus in 

LIST INPUTS. 

The user may view a summary of the inputs for fuel price projection, weights, and shipments 

in the upper right of the RESULTS screen; the summary is updated after each calculation. In 

Weights Assumptions, % Converted to Electronic, the first period is listed as "skipped" if there is 

no delay period. 

IMPORTANT NOTE: Input changes should always be made in the LIST INPUTS menu. 

The user may view the impacts on the other worksheets, but editing those sheets could cause 

calculation etTors, especially if formulae are overwritten. 

The exception is the Default Engr worksheet; here the user may change the default 

engineering values. These values will then be loaded when Load Defaults is selected. (Results will 

then be different than those from the original NES _vA model.) 

Fuel Price Projection Menu 

The user selects one of the five available electricity forecasts for 1999. The selected forecast 

trend is used for projecting future marginal electricity prices, which are shown in graphic and table 

form for the commercial and industrial sectors. Selecting <Show Comparison> produces a graph 

ofthe average prices for the five forecasts (the trend line is similar to that of the marginal prices). 

The default is the AEO 1999 Reference Case projection. 

Marginal prices for both the commercial and industrial sectors are used in the NPV 

calculations. The NES model and analysis uses a single point value for each year (rather than a 

distribution of marginal prices as used for the LCC analysis). The marginal prices used in this 

analysis, calculated from the AEO Reference Case forecast, are shown in Table BA above. 

B-14 

Delay Period, (3) Discount Rate and Discount Year, (4) Shipments Forecast, (5) Ballast 

Assumptions, and (6) Ballast Weights by clicking the cOtTesponding button. 

When inside anyone of the six input screens, the user can switch over to any other screen 

or back to the LIST INPUTS menu by clicking the cOlTesponding button at the bottom. 

After making the selections, the user can start the calculation by clicking the 

[CALCULATE] button or by hitting <AL T>C. In case the user wants to disregard the selections 

that have been made, the setup session can be canceled by clicking the [CANCEL] button in the 

LIST INPUTS menu, or the X at the top right of any screen. Note that the RESULTS screen will 

still reflect any changes made prior to the last calculation. 

In the Ballast Assumptions screen, to return to the default input values, click on the [Load 

Defaults] button. If new results were previously calculated, another calculation must be done (using 

[CALCULA TE] or <AL T>C) in order to produce the default results. This button resets only the 

Ballast Assumption and Weights inputs; it does not affect changed inputs from other menus in 

LIST INPUTS. 

The user may view a summary of the inputs for fuel price projection, weights, and shipments 

in the upper right of the RESULTS screen; the summary is updated after each calculation. In 

Weights Assumptions, % Converted to Electronic, the first period is listed as "skipped" if there is 

no delay period. 

IMPORTANT NOTE: Input changes should always be made in the LIST INPUTS menu. 

The user may view the impacts on the other worksheets, but editing those sheets could cause 

calculation etTors, especially if formulae are overwritten. 

The exception is the Default Engr worksheet; here the user may change the default 

engineering values. These values will then be loaded when Load Defaults is selected. (Results will 

then be different than those from the original NES _vA model.) 

Fuel Price Projection Menu 

The user selects one of the five available electricity forecasts for 1999. The selected forecast 

trend is used for projecting future marginal electricity prices, which are shown in graphic and table 

form for the commercial and industrial sectors. Selecting <Show Comparison> produces a graph 

ofthe average prices for the five forecasts (the trend line is similar to that of the marginal prices). 

The default is the AEO 1999 Reference Case projection. 

Marginal prices for both the commercial and industrial sectors are used in the NPV 

calculations. The NES model and analysis uses a single point value for each year (rather than a 

distribution of marginal prices as used for the LCC analysis). The marginal prices used in this 

analysis, calculated from the AEO Reference Case forecast, are shown in Table BA above. 

B-14 
Marginal prices for any NES electricity price scenario are calculated in the model as follows.

As shown in cells G5 and H5 of the Setup worksheet, a ratio is used to calculate the 1997 marginal

price from the 1997 EIA weighted average electricity price (see section B.1.6 above). For any

projection selected, the model uses the resulting 1997 estimated marginal price and adjusts electricity

prices for future years based on the price trend for 1997 through 2030 in that projection.

The average prices as well as marginal prices appear in the Com Elec Price and Ind Elec

Price sheets for all the price projections. The marg01inal prices appear also in the Setup sheet under

"Marginal Elec Price Projection."

Year to Start Standard Menu

The assumed effective date for the ballast standards is chosen from the menu. The user may

also select a Delay Period for Replacement Ballast Market in years, assuming that the standards

would apply to ballasts sold in luminaires in the start year and apply to ballasts sold separately (for

the replacement market) a specified number of years later. The default values are the year 2003 with

no delay period (0 years). To select the relative sizes of these markets, use the Weights menu as

described below.

Discounting Menu

The user selects the social discount rate. Note that this is the discount rate used for national

Net Present Value calculations, and is not the same as the discount rate used in the LCC analysis.

The default value is 7 percent.

The user selects the year to which the equipment costs are to be discounted for the NPV

calculation. The default value is 1997.

Shipments Menu

The Shipments menu allows the user to select Decreasing Shipments or Constant

Shipments. In the decreasing shipments scenario, magnetic ballasts shipments decrease to a selected

base value by a certain year, and remain constant at that value through 2030. The user selects these

shipment values in the Reduce Shipments to (millions) and By Year boxes. The default

Decreasing Shipments scenario uses a magnetic ballast shipments forecast for the start year based

on a linear regression through actual 1993 - 1997 shipments for the 2F40 ballast class until 2015 and

assumes that a small number of magnetic ballasts (10 percent of the 1997 shipments) will continue

to be sold each year after 2015. This is called the "Decreasing Shipments to 2015" scenario in the

ballast analysis. To run the "Decreasing Shipments to 2027" scenario, select 2027 from the pulldown menu right under the By Year label. To select another year (other than 2015 and 2027), or

to enter different years for different lamp/ballast combinations, enter the years in the four separate

boxes below.

B-15

EE-RM-97-500 COMMENT 104

Marginal prices for any NES electricity price scenario are calculated in the model as follows. 

As shown in cells 05 and H5 of the Setup worksheet, a ratio is used to calculate the 1997 marginal 

price from the 1997 EIA weighted average electricity price (see section B.1.6 above). For any 

projection selected, the model uses the resulting 1997 estimated marginal price and adjusts electricity 

prices for future years based on the price trend for 1997 through 2030 in that projection. 

The average prices as well as marginal prices appear in the Com Elec Price and Ind Elec 

Price sheets for all the price projections. The marginal prices appear also in the Setup sheet under 

"Marginal Elec Price Projection." 

Year to Start Standard Menu 

The assumed effective date for the ballast standards is chosen from the menu. The user may 

also select a Delay Period for Replacement Ballast Market in years, assuming that the standards 

would apply to ballasts sold in luminaires in the start year and apply to ballasts sold separately (for 

the replacement market) a specified number of years later. The default values are the year 2003 with 

no delay period (0 years). To select the relative sizes of these markets, use the Weights menu as 

described below. 

Discounting Menu 

The user selects the social discount rate. Note that this is the discount rate used for national 

Net Present Value calculations, and is not the same as the discount rate used in the LCC analysis. 

The default value is 7 percent. 

The user selects the year to which the equipment costs are to be discounted for the NPV 

calculation. The default value is 1997. 

Shipments Menu 

The Shipments menu allows the user to select Decreasing Shipments or Constant 

Shipments. In the decreasing shipments scenario, magnetic ballasts shipments decrease to a selected 

base value by a certain year, and remain constant at that value through 2030. The user selects these 

shipment values in the Reduce Shipments to (millions) and By Year boxes. The default 

Decreasing Shipments scenario uses a magnetic ballast shipments forecast for the start year based 

on a linear regression through actual 1993 - 1997 shipments for the 2F40 ballast class until 20 15 and 

assumes that a small number of magnetic ballasts (10 percent ofthe 1997 shipments) will continue 

to be sold each year after 2015. This is called the '~Decreasing Shipments to 2015" scenario in the 

ballast analysis. To run the "Decreasing Shipments to 2027" scenario, select 2027 from the pulldown menu right under the By Year label. To select another year (other than 2015 and 2027), or 

to enter different years for different lamp/ballast combinations, enter the years in the four separate 

boxes below. 

B-15 

Marginal prices for any NES electricity price scenario are calculated in the model as follows. 

As shown in cells 05 and H5 of the Setup worksheet, a ratio is used to calculate the 1997 marginal 

price from the 1997 EIA weighted average electricity price (see section B.1.6 above). For any 

projection selected, the model uses the resulting 1997 estimated marginal price and adjusts electricity 

prices for future years based on the price trend for 1997 through 2030 in that projection. 

The average prices as well as marginal prices appear in the Com Elec Price and Ind Elec 

Price sheets for all the price projections. The marginal prices appear also in the Setup sheet under 

"Marginal Elec Price Projection." 

Year to Start Standard Menu 

The assumed effective date for the ballast standards is chosen from the menu. The user may 

also select a Delay Period for Replacement Ballast Market in years, assuming that the standards 

would apply to ballasts sold in luminaires in the start year and apply to ballasts sold separately (for 

the replacement market) a specified number of years later. The default values are the year 2003 with 

no delay period (0 years). To select the relative sizes of these markets, use the Weights menu as 

described below. 

Discounting Menu 

The user selects the social discount rate. Note that this is the discount rate used for national 

Net Present Value calculations, and is not the same as the discount rate used in the LCC analysis. 

The default value is 7 percent. 

The user selects the year to which the equipment costs are to be discounted for the NPV 

calculation. The default value is 1997. 

Shipments Menu 

The Shipments menu allows the user to select Decreasing Shipments or Constant 

Shipments. In the decreasing shipments scenario, magnetic ballasts shipments decrease to a selected 

base value by a certain year, and remain constant at that value through 2030. The user selects these 

shipment values in the Reduce Shipments to (millions) and By Year boxes. The default 

Decreasing Shipments scenario uses a magnetic ballast shipments forecast for the start year based 

on a linear regression through actual 1993 - 1997 shipments for the 2F40 ballast class until 20 15 and 

assumes that a small number of magnetic ballasts (10 percent ofthe 1997 shipments) will continue 

to be sold each year after 2015. This is called the '~Decreasing Shipments to 2015" scenario in the 

ballast analysis. To run the "Decreasing Shipments to 2027" scenario, select 2027 from the pulldown menu right under the By Year label. To select another year (other than 2015 and 2027), or 

to enter different years for different lamp/ballast combinations, enter the years in the four separate 

boxes below. 

B-15 
The Constant Shipments scenario assumes that magnetic ballast shipments remain constant

at near- 1997 levels through 2030.

By selecting <Show Shipments> on this menu, the user may view the graphs of shipments

for the four lamp-ballast combinations. Note that the 1 F40 graph has two lines; the lower line is for

1-lamp F40T 12 ballasts in one-lamp fixtures, and the upper line is for all 1F40 shipments, including

those in 3-lamp fixtures. The linear regression through historical data for 1993 - 1997 is also shown.

For the default scenario, the 2F40 and 2F96 lines follow this linear regression, while the 1F40 and

2F96/HO lines do not; however, the same year (2015) is chosen for all four lamp-ballast

combinations for simplicity.

Ballast Assumptions Menu

This menu has categories for 1-lamp four-foot, 2 to 4-lamp four-foot, 2F96T12, and

2F96T12HO ballasts. The four-foot categories have data for ballasts in 1-lamp, 2-lamp, 3-lamp

tandem-wired, 3-lamp non-tandem-wired, and 4-lamp fixtures.

In view/edit, the user can observe or adjust:

® Ballast Price, which automatically changes these values, shown for reference:

Total Equip + Install. Cost = Ballast Price + Install. + Lamp Cost, where

Install. + Lamp Cost = Ballast Labor Cost + Initial Lamp Equipment + Lamp Labor

Cost

Note that EEM 3-lamp and 4-lamp ballast prices adjust automatically as 2-lamp and 1-lamp

ballast prices are changed by the user. CC ballast prices for 3- and 4-lamp fixtures are not shown,

but they function in the same manner as the EEM ballast prices.

® Mean Lumens (four-foot fixtures) for 1-lamp and 2-lamp fixtures

® CU (coefficient of utilization) for four-foot fixtures using 2, 3, and 4 lamps

Load Defaults restores the default ballast assumptions.

The default values for mean lumens are found in Chapter 3, Table 3.8. The default CU

values are 1.0; see the discussion in Chapter 3, section 3.6. Other default values in this screen

(annual lighting hours, ballast lifetime, and equipment and labor costs) are the same as those in the

LCC spreadsheet, as described in Chapter 3 and Appendix A.

Weights Menu

This menu allows the user to make three different types of choices, reflecting different

weights or percentages of the total for each category.

B-16

EE-RM-97-500 COMMENT 104

The Constant Shipments scenario assumes that magnetic ballast shipments remain constant 

at near-1997 levels through 2030. 

By selecting <Show Shipments> on this menu, the user may view the graphs of shipments 

for the four lamp-ballast combinations. Note that the 1 F40 graph has two lines; the lower line is for 

I-lamp F 40T 12 ballasts in one-lamp fixtures, and the upper line is for all 1 F 40 shipments, including 

those in 3-lamp fixtures. The linearregression through historical data for 1993 - 1997 is also shown. 

For the default scenario, the 2F40 and 2F96 lines follow this linear regression, while the IF40 and 

2F96/HO lines do not; however, the same year (2015) is chosen for all four lamp-ballast 

combinations for simplicity. 

Ballast Assumptions Menu 

This menu has categories for I-lamp four-foot, 2 to 4-lamp four-foot, 2F96TI2, and 

2F96T12HO ballasts. The four-foot categories have data for ballasts in I-lamp, 2-lamp, 3-lamp 

tandem-wired, 3-lamp non-tandem-wired, and 4-lamp fixtures. 

In view/edit, the user can observe or adjust: 

• Ballast Price, which automatically changes these values, shown for reference: 

Total Equip + Install. Cost = Ballast Price + Install. + Lamp Cost, where 

Install. + Lamp Cost = Ballast Labor Cost + Initial Lamp Equipment + Lamp Labor 

Cost 

Note that EEM 3-lamp and 4-lamp ballast prices adjust automatically as 2-lamp and I-lamp 

ballast prices are changed by the user. CC ballast prices for 3- and 4-lamp fixtures are not shown, 

but they function in the same manner as the EEM ballast prices. 

• Mean Lumens (four-foot fixtures) for I-lamp and 2-lamp fixtures 

• CU (coefficient of utilization) for four-foot fixtures using 2,3, and 4 lamps 

Load Defaults restores the default ballast assumptions. 

The default values for mean lumens are found in Chapter 3, Table 3.8. The default CD 

values are 1.0; see the discussion in Chapter 3, section 3.6. Other default values in this screen 

(annual lighting hours, ballast lifetime, and equipment and labor costs) are the same as those in the 

LCC spreadsheet, as described in Chapter 3 and Appendix A. 

Weights Menu 

This menu allows the user to make three different types of choices, reflecting different 

weights or percentages of the total for each category. 

B-I6 

The Constant Shipments scenario assumes that magnetic ballast shipments remain constant 

at near-1997 levels through 2030. 

By selecting <Show Shipments> on this menu, the user may view the graphs of shipments 

for the four lamp-ballast combinations. Note that the 1 F40 graph has two lines; the lower line is for 

I-lamp F 40T 12 ballasts in one-lamp fixtures, and the upper line is for all 1 F 40 shipments, including 

those in 3-lamp fixtures. The linearregression through historical data for 1993 - 1997 is also shown. 

For the default scenario, the 2F40 and 2F96 lines follow this linear regression, while the IF40 and 

2F96/HO lines do not; however, the same year (2015) is chosen for all four lamp-ballast 

combinations for simplicity. 

Ballast Assumptions Menu 

This menu has categories for I-lamp four-foot, 2 to 4-lamp four-foot, 2F96TI2, and 

2F96T12HO ballasts. The four-foot categories have data for ballasts in I-lamp, 2-lamp, 3-lamp 

tandem-wired, 3-lamp non-tandem-wired, and 4-lamp fixtures. 

In view/edit, the user can observe or adjust: 

• Ballast Price, which automatically changes these values, shown for reference: 

Total Equip + Install. Cost = Ballast Price + Install. + Lamp Cost, where 

Install. + Lamp Cost = Ballast Labor Cost + Initial Lamp Equipment + Lamp Labor 

Cost 

Note that EEM 3-lamp and 4-lamp ballast prices adjust automatically as 2-lamp and I-lamp 

ballast prices are changed by the user. CC ballast prices for 3- and 4-lamp fixtures are not shown, 

but they function in the same manner as the EEM ballast prices. 

• Mean Lumens (four-foot fixtures) for I-lamp and 2-lamp fixtures 

• CU (coefficient of utilization) for four-foot fixtures using 2,3, and 4 lamps 

Load Defaults restores the default ballast assumptions. 

The default values for mean lumens are found in Chapter 3, Table 3.8. The default CD 

values are 1.0; see the discussion in Chapter 3, section 3.6. Other default values in this screen 

(annual lighting hours, ballast lifetime, and equipment and labor costs) are the same as those in the 

LCC spreadsheet, as described in Chapter 3 and Appendix A. 

Weights Menu 

This menu allows the user to make three different types of choices, reflecting different 

weights or percentages of the total for each category. 

B-I6 
In the upper left section of the sheet, % Converted to Electronic allows the ballast market

under an electronic ballast standard to be separated into the New/Renovation segment, for ballasts

sold in luminaires and the Replacement segment, for ballasts sold separately. These weights

(percentages) apply to 4-foot, 8-foot, and 8-foot HO ballasts. These percentages may be selected

separately for the 1st Period and the 2IId Period. The delay period chosen in the Year to Start

Standard menu is automatically displayed to the right as (x) Years Later. If there is a delay period,

the value for the first period for the (Replacement Market) should be set as 0 percent; this is the

default value. This means that the standard will not apply to this percentage of the market during

the delay period, while the percentage comprised by ballasts for new/renovation period (default 70

percent) will be affected. The default value for the ( New/Renovation Market) lst Period is 70

percent. The default values for the second period are 70 percent for the new/renovation market and

30 percent for the replacement market, selected as described in Chapter 5, section 5.3. For the

second period, the two percentages should always add to 100 percent.

In the upper right section, New/Renovation % Converted to... and Replacement %

Converted to... allow the user to select percentages of 4-foot magnetic ballasts converted to either

T8 ERS ballast/lamp systems or T12 ERS systems. These may be selected separately for the

new/renovation and the replacement markets (headings on the left). The user may select a single

value for all 5 system types in the box outlined in red, or individually in the boxes below. The user

selects the T8 percentages and the T12 percentages automatically adjust to equal 100 percent. The

default values are 100 percent T12 ERS.

In the middle section, Ballast Weights in Fixtures shows the percentage of four-foot 2-lamp

magnetic ballasts in fixture types by number of lamps and configuration (2-lamp, 3-lamp tandemwired, 3-lamp non-tandem-wired, and 4-lamp fixtures). The model uses the weights for the years

1997 and 2009, assigning weights linearly to each year in between, and uses the 2009 weights for

all years thereafter. The user may specify the weights for 2009. The default weights for both years

are estimated by LBNL (see section B.3 below). By 2009, the weights are assumed to have saturated

the building stock and are used for all years thereafter.

Load Defaults restores the default weights.

Weights for the Cathode Cutout Standard

In the List Inputs menu lower left corner, For CC Standard, % of EEM Conversion to

allows the user to select the percentage of magnetic ballasts converted to cathode cutout and

percentage to electronic ballasts under a cathode cutout standard. The default value is 100 percent

cathode cutout. The weights selected in Weights for the New/Renovation market segment determine

the percentage of the electronic ballasts that are T12 ERS and T8 ERS.

Shipments Worksheet

The Shipments worksheet contains the shipments that result from user inputs as described

below. These are used as input to the GRIM (Government Regulatory Impact Model). The NES

B-17

EE-RM-97-500 COMMENT 104

In the upper left section of the sheet, % Converted to Electronic allows the ballast market 

under an electronic ballast standard to be separated into the New/Renovation segment, for ballasts 

sold in luminaires and the Replacement segment, for ballasts sold separately. These weights 

(percentages) apply to 4-foot, 8-foot, and 8-foot HO ballasts. These percentages may be selected 

separately for the 1st Period and the 2nd Period. The delay period chosen in the Year to Start 

Standard menu is automatically displayed to the right as (x) Years Later. Ifthere is a delay period, 

the value for the first period for the (Replacement Market) should be set as 0 percent; this is the 

default value. This means that the standard will not apply to this percentage ofthe market during 

the delay period, while the percentage comprised by ballasts for new/renovation period (default 70 

percent) will be affected. The default value for the (New/Renovation Market) r t Period is 70 

percent. The default values for the second period are 70 percent for the new/renovation market and 

30 percent for the replacement market, selected as described in Chapter 5, section 5.3. For the 

second period, the two percentages should always add tol00 percent. 

In the upper right section, New/Renovation % Converted to ••• and Replacement % 

Converted to .•• allow the user to select percentages of 4-foot magnetic ballasts converted to either 

T8 ERS ballast/lamp systems or T12 ERS systems. These may be selected separately for the 

new/renovation and the replacement markets (headings on the left). The user may select a single 

value for all 5 system types in the box outlined in red, or individually in the boxes below. The user 

selects the T8 percentages and the T12 percentages automatically adjust to equal 100 percent. The 

default values are 100 percent T12 ERS. 

In the middle section, Ballast Weights in Fixtures shows the percentage offour-foot 2-lamp 

magnetic ballasts in fixture types by number of lamps and configuration (2-lamp, 3-lamp tandemwired, 3-lamp non-tandem-wired, and 4-lamp fixtures). The model uses the weights for the years 

1997 and 2009, assigning weights linearly to each year in between, and uses the 2009 weights for 

all years thereafter. The user may specify the weights for 2009. The default weights for both years 

are estimated by LBNL (see section B.3 below). By 2009, the weights are assumed to have saturated 

the building stock and are used for all years thereafter. 

Load Defaults restores the default weights. 

Weights for the Cathode Cutout Standard 

In the List Inputs menu lower left comer, For CC Standard, % of EEM Conversion to 

allows the user to select the percentage of magnetic ballasts converted to cathode cutout and 

percentage to electronic ballasts under a cathode cutout standard. The default value is 100 percent 

cathode cutout. The weights selected in Weights for the New/Renovation market segment detemline 

the percentage ofthe electronic ballasts that are T12 ERS and T8 ERS. 

Shipments Worksheet 

The Shipments worksheet contains the shipments that result from user inputs as described 

below. These are used as input to the GRIM (Government Regulatory Impact Model). The NES 

B-17 

In the upper left section of the sheet, % Converted to Electronic allows the ballast market 

under an electronic ballast standard to be separated into the New/Renovation segment, for ballasts 

sold in luminaires and the Replacement segment, for ballasts sold separately. These weights 

(percentages) apply to 4-foot, 8-foot, and 8-foot HO ballasts. These percentages may be selected 

separately for the 1st Period and the 2nd Period. The delay period chosen in the Year to Start 

Standard menu is automatically displayed to the right as (x) Years Later. Ifthere is a delay period, 

the value for the first period for the (Replacement Market) should be set as 0 percent; this is the 

default value. This means that the standard will not apply to this percentage ofthe market during 

the delay period, while the percentage comprised by ballasts for new/renovation period (default 70 

percent) will be affected. The default value for the (New/Renovation Market) r t Period is 70 

percent. The default values for the second period are 70 percent for the new/renovation market and 

30 percent for the replacement market, selected as described in Chapter 5, section 5.3. For the 

second period, the two percentages should always add tol00 percent. 

In the upper right section, New/Renovation % Converted to ••• and Replacement % 

Converted to .•• allow the user to select percentages of 4-foot magnetic ballasts converted to either 

T8 ERS ballast/lamp systems or T12 ERS systems. These may be selected separately for the 

new/renovation and the replacement markets (headings on the left). The user may select a single 

value for all 5 system types in the box outlined in red, or individually in the boxes below. The user 

selects the T8 percentages and the T12 percentages automatically adjust to equal 100 percent. The 

default values are 100 percent T12 ERS. 

In the middle section, Ballast Weights in Fixtures shows the percentage offour-foot 2-lamp 

magnetic ballasts in fixture types by number of lamps and configuration (2-lamp, 3-lamp tandemwired, 3-lamp non-tandem-wired, and 4-lamp fixtures). The model uses the weights for the years 

1997 and 2009, assigning weights linearly to each year in between, and uses the 2009 weights for 

all years thereafter. The user may specify the weights for 2009. The default weights for both years 

are estimated by LBNL (see section B.3 below). By 2009, the weights are assumed to have saturated 

the building stock and are used for all years thereafter. 

Load Defaults restores the default weights. 

Weights for the Cathode Cutout Standard 

In the List Inputs menu lower left comer, For CC Standard, % of EEM Conversion to 

allows the user to select the percentage of magnetic ballasts converted to cathode cutout and 

percentage to electronic ballasts under a cathode cutout standard. The default value is 100 percent 

cathode cutout. The weights selected in Weights for the New/Renovation market segment detemline 

the percentage ofthe electronic ballasts that are T12 ERS and T8 ERS. 

Shipments Worksheet 

The Shipments worksheet contains the shipments that result from user inputs as described 

below. These are used as input to the GRIM (Government Regulatory Impact Model). The NES 

B-17 
user should not change shipments in this sheet. The magnetic ballast shipments from 1998 on are

calculated by the model based on the scenario and inputs chosen by the user in List Inputs,

Shipments menu. The electronic and cathode cutout shipments are calculated by the model based

on user selections in Weights and the percent of EEM Conversion to CC and Electronic in List

Inputs. As described above, the weights for the 2-lamp ballasts in four-foot fixtures vary from 1997

to 2009.

This worksheet contains two alternative sets of shipments from 1997 to 2030. The upper half

shows shipments for the electronic ballast standard (which can include conversion to T 12 as well

as T8, depending on user input), and the lower half for the cathode cutout standard (which can

include some electronic ballast T12 and T8 conversion, depending on user input).

Note that these shipments represent only those ballasts that would replace magnetic ballasts

sold in the base case. Three-lamp fixtures with magnetic ballasts that are not tandem wired being

replaced by electronic ballasts are assumed to use single 3-lamp electronic ballasts, while tandemwired magnetic ballasts are replaced by tandem-wired electronic ballasts. Four-lamp fixtures with

magnetic ballasts being replaced by electronic T8 ballasts are assumed to use single 4-lamp ballasts,

while those with T12 electronic ballast replacements have 2 ballasts. Therefore, the electronic

shipment totals are lower than the magnetic shipment totals.

In the cathode cutout standard, since there are no F96T12 cathode cutout ballasts, the

magnetic shipments are assumed to remain magnetic ballasts.

B.2.4 Instructions for Modeling the Standards Scenarios

This section contains instructions on how to change the Default settings to reflect each

scenario's assumptions.

Scenario IA&5A: Use Default inputs

Scenario IB&5B: In Shipments, change "By Year" value from 2015 to 2027.

Scenario 2A&5A: In List Inputs, change "For CC Standard, % of EEM Conversion" to 30%

from 100%. In Weights, change "% Converted to T8 ERS" to 95% for both

New and Replacement markets.

Scenario 2B&5B: Ditto, and in Shipments, change "By Year" value from 2015 to 2027.

Scenario 3A: In Year to Start Standard, change the "Delay Period" to 5 years. In Weights,

change "% Converted to T8 ERS" to 100% for New/Renovation market and

95% for Replacement market.

Scenario 3B: Ditto, and in Shipments, change "By Year" value from 2015 to 2027.

Scenario 4A: In Year to Start Standard, change the "Delay Period" to 2 years. In Weights,

change "% Converted to T8 ERS" to 100% for New market and 95% for

Replacement market.

Scenario 4B: Ditto, and in Shipments, change "By Year" value from 2015 to 2027.

B-18

EE-RM-97-500 COMMENT 104

user should not change shipments in this sheet. The magnetic ballast shipments from 1998 on are 

calculated by the model based on the scenario and inputs chosen by the user in List Inputs, 

Shipments menu. The electronic and cathode cutout shipments are calculated by the model based 

on user selections in Weights and the percent of EEM Conversion to CC and Electronic in List 

Inputs. As described above, the weights for the 2-lamp ballasts in four-foot fixtures vary from 1997 

to 2009. 

This worksheet contains two alternative sets of shipments from 1997 to 2030. The upper half 

shows shipments for the electronic ballast standard (which can include conversion to T12 as well 

as T8, depending on user input), and the lower half for the cathode cutout standard (which can 

include some electronic ballast TI2 and T8 conversion, depending on user input). 

Note that these shipments represent only those ballasts that would replace magnetic ballasts 

sold in the base case. Three-lamp fixtures with magnetic ballasts that are not tandem wired being 

replaced by electronic ballasts are assumed to use single 3-lamp electronic ballasts, while tandemwired magnetic ballasts are replaced by tandem-wired electronic ballasts. Four-lamp fixtures with 

magnetic ballasts being replaced by electronic T8 ballasts are assumed to use single 4-lamp ballasts, 

while those with TI2 electronic ballast replacements have 2 ballasts. Therefore, the electronic 

shipment totals are lower than the magnetic shipment totals. 

In the cathode cutout standard, since there are no F96T12 cathode cutout ballasts, the 

magnetic shipments are assumed to remain magnetic ballasts. 

B.2.4 Instructions for Modeling the Standards Scenarios 

This section contains instructions on how to change the Default settings to reflect each 

scenario's assumptions. 

Scenario IA&5A: 

Scenario IB&5B: 

Scenario 2A&5A: 

Scenario 2B&5B: 

Scenario 3A: 

Scenario 3B: 

Scenario 4A: 

Scenario 4B: 

Use Default inputs 

In Shipments, change "By Year" value from 2015 to 2027. 

In List Inputs, change "For CC Standard, % of EEM Conversion" to 30% 

from 100%. In Weights, change "% Converted to T8 ERS" to 95% for both 

New and Replacement markets. 

Ditto, and in Shipments, change "By Year" value from 2015 to 2027. 

In Year to Start Standard, change the "Delay Period" to 5 years. In Weights, 

change "% Converted to T8 ERS" to 100% for New/Renovation market and 

95% for Replacement market. 

Ditto, and in Shipments, change "By Year" value from 2015 to 2027. 

In Year to Start Standard, change the "Delay Period" to 2 years. In Weights, 

change "% Converted to T8 ERS" to 1 00% for New market and 95% for 

Replacement market. 

Ditto, and in Shipments, change "By Year" value from 2015 to 2027. 

B-18 

user should not change shipments in this sheet. The magnetic ballast shipments from 1998 on are 

calculated by the model based on the scenario and inputs chosen by the user in List Inputs, 

Shipments menu. The electronic and cathode cutout shipments are calculated by the model based 

on user selections in Weights and the percent of EEM Conversion to CC and Electronic in List 

Inputs. As described above, the weights for the 2-lamp ballasts in four-foot fixtures vary from 1997 

to 2009. 

This worksheet contains two alternative sets of shipments from 1997 to 2030. The upper half 

shows shipments for the electronic ballast standard (which can include conversion to T12 as well 

as T8, depending on user input), and the lower half for the cathode cutout standard (which can 

include some electronic ballast TI2 and T8 conversion, depending on user input). 

Note that these shipments represent only those ballasts that would replace magnetic ballasts 

sold in the base case. Three-lamp fixtures with magnetic ballasts that are not tandem wired being 

replaced by electronic ballasts are assumed to use single 3-lamp electronic ballasts, while tandemwired magnetic ballasts are replaced by tandem-wired electronic ballasts. Four-lamp fixtures with 

magnetic ballasts being replaced by electronic T8 ballasts are assumed to use single 4-lamp ballasts, 

while those with TI2 electronic ballast replacements have 2 ballasts. Therefore, the electronic 

shipment totals are lower than the magnetic shipment totals. 

In the cathode cutout standard, since there are no F96T12 cathode cutout ballasts, the 

magnetic shipments are assumed to remain magnetic ballasts. 

B.2.4 Instructions for Modeling the Standards Scenarios 

This section contains instructions on how to change the Default settings to reflect each 

scenario's assumptions. 

Scenario IA&5A: 

Scenario IB&5B: 

Scenario 2A&5A: 

Scenario 2B&5B: 

Scenario 3A: 

Scenario 3B: 

Scenario 4A: 

Scenario 4B: 

Use Default inputs 

In Shipments, change "By Year" value from 2015 to 2027. 

In List Inputs, change "For CC Standard, % of EEM Conversion" to 30% 

from 100%. In Weights, change "% Converted to T8 ERS" to 95% for both 

New and Replacement markets. 

Ditto, and in Shipments, change "By Year" value from 2015 to 2027. 

In Year to Start Standard, change the "Delay Period" to 5 years. In Weights, 

change "% Converted to T8 ERS" to 100% for New/Renovation market and 

95% for Replacement market. 

Ditto, and in Shipments, change "By Year" value from 2015 to 2027. 

In Year to Start Standard, change the "Delay Period" to 2 years. In Weights, 

change "% Converted to T8 ERS" to 1 00% for New market and 95% for 

Replacement market. 

Ditto, and in Shipments, change "By Year" value from 2015 to 2027. 

B-18 
Scenario 7A: In Year to Start Standard, change the "Effective Date" to 2005 and "Delay

Period" to 5 years. In Weights, change "% Converted to T8 ERS" to 100%

for New/Renovation market and 95% for Replacement market for all 5 types.

Scenario 7B: Ditto, and in Shipments, change "By Year" value from 2015 to 2027.

Scenario 7C.• Use inputs for 7A, and in Shipments, select "Constant Shipments."

B.2.5. Modeling the Regulatory Impact Analysis Alternative Cases

We used the NES model for the Regulatory Impact Analysis in the RIA section of this

document. Refer to that section for discussion of the basis of the assumptions. As defined in that

section, "conversion rate" means the percentage of ballasts that would be magnetic for any year in

the base case that are electronic T8 in the alternative case (the base case already assumes that some

ballasts would be electronic.) The standards cases shown in Chapter 5 have a 100 percent

conversion rate to electronic ballasts (either T12 or T8).

We implemented the conversion rate in Weights, "% Converted to Electronic" for the

New/Renovation Market for both periods, using 0 percent in the Replacement Market. Whenever

0 percent is used in the two New/Renovation fields, the NES model requires a non-zero value

because of calculation equations, so we used 0.0001d. We assumed that the policy would affect this

market (in the model this input simply means that x percent of the total market is affected, and the

results are the same as if the input were applied to the other market segment). In Weights, "%

Converted to T8ERS," we used 100 percent for both the New/Renovation and the Replacement

Markets.

Following are instructions for modeling the RIA cases. To adjust the conversion rates, follow

the method in the paragraph above. Each case was run under the 2027 Shipments scenario; adjust

the "By Year" values in the Shipments menu accordingly. The Start Year was 2003 for all the

alternative policies except Voluntary Efficiency Targets, which was modeled with a start year of

2005.

Tax Credits to Consumers

Approach: Reduce the incremental ballast price by 50 percent for 1-lamp and 2-lamp fourfoot and all 8-foot and 8-foot HO ballasts. Use a 7 percent conversion rate.

Method: Reduce the ballast price by half in 1F40T12, 2F40T12 (2-lamp and 3-lamp tandem

wired), 2F96T12, and 2F96H®T12 worksheets, in the cells for Unit Incr. Pricefrorn EEM(for fourfoot systems adjust the T8 ERS price and for eight-foot systems adjust the EIS or ERS price). In

Weights, use a 7 percent conversion rate as described above. (Note: It would be difficult to adjust

the conversion rate so that it didn't apply to 3-lamp non-tandem-wired and 4-lamp ballasts; we

'Note also that the percentage values after the decimal point no longer appear after the calculation, but they do affect

the results.

B-19

EE-RM-97-500 COMMENT 104

Scenario 7 A: 

Scenario 7B: 

Scenario 7 C: 

In Year to Start Standard, change the "Effective Date" to 2005 and "Delay 

Period" to 5 years. In Weights, change "% Converted to T8 ERS" to 100% 

for New/Renovation market and 95% for Replacement market for all 5 types. 

Ditto, and in Shipments, change "By Year" value from 2015 to 2027. 

Use inputs for 7 A, and in Shipments, select "Constant Shipments." 

B.2.5 Modeling the Regulatory Impact Analysis Alternative Cases 

We used the NES model for the Regulatory Impact Analysis in the RIA section of this 

document. Refer to that section for discussion ofthe basis of the assumptions. As defined in that 

section, "conversion rate" means the percentage of ballasts that would be magnetic for any year in 

the base case that are electronic T8 in the alternative case (the base case already assumes that some 

ballasts would be electronic.) The standards cases shown in Chapter 5 have a 100 percent 

conversion rate to electronic ballasts (either T12 or T8). 

We implemented the conversion rate in Weights, "% Converted to Electronic" for the 

New/Renovation Market for both periods, using 0 percent in the Replacement Market. Whenever 

o percent is used in the two New/Renovation fields, the NES model requires a non-zero value 

because of calculation equations, so we used 0.000 I d. We assumed that the policy would affect this 

market (in the model this input simply means that x percent of the total market is affected, and the 

results are the same as if the input were applied to the other market segment). In Weights, "% 

Converted to T8ERS," we used 100 percent for both the New/Renovation and the Replacement 

Markets. 

Following are instructions for modeling the RIA cases. To adjust the conversion rates, follow 

the method in the paragraph above. Each case was run under the 2027 Shipments scenario; adjust 

the "By Year" values in the Shipments menu accordingly. The Start Year was 2003 for all the 

alternative policies except Voluntary Efficiency Targets, which was modeled with a start year of 

2005. 

Tax Credits to Consumers 

Approach: Reduce the incremental ballast price by 50 percent for I-lamp and 2-lamp fourfoot and all8-foot and 8-foot HO ballasts. Use a 7 percent conversion rate. 

Method: Reduce the ballast price by half in IF40T12, 2F40T12 (2-lamp and 3-lamp tandem 

wired), 2F96T12, and 2F96HOT12 worksheets, in the cells for Unit Incl'. Price from EEM (for fourfoot systems adjust the T8 ERS price and for eight-foot systems adjust the EIS or ERS price). In 

Weights, use a 7 percent conversion rate as described above. (Note: It would be difficult to adjust 

the conversion rate so that it didn't apply to 3-lamp non-tandem-wired and 4-lamp ballasts; we 

'Note also that the percentage values after the decimal point no longer appear after the calculation, but they do affect 

the results. 

B-19 

Scenario 7 A: 

Scenario 7B: 

Scenario 7 C: 

In Year to Start Standard, change the "Effective Date" to 2005 and "Delay 

Period" to 5 years. In Weights, change "% Converted to T8 ERS" to 100% 

for New/Renovation market and 95% for Replacement market for all 5 types. 

Ditto, and in Shipments, change "By Year" value from 2015 to 2027. 

Use inputs for 7 A, and in Shipments, select "Constant Shipments." 

B.2.5 Modeling the Regulatory Impact Analysis Alternative Cases 

We used the NES model for the Regulatory Impact Analysis in the RIA section of this 

document. Refer to that section for discussion ofthe basis of the assumptions. As defined in that 

section, "conversion rate" means the percentage of ballasts that would be magnetic for any year in 

the base case that are electronic T8 in the alternative case (the base case already assumes that some 

ballasts would be electronic.) The standards cases shown in Chapter 5 have a 100 percent 

conversion rate to electronic ballasts (either T12 or T8). 

We implemented the conversion rate in Weights, "% Converted to Electronic" for the 

New/Renovation Market for both periods, using 0 percent in the Replacement Market. Whenever 

o percent is used in the two New/Renovation fields, the NES model requires a non-zero value 

because of calculation equations, so we used 0.000 I d. We assumed that the policy would affect this 

market (in the model this input simply means that x percent of the total market is affected, and the 

results are the same as if the input were applied to the other market segment). In Weights, "% 

Converted to T8ERS," we used 100 percent for both the New/Renovation and the Replacement 

Markets. 

Following are instructions for modeling the RIA cases. To adjust the conversion rates, follow 

the method in the paragraph above. Each case was run under the 2027 Shipments scenario; adjust 

the "By Year" values in the Shipments menu accordingly. The Start Year was 2003 for all the 

alternative policies except Voluntary Efficiency Targets, which was modeled with a start year of 

2005. 

Tax Credits to Consumers 

Approach: Reduce the incremental ballast price by 50 percent for I-lamp and 2-lamp fourfoot and all8-foot and 8-foot HO ballasts. Use a 7 percent conversion rate. 

Method: Reduce the ballast price by half in IF40T12, 2F40T12 (2-lamp and 3-lamp tandem 

wired), 2F96T12, and 2F96HOT12 worksheets, in the cells for Unit Incl'. Price from EEM (for fourfoot systems adjust the T8 ERS price and for eight-foot systems adjust the EIS or ERS price). In 

Weights, use a 7 percent conversion rate as described above. (Note: It would be difficult to adjust 

the conversion rate so that it didn't apply to 3-lamp non-tandem-wired and 4-lamp ballasts; we 

'Note also that the percentage values after the decimal point no longer appear after the calculation, but they do affect 

the results. 

B-19 
assumed that the existence of rebates for 1- and 2-lamp ballasts would influence these systems,

whose price differential was minimal or negative, to convert to electronic at the same rate.)

Tax Credits to Manufacturers

Approach: Reduce incremental ballast price by $0.04.

Method.' Reduce the Unit Incr. Price from EEM in the 4 worksheets for all systems.

Rebates to Consumers

Approach: Reduce the incremental price by 50 percent. Use a 12 percent conversion rate.

Method: Implement in a similar manner to Tax Credits to Consumers, but with a 12 percent

conversion rate in Weights.

Voluntary Targets, 10 Years

Approach: Assume that the entire market experiences a 10-year delay after 2005.

Method: In Year to Start Standard, select 2005 as the Effective Date and select a 10 year

delay period. In Weights, "Pt Period", use 0 percent for both market segments.

Voluntary Targets, 5 Years

Approach: Assume that the entire market experiences a 5-year delay after 2005.

Method' In Year to Start Standard, select 2005 as the Effective Date and select a 5 year

delay period. In Weights, "l u Period", use 0 percent for both market segments.

Labels and Consumer Education, Current Technologies

Approach: Assume a 3 percent conversion rate.

Method: Use a 3 percent conversion rate in Weights.

Enhanced Labeling and Consumer Education, Alternative Technologies

Approach: Increase ballast kWh savings by 40 percent for F40 and F96. Assume no

daylighting potential for industrial sector F96H0 . Increase electronic ballast incremental cost by $7.

Assume 0.6 percent conversion rate to dimming ballasts. Assume that consumers not converting to

dimming ballasts would still convert to regular electronic ballasts due to consumer education.

B-20

EE-RM-97-500 COMMENT 104

assumed that the existence of rebates for 1- and 2-lamp ballasts would influence these systems, 

whose price differential was minimal or negative, to convert to electronic at the same rate.) 

Tax Credits to Manufacturers 

Approach: Reduce incremental ballast price by $0.04. 

Method: Reduce the Unit Incr. Price from EEM in the 4 worksheets for all systems. 

Rebates to Consumers 

Approach: Reduce the incremental price by 50 percent. Use a 12 percent conversion rate. 

Method: Implement in a similar manner to Tax Credits to Consumers, but with a 12 percent 

conversion rate in Weights. 

Voluntary Targets, lOY ears 

Approach.' Assume that the entire market experiences a 10-year delay after 2005. 

Method: In Year to Start Standard, select 2005 as the Effective Date and select a 10 year 

delay period. In Weights, "1 st Period", use 0 percent for both market segments. 

Voluntary Targets, 5 Years 

Approach: Assume that the entire market experiences a 5-year delay after 2005. 

Method: In Year to Start Standard, select 2005 as the Effective Date and select a 5 year 

delay period. In Weights, "1 st Period", use 0 percent for both market segments. 

Labels and Consumer Education, CUlTent Technologies 

Approach: Assume a 3 percent conversion rate. 

Method: Use a 3 percent conversion rate in Weights. 

Enhanced Labeling and Consumer Education, Alternative Technologies 

Approach: Increase ballast kWh savings by 40 percent for F40 and F96. Assume no 

daylightingpotential for industrial sector F96HO. Increase electronic ballast incremental cost by $7. 

Assume 0.6 percent conversion rate to dimming ballasts. Assume that consumers not converting to 

dimming ballasts would still conveli to regular electronic ballasts due to consumer education. 

B-20 

assumed that the existence of rebates for 1- and 2-lamp ballasts would influence these systems, 

whose price differential was minimal or negative, to convert to electronic at the same rate.) 

Tax Credits to Manufacturers 

Approach: Reduce incremental ballast price by $0.04. 

Method: Reduce the Unit Incr. Price from EEM in the 4 worksheets for all systems. 

Rebates to Consumers 

Approach: Reduce the incremental price by 50 percent. Use a 12 percent conversion rate. 

Method: Implement in a similar manner to Tax Credits to Consumers, but with a 12 percent 

conversion rate in Weights. 

Voluntary Targets, lOY ears 

Approach.' Assume that the entire market experiences a 10-year delay after 2005. 

Method: In Year to Start Standard, select 2005 as the Effective Date and select a 10 year 

delay period. In Weights, "1 st Period", use 0 percent for both market segments. 

Voluntary Targets, 5 Years 

Approach: Assume that the entire market experiences a 5-year delay after 2005. 

Method: In Year to Start Standard, select 2005 as the Effective Date and select a 5 year 

delay period. In Weights, "1 st Period", use 0 percent for both market segments. 

Labels and Consumer Education, CUlTent Technologies 

Approach: Assume a 3 percent conversion rate. 

Method: Use a 3 percent conversion rate in Weights. 

Enhanced Labeling and Consumer Education, Alternative Technologies 

Approach: Increase ballast kWh savings by 40 percent for F40 and F96. Assume no 

daylightingpotential for industrial sector F96HO. Increase electronic ballast incremental cost by $7. 

Assume 0.6 percent conversion rate to dimming ballasts. Assume that consumers not converting to 

dimming ballasts would still conveli to regular electronic ballasts due to consumer education. 

B-20 
Method: In the 1F40T12, 2F40T12, and 2F96T12 worksheets, increase Unit kWh Savings

from EEMby 40 percent. In the same worksheets, increase Unit Incr. Price from EEMby $7. In

Weights, use a 0.6 percent conversion rate.

Run another case with default kWh savings and default ballast prices and a 2.4 percent

conversion rate (3 percent - 0.6 percent) in Weights. Add the results to those from the first run.

Government Purchases

Approach: Use a 10 percent conversion rate.

Method- Use a 10 percent conversion rate in Weights.

Policy 9. Lighting Research

Approach: Increase the incremental kWh savings by 40 percent, incremental cost by $7. Use

a conversion rate of 1.6 percent and a time delay of 5 years. Assume these impacts are also

achievable in the industrial sector.

Method: In the 1F40T12, 2F40T12, 2F96T12, and 2F96T12H® worksheets, increase Unit

kWh Savings from EEMby 40 percent. In the same worksheets, increase Unit Incr. Price from EEM

by $7. In Weights, use a 1.6 percent conversion rate (may be entered as 1.1 percent New/Renovation

and 0.5 percent Replacement to represent their relative sizes). In Year to Start Standard, use a 5

year delay period. In Weights, "1st Period", use 0 percent for both market segments.

Building Codes

Approach: Use a 1 percent conversion rate for the 2015 scenario and a 3 percent conversion

rate for the 2027 scenario. Assume that the code takes effect in 2005.

Method- In the 2015 base case, use a 1 percent conversion rate in Weights. In Year to Start

Standard, use a 2 year delay period. In Weights, "I" Period", use 0 percent for both market

segments.

In the 2027 base case, assume 3 percent conversion rate in Weights. In Year to Start

Standard, use a 2 year delay period. In Weights, "I" Period", use 0 percent for both market

segments.

B.3 SHIPMENTS AND BALLAST WEIGHTS

The shipments scenarios used for the NES analysis are described in Chapter 5 (sections 5.2.1

and 5.3.1).

B-21

EE-RM-97-500 COMMENT 104

Method: In the IF40T12, 2F40T12, and 2F96T12 worksheets, increase Unit kWh Savings 

from EEMby 40 percent. In the same worksheets, increase Unit Incr. Price from EEMby $7. In 

Weights, use a 0.6 percent conversion rate. 

Run another case with default kWh savings and default ballast prices and a 2.4 percent 

conversion rate (3 percent - 0.6 percent) in Weights. Add the results to those from the first run. 

Government Purchases 

Approach: Use a 10 percent conversion rate. 

Method: Use a 10 percent conversion rate in Weights. 

Policy 9. Lighting Research 

Approach: Increase the incremental kWh savings by 40 percent, incremental cost by $7. Use 

a conversion rate of 1.6 percent and a time delay of 5 years. Assume these impacts are also 

achievable in the industrial sector. 

Method' In the IF40T12, 2F40T12, 2F96T12, and 2F96T12HO worksheets, increase Unit 

kWh Savings from EEMby 40 percent. In the same worksheets, increase Unit Incr. Price from EEM 

by$7. In Weights, use a 1.6 percent conversion rate (maybe entered as 1.1 percent New/Renovation 

and 0.5 percent Replacement to represent their relative sizes). In Year to Start Standard, use a 5 

year delay period. In Weights, "1 sl Period", use 0 percent for both market segments. 

Building Codes 

Approach: Use a 1 percent conversion rate for the 2015 scenario and a 3 percent conversion 

rate for the 2027 scenario. Assume that the code takes effect in 2005. 

Method: In the 2015 base case, use a 1 percent conversion rate in Weights. In Year to Start 

Standard, use a 2 year delay period. In Weights, "lsI Period", use 0 percent for both market 

segments. 

In the 2027 base case, assume 3 percent conversion rate in Weights. In Year to Start 

Standard, use a 2 year delay period. In Weights, "lSI Period", use 0 percent for both market 

segments. 

8.3 SIDPMENTS AND BALLAST WEIGHTS 

The shipments scenarios used for the NES analysis are described in Chapter 5 (sections 5.2.1 

and 5.3.1). 

B-21 

Method: In the IF40T12, 2F40T12, and 2F96T12 worksheets, increase Unit kWh Savings 

from EEMby 40 percent. In the same worksheets, increase Unit Incr. Price from EEMby $7. In 

Weights, use a 0.6 percent conversion rate. 

Run another case with default kWh savings and default ballast prices and a 2.4 percent 

conversion rate (3 percent - 0.6 percent) in Weights. Add the results to those from the first run. 

Government Purchases 

Approach: Use a 10 percent conversion rate. 

Method: Use a 10 percent conversion rate in Weights. 

Policy 9. Lighting Research 

Approach: Increase the incremental kWh savings by 40 percent, incremental cost by $7. Use 

a conversion rate of 1.6 percent and a time delay of 5 years. Assume these impacts are also 

achievable in the industrial sector. 

Method' In the IF40T12, 2F40T12, 2F96T12, and 2F96T12HO worksheets, increase Unit 

kWh Savings from EEMby 40 percent. In the same worksheets, increase Unit Incr. Price from EEM 

by$7. In Weights, use a 1.6 percent conversion rate (maybe entered as 1.1 percent New/Renovation 

and 0.5 percent Replacement to represent their relative sizes). In Year to Start Standard, use a 5 

year delay period. In Weights, "1 sl Period", use 0 percent for both market segments. 

Building Codes 

Approach: Use a 1 percent conversion rate for the 2015 scenario and a 3 percent conversion 

rate for the 2027 scenario. Assume that the code takes effect in 2005. 

Method: In the 2015 base case, use a 1 percent conversion rate in Weights. In Year to Start 

Standard, use a 2 year delay period. In Weights, "lsI Period", use 0 percent for both market 

segments. 

In the 2027 base case, assume 3 percent conversion rate in Weights. In Year to Start 

Standard, use a 2 year delay period. In Weights, "lSI Period", use 0 percent for both market 

segments. 

8.3 SIDPMENTS AND BALLAST WEIGHTS 

The shipments scenarios used for the NES analysis are described in Chapter 5 (sections 5.2.1 

and 5.3.1). 

B-21 
This section describes the calculations of the "ballast weights" assumed for two-lamp

magnetic ballasts in four-lamp fixtures for use in the NES model. Two-lamp ballasts may be used

in two-lamp fixtures, three-lamp tandem-wired fixtures, three-lamp non-tandem wired fixtures (with

a one-lamp ballast), and four-lamp fixtures.

The goal was to estimate the weights, or percentages, oftwo-lamp magnetic ballasts that were

used in each of these fixture types. The first step was to estimate the "fixture weights" for

one-, two-, three-, and four-lamp fixtures, and the second step was to estimate the "ballast weights."

Both types of weights were estimated for the year 1997 and the year 2009, which is the midpoint

between the year the standard starts, 2003, and the year the magnetic ballast shipments reach their

base level in the "Decreasing Shipments to 2015" scenario. The NES model uses the ballast weights

for these two years and estimates those for the intervening and succeeding years. See section B.2.3,

Weights Menu, for discussion of user inputs.

The fixture weights (and therefore the ballast weights) were assumed to be changing over

time. Fixture weights in existing buildings were taken from the Xenergy database and were assumed

to represent the year 1995. Fixture weights for new buildings were estimated from the Bureau of

Census report on Electric Lighting Fixtures (MA-36L) for 1997, which reports the fluorescent fixture

sales by number of lamps for common fixture types. We assumed that the fixtures with the 1995

weights would be replaced by those with the new building fixture rates, and estimated that fixtures

in new buildings or in renovated systems had an average turnover rate of 16 years. The resulting

fixture weights for existing buildings in 1997 and in 2009 are shown in Table B.5. By 2009, the new

weights are assumed to have saturated the building stock and are used in the NES model for all years

thereafter. Note that three-lamp fixtures are becoming increasingly popular.

From these fixture weights, we calculated the ballast weights, making the following

assumptions: Two-lamp fixtures each had 1 two-lamp ballast; three-lamp fixtures were 20 percent

tandem wired with 1.5 two-lamp ballasts per fixture and 80 percent non-tandem-wired with 1 twolamp ballast (and a one-lamp ballast); and four-lamp fixtures had 2 two-lamp ballasts. The resulting

ballast weights for 1997 and for 2009 are shown in Table B.6. These are the weights used in the

NES analysis. As noted in section B.2.3, the weights for 1997 are fixed in the NES model, but the

user can adjust the 2009 ballast weights.

Table B.5. Fluorescent Fixture Weights by Number of Lamps (percent of market)

Fixture Type Existing

1995

New Existing

1997

Existing

2009

One-lamp 9% 7% 9% 7%

Two-lamp 51 % 38 % 49 % 40 %

Three-lamp 7% 24 % 10 % 22 %

Four-lamp 33 % 31 % 33 % 31 %

B-22

EE-RM-97-500 COMMENT 104

This section describes the calculations of the "ballast weights" assumed for two-lamp 

magnetic ballasts in four-lamp fixtures for use in the NES model. Two-lamp ballasts may be used 

in two-lamp fixtures, three-lamp tandem-wired fixtures, three-lamp non-tandem wired fixtures (with 

a one-lamp ballast), and four-lamp fixtures. 

The goal was to estimate the weights, or percentages, of two-lamp magnetic ballasts that were 

used in each of these fixture types. The first step was to estimate the "fixture weights" fo~ 

one-, two-, three-, and four-lamp fixtures, and the second step was to estimate the "ballast weights." 

Both types of weights were estimated for the year 1997 and the year 2009, which is the midpoint 

between the year the standard statts, 2003, and the year the magnetic ballast shipments reach their 

base level in the "Decreasing Shipments to 2015" scenario. The NES model uses the ballast weights 

for these two years and estimates those for the intervening and succeeding years. See section B.2.3, 

Weights Menu, for discussion of user inputs. 

The fixture weights (and therefore the ballast weights) were assumed to be changing over 

time. Fixture weights in existing buildings were taken from the Xenergy database and were assumed 

to represent the year 1995. Fixture weights for new buildings were estimated from the Bureau of 

Census report on Electric Lighting Fixtures (MA -36L) for 1997, which reports the fluorescent fixture 

sales by number oflamps for common fixture types. We assumed that the fixtures with the 1995 

weights would be replaced by those with the new building fixture rates, and estimated that fixtures 

in new buildings or in renovated systems had an average turnover rate of 16 years. The resulting 

fixture weights for existing buildings in 1997 and in 2009 are shown in Table B.5. By 2009, the new 

weights are assumed to have saturated the building stock and are used in the NES model for all years 

thereafter. Note that three-lamp fixtures are becoming increasingly popular. 

From these fixture weights, we calculated the ballast weights, making the following 

assumptions: Two-lamp fixtures each had 1 two-lamp ballast; three-lamp fixtures were 20 percent 

tandem wired with 1.5 two-lamp ballasts per fixture and 80 percent non-tandem-wired with 1 twolamp ballast (and a one-lamp ballast); and four-lamp fixtures had 2 two-lamp ballasts. The resulting 

ballast weights for 1997 and for 2009 are shown in Table B.6. These are the weights used in the 

NES analysis. As noted in section B.2.3, the weights for 1997 are fixed in the NES model, but the 

user can adjust the 2009 ballast weights. 

T a bi e B 5 . . FI uorescen t F' IX t ure w· eigi ht s b N b Iy urn er 0 fL amps ( lpercen t 0 f mar k e t) 

Fixture Type Existing New Existing Existing 

1995 1997 2009 

One-lamp 9% 7% 9% 7% 

Two-lamp 51 % 38% 49% 40% 

Three-lamp 7% 24% 10% 22% 

Four-lamp 33 % 31 % 33 % 31 % 

B-22 

This section describes the calculations of the "ballast weights" assumed for two-lamp 

magnetic ballasts in four-lamp fixtures for use in the NES model. Two-lamp ballasts may be used 

in two-lamp fixtures, three-lamp tandem-wired fixtures, three-lamp non-tandem wired fixtures (with 

a one-lamp ballast), and four-lamp fixtures. 

The goal was to estimate the weights, or percentages, of two-lamp magnetic ballasts that were 

used in each of these fixture types. The first step was to estimate the "fixture weights" fo~ 

one-, two-, three-, and four-lamp fixtures, and the second step was to estimate the "ballast weights." 

Both types of weights were estimated for the year 1997 and the year 2009, which is the midpoint 

between the year the standard statts, 2003, and the year the magnetic ballast shipments reach their 

base level in the "Decreasing Shipments to 2015" scenario. The NES model uses the ballast weights 

for these two years and estimates those for the intervening and succeeding years. See section B.2.3, 

Weights Menu, for discussion of user inputs. 

The fixture weights (and therefore the ballast weights) were assumed to be changing over 

time. Fixture weights in existing buildings were taken from the Xenergy database and were assumed 

to represent the year 1995. Fixture weights for new buildings were estimated from the Bureau of 

Census report on Electric Lighting Fixtures (MA -36L) for 1997, which reports the fluorescent fixture 

sales by number oflamps for common fixture types. We assumed that the fixtures with the 1995 

weights would be replaced by those with the new building fixture rates, and estimated that fixtures 

in new buildings or in renovated systems had an average turnover rate of 16 years. The resulting 

fixture weights for existing buildings in 1997 and in 2009 are shown in Table B.5. By 2009, the new 

weights are assumed to have saturated the building stock and are used in the NES model for all years 

thereafter. Note that three-lamp fixtures are becoming increasingly popular. 

From these fixture weights, we calculated the ballast weights, making the following 

assumptions: Two-lamp fixtures each had 1 two-lamp ballast; three-lamp fixtures were 20 percent 

tandem wired with 1.5 two-lamp ballasts per fixture and 80 percent non-tandem-wired with 1 twolamp ballast (and a one-lamp ballast); and four-lamp fixtures had 2 two-lamp ballasts. The resulting 

ballast weights for 1997 and for 2009 are shown in Table B.6. These are the weights used in the 

NES analysis. As noted in section B.2.3, the weights for 1997 are fixed in the NES model, but the 

user can adjust the 2009 ballast weights. 

T a bi e B 5 . . FI uorescen t F' IX t ure w· eigi ht s b N b Iy urn er 0 fL amps ( lpercen t 0 f mar k e t) 

Fixture Type Existing New Existing Existing 

1995 1997 2009 

One-lamp 9% 7% 9% 7% 

Two-lamp 51 % 38% 49% 40% 

Three-lamp 7% 24% 10% 22% 

Four-lamp 33 % 31 % 33 % 31 % 

B-22 
Table B.6. Fluorescent Ballast Weight s (percent of two-lama market)

Fixture Type Existing

1995

Existing

1997

Existing

2009

Two-lamp 41 % 39 % 32 %

Three-lamp tandem wired 2% 2% 5%

Three-lamp non-tandem-wired 4% 6% 14 %

Four-lamp 53 % 52 % 49 %

B.4 LIGHTING/HVAC INTE RACTIONS

B.4.1 Methodology

We used the DOE-2 building simulation model to calculate energy load changes due to

installation of more efficient lighting for each of eleven major building prototypes with several

subtypes in five different climate zones (a total of 36 combinations). Also, we used CBECS data to

determine the square footage of each building type found in each climate zone. Using a spreadsheet,

we combined the load information with the following additional information to calculate the change

in HVAC energy use resulting from a unit reduction in lighting energy. The multipliers that

calculate the change in cooling or heating energy per unit of lighting energy are called the cooling

or heating coincidence factors. We combined market shares for the different equipment types (e.g.

oil-fired boiler, gas furnace, heat pump) by building type with heating and cooling coincidence

factors by building type, equipment efficiencies and distribution system losses.

In hot climates the use of more efficient lighting results in smaller heat losses that generate

large savings from reductions in cooling energy use. However, in cold climates the additional energy

needed for heating generally outweighs the cooling savings. Our study attempted to estimate the net

average national energy impacts for all climates and building types in the nation.

B.4.2 Savings in Existing Buildings

The first approach used CBECS 1992' data that represented the building stock in 1989. The

results showed averages for this building stock for each building type across several climate zones,

weighted by floor space of that building type found in each climate zone. The results are shown in

Table B.7. We found that substantial net HVAC 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 (see next paragraph for building type coverage).

Schools, on the other hand, constitute close to 20 percent of the same commercial floor area, and

lighting efficiency improvements generate large heating increases in this building type. We obtained

this result because in schools the cooling-lighting coincidence is low and the heating-lighting

B-23

EE-RM-97-500 COMMENT 104

T a bi e B 6 . . Fl uorescen t B a n as tw· eIgl ht s ( lpercen t 0 ftw 0-I amp mar k e t) 

Fixture Type Existing Existing Existing 

1995 1997 2009 

Two-lamp 41 % 39% 32% 

Three-lamp tandem wired 2% 2% 5% 

Three-lamp non-tandem-wired 4% 6% 14% 

Four-lamp 53 % 52% 49% 

B.4 LIGHTING/HV AC INTERACTIONS 

B.4.1 Methodology 

We used the DOE-2 building simulation model to calculate energy load changes due to 

installation of more efficient lighting for each of eleven major building prototypes with several 

subtypes in five different climate zones (a total of36 combinations). Also, we used CBECS data to 

determine the square footage of each building type found in each climate zone. Using a spreadsheet, 

we combined the load information with the following additional infornlation to calculate the change 

in HV AC energy use resulting from a unit reduction in lighting energy. The multipliers that 

calculate the change in cooling or heating energy per unit of lighting energy are called the cooling 

or heating coincidence factors. We combined market shares for the different equipment types (e.g. 

oil-fired boiler, gas furnace, heat pump) by building type with heating and cooling coincidence 

factors by building type, equipment efficiencies and distribution system losses. 

In hot climates the use of more efficient lighting results in smaller heat losses that generate 

large savings from reductions in cooling energy use. However, in cold climates the additional energy 

needed for heating generally outweighs the cooling savings. Our study attempted to estimate the net 

average national energy impacts for all climates and building types in the nation. 

B.4.2 Savings in Existing Buildings 

The first approach used CBECS 19926 data that represented the building stock in 1989. The 

results showed averages for this building stock for each building type across several climate zones, 

weighted by floor space ofthat building type found in each climate zone. The results are shown in 

Table B.7. We found that substantial net HVAC 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 perforn1ed (see next paragraph for building type coverage). 

Schools, on the other hand, constitute close to 20 percent of the same commercial floor area, and 

lighting efficiency improvements generate large heating increases in this building type. We obtained 

this result because in schools the cooling-lighting coincidence is low and the heating-lighting 

B-23 

T a bi e B 6 Fl " . uorescen tBn a as tW"h eIg] ts ( lpercent 0 f two-I amp mar k ) et 

Fixture Type Existing Existing Existing 

1995 1997 2009 

Two-lamp 41 % 39% 32% 

Three-lamp tandem wired 2% 2% 5% 

Three-lamp non-tandem-wired 4% 6% 14% 

Four-lamp 53 % 52% 49% 

B.4 LIGHTING/HV AC INTERACTIONS 

B.4.1 Methodology 

We used the DOE-2 building simulation model to calculate energy load changes due to 

installation of more efficient lighting for each of eleven major building prototypes with several 

subtypes in five different climate zones (a total of36 combinations). Also, we used CBECS data to 

determine the square footage of each building type found in each climate zone. Using a spreadsheet, 

we combined the load information with the following additional infornlation to calculate the change 

in HV AC energy use resulting from a unit reduction in lighting energy. The multipliers that 

calculate the change in cooling or heating energy per unit of lighting energy are called the cooling 

or heating coincidence factors. We combined market shares for the different equipment types (e.g. 

oil-fired boiler, gas furnace, heat pump) by building type with heating and cooling coincidence 

factors by building type, equipment efficiencies and distribution system losses. 

In hot climates the use of more efficient lighting results in smaller heat losses that generate 

large savings from reductions in cooling energy use. However, in cold climates the additional energy 

needed for heating generally outweighs the cooling savings. Our study attempted to estimate the net 

average national energy impacts for all climates and building types in the nation. 

B.4.2 Savings in Existing Buildings 

The first approach used CBECS 19926 data that represented the building stock in 1989. The 

results showed averages for this building stock for each building type across several climate zones, 

weighted by floor space ofthat building type found in each climate zone. The results are shown in 

Table B.7. We found that substantial net HVAC 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 perforn1ed (see next paragraph for building type coverage). 

Schools, on the other hand, constitute close to 20 percent of the same commercial floor area, and 

lighting efficiency improvements generate large heating increases in this building type. We obtained 

this result because in schools the cooling-lighting coincidence is low and the heating-lighting 

B-23 
coincidence is high. The other building types (small office, large and small retail, grocery,

restaurant, small hotel, and warehouse) showed small net increases in energy consumption from

installation of more efficient lighting.

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

very small (approximately zero). It should be noted that this calculation covered about 76 percent

ofthe commercial building area. The other 24 percent included assembly buildings, parking garages,

public order and safety buildings, building types 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 we performed

calculations!

In response to comments that these savings were lower than those of other researchers, we

compared our findings to theirs and found them consistent. E-source indicated that the bonus

savings presented in their document are representative of typical large office buildings'. They

presented two sets of calculations. The first set was based on a previous LBNL publication' which

formed the basis of the more recent LBNL study.' The second set was based on Rundquist, et al.

(1993).L0 For large offices, E-source indicated that cooling site energy savings are in the range of

28 - 36 percent. The Eley Memo was based on DOE-2 runs that showed that the cooling bonus is

about 10 percent to 20 percent of the lighting savings." As shown in Table B.7, we calculated a 28

percent reduction in cooling energy in large office buildings due to the installation of efficient

lighting.

The numbers for heating penalties were not directly comparable because we presented results

in terms of primary energy, while E-Source presented results as site energy. Nevertheless, E-source

indicated that the heating site energy penalty is 0 to 21 percent. We showed an average heating

penalty of 16 percent for large office buildings in the United States in terms of primary energy. It

should be noted that the Rundquist study used two generic commercial building types (small and

large). In the LBNL studies, 36 prototypical building models were simulated.

° The calculations were not performed for the other 24 percent of buildings because there were not enough buildings

of those types in enough different climate zones in the CBECS data set. Although these building types represent 24

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

B-24

EE-RM-97-500 COMMENT 104

coincidence is high. The other building types (small office, large and small retail, grocery, 

restaurant, small hotel, and warehouse) showed small net increases in energy consumption from 

installation of more efficient lighting. 

When averaged, the net effect of lightinglHV A C interactions for the U. S. building stock was 

very small (approximately zero). It should be noted that this calculation covered about 76 percent 

ofthe commercial building area. The other 24 percent included assembly buildings, parking garages, 

public order and safety buildings, building types not covered by the conventional categories, and 

vacant buildings. We assumed that the average lighting/HV AC interactions for these building types 

would be approximately equal to that found in the 76 percent of buildings for which we perfomled 

calculations. e 

In response to comments that these savings were lower than those of other researchers, we 

compared our findings to theirs and found them consistent. E-source indicated that the bonus 

savings presented in their document are representative of typical large office buildings7. They 

presented two sets of calculations. The first set was based on a previous LBNL pUblication8 which 

formed the basis of the more recent LBNL study.9 The second set was based on Rundquist, et al. 

(1993).10 For large offices, E-source indicated that cooling site energy savings are in the range of 

28 - 36 percent. The Eley Memo was based on DOE-2 runs that showed that the cooling bonus is 

about 10 percentto 20 percent ofthe lighting savings.ll As shown in Table B. 7, we calculated a 28 

percent reduction in cooling energy in large office buildings due to the installation of efficient 

lighting. 

The numbers for heating penalties were not directly comparable because we presented results 

in temlS of primary energy, while E-Source presented results as site energy. Nevertheless, E-source 

indicated that the heating site energy penalty is 0 to 21 percent. We showed an average heating 

penalty of 16 percent for large office buildings in the United States in temlS of primary energy. It 

should be noted that the Rundquist study used two generic commercial building types (small and 

large). In the LBNL studies, 36 prototypical building models were simulated. 

C The calculations were not perfOlmed for the other 24 percent of buildings because there were not enough buildings 

of those types in enough different climate zones in the CBECS data set. Although these building types represent 24 

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

B-24 

coincidence is high. The other building types (small office, large and small retail, grocery, 

restaurant, small hotel, and warehouse) showed small net increases in energy consumption from 

installation of more efficient lighting. 

When averaged, the net effect of lightinglHV A C interactions for the U. S. building stock was 

very small (approximately zero). It should be noted that this calculation covered about 76 percent 

ofthe commercial building area. The other 24 percent included assembly buildings, parking garages, 

public order and safety buildings, building types not covered by the conventional categories, and 

vacant buildings. We assumed that the average lighting/HV AC interactions for these building types 

would be approximately equal to that found in the 76 percent of buildings for which we perfomled 

calculations. e 

In response to comments that these savings were lower than those of other researchers, we 

compared our findings to theirs and found them consistent. E-source indicated that the bonus 

savings presented in their document are representative of typical large office buildings7. They 

presented two sets of calculations. The first set was based on a previous LBNL pUblication8 which 

formed the basis of the more recent LBNL study.9 The second set was based on Rundquist, et al. 

(1993).10 For large offices, E-source indicated that cooling site energy savings are in the range of 

28 - 36 percent. The Eley Memo was based on DOE-2 runs that showed that the cooling bonus is 

about 10 percentto 20 percent ofthe lighting savings.ll As shown in Table B. 7, we calculated a 28 

percent reduction in cooling energy in large office buildings due to the installation of efficient 

lighting. 

The numbers for heating penalties were not directly comparable because we presented results 

in temlS of primary energy, while E-Source presented results as site energy. Nevertheless, E-source 

indicated that the heating site energy penalty is 0 to 21 percent. We showed an average heating 

penalty of 16 percent for large office buildings in the United States in temlS of primary energy. It 

should be noted that the Rundquist study used two generic commercial building types (small and 

large). In the LBNL studies, 36 prototypical building models were simulated. 

C The calculations were not perfOlmed for the other 24 percent of buildings because there were not enough buildings 

of those types in enough different climate zones in the CBECS data set. Although these building types represent 24 

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

B-24 
Table B.7 Lighting/IIVAC Interactions for the U.S. Building Stock in 1989: Averaged Using Floor Area

Percent change in HVAC source ener per one unit Chan e in lighting primary ener ry)

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All

Offic Offic Retail Retail Hotel Hotel Buildings

e e

Floor Area Share 7% 17% 14% 12% 2% 3% 3% 2% 17% 2% 19% 100%

Change in Heating 23% 16% 21% 30% 22% 17% 13% 36% 31% 28% 6% 20%

(kWh/kWh Lighting)(1)

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

(kWh/kWh Lightin )( 1 ) 1

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

(kWh/kWh Lighting)(1)

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy

In response to further comments that the results be weight-averaged from the lighting energy consumption of each building type

rather than by the floor area, we used lighting consumption numbers from EIA12 and calculated an overall HVAC bonus of 4 percent (in

contrast to 0 percent calculated using floor area).

There were also comments that hot climates with high cooling loads were under-represented and that one climate zone used weather

data from a non-representative city. We selected another city for that climate zone, and the results for the 1989 existing building stock

increased from 4 to 5 percent. The final results for the existing buildings are found in Table B.B.

B-25

EE-RM-97-500 COMMENT 104

Table B.7 Lighting/HV AC Interactions for the U.S. Building Stock in 1989: Averaged Using Floor Area 

(Percent change in HV AC source energy per one unit change in lighting primary ener)0') 

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All 

Offic Offic Retail Retail Hotel Hotel Buildings 

e e 

Floor Area Share 7% 17% 14% 12% 2% 3% 3% 2% 17% 2% 19% 100% 

Change in Heating 23% 16% 21% 30% 22% 17% 13% 36% 31% 28% 6% 20% 

(kWh/kWh Lighting)(I) 

Change in coolingl-23% -30% -14% -27% -21% -27% -33% -34% .-18% -24% -5% -20% 

(kWh/kWh Lighting)( 1) 

Change in HV AC .10% -14% 6% 3% 1% -11% -20% 2% 13% 4% 1% 0% 

(kWh/kWh Lighting)( 1) 

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy 

In response to further comments that the results be weight-averaged from the lighting energy consumption of each building type 

rather than by the floor area, we used lighting consumption numbers from EIA l2 and calculated an overall HV AC bonus of 4 percent (in 

contrast to 0 percent calculated using floor area). 

There were also comments that hot climates with high cooling loads were under-represented and that one climate zone used weather 

data from a non-representative city. We selected another city for that climate zone, and the results for the 1989 existing building stock 

increased from 4 to 5 percent. The final results for the existing buildings are found in Table B.8. 

B-25 

Table B.7 Lighting/HV AC Interactions for the U.S. Building Stock in 1989: Averaged Using Floor Area 

(Percent change in HV AC source energy per one unit change in lighting primary ener~) 

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All I 

Offic Offic Retail Retail Hotel Hotel Buildings 

e e 

Floor Area Share 7% 17% 14% 12% 2% 3% 3% 2% 17% 2% 19% 100% 

Change in Heating 23% 16% 21% 30% 22% 17% 13% 36% 31% 28% 6% 20% 

(kWh/kWh Lighting)(I) 

Change in coolingl-23% -30% -14% -27% -21% -27% -33% -34% .-18% -24% -5% -20% 

(kWh/kWh Li ohting)(l) 

Change in HV AC 10% -14% 6% 3% 1% -11% -20% 2% 13% 4% 1% 0% 

(kWh/kWh Lighting)( 1) 

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy 

In response to further comments that the results be weight-averaged from the lighting energy consumption of each building type 

rather than by the floor area, we used lighting consumption numbers from EIA l2 and calculated an overall HV AC bonus of 4 percent (in 

contrast to 0 percent calculated using floor area). 

There were also comments that hot climates with high cooling loads were under-represented and that one climate zone used weather 

data from a non-representative city. We selected another city for that climate zone, and the results for the 1989 existing building stock 

increased from 4 to 5 percent. The final results for the existing buildings are found in Table B.8. 

B-25 
Table B.8 Lighting/HVAC Interactions for the U.S. Building Stock in 1989: Averaged Using Lighting

Energy

(Percent change in HVAC source energy per one unit change in lighting primary energy where the

unit change is distributed over the bu'ld'mt es ortional to the lighting energy use i ro

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All

Office Office Retail Retail Hotel Hotel Buildings

Lighting Energy 8% 18% 9% 8% 6% 9% 17% 2% 18% 1% 4% 100%

Share

Change in Heating 23% 16% 21% 30% 22% 17% 13% 36% 31% 28% 6% 21%

(kWh/kWh Lighting)(1)

Change in Cooling -23% -30% -14% -27% -21% -27% -33% -34% -18% -24% -5% -24%

(kWh/1cWh Lighting)(1)

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

(kWh/kWh Lighting)(1)

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy

B-26

EE-RM-97-500 COMMENT 104

Table B.8 Lighting/HV AC Interactions for the U.S. Building Stock in 1989: Averaged Using Lighting 

Energy 

(Percent change in HV AC source energy per one unit change in lighting primary energy where the 

unit change is distributed over the building types proportional to the lightin energy use ) 

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All 

Office Office Retail Retail Hotel Hotel Buildings 

Lighting Energ, 8% 18% 9% 8% 6% 9% 17% 2% 18% 1% 4% 100% 

Share 

Change in Heating 23% 16% 21% 30% 22% 17% 13% 36% 31% 28% 6% 21% 

(kWh/kWh Lighting)(l) 

Change in Cooling 1-23% -30% -14% -27% -21% -27% -33% -34% -18% -24% -5% -24% 

(kWh/kWh Lighting)(I) 

Change in HV AC _\0% -14% 6% 3% 1% -11% -20% 2% 13% 4% 1% -4% 

(kWh/kWh Lighting)(I) 

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy 

B-26 

Table B.8 Lighting/HV AC Interactions for the U.S. Building Stock in 1989: Averaged Using Lighting 

Energy 

(Percent change in HV AC source energy per one unit change in lighting primary energy where the 

unit change is distributed over the building types proportional to the lightin energy use ) 

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All 

Office Office Retail Retail Hotel Hotel Buildings 

Lighting Energy 8% 18% 9% 8% 6% 9% 17% 2% 18% 1% 4% 100% 

Share 

Change in Heating 23% 16% 21% 30% 22% 17% 13% 36% 31% 28% 6% 21% 

(kWh/kWh Lighting)(l) 

Change in Cooling 1-23% -30% -14% -27% -21% -27% -33% -34% -18% -24% -5% -24% 

(kWh/kWh Lighting)(I) 

Change in HV AC \0% -14% 6% 3% 1% -11% -20% 2% 13% 4% 1% -4% 

(kWh/kWh Lighting)(I) 

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy 

B-26 
B.4.3 Savings in Newer Buildings

The comments noted that the analysis was for the existing building stock as a whole in 1989,

and suggested that recent higher construction rates in the south be considered to better represent

future interactions over the analysis period. We addressed these issues by performing an analysis

for buildings built from 1990-1995. We took the floor area developed by building type and climate

region from CBECS 1995.13 The commercial floor area developed during 1990-1995 was about 4

billion square feet, while the total floor area for the commercial sector as a whole is about 60 billion

square feet.

One of the main reasons for the higher savings was that during the period 1990 to 1995, the

proportion of health care buildings to the total buildings was double that for the building stock in

1989. The hospital lighting energy use and cooling coincidence factor were both very high. A few

other building types (grocery and restaurant) also showed small HVAC savings because of higher

construction rates in the South. These factors shifted the average net savings from lighting/HVAC

interactions to 10 percent.

Tables B.9 and B. 10 show the results for buildings built from 1990- 1995, using floor area

and lighting energy, respectively, as the weighting factor.

EE-RM-97-500 COMMENT 104

B.4.3 Savings in Newer Buildings 

The comments noted that the analysis was for the existing building stock as a whole in 1989, 

and suggested that recent higher construction rates in the south be considered to better represent 

future interactions over the analysis period. We addressed these issues by performing an analysis 

for buildings built from 1990-1995. We took the floor area developed by building type and climate 

region from CBECS 1995Y The commercial floor area developed during 1990-1995 was about 4 

billion square feet, while the total floor area for the commercial sector as a whole is about 60 billion 

square feet. 

One ofthe main reasons for the higher savings was that during the period 1990 to 1995, the 

proportion of health care buildings to the total buildings was double that for the building stock in 

1989. The hospital lighting energy use and cooling coincidence factor were both very high. A few 

other building types (grocery and restaurant) also showed small HVAC savings because of higher 

construction rates in the South. These factors shifted the average net savings from lighting/HV AC 

interactions to 10 percent. 

Tables B.9 and B.I0 show the results for buildings built from 1990-1995, using floor area 

and lighting energy, respectively, as the weighting factor. 

B-27 

B.4.3 Savings in Newer Buildings 

The comments noted that the analysis was for the existing building stock as a whole in 1989, 

and suggested that recent higher construction rates in the south be considered to better represent 

future interactions over the analysis period. We addressed these issues by performing an analysis 

for buildings built from 1990-1995. We took the floor area developed by building type and climate 

region from CBECS 1995Y The commercial floor area developed during 1990-1995 was about 4 

billion square feet, while the total floor area for the commercial sector as a whole is about 60 billion 

square feet. 

One ofthe main reasons for the higher savings was that during the period 1990 to 1995, the 

proportion of health care buildings to the total buildings was double that for the building stock in 

1989. The hospital lighting energy use and cooling coincidence factor were both very high. A few 

other building types (grocery and restaurant) also showed small HVAC savings because of higher 

construction rates in the South. These factors shifted the average net savings from lighting/HV AC 

interactions to 10 percent. 

Tables B.9 and B.I0 show the results for buildings built from 1990-1995, using floor area 

and lighting energy, respectively, as the weighting factor. 

B-27 
Table B.9 Lighting/HVAC Interactions for U.S. Buildings Built During 1990-1995: Averaged Using Floor Area

Percent chars e in HVAC source energy per one unit chars e in li htin primary energy)

Small

Office

Large

Office

Small

Retail

Large

Retail

Small

Hotel

Large

Hotel

Hospital Grocery School Restaurant Warehouse All

Buildings

Floor Area Share 5% 16% 15% 10% 2% 4% % 3% 15% 3% 20% 100%

Change in Heating 24% 17% 15% 27% 21% 9% 5% 25% 31% 23% 7% 17%

(kWh/kWh Lighting)(1)

Change in Cooling -18% -27% -14% -31% -20% -30% -29% -32% -22% -24% -4% -20%

(kWh/kWh Lighting)(1)

Change in HVAC 5% -10% 2% -5% 1% -21% -24% -7% 9% -1% 3% -2%

(kWh/kWh Lighting)(1)

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy

B-28

EE-RM-97-500 COMMENT 104

Table B.9 Lighting/HV AC Interactions for U.S. Buildings Built During 1990-1995: Averaged Using Floor Area 

Percent change in IN AC source energ'y per one unit chane:e in lighting primary energy) 

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All 

Office Office Retail Retail Hotel Hotel Buildings 

Floor Area Share 5% 16% 15% 10% 2% 4% 7% 3% 15% 3% 20% 100% 

Change in Heating 24% 17% 15% 27% 21% 9% 5% 25% 31% 23% 7% 17% 

(kWh/kWh Lighting)(l) 

Change in Cooling .1-18% -27% -14% -31% -20% -30% -29% -32% -22% -24% -4% -20% 

(kWh/kWh Lighting)( 1) 

Change in IN AC .15% -10% 2% -5% 1% -21% -24% -7% 9% -1% 3% -2% 

(kWh/kWh Lighting)( 1) 

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy 

B-28 

Table B.9 Lighting/HV AC Interactions for U.S. Buildings Built During 1990-1995: Averaged Using Floor Area 

JPercent change in IN AC source energ'V per one unit chan2:e in lighting primary enerj;! y') 

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All 

Office Office Retail Retail Hotel Hotel Buildinas 

Floor Area Share 5% 16% 15% 10% 2% 4% 7% 3% 15% 3% 20% 100% 

Change in Heating 24% 17% 15% 27% 21% 9% 5% 25% 31% 23% 7% 17% 

(kWh/kWh Lighting)(l) 

Change in Cooling .1-18% -27% -14% -31% -20% -30% -29% -32% -22% -24% -4% -20% 

(kWh/kWh Lighting)( 1) 

Change in IN AC 15% -10% 2% -5% 1% -21% -24% -7% 9% -1% 3% -2% 

(kWh/kWh Lighting)( 1) 

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy 

B-28 
Table B.10 Lighting/HVAC Interactions for U.S. Buildings Built During 1990-1995: Averaged Using Lighting Energy

(Percent change in HVAC source energy per one unit change in lighting primary energy where the

unit change is distributed over th e building es ro ortf'onal to the lighting energy us e

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All

Office Office Retail Retail Hotel Hotel Buildin s

Lighting Energy Share 5% 15% 9% 6% 4% 9% 31% 3% 14% 1% 4% 100%

Change in Heating 24% 17% 15% 27% 21% 9% 5% 25% 31% 23% 7% 15%

(kWh/kWh Lighting)(1)

Change in Cooling -18% -27% -14% -31% -20% -30% -29% -32% -22% -24% -4% -25%

(kWh/kWh Lighting)(1)

Change in HVAC 5% -10% 2% -5% 1% -21% -24% -7% 9% -1% 3% -10%

(kWh/kWh Lighting)(1)

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy

EE-RM-97-500 COMMENT 104

Table RIO Lighting/HV AC Interactions for U.S. Buildings Built During 1990-1995: Averaged Using Lighting Energy 

(Percent change in HV AC source energy per one unit change in lighting primary energy where the 

unit change is distributed over the buildingJYpes proportional to the lighting energy use) 

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All 

Office Office Retail Retail Hotel Hotel Buildings 

Lighting Energy Share 5% 15% 9% 6% 4% 9% 31% 3% 14% 1% 4% 100% 

Change in Heating 24% 17% 15% 27% 21% 9% 5% 25% 31% 23% 7% 15% 

(kWh/kWh Lighting)(l) 

Change in Cooling 1-18% -27% -14% -31% -20% -30% -29% -32% -22% -24% -4% -25% 

(kWh/kWh Lighting)(I) 

Change in HV AC .15% -10% 2% -5% 1% -21% -24% -7% 9% -1% 3% -10% 

(kWh/kWh Lighting)(I) 

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy 

B-29 

Table RIO Lighting/HV AC Interactions for U.S. Buildings Built During 1990-1995: Averaged Using Lighting Energy 

(Percent change in HV AC source energy per one unit change in lighting primary energy where the 

unit change is distributed over the building types roportional to the lighting energy use) 

Small Large Small Large Small Large Hospital Grocery School Restaurant Warehouse All 

Office Office Retail Retail Hotel Hotel Buildings 

Lighting Energy Share 5% 15% 9% 6% 4% 9% 31% 3% 14% 1% 4% 100% 

Change in Heating 24% 17% 15% 27% 21% 9% 5% 25% 31% 23% 7% 15% 

(kWh/kWh Lighting)(l) 

Change in Cooling 1-18% -27% -14% -31% -20% -30% -29% -32% -22% -24% -4% -25% 

(kWh/kWh Lighting)(I) 

Change in HV AC .15% -10% 2% -5% 1% -21% -24% -7% 9% -1% 3% -10% 

(kWh/kWh Lighting)(I) 

(1) Values in source energy: 1 kWh lighting electricity corresponds to 3.2 kWh source energy 

B-29 
B.4.4 Average Savings During the Analysis Period

Table B. 11 compares the savings for the stock in 1989 to those of buildings built during

1990-1995, using lighting energy as the weighting factor.

Table B.11. Energy Savings due to Lighting/HVAC Interactions in Buildings of Different

Uinta es

Building Stock in Buildings Developed During

1989 1990-1995

Extra Savings due to Lighting/ HVAC

Interactionsas a Percentage of the 5% 10%

Savings in Lighting Source Energy

Clearly, the lighting/HVAC interactions in the newer buildings cause more net savings than

they do in the older buildings. It is not unreasonable to assume that over the next 30 years, the net

savings for the building stock will move from 5 percent towards 10 percent. Assuming that

commercial buildings have a 60 year lifetime, we reasoned that the building stock would be at about

the midpoint by 2030, for a net savings of 7.5 percent. To represent the analysis period between

2003 and 2030, we assumed a midpoint between 5 percent and 7.5 percent, resulting in a 6.25

percent additional national energy savings. The analysis indicated that extra savings due to

interactions are very sensitive to the proportion of health care buildings in the total commercial floor

stock, and future values depend on the growth of floor stock for the different building types relative

to each other.

As discussed in Chapter 5, the analysis presented in this document considered HVAC

interactions for energy savings, but did not include them in the NPV calculations. Estimation of the

relatively small impacts on the energy cost savings would be complicated by the need to analyze

shifting fuel prices as well as changing building heating and cooling types over time.

B.5 NON-REGULATORY PROGRAMS

This section summarizes our research on quantifying the impacts of key non-regulatory

programs on the market for efficient fluorescent ballasts. These analyses was intended to serve as

a background for the base case assumptions used in projecting national energy savings. We analyzed

the following non-regulatory programs that are implicit in the base case forecasts. Where possible,

we have attempted to quantify the impacts in terms of number of.electronic ballasts that would be

purchased as a result of each program. The programs that influence the ballast market that were

studied in this analysis were:

B-30

EE-RM-97-500 COMMENT 104

BAA Average Savings During the Analysis Period 

Table B.ll compares the savings for the stock in 1989 to those of buildings built during 

1990-1995, using lighting energy as the weighting factor. 

Table Rll. Energy Savings due to Lighting!HV AC Interactions in Buildings of Different 

V' mtages 

Building Stock in Buildings Developed During 

1989 1990-1995 

Extra Savings due to Lighting! HV AC 

Interactionsas a Percentage of the 5% 10% 

Savings in Lighting Source Energy 

Clearly, the lightinglHV AC interactions in the newer buildings cause more net savings than 

they do in the older buildings. It is not unreasonable to assume that over the next 30 years, the net 

savings for the building stock will move from 5 percent towards 10 percent. Assuming that 

commercial buildings have a 60 year lifetime, we reasoned that the building stock would be at about 

the midpoint by 2030, for a net savings of 7.5 percent. To represent the analysis period between 

2003 and 2030, we assumed a midpoint between 5 percent and 7.5 percent, resulting in a 6.25 

percent additional national energy savings. The analysis indicated that extra savings due to 

interactions are very sensitive to the proportion of health care buildings in the total commercial floor 

stock, and future values depend on the growth of floor stock for the different building types relative 

to each other. 

As discussed in Chapter 5, the analysis presented in this document considered HVAC 

interactions for energy savings, but did not include them in the NPV calculations. Estimation of the 

relatively small impacts on the energy cost savings would be complicated by the need to analyze 

shifting fuel prices as well as changing building heating and cooling types over time. 

B.S NON-REGULATORY PROGRAMS 

This section summarizes our research on quantifying the impacts of key non-regulatory 

programs on the market for efficient fluorescent ballasts. These analyses was intended to serve as 

a background for the base case assumptions used in projecting national energy savings. We analyzed 

the following non-regulatory programs that are implicit in the base case forecasts. Where possible, 

we have attempted to quantify the impacts in terms of number of .electronic ballasts that would be 

purchased as a result of each program. The programs that influence the ballast market that were 

studied in this analysis were: 

B-30 

BAA Average Savings During the Analysis Period 

Table B.ll compares the savings for the stock in 1989 to those of buildings built during 

1990-1995, using lighting energy as the weighting factor. 

Table Rll. Energy Savings due to Lighting!HV AC Interactions in Buildings of Different 

V' In t ages 

Building Stock in Buildings Developed During 

1989 1990-1995 

Extra Savings due to Lighting! HV AC 

Interactionsas a Percentage of the 5% 10% 

Savings in Lighting Source Energy 

Clearly, the lightinglHV AC interactions in the newer buildings cause more net savings than 

they do in the older buildings. It is not unreasonable to assume that over the next 30 years, the net 

savings for the building stock will move from 5 percent towards 10 percent. Assuming that 

commercial buildings have a 60 year lifetime, we reasoned that the building stock would be at about 

the midpoint by 2030, for a net savings of 7.5 percent. To represent the analysis period between 

2003 and 2030, we assumed a midpoint between 5 percent and 7.5 percent, resulting in a 6.25 

percent additional national energy savings. The analysis indicated that extra savings due to 

interactions are very sensitive to the proportion of health care buildings in the total commercial floor 

stock, and future values depend on the growth of floor stock for the different building types relative 

to each other. 

As discussed in Chapter 5, the analysis presented in this document considered HVAC 

interactions for energy savings, but did not include them in the NPV calculations. Estimation of the 

relatively small impacts on the energy cost savings would be complicated by the need to analyze 

shifting fuel prices as well as changing building heating and cooling types over time. 

B.S NON-REGULATORY PROGRAMS 

This section summarizes our research on quantifying the impacts of key non-regulatory 

programs on the market for efficient fluorescent ballasts. These analyses was intended to serve as 

a background for the base case assumptions used in projecting national energy savings. We analyzed 

the following non-regulatory programs that are implicit in the base case forecasts. Where possible, 

we have attempted to quantify the impacts in terms of number of .electronic ballasts that would be 

purchased as a result of each program. The programs that influence the ballast market that were 

studied in this analysis were: 

B-30 
Electric Utility Demand-Side Management (DSM) Programs

ASHRAE/IES 90 . 1-1999 Building Code

Environmental Protection Agency (EPA) Energy Star Buildings/Green Lights

Federal Energy Management Program (FEMP)

Energy Cost Savings Council (NEMA)

Other programs will have an influence on the future of energy -efficient lighting, but were too

new or too broad in scope to analyze in quantitative detail. For example , the Department of

Energy's Lighting Technology Roadmap is a comprehensive approach with many interactive options

that promote the use of more efficient lighting equipment.

Quantifying the impacts of non-regulatory programs was a challenge due to several factors,

including the following:

Lack of data on most programs, particularly projections for future years

Interactions between various non-regulatory programs as well as with building codes (which

are regulatory but not component-based, as discussed below).

B.5.1. Utility DSM Rebates

B.5.1.1 Introduction

LBNL analyzed utility and national energy-efficiency spending data from several major U. S.

electric utilities to produce national estimates of Demand -Side Management (DSM) rebates for

electronic ballasts. 14 The study estimated the number of rebates paid for electronic ballasts annually

and the monetary value of these rebates since their widespread emergence in the early 1990s, and

the fraction of national ballast sales that rebates comprised . The objective was to discern trends over

time in DSM rebates and their impact on the market share garnered by electronic ballasts.

B.5.1.2 Utility Data

Nine utilities provided time series data on their DSM electronic ballast rebates from 1992 -

1997. Two sample data sets, referred to as the core and expanded samples, were created. The core

sample data set included information from six utilities, whose total energy efficiency (EE) spending

represented about 17 percent of national utility spending on energy efficiency programs during that

period. The expanded sample data set added three utilities , which provided partial data, to the core

sample. Total energy efficiency expenditures for utilities in the expanded sample accounted for

about 30 percent of national expenditures. The estimation technique used to incorporate these three

utilities in the analysis is described below.

The six utilities that comprised the core sample data set were : Central Maine Power

Company (ME), the Long Island Lighting Company (NY), Pacific Gas & Electric (CA), Rochester

B-31

EE-RM-97-500 COMMENT 104

• Electric Utility Demand-Side Management (DSM) Programs 

.. ASHRAE/IES 90.1-1999 Building Code 

.. Environmental Protection Agency (EPA) Energy Star Buildings/Green Lights 

.. Federal Energy Management Program (FEMP) 

" Energy Cost Savings Council (NEMA) 

Other programs will have an influence on the future of energy-efficient lighting, but were too 

new or too broad in scope to analyze in quantitative detail. For example, the Department of 

Energy's Lighting Technology Roadmap is a comprehensive approach with many interactive options 

that promote the use of more efficient lighting equipment. 

Quantifying the impacts of non-regulatory programs was a challenge due to several factors, 

including the following: 

.. Lack of data on most programs, particularly projections for future years 

" Interactions between various non-regulatory programs as well as with building codes (which 

are regulatory but not component-based, as discussed below). 

B.S.1. Utility DSM Rebates 

B.S.1.1 Introduction 

LBNL analyzed utility and national energy-efficiency spending data from several major U.S. 

electric utilities to produce national estimates of Demand-Side Management (DSM) rebates for 

electronic ballasts. 14 The study estimated the number of rebates paid for electronic ballasts annually 

and the monetary value of these rebates since their widespread emergence in the early 1990s, and 

the fraction of national ballast sales that rebates comprised. The objective was to discern trends over 

time in DSM rebates and their impact on the market share garnered by electronic ballasts. 

B.S.1.2 Utility Data 

Nine utilities provided time series data on their DSM electronic ballast rebates from 1992 -

1997. Two sample data sets, referred to as the core and expanded samples, were created. The core 

sample data set included information from six utilities, whose total energy efficiency (EE) spending 

represented about 17 percent of national utility spending on energy efficiency programs during that 

period. The expanded sample data set added three utilities, which provided partial data, to the core 

sample. Total energy efficiency expenditures for utilities in the expanded sample accounted for 

about 30 percent of national expenditures. The estimation technique used to incorporate these three 

utilities in the analysis is described below. 

The six utilities that comprised the core sample data set were: Central Maine Power 

Company (ME), the Long Island Lighting Company (NY), Pacific Gas & Electric (CA), Rochester 

B-31 

• Electric Utility Demand-Side Management (DSM) Programs 

.. ASHRAE/IES 90.1-1999 Building Code 

.. Environmental Protection Agency (EPA) Energy Star Buildings/Green Lights 

.. Federal Energy Management Program (FEMP) 

" Energy Cost Savings Council (NEMA) 

Other programs will have an influence on the future of energy-efficient lighting, but were too 

new or too broad in scope to analyze in quantitative detail. For example, the Department of 

Energy's Lighting Technology Roadmap is a comprehensive approach with many interactive options 

that promote the use of more efficient lighting equipment. 

Quantifying the impacts of non-regulatory programs was a challenge due to several factors, 

including the following: 

.. Lack of data on most programs, particularly projections for future years 

" Interactions between various non-regulatory programs as well as with building codes (which 

are regulatory but not component-based, as discussed below). 

B.S.1. Utility DSM Rebates 

B.S.1.1 Introduction 

LBNL analyzed utility and national energy-efficiency spending data from several major U.S. 

electric utilities to produce national estimates of Demand-Side Management (DSM) rebates for 

electronic ballasts. 14 The study estimated the number of rebates paid for electronic ballasts annually 

and the monetary value of these rebates since their widespread emergence in the early 1990s, and 

the fraction of national ballast sales that rebates comprised. The objective was to discern trends over 

time in DSM rebates and their impact on the market share garnered by electronic ballasts. 

B.S.1.2 Utility Data 

Nine utilities provided time series data on their DSM electronic ballast rebates from 1992 -

1997. Two sample data sets, referred to as the core and expanded samples, were created. The core 

sample data set included information from six utilities, whose total energy efficiency (EE) spending 

represented about 17 percent of national utility spending on energy efficiency programs during that 

period. The expanded sample data set added three utilities, which provided partial data, to the core 

sample. Total energy efficiency expenditures for utilities in the expanded sample accounted for 

about 30 percent of national expenditures. The estimation technique used to incorporate these three 

utilities in the analysis is described below. 

The six utilities that comprised the core sample data set were: Central Maine Power 

Company (ME), the Long Island Lighting Company (NY), Pacific Gas & Electric (CA), Rochester 

B-31 
Gas & Electric (NY), San Diego Gas and Electric (CA) and one utility that requested that its identity

remain confidential.

Three large utilities- Baltimore Gas & Electric (MD), Southern California Edison (CA), and

Consolidated Edison (NY) - also provided data that were useful and extensive, but not as complete.

Baltimore Gas & Electric provided annual figures on the number of rebated ballasts installed, but

not the dollar value of these rebates. Southern California Edison provided data on yearly spending

on ballasts, but not the corresponding number of rebated ballasts. Consolidated Edison provided

data that involved estimation of the number of electronic ballasts rebated under some measures for

each year, while later years lacked specific information on the value of each rebate.

Using the average rebate value from the core sample data set, it was possible to incorporate

the information provided by the three utilities listed above to create an expanded sample data set.

In the case of Southern California Edison, the figures provided on annual expenditures for electronic

ballast rebates were divided by the average rebate value from the core sample to estimate the number

of ballasts rebated annually (expenditures on electronic ballast rebates - $s per rebate = total number

of electronic ballasts rebated). In the case of Baltimore Gas & Electric, the figures for the annual

number of rebates were multiplied by the average rebate value to estimate the utility's annual

spending on electronic ballast rebates (number of rebates x $s per rebate = total spending on

electronic ballast rebates). For the years for which Consolidated Edison provided information on

the number of rebates only, the same approach was used as in the case of Baltimore Gas & Electric.

While the expanded sample data enlarged the sample size and provided a greater breadth of

data, the core data set contained the more reliable data and might represent the better national

estimate.

B.5.1.3 DSM and Energy Efficiency Spending

Electric utility DSM programs grew rapidly in the late 1980s and early 1990s due to

incentives created by state regulatory commissions to encourage integrated resource planning."

Electronic ballast rebates fell into the Energy Efficiency (EE) component of DSM programs-'

National EE spending reached a peak in 1993, leveled off, and declined sharply in 1996 and1997.

In 1995, EE spending fell by 12 percent from the year before and in 1996 it fell by 26 percent. Table

B.12 lists national spending on the EE programs from 1992-1997, as well as the percentage of that

national total accounted for by the two sample data sets analyzed in this study.

'In addition to energy efficiency, the other components of DSM programs are: direct load control,

interruptible load, and other load management.

B-32

EE-RM-97-500 COMMENT 104

Gas & Electric (NY), San Diego Gas and Electric (CA) and one utility that requested that its identity 

remain confidential. 

Three large utilities-Baltimore Gas & Electric (MD), Southern California Edison (CA), and 

Consolidated Edison (NY) - also provided data that were useful and extensive, but not as complete. 

Baltimore Gas & Electric provided annual figures on the number of rebated ballasts installed, but 

not the dollar value of these rebates. Southern California Edison provided data on yearly spending 

on ballasts, but not the corresponding number of rebated ballasts. Consolidated Edison provided 

data that involved estimation ofthe number of electronic ballasts rebated under some measures for 

each year, while later years lacked specific infonnation on the value of each rebate. 

Using the average rebate value from the core sample data set, it was possible to incorporate 

the information provided by the three utilities listed above to create an expanded sample data set. 

In the case of Southern California Edison, the figures provided on annual expenditures for electronic 

ballast rebates were divided by the average rebate value from the core sample to estimate the number 

of ballasts rebated annually (expenditures on electronic ballast rebates -+- $s per rebate = total number 

of electronic ballasts rebated). In the case of Baltimore Gas & Electric, the figures for the annual 

number of rebates were multiplied by the average rebate value to estimate the utility's annual 

spending on electronic ballast rebates (number of rebates x $s per rebate = total spending on 

electronic ballast rebates). For the years for which Consolidated Edison provided information on 

the number of rebates only, the same approach was used as in the case of Baltimore Gas & Electric. 

While the expanded sample data enlarged the sample size and provided a greater breadth of 

data, the core data set contained the more reliable data and might represent the better national 

estimate. 

B.5.1.3 DSM and Energy Efficiency Spending 

Electric utility DSM programs grew rapidly in the late 1980s and early 1990s due to 

incentives created by state regulatory commissions to encourage integrated resource planning. [5 

Electronic ballast rebates fell into the Energy Efficiency (EE) component of DSM programs·f 

National EE spending reached a peak in 1993, leveled off, and declined sharply in 1996 and1997. 

In 1995, EE spending fell by 12 percent from the year before and in 1996 it fell by 26 percent. Table 

B.12lists national spending on the EE programs from 1992-1997, as well as the percentage of that 

national total accounted for by the two sample data sets analyzed in this study. 

f In addition to energy efficiency, the other components of DSM programs are: direct load control, 

interruptible load, and other load management. 

B-32 

Gas & Electric (NY), San Diego Gas and Electric (CA) and one utility that requested that its identity 

remain confidential. 

Three large utilities-Baltimore Gas & Electric (MD), Southern California Edison (CA), and 

Consolidated Edison (NY) - also provided data that were useful and extensive, but not as complete. 

Baltimore Gas & Electric provided annual figures on the number of rebated ballasts installed, but 

not the dollar value of these rebates. Southern California Edison provided data on yearly spending 

on ballasts, but not the corresponding number of rebated ballasts. Consolidated Edison provided 

data that involved estimation ofthe number of electronic ballasts rebated under some measures for 

each year, while later years lacked specific infonnation on the value of each rebate. 

Using the average rebate value from the core sample data set, it was possible to incorporate 

the information provided by the three utilities listed above to create an expanded sample data set. 

In the case of Southern California Edison, the figures provided on annual expenditures for electronic 

ballast rebates were divided by the average rebate value from the core sample to estimate the number 

of ballasts rebated annually (expenditures on electronic ballast rebates -+- $s per rebate = total number 

of electronic ballasts rebated). In the case of Baltimore Gas & Electric, the figures for the annual 

number of rebates were multiplied by the average rebate value to estimate the utility's annual 

spending on electronic ballast rebates (number of rebates x $s per rebate = total spending on 

electronic ballast rebates). For the years for which Consolidated Edison provided information on 

the number of rebates only, the same approach was used as in the case of Baltimore Gas & Electric. 

While the expanded sample data enlarged the sample size and provided a greater breadth of 

data, the core data set contained the more reliable data and might represent the better national 

estimate. 

B.5.1.3 DSM and Energy Efficiency Spending 

Electric utility DSM programs grew rapidly in the late 1980s and early 1990s due to 

incentives created by state regulatory commissions to encourage integrated resource planning. [5 

Electronic ballast rebates fell into the Energy Efficiency (EE) component of DSM programs·f 

National EE spending reached a peak in 1993, leveled off, and declined sharply in 1996 and1997. 

In 1995, EE spending fell by 12 percent from the year before and in 1996 it fell by 26 percent. Table 

B.12lists national spending on the EE programs from 1992-1997, as well as the percentage of that 

national total accounted for by the two sample data sets analyzed in this study. 

f In addition to energy efficiency, the other components of DSM programs are: direct load control, 

interruptible load, and other load management. 

B-32 
Table B.12: Energy Efficiency Spending as Percent of National Total

Year National EE $s

(millions)*

Core Sample % of

National

Expanded Sample

% of National

1992 1,205 18% 33%

1993 1,608 14% 29%

1994 1,592 18% 31%

1995 1,409 19% 27%

1996 1,052 18% 30%

1997 892 17% NA**

17% 30%

as reported in the DOE/EIA Annual Reports on DSM, "U.S. Electric Utility Demand-Side Management," for 1993,

1994, 1995, 1996, 1997 (EIA-0589(93-97). No report was published for 1992; data for the year were provided

directly by EIA.

* not available

In Table B. 12, the column "National EE $s" indicates total national DSM expenditures on

energy efficiency for each year. The column "Core Sample %" lists the percentage of national EE

expenditures represented by the energy efficiency spending of the six utilities that comprised the core

sample data set. The column "Expanded Sample %" lists the percentage of national EE expenditures

represented by the nine utilities that comprised the expanded sample data set.

B.5.1.4 Methodology

We used two values derived from each sample to estimate the number of electronic ballasts

for the nation and their dollar value annually for the years 1992 to 1997. First, we calculated the

fraction of energy efficiency spending devoted to electronic ballast rebates and the average value of

the rebate per ballast for the sample data. Next, we assumed that the average rebate value and

fraction of EE spending going to ballast rebates was the same for the nation as in the sample. For

each year, nationally-aggregated energy efficiency spending was multiplied by the fraction of EE

spending that went to ballast rebates in the sample to estimate national spending on ballast rebates.

Then, we divided this estimate of spending on ballast rebates by the average rebate per ballast to

estimate the number of ballasts rebated for the year.

National Rebate $s

National Ballast Rebates (ballasts) = Average Rebate in Sample ($ per ballast) where:

National Rebate $s = Percent EE $s to Rebates in Sample * National EE $s

EE-RM-97-500 COMMENT 104

T able Bt2 . : E nergy Em IClency . S ipen d" mg as p ercent 0 fN" abona ITt oa I 

Year National EE $s Core Sample % of Expanded Sample 

(millions)* National % of National 

1992 1,205 18% 33% 

1993 1,608 14% 29% 

1994 1,592 18% 31% 

1995 1,409 19% 27% 

1996 1,052 18% 30% 

1997 892 17% NA** 

Avg 17% 30% .. * as reported in the DOE/EIA Annual Reports on DSM, "U.S. Electnc UtilIty Demand-Side Management," for 1993, 

1994, 1995, 1996, 1997 (EIA-0589(93-97). No report was published for 1992; data for the year were provided 

directly by EIA . 

• * not available 

In Table B.12, the column "National EE $s" indicates total national DSM expenditures on 

energy efficiency for each year. The colu11111 "Core Sample %" lists the percentage of national EE 

expenditures represented by the energy efficiency spending ofthe six utilities that comprised the core 

sample dataset. The column "Expanded Sample %" lists the percentage of national EE expenditures 

represented by the nine utilities that comprised the expanded sample data set. 

B.S.1.4 Methodology 

We used two values derived from each sample to estimate the number of electronic ballasts 

for the nation and their dollar value annually for the years 1992 to 1997. First, we calculated the 

fraction of energy efficiency spending devoted to electronic ballast rebates and the average value of 

the rebate per ballast for the sample data. Next, we assumed that the average rebate value and 

fraction ofEE spending going to ballast rebates was the same for the nation as in the sample. For 

each year, nationally-aggregated energy efficiency spending was multiplied by the fraction of EE 

spending that went to ballast rebates in the sample to estimate national spending on ballast rebates. 

Then, we divided this estimate of spending on ballast rebates by the average rebate per ballast to 

estimate the number of ballasts rebated for the year. 

National Rebate $s 

National Ballast Rebates (ballasts) = Average Rebate in Sample ($ per ballast), where: 

National Rebate $s = Percent EE $s to Rebates in Sample * National EE $s 

B-33 

Table B.t 2 : Energy Em IClency . S ipen d" mg as p ercent 0 fN" abona IT ota 

Year National EE $s Core Sample % of Expanded Sample 

(millions)* National % of National 

1992 1,205 18% 33% 

1993 1,608 14% 29% 

1994 1,592 18% 31% 

1995 1,409 19% 27% 

1996 1,052 18% 30% 

1997 892 17% NA** 

Avg 17% 30% 

* .. as reported m the DOE/EIA Annual Reports on DSM, "U.S. Electnc UtilIty Demand-Side Management," for 1993, 

1994, 1995, 1996, 1997 (EIA-0589(93-97). No report was published for 1992; data for the year were provided 

directly by EIA . 

• * not available 

In Table B.12, the column "National EE $s" indicates total national DSM expenditures on 

energy efficiency for each year. The colu11111 "Core Sample %" lists the percentage of national EE 

expenditures represented by the energy efficiency spending ofthe six utilities that comprised the core 

sample dataset. The column "Expanded Sample %" lists the percentage of national EE expenditures 

represented by the nine utilities that comprised the expanded sample data set. 

8.5.1.4 Methodology 

We used two values derived from each sample to estimate the number of electronic ballasts 

for the nation and their dollar value annually for the years 1992 to 1997. First, we calculated the 

fraction of energy efficiency spending devoted to electronic ballast rebates and the average value of 

the rebate per ballast for the sample data. Next, we assumed that the average rebate value and 

fraction ofEE spending going to ballast rebates was the same for the nation as in the sample. For 

each year, nationally-aggregated energy efficiency spending was multiplied by the fraction of EE 

spending that went to ballast rebates in the sample to estimate national spending on ballast rebates. 

Then, we divided this estimate of spending on ballast rebates by the average rebate per ballast to 

estimate the number of ballasts rebated for the year. 

National Rebate $s 

National Ballast Rebates (ballasts) = Average Rebate in Sample ($ per ballast), where: 

National Rebate $s = Percent EE $s to Rebates in Sample * National EE $s 

B-33 
B.5.1.5 Results

Analysis of the core and expanded samples using this approach led to the two national

estimates of the sample percentage of EE expenditures for rebates, national rebate spending, average

sample rebate amount, the national number of electronic ballast DSM rebates, shown in Tables B. 13

and B. 14. The tables also show the estimated percentage of national electronic ballast shipments that

received rebates; the average percentage for the core sample was 41 percent (1993-1997) and for

the expanded sample it was 37 percent (1993-1996).

Table 8.13. Core Sample Data Set Estimates

Year 1992 1993 1994 1995 1996 1997°

% of EE $s to electronic ballast rebates in the

sample

5% 8% 11% 13% 7% 2%

National estimate of rebate spending

(millions of $s

$64 $126 $171 $183 $78 $21

Average $/rebate

in the sample

$14.16 $13.18 $9.34 $11.91 $7.64 $3.35

Estimated number of rebates nationwide (millions) 4.5 9.6 18.3 15.4 10.2 6.4

National electronic ballasts shipments (millions) 22.9 26.0 33.1 29.7 35.4

Percent of electronic ballasts with rebates 42% 70% 46% 34% 18%

Following the equation above, the estimate ofthe number of rebates nationwide, listed in row

four, results from dividing the estimate of national spending on rebates in row two by the weighted

average rebate value found in row three (row four = row two / row three). The national estimate of

spending on electronic ballast rebates in row two results from multiplying national spending on

rebates (listed in Table B. 14) by the fraction of energy efficiency dollars going to rebates listed in

row one.

Table 8.14. Expanded Sample Data Set Estimates

Year 1992 1993 1994 1995 1996

% of EE $s to electronic ballast rebates in the sample 11% 6% 7% 12% 5%

National estimate of rebate spending (millions of $s) $131 $98 $112 $165 $54

Average $s/rebate

in the sample

$20.65 $12.01 $9.42 $11.91 $7.64

Estimated number of rebates nationwide 6.3 8.1 11.8 13.9 7.0

National electronic ballasts shipments (millions) 22.9 26.0 33.1 29.7

Percent of electronic ballasts with rebates 36% 46% 42% 24%

`None of the three utilities added to the core data set provided information for 1997.

Analysis of the core sample data set showed that electronic ballast rebates peaked in 1994

in terms of the number of ballasts rebated and utility spending on rebates. Estimates of the number

of rebates declined slightly in 1995 and substantially in the last two years. Analysis of the expanded

B-34

EE-RM-97-500 COMMENT 104

B.S.1.S Results 

Analysis of the core and expanded samples using this approach led to the two national 

estimates of the sample percentage ofEE expenditures for rebates, national rebate spending, average 

sample rebate amount, the national number of electronic ballast DSM rebates, shown in Tables B.13 

and B.14. The tables also show the estimated percentage of national electronic ballast shipments that 

received rebates; the average percentage for the core sample was 41 percent (1993-1997) and for 

the expanded sample it was 37 percent (1993-1996). 

T a hI e B . 13 . C ore S ample IDtStEf aa e sima t es 

Year 1992 1993 1994 1995 1996 1997' 

% of EE $s to electronic ballast rebates in the 5% 8% 11% 13% 7% 2% 

sample 

National estimate of rebate spending $64 $126 $171 $183 $78 $21 

I (millions of $s) 

Average $/rebate $14.16 $13.18 $9.34 $11.91 $7.64 $3.35 

in the sample 

Estimated number of rebates nationwide (millions) 4.5 9.6 18.3 15.4 10.2 6.4 

National electronic ballasts shipments (millions) 22.9 26.0 33.1 29.7 35.4 

Percent of electronic ballasts with rebates 42% 70% 46% 34% 18% 

Following the equation above, the estimate of the number of rebates nationwide, listed in row 

four, results from dividing the estimate of national spending on rebates in row two by the weighted 

average rebate value found in row three (row four = row two / row three). The national estimate of 

spending on electronic ballast rebates in row two results from multiplying national spending on 

rebates (listed in Table B.14) by the fraction of energy efficiency dollars going to rebates listed in 

row one. 

T a hI e B . 14 . E xpan d e dS ample IDtStEf aa e S lmates 

Year 1992 1993 1994 1995 1996 

% of EE $s to electronic ballast rebates in the sample 11% 6% 7% 12% 5% 

National estimate of rebate sj)ending (millions of $s) $131 $98 $112 $165 $54 

Average $s/rebate $20.65 $12.01 $9.42 $11.91 $7.64 

in the sample 

Estimated number of rebates nationwide 6.3 8.1 11.8 13.9 7.0 

National electronic ballasts shipments (millions) 22.9 26.0 33.1 29.7 

Percent of electronic ballasts with rebates 36% 46% 42% 24% .. . 'None of the three utlhtles added to the core data set provlded mformatlOn for 1997 . 

Analysis of the core sample data set showed that electronic ballast rebates peaked in 1994 

in terms of the number of ballasts rebated and utility spending on rebates. Estimates of the number 

of rebates declined slightly in 1995 and substantially in the last two years. Analysis ofthe expanded 

B-34 

B.S.1.S Results 

Analysis of the core and expanded samples using this approach led to the two national 

estimates of the sample percentage ofEE expenditures for rebates, national rebate spending, average 

sample rebate amount, the national number of electronic ballast DSM rebates, shown in Tables B.13 

and B.14. The tables also show the estimated percentage of national electronic ballast shipments that 

received rebates; the average percentage for the core sample was 41 percent (1993-1997) and for 

the expanded sample it was 37 percent (1993-1996). 

T a hI e B . 13 . C ore S ample IDtStEf aa e sima t es 

Year 1992 1993 1994 1995 1996 1997' 

% of EE $s to electronic ballast rebates in the 5% 8% 11% 13% 7% 2% 

sample 

National estimate of rebate spending $64 $126 $171 $183 $78 $21 

I (millions of $s) 

Average $/rebate $14.16 $13.18 $9.34 $11.91 $7.64 $3.35 

in the sample 

Estimated number of rebates nationwide (millions) 4.5 9.6 18.3 15.4 10.2 6.4 

National electronic ballasts shipments (millions) 22.9 26.0 33.1 29.7 35.4 

Percent of electronic ballasts with rebates 42% 70% 46% 34% 18% 

Following the equation above, the estimate of the number of rebates nationwide, listed in row 

four, results from dividing the estimate of national spending on rebates in row two by the weighted 

average rebate value found in row three (row four = row two / row three). The national estimate of 

spending on electronic ballast rebates in row two results from multiplying national spending on 

rebates (listed in Table B.14) by the fraction of energy efficiency dollars going to rebates listed in 

row one. 

T a hI e B . 14 . E xpan d e dS ample IDtStEf aa e S lmates 

Year 1992 1993 1994 1995 1996 

% of EE $s to electronic ballast rebates in the sample 11% 6% 7% 12% 5% 

National estimate of rebate sj)ending (millions of $s) $131 $98 $112 $165 $54 

Average $s/rebate $20.65 $12.01 $9.42 $11.91 $7.64 

in the sample 

Estimated number of rebates nationwide 6.3 8.1 11.8 13.9 7.0 

National electronic ballasts shipments (millions) 22.9 26.0 33.1 29.7 

Percent of electronic ballasts with rebates 36% 46% 42% 24% .. . 'None of the three utlhtles added to the core data set provlded mformatlOn for 1997 . 

Analysis of the core sample data set showed that electronic ballast rebates peaked in 1994 

in terms of the number of ballasts rebated and utility spending on rebates. Estimates of the number 

of rebates declined slightly in 1995 and substantially in the last two years. Analysis ofthe expanded 

B-34 
sample data set showed the same general trend in DSM rebates. The expanded data set indicated that

rebates peaked in 1995 rather than 1994. However, as with the core sample, the analysis pointed to

a sharp drop from 1995 to 1996.

Tables B. 12 through B. 14 also show that the decrease in spending on electronic ballast

rebates fell much more sharply than did utility spending on energy-efficiency in DSM programs in

the later analysis years. In the core sample, rebate spending in 1996 fell to less than half of the 1995

spending, and in the expanded sample it fell to one-third of its 1995 level. In contrast, national

energy-efficiency spending in 1996 decreased to only about 3/4 of its 1995 level.

B.5.1.6 Market Impacts of DSM Rebates

Table B. 15 shows the number of magnetic ballast shipments falling from 1993 to 1996. In

1997, the number of magnetic ballast shipments rose slightly and in 1998 essentially leveled off. As

rebates for electronic ballasts were on the increase in 1994 and 1995 (see Tables B. 13 and B. 14), the

number of magnetic ballast shipments was declining. The numbers suggested some correlation

between DSM rebates and magnetic ballast shipments.

Data on market share shown in Table B. 15 revealed a similar pattern to data on the number

of shipments. The table shows the shares of the total market held by magnetic ballasts. The market

share for magnetic ballasts decreased from 1993 to 1995 when rebates for electronic ballasts were

still on the rise. A levelling-off of magnetic market share similar to that of the magnetic ballast

shipments can be seen from 1995 to 1997, during the time when ballast rebates were on the wane.

This demonstrated the potential impact of the decline of the rebate programs.

Table B.15. Shipments and Market Shares of Rebated Ballasts

Year Magnetic Ballast Shipments

(millions)'

Total Ballast Shipments (T12

and T8, millions)

Magnetic Ballast Market Share

(% of Ballast Market)

1993 39.0 61.9 63%

1994 36.0 62.0 58%

1995 32.7 65.8 50%

1996 30.1 59.8 50%

1997 31.1 66.5 47%

1998 30.9 68.7 45%

I Shipments data from NEMA (see Chapter 5, Table 5 . 2). Almost all magnetic ballasts sold were for T12lamps.

Data are for those operating both 4-foot and 8-foot lamps.

EE-RM-97-500 COMMENT 104

sample data set showed the same general trend in DSM rebates. The expanded data set indicated that 

rebates peaked in 1995 rather than 1994. However, as with the core sample, the analysis pointed to 

a sharp drop from 1995 to 1996. 

Tables B.12 through B.14 also show that the decrease in spending on electronic ballast 

rebates fell much more sharply than did utility spending on energy-efficiency in DSM programs in 

the later analysis years. In the core sample, rebate spending in 1996 fell to less than half of the 1995 

spending, and in the expanded sample it fell to one-third of its 1995 level. In contrast, national 

energy-efficiency spending in 1996 decreased to only about 3/4 of its 1995 level. 

B.5.1.6 Market Impacts of DSM Rebates 

Table B.15 shows the number of magnetic ballast shipments falling from 1993 to 1996. In 

1997, the number of magnetic ballast shipments rose slightly and in 1998 essentially leveled off. As 

rebates for electronic ballasts were on the increase in 1994 and 1995 (see Tables B.13 and B.14), the 

number of magnetic ballast shipments was declining. The numbers suggested some correlation 

between DSM rebates and magnetic ballast shipments. 

Data on market share shown in Table B.15 revealed a similar pattern to data on the number 

of shipments. The table shows the shares of the total market held by magnetic ballasts. The market 

share for magnetic ballasts decreased from 1993 to 1995 when rebates for electronic ballasts were 

still on the rise. A levelling-off of magnetic market share similar to that of the magnetic ballast 

shipments can be seen from 1995 to 1997, during the time when ballast rebates were on the wane. 

This demonstrated the potential impact of the decline of the rebate programs. 

T a bi e B . 15 . Sh' Ipmen t san d M k ar e tSh ares 0 fRbtdBll e a e a as t s 

Year Magnetic Ballast Shipments Total Ballast Shipments (TI2 Magnetic Ballast Market Share 

(millions) 1 and T8, millions) (% of Ballast Market) 

1993 39.0 61.9 63% 

1994 36.0 62.0 58% 

1995 32.7 65.8 50% 

1996 30.1 59.8 50% 

1997 31.1 66.5 47% 

1998 30.9 68.7 45% 

1 Shipments data from NEMA (see Chapter 5, Table 5.2). Almost all magnetic ballasts sold were for TI2lamps. 

Data are for those operating both 4-foot and 8-foot lamps. 

B-35 

sample data set showed the same general trend in DSM rebates. The expanded data set indicated that 

rebates peaked in 1995 rather than 1994. However, as with the core sample, the analysis pointed to 

a sharp drop from 1995 to 1996. 

Tables B.12 through B.14 also show that the decrease in spending on electronic ballast 

rebates fell much more sharply than did utility spending on energy-efficiency in DSM programs in 

the later analysis years. In the core sample, rebate spending in 1996 fell to less than half of the 1995 

spending, and in the expanded sample it fell to one-third of its 1995 level. In contrast, national 

energy-efficiency spending in 1996 decreased to only about 3/4 of its 1995 level. 

B.5.1.6 Market Impacts of DSM Rebates 

Table B.15 shows the number of magnetic ballast shipments falling from 1993 to 1996. In 

1997, the number of magnetic ballast shipments rose slightly and in 1998 essentially leveled off. As 

rebates for electronic ballasts were on the increase in 1994 and 1995 (see Tables B.13 and B.14), the 

number of magnetic ballast shipments was declining. The numbers suggested some correlation 

between DSM rebates and magnetic ballast shipments. 

Data on market share shown in Table B.15 revealed a similar pattern to data on the number 

of shipments. The table shows the shares of the total market held by magnetic ballasts. The market 

share for magnetic ballasts decreased from 1993 to 1995 when rebates for electronic ballasts were 

still on the rise. A levelling-off of magnetic market share similar to that of the magnetic ballast 

shipments can be seen from 1995 to 1997, during the time when ballast rebates were on the wane. 

This demonstrated the potential impact of the decline of the rebate programs. 

T a bi e B . 15 . Sh' Ipments an dM ar k et Sh ares 0 fRb e ate dBll a asts 

Year Magnetic Ballast Shipments Total Ballast Shipments (TI2 Magnetic Ballast Market Share 

(millions) 1 and T8, millions) (% of Ballast Market) 

1993 39.0 61.9 63% 

1994 36.0 62.0 58% 

1995 32.7 65.8 50% 

1996 30.1 59.8 50% 

1997 31.1 66.5 47% 

1998 30.9 68.7 45% 

1 Shipments data from NEMA (see Chapter 5, Table 5.2). Almost all magnetic ballasts sold were for TI2lamps. 

Data are for those operating both 4-foot and 8-foot lamps. 

B-35 
B.5.2 ASH E/IES 90.1-1999 Building Code

This section reports (draft) results of analysis of the impact of the lighting provisions in the

upcoming ASHRAE/IES 90.1 revised building code on electronic ballast shipments. This code is

not actually "non-regulatory," since state compliance with the code was mandated by EPAct.

However, it is included in this section as a base case market driver that is not a component standard.

The building code sets limits on the lighting power density (or unit power density) in Watts per

square foot or square meter, but these limits do not prescribe any specific equipment type that must

be used to meet them. Nonetheless, this analysis assumed that the code would strongly encourage

the specification of electronic ballasts (with T8 lamps) for new and renovated lighting systems.

B.5.2.1 Introduction

A study by Pacific Northwest National Laboratories (PNNL) and LBNL attempted to quantify

the number of magnetic ballasts that would become electronic as a result of the adoption of the

lighting provisions in ASHRAEIIES 90.1-1999 building code by the states.` This study assumed

that the code revision (version 90.1-1999) would be published and that DOE would designate it as

the appropriate code for state adoption under EPAct. The code was estimated to begin affecting new

and renovated buildings in the year 2005. It was assumed that electronic ballasts would be used to

achieve compliance with the lighting power density requirements in the code. These ballasts would

be purchased in place of the magnetic ballasts that would be used in the absence of the revised code.

However, the rate of adoption by the states, as well as the rate of compliance by building owners,

were both assumed to be less than 100 percent and to increase over time. Both rates would increase

as a result of DOE support through PNNL's Building Standards and Guidelines (BSG) program,

which provides technical assistance to the states in adopting and using the code. Impacts were

analyzed through the year 2020.

B.5.2.2 Methodology

A base case forecast of ballast sales was established using the National Energy Modeling

System (NEMS) output forecasted for the Annual Energy Outlook 1999." The projections of

lighting service demand were developed in NEMS using a market-based approach which takes into

account the annualized life cycle costs of competing lighting technologies. Additionally, current

EPAct lamp standards, current NAECA ballast standards, and impacts of EPA's lighting programs

were incorporated into NEMS. The NEMS output provided a forecast of lighting service demand

from 1996 through 2020 for representative fluorescent system types.

The lighting service demand was translated into ballast sales using the following equations:

E

BS = CapFac x BF

8,760 x FW

B-36

EE-RM-97-500 COMMENT 104

B.S.2 ASHRAEIIES 90.1-1999 Building Code 

This section reports (draft) results of analysis of the impact of the lighting provisions in the 

upcoming ASHRAEIIES 90.1 revised building code on electronic ballast shipments. This code is 

not actually "non-regulatory," since state compliance with the code was mandated by EPAct. 

However, it is included in this section as a base case market driver that is not a component standard. 

The building code sets limits on the lighting power density (or unit power density) in Watts per 

square foot or square meter, but these limits do not prescribe any specific equipment type that must 

be used to meet them. Nonetheless, this analysis assumed that the code would strongly encourage 

the specification of electronic ballasts (with T8 lamps) for new and renovated lighting systems. 

B.S.2.1 Introduction 

A study by Pacific Northwest National Laboratories (PNNL) and LBNL attempted to quantifY 

the number of magnetic ballasts that would become electronic as a result of the adoption of the 

lighting provisions in ASHRAE/IES 90.1-1999 building code by the states. 16 This study assumed 

that the code revision (version 90.1-1999) would be published and that DOE would designate it as 

the appropriate code for state adoption under EP Act. The code was estimated to begin affecting new 

and renovated buildings in the year 2005. It was assumed that electronic ballasts would be used to 

achieve compliance with the lighting power density requirements in the code. These ballasts would 

be purchased in place of the magnetic ballasts that would be used in the absence ofthe revised code. 

However, the rate of adoption by the states, as well as the rate of compliance by building owners, 

were both assumed to be less than 100 percent and to increase over time. Both rates would increase 

as a result of DOE suppo11 through PNNL's Building Standards and Guidelines (BSG) program, 

which provides technical assistance to the states in adopting and using the code. Impacts were 

analyzed through the year 2020. 

B.S.2.2 Methodology 

A base case forecast of ballast sales was established using the National Energy Modeling 

System (NEMS) output forecasted for the Annual Energy Outlook 1999. 17 The projections of 

lighting service demand were developed in NEMS using a market-based approach which takes into 

account the annualized life cycle costs of competing lighting technologies. Additionally, current 

EPAct lamp standards, current NAECA ballast standards, and impacts of EPA's lighting programs 

were incorporated into NEMS. The NEMS output provided a forecast of lighting service demand 

from 1996 through 2020 for representative fluorescent system types. 

The lighting service demand was translated into ballast sales using the following equations: 

E 

BS = CapFac x BF 

8,760 x FW 

B-36 

B.S.2 ASHRAEIIES 90.1-1999 Building Code 

This section reports (draft) results of analysis of the impact of the lighting provisions in the 

upcoming ASHRAEIIES 90.1 revised building code on electronic ballast shipments. This code is 

not actually "non-regulatory," since state compliance with the code was mandated by EPAct. 

However, it is included in this section as a base case market driver that is not a component standard. 

The building code sets limits on the lighting power density (or unit power density) in Watts per 

square foot or square meter, but these limits do not prescribe any specific equipment type that must 

be used to meet them. Nonetheless, this analysis assumed that the code would strongly encourage 

the specification of electronic ballasts (with T8 lamps) for new and renovated lighting systems. 

B.S.2.1 Introduction 

A study by Pacific Northwest National Laboratories (PNNL) and LBNL attempted to quantifY 

the number of magnetic ballasts that would become electronic as a result of the adoption of the 

lighting provisions in ASHRAE/IES 90.1-1999 building code by the states. 16 This study assumed 

that the code revision (version 90.1-1999) would be published and that DOE would designate it as 

the appropriate code for state adoption under EP Act. The code was estimated to begin affecting new 

and renovated buildings in the year 2005. It was assumed that electronic ballasts would be used to 

achieve compliance with the lighting power density requirements in the code. These ballasts would 

be purchased in place of the magnetic ballasts that would be used in the absence ofthe revised code. 

However, the rate of adoption by the states, as well as the rate of compliance by building owners, 

were both assumed to be less than 100 percent and to increase over time. Both rates would increase 

as a result of DOE suppo11 through PNNL's Building Standards and Guidelines (BSG) program, 

which provides technical assistance to the states in adopting and using the code. Impacts were 

analyzed through the year 2020. 

B.S.2.2 Methodology 

A base case forecast of ballast sales was established using the National Energy Modeling 

System (NEMS) output forecasted for the Annual Energy Outlook 1999. 17 The projections of 

lighting service demand were developed in NEMS using a market-based approach which takes into 

account the annualized life cycle costs of competing lighting technologies. Additionally, current 

EPAct lamp standards, current NAECA ballast standards, and impacts of EPA's lighting programs 

were incorporated into NEMS. The NEMS output provided a forecast of lighting service demand 

from 1996 through 2020 for representative fluorescent system types. 

The lighting service demand was translated into ballast sales using the following equations: 

E 

BS = CapFac x BF 

8,760 x FW 

B-36 
and

E _ 8,760 X SD

e

where BS = Ballast Sales

E = Annual energy use (watt hours)

CapFac = Weighted Lighting Capacity Factor

FW = Weighted Fixture wattage

BF =Weighted Ballasts per fixture

SD = Service Demand (billion lumen years)

e = Fixture efficacy (lumens/watt)

8,760 = hours per year

The NEMS output provided lighting service demand. NEMS inputs provided and lighting

capacity factors by building type; these were weighted using 1995 CBECS floorspace information

to determine a weighted lighting capacity factor. Fixture wattages and efficacies were the same as

those LBNL provided to EIA for AEO 98 (used again for AEO 99); these data were from the LBNL

1997 Draft Report.

To estimate a weighted fixture wattage, fixture sales by 2-, 3-, and 4-lamp fixtures from the

Bureau of Census report "Current Industrial Reports - Electric Lighting Fixtures - 1998," Table 2

were used. Fixture sales by number of lamps were only available for 4-foot fixtures, so sales by

lamp for 8-foot fixtures were estimated to be 55 percent 2-lamp, 40 percent 4-lamp, and 5 percent

1-lamp. A weighted average of ballasts per fixture was derived using ballast sales information from

the Bureau of Census report "Current Industrial Reports - Fluorescent Lamp Ballasts - Summary for

1998," and "Summary for 1999", Table 3.

The current model inputs used in this TSD that have been updated since the 1997 Draft

Report were not updated for this building code study, since EIA was unable to rerun the NEMS

analysis to reflect the changes.' Inputs that might affect lighting service demand that should be

updated in future analyses were ballast service life and ballast prices, both of which received

relatively small adjustments.

The NEMS output is separated into four decision categories for equipment purchases: New,

Replacement, Retrofit/ Surviving, and Total. In NEMS, the Replacement category signifies

substantially renovated lighting systems, while Retrofit/Surviving signifies replacement at failure

in existing buildings. In this analysis, the LBNL/PNNL base case contained only those ballasts

associated with New and Replacement decisions, as it was assumed that the revised codes would

only be applicable to new construction and substantial renovation. Within the NEMS base case, use

of magnetic ballasts shifted down after 2005 because of a switch from the F32T8 - Magnetic Ballast

'The current model inputs for ballast and lamp lifetimes, wattage, and mean lumen output were used for AEO 2000;

ballast prices will be provided to EIA for future AEO analyses.

B-37

EE-RM-97-500 COMMENT 104

and 

E 8,760 x SD 

e 

where BS = Ballast Sales 

E = Annual energy use (watt hours) 

CapFac = Weighted Lighting Capacity Factor 

. FW = Weighted Fixture wattage 

BF =Weighted Ballasts per fixture 

SD = Service Demand (billion lumen years) 

e = Fixture efficacy (lumens/watt) 

8,760 = hours per year 

The NEMS output provided lighting service demand. NEMS inputs provided and lighting 

capacity factors by building type; these were weighted using 1995 CBECS floorspace information 

to determine a weighted lighting capacity factor. Fixture wattages and efficacies were the same as 

those LBNL provided to EIA for AEO 98 (used again for AEO 99); these data were from the LBNL 

1997 Draft Report. 

To estimate a weighted fixture wattage, fixture sales by 2-,3-, and 4-lamp fixtures from the 

I;lureau of Census report "Current Industrial Repolis - Electric Lighting Fixtures - 1998," Table 2 

were used. Fixture sales by number of lamps were only available for 4-foot fixtures, so sales by 

lamp for 8-foot fixtures were estimated to be 55 percent 2-lamp, 40 percent 4-lamp, and 5 percent 

I-lamp. A weighted average of ballasts per fixture was derived using ballast sales information from 

the Bureau of Census report "Current Industrial Reports - Fluorescent Lamp Ballasts - Summary for 

1998," and "Summary for 1999", Table 3. 

The current model inputs used in this TSD that have been updated since the 1997 Draft 

Report were not updated for this building code study, since EIA was unable to rerun the NEMS 

analysis to reflect the changes.g Inputs that might affect lighting service demand that should be 

updated in future analyses were ballast service life and ballast prices, both of which received 

relatively small adjustments. 

The NEMS output is separated into four decision categories for equipment purchases: New, 

Replacement, Retrofit! Surviving, and Total. In NEMS, the Replacement category signifies 

substantially renovated lighting systems, while Retrofit/Surviving signifies replacement at failure 

in existing buildings. In this analysis, the LBNLlPNNL base case contained only those ballasts 

associated with New and Replacement decisions, as it was assumed that the revised codes would 

only be applicable to new construction and substantial renovation. Within the NEMS base case, use 

of magnetic ballasts shifted down after 2005 because ofa switch from the F32T8 - Magnetic Ballast 

Il'fhe current model inputs for ballast and lamp lifetimes, wattage, and mean lumen output were used for AEO 2000; 

ballast prices will be provided to EIA for future AEO analyses. 

B-37 

and 

E 8,760 x SD 

e 

where BS = Ballast Sales 

E = Annual energy use (watt hours) 

CapFac = Weighted Lighting Capacity Factor 

. FW = Weighted Fixture wattage 

BF =Weighted Ballasts per fixture 

SD = Service Demand (billion lumen years) 

e = Fixture efficacy (lumens/watt) 

8,760 = hours per year 

The NEMS output provided lighting service demand. NEMS inputs provided and lighting 

capacity factors by building type; these were weighted using 1995 CBECS floorspace information 

to determine a weighted lighting capacity factor. Fixture wattages and efficacies were the same as 

those LBNL provided to EIA for AEO 98 (used again for AEO 99); these data were from the LBNL 

1997 Draft Report. 

To estimate a weighted fixture wattage, fixture sales by 2-,3-, and 4-lamp fixtures from the 

I;lureau of Census report "Current Industrial Repolis - Electric Lighting Fixtures - 1998," Table 2 

were used. Fixture sales by number of lamps were only available for 4-foot fixtures, so sales by 

lamp for 8-foot fixtures were estimated to be 55 percent 2-lamp, 40 percent 4-lamp, and 5 percent 

I-lamp. A weighted average of ballasts per fixture was derived using ballast sales information from 

the Bureau of Census report "Current Industrial Reports - Fluorescent Lamp Ballasts - Summary for 

1998," and "Summary for 1999", Table 3. 

The current model inputs used in this TSD that have been updated since the 1997 Draft 

Report were not updated for this building code study, since EIA was unable to rerun the NEMS 

analysis to reflect the changes.g Inputs that might affect lighting service demand that should be 

updated in future analyses were ballast service life and ballast prices, both of which received 

relatively small adjustments. 

The NEMS output is separated into four decision categories for equipment purchases: New, 

Replacement, Retrofit! Surviving, and Total. In NEMS, the Replacement category signifies 

substantially renovated lighting systems, while Retrofit/Surviving signifies replacement at failure 

in existing buildings. In this analysis, the LBNLlPNNL base case contained only those ballasts 

associated with New and Replacement decisions, as it was assumed that the revised codes would 

only be applicable to new construction and substantial renovation. Within the NEMS base case, use 

of magnetic ballasts shifted down after 2005 because ofa switch from the F32T8 - Magnetic Ballast 

Il'fhe current model inputs for ballast and lamp lifetimes, wattage, and mean lumen output were used for AEO 2000; 

ballast prices will be provided to EIA for future AEO analyses. 

B-37 
fixtures to primarily F32T8 - Electronic Ballast fixtures due to the NEMS assumptions regarding

their relative annualized life cycle costs.

B.5.2.3 Base Case Forecast without Upgraded Code

The base case ballast sales forecast is presented in Table B. 16. The results were based on

the NEMS output, which in turn was based on that model's assumptions of consumer behavior in

response to life-cycle cost. The magnetic ballast shipments were greater than in the LBNL NES

model forecast, in part because these shipments represented the new and renovation component of

all fluorescent ballast sales, rather than those of the smaller subset of "covered products" considered

in this analysis and shown in Chapter 5, Table 5.1 and 5.2.

The magnetic market share of 51 percent was congruent with the data submitted by Osram

Sylvania, Inc. in its comments to DOE on December 14, 1998, which showed the magnetic market

share for total new and renovation (OEM) ballasts as 56 percent." We compared only the ballasts

for the new and renovation markets, since the NEMS forecast did not have many "early replacement"

retrofits; the NEMS model assumed that these occurred only when the capital cost plus operation and

maintenance of the new system was lower than the operation and maintenance cost of the old system,

without accounting for financial incentives such as rebates. Note that the electronic share grew

rapidly in the base case shown in Table B.16, surpassing 85 percent by the year 2006.

B.5.2.4 Impact of Upgraded Code

To estimate the impact on ballast sales due to the proposed changes in ASHRAE 90.1-1999,

the proposed lighting standards were compared to the current standard , ASHRAE 90.1-1989. When

comparing the allowable lighting power densities for whole buildings (watts per square foot), on

average, the 90.1-1999 would allow about 10 percent fewer watts per square foot than the current

standard for those building types where standards existed previously . To translate this into an impact

on ballast sales, it was assumed that fluorescent fixtures would need to use, on average , 10 percent

fewer watts than those that met the current standard. In effect, an electronic ballast would need to

be used to meet the proposed standard.

" Those data distinguished ballasts from the early retrofit market from those in the new/renovation market; more

recent NEMA data provided in their comments of April 15 `x', 1999 included (early) retrofits with new and renovation.

B-38

EE-RM-97-500 COMMENT 104

fixtures to primarily F32T8 - Electronic Ballast fixtures due to the NEMS assumptions regarding 

their relative annualized life cycle costs. 

B.5.2.3 Base Case Forecast without Upgraded Code 

The base case ballast sales forecast is presented in Table B.16. The results were based on 

the NEMS output, which in tum was based on that model's assumptions of consumer behavior in 

response to life-cycle cost. The magnetic ballast shipments were greater than in the LBNL NES 

model forecast, in part because these shipments represented the new and renovation component of 

all fluorescent ballast sales, rather than those of the smaller subset of "covered products" considered 

in this analysis and shown in Chapter 5, Table 5.1 and 5.2. 

The magnetic market share of 51 percent was congruent with the data submitted by Osram 

Sylvania, Inc. in its comments to DOE on December 14, 1998, which showed the magnetic market 

share for total new and renovation (OEM) ballasts as 56 percent.h We compared only the ballasts 

for the new and renovation markets, since the NEMS forecast did not have many "early replacement" 

retrofits; the NEMS model assumed that these occurred only when the capital cost plus operation and 

maintenance ofthe new system was lower than the operation and maintenance cost ofthe old system, 

without accounting for financial incentives such as rebates. Note that the electronic share grew 

rapidly in the base case shown in Table B.16, surpassing 85 percent by the year 2006. 

B.5.2.4 Impact of Upgraded Code 

To estimate the impact on ballast sales due to the proposed changes in ASHRAE 90.1-1999, 

the proposed lighting standards were compared to the current standard, ASHRAE 90.1-1989. When 

comparing the allowable lighting power densities for whole buildings (watts per square foot), on 

average, the 90.1-1999 would allow about 10 percent fewer watts per square foot than the current 

standard for those building types where standards existed previously. To translate this into an impact 

on ballast sales, it was assumed that fluorescent fixtures would need to use, on average, 10 percent 

fewer watts than those that met the current standard. In effect, an electronic ballast would need to 

be used to meet the proposed standard. 

h Those data distinguished ballasts from the early retrofit market from those in the new/renovation market; more 

recent NEMA data provided in their comments of April 15'h, 1999 included (early) retrofits with new and renovation. 

B-38 

fixtures to primarily F32T8 - Electronic Ballast fixtures due to the NEMS assumptions regarding 

their relative annualized life cycle costs. 

B.5.2.3 Base Case Forecast without Upgraded Code 

The base case ballast sales forecast is presented in Table B.16. The results were based on 

the NEMS output, which in tum was based on that model's assumptions of consumer behavior in 

response to life-cycle cost. The magnetic ballast shipments were greater than in the LBNL NES 

model forecast, in part because these shipments represented the new and renovation component of 

all fluorescent ballast sales, rather than those of the smaller subset of "covered products" considered 

in this analysis and shown in Chapter 5, Table 5.1 and 5.2. 

The magnetic market share of 51 percent was congruent with the data submitted by Osram 

Sylvania, Inc. in its comments to DOE on December 14, 1998, which showed the magnetic market 

share for total new and renovation (OEM) ballasts as 56 percent.h We compared only the ballasts 

for the new and renovation markets, since the NEMS forecast did not have many "early replacement" 

retrofits; the NEMS model assumed that these occurred only when the capital cost plus operation and 

maintenance ofthe new system was lower than the operation and maintenance cost ofthe old system, 

without accounting for financial incentives such as rebates. Note that the electronic share grew 

rapidly in the base case shown in Table B.16, surpassing 85 percent by the year 2006. 

B.5.2.4 Impact of Upgraded Code 

To estimate the impact on ballast sales due to the proposed changes in ASHRAE 90.1-1999, 

the proposed lighting standards were compared to the current standard, ASHRAE 90.1-1989. When 

comparing the allowable lighting power densities for whole buildings (watts per square foot), on 

average, the 90.1-1999 would allow about 10 percent fewer watts per square foot than the current 

standard for those building types where standards existed previously. To translate this into an impact 

on ballast sales, it was assumed that fluorescent fixtures would need to use, on average, 10 percent 

fewer watts than those that met the current standard. In effect, an electronic ballast would need to 

be used to meet the proposed standard. 

h Those data distinguished ballasts from the early retrofit market from those in the new/renovation market; more 

recent NEMA data provided in their comments of April 15'h, 1999 included (early) retrofits with new and renovation. 

B-38 
Table B.16. Base Case Ballast Sales, New and Renovated Systems (Thousands )

Magnetic Ballast Electronic Ballast

Year Sales % of Total Sal 1w of Total

2000 32796 51% 31786 49%

2001 31362 48% 33449 52%

2002 26664 41% 38656 59%

2003 18900 28% 47909 72%

2004 15782 23% 52034 77%

2005 15912 23% 52331 77%

2006 8819 13% 60629 87%

2007 8904 13% 61273 87%

2008 9004 13% 61857 87%

2009 9089 13% 62361 87%

2010 9138 13% 62748 87%

2011 9163 13% 63018 87%

2012 9191 13% 63192 87%

2013 9196 13% 63255 87%

2014 9177 13% 63108 87%

2015 9161 14% 57767 86%

2016 9137 14% 57310 86%

2017 9088 14% 56785 86%

2018 9030 14% 56066 86%

2019 8966 14% 54948 86%

2020 8906 14% 53993 86%

Using the base case sales information, the number of ballasts sold for fixtures not expected

to meet the 2005 standard was calculated. As discussed above, this was all of the magnetic ballasts

forecasted to be sold in the base case beginning in 2005. Changes in sales patterns based on

increased state adoption and code compliance for the BSG program were applied to those sales to

determine the estimated impact of the program on ballast sales. The assumed rates for state adoption

and code compliance (from the FY99 DOE-EE GPRA` Metrics effort) are shown in Table B. 17. The

decrease in state adoption rates after 2010 was based on the assumption that adoption rates would

slip slightly over time.

'Government and Performance Results Act

B-39

EE-RM-97-500 COMMENT 104

Table B.16. Base Case Ballast Sales, New and Renovated Systems (Thousands) 

Magnetic Ballast Electronic Banast 

Year Sales % of Total Sales % of Total 

2000 32796 51% 31786 49% 

2001 31362 48% 33449 52% 

2002 26664 41% 38656 59% 

2003 18900 28% 47909 72%. 

2004 15782 23% 52034 77% 

2005 15912 23% 52331 77% 

2006 8819 13% 60629 87% 

2007 8904 13% 61273 87% 

2008 9004 13% 61857 87% 

2009 9089 13% 62361 87% 

2010 9138 13% 62748 87% 

2011 9163 13% 63018 87% 

2012 9191 13% 63192 87% 

2013 9196 13% 63255 87% 

2014 9177 13% 63108 87% 

2015 9161 14% 57767 86% 

2016 9137 14% 57310 86% 

2017 9088 14% 56785 86% 

2018 9030 14% 56066 86% 

2019 8966 14% 54948 86% 

2020 8906 14% 53993 86% 

Using the base case sales information, the number of ballasts sold for fixtures not expected 

to meet the 2005 standard was calculated. As discussed above, this was all of the magnetic ballasts 

forecasted to be sold in the base case beginning in 2005. Changes in sales patterns based on 

increased state adoption and code compliance for the BSG program were applied to those sales to 

determine the estimated impact of the program on ballast sales. The assumed rates for state adoption 

and code compliance (from the FY99 DOE-EE GPRAi Metrics effort) are shown in Table B.17. The 

decrease in state adoption rates after 2010 was based on the assumption that adoption rates would 

slip slightly over time. 

i Govemment and Performance Results Act 

B-39 

Table B.16. Base Case Ballast Sales, New and Renovated Systems (Thousands) 

Magnetic Ballast Electronic Banast 

Year Sales 0/0 ......... .1 Sales % of Total 

2000 32796 51% 31786 49% 

2001 31362 48% 33449 52% 

2002 26664 41% 38656 59% 

2003 18900 28% 47909 72%. 

2004 15782 23% 52034 77% 

2005 15912 23% 52331 77% 

2006 8819 13% 60629 87% 

2007 8904 13% 61273 87% 

2008 9004 13% 61857 87% 

2009 9089 13% 62361 87% 

2010 9138 13% 62748 87% 

2011 9163 13% 63018 87% 

2012 9191 13% 63192 87% 

2013 9196 13% 63255 87% 

2014 9177 13% 63108 87% 

2015 9161 14% 57767 86% 

2016 9137 14% 57310 86% 

2017 9088 14% 56785 86% 

2018 9030 14% 56066 86% 

2019 8966 14% 54948 86% 

2020 8906 14% 53993 86% 

Using the base case sales information, the number of ballasts sold for fixtures not expected 

to meet the 2005 standard was calculated. As discussed above, this was all of the magnetic ballasts 

forecasted to be sold in the base case beginning in 2005. Changes in sales patterns based on 

increased state adoption and code compliance for the BSG program were applied to those sales to 

determine the estimated impact of the program on ballast sales. The assumed rates for state adoption 

and code compliance (from the FY99 DOE-EE GPRAi Metrics effort) are shown in Table B.17. The 

decrease in state adoption rates after 2010 was based on the assumption that adoption rates would 

slip slightly over time. 

i Govemment and Performance Results Act 

B-39 
and Code Compliance tion

ected Im act of Existin Pro races on State Adop

Table B.17 Exp

State Adoption Code Compliance

Year (% of Buildings) (% of Net Potential) Total Impact

2006 66% 58% 39%

2007 70% 61% 43%

12008 73% 64% 47%

67% 51%

{2009 77%

{2010 80% 70% 56%

{2011 79% 70% 56%

12012 79% 70% 55%

{2013 78% 70% 55%

2014 77% 70% 54%

2015 77% 70% 54%

70% 53%

(2016 76%

0% 53%

2017 75% 7

2018 74% 70% 52%

2019 74% 70% 52%

020 73% 70% 51%

EE-RM-97-500 COMMENT 104

Ta bI e B . 17 E xpecte dI mpacto fE Xlstm2 .. p r02rams on S tate AdoptIOn an dC d C o e OmpJlance r 

State Adoption Code Compliance 

Year (% of Buildings) (% of Net Potential) Total Impact 

2006 66% 58% 39% 

2007 70% 61% 43% 

2008 73% 64% 47% 

2009 77% 67% 51% 

2010 80% 70% 56% 

2011 79% 70% 56% 

2012 79% 70% 55% 

2013 78% 70% 55% 

2014 77% 70% 54% 

2015 77% 70% 54% 

~016 76% 70% 53% 

~017 75% 70% 53% 

~018 74% 70% 52% 

~019 74% 70% 52% 

~020 73% 70% 51% 

B-40 

T a bI e B . 17 E xpecte dI mpacto fE Xlstm~ .. P ro~rams on S tate Ad optIOn an dC d C o e OmpJlanCe r 

State Adoption Code Compliance 

Year (% of Buildings) (% of Net Potential) Total Impact 

~006 66% 58% 39% 

~OO7 70% 61% 43% 

lO08 73% 64% 47% 

2009 77% 67% 51% 

2010 80% 70% 56% 

2011 79% 70% 56% 

lO12 79% 70% 55% 

2013 78% 70% 55% 

2014 77% 70% 54% 

2015 77% 70% 54% 

2016 76% 70% 53% 

2017 75% 70% 53% 

2018 74% 70% 52% 

2019 74% 70% 52% 

2020 73% 70% 51% 

B-40 
The estimated impact on ballast sales is presented in Table B.18.

Table B.18. Ballast Sales with Upgraded Code with BSG Program Support (Thousands)

Magnetic Ballast Change

Year 0 0 Ballast

2000 32796 51% 31786 49% 0

2001 31362 48% 33449 52% 0

2002 26664 41% 38656 59% 0

2003 18900 28% 47909 72% 0

2004 15782 23% 52034 77% 0

2005 10399 15% 58045 85% 5514

2006 5423 8% 64025 92% 3996

2007 5113 7% 65064 93% 3791

2008 4786 7% 66076 93% 4218

2009 4424 6% 67026 94% 4664

2010 4021 6% 67865 94% 5117

2011 4090 6% 68120 94% 5103

2012 4134 6% 68249 94% 5057

2013 4182 6% 68270 94% 5015

2014 4218 6% 68067 94% 4959

2015 4255 6% 62673 94% 4906

2016 4289 6% 62158 94% 4848

2017 4310 7% 61562 93% 4777

2018 4327 7% 60769 93% 4703

2019 4341 7% 59574 93% 4626

2020 4355 7% 58544 93% 4551

The resulting magnetic ballast shipments were slightly greater than those in the NES model

after the year 2014 for the 2015 Base Case and after the year 2024 for the 2027 Base Case. This

indicated that the while the assumptions for both scenarios about the decline of magnetic ballast

future shipments included some impacts from the upgraded building code, they also included

additional impacts from other programs. The building code was estimated to cause an overall

reduction of magnetic ballast shipments from 2003 to 2030 (the analysis period for the NES model)

of 27 percent from the base case without the code.

We analyzed the implied "conversion rate" from magnetic to electronic ballasts by

calculating the conversion rate for each year, summing the annual rates, and dividing by the number

B-41

EE-RM-97-500 COMMENT 104

The estimated impact on ballast sales is presented in Table R18. 

Table B.1S. Ballast Sales with Upgraded Code with BSG Program Support (Thousands) 

Magnetic Ballast Electronic Ballast Change 

Year Sales % of Total Sales % of Total (Ballasts) 

2000 32796 51% 31786 49% 0 

2001 31362 48% 33449 52% 0 

2002 26664 41% 38656 59% 0 

2003 18900 28% 47909 72% 0 

2004 15782 23% 52034 77% 0 

2005 10399 15% 58045 85% 5514 

2006 5423 8% 64025 92% 3996 

2007 5113 7% 65064 93% 3791 

2008 4786 7% 66076 93% 4218 

2009 4424 6% 67026 94% 4664 

2010 4021 6% 67865 94% 5117 

2011 4090 6% 68120 94% 5103 

2012 4134 6% 68249 94% 5057 

2013 4182 6% 68270 94% 5015 

2014 4218 6% 68067 94% 4959 

2015 4255 6% 62673 94% 4906 

2016 4289 6% 62158 94% 4848 

2017 4310 7% 61562 93% 4777 

2018 4327 7% 60769 93% 4703 

2019 4341 7% 59574 93% 4626 

2020 4355 7% 58544 93% 4551 

The resulting magnetic ballast shipments were slightly greater than those in the NES model 

after the year 2014 for the 2015 Base Case and after the year 2024 for the 2027 Base Case. This 

indicated that the while the assumptions for both scenarios about the decline of magnetic ballast 

future shipments included some impacts from the upgraded building code, they also included 

additional impacts from other programs. The building code was estimated to cause an overall 

reduction of magnetic ballast shipments from 2003 to 2030 (the analysis period for the NES model) 

of 27 percent from the base case without the code. 

We analyzed the implied "conversion rate" from magnetic to electronic ballasts by 

calculating the conversion rate for each year, summing the annual rates, and dividing by the number 

B-41 

The estimated impact on ballast sales is presented in Table R18. 

Table B.1S. Ballast Sales with Upgraded Code with BSG Program Support (Thousands) 

Magnetic Ballast Electronic Ballast Change 

Year Sales % of Total Sales % of Total (Ballasts) 

2000 32796 51% 31786 49% 0 

2001 31362 48% 33449 52% 0 

2002 26664 41% 38656 59% 0 

2003 18900 28% 47909 72% 0 

2004 15782 23% 52034 77% 0 

2005 10399 15% 58045 85% 5514 

2006 5423 8% 64025 92% 3996 

2007 5113 7% 65064 93% 3791 

2008 4786 7% 66076 93% 4218 

2009 4424 6% 67026 94% 4664 

2010 4021 6% 67865 94% 5117 

2011 4090 6% 68120 94% 5103 

2012 4134 6% 68249 94% 5057 

2013 4182 6% 68270 94% 5015 

2014 4218 6% 68067 94% 4959 

2015 4255 6% 62673 94% 4906 

2016 4289 6% 62158 94% 4848 

2017 4310 7% 61562 93% 4777 

2018 4327 7% 60769 93% 4703 

2019 4341 7% 59574 93% 4626 

2020 4355 7% 58544 93% 4551 

The resulting magnetic ballast shipments were slightly greater than those in the NES model 

after the year 2014 for the 2015 Base Case and after the year 2024 for the 2027 Base Case. This 

indicated that the while the assumptions for both scenarios about the decline of magnetic ballast 

future shipments included some impacts from the upgraded building code, they also included 

additional impacts from other programs. The building code was estimated to cause an overall 

reduction of magnetic ballast shipments from 2003 to 2030 (the analysis period for the NES model) 

of 27 percent from the base case without the code. 

We analyzed the implied "conversion rate" from magnetic to electronic ballasts by 

calculating the conversion rate for each year, summing the annual rates, and dividing by the number 

B-41 
of years from 2005 to 2020. The magnetic ballast shipments (for all uses) implied by the EIA NEMS

output had a 3 percent average annual conversion rate without an upgraded building code for the

period 2005 - 2020 . With the fairly aggressive partial adoption and compliance rates assumed in this

analysis, the upgraded ASHRAE/IES 90.1 - 1999 code caused a conversion rate of 5 percent. The

LBNL NES model 2027 base case shipments had a 6 percent average annual conversion rate from

2005 to 2020 due to all programs (including EPA programs as well as an upgraded building code).

The LBNL NES 2015 base case had an 11 percent conversion rate from 2005 to 2020. Again, this

implied that both NES model base cases incorporate the impacts of the building code , but included

the impacts of other non-regulatory programs as well.

While the NEMS base case did include impacts from EPA programs, EIA did not assume

those impacts to be as great in the early years of the forecast as the LBNL base cases imply. See

further discussion contrasting the EIA and EPA assumptions in section B.5.3 below.

only when the capital cost plus operation and maintenance of the new system are lower than the

operation and maintenance of the old system, without accounting for financial incentives such as

rebates

B.5.3. EPA Energy Star Buildings and Green Lights Programs

In 1997, the EPA provided data to LBNL from its Green Lights database as well as its

Climate Change Action Plan. The Green Lights Program, Marketing and Implementation Data Base

(Program Snapshot) showed the annual total sales of electronic ballasts from 1992 - 1996. EPA's

forecast for the revised Climate Change Action Plan' contained the historical and projected building

floor space (in square feet) retrofit from 1992 - 2015. From these EPA estimated the electronic

ballasts that would be used to retrofit this floor space from 1997 - 2015 . We have been unable to

determine whether the EPA has prepared updated forecasts since 1997.

Integration of these data with the analyses from the other programs proved to be complex.

EPA assumed that their projections represented the impacts of their programs on the market for

electronic ballasts, as well as past, present, and future market drivers including the Green Lights and

Energy Star Buildings programs, the success of past research and development in advancing the

technologies, and utility DSM programs in jump-starting the market. Therefore, while the historical

impacts of the Green Lights program were fairly straightforward, the interactions between all the

market drivers in the future forecast years were not quantifiable.

EPA projected that about 7 percent of all commercial and industrial floor space would be

retrofit by the year 2000 . By the year 2015, this increased to about 48 percent of floor space.

The EPA data analysis is shown in Table B.19.

'EPA projections were revised due to funding cuts for program budgets.

B-42

EE-RM-97-500 COMMENT 104

of years from 2005 to 2020. The magnetic ballast shipments (for all uses) implied by the EIA NEMS 

output had a 3 percent average annual conversion rate without an upgraded building code for the 

period 2005 - 2020. With the fairly aggressive partial adoption and compliance rates assumed in this 

analysis, the upgraded ASHRAE/IES 90.1-1999 code caused a conversion rate of 5 percent. The 

LBNL NES model 2027 base case shipments had a 6 percent average annual conversion rate from 

2005 to 2020 due to all programs (including EPA programs as well as an upgraded building code). 

The LBNL NES 2015 base case had an 11 percent conversion rate from 2005 to 2020. Again, this 

implied that both NES model base cases incorporate the impacts ofthe building code, but included 

the impacts of other non-regulatOlY programs as well. 

While the NEMS base case did include impacts from EPA programs, EIA did not assume 

those impacts to be as great in the early years of the forecast as the LBNL base cases imply. See 

further discussion contrasting the EIA and EPA assumptions in section B.5.3 below. 

only when the capital cost plus operation and maintenance of the new system are lower than the 

operation and maintenance of the old system, without accounting for financial incentives such as 

rebates 

B.5.3. EPA Energy Star Buildings and Green Lights Programs 

In 1997, the EPA provided data to LBNL from its Green Lights database as well as its 

Climate Change Action Plan. The Green Lights Program, Marketing and Implementation Data Base 

(Program Snapshot) showed the annual total sales of electronic ballasts from 1992 - 1996. EPA's 

forecast for the revised Climate Change Action Plad contained the historical and proj ected building 

floor space (in square feet) retrofit from 1992 - 2015. From these EPA estimated the electronic 

ballasts that would be used to retrofit this floor space from 1997 - 2015. We have been unable to 

determine whether the EPA has prepared updated forecasts since 1997. 

Integration of these data with the analyses fi'om the other programs proved to be complex. 

EPA assumed that their projections represented the impacts of their programs on the market for 

electronic ballasts, as well as past, present, and future market drivers including the Green Lights and 

Energy Star Buildings programs, the success of past research and development in advancing the 

technologies, and utility DSM programs in jump-starting the market. Therefore, while the historical 

impacts of the Green Lights program were fairly straightforward, the interactions between all the 

market drivers in the future forecast years were not quantifiable. 

EPA projected that about 7 percent of all commercial and industrial floor space would be 

retrofit by the year 2000. By the year 2015, this increased to about 48 percent of floor space. 

The EPA data analysis is shown in Table B.19. 

iEPA projections were revised due to funding cuts for program budgets. 

B-42 

of years from 2005 to 2020. The magnetic ballast shipments (for all uses) implied by the EIA NEMS 

output had a 3 percent average annual conversion rate without an upgraded building code for the 

period 2005 - 2020. With the fairly aggressive partial adoption and compliance rates assumed in this 

analysis, the upgraded ASHRAE/IES 90.1-1999 code caused a conversion rate of 5 percent. The 

LBNL NES model 2027 base case shipments had a 6 percent average annual conversion rate from 

2005 to 2020 due to all programs (including EPA programs as well as an upgraded building code). 

The LBNL NES 2015 base case had an 11 percent conversion rate from 2005 to 2020. Again, this 

implied that both NES model base cases incorporate the impacts ofthe building code, but included 

the impacts of other non-regulatOlY programs as well. 

While the NEMS base case did include impacts from EPA programs, EIA did not assume 

those impacts to be as great in the early years of the forecast as the LBNL base cases imply. See 

further discussion contrasting the EIA and EPA assumptions in section B.5.3 below. 

only when the capital cost plus operation and maintenance of the new system are lower than the 

operation and maintenance of the old system, without accounting for financial incentives such as 

rebates 

B.5.3. EPA Energy Star Buildings and Green Lights Programs 

In 1997, the EPA provided data to LBNL from its Green Lights database as well as its 

Climate Change Action Plan. The Green Lights Program, Marketing and Implementation Data Base 

(Program Snapshot) showed the annual total sales of electronic ballasts from 1992 - 1996. EPA's 

forecast for the revised Climate Change Action Plad contained the historical and proj ected building 

floor space (in square feet) retrofit from 1992 - 2015. From these EPA estimated the electronic 

ballasts that would be used to retrofit this floor space from 1997 - 2015. We have been unable to 

determine whether the EPA has prepared updated forecasts since 1997. 

Integration of these data with the analyses fi'om the other programs proved to be complex. 

EPA assumed that their projections represented the impacts of their programs on the market for 

electronic ballasts, as well as past, present, and future market drivers including the Green Lights and 

Energy Star Buildings programs, the success of past research and development in advancing the 

technologies, and utility DSM programs in jump-starting the market. Therefore, while the historical 

impacts of the Green Lights program were fairly straightforward, the interactions between all the 

market drivers in the future forecast years were not quantifiable. 

EPA projected that about 7 percent of all commercial and industrial floor space would be 

retrofit by the year 2000. By the year 2015, this increased to about 48 percent of floor space. 

The EPA data analysis is shown in Table B.19. 

iEPA projections were revised due to funding cuts for program budgets. 

B-42 
Table B.19 Green Lights/En rStar Buildings Pro ram Impacts on Electronic Ballast Sales

Year

Ballasts

Annual

(million)

Sq. Ft.

Upgraded

(billion)

Percent

Total Sq. Ft.

Upgraded

Sq. Ft.

Recruited

(billion)

Sq. Ft. Projected

Upgraded

(billion)

Percent

Total Sq. Ft.

Projected Upgraded

1992 0.1

1993 0.7

1994 1.8 0.48

1995 3.6 0.98

1996 3.4 1.27 1% 6 4.8 6%

1997 8.3 2.36 7.5 6

1998 10.8 3.77 9 7.2

1999 12.2 5.37 10.25 8.2

2000 12.8 7.05 8% 11 8.8 10%

2001 18.2 9.44

2002 15.7 11.49

2003 16.3 13.63

2004 18.1 16.00

2005 21.0 18.75 19%

2006 24.1 21.91

2007 23.7 25.02

2008 26.6 28.51

2009 26.1 31.93

2010 22.4 34.87 34%

2011 29.6 38.75

2012 26.2 42.18

2013 25.9 45.58

2014 25.6 48.93

2015 , 25.3 52.25 48%

Ballast counts from 1991 - 1996 were from Green Lights Program Snapshots.

Annual ballast counts included an estimate for January 1992 (data gathering began in 2/92).

Ballast counts from 1997 - 2015 were calculated from square feet retrofit,

assuming a constant ratio of electronic ballasts/square foot retrofit from 1992 - 1996.

Sq. ft. estimates from EPA: 1996 from Snapshot, 1997 - 2000 from Climate Change Action Plan (revised).

Sq. ft. recruited * 0.9 = sq. ft. committed.

Sq. ft. projected upgraded = sq. ft. committed * 0.8 (this represented EPA's 10 percent dropout rate).

(A more conservative estimate of dropout rate could be used for another scenario).

Note from Table B.12 that the actual sales of electronic ballasts for 1995 were higher than

the estimates in Table B. 19 that supposedly represent all market drivers, especially in the early years.

The EPA ballast sales estimates in the early years included only the EPA program impacts. Also,

the EPA estimates are built up from the Energy Information Administration's Annual Energy

Outlook 1997 Reference Case forecast, and that forecast implied a much more modest penetration

of electronic ballasts. The AEO 1997 Reference Case showed a very modest penetration of

B-43

EE-RM-97-500 COMMENT 104

Table B.19 Green Lights/Energy Star Buildings Pro ram Impacts on Electronic Ballast Sales 

Ballasts Sq. Ft. Percent Sq. Ft. Sq. Ft. Projected Percent 

Year Annual Upgraded Total Sq. Ft. Recruited Upgraded Total Sq. Ft. 

(million) . (billion) Upgraded (billion) (billion) Projected Upgraded 

1992 0.1 

1993 0.7 

1994 1.8 

1995 3.6 

1996 3.4 

1997 8.3 

1998 10.8 

1999 12.2 

2000 12.8 

2001 18.2 

2002 15.7 

2003 16.3 

2004 18.1 

2005 21.0 

2006 24.1 

2007 23.7 

2008 26.6 

2009 26.1 

2010 22.4 

2011 29.6 

2012 26.2 

2013 25.9 

2014 25.6 

2015 25.3 

0.48 

0.98 

1.27 

2.36 

3.77 

5.37 

7.05 

9.44 

11.49 

13.63 

16.00 

18.75 

21.91 

25.02 

28.51 

31.93 

34.87 

38.75 

42.18 

45.58 

48.93 

52.25 

1% 

8% 

19% 

34% 

48% 

6 

7.5 

9 

10.25 

11 

4.8 

6 

7.2 

8.2 

8.8 

Ballast counts from 1991 - 1996 were from Green Lights Program Snapshots. 

Annual ballast counts included an estimate for January 1992 (data gathering began in 2/92). 

Ballast counts from 1997 - 2015 were calculated from square feet retrofit, 

assuming a constant ratio of electronic ballasts/square foot retrofit from 1992 - 1996. 

6% 

10% 

Sq. ft. estimates from EPA: 1996 from Snapshot, 1997 - 2000 from Climate Change Action Plan (revised). 

Sq. ft. recruited * 0.9 = sq. ft. committed. 

Sq. ft. projected upgraded = sq. ft. committed * 0.8 (this represented EPA's 10 percent dropout rate). 

(A more conservative estimate of dropout rate could be used for another scenario). 

Note from Table B.12 that the actual sales of electronic ballasts for 1995 were higher than 

the estimates in Table B.19 that supposedly represent all market drivers, especially in the early years. 

The EPA ballast sales estimates in the early years included only the EPA program impacts. Also, 

the EPA estimates are built up from the Energy Information Administration's Annual Energy 

Outlook 1997 Reference Case forecast, and that forecast implied a much more modest penetration 

of electronic ballasts. The AEO 1997 Reference Case showed a very modest penetration of 

B-43 

Table B.19 Green Lights/Energy Star Buildings Prol.!ram Impacts on Electronic Ballast Sales 

Ballasts Sq. Ft. Percent Sq. Ft. Sq. Ft. Projected Percent 

Year Annual Upgraded Total Sq. Ft. Recruited Upgraded Total Sq. Ft. 

(million) . (billion) Upgraded (billion) (billion) Projected Upgraded 

1992 0.1 

1993 0.7 

1994 1.8 

1995 3.6 

1996 3.4 

1997 8.3 

1998 10.8 

1999 12.2 

2000 12.8 

2001 18.2 

2002 15.7 

2003 16.3 

2004 18.1 

2005 21.0 

2006 24.1 

2007 23.7 

2008 26.6 

2009 26.1 

2010 22.4 

2011 29.6 

2012 26.2 

2013 25.9 

2014 25.6 

2015 25.3 

0.48 

0.98 

1.27 

2.36 

3.77 

5.37 

7.05 

9.44 

11.49 

13.63 

16.00 

18.75 

21.91 

25.02 

28.51 

31.93 

34.87 

38.75 

42.18 

45.58 

48.93 

52.25 

1% 

8% 

19% 

34% 

48% 

6 

7.5 

9 

10.25 

11 

4.8 

6 

7.2 

8.2 

8.8 

Ballast counts from 1991 - 1996 were from Green Lights Program Snapshots. 

Annual ballast counts included an estimate for January 1992 (data gathering began in 2/92). 

Ballast counts from 1997 - 2015 were calculated from square feet retrofit, 

assuming a constant ratio of electronic ballasts/square foot retrofit from 1992 - 1996. 

6% 

10% 

Sq. ft. estimates from EPA: 1996 from Snapshot, 1997 - 2000 from Climate Change Action Plan (revised). 

Sq. ft. recruited * 0.9 = sq. ft. committed. 

Sq. ft. projected upgraded = sq. ft. committed * 0.8 (this represented EPA's 10 percent dropout rate). 

(A more conservative estimate of dropout rate could be used for another scenario). 

Note from Table B.12 that the actual sales of electronic ballasts for 1995 were higher than 

the estimates in Table B.19 that supposedly represent all market drivers, especially in the early years. 

The EPA ballast sales estimates in the early years included only the EPA program impacts. Also, 

the EPA estimates are built up from the Energy Information Administration's Annual Energy 

Outlook 1997 Reference Case forecast, and that forecast implied a much more modest penetration 

of electronic ballasts. The AEO 1997 Reference Case showed a very modest penetration of 

B-43 
electronic ballasts in early years. This was partly because that forecast was based on the penetrations

of efficient technologies from the 1992 CBECS survey.' Also, as mentioned in section B.5.2, in the

EIA NEMS model the economic criteria for the early replacement retrofits were strict enough that

they didn't often meet most consumers'discount rate criteria for investment. EIA did attempt to

include the impacts of EPA programs and utility DSM programs. However, the two estimates

showed considerable variation because of the different original assumptions.

To answer the question of whether the lack of utility rebates would cause fewer ballast

retrofits in the future, EPA stated that the internal rate of return for electronic ballast retrofits was

typically well over the Green Lights IRR criterion of 20 percent. Therefore, EPA assumed that the

retrofit rate would be unaffected as utility rebates diminish. From the Green Lights Program

Database, based on reports submitted by Green Lights program participants on completed upgrades,

we calculate that about 35 percent of building retrofit projects took advantage of utility rebates.

However, the database did not link these rebates specifically to electronic ballasts.

We were unable to locate an independent assessment of the EPA's program projections,

dropout rates, overlap with ballast rebates, etc. EPA asked its partners to retrofit 90 percent of their

recruited floor space, and estimated that they would convert all but 10 percent of this. Since partners

pledged to retrofit their floor space within 5 years, the first partners to join the program in 1991 and

1992 were just finishing their upgrades when these data were taken. EPA attributed their higher

dropout rates to early emphasis on recruitment rather than assistance and follow-up.

B.5.4 Federal Energy Management Program

This section presents results from LBNL's analysis of the Federal Energy Management's

Procurement Challenge recommendations for fluorescent ballasts and fluorescent luminaires . It also

shows our estimates of the impacts of the Federal Relighting Initiative.

B.5.4.1 Procurement Challenge (PEERs)

Product Energy Efficiency Recommendations (PEERS) are issued by the Federal Energy

Management Program (FEMP) to assist Federal agencies in complying with the requirements of the

Energy Policy Act of 1992 (EPACT) and Executive Order 12902. The Executive Order directs all

Federal agencies to buy products in the upper quartile with respect to energy and water efficiency,

or to choose products that are at least 10 percent more efficient than existing Department of Energy

(DOE) standards. A further goal of EPACT and the Executive Order is to use the buying power of

the Federal government to "pull" the overall commercial market for particular products towards

greater efficiency. The PEERs offer a mechanism with which to implement these goals by providing

k The AEO 1999 forecasts used data from CBECS 1995 survey. Subsequent AEO forecasts also use data and

modeling assumptions from LBNL more consistent with the analysis in this document

B-44

EE-RM-97-500 COMMENT 104

electronic ballasts in early years. This was partly because that forecast was based on the penetrations 

of efficient technologies from the 1992 CBECS survey.k Also, as mentioned in section B.5.2, in the 

EIA NEMS model the economic criteria for the early replacement retrofits were strict enough that 

they didn't often meet most consumers' discount rate criteria for investment. EIA did attempt to 

include the impacts of EPA programs and utility DSM programs. However, the two estimates 

showed considerable variation because of the different original assumptions. 

To answer the question of whether the lack of utility rebates would cause fewer ballast 

retrofits in the future, EPA stated that the intemal rate of retum for electronic ballast retrofits was 

typically well over the Green Lights IRR criterion of 20 percent. Therefore, EPA assumed that the 

retrofit rate would be unaffected as utility rebates diminish. From the Green Lights Program 

Database, based on reports submitted by Green Lights program participants on completed upgrades, 

we calculate that about 35 percent of building retrofit projects took advantage of utility rebates. 

However, the database did not link these rebates specifically to electronic ballasts. 

We were unable to locate an independent assessment of the EPA's program projections, 

dropout rates, overlap with ballast rebates, etc. EPA asked its partners to retrofit 90 percent of their 

recruited floor space, and estimated that they would convert all but 10 percent of this. Since partners 

pledged to retrofit their floor space within 5 years, the first partners to join the program in 1991 and 

1992 were just finishing their upgrades when these data were taken. EPA attributed their higher 

dropout rates to early emphasis on recruitment rather than assistance and follow-up. 

B.5.4 Federal Energy Management Program 

This section presents results from LBNL's analysis of the Federal Energy Management's 

Procurement Challenge recommendations for fluorescent ballasts and fluorescent luminaires. It also 

shows our estimates of the impacts of the Federal Relighting hlitiative. 

B.5.4.1 Procurement Challenge (PEERs) 

Product Energy Efficiency Recommendations (PEERs) are issued by the Federal Energy 

Management Program (FEMP) to assist Federal agencies in complying with the requirements ofthe 

Energy Policy Act of 1992 (EP ACT) and Executive Order 12902. The Executive Order directs all 

Federal agencies to buy products in the upper quartile with respect to energy and water efficiency, 

or to choose products that are at least 10 percent more efficient than existing Department of Energy 

(DOE) standards. A further goal of EP ACT and the Executive Order is to use the buying power of 

the Federal govemment to "pull" the overall commercial market for particular products towards 

greater efficiency. The PEERs offer a mechanism with which to implement these goals by providing 

k . The AEO 1999 forecasts used data from CBECS 1995 survey. Subsequent AEO forecasts also use data and 

modeling assumptions from LBNL more consistent with the analysis in this document 

B-44 

electronic ballasts in early years. This was partly because that forecast was based on the penetrations 

of efficient technologies from the 1992 CBECS survey.k Also, as mentioned in section B.5.2, in the 

EIA NEMS model the economic criteria for the early replacement retrofits were strict enough that 

they didn't often meet most consumers' discount rate criteria for investment. EIA did attempt to 

include the impacts of EPA programs and utility DSM programs. However, the two estimates 

showed considerable variation because of the different original assumptions. 

To answer the question of whether the lack of utility rebates would cause fewer ballast 

retrofits in the future, EPA stated that the intemal rate of retum for electronic ballast retrofits was 

typically well over the Green Lights IRR criterion of 20 percent. Therefore, EPA assumed that the 

retrofit rate would be unaffected as utility rebates diminish. From the Green Lights Program 

Database, based on reports submitted by Green Lights program participants on completed upgrades, 

we calculate that about 35 percent of building retrofit projects took advantage of utility rebates. 

However, the database did not link these rebates specifically to electronic ballasts. 

We were unable to locate an independent assessment of the EPA's program projections, 

dropout rates, overlap with ballast rebates, etc. EPA asked its partners to retrofit 90 percent of their 

recruited floor space, and estimated that they would convert all but 10 percent of this. Since partners 

pledged to retrofit their floor space within 5 years, the first partners to join the program in 1991 and 

1992 were just finishing their upgrades when these data were taken. EPA attributed their higher 

dropout rates to early emphasis on recruitment rather than assistance and follow-up. 

B.5.4 Federal Energy Management Program 

This section presents results from LBNL's analysis of the Federal Energy Management's 

Procurement Challenge recommendations for fluorescent ballasts and fluorescent luminaires. It also 

shows our estimates of the impacts of the Federal Relighting hlitiative. 

B.5.4.1 Procurement Challenge (PEERs) 

Product Energy Efficiency Recommendations (PEERs) are issued by the Federal Energy 

Management Program (FEMP) to assist Federal agencies in complying with the requirements ofthe 

Energy Policy Act of 1992 (EP ACT) and Executive Order 12902. The Executive Order directs all 

Federal agencies to buy products in the upper quartile with respect to energy and water efficiency, 

or to choose products that are at least 10 percent more efficient than existing Department of Energy 

(DOE) standards. A further goal of EP ACT and the Executive Order is to use the buying power of 

the Federal govemment to "pull" the overall commercial market for particular products towards 

greater efficiency. The PEERs offer a mechanism with which to implement these goals by providing 

k . The AEO 1999 forecasts used data from CBECS 1995 survey. Subsequent AEO forecasts also use data and 

modeling assumptions from LBNL more consistent with the analysis in this document 

B-44 
Federal purchasers with technical and market guidance on efficiency levels for a given product in

order to meet the requirements of the Executive Order.

The PEER for ballasts recommends electronic ballasts. The PEER for fluorescent luminaires

is based on the LER or luminaire efficacy rating, a metric with units of lumens/Watt that

incorporates the effects of lamp/ballast input wattage, lamp light output, ballast factor, and luminaire

efficiency. The higher LERs required to meet the recommendation are achievable with electronic

ballasts. Thus, for both products, selection of an electronic ballast either individually or in a

luminaire was assumed to meet the recommendation.

LBNL estimated the impacts of the PEERs for fluorescent ballasts and fluorescent luminaires

as part of a larger study on all the product recommendations. `S We provided a characterization of

the federal sector building and equipment stock, including the data and assumptions used to estimate

impacts for the PEERS We first needed to quantify both the ballasts purchased individually and those

purchased in luminaires for the federal sector in the absence of PEERs. Data on compliance with

the PEERs would be difficult and expensive to gather, and require detailed knowledge about federal

purchases. At this stage of the program, such data were not available, and consequently we relied

on assumptions concerning the Federal building stock, Federal market data, and product/end-use

definitions.

Background and Methodology

The Federal market for energy-related products was estimated at $12 billion annually, of

which about $4 billion or 25 percent were building-related purchases.19 Of this $4 billion,

approximately 10 percent were lighting equipment purchases. The installed ballast stock and usage

were calculated using the lighting equipment densities and usage from the XENERGY commercial

buildings database. Lighting technology sales are relatively constant because we have assumed no

changes in product density. The usage assumption for fluorescent lighting was 3600 hours per year.

We developed a modeling framework based on the building and equipment stock in order

to estimate stock turnover and purchases for each product over the period from 1996-2010. We then

adopted a number of energy-economic assumptions for trends in efficiency.

The analysis covered the period from 1996 to 2010. The recommendations were assumed

to apply to ballasts and luminaires starting in 1998. The analysis went out to 2010, which

corresponded to a target year for federal energy savings-a 35 percent reduction on a square foot

basis from the 1985 levels.20 Extension of the analysis beyond 2010 would be straightforward, but

the assumptions regarding changes in efficiency and technologies would be less stable over a longer

time frame.

Three characteristic assumptions were required in defining the scenarios-eligibility,

program penetration, and factors to avoid double-counting of ballast and luminaire stock. Eligibility

referred to the share of products that were technically eligible (or compatible) for replacement.

Program penetration, ranging from 0 percent to 100 percent, referred to the proportion of federal

B-45

EE-RM-97-500 COMMENT 104

Federal purchasers with technical and market guidance on efficiency levels for a given product in 

order to meet the requirements of the Executive Order. 

The PEER for ballasts recommends electronic ballasts. The PEER for fluorescent luminaires 

is based on the LER or luminaire efficacy rating, a metric with units of lumens/Watt that 

incorporates the effects oflamp/ballast input wattage, lamp light output, ballast factor, and luminaire 

efficiency. The higher LERs required to meet the recommendation are achievable with electronic 

ballasts. Thus, for both products, selection of an electronic ballast either individually or in a 

luminaire was assumed to meet the recommendation. 

LBNL estimated the impacts of the PEERs for fluorescent ballasts and fluorescent luminaires 

as part of a larger study on all the product recommendations. l8 We provided a characterization of 

the federal sector building and equipment stock, including the data and assumptions used to estimate 

impacts for the PEERs We first needed to quantify both the ballasts purchased individually and those 

purchased in luminaires for the federal sector in the absence of PEERs. Data on compliance with 

the PEERs would be difficult and expensive to gather, and require detailed knowledge about federal 

purchases. At this stage ofthe program, such data were not available, and consequently we relied 

on assumptions concerning the Federal building stock, Federal market data, and product/end-use 

definitions. 

Background and Methodology 

The Federal market for energy-related products was estimated at $12 billion annually, of 

which about $4 billion or 25 percent were building-related purchases. l9 Of this $4 billion, 

approximately 10 percent were lighting equipment purchases. The installed ballast stock and usage 

were calculated using the lighting equipment densities and usage from the XENERGY commercial 

buildings database. Lighting technology sales are relatively constant because we have assumed no 

changes in product density. The usage assumption for fluorescent lighting was 3600 hours per year. 

We developed a modeling framework based on the building and equipment stock in order 

to estimate stock turnover and purchases for each product over the period from 1996-2010. We then 

adopted a number of energy-economic assumptions for trends in efficiency. 

The analysis covered the period from 1996 to 2010. The recommendations were assumed 

to apply to ballasts and luminaires starting in 1998. The analysis went out to 2010, which 

corresponded to a target year for federal energy savings-a 35 percent reduction on a square foot 

basis from the 19851evels.20 Extension ofthe analysis beyond 2010 would be straightforward, but 

the assumptions regarding changes in efficiency and technologies would be less stable over a longer 

time frame. 

Three characteristic assumptions were required in defining the scenarios-eligibility, 

program penetration, and factors to avoid double-counting of ballast and luminaire stock. Eligibility 

referred to the share of products that were technically eligible (or compatible) for replacement. 

Program penetration, ranging from 0 percent to 100 percent, referred to the proportion of federal 

B-45 

Federal purchasers with technical and market guidance on efficiency levels for a given product in 

order to meet the requirements of the Executive Order. 

The PEER for ballasts recommends electronic ballasts. The PEER for fluorescent luminaires 

is based on the LER or luminaire efficacy rating, a metric with units of lumens/Watt that 

incorporates the effects oflamp/ballast input wattage, lamp light output, ballast factor, and luminaire 

efficiency. The higher LERs required to meet the recommendation are achievable with electronic 

ballasts. Thus, for both products, selection of an electronic ballast either individually or in a 

luminaire was assumed to meet the recommendation. 

LBNL estimated the impacts of the PEERs for fluorescent ballasts and fluorescent luminaires 

as part of a larger study on all the product recommendations. l8 We provided a characterization of 

the federal sector building and equipment stock, including the data and assumptions used to estimate 

impacts for the PEERs We first needed to quantify both the ballasts purchased individually and those 

purchased in luminaires for the federal sector in the absence of PEERs. Data on compliance with 

the PEERs would be difficult and expensive to gather, and require detailed knowledge about federal 

purchases. At this stage ofthe program, such data were not available, and consequently we relied 

on assumptions concerning the Federal building stock, Federal market data, and product/end-use 

definitions. 

Background and Methodology 

The Federal market for energy-related products was estimated at $12 billion annually, of 

which about $4 billion or 25 percent were building-related purchases. l9 Of this $4 billion, 

approximately 10 percent were lighting equipment purchases. The installed ballast stock and usage 

were calculated using the lighting equipment densities and usage from the XENERGY commercial 

buildings database. Lighting technology sales are relatively constant because we have assumed no 

changes in product density. The usage assumption for fluorescent lighting was 3600 hours per year. 

We developed a modeling framework based on the building and equipment stock in order 

to estimate stock turnover and purchases for each product over the period from 1996-2010. We then 

adopted a number of energy-economic assumptions for trends in efficiency. 

The analysis covered the period from 1996 to 2010. The recommendations were assumed 

to apply to ballasts and luminaires starting in 1998. The analysis went out to 2010, which 

corresponded to a target year for federal energy savings-a 35 percent reduction on a square foot 

basis from the 19851evels.20 Extension ofthe analysis beyond 2010 would be straightforward, but 

the assumptions regarding changes in efficiency and technologies would be less stable over a longer 

time frame. 

Three characteristic assumptions were required in defining the scenarios-eligibility, 

program penetration, and factors to avoid double-counting of ballast and luminaire stock. Eligibility 

referred to the share of products that were technically eligible (or compatible) for replacement. 

Program penetration, ranging from 0 percent to 100 percent, referred to the proportion of federal 

B-45 
purchases that adhere to the Recommendations. For program penetration, we recognized that it was

perhaps unrealistic to assume that all purchasers would use the guidelines, in spite of the Executive

Order and the Procurement Challenge, and the simplicity-of-use of the PEERs themselves. However,

as the program continued and reached more and more purchasers, it made sense to assume that the

penetration would increase. Consequently we assumed that 20 percent of purchases were at the

Recommended levels for each product in the first year, but this percentage increased proportionally

over the ensuing years until it reached 80 percent in 2010, such that the average annual rate over the

12-year period is 50 percent. For eligibility, the fraction of federal purchases that were magnetic

ballasts was assumed to be 94 percent for ballasts in 1998, based on the replacement market shares

from the NEMA data (NEMA comments, April 15, 1999). In succeeding years the eligibility was

reduced by 2 percent, assuming that a smaller percentage of these ballasts would be magnetic. The

eligibility for luminaires was assumed to be 56 percent in 1998, based on the Osram Sylvania

comments as noted in section B.5.3, since this analysis did not cover early retrofits. The eligibility

was reduced by 5 percent in future years, again assuming that the market share of magnetic ballasts

in luminaires would decrease.

An implicit assumption in the analysis was that there was no early replacement of equipment;

products were assumed to be replaced at the end of their useful lives. In practice, there would indeed

be early replacement for various reasons, and consequently the above analysis may not have

estimated these savings. Early replacement might be undertaken mainly on the basis of costeffective energy efficiency investments, or it might be based on other sources of changing product

demand. Certain types of construction, such as renovation projects, would generally result in early

product replacement, and the PEERs could serve as a mechanism to coordinate energy-efficient

purchasing with product replacement that occurred for other reasons. Early replacement retrofits

were analyzed as discussed in the Federal Relighting Initiative, as discussed in section B. 5.4.2 below.

Results

Table B.20 shows the estimated numbers of ballasts and luminaires affected by the PEERs

for the analysis period. The results implied a average annual percentage decrease of magnetic

ballasts in federal buildings of 49 percent from 1998 - 2010 resulting from this program.

EE-RM-97-500 COMMENT 104

purchases that adhere to the Recommendations. For program penetration, we recognized that it was 

perhaps unrealistic to assume that all purchasers would use the guidelines, in spite ofthe Executive 

Order and the Procurement Challenge, and the simplicity-of-use of the PEERs themselves. However, 

as the program continued and reached more and more purchasers, it made sense to assume that the 

penetration would increase. Consequently we assumed that 20 percent of purchases were at the 

Recommended levels for each product in the first year, but this percentage increased proportionally 

over the ensuing years until it reached 80 percent in 2010, such that the average annual rate over the 

12-year period is 50 percent. For eligibility, the fraction of federal purchases that were magnetic 

ballasts was assumed to be 94 percent for ballasts in 1998, based on the replacement market shares 

from the NEMA data (NEMA comments, Apri115, 1999). In succeeding years the eligibility was 

reduced by 2 percent, assuming that a smaller percentage of these ballasts would be magnetic. The 

eligibility for luminaires was assumed to be 56 percent in 1998, based on the Osram Sylvania 

comments as noted in section B.5.3, since this analysis did not cover early retrofits. The eligibility 

was reduced by 5 percent in future years, again assuming that the market share of magnetic ballasts 

in luminaires would decrease. 

An implicit assumption in the analysis was that there was no early replacement of equipment; 

products were assumed to be replaced at the end oftheir useful lives. In practice, there would indeed 

be early replacement for various reasons, and consequently the above analysis may not have 

estimated these savings. Early replacement might be undertaken mainly on the basis of costeffective energy efficiency investments, or it might be based on other sources of changing product 

demand. Certain types of construction, such as renovation projects, would generally result in early 

product replacement, and the PEERs could serve as a mechanism to coordinate energy-efficient 

purchasing with product replacement that occurred for other reasons. Early replacement retrofits 

were analyzed as discussed in the Federal Relighting Initiative, as discussed in section B.5.4.2 below. 

Results 

Table B.20 shows the estimated numbers of ballasts and luminaires affected by the PEERs 

for the analysis period. The results implied a average annual percentage decrease of magnetic 

ballasts in federal buildings of 49 percent from 1998 - 2010 resulting from this program. 

B-46 

purchases that adhere to the Recommendations. For program penetration, we recognized that it was 

perhaps unrealistic to assume that all purchasers would use the guidelines, in spite ofthe Executive 

Order and the Procurement Challenge, and the simplicity-of-use of the PEERs themselves. However, 

as the program continued and reached more and more purchasers, it made sense to assume that the 

penetration would increase. Consequently we assumed that 20 percent of purchases were at the 

Recommended levels for each product in the first year, but this percentage increased proportionally 

over the ensuing years until it reached 80 percent in 2010, such that the average annual rate over the 

12-year period is 50 percent. For eligibility, the fraction of federal purchases that were magnetic 

ballasts was assumed to be 94 percent for ballasts in 1998, based on the replacement market shares 

from the NEMA data (NEMA comments, Apri115, 1999). In succeeding years the eligibility was 

reduced by 2 percent, assuming that a smaller percentage of these ballasts would be magnetic. The 

eligibility for luminaires was assumed to be 56 percent in 1998, based on the Osram Sylvania 

comments as noted in section B.5.3, since this analysis did not cover early retrofits. The eligibility 

was reduced by 5 percent in future years, again assuming that the market share of magnetic ballasts 

in luminaires would decrease. 

An implicit assumption in the analysis was that there was no early replacement of equipment; 

products were assumed to be replaced at the end oftheir useful lives. In practice, there would indeed 

be early replacement for various reasons, and consequently the above analysis may not have 

estimated these savings. Early replacement might be undertaken mainly on the basis of costeffective energy efficiency investments, or it might be based on other sources of changing product 

demand. Certain types of construction, such as renovation projects, would generally result in early 

product replacement, and the PEERs could serve as a mechanism to coordinate energy-efficient 

purchasing with product replacement that occurred for other reasons. Early replacement retrofits 

were analyzed as discussed in the Federal Relighting Initiative, as discussed in section B.5.4.2 below. 

Results 

Table B.20 shows the estimated numbers of ballasts and luminaires affected by the PEERs 

for the analysis period. The results implied a average annual percentage decrease of magnetic 

ballasts in federal buildings of 49 percent from 1998 - 2010 resulting from this program. 

B-46 
Table B.20 Fluorescent Electronic Ballasts Purchased from FEMP Prog rams (thousands

Program 1992 1993 1994 1995 1996 1997 1998 1999 2000

PEERS Ballasts 562 690 812

PEERS Luminaires 441 553 664

PEERS Total 1104 1370 1629

Federal Relighting Initiative 1256 2462 2484 2463 1875 1860 1860 1860 1860

Program 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

PEERS Ballasts 928 1042 1159 1269 1372 1468 1559 1649 1737 1814

PEERS Luminaires 773 885 1004 1121 1236 1349 1462 1577 1695 1806

PEERS Total 1879 2131 2394 2648 2892 3128 3358 3589 3822 4036

Federal Relighting Initiative 1860

B.5.4.2 Federal Relighting Initiative (FRI)

The Federal Relighting Initiative encouraged early replacement retrofits of federal building

lighting systems, including fluorescent ballasts. The lighting PEERs were not considered to impact

these types of retrofits, since the products were replaced at the end of their useful lives. FEMP has

done no evaluation of the impacts of the FRI program on building retrofits . The program

concentrated on training and specifications . A survey of a subset of those in the training courses was

performed, but this was not specific to the impact on specific technologies.

For background, we estimated the federal ballast existing building stock as about 28 million

ballasts. In 1997, there were about 700 million ballasts in the commercial and industrial sectors (see

section B.2 above). The PEERs analysis estimated 2.5 million square feet in U.S. non-residential

floor space for federal buildings in 1997. This was about 4.3 percent of the national total from

CBECS 1995; assuming that national floorspace grew from 1995 to 1997, we rounded the percentage

to 4 percent. Thus, the federal ballast existing stock would be about 4 percent of the 700 million,

or 28 million ballasts.

In 1993, Pacific Northwest National Laboratory prepared "Estimate of Federal Relighting

Potential and Demand for Energy-Efficient Lighting Products," a study of potential impacts of FRI."

This study included both federally owned and leased space, from the GSA Summary Report of Real

Property Owned by the United States (1989). Since we could not assume that the federal

government would be able to retrofit its federally-leased space, we calculated the federally-owned

floor space using separate figures from GSA for 1995 for federally-owned and federally-leased

space, as described above. About 90 percent percent of federally-owned and leased space was

estimated as federally-owned in 1995.

The PNNL report estimated potential sales of 22 million electronic ballasts if all retrofit

projects were implemented at once. Assuming that 90 percent of these were for federally-owned

space, the total potential was 19.8 million ballasts.

B-47

EE-RM-97-500 COMMENT 104

Table 8.20 Fluorescent Electronic Ballasts Purchased from FEMP Programs (thousands) 

Program 1992 1993 1994 1995 1996 1997 1998 1999 2000 

PEERS Ballasts 562 690 812 

PEERS Luminaires 441 553 664 

PEERS Total 1104 1370 1629 

Federal Relighting Initiative 1256 2462 2484 2463 1875 1860 1860 1860 1860 

Program 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 

PEERS Ballasts 928 1042 1159 1269 1372 1468 1559 1649 1737 1814 

PEERS Luminaires 773 885 1004 1121 1236 1349 1462 1577 1695 1806 

PEERS Total 1879 2131 2394 2648 2892 3128 3358 3589 3822 4036 

Federal Relighting Initiative 1860 

8.5.4.2 Federal Relighting Initiative (FRI) 

The Federal Relighting Initiative encouraged early replacement retrofits of federal building 

lighting systems, including fluorescent ballasts. The lighting PEERs were not considered to impact 

these types of retrofits, since the products were replaced at the end of their useful lives. FEMP has 

done no evaluation of the impacts of the FRI program on building retrofits. The program 

concentrated on training and specifications. A survey of a subset ofthose in the training courses was 

performed, but this was not specific to the impact on specific technologies. 

For background, we estimated the federal ballast existing building stock as about 28 million 

ballasts. In 1997, there were about 700 million ballasts in the commercial and industrial sectors (see 

section B.2 above). The PEERs analysis estimated 2.5 million square feet in U.S. non-residential 

floor space for federal buildings in 1997. This was about 4.3 percent of the national total from 

CBECS 1995; assuming that national floorspace grew from 1995 to 1997, we rounded the percentage 

to 4 percent. Thus, the federal ballast existing stock would be about 4 percent of the 700 million, 

or 28 million ballasts. 

In 1993, Pacific Northwest National Laboratory prepared "Estimate of Federal Relighting 

Potential and Demand for Energy-Efficient Lighting Products," a study of potential impacts of FRI. 21 

This study included both federally owned and leased space, from the GSA Summary Report of Real 

Property Owned by the United States (1989). Since we could not assume that the federal 

government would be able to retrofit its federally-leased space, we calculated the federally-owned 

floor space using separate figures from GSA for 1995 for federally-owned and federally-leased 

space, as described above. About 90 percent percent of federally-owned and leased space was 

estimated as federally-owned in 1995. 

The PNNL report estimated potential sales of 22 million electronic ballasts if all retrofit 

projects were implemented at once. Assuming that 90 percent of these were for federally-owned 

space, the total potential was 19.8 million ballasts. 

B-47 

Table 8.20 Fluorescent Electronic Ballasts Purchased from FEMP Pro2rams (thousands) 

Program 1992 1993 1994 1995 1996 1997 1998 1999 2000 

PEERS Ballasts 562 690 812 

PEERS Luminaires 441 553 664 

PEERS Total 1104 1370 1629 

Federal Relighting Initiative 1256 2462 2484 2463 1875 1860 1860 1860 1860 

Program 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 

PEERS Ballasts 928 1042 1159 1269 1372 1468 1559 1649 1737 1814 

PEERS Luminaires 773 885 1004 1121 1236 1349 1462 1577 1695 1806 

PEERS Total 1879 2131 2394 2648 2892 3128 3358 3589 3822 4036 

Federal Relighting Initiative 1860 

8.5.4.2 Federal Relighting Initiative (FRI) 

The Federal Relighting Initiative encouraged early replacement retrofits of federal building 

lighting systems, including fluorescent ballasts. The lighting PEERs were not considered to impact 

these types of retrofits, since the products were replaced at the end of their useful lives. FEMP has 

done no evaluation of the impacts of the FRI program on building retrofits. The program 

concentrated on training and specifications. A survey of a subset ofthose in the training courses was 

performed, but this was not specific to the impact on specific technologies. 

For background, we estimated the federal ballast existing building stock as about 28 million 

ballasts. In 1997, there were about 700 million ballasts in the commercial and industrial sectors (see 

section B.2 above). The PEERs analysis estimated 2.5 million square feet in U.S. non-residential 

floor space for federal buildings in 1997. This was about 4.3 percent of the national total from 

CBECS 1995; assuming that national floorspace grew from 1995 to 1997, we rounded the percentage 

to 4 percent. Thus, the federal ballast existing stock would be about 4 percent of the 700 million, 

or 28 million ballasts. 

In 1993, Pacific Northwest National Laboratory prepared "Estimate of Federal Relighting 

Potential and Demand for Energy-Efficient Lighting Products," a study of potential impacts of FRI. 21 

This study included both federally owned and leased space, from the GSA Summary Report of Real 

Property Owned by the United States (1989). Since we could not assume that the federal 

government would be able to retrofit its federally-leased space, we calculated the federally-owned 

floor space using separate figures from GSA for 1995 for federally-owned and federally-leased 

space, as described above. About 90 percent percent of federally-owned and leased space was 

estimated as federally-owned in 1995. 

The PNNL report estimated potential sales of 22 million electronic ballasts if all retrofit 

projects were implemented at once. Assuming that 90 percent of these were for federally-owned 

space, the total potential was 19.8 million ballasts. 

B-47 
B.5.6 Voluntary Luminaire Program

The voluntary luminaire program presents energy-efficiency information in the form of a

"Luminaire Efficacy Rating" (LER) for the luminaire system (defined in the FEMP PEERs

discussion in 5.4.1 above). Using an electronic ballast greatly improves the LER of a luminaire. A

1999 report to DOE on the program showed widespread manufacturer participation, with 75 percent

of manufacturers publishing LER information in their product literature in 1997, and 100 percent

doing so by 1999.

It was difficult to evaluate the direct impact this voluntary program has had and will have on

the fluorescent ballast market. The Bureau of Census began to collect data on ballast usage in

luminaires in 1998, and has collected data on the number of lamps per luminaire since 1995.

However, the voluntary luminaire program interacts with and reinforces other educational programs

to encourage the use of electronic ballasts, and its specific influence was hard to quantify.

B.5.7 Summary

Table B.21 shows the number of electronic ballast shipments affected by each of the

programs analyzed above. We have not attempted to calculate the overlap between programs.

Remember that the EPA projections, as discussed above, were intended to represent the impacts of

all market forces. Therefore a sum of the individual program impacts shown in the table is not

meaningful.

Table B.21 Shipments of Electronic Ballasts Affected by Programs ( millions)

1992 1993 1994 1995 1996 1997 1998 1999 2000

Total Electronic Shipments (NEMA) 22.9 26.0 33.1 29.7 35.4 37.8

DSM (Core Sample Estimates) 4.5 9.6 18.3 15.4 10.2 6.4

Green Lights/Energy Star Buildings 0.1 0.7 1.8 3.6 3.4 8.3 10.8 12.2 12.8

ASHRAE/IES 90.1-1999 Code

FEMP Programs 1.3 2.5 2.5 2.5 1.9 1.9 3.0 3.2 3.5

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Total Electronic Shipments (NEMA)

DSM (Core Sample Estimates)

Green Lights/Energy Star Buildings 18.2 15.7 16.3 18.1 21.0 24.1 23.7 26.6 26.1 22.4

ASHRAE/IES 90.1-1999 Code 5.5 3.4 3.8 4.2 4.7 5.1

FEMP Programs 3.7 2.1 2.4 2.6 2.9 3.1 3.4 3.6 3.8 4.0

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Total Electronic Shipments (NEMA)

DSM (Core Sample Estimates)

Green Lights/Energy Star Buildings 29.6 26.2 25.9 25.6 25.3

ASHRAE/IES 90.1-1999 Code 5.1 5.1 5.0 5.0 4.9 4.8 4.8 4.7 4.6 4.6

FEMP Programs

B-49

EE-RM-97-500 COMMENT 104

B.5.6 Voluntary Luminaire Program 

The voluntary luminaire program presents energy-efficiency information in the form of a 

"Luminaire Efficacy Rating" (LER) for the luminaire system (defined in the FEMP PEERs 

discussion in 5.4.1 above). Using an electronic ballast greatly improves the LER ofa luminaire. A 

1999 report to DOE on the program showed widespread manufacturer participation, with 75 percent 

of manufacturers publishing LER information in their product literature in 1997, and 100 percent 

doing so by 1999. 

It was difficult to evaluate the direct impact this voluntary program has had and will have on 

the fluorescent ballast market. The Bureau of Census began to collect data on ballast usage in 

luminaires in 1998, and has collected data on the number of lamps per luminaire since 1995. 

However, the voluntary luminaire program interacts with and reinforces other educational programs 

to encourage the use of electronic ballasts, and its specific influence was hard to quantify. 

B.5.7 Summary 

Table B.21 shows the number of electronic ballast shipments affected by each of the 

programs analyzed above. We have not attempted to calculate the overlap between programs. 

Remember that the EPA projections, as discussed above, were intended to represent the impacts of 

all market forces. Therefore a sum of the individual program impacts shown in the table is not 

meaningful. 

Table B.2l Shipments of Electronic Ballasts Affected by Programs (millions) 

1992 1993 1994 1995 1996 1997 1998 1999 2000 

Total Electronic Shipments (NEMA) 22.9 26.0 33.1 29.7 35.4 37.8 

DSM (Core Sample Estimates) 4.5 9.6 18.3 15.4 10.2 6.4 

Green LightslEnergy Star Buildings O.l 0.7 1.8 3.6 3.4 8.3 10.8 12.2 12.8 

ASHRAEIIES 90.1-1999 Code 

FEMP Programs 1.3 2.5 2.5 2.5 1.9 1.9 3.0 3.2 3.5 

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 

Total Electronic Shipments (NEMA) 

DSM (Core Sample Estimates) 

Green LightslEnergy Star Buildings 18.2 15.7 16.3 18.l 21.0 24.l 23.7 26.6 26.1 22.4 

ASHRAEIIES 90.l-1999 Code 5.5 3.4 3.8 4.2 4.7 5.l 

FEMP Programs 3.7 2.l 2.4 2.6 2.9 3.l 3.4 3.6 3.8 4.0 

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 

Total Electronic Shipments (NEMA) 

DSM (Core Sample Estimates) 

Green LightslEnergy Star Buildings 29.6 26.2 25.9 25.6 25.3 

ASHRAEIIES 90.1-1999 Code 5.1 5.1 5.0 5.0 4.9 4.8 4.8 4.7 4.6 4.6 

FEMP Programs 

B-49 

B.5.6 Voluntary Luminaire Program 

The voluntary luminaire program presents energy-efficiency information in the form of a 

"Luminaire Efficacy Rating" (LER) for the luminaire system (defined in the FEMP PEERs 

discussion in 5.4.1 above). Using an electronic ballast greatly improves the LER ofa luminaire. A 

1999 report to DOE on the program showed widespread manufacturer participation, with 75 percent 

of manufacturers publishing LER information in their product literature in 1997, and 100 percent 

doing so by 1999. 

It was difficult to evaluate the direct impact this voluntary program has had and will have on 

the fluorescent ballast market. The Bureau of Census began to collect data on ballast usage in 

luminaires in 1998, and has collected data on the number of lamps per luminaire since 1995. 

However, the voluntary luminaire program interacts with and reinforces other educational programs 

to encourage the use of electronic ballasts, and its specific influence was hard to quantify. 

B.5.7 Summary 

Table B.21 shows the number of electronic ballast shipments affected by each of the 

programs analyzed above. We have not attempted to calculate the overlap between programs. 

Remember that the EPA projections, as discussed above, were intended to represent the impacts of 

all market forces. Therefore a sum of the individual program impacts shown in the table is not 

meaningful. 

Table B.2l Shipments of Electronic Ballasts Affected by Programs (millions) 

1992 1993 1994 1995 1996 1997 1998 1999 2000 

Total Electronic Shipments (NEMA) 22.9 26.0 33.1 29.7 35.4 37.8 

DSM (Core Sample Estimates) 4.5 9.6 18.3 15.4 10.2 6.4 

Green LightslEnergy Star Buildings O.l 0.7 1.8 3.6 3.4 8.3 10.8 12.2 12.8 

ASHRAEIIES 90.1-1999 Code 

FEMP Programs 1.3 2.5 2.5 2.5 1.9 1.9 3.0 3.2 3.5 

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 

Total Electronic Shipments (NEMA) 

DSM (Core Sample Estimates) 

Green LightslEnergy Star Buildings 18.2 15.7 16.3 18.l 21.0 24.l 23.7 26.6 26.1 22.4 

ASHRAEIIES 90.l-1999 Code 5.5 3.4 3.8 4.2 4.7 5.l 

FEMP Programs 3.7 2.l 2.4 2.6 2.9 3.l 3.4 3.6 3.8 4.0 

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 

Total Electronic Shipments (NEMA) 

DSM (Core Sample Estimates) 

Green LightslEnergy Star Buildings 29.6 26.2 25.9 25.6 25.3 

ASHRAEIIES 90.1-1999 Code 5.1 5.1 5.0 5.0 4.9 4.8 4.8 4.7 4.6 4.6 

FEMP Programs 

B-49 
The report projected ballast retrofits under two scenarios , one with utility rebates at 1992

levels projected through 1995, and the other with higher rebate levels due to FRI. However, FRI did

not track the actual retrofits made through the program during these years. Also , as discussed in

section B. 5.1, utility rebates have been diminishing from 1996 on.

In the absence of verifying data, we simply used the "FRI Scenario" estimates for ballasts

installed from 1992 - 1996. Taking 90 percent of these values gave a total of 10.5 million. The

remaining 9.3 million ballasts were apportioned equally through 2001. The results are shown in

Table B.20 above.

B.5.5. Energy Cost Savings Council (ECSC)

The Energy Cost Savings Council program created by NEMA is a broad and ambitious

program that aims to convince key business decision makers, such as chief financial officers, to

upgrade their buildings with more efficient electrical equipment. The ECSC complements EPA's

program, advising building owners they can achieve up to 50 percent savings ($ 1/sq ft) and paybacks

of 3 to 5 years (30 to 50 percent return on investment or ROI).22 Note that these estimates are for

all end-uses, not only for lighting. An ECSC analysis of 248 lighting upgrade projects documented

by Energy User News showed an average project payback period of 2.2 years and an ROI of 45

percent.23

ECSC has estimated that despite the successes of the non-regulatory programs already

discussed in this section , the potential for lighting retrofits is very large. The Council estimates that

the floor space committed to be retrofit under the EPA Green Lights /Energy Star Buildings programs

still represents only 10 percent of the potential national commercial , industrial, and institutional

square footage. ECSC estimates that all the electronic ballasts sold since 1989 have replaced only

10 percent of the installed base of ballasts in existing buildings (which they estimate at 1.6 billion

ballasts, considerably higher than the LBNL estimate described in Chapter 5, section 5.3.6). Since

conversion of magnetic to electronic ballasts is one of the primary lighting retrofits , the expansion

of efficiency upgrades through the ECSC's influence could cause an increase in electronic ballast

sales.

However, we determined from interviewing ECSC staffthat data on ballast retrofits directly

attributable to ECSC efforts would be difficult to obtain. Unlike the EPA programs, which recruit

"partners" and "allies" and ask them to report on their efficiency retrofits , ECSC does not gather data

on the activities of those reached through their educational programs. A participant could be

motivated to seek the services of an energy service company (ESCO), for instance, but only if that

ESCO were an ECSC member could the Council track the resulting retrofits. Therefore, this

program will most likely have to be evaluated through qualitative rather than quantitative means.

B-48

EE-RM-97-500 COMMENT 104

The report projected ballast retrofits under two scenarios, one with utility rebates at 1992 

levels projected through 1995, and the other with higher rebate levels due to FRI. However, FRI did 

not track the actual retrofits made through the program during these years. Also, as discussed in 

section B.5.1, utility rebates have been diminishing from 1996 on. 

In the absence of verifying data, we simply used the "FRI Scenario" estimates for ballasts 

installed from 1992 - 1996. Taking 90 percent of these values gave a total of 10.5 million. The 

remaining 9.3 million ballasts were apportioned equally through 2001. The results are shown in 

Table B.20 above. 

B.S.S. Energy Cost Savings Council (ECSC) 

The Energy Cost Savings Council program created by NEMA is a broad and ambitious 

program that aims to convince key business decision makers, such as chief financial officers, to 

upgrade their buildings with more efficient electrical equipment. The ECSC complements EPA's 

program, advising building owners they can achieve up to 50 percent savings ($1/sq ft) and paybacks 

of 3 to 5 years (30 to 50 percent return on investment or ROI).22 Note that these estimates are for 

all end-uses, not only for lighting. An ECSC analysis of248 lighting upgrade projects documented 

by Energy User News showed an average project payback period of 2.2 years and an ROI of 45 

percent.23 

ECSC has estimated that despite the successes of the non-regulatory programs already 

discussed in this section, the potential for lighting retrofits is very large. The Council estimates that 

the floor space committed to be retrofit under the EPA Green Lights/Energy Star Buildings programs 

still represents only 10 percent of the potential national commercial, industrial, and institutional 

square footage. ECSC estimates that all the electronic ballasts sold since 1989 have replaced only 

10 percent of the installed base of ballasts in existing buildings (which they estimate at 1.6 billion 

ballasts, considerably higher than the LBNL estimate described in Chapter 5, section 5.3.6). Since 

conversion of magnetic to electronic ballasts is one of the primary lighting retrofits, the expansion 

of efficiency upgrades through the ECSC's influence could cause an increase in electronic ballast 

sales. 

However, we determined from interviewing ECSC staffthat data on ballast retrofits directly 

attributable to ECSC efforts would be difficult to obtain. Unlike the EPA programs, which recruit 

"partners" and "allies" and ask them to report on their efficiency retrofits, ECSC does not gather data 

on the activities of those reached through their educational programs. A participant could be 

motivated to seek the services of an energy service company (ESCO), for instance, but only ifthat 

ESCO were an ECSC member could the Council track the resulting retrofits. Therefore, this 

program will most likely have to be evaluated through qualitative rather than quantitative means. 

B-48 

The report projected ballast retrofits under two scenarios, one with utility rebates at 1992 

levels projected through 1995, and the other with higher rebate levels due to FRI. However, FRI did 

not track the actual retrofits made through the program during these years. Also, as discussed in 

section B.5.1, utility rebates have been diminishing from 1996 on. 

In the absence of verifying data, we simply used the "FRI Scenario" estimates for ballasts 

installed from 1992 - 1996. Taking 90 percent of these values gave a total of 10.5 million. The 

remaining 9.3 million ballasts were apportioned equally through 2001. The results are shown in 

Table B.20 above. 

B.S.S. Energy Cost Savings Council (ECSC) 

The Energy Cost Savings Council program created by NEMA is a broad and ambitious 

program that aims to convince key business decision makers, such as chief financial officers, to 

upgrade their buildings with more efficient electrical equipment. The ECSC complements EPA's 

program, advising building owners they can achieve up to 50 percent savings ($1/sq ft) and paybacks 

of 3 to 5 years (30 to 50 percent return on investment or ROI).22 Note that these estimates are for 

all end-uses, not only for lighting. An ECSC analysis of248 lighting upgrade projects documented 

by Energy User News showed an average project payback period of 2.2 years and an ROI of 45 

percent.23 

ECSC has estimated that despite the successes of the non-regulatory programs already 

discussed in this section, the potential for lighting retrofits is very large. The Council estimates that 

the floor space committed to be retrofit under the EPA Green Lights/Energy Star Buildings programs 

still represents only 10 percent of the potential national commercial, industrial, and institutional 

square footage. ECSC estimates that all the electronic ballasts sold since 1989 have replaced only 

10 percent of the installed base of ballasts in existing buildings (which they estimate at 1.6 billion 

ballasts, considerably higher than the LBNL estimate described in Chapter 5, section 5.3.6). Since 

conversion of magnetic to electronic ballasts is one of the primary lighting retrofits, the expansion 

of efficiency upgrades through the ECSC's influence could cause an increase in electronic ballast 

sales. 

However, we determined from interviewing ECSC staffthat data on ballast retrofits directly 

attributable to ECSC efforts would be difficult to obtain. Unlike the EPA programs, which recruit 

"partners" and "allies" and ask them to report on their efficiency retrofits, ECSC does not gather data 

on the activities of those reached through their educational programs. A participant could be 

motivated to seek the services of an energy service company (ESCO), for instance, but only ifthat 

ESCO were an ECSC member could the Council track the resulting retrofits. Therefore, this 

program will most likely have to be evaluated through qualitative rather than quantitative means. 

B-48 
B.6 NATIONAL ENERGY SAVINGS DETAILED RESULTS

B.6.1 Electronic Ballast Standards

This section contains the NES results for the electronic ballast standards for the four lampballast combinations using the AEO Reference Case electricity price projection. The results reported

are for the same Scenarios found in Chapter 5, Table 5.5. The energy savings include HVAC

impacts, while the total benefit (energy cost savings) and equipment costs do not.

Table B.22 Scenario 1A

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 1F40 2F96/ES 2F96HO/ES Total

Total Quads Saved 0.71 0.02 0.26 0.02 1.01

Total Quads Saved w/ HVAC 0.75 0.02 0.28 0.02 1.08

Total Benefit 1.38 0.04 0.51 0.03 1.97

Total Equipment Cost 0.66 0.03 0.31 0.01 1.01

Net Present Value 0.72 0.02 0.21 0.02 0.96

Table B.23 Scenario 1B

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 IF40 2F96/ES 2F96HO/ES Total

Total Quads Saved 1.25 0.04 0.46 0.04 1.79

Total Quads Saved w/ HVAC 1.33 0.04 0.49 0.04 1.90

Total Benefit 2.20 0.07 0.81 0.04 3.13

Total Equipment Cost 1.06 0.05 0.49 0.02 1.62

Net Present Value 1.13 0.03 0.32 0.03 1.51

Table B.24 Scenario 2A

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 1F40 2F96/ES 2F96HO/ES Total

Total Quads Saved 1.34 0.03 0.26 0.02 1.66

Total Quads Saved with 1.42 0.04 0.28 0.02 1.76

HVAC*

Total Benefit 2.62 0.07 0.51 0.03 3.22

Total Equipment Cost 0.46 0.02 0.31 0.01 0.80

Net Present Value 2.16 0.05 0.21 0.02 2.43

B-50

EE-RM-97-500 COMMENT 104

B.6 NATIONAL ENERGY SAVINGS DETAILED RESULTS 

B.6.1 Electronic Ballast Standards 

This section contains the NES results for the electronic ballast standards for the four lampballast combinations using the AEO Reference Case electricity price projection. The results reported 

are for the same Scenarios found in Chapter 5, Table 5.5. The energy savings include HVAC 

impacts, while the total benefit (energy cost savings) and equipment costs do not. 

Table B.22 Scenario 1A 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F96/ES 2F96HO/ES Total 

Total Quads Saved 0.71 0.02 0.26 

Total Quads Saved wi HV AC 0.75 0.02 0.28 

Total Benefit 1.38 0.04 0.51 

Total Equipment Cost 0.66 0.03 0.31 

Net Present Value 0.72 0.02 0.21 

Table B.23 Scenario 1B 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 

Total Quads Saved 1.25 0.04 0.46 

Total Quads Saved wi HV AC 1.33 0.04 0.49 

Total Benefit 2.20 0.07 0.81 

Total Equipment Cost 1.06 0.05 0.49 

Net Present Value 1.13 0.03 0.32 

Table B.24 Scenario 2A 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 

Total Quads Saved 1.34 0.03 0.26 

Total Quads Saved with 1.42 0.04 0.28 

HVAC* 

Total Benefit 2.62 0.07 0.51 

Total Equipment Cost 0.46 0.02 0.31 

Net Present Value 2.16 0.05 0.21 

B-50 

0.02 

0.02 

0.03 

0.01 

0.02 

2F96HOIES 

0.04 

0.04 

0.04 

0.02 

0.03 

2F96HOIES 

0.02 

0.02 

0.03 

0.01 

0.02 

1.01 

1.08 

1.97 

1.01 

0.96 

Total 

1.79 

1.90 

3.13 

1.62 

1.51 

Total 

1.66 

1.76 

3.22 

0.80 

2.43 

B.6 NATIONAL ENERGY SAVINGS DETAILED RESULTS 

B.6.1 Electronic Ballast Standards 

This section contains the NES results for the electronic ballast standards for the four lampballast combinations using the AEO Reference Case electricity price projection. The results reported 

are for the same Scenarios found in Chapter 5, Table 5.5. The energy savings include HVAC 

impacts, while the total benefit (energy cost savings) and equipment costs do not. 

Table B.22 Scenario 1A 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F96/ES 2F96HO/ES Total 

Total Quads Saved 0.71 0.02 0.26 

Total Quads Saved wi HV AC 0.75 0.02 0.28 

Total Benefit 1.38 0.04 0.51 

Total Equipment Cost 0.66 0.03 0.31 

Net Present Value 0.72 0.02 0.21 

Table B . 23 Scenario 1B 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 

Total Quads Saved 1.25 0.04 0.46 

Total Quads Saved wi HV AC 1.33 0.04 0.49 

Total Benefit 2.20 0.07 0.81 

Total Equipment Cost 1.06 0.05 0.49 

Net Present Value 1.13 0.03 0.32 

Table B . 24 Scenario 2A 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 

Total Quads Saved 1.34 0.03 0.26 

Total Quads Saved with 1.42 0.04 0.28 

HVAC* 

Total Benefit 2.62 0.07 0.51 

Total Equipment Cost 0.46 0.02 0.31 

Net Present Value 2.16 0.05 0.21 

B-50 

0.02 

0.02 

0.03 

0.01 

0.02 

2F96HOIES 

0.04 

0.04 

0.04 

0.02 

0.03 

2F96HOIES 

0.02 

0.02 

0.03 

0.01 

0.02 

1.01 

1.08 

1.97 

1.01 

0.96 

Total 

1.79 

1.90 

3.13 

1.62 

1.51 

Total 

1.66 

1.76 

3.22 

0.80 

2.43 
Table B.25 Scenario 2B

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 IF40 2F96/ES 2F96HO/ES Total

Total Quads Saved 2.37 0.06 0.46 0.04 2.93

Total Quads Saved with 2.52 0.07 0.49 0.04 3.12

HVAC*

Total Benefit 4.17 0.11 0.8 0.04 5.13

Total Equipment Cost 0.73 0.03 0.49 0.02 1.27

Net Present Value 3.43 0.08 0.32 0.03 3.86

Table B.26 Scenario 3A

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 I F40 2F96/ES 2F96H0/ES Total

Total Quads Saved 1.16 0.03 0.22 0.02 1.43

Total Quads Saved with 1.23 0.03 0.24 0.02 1.52

HVAC*

Total Benefit 2.18 0.05 0.42 0.02 2.68

Total Equipment Cost 0.37 0.02 0.25 0.01 0.64

Net Present Value 1.81 0.04 0.17 0.01 2.03

Table B.27 Scenario 3B

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 IF40 2F96/ES 2F96HO/ES Total

Total Quads Saved 2.15 0.06 0.41 0.03 2.66

Total Quads Saved with HVAC* 2.29 0.06 0.44 0.04 2.82

Total Benefit 3.63 0.09 0.69 0.04 4.46

Total Equipment Cost 0.62 0.03 0.42 0.01 1.08

Net Present Value 3.02 0.07 0.27 0.02 3.38

Table B.28 Scenario 4A

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $

2F40 IF40 2F96/ES 2F96HO/ES Total

Total Quads Saved 1.27 0.03 0.24 0.02 1.57

Total Quads Saved with 1.35 0.03 0.26 0.02 1.67

HVAC*

Total Benefit 2.43 0.06 0.46 0.03 2.98

Total Eq uipment Cost 0.41 0.02 0.28 0.01 0.72

Net Present Value 2.02 0.04 0.19 0.02 2.26

B-51

EE-RM-97-500 COMMENT 104

Table 8.25 Scenario 2B 

Total Quads Saved 

Total Quads Saved 

HVAC* 

Total Benefit 

Total Equipment Cost 

Net Present Value 

Table 8.26 Scenario 3A 

Total Quads Saved 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F96/ES 

2.37 0.06 0.46 

with 2.52 0.Q7 0.49 

4.17 0.11 0.8 

0.73 0.Q3 0.49 

3.43 0.08 0.32 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 

1.16 0.03 0.22 

Total Quads Saved with 

HVAC* 

1.23 0.03 0.24 

Total Benefit 

Total Equipment Cost 

Net Present Value 

Table B.27 Scenario 3B 

Total Quads Saved 

Total Quads Saved with HV AC* 

Total Benefit 

Total Equipment Cost 

Net Present Value 

Table 8.28 Scenario 4A 

Total Quads Saved 

2.18 

0.37 

1.81 

0.05 

0.02 

0.04 

0.42 

0.25 

0.17 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 

2.15 0.06 0.41 

2.29 0.06 0.44 

3.63 0.09 0.69 

0.62 0.Q3 0.42 

3.02 0.Q7 0.27 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 fin billion 1997 $} 

2F40 1F40 2F96/ES 

1.27 0.03 0.24 

Total Quads Saved with 1.35 0.03 0.26 

HVAC* 

Total Benefit 2.43 0.06 0.46 

Total EQuipment Cost 0.41 0.02 0.28 

Net Present Value 2.02 0.04 0.19 

B-51 

2F96HOIES Total 

0.04 2.93 

0.04 3.12 

0.04 5.13 

0.02 1.27 

0.03 3.86 

2F96HO/ES Total 

0.02 1.43 

0.02 1.52 

0.02 2.68 

0.01 0.64 

0.01 2.03 

2F96HOIES Total 

0.03 2.66 

0.04 2.82 

0.04 4.46 

0.0) 1.08 

0.02 3.38 

2F96HO/ES Total 

0.02 1.57 

0.02 1.67 

0.03 2.98 

0.01 0.72 

0.02 2.26 

Table 8.25 Scenario 2B 

Total Quads Saved 

Total Quads Saved 

HVAC* 

Total Benefit 

Total Equipment Cost 

Net Present Value 

Table 8.26 Scenario 3A 

Total Quads Saved 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F96/ES 

2.37 0.06 0.46 

with 2.52 0.Q7 0.49 

4.17 0.11 0.8 

0.73 0.Q3 0.49 

3.43 0.08 0.32 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 

1.16 0.03 0.22 

Total Quads Saved with 

HVAC* 

1.23 0.03 0.24 

Total Benefit 

Total Equipment Cost 

Net Present Value 

Table B.27 Scenario 3B 

Total Quads Saved 

Total Quads Saved with HV AC* 

Total Benefit 

Total Equipment Cost 

Net Present Value 

Table 8.28 Scenario 4A 

Total Quads Saved 

2.18 

0.37 

1.81 

0.05 

0.02 

0.04 

0.42 

0.25 

0.17 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 

2.15 0.06 0.41 

2.29 0.06 0.44 

3.63 0.09 0.69 

0.62 0.Q3 0.42 

3.02 0.Q7 0.27 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F96/ES 

1.27 0.03 0.24 

Total Quads Saved with 1.35 0.03 0.26 

HVAC* 

Total Benefit 2.43 0.06 0.46 

Total Equipment Cost 0.41 0.02 0.28 

Net Present Value 2.02 0.04 0.19 

B-51 

2F96HOIES Total 

0.04 2.93 

0.04 3.12 

0.04 5.13 

0.02 1.27 

0.03 3.86 

2F96HO/ES Total 

0.02 1.43 

0.02 1.52 

0.02 2.68 

0.01 0.64 

0.01 2.03 

2F96HOIES Total 

0.03 2.66 

0.04 2.82 

0.04 4.46 

0.0) 1.08 

0.02 3.38 

2F96HO/ES Total 

0.02 1.57 

0.02 1.67 

0.03 2.98 

0.01 0.72 

0.02 2.26 
Table B.29 Scenario 4B

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $

2F40 1F40 2F96/ES 2F96HO/ES Total

Total Quads Saved 2.30 0.06 0.44 0.04 2.84

Total Quads Saved with 2.45 0.06 0.47 0.04 3.02

HVAC*

Total Benefit 3.95 0.10 0.76 0.04 4.85

Total Equipment Cost 0.67 0.03 0.46 0.02 1.18

Net Present Value 3.28 0.07 0.30 0.03 3.68

B.6.2 Cathode Cutout Standards

This section contains the NES results for the cathode cutout ballast standards for the four

lamp-ballast combinations using the AEO Reference Case electricity price projection. The cases

reported are the same Scenarios found in Chapter 5, Table 5.6.

Table B.30 Scenario 5A

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 I F40 2F96/ES 2F96H0/ES Total

Total Quads Saved 0.46 0.01 0.00 0.01 0.48

Total Quads Saved w/ HVAC 0.49 0.01 0.00 0.01 0.51

Total Benefit 0.90 0.02 0.00 0.02 0.94

Total Equipment Cost 0.76 0.02 0.00 0.01 0.78

Net Present Value 0.15 0.00 0.00 0.01 0.16

Table B31. Scenario 5B

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 I F40 2F96/ES 2F96H4/ES Total

Total Quads Saved 0.82 0.01 0.00 0.02 0.85

Total Quads Saved w/ HVAC 0.87 0.02 0.00 0.03 0.91

Total Benefit 1 .44 0.03 0.00 0.03 1.49

Total Equipment Cost 1.21 0.03 0.00 0.01 1.26

Net Present Value 0.22 -0.01 0.00 0.02 0.23

EE-RM-97-500 COMMENT 104

Table 8.29 Scenario 48 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 IF40 2F961ES 2F96HOIES Total 

Total Quads Saved 2.30 0.06 0.44 0.04 2.84 

Total Quads Saved with 2.45 0.06 0.47 0.04 3.02 

HVAC* 

Total Benefit 3.95 0.10 0.76 0.04 4.85 

Total Equipment Cost 0.67 0.03 0.46 0.02 1.18 

Net Present Value 3.28 0.07 0.30 0.03 3.68 

8.6.2 Cathode Cutout Standards 

This section contains the NES results for the cathode cutout ballast standards for the four 

lamp-ballast combinations using the AEO Reference Case electricity price projection. The cases 

reported are the same Scenarios found in Chapter 5, Table 5.6. 

Table 8.30 Scenario 5A 

For Units Sold from 2003 to 2030 

Di<;collnted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 2F96HOIES Total 

Total Quads Saved 0.46 0.01 0.00 0.01 0.48 

Total Quads Saved wi HV AC 0.49 0.01 0.00 0.01 0.51 

Total Benefit 0.90 0.02 0.00 0.02 0.94 

Total Equipment Cost 0.76 0.02 0.00 0.01 0.78 

Net Present Value 0.15 0.00 0.00 0.01 0.16 

Table 831. Scenario 58 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 2F96HOIES Total 

Total Quads Saved 0.82 0.01 0.00 0.02 0.85 

Total Quads Saved wi HV AC 0.87 0.02 0.00 0.03 0.91 

Total Benefit 1.44 0.03 0.00 0.03 1.49 

Total Equipment Cost 1.21 0.03 0.00 0.01 1.26 

Net Present Value 0.22 -0.01 0.00 0.02 0.23 

B-52 

Table 8.29 Scenario 48 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 IF40 2F961ES 2F96HOIES Total 

Total Quads Saved 2.30 0.06 0.44 0.04 2.84 

Total Quads Saved with 2.45 0.06 0.47 0.04 3.02 

HVAC* 

Total Benefit 3.95 0.10 0.76 0.04 4.85 

Total Equipment Cost 0.67 0.03 0.46 0.02 1.18 

Net Present Value 3.28 0.07 0.30 0.03 3.68 

8.6.2 Cathode Cutout Standards 

This section contains the NES results for the cathode cutout ballast standards for the four 

lamp-ballast combinations using the AEO Reference Case electricity price projection. The cases 

reported are the same Scenarios found in Chapter 5, Table 5.6. 

Table 8.30 Scenario 5A 

For Units Sold from 2003 to 2030 

Di<;collnted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 2F96HOIES Total 

Total Quads Saved 0.46 0.01 0.00 0.01 0.48 

Total Quads Saved wi HV AC 0.49 0.01 0.00 0.01 0.51 

Total Benefit 0.90 0.02 0.00 0.02 0.94 

Total Equipment Cost 0.76 0.02 0.00 0.01 0.78 

Net Present Value 0.15 0.00 0.00 0.01 0.16 

Table 831. Scenario 58 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 2F96HOIES Total 

Total Quads Saved 0.82 0.01 0.00 0.02 0.85 

Total Quads Saved wi HV AC 0.87 0.02 0.00 0.03 0.91 

Total Benefit 1.44 0.03 0.00 0.03 1.49 

Total Equipment Cost 1.21 0.03 0.00 0.01 1.26 

Net Present Value 0.22 -0.01 0.00 0.02 0.23 

B-52 
Table B.32 Scenario 6A

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 1F40 2F96/ES 2F96HO/ES Total

Total Quads Saved 1.08 0.03 0.00 0.02 1.12

Total Quads with HVAC 1.14 0.03 0.00 0.02 1.19

Total Benefit 2.10 0.05 0.00 0.02 2.18

Total Equipment Cost 0.55 0.02 0.00 0.01 0.58

Net Present Value 1.55 0.03 0.00 0.02 1.60

Table B.33 Scenario 6B

For Units Sold from 2003 to 2030

Discounted at 7% to 1997 (in billion 1997 $)

2F40 I F40 2F96/ES 2F96HO/ES Total

Total Quads Saved 1.90 0.05 0.00 0.03 1.98

Total Quads with HVAC 2.02 0.05 0.00 0.04 2.11

Total Benefit 3.35 0.08 0.00 0.04 3.47

Total Equipment Cost 0.88 0.03 0.00 0.01 0.93

Net Present Value 2.47 0.05 0.00 0.02 2.54

B.7 FLOOR SPACE PROJECTIONS

Table B.34. shows the commercial and industrial historical and projected floor space

(including federal buildings) from 1980 to 2030. The forecasts by building types were based on data

developed by Regional Economic Research (RER).

The commercial floor space was estimated by RER based on the 1992 Commercial Buildings

Energy Consumption Survey (CBECS).24 The industrial floor stock forecast was estimated by RER

based on the 1991 Manufacturing Energy Consumption Survey (MECS)25, and on the 1995 Annual

Energy Outlook26 manufacturing employment forecast to 2010 and then extended to 2030, using the

same growth rate.

EE-RM-97-500 COMMENT 104

Table B.32 Scenario 6A 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 2F96HOIES Total 

Total Quads Saved 1.08 0.03 0.00 0.02 1.12 

Total Quads with HVAC 1.14 0.03 0.00 0.02 1.19 

Total Benefit 2.10 0.05 0.00 0.02 2.18 

Total Equipment Cost 0.55 0.02 0.00 0.01 0.58 

Net Present Value 1.55 0.03 0.00 0.02 1.60 

Table B . 33 Scenario 6B 

For Units Sold from 2003 to 2030 

Di.,counted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 2F96HOIES Total 

Total Quads Saved 1.90 0.05 0.00 0.03 1.98 

Total Quads with HV AC 2.02 0.05 0.00 0.04 2.11 

Total Benefit 3.35 0.08 0.00 0.04 3.47 

Total Equipment Cost 0.88 0.03 0.00 0.01 0.93 

Net Present Value 2.47 0.05 0.00 0.02 2.54 

B.7 FLOOR SPACE PROJECTIONS 

Table B.34. shows the commercial and industrial historical and projected floor space 

(including federal buildings) from 1980 to 2030. The forecasts by building types were based on data 

developed by Regional Economic Research (RER). 

The commercial floor space was estimated by RER based on the 1992 Commercial Buildings 

Energy Consumption Survey (CBECS).24 The industrial floor stock forecast was estimated by RER 

based on the 1991 Manufacturing Energy Consumption Survey (MECS)25, and on the 1995 Annual 

Energy Outlook!-6 manufacturing employment forecast to 2010 and then extended to 2030, using the 

same growth rate. 

B-53 

Table B.32 Scenario 6A 

For Units Sold from 2003 to 2030 

Discounted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 2F96HOIES Total 

Total Quads Saved 1.08 0.03 0.00 0.02 1.12 

Total Quads with HVAC 1.14 0.03 0.00 0.02 1.19 

Total Benefit 2.10 0.05 0.00 0.02 2.18 

Total Equipment Cost 0.55 0.02 0.00 0.01 0.58 

Net Present Value 1.55 0.03 0.00 0.02 1.60 

Table B . 33 Scenario 6B 

For Units Sold from 2003 to 2030 

Di.,counted at 7% to 1997 (in billion 1997 $) 

2F40 1F40 2F961ES 2F96HOIES Total 

Total Quads Saved 1.90 0.05 0.00 0.03 1.98 

Total Quads with HV AC 2.02 0.05 0.00 0.04 2.11 

Total Benefit 3.35 0.08 0.00 0.04 3.47 

Total Equipment Cost 0.88 0.03 0.00 0.01 0.93 

Net Present Value 2.47 0.05 0.00 0.02 2.54 

B.7 FLOOR SPACE PROJECTIONS 

Table B.34. shows the commercial and industrial historical and projected floor space 

(including federal buildings) from 1980 to 2030. The forecasts by building types were based on data 

developed by Regional Economic Research (RER). 

The commercial floor space was estimated by RER based on the 1992 Commercial Buildings 

Energy Consumption Survey (CBECS).24 The industrial floor stock forecast was estimated by RER 

based on the 1991 Manufacturing Energy Consumption Survey (MECS)25, and on the 1995 Annual 

Energy Outlook!-6 manufacturing employment forecast to 2010 and then extended to 2030, using the 

same growth rate. 

B-53 
Table B.34 US Commercial and Industrial Floor Stock Projections (Billions of square feet)

Year Small Large Retail Restaurant Grocery Warehouse School College Health Lodging Assembly Misc

Office Office

Indus-trial Total

1980 4.64 5.65 9.20 1.10 0.57 9.41 4.73 2.82 1.23 1.75 5.91 5.66 9.87 62.54

1985 4.93 6.01 10.70 1.28 0.66 10.13 4.83 2.86 1.43 2.17 6.76 6.47 11.28 69.53

1990 5.39 6.56 11.98 1.44 0.74 11.11 5.24 3.01 1.68 2.68 7.88 7.54 13.15 78.39

1995 5.89 7.17 13.26 1.59 0.82 12.06 5.59 3.16 1.93 3.13 8.68 8.44 13.96 85.66

2000 6.36 7.75 14.58 1.75 0.90 13.09 5.57 3.16 2.05 3.46 9.27 9.30 14.11 91.36

2005 6.78 8.26 16.01 1.92 0.98 13.99 5.48 3.14 2.12 3.59 9.78 10.26 14.28 96.5.9

2010 7.19 8.76 17.64 2.11 1.08 14.67 5.39 3.13 2.18 3.63 10.10 11.40 14.40 101.68

2015 7.66 9.34 19.49 2.33 1.20 15.55 5.30 3.12 2.26 3.72 10.54 12.69 14.54 107.73

2020 8.13 9.91 21.34 2.56 1.31 16.44 5.22 3.10 2.33 3.80 10.99 13.98 14.68 113.78

2025 8.60 10.48 23.19 2.78 1.43 17.32 5.13 3.09 2.40 3.89 11.44 15.26 14.83 119.82

2030 9.07 11.05 25.04 3.00 1.54 18.20 5.04 3.07 2.47 3.98 11.89 16.55 14.97 125.87

Note: To convert values above to metric units, divide by 10.764 to get square meters (m'-).

B-54

EE-RM-97-500 COMMENT 104

Table B.34 US Commercial and Industrial Floor Stock Projections (Billions of square feet) , 

Year Small Large Retail Restaurant Grocery Warehouse School College Health Lodging Assembly Mise Indus-trial Total Office Office 

1980 4.64 5.65 9.20 1.10 0.57 9.41 4.73 2.82 1.23 1.75 5.91 5.66 9.87 62.54 

1985 4.93 6.01 10.70 1.28 0.66 10.13 4.83 2.86 1.43 2.17 6.76 6.47 11.28 69.53 

1990 5.39 6.56 11.98 1.44 0.74 11.11 5.24 3.01 1.68 2.68 7.88 7.54 13.15 78.39 

1995 5.89 7.17 13.26 1.59 0.82 12.06 5.59 3.16 1.93 3.13 8.68 8.44 13.96 85.66 

2000 6.36 7.75 14.58 1.75 0.90 13.09 5.57 3.16 2.05 3.46 9.27 9.30 14.11 91.36 

2005 6.78 8.26 16.01 1.92 0.98 13.99 5.48 3.14 2.12 3.59 9.78 10.26 14.28 96.5.9 

2010 7.19 8.76 17.64 2.11 1.08 14.67 5.39 3.13 2.18 3.63 10.10 11.40 14.40 101.68 

2015 7.66 9.34 19.49 2.33 1.20 15.55 5.30 3.12 2.26 3.72 10.54 12.69 14.54 107.73 

2020 8.13 9.91 21.34 2.56 1.31 16.44 5.22 3.10 2.33 3.80 10.99 13.98 14.68 113.78 

2025 8.60 10.48 23.19 2.78 1.43 17.32 5.13 3.09 2.40 3.89 11.44 15.26 14.83 119.82 

2030 9.07 11.05 25.04 3.00 1.54 18.20 5.04 3.07 2.47 3.98 11.89 16.55 14.97 125.87 . . Note: To convert values above to metriC umts, divide by 10.764 to get square meters (m2

) • 

B-54 

Table B.34 US Commercial and Industrial Floor Stock Projections (Billions of square feet) , 

Year Small Large Retail Restaurant Grocery Warehouse School College Health Lodging Assembly Mise Indus-trial Total Office Offi;' .. 

1980 4.64 5.65 9.20 1.10 0.57 9.41 4.73 2.82 1.23 1.75 5.91 5.66 9.87 62.54 

1985 4.93 6.01 10.70 1.28 0.66 10.13 4.83 2.86 1.43 2.17 6.76 6.47 11.28 69.53 

1990 5.39 6.56 11.98 1.44 0.74 11.11 5.24 3.01 1.68 2.68 7.88 7.54 13.15 78.39 

1995 5.89 7.17 13.26 1.59 0.82 12.06 5.59 3.16 1.93 3.13 8.68 8.44 13.96 85.66 

2000 6.36 7.75 14.58 1.75 0.90 13.09 5.57 3.16 2.05 3.46 9.27 9.30 14.11 91.36 

2005 6.78 8.26 16.01 1.92 0.98 13.99 5.48 3.14 2.12 3.59 9.78 10.26 14.28 96.5.9 

2010 7.19 8.76 17.64 2.11 1.08 14.67 5.39 3.13 2.18 3.63 10.10 11.40 14.40 101.68 

2015 7.66 9.34 19.49 2.33 1.20 15.55 5.30 3.12 2.26 3.72 10.54 12.69 14.54 107.73 

2020 8.13 9.91 21.34 2.56 1.31 16.44 5.22 3.10 2.33 3.80 10.99 13.98 14.68 113.78 

2025 8.60 10.48 23.19 2.78 1.43 17.32 5.13 3.09 2.40 3.89 11.44 15.26 14.83 119.82 

2030 9.07 11.05 25.04 3.00 1.54 18.20 5.04 3.07 2.47 3.98 11.89 16.55 14.97 125.87 . . Note: To convert values above to metriC units, divide by 10.764 to get square meters (m2

) • 

B-54 
REFERENCES

1. See DOE's Ballast rulemaking website:

http://www.eren. doe.gov/buildings/codes_standards/applbrf/ballast.html.

2. MAISY®, the Market Analysis and Information System, is a proprietary product that was

acquired by LBNL from Jackson Associates.

3. Department of Energy/Energy Information Administration (DOE/EIA). 1998. A Look at

Commercial Buildings in 1995. Characteristics, Energy Consumption, and Energy Expenditures.

DOE/EIA-0625(95). Table BC-39: Lighting Equipment, Number of Buildings.

4. See comment #12 on the Draft Report on Potential Impact of Possible Energy Efficiency Levels

for Fluorescent Lamp Ballasts, received by DOE from Steve Rosenstock of EEI on August 28,

1997.

5. Ibid.

6. Department of Energy/Energy Information Administration (DOE/EIA). 1994. Commercial

Buildings Characteristics 1992: Commercial Buildings Energy Consumption Survey. DOE/EIA0246(92).

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

8. 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.

9. Sezgen, 0., and J.G. Koomey, April 1998, Interactions Between Lighting and Space

Conditioning Energy Use in U.S. Commercial Buildings. LBNL-39795, Lawrence Berkeley

National Laboratory, Berkeley, California.

10. Rundquist, R. et al. 1993. "Calculating Lighting and HVAC Interactions," ASHRAE Journal,

Nov. 1993.

11. Charles Eley Associates. Memo to ASHRAE SPCC 90. 1 re "Impact of Internal Gain on

Cooling and Heating Energy." San Francisco, California.

12. Department of Energy /Energy Information Administration. 1992. Lighting in Commercial

Buildings. DOE/EIA-0555(92)/1.

13. Department of Energy/Energy Information Administration, 1998, op cit.

14. Busch, Chris B., I. Turiel, B.A. Atkinson, J.E. McMahon, and J.H. Eto. 1999. "DSM Rebates

for Electronic Ballasts: National Estimates (1992-1997) and Assessment of Market Impact. "

Lawrence Berkeley National Laboratory (Draft).

B-55

EE-RM-97-500 COMMENT 104

REFERENCES 

1. See DOE's Ballast mlemaking website: 

http://www .eren. doe.gov /buildings/codes _ standards/app lbrflballast.html. 

2. MAISY®, the Market Analysis and Infom1ation System, is a proprietary product that was 

acquired by LBNL from Jackson Associates. 

3. Department of Energy/Energy Information Administration (DOEIEIA). 1998. A Look at 

Commercial Buildings in 1995: Characteristics, Energy Consumption, and Energy Expenditures. 

DOE/EIA-0625(95). Table BC-39: Lighting Equipment, Number of Buildings. 

4. See comment #12 on the Draft Report on Potential Impact of Possible Energy Efficiency Levels 

for Fluorescent Lamp Ballasts, received by DOE from Steve Rosenstock of EEl on August 28, 

1997. 

5. Ibid. 

6. Department of Energy/Energy Information Administration (DOE/EIA). 1994. Commercial 

Buildings Characteristics 1992: Commercial Buildings Energy Consumption Survey. DOE/EIA0246(92). 

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

8. Sezgen, A.O., and Y.J. Huang, 1994, "Ughting/HV AC interactions and their effects on energy 

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

Study on Energy Efficiency in Buildings. 

9. Sezgen, 0., and J.G. Koomey, April 1998, Interactions Between Lighting and Space 

Conditioning Energy Use in U.S. Commercial Buildings. LBNL-39795, Lawrence Berkeley 

National Laboratory, Berkeley, Califomia. 

10. Rundquist, R. et al. 1993. "Calculating Lighting and HVAC Interactions," ASHRAE Jouma1, 

Nov. 1993. 

11. Charles Eley Associates. Memo to ASHRAE SPCC 90.1 re "Impact ofIntemal Gain on 

Cooling and Heating Energy." San Francisco, Califomia. 

12. Department of Energy /Energy Information Administration. 1992. Lighting in Commercial 

BUildings. DOE/EIA-0555(92)/1. 

13. Department of Energy/Energy Infom1ation Administration, 1998, op cit. 

14. Busch, Chris B., 1. Turiel, B.A. Atkinson, J.E. McMahon, and J.H. Eto. 1999. "DSM Rebates 

for Electronic Ballasts: National Estimates (1992-1997) and Assessment of Market Impact. " 

Lawrence Berkeley National Laboratory (Draft). 

B-55 

REFERENCES 

1. See DOE's Ballast mlemaking website: 

http://www .eren. doe.gov /buildings/codes _ standards/app lbrflballast.html. 

2. MAISY®, the Market Analysis and Infom1ation System, is a proprietary product that was 

acquired by LBNL from Jackson Associates. 

3. Department of Energy/Energy Information Administration (DOEIEIA). 1998. A Look at 

Commercial Buildings in 1995: Characteristics, Energy Consumption, and Energy Expenditures. 

DOE/EIA-0625(95). Table BC-39: Lighting Equipment, Number of Buildings. 

4. See comment #12 on the Draft Report on Potential Impact of Possible Energy Efficiency Levels 

for Fluorescent Lamp Ballasts, received by DOE from Steve Rosenstock of EEl on August 28, 

1997. 

5. Ibid. 

6. Department of Energy/Energy Information Administration (DOE/EIA). 1994. Commercial 

Buildings Characteristics 1992: Commercial Buildings Energy Consumption Survey. DOE/EIA0246(92). 

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

8. Sezgen, A.O., and Y.J. Huang, 1994, "Ughting/HV AC interactions and their effects on energy 

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

Study on Energy Efficiency in Buildings. 

9. Sezgen, 0., and J.G. Koomey, April 1998, Interactions Between Lighting and Space 

Conditioning Energy Use in U.S. Commercial Buildings. LBNL-39795, Lawrence Berkeley 

National Laboratory, Berkeley, Califomia. 

10. Rundquist, R. et al. 1993. "Calculating Lighting and HVAC Interactions," ASHRAE Jouma1, 

Nov. 1993. 

11. Charles Eley Associates. Memo to ASHRAE SPCC 90.1 re "Impact ofIntemal Gain on 

Cooling and Heating Energy." San Francisco, Califomia. 

12. Department of Energy /Energy Information Administration. 1992. Lighting in Commercial 

BUildings. DOE/EIA-0555(92)/1. 

13. Department of Energy/Energy Infom1ation Administration, 1998, op cit. 

14. Busch, Chris B., 1. Turiel, B.A. Atkinson, J.E. McMahon, and J.H. Eto. 1999. "DSM Rebates 

for Electronic Ballasts: National Estimates (1992-1997) and Assessment of Market Impact. " 

Lawrence Berkeley National Laboratory (Draft). 

B-55 
15. Eto, Joseph. 1996. "The Past, Present, and Future of U.S. Utility Demand-Side Management

Programs." Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-3993 1.

16. Internal Memoranda, PNNL and LBNL, 1996 - 1999.

17. Data provided by Energy Information Administration. 1996 - 1999.

18. Johnson, Frank X, Harris, J.P., Coleman, P.E. 1999. "Energy and Economic Impacts from

Product Energy Efficiency Recommendations for Federal Purchasing," Lawrence Berkeley

National Laboratory (draft).

19. Casey-McCabe, Nancy 1995. Federal Purchasing Volume. Internal Memorandum,. Lawrence

Berkeley National Laboratory, Washington, DC.

20. Clinton, William J. 1999. Executive Order 13123-Greening the Government Through

Efficient Energy Management. Federal Register, Vol. 64, No. 109. Tuesday, June 8, 1999.

21. Shankle, S.A., Dirks, J.A., Elliott, D.B., Richman, E.E., and Grover, S.E. 1993. Estimate of

Federal Relighting Potential and Demand for Efficient Products. Pacific Northwest Laboratory.

PNL-8930.

22. Energy Cost Savings Council. 1998. "Re-electrification of America: An Overview." March

1998.

23. Energy Cost Savings Council. 1998. "Analysis: 1,000 U.S. Electrical Product Upgrade

Projects, 1988-1996."

24. U.S. Department of Energy/Energy Information Administration (DOE/EIA), 1994, op cit.

25. Department of Energy/Energy Information Administration. 1994. Manufacturing

Consumption of Energy 1991: Manufacturing Energy Consumption Survey. DOE/EIA-0512(91).

26. Department of Energy/Energy Information Administration. 1995. Annual Energy Outlook,

1995. DOE/EIA-0383(95).

EE-RM-97-500 COMMENT 104

15. Eto, Joseph. 1996. "The Past, Present, and Future of U.S. Utility Demand-Side Management 

Programs." Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-39931. 

16. Internal Memoranda, PNNL and LBNL, 1996 - 1999. 

17. Data provided by Energy Information Administration. 1996 - 1999. 

18. Johnson, Frank X, Harris, J.P., Coleman, P.E. 1999. "Energy and Economic Impacts from 

Product Energy Efficiency Recommendations for Federal Purchasing," Lawrence Berkeley 

National Laboratory (draft). 

19. Casey-McCabe, Nancy 1995. Federal Purchasing Volume. Internal Memorandum,. Lawrence 

Berkeley National Laboratory, Washington, DC. 

20. Clinton, William J. 1999. Executive Order BI23-Greening the Government Through 

Efficient Energy Management. Federal Register, Vol. 64, No. 109. Tuesday, June 8, 1999. 

21. Shankle, S.A., Dirks, J.A., Elliott, D.B., Richman, E.E., and Grover, S.E. 1993. Estimate of 

Federal Relighting Potential and Demand for Efficient Products. Pacific Northwest Laboratory. 

PNL-8930. 

22. Energy Cost Savings Council. 1998. "Re-electrification of America: An Overview." March 

1998. 

23. Energy Cost Savings Council. 1998. "Analysis: 1,000 U.S. Electrical Product Upgrade 

Projects, 1988-1996." 

24. U.S. Department of Energy/Energy Information Administration (DOE/EIA), 1994, op cit. 

25. Department of Energy/Energy Information Administration. 1994. Manufacturing 

Consumption of Energy 1991: Manufacturing Energy Consumption Survey. DOE/EIA-0512(91). 

26. Department of Energy/Energy Information Administration. 1995. Annual Energy Outlook, 

1995. DOE/EIA-0383(95). 

B-56 

15. Eto, Joseph. 1996. "The Past, Present, and Future of U.S. Utility Demand-Side Management 

Programs." Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-39931. 

16. Internal Memoranda, PNNL and LBNL, 1996 - 1999. 

17. Data provided by Energy Information Administration. 1996 - 1999. 

18. Johnson, Frank X, Harris, J.P., Coleman, P.E. 1999. "Energy and Economic Impacts from 

Product Energy Efficiency Recommendations for Federal Purchasing," Lawrence Berkeley 

National Laboratory (draft). 

19. Casey-McCabe, Nancy 1995. Federal Purchasing Volume. Internal Memorandum,. Lawrence 

Berkeley National Laboratory, Washington, DC. 

20. Clinton, William J. 1999. Executive Order BI23-Greening the Government Through 

Efficient Energy Management. Federal Register, Vol. 64, No. 109. Tuesday, June 8, 1999. 

21. Shankle, S.A., Dirks, J.A., Elliott, D.B., Richman, E.E., and Grover, S.E. 1993. Estimate of 

Federal Relighting Potential and Demand for Efficient Products. Pacific Northwest Laboratory. 

PNL-8930. 

22. Energy Cost Savings Council. 1998. "Re-electrification of America: An Overview." March 

1998. 

23. Energy Cost Savings Council. 1998. "Analysis: 1,000 U.S. Electrical Product Upgrade 

Projects, 1988-1996." 

24. U.S. Department of Energy/Energy Information Administration (DOE/EIA), 1994, op cit. 

25. Department of Energy/Energy Information Administration. 1994. Manufacturing 

Consumption of Energy 1991: Manufacturing Energy Consumption Survey. DOE/EIA-0512(91). 

26. Department of Energy/Energy Information Administration. 1995. Annual Energy Outlook, 

1995. DOE/EIA-0383(95). 

B-56