DRAFT 

Marginal Energy Prices Report 

July 1999 

U.S. Department of Energy 

Assistant Secretary, 

Energy Efficiency & Renewable Energy 

Office of Codes and Standards 

Washington, DC 20585 

EE-RM-STD-98-440 COMMENT 241ZZ

DRAFT 

Marginal Energy Prices Report 

July 1999 

U.S. Department of Energy 

Assistant Secretary, 

Energy Efficiency & Renewable Energy 

Office of Codes and Standards 

Washington, DC 20585 

J :\marginal\FinalDraftReport7 -29-99 .wpd 
This document was prepared for the Department of Energy 

by staff members of the 

Lawrence Berkeley National Laboratory (LBNL) 

EE-RM-STD-98-440 COMMENT 241ZZ

This document was prepared for the Department of Energy 

by staff members of the 

Lawrence Berkeley National Laboratory (LBNL) 

J :\marginal\FinalDraftReport7 -29-99. wpd ii 
Executive Summary 

MARGINAL ENERGY PRICES 

Final Report 

Outline 

I. Background 

II. Methods 

IE Analysis and Results - Commercial 

W. Analysis and Results -Residential 

V. Residential Heating Oil and Propane 

VI. Taxes 

Appendices 

Appendix 1. Tariffs Used in the Commercial Analysis 

Appendix 2. Examples of Commercial Bill Calculations 27 

Appendix 3. Demand Decrement Due to Standards - The Role of Lighting Coincidence and Diversity 

Appendix 4. Weighting Method for Commercial Sample 

Appendix 5. Tax Tables 

Tables 

Table 1. Characterization and Summary of Commercial Electric Utility Sample 

Table 2. Distribution of r-squared Values for Electricity (Summer and Winter) and Gas Regressions 14 

Table 3. Number of Households in Each Appliance Data Set, and in Subsets with Marginal Prices 

Table 4. Heating Oil Prices 19 

Table 5. Propane Prices 

Table 6. State Sales Taxes 3 2 

Table 7. Gas and Electric Utility Regulatory Assessments by States 

Table 8. kwh Taxes on Electricity 

Figures 

Figure 1. Commercial Electricity Marginal Prices 

Figure 2. RECS 1993 Linear Regression on Electricity Bills, Household 41 64 

Figure 3. RECS 1993 Linear Regression on Gas Bills, Household 1001 

Figure 4. Residential Summer Electricity Marginal Prices 

Figure 5. Residential Winter Electricity Marginal Prices 

Figure 6. Residential Gas Marginal Prices 

Contributors 

Lawrence Berkeley National Laboratory (LBNL) Report Coordinator: Stuart Chaitkin 

LBNL Report Contributors: Peter Biennayer, Sarah Bretz, Steve Brown, Stuart Chaitkin, Sachu Constantine, 

Diane Fisher, Sajid Hakim, Lucy Liew, Jim Lutz, Chris Mamay, James E. McMahon, Mithra Moezzi, 

Julie Osborn, Esther Rawner, Judy Roberson, Greg Rosenquist, Nancy Ryan, Isaac Turiel, Stephen Wiel 

Acknowledgments 

EE-RM-STD-98-440 COMMENT 241ZZ

MARGINAL ENERGY PRICES 

Outline 

Executive Summary 

L 

IL 

ill. 

IV. 

v. 

VI. 

Background 

Methods 

Analysis and Results - Commercial 

Analysis and Results - Residential 

Residential Heating Oil and Propane 

Taxes 

Appendices 

Final Report 

4 

12 

1 

2 

6 

19 

22 

Appendix 1. Tariffs Used in the Commercial Analysis 26 

Appendix 2. Examples of Commercial Bill Calculations 27 

Appendix 3. Demand Decrement Due to Standards - The Role of Lighting Coincidence and Diversity 28 

Appendix 4. Weighting Method for Commercial Sample 29 

Appendix 5. Tax Tables 32 

Tables 

Table 1. Characterization and Summary of Commercial Electric Utility Sample 6 

Table 2. Distribution ofr-squared Values for Electricity (Summer and Winter) and Gas Regressions 14 

Table 3. Number of Households in Each Appliance Data Set, and in Subsets with Marginal Prices 15 

Table 4. Heating Oil Prices 19 

Table 5. Propane Prices 21 

Table 6. State Sales Taxes 32 

Table 7. Gas and Electric Utility Regulatory Assessments by States 38 

Table 8. kWh Taxes on Electricity 38 

Figures 

Figure 1. Commercial Electricity Marginal Prices 

Figure 2. RECS 1993 Linear Regression on Electricity Bills, Household 4 J 64 

Figure 3. RECS 1993 Linear Regression on Gas Bills, Household 1001 

Figure 4. Residential Summer Electricity Marginal Prices 

Figure 5. Residential Winter Electricity Marginal Prices 

Figure 6. Residential Gas Marginal Prices 

Contributors 

Lawrence Berkeley National Laboratory (LBNL) Report Coordinator: Stuart Chaitkin 

14 

LBNL Report Contributors: Peter Biennayer, Sarah Bretz, Steve Brown, Stuart Chaitkin, Sachu Constantine, 

Diane Fisher, Sajid Hakim, Lucy Liew, Jim Lutz, Chris Marnay, James E. McMahon, Mithra Moezzi, 

Julie Osborn, Esther Rawner, Judy Roberson, Greg Rosenquist, Nancy Ryan, Isaac Turiel, Stephen WieJ 

Acknowledgments 

J: \marginal\FinalDraftReport7 -29-99. wpd 

11 

13 

16 

17 

18 

111 
This work was prepared for the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Codes and 

Standards, of the U.S. Department of Energy, under Contract No. DE-AC03-76SF0098. Data and advice were 

graciously provided by Robert Latta and Steve Wade of the Energy Information Administration. 

EE-RM-STD-98-440 COMMENT 241ZZ

This work was prepared for the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Codes and 

Standards, of the U.S. Department of Energy, under Contract No. DE-AC03-76SF0098. Data and advice were 

graciously provided by Robert Latta and Steve Wade ofthe Energy Information Administration. 

J: \marginal\FinalDraftReport7 -29-99. wpd IV 
MARGINAL ENERGY PRICES - FINAL REPORT 

This report responds to a recommendation from the Department of Energy's (DOE) Advisory Committee on Appliance 

Energy Efficiency Standards. It presents the derivation of estimated consumer marginal energy prices for the 

commercial and residential sectors for use in the life-cycle cost (LCC) analyses for four of the high priority appliances' 

energy efficiency standards rulemakings - clothes washers, water heaters, fluorescent lamp ballasts, and central air 

conditionerslheat pumps. 

Marginal prices as discussed here are those prices consumers pay (or save) for their last units of energy used (or 

saved). Marginal prices reflect a change in a consumer's bill (that might be associated with new energy efficiency 

standards) divided by the corresponding change in the amount of energy the consumer used. 

Previous LCC analyses had either used Energy Information Administration's (EIA) Residential Energy Consumption 

Survey (RECS) household average prices for valuing energy savings from residential sector energy efficiency 

standards or a distribution of average prices derived from electric utility data for valuing energy savings from 

commercial sector energy efficiency standards. Average energy prices for a consumer are derived by dividing annual 

energy costs by annual energy consumption. At the utility level, average energy prices are derived by dividing 

annual revenues by annual energy sales. 

Section I below provides background on the work developed for marginal energy prices. Section I1 describes the 

methods used to derive marginal energy prices. Other credible methods to calculate or estimate consumers' marginal 

energy prices were not found. Because of the complexity inherent in utility tariffs, methods that simply subtract fixed 

costs from average costs do not constitute a credible method for calculating marginal prices. 

Section 111 shows the analysis for the commercial sector (for application to fluorescent lamp ballasts). The analysis 

centered on the calculation of electricity bills for a distribution of sample buildings in conjunction with a distribution 

of commercial tariffs. Jackson Associates' Market Analysis and Information System (MAISPI database provided the 

source of information for the building energy and demand levels among buildings of different types. Commercial 

tariffs from a sample of utilities were collected and modeled.. 

Section IV describes the analysis for the residential sector (for application to three products: clothes washers, water 

heaters, and central air conditionerslheat pumps). The analysis centered on use of consumer bills from the RECS 1993 

database. Seasonal marginal electricity prices and annual marginal natural gas prices were derived by calculating the 

slope of the regression lines associated with monthly bill and consumption data. 

Prices of residential heating oil and propane were also examined. Because bills paid by residential consumers for these 

fuels are determined almost wholly by the volume purchased, quoted prices are essentially marginal prices. Section V 

presents the results of our examination of residential heating oil and propane prices. 

Section VI discusses the effect of taxes on marginal prices. 

A summary of the results of the analyses to derive commercial and residential marginal prices follows: 

Commercial 

- Analysis based on the LBNL/MAISYa approach. 

- Used tariffs for 2 1% of national commercial electricity customers. 

- Electricity marginal prices for individual customers range from 8.5% below to 5 1 % above the average price 

for the same customer. At the consumption-weighted mean of the differences, electricity marginal prices 

are 5.2% lower than average prices. 

Residential 

- Analysis based on the RECS national sample. 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

MARGINAL ENERGY PRICES - FINAL REPORT 

EXECUTIVE SUMMARY 

This report responds to a recommendation from the Department of Energy's (DOE) Advisory Committee on Appliance 

Energy Efficiency Standards. It presents the derivation of estimated consumer marginal energy prices for the 

commercial and residential sectors for use in the life-cycle cost (LCC) analyses for four of the high priority appliances' 

energy efficiency standards rulemakings - clothes washers, water heaters, fluorescent lamp ballasts, and central air 

conditioners/heat pumps. 

Marginal prices as discussed here are those prices consumers pay (or save) for their last units of energy used (or 

saved). Marginal prices reflect a change in a consumer's bill (that might be associated with new energy efficiency 

standards) divided by the corresponding change in the amount of energy the consumer used. 

Previous LCC analyses had either used Energy Information Administration's (EIA) Residential Energy Consumption 

Survey (RECS) household average prices for valuing energy savings from residential sector energy efficiency 

standards or a distribution of average prices derived from electric utility data for valuing energy savings from 

commercial sector energy efficiency standards. Average energy prices for a consumer are derived by dividing annual 

energy costs by annual energy consumption. At the utility level, average energy prices are derived by dividing 

annual revenues by annual energy sales. 

Section I below provides background on the work developed for marginal energy prices. Section II describes the 

methods used to derive marginal energy prices. Other credible methods to calculate or estimate consumers' marginal 

energy prices were not found. Because ofthe complexity inherent in utility tariffs, methods that simply subtract fixed 

costs from average costs do not constitute a credible method for calculating marginal prices. 

Section III shows the analysis for the commercial sector (for application to fluorescent lamp ballasts). The analysis 

centered on the calculation of electricity bills for a distribution of sample buildings in conjunction with a distribution 

of commercial tariffs. Jackson Associates' Market Analysis and Information System (MAISY®) database provided the 

source of information for the building energy and demand levels among buildings of different types. Commercial 

tariffs from a sample of utilities were collected and modeled .. 

Section IV describes the analysis for the residential sector (for application to three products: clothes washers, water 

heaters, and central air conditioners/heat pumps). The analysis centered on use of consumer bills from the RECS 1993 

database. Seasonal marginal electricity prices and annual marginal natural gas prices were derived by calculating the 

slope of the regression lines associated with monthly bill and consumption data. 

Prices of residential heating oil and propane were also examined. Because bills paid by residential consumers for these 

fuels are determined almost wholly by the volume purchased, quoted prices are essentially marginal prices. Section V 

presents the results of our examination of residential heating oil and propane prices. 

Section VI discusses the effect of taxes on marginal prices. 

A summary of the results of the analyses to derive commercial and residential marginal prices follows: 

Commercial 

Analysis based on the LBNL!MAISY~ approach. 

U sed tariffs for 21 % of national commercial electricity customers. 

Electricity marginal prices for individual customers range from 85% below to 51 % above the average price 

for the same customer. At the consumption-weighted mean of the differences, electricity marginal prices 

are 5.2% lower than average prices. 

Residential 

Analysis based on the RECS national sample. 

J :\marginal\FinalDraftReport7 -29-99. wpd 
- Electricity marginal prices for individual customers in the summer (June-September) range from 98% 

below to 175% above the average price for the same customer. At the consumption-weighted mean of 

the differences, electricity marginal prices are 2.5% higher than average prices in the summer. 

- Electricity marginal prices for individual customers in the winter (the remaining eight months) range from 

100% below to 130% above the average price for the same customer. At the consumption-weighted 

mean of the differences, electricity marginal prices are 5.3% lower than average prices in the winter. 

- Gas marginal prices range for individual customers from 98% below to 248% above the average price for 

the same customer. At the consumption-weighted mean of the differences, gas marginal prices are 7.8% 

lower than average prices. 

L BACKGROUND 

The Issue 

In the past, DOE'S analyses of the life-cycle costs of and consumer bill savings possible from appliance energy 

efficiency standards were based on average energy prices. Using marginal energy prices in these analyses is more 

theoretically sound because consumers would save energy on the margin (that is, at the price they pay for their last 

unit of energy), not at the average price they pay for their energy. Unfortunately, neither published nor readily 

available data existed for consumer marginal energy prices. Indeed, a major research effort was required to derive 

consumer marginal energy prices. 

DOE Advisorv Committee 

In its April 2 1, 1998 letter to Secretary of Energy Pena, the Advisory Committee on Appliance Energy Efficiency 

Standards delivered recon~mendations to the Department of Energy regarding, anlong other things, the use of energy 

prices in future appliance standards rulemakings. For life-cycle cost analyses the Committee recommended that DOE 

should replace the use of national average energy prices with the full range of consumer marginal energy rates. 

Absent consumer marginal energy rate information, the Committee recommended DOE use a range of net energy rates, 

calculated by removing all fixed charges (such as monthly customer charges that consumers incur regardless of their 

monthly energy usage). In response to the Committee's recommendations, DOE' agreed that the use of marginal 

energy rates would improve the theoretical soundness of the analysis and decided to determine marginal rates using 

either RECS or comn~ercially available databases. While the Department believed at that time that it was unknown if 

removing fixed costs is more or less reflective of marginal rates, it did not intend to take that intermediate step without 

evidence that the result would improve the accuracy of the analysis. 

Fixed Charges 

In considering the recommendation to subtract fixed charges, the Department asked LBNL to examine the data 

provided by the Edison Electric Institute (EEI) concerning electric rates for 104 utilities, serving over 63 million 

customers. The data included average prices, tail block rates and fixed charges, but not marginal prices. EEI reported 

that fixed charges as a weighted percentage of average prices represent 7.5 percent. However, in examining the actual 

rate schedules of a few of these 104 utilities, it was found that some of the reported fixed charges were actually 

minimum charges, not customer charges. Thus the actual fixed charges, which are independent of kilowatthour (kwh) 

usage, would be somewhat lower than the reported 7.5 percent depending upon how often minimum charges were 

considered to be customer charges within the whole EEI sample of utilities. 

The Department agreed that for flat rate schedules, the removal of these adjusted fixed charges would yield the 

marginal rate. However, upon examining the EEI data, it was determined that most rates are not flat. Of the 104 utilities 

represented in the data, 100 contained average price, tail block rate and fixed charges. For utilities with a single flat 

rate schedule, the average price absent the fixed charges should equal the tail block rate. Of the 100 utilities, only 16 

met this test within an assumed one percent reporting and rounding error. Thus, 84 percent of the utilities had non-flat 

rates or a mix of rate schedules which greatly complicates the determination of the marginal price. Of these 84 utilities, 

' Letter from Dan Reicher, DOE Assistant Secretary, Energy Efficiency and Renewable Energy, to the Advisory 

Committee members, July 28, 1998. 

J :\marginal\FinalDraftReport7-29-99. wpd 2 

EE-RM-STD-98-440 COMMENT 241ZZ

Electricity marginal prices for individual customers in the summer (June-September) range from 98% 

below to 175% above the average price for the same customer. At the consumption-weighted mean of 

the differences, electricity marginal prices are 2.5% higher than average prices in the summer. 

Electricity marginal prices for individual customers in the winter (the remaining eight months) range from 

100% below to 130% above the average price for the same customer. At the consumption-weighted 

mean of the differences, electricity marginal prices are 5.3% lower than average prices in the winter. 

Gas marginal prices range for individual customers from 98% below to 248% above the average price for 

the same customer. At the consumption-weighted mean ofthe differences, gas marginal prices are 7.8% 

lower than average prices. 

L BACKGROUND 

The Issue 

In the past, DOE's analyses of the life-cycle costs of and consumer bill savings possible from appliance energy 

efficiency standards were based on average energy prices. Using marginal energy prices in these analyses is more 

theoretically sound because consumers would save energy on the margin (that is, at the price they pay for their last 

unit of energy), not at the average price they pay for their energy. Unfortunately, neither published nor readily 

available data existed for consumer marginal energy prices. Indeed, a major research effort was required to derive 

consumer marginal energy prices. 

DOE Advisory Committee 

In its April 21 , 1998 letter to Secretary of Energy Pena, the Advisory Committee on Appliance Energy Efficiency 

Standards delivered recommendations to the Department of Energy regarding, among other things, the use of energy 

prices in future appliance standards rulemakings. For life-cycle cost analyses the Committee recommended that DOE 

should replace the use of national average energy prices with the full range of consumer marginal energy rates. 

Absent consumer marginal energy rate information, the Committee recommended DOE use a range of net energy rates, 

calculated by removing all fixed charges (such as monthly customer charges that consumers incur regardless of their 

monthly energy usage). In response to the Committee's recommendations, DOEi agreed that the use of marginal 

energy rates would improve the theoretical soundness ofthe analysis and decided to determine marginal rates using 

either RECS or commercially available databases. While the Department believed at that time that it was unknown if 

removing fixed costs is more or less reflective of marginal rates, it did not intend to take that intermediate step without 

evidence that the result would improve the accuracy of the analysis. 

Fixed Charges 

In considering the recommendation to subtract fixed charges, the Department asked LBNL to examine the data 

provided by the Edison Electric Institute (EEl) concerning electric rates for 104 utilities, serving over 63 million 

customers. The data included average prices, tail block rates and fixed charges, but not marginal prices. EEl reported 

that fixed charges as a weighted percentage of average prices represent 7.5 percent. However, in examining the actual 

rate schedules of a few of these 104 utilities, it was found that some of the reported fixed charges were actually 

minimum charges, not customer charges. Thus the actual fixed charges, which are independent of kilowatt hour (kWh) 

usage, would be somewhat lower than the reported 7.5 percent depending upon how often minimum charges were 

considered to be customer charges within the whole EEl sample of utilities. 

The Department agreed that for flat rate schedules, the removal of these adjusted fixed charges would yield the 

marginal rate. However, upon examining the EEl data, it was determined that most rates are not flat. Of the 104 utilities 

represented in the data, 100 contained average price, tail block rate and fixed charges. For utilities with a single flat 

rate schedule, the average price absent the fixed charges should equal the tail block rate. Of the 100 utilities, only 16 

met this test within an assumed one percent reporting and rounding error. Thus, 84 percent ofthe utilities had non-flat 

rates or a mix of rate schedules which greatly complicates the determination of the marginal price. Ofthese 84 utilities, 

i Letter from Dan Reicher, DOE Assistant Secretary, Energy Efficiency and Renewable Energy, to the Advisory 

Committee members, July 28,1998. 

J :\marginal\FinaIDraftReport7 -29-99. wpd 2 
roughly half had tail blocks higher than the average price, indicating inclining block rates, and half had lower tail 

blocks indicating declining block rates. Seasonal rates further complicate this picture. 

The American Gas Association (AGA) had conducted a similar analysis using 1996 data for 264 gas utilities serving 46 

million residential customers. AGA found that fixed customer charges constituted approximately 13.5% of those 

utilities' total gas revenues from their residential consumers. Beyond noticing that the AGA analysis included only 

two of California's gas utilities fe.g., it did not include data for either Pacific Gas & Electric or San Diego Gas & 

Electric), AGA's analysis was not closely evaluated. 

Marginal Prices 

With inclining or declining block rates, the actual value of consumer energy savings is the product of the marginal 

price, plus any applicable taxes or fees, times the energy units saved. While simple in theory, the accurate derivation 

of this saving is very difficult. Consumers are typically billed monthly for their energy usage and will face different 

marginal rates during the year depending on where their energy consumption places them in the rate schedule in any 

given month and how that rate may change with the time of year. It should be noted that just examining the last or tail 

block of a rate schedule is not sufficient since many, if not most, consumers do not progress to the last block of the 

rate schedule each and every month of the year. The seasonal variability of appliance usage is most obvious for 

space conditioning products such as air conditioners, but, in reality, almost all appliances undergo some seasonal 

variation in their usage. Given the possible wide range of marginal prices between winter and summer months, any 

significant seasonal variation in usage could have a significant effect on the marginally calculated savings. 

Using actual marginal prices may yield consumer energy savings higher or lower than the past method of using 

average costs based on total revenue. Given the prevalence of summer peaking utilities and seasonal rates, it seems 

likely that appliances with heavier summer usage, particularly central air conditioners, would most probably produce a 

greater consumer value of savings using marginal prices. Therefore, removing fixed costs, which only guarantees 

lower estimates of energy prices, would then be a correction in the wrong direction. 

Restructurinp, 

Given restructuring of parts of the energy supply sector, customers may soon have more than one bill (e.g., one from 

the distribution company, and one or more from generators or suppliers). To capture complete information, future 

surveys would best gather energy pricing information directly from the customers, rather than from utilities or local 

distribution companies. The most efficient means to collect energy pricing information in the future involves including 

consumption by month and pricing information in future bills. The pricing information would include for each 

customer, the rate schedule: marginal rates, fixed charges, demand charges for commercial and industrial customers, or 

time-of-use rates where applicable. 

Data Sources 

In the near term (before new surveys can be crafted and implemented), several sources of information were available 

for this current research and analysis on marginal prices. Residential consumers' utility bills were available from the 

RECS 1993 survey. Commercial customers' utility bills could be approximated from monthly usage and likely utility 

tariff. Monthly usage for a large sample of commercial buildings was available from a commercial product, the 

MAISP commercial databa~e.~ Utility tariff sheets were available from utility web sites, commercial services, or the 

utilities themselves. Data on utility sales and number of customers on various tariffs were available for investorowned utilities from the Federal Energy Regulatory Commission (FERC) Form 1 filings made by those utilities. For 

municipal utilities and co-ops, equivalent tariff-level data were obtained from either the utility itself or were available 

from the Rural Utility Service. 

MATSF is a proprietary product that was acquired by LBNL from Jackson Associates, Suite 200,4825 Creekstone 

Drive, Durham, NC 27703; 91 9-967-90001919-967-8040 (fax);e-maiI:jjackson@ntrnet.net 

EE-RM-STD-98-440 COMMENT 241ZZ

I 

roughly half had tail blocks higher than the average price, indicating inclining block rates, and half had lower tail 

blocks indicating declining block rates. Seasonal rates further complicate this picture. 

The American Gas Association (AGA) had conducted a similar analysis using 1996 data for 264 gas utilities serving 46 

million residential customers. AGA found that fixed customer charges constituted approximately 13.5% of those 

utilities' total gas revenues from their residential consumers. Beyond noticing that the AGA analysis included only 

two of California's gas utilities (e.g., it did not include data for either Pacific Gas & Electric or San Diego Gas & 

Electric), AGA's analysis was not closely evaluated. 

Marginal Prices 

With inclining or declining block rates, the actual value of consumer energy savings is the product ofthe marginal 

price, plus any applicable taxes or fees, times the energy units saved. While simple in theory, the accurate derivation 

ofthis saving is very difficult. Consumers are typically billed monthly for their energy usage and will face different 

marginal rates during the year depending on where their energy consumption places them in the rate schedule in any 

given month and how that rate may change with the time of year. It should be noted that just examining the last or tail 

block of a rate schedule is not sufficient since many, ifnot most, consumers do not progress to the last block of the 

rate schedule each and every month of the year. The seasonal variability of appliance usage is most obvious for 

space conditioning products such as air conditioners, but, in reality, almost all appliances undergo some seasonal 

variation in their usage. Given the possible wide range of marginal prices between winter and summer months, any 

significant seasonal variation in usage could have a significant effect on the marginally calculated savings. 

Using actual marginal prices may yield consumer energy savings higher or lower than the past method of using 

average costs based on total revenue. Given the prevalence of summer peaking utilities and seasonal rates, it seems 

likely that appliances with heavier summer usage, particularly central air conditioners, would most probably produce a 

greater consumer value of savings using marginal prices. Therefore, removing fixed costs, which only guarantees 

lower estimates of energy prices, would then be a correction in the wrong direction. 

Restructuring 

Given restructuring of parts of the energy supply sector, customers may soon have more than one bill (e.g., one from 

the distribution company, and one or more from generators or suppliers). To capture complete information, future 

surveys would best gather energy pricing infonnation directly from the customers, rather than from utilities or local 

distribution companies. The most efficient means to collect energy pricing information in the future involves including 

consumption by month and pricing information in future bills. The pricing information would include for each 

customer, the rate schedule: marginal rates, fixed charges, demand charges for commercial and industrial customers, or 

time-of-use rates where applicable. 

Data Sources 

In the near term (before new surveys can be crafted and implemented), several sources of information were available 

for this current research and analysis on marginal prices. Residential consumers' utility bills were available from the 

RECS 1993 survey. Commercial customers' utility bills could be approximated from monthly usage and likely utility 

tariff. Monthly usage for a large sample of commercial buildings was available from a commercial product, the 

MAISY® commercial database. 2 Utility tariff sheets were available from utility web sites, commercial services, or the 

utilities themselves. Data on utility sales and number of customers on various tariffs were available for investorowned utilities from the Federal Energy Regulatory Commission (FERC) Form 1 filings made by those utilities. For 

municipal utilities and co-ops, equivalent tariff-level data were obtained from either the utility itself or were available 

from the Rural Utility Service. 

2 MAISY' is a proprietary product that was acquired by LBNL from Jackson Associates, Suite 200, 4825 Creekstone 

Drive, Durham, NC 27703; 919-967-9000/919-967-8040 (fax);e-mail:jjackson@ntrnet.net 

J: \marginal\FinalDraftReport7 -29-99. wpd 3 
n METHODS 

Calculating the true bill effects of proposed appliance energy efficiency standards on diverse U.S. consumers is 

difficult because of the many electricity and natural gas tariffs in place, and the complexity of these tariffs. Since the 

set of end-uses currently under consideration for new standards includes fluorescent lamp ballasts, calculations for 

commcrcia! as well as residential electricity customers must be included, which complicates the task considerably 

because commercial tariffs are generally more complex. 

To address this difficult research question, several complementary approaches were developed. Those approaches 

are briefly outlined below. Each of these approaches was a step in the overall sequence of setting up the modeling 

capability to allow calculation of samples of customer bills. 

"Analvtic" Approach 

This approach was the first-cut analysis in which residential electricity and gas tariffs and commercial electricity tariffs 

were examined as they were collected. Comparing each particular tariff's rate blocks to average prices yielded the 

maximum and nlinirnum differences between marginal and average price possible for that particular tariff. Because 

consumption is unknown, actual customer marginal prices cannot be obtained by this method; only the outer bounds 

to those prices can be obtained. The findings from this approach were: 

a) Care must be taken to find and account for all charges, since the tariffs are often complex, with a number 

of charges based on demand levels, voltage levels, seasons, etc. Residential tariffs tend, though, to be 

less complex than commercial tariffs. 

b) For residential gas, individual customer marginal prices are most likely to be below average prices, but 

with a large range in results. 

c) For residential electricity, individual customer marginal prices can show a very large range, with 

differences ranging from -79% to +344% compared to the average price for customers having a particular 

tariff, although many of the extreme results appear on tariffs that cover only a few customers and thus 

would be seldom experienced. 

d) For commercial electricity, individual customer marginal prices also show a large range and no discernable 

pattern emerged from the analysis. 

Because the range of differences between marginal and average prices from this analysis was large, it was concluded 

that simple patterns in the relationship between average and marginal prices did not seem to exist. Therefore, the 

focus was on the methods that permit estimation of individual customer bills: the experimental, empirical, and RECS 

analyses. 

"Emvirical" Avvroach 

LBNL contracted with Jackson Associates for use of and enhancement to its proprietary MAISP database of U.S. 

residential and commercial building energy characteristics. This approach involves allocating these residences and 

commercial buildings, currently only identified by state, to electric utility service territories and then to particular utility 

tariffs. Monthly customer bills can then be calculated with and without energy and demand savings, yielding marginal 

prices for each particular consumer. The method and code for conducting the allocation of customers and estimation 

of bills was developed using two utilities in a southern California test area, and a bill calculator function using the 

actual tariffs of electric and gas utilities was developed. For commercial buildings, this approach evolved into the one 

explained in Section 111. Since an estimate of the effect on the house's natural gas bill was also required for analysis of 

the three residential products (clothes washers, water heaters, and central air conditionerslheat pumps), the 

households were further assigned to a natural gas local distribution company (LDC) that serves a part of the territory 

of that residence's electric utility. While for residential buildings, this "empirical" approach was put aside in favor of 

the RECS approach described below, it was used as the foundation for the "experimental" approach in the analysis of 

commercial sector fluorescent lamp ballasts. 

"Exverimental" Approach 

During the collection of tariffs and the designing of the computer code for the empirical approach, a spreadsheet bill 

calculator was developed. Exploratory experimental tariffs could be modeled in this spreadsheet, and, together with 

EE-RM-STD-98-440 COMMENT 241ZZ

11 METIIODS 

Calculating the true bilI effects of proposed appliance energy efficiency standards on diverse U.S. consumers is 

difficult because of the many electricity and natural gas tariffs in place, and the complexity of these tariffs. Since the 

set of end-uses currently under consideration for new standards includes fluorescent lamp balIasts, calculations for 

commercia! as welI as residential electricity customers must be included, which complicates the task considerably 

because commercial tariffs are generalIy more complex. 

To address this difficult research question, several complementary approaches were developed. Those approaches 

are briefly outlined below. Each of these approaches was a step in the overall sequence of setting up the modeling 

capability to allow calculation of samples of customer bilIs. 

"Analytic" Approach 

This approach was the first-cut analysis in which residential electricity and gas tariffs and commercial electricity tariffs 

were examined as they were collected. Comparing each particular tariff's rate blocks to average prices yielded the 

maximum and minimum differences between marginal and average price possible for that particular tariff. Because 

consumption is unknown, actual customer marginal prices cannot be obtained by this method; only the outer bounds 

to those prices can be obtained. The findings from this approach were: 

a) Care must be taken to find and account for alI charges, since the tariffs are often complex, with a number 

of charges based on demand levels, voltage levels, seasons, etc. Residential tariffs tend, though, to be 

less complex than commercial tariffs. 

b) F or residential gas, individual customer marginal prices are most likely to be below average prices, but 

with a large range in results. 

c) For residential electricity, individual customer marginal prices can show a very large range, with 

differences ranging from -79% to +344% compared to the average price for customers having a particular 

tariff, although many of the extreme results appear on tariffs that cover only a few customers and thus 

would be seldom experienced. 

d) For commercial electricity, individual customer marginal prices also show a large range and no discernable 

pattern emerged from the analysis. 

Because the range of differences between marginal and average prices from this analysis was large, it was concluded 

that simple patterns in the relationship between average and marginal prices did not seem to exist. Therefore, the 

focus was on the methods that permit estimation of individual customer bills: the experimental, empirical, and RECS 

analyses. 

"Empirical" Approach 

LBNL contracted with Jackson Associates for use of and enhancement to its proprietary MAISY® database of U.S. 

residential and commercial building energy characteristics. This approach involves allocating these residences and 

commercial buildings, currently only identified by state, to electric utility service territories and then to particular utility 

tariffs. Monthly customer bills can then be calculated with and without energy and demand savings, yielding marginal 

prices for each particular consumer. The method and code for conducting the allocation of customers and estimation 

of bills was developed using two utilities in a southern California test area, and a bill calculator function using the 

actual tariffs of electric and gas utilities was developed. For commercial buildings, this approach evolved into the one 

explained in Section III. Since an estimate of the effect on the house's natural gas bill was also required for analysis of 

the three residential products (clothes washers, water heaters, and central air conditioners/heat pumps), the 

households were further assigned to a natural gas local distribution company (LDC) that serves a part of the territory 

of that residence's electric utility. While for residential buildings, this "empirical" approach was put aside in favor of 

the RECS approach described below, it was used as the foundation for the "experimental" approach in the analysis of 

commercial sector fluorescent lamp ballasts. 

"Experimental" Approach 

During the collection of tariffs and the designing of the computer code for the empirical approach, a spreadsheet bill 

calculator was developed. Exploratory experimental tariffs could be modeled in this spreadsheet, and, together with 

]: \marginal\FinalDraftReport7 -29-99. wpd 4 
load shapes from the general literature and estimates of nionthly customer energy use effects, bills can be estimated. 

A variation of this method that used actual commercial tariffs and load shapes from MAISP commercial buildings 

provided the technical approach used for the commercial sector analysis. See Section I11 for the details and results of 

this method as applied in final form to the analysis of the commercial sector. 

"Big ban^" Ao~roach 

The initial sample of electric utility tariffs that was analyzed had been chosen with consideration of diversity in terms 

of utility ownership, geography, and utility size in mind. That sample was extended, as time permitted, but the utilityby-utility detailed approach necessarily covered only a limited fraction of the total customer population. On the other 

hand, a large fraction of customers in the country are actually served on a rather small number of tariffs from a limited 

number of large utilities. This fact suggested an alternative "big bang" approach. In this approach, the tariffs with the 

greatest number of customers would be examined. This approach was drawn upon to a limited degree in the expansion 

of the set of commercial tariffs used in the final commercial sector analysis. 

RECS Amroach 

Consumer marginal energy prices were estimated directly from household RECS data by calculating the slopes of the 

regression lines that relate customer bills and customer usage. For electricity, the slopes of the regressions for four 

summer months (June-September) and, separately, for the remaining ("winter") months were calculated. For natural 

gas, the data did not support calculations beyond the annual level. After exploring the empirical method outlined 

above for use in the analysis for the residential sector, the RECS method (because of its advantages which are 

explained further in Section IV) was adopted for the analysis of residential marginal prices in this project. See Section 

IV for the details and the results of this RECS method. 

Linkage to the Product LCC Analvses 

To anticipate the impact of using marginal prices in place of average prices, a simple sensitivity analysis on the LCC 

for each product was conducted, using a range of possible energy price variations. The results were: 

a) For central air conditioners, the efficiency level having the minimum LCC was not affected over the range 

-25 to +25% from average energy prices. 

b) For water heaters, the design option for electric water heaters having the minimum life cycle cost is 

sensitive to the value of marginal energy prices, but less so for natural gas water heaters. 

c) For clothes washers, the efficiency level having the minimum LCC was not affected over the range -25% 

to +25% from average energy prices. In addition, payback period was shown to be affected by a lower 

percentage than the change in energy prices, because the operating savings due to water is unaffected by 

a change in energy price. 

d) For ballasts, electronic ballasts were shown to reduce LCC at energy prices down to 45% below average, 

compared to energy efficient magnetic (EEM). For cathode cutouts, energy prices down to 10% below 

average still yielded a reduced LCC compared to EEM. 

See the relevant Technical Support Documents (TSDs), Notice of Proposed Rulemakings (NOPRs), and the Advanced 

Notice of Proposed Rulemaking (ANOPR) - all in progress -- for information on the application of the results of the 

marginal energy price study to specific products. 

EE-RM-STD-98-440 COMMENT 241ZZ

load shapes from the general literature and estimates of monthly customer energy use effects, bills can be estimated. 

A variation of this method that used actual commercial tariffs and load shapes from MAISY® commercial buildings 

provided the technical approach used for the commercial sector analysis. See Section III for the details and results of 

this method as applied in final form to the analysis ofthe commercial sector. 

"Big Bang" Approach 

The initial sample of electric utility tariffs that was analyzed had been chosen with consideration of diversity in terms 

of utility ownership, geography, and utility size in mind. That sample was extended, as time permitted, but the utilityby-utility detailed approach necessarily covered only a limited fraction of the total customer population. On the other 

hand, a large fraction of customers in the country are actually served on a rather small number of tariffs from a limited 

number oflarge utilities. This fact suggested an alternative "big bang" approach. In this approach, the tariffs with the 

greatest number of customers would be examined. This approach was drawn upon to a limited degree in the expansion 

of the set of commercial tariffs used in the final commercial sector analysis. 

RECS Approach 

Consumer marginal energy prices were estimated directly from household RECS data by calculating the slopes of the 

regression lines that relate customer bills and customer usage. For electricity, the slopes of the regressions for four 

summer months (June-September) and, separately, for the remaining ("winter") months were calculated. For natural 

gas, the data did not support calculations beyond the annual level. After exploring the empirical method outlined 

above for use in the analysis for the residential sector, the RECS method (because of its advantages which are 

explained further in Section IV) was adopted for the analysis of residential marginal prices in this project. See Section 

IV for the details and the results of this RECS method. 

Linkage to the Product LCC Analyses 

To anticipate the impact of using marginal prices in place of average prices, a simple sensitivity analysis on the LCC 

for each product was conducted, using a range of possible energy price variations. The results were: 

a) For central air conditioners, the efficiency level having the minimum LCC was not affected over the range 

-25 to +25% from average energy prices. 

b) For water heaters, the design option for electric water heaters having the minimum life cycle cost is 

sensitive to the value of marginal energy prices, but less so for natural gas water heaters. 

c) For clothes washers, the efficiency level having the minimum LCC was not affected over the range -25% 

to +25% from average energy prices. In addition, payback period was shown to be affected by a lower 

percentage than the change in energy prices, because the operating savings due to water is unaffected by 

a change in energy price. 

d) For ballasts, electronic ballasts were shown to reduce LCC at energy prices down to 45% below average, 

compared to energy efficient magnetic (EEM). For cathode cutouts, energy prices down to 10% below 

average still yielded a reduced LCC compared to EEM. 

See the relevant Technical Support Documents (TSDs), Notice of Proposed Rulemakings (NOPRs), and the Advanced 

Notice of Proposed Rulemaking (ANOPR) all in progress -- for information on the application of the results of the 

marginal energy price study to specific products. 

J : \marginal\F inalDraftReport 7 -29-99. wpd 5 
III. AYALYSIS AND RESULTS - COMMERCIAL 

Commercial marginal electricity prices were derived by calculating monthly bills for a distribution of buildings (with 

energy usage information) using a set of modeled commercial tariffs. This section of the Marginal Energy Prices report 

first describes the utility tariffs that were chosen and modeled for this analysis. Then the process for calculating the 

electricity bills and the marginal electricity prices for the sampled commercial buildings is described. Because 

commercial tariffs tend to have dernand charges, the adjustment applicd to represent the reduction in demand 

associated with new energy efficiency standards is described next. The method by which the sampled buildings are 

weighted in the analysis is also described. Lastly, the results of the commercial analysis are presented. 

A. Tariff Collection and Modeling 

Tariff Collection 

For a variety of utilities around the country, commercial tariffs were collected and then modeled for use in the bill 

calculation process. Utilities were chosen in an attempt to obtain a sample that is generally representative of 

commercial customers throughout the nation. In choosing utilities, the key factors considered were geographic 

location, type of ownership (e.g. investor owned, publicly owned, or cooperatively owned), and size. Those factors 

were chosen to capture regional differences in such issues as climate, the availability of inexpensive sources of 

energy, the structure of regulatory oversight, and the scale of operation. Table 1 below is a list of the commercial 

utility sample used in this analysis. Utility tariffs were obtained from the following sources: utility web sites, a 

consulting company, and utilities themselves. In order to properly weight results, FERC Form 1 (or equivalent) data 

for the chosen utilities showing the number of customers and the sales for each of a utility's tariffs were obtained. 

Table 1. Characterization and Summary of Commercial Electric Utility Sample 

By Utility 

US TOTAL 13,540,374 928,440,265 

By Modeled Tan'm Percent of US in 

Modeled Tariffs 

Cust. 

3.0% 

3.1% 

2.2% 

1.0% 

1.2% 

1.1% 

1.1% 

1.1% 

0.8% 

0.9% 

0.6% 

0.8% 

0.6% 

0.6% 

0.0% 

0.5% 

0.6% 

0.4% 

0.5% 

0.3% 

0.2% 

0.1% 

0.0% 

0.0% 

20.7% 

Our Sample as 

Percent of Utility 

MWh 

Sales 

1.7% 

2.5% 

2.8% 

0.3% 

0.8% 

0.9% 

0.9% 

0.5% 

0.5% 

0.9% 

0.1% 

0.6% 

0.3% 

0.3% 

0.7% 

0.2% 

0.8% 

0.1% 

0.3% 

0.3% 

0.1% 

0.1% 

0.1% 

0.0% 

15.7% 

- 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 lllum 

Seattle City Light 

NSP (WI) 

Madison Gas & Electric 

Savannah Elec & Power 

Poudre Vallev REA 

Sample Total 

cust. 

73% 

8Ph 

Wh 

73% 

94% 

83% 

Wh 

QP? 

820h 

Wh 

71% 

Wh 

91% 

Ph 

7% 

87% 

98% 

76% 

97% 

100% 

88% 

91% 

5% 

76% 

84% 

a, 

- 

CA 

CA 

IL 

VA 

MI 

AL 

PA 

NY 

MN 

MO 

VA 

NJ 

WI 

A 

OH 

TX 

AZ 

MD 

OH 

WA 

WI 

WI 

GA 

CO 

MWh 

Sales 

32% 

45% 

!Ph 

13% 

42?h 

7G% 

43% 

200h 

Wh 

720h 

4% 

51% 

15% 

35% 

25% 

16% 

82% 

9% 

4Ph 

100% 

Wh 

Wh 

45% 

2% 

38% 

Megawatt-hour 

Sales 

16,025,437 

23,057,727 

25,859,649 

2,696,019 

7,613,394 

7,917,907 

8,781,171 

4,550,295 

4,461,852 

8,735,132 

674,926 

5,358,036 

2,705,293 

2,820,415 

6,348,160 

2,099,439 

6,989,968 

1,317,800 

2,765,339 

3,081,941 

831,739 

685,233 

517,052 

96.424 

145,990,348 

a, 

P Total Number Total Megawatt- % 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

IOU 

Muni 

IOU 

IOU 

IOU 

Cwo 

552,901 

481,417 

292,709 

189,301 

177,088 

174.602 

149,221 

148,124 

135,183 

134,699 

110,740 

106,157 

93,973 

87,644 

87,314 

85,311 

79,755 

70,909 

64,907 

43,497 

31,236 

16,220 

14,100 

2.774 

3,329,782 

49,530,911 

51,410,408 

49,933,859 

20,061,476 

17,997,214 

11,330,312 

20,475,206 

23,279,622 

5,009,755 

12,189,235 

16,390,313 

10,510,309 

18,454,563 

7,991,349 

24,917,126 

12,844,712 

8,524,882 

15,307,001 

5,883,328 

3,081,941 

866,425 

1,727,832 

1,156,078 

377.974 

389,251,831 

Cust. 

401.747 

419,163 

291,143 

137,813 

166,003 

154,077 

143,849 

143,590 

110,807 

120,596 

78.554 

104,922 

85,735 

80,255 

6,442 

74,107 

78,530 

53,620 

63.161 

43,497 

27,449 

14,815 

708 

2.101 

2,802,684 

EE-RM-STD-98-440 COMMENT 241ZZ

m. Al'lALYSIS Al'W RESULTS - COMMERCIAL 

Commercial marginal electricity prices were derived by calculating monthly bills for a distribution of buildings (with 

energy usage information) using a set of modeled commercial tariffs. This section ofthe Marginal Energy Prices report 

first describes the utility tariffs that were chosen and modeled for this analysis. Then the process for calculating the 

electricity bills and the marginal electricity prices for the sampled commercial buildings is described. Because 

commercial tariffs tend to have demand charges, the adjustment applied to represent the reduction in demand 

associated with new energy efficiency standards is described next. The method by which the sampled buildings are 

weighted in the analysis is also described. Lastly, the results of the commercial analysis are presented. 

A. Tariff Collection and Modeling 

Tariff Collection 

For a variety of utilities around the country, commercial tariffs were collected and then modeled for use in the bill 

calculation process. Utilities were chosen in an attempt to obtain a sample that is generally representative of 

commercial customers throughout the nation. In choosing utilities, the key factors considered were geographic 

location, type of ownership (e.g. investor owned, publicly owned, or cooperatively owned), and size. Those factors 

were chosen to capture regional differences in such issues as climate, the availability of inexpensive sources of 

energy, the structure of regulatory oversight, and the scale of operation. Table I below is a list of the commercial 

utility sample used in this analysis. Utility tariffs were obtained from the following sources: utility web sites, a 

consulting company, and utilities themselves. In order to properly weight results, FERC Form 1 (or equivalent) data 

for the chosen utilities showing the number of customers and the sales for each of a utility's tariffs were obtained. 

Table 1. 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 Pu blic Service 

PEPCO 

Cleveland Electric ilium 

Seattle City Light 

NSP(WI) 

Madison Gas & Electric 

Savannah Elec & Power 

Poudre Vallev REA 

Sample Total 

US TOTAL 

~ '" c. 

m :>. 

CA I IOU I CA IOU 

IL IOU 

VA IOU 

MI IOU 

AL IOU 

PA IOU 

NY IOU 

MN IOU 

MO IOU 

VA IOU 

NJ IOU 

WI IOU 

MA IOU 

OH IOU 

TX IOU 

AZ IOU 

MD IOU 

OH IOU 

WA Muni 

WI IOU 

WI IOU 

GA IOU 

CO (;o.{)p 

By Utility 

Total Number 

552.901 

481,417 

292,709 

189,301 

177,088 

174,602 

149,221 

148,124 

135,183 

134,699 

110,740 

106,157 

93,973 

87,644 

87,314 

85,311 

79,755 

70,909 

64,907 

43,497 

31,236 

16,220 

14,100 

2774 

3,329,782 

13,540,374 

Total Megawatt· 

49,530,911 

51,410,408 

49,933,859 

20,061,476 

17,997,214 

11,330,312 

20,475,206 

23,279,622 

5,009,755 

12,189,235 

16,390,313 

10,510,309 

18,454,563 

7,991,349 

24,917,126 

12,844,712 

8,524,882 

15,307,001 

5,883,328 

3,081,941 

866,425 

1,727,832 

1,156,078 

377974 

389,251,831 

928,440,265 

J :\marginal\FinalDraftReport7 -29-99. wpd 

By Modeled Tariffs 

Megawatt·hour 

Cll~t Sale~ 

401.747 16,025,437 

419,163 23,057.727 

291.143 25,859.649 

137,813 2,696,019 

166,003 7,613,394 

154,077 7,917,907 

143,849 8,781,171 

143,590 4,550,295 

110,807 4,461,852 

120,596 8,735,132 

78,554 674,926 

104,922 5,358,036 

85,735 2,705,293 

80,255 2,820,415 

6,442 6,348,160 

74,107 2,099,439 

78,530 6,989,968 

53,620 1,317,800 

63,161 2,765,339 

43,497 3,081,941 

27,449 831,739 

14,815 685,233 

708 517,052 

2101 96424 

2,802,664 145,990,348 

Percent of US in Our Sample as 

Modeled Tariffs Percent of Utility 

MWh MWh 

Cust. Sales Cust. Sales 

3.0% 1.7% 73% 32"10 

3.1% 2.5% 87% 45% 

2.2% 2.8% 99% 52% 

1.0% 0.3% 73% 13% 

1.2% 0.8% 94% 42% 

1.1% 0.9% 88% 70% 

1.1% 0.9% 96% 43% 

1.1% 0.5% 97% 20% 

0.8% 0.5% 82% 89% 

0.9% 0.9% 90% 72:'1. 

0.6% 0.1% 71% 4% 

0.8% 0.6% 99% 51% 

0.6% 0.3% 91% 15% 

0.6% 0.3% 92% 35% 

0.0% 0.7% 70;. 25% 

0.5% 0.2% 87% 16% 

0.6% 0.8% 98% 82% 

0.4% 0.1% 76% 9% 

0.5% 0.3% 97% 47% 

0.3% 0.3% 100% 100% 

0.2% 0.1% 88% 96% 

0.1% 0.1% 91% 40% 

0.0% 0.1% 50;. 45% 

0.0% 0.0% 76% 26% 

20.7% 15.7% 84% 38% 

6 
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, the sample contained only one 

municipal utility and one co-op. While the co-op incl~tded in the 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, several more municipal utilities were later added, which brought the weighted 

average electricity price of the municipal utilities in the sample closer to the average commercial price of all of the 

municipal utilities in the country. The bill calculator (which is described below) was then re-run using the expanded 

set of tariffs. See Section D below for the results of this sensitivity analysis. 

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 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. 

Minimum monthly charge. 

Power factor adjustments and reactive power demand charges. 

Commercial Soreadsheet Modeling 

Generally, the commercial tariff modeling effort focused on two common categories of commercial tariffs: small general 

service and general service. The collected commercial tariffs were modeled in spreadsheet form, excluding some tariffs 

and/or components that could not be applied to buildings in MAISY'. For instance, some tariffs provide charges for 

both single and three-phase service. Because MAISV does not distinguish between these two types of service, only 

the charges for single phase were modeled. Also, some tariffs charge different prices for primary, secondary, and 

transmission voltage levels. Because of the types of buildings used in MAISYX, only the secondary voltage levels 

were considered. Power factor and reactive demand charges, which are adjustments based on voltage and demand 

usage, were ignored. Other factors were ignored if the information could not be used in MAISP or are rarely 

applicable. The spreadsheet also contains 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. 

B, Calculation of Commercial Bills and Marginal Prices 

Overview 

The Commercial Tariffs Spreadsheet, or CTAS, contains a collection of modeled commercial electric utility tariffs, a 

collection of modeled commercial buildings, and Visual Basic program modules. Each building is assigned to a group 

of applicable tariffs, based on building demand, and monthly bills are calculated for each tariff for a calendar year. 

Then, building energy usage and demands are decremented to account for savings expected from a new energy 

efficiency standard, monthly bills are recalculated, and the differences in bills and energy use are used to calculate 

marginal prices. 

Commercial Tariffs 

Electric utilities calculate customer bills from 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. These tariffs were collected for a number of US electric utilities and modeled for inclusion in CTAS 

calculations. CTAS contains the specific commercial tariffs for the utilities shown in Appendix 1. 

In addition to the parameters necessary to calculate customer bills, information was collected for each tariff regarding 

the number of customers, revenues, and delivered energy. This tariff-level data on customers and delivered energy 

were used to weight the resulting bill and marginal price calculations to represent a national distribution of marginal 

prices. The 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 

EE-RM-STD-98-440 COMMENT 241ZZ

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, the sample contained only one 

municipal utility and one co-op. While the co-op included in the 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, several more municipal utilities were later added, which brought the weighted 

average electricity price of the municipal utilities in the sample closer to the average commercial price of all of the 

municipal utilities in the country. The bill calculator (which is described below) was then re-run using the expanded 

set of tariffs. See Section D below for the results ofthis sensitivity analysis. 

Commercial Tariff Structure 

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

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

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

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

Definition of demand charges. 

Rate periods (iftime-of-use) and definition of seasons. 

Minimum monthly charge. 

Power factor adjustments and reactive power demand charges. 

Commercial Spreadsheet Modeling 

Generally, the commercial tariff modeling effort focused on two common categories of commercial tariffs: small general 

service and general service. The collected commercial tariffs were modeled in spreadsheet fonn, excluding some tariffs 

and/or components that could not be applied to buildings in MAISY~. For instance, some tariffs provide charges for 

both single and three-phase service. Because MAIS~ does not distinguish between these two types of service, only 

the charges for single phase were modeled. Also, some tariffs charge different prices for primary, secondary, and 

transmission voltage levels. Because of the types of buildings used in MAIS~, only the secondary voltage levels 

were considered. Power factor and reactive demand charges, which are adjustments based on voltage and demand 

usage, were ignored. Other factors were ignored if the information could not be used in MAIS~ or are rarely 

applicable. The spreadsheet also contains 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 Fonn 1 for IOU's and from comparable sources for municipal utilities and co-ops. 

B. Calculation of Commercial Bills and Marginal Prices 

Overview 

The Commercial Tariffs Spreadsheet, or CT AS, contains a collection of modeled commercial electric utility tariffs, a 

collection of modeled commercial buildings, and Visual Basic program modules. Each building is assigned to a group 

of applicable tariffs, based on building demand, and monthly bills are calculated for each tariff for a calendar year. 

Then, building energy usage and demands are decremented to account for savings expected from a new energy 

efficiency standard, monthly bills are recalculated, and the differences in bills and energy use are used to calculate 

marginal prices. 

Commercial Tariffs 

Electric utilities calculate customer bills from 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. These tariffs were collected for a number of US electric utilities and modeled for inclusion in CTAS 

calculations. CT AS contains the specific commercial tariffs for the utilities shown in Appendix 1. 

In addition to the parameters necessary to calculate customer bills, information was collected for each tariff regarding 

the number of customers, revenues, and delivered energy. This tariff-level data on customers and delivered energy 

were used to weight the resulting bill and marginal price calculations to represent a national distribution of marginal 

prices. The 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 

J: \marginal\FinalDraftReport7 -29-99. wpd 7 
utility's tariffs). Industrial tariffs, as contrasted to commercial tariffs, have not been included in CTAS. This is 

primarily because the MAISP database does not include industrial buildings. 

Electric utilities typically have many con~mercial 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. To maximize coverage in the limited time available, only the most-used two or three 

commercial tariffs for each utility have been included in CTAS. Taken together, these few tariffs cover 84% of the 

customers in our utility sample and 38% of the commercial electricity sales by those sampled utilities. 

Generally speaking, commercial customers are assigned to a particular tariff based on their peak monthly andlor annual 

demand as measured in kilowatts over a short period of time, e.g. 15 or 30 minutes. This demand "window" was used 

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 is applied to a customer/building whose peak demand is between 20 and 500 

kW. This assignment by demand window means that a particular building will have bills calculated using some subset 

of all the modeled tariffs, typically one tariff per utility. 

CTAS Buildings/Customers 

The collection of buildings contained in CTAS comes from MA IS^, a proprietary database containing weighted, 

representative building collections for each state. A subset of buildings was selected from MAISP, 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 building in MAISP has 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 MAISP 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 EIA's Commercial Building Energy Consunlption Survey (CBECS) as 

follows: 

CBECS % of Total Commercial 

Building tvoe Buildings in US3 

Education 7.6% 

Food Sales 3.5% 

Food Service 6.7% 

Health Care 2.5% 

Lodging 3.4% 

Mercantile and Service 30.9% 

Office 17.9% 

Public Assembly 7.6% 

Public Order & Safety 2.1% 

Religious 5.0% 

Warehouse & Storage 9.7% 

Othermacant 3.0% 

Number of % Buildings 

Buildings in Samole in Sam~le 

98 7.4% 

63 4.8% 

9 1 6.9% 

35 2.6% 

42 3.2% 

406 30.7% 

23 1 17.5% 

98 7.4% 

28 2.1% 

63 4.8% 

126 9.5% 

42 3.2% 

----------- - - - - - - -- - ------ - - - - - ---- 

TOTAL 100.0% 1323 100.0% 

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: 

Source: CBECS Table BC-39: Lighting Equipment, Number of Buildings, 1995. 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

utility's tariffs). Industrial tariffs, as contrasted to commercial tariffs, have not been included in CTAS. This is 

primarily because the MAISyt) database does not include industrial buildings. 

Electric utilities typically have many commercial tariffs. Southern California Edison, for instanee, has over 100 

commercial tariffs. Most ofthese tariffs target a very small number of customers, or even individual customers, and 

thus have been ignored in CTAS. To maximize coverage in the limited time available, only the most-used two or three 

commercial tariffs for each utility have been included in CTAS. Taken together, these few tariffs cover 84% of the 

customers in our utility sample and 38% of the commercial electricity sales by those sampled utilities. 

Generally speaking, commercial customers are 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. This demand "window" was used 

to determine which of all the tariffs to apply to a particular building in the CT AS collection. For instance the GS-2 tariff 

carried by Southern California Edison is applied to a customerlbuilding whose peak demand is between 20 and 500 

kW. This assignment by demand window means that a particular building will have bills ealculated using some subset 

of all the modeled tariffs, typically one tariff per utility. 

CT AS Buildings/Customers 

The collection of buildings contained in CT AS comes from MAISY"", a proprietary database containing weighted, 

representative building collections for each state. A subset of buildings was selected 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 building in MAISY® has 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 CT AS. 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 EIA's Commercial Building Energy Consumption Survey (CBECS) as 

follows: 

CBECS % of Total Commercial Number of % Buildings 

Building type Buildings in US3 Buildings in Sample 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% 

OtherN acant 3.0% 42 3.2% 

-----------

TOTAL 100.0% 1323 100.0% 

CT AS Calculations 

CT AS 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: 

3 Source: CBECS Table BC-39: Lighting Equipment, Number of Buildings, 1995. 

J :\marginal\FinalDraftReport7 -29-99. wpd 8 
Each eustomer/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 representative change in the building's bill (in order to calculate the marginal 

energy price). The assumed energy use decrement is 5%; the demand decrement is 80% of 5%, that is, 4%.j 

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. 

CTAS OutDut 

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 MAISP customer / building ID number 

STATE State from which sample customer was drawn 

ANPKKW Building annual peak demand in kW measured over 1 hour 

TARFTYP Tariff group index defined by demand "window" 

ARFINDX Index used in program 

TARPTR Index used in program 

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

TARFNAME Name of particular tariff, e.g. GS-I 

NCUS Number of customers billed under that tariff 

DELVDMWH Annual megawatthours (MWh) delivered by utility to customers under this tariff 

BLDGWT Calculated weight of building, derived in part from MAISP weight 

BLDGOPHRWeekly hours of operation of building 

ANAVGRAT Annual average electricity price 

ANMR 1 Annual average electricity marginal price 

ANE 1 Annual percent difference between marginal and average electricity price 

AVGBILL Annual average bill, defined as average of 12 monthly bills 

AVGBILLl Annual average decremented bill, defined as average of 12 monthly average bills 

AVGKWH Annual average energy use, defined as average of 12 monthly energy usage levels 

AVGKWHl Annual average decremented energy use, defined as average of 12 monthly decremented energy 

usage levels 

See Appendix 2 for sample commercial bill calculations. 

The explanation of the derivation of the demand decrement appears in Appendix 3: "Demand Decrement Due to 

Standards - The Role of Lighting Coincidence and Diversity." 

EE-RM-STD-98-440 COMMENT 241ZZ

Each customerlbuilding is considered in tum. 

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

F or each tariff a calculation of a monthly bill in $/month is performed.4 

A second calculation with the same month, tariff and building is performed with decremented monthly energy 

use and peak demand to obtain a representative change in the building's bill (in order to calculate the marginal 

energy price). The assumed energy use decrement is 5%; the demand decrement is 80% of 5%, that is, 4%.5 

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, un decremented 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 tarifffor the particular customer/building. 

The next building is considered and the complete process repeated, until all customer/buildings have been 

processed. 

CTAS Output 

The output from a CT AS 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 

TARFTYP 

ARFINDX 

TARPTR 

UTILCO 

TARFNAME 

NCUS 

Field Description 

MAISyID customer / building ID number 

State from which sample customer was drawn 

Building annual peak demand in kW measured over 1 hour 

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-l 

Number of customers billed under that tariff 

DEL VDMWH Annual megawatthours (MWh) delivered by utility to customers under this tariff 

BLDGWT Calculated weight of building, derived in part from MAISY'"' weight 

BLDGOPHRWeekly hours of operation of building 

ANA VGRA T Annual average electricity price 

ANMRI Annual average electricity marginal price 

ANEI Annual percent difference between marginal and average electricity price 

A VGBILL Annual average bill, defined as average of 12 monthly bills 

AVGBILLl Annual average decremented bill, defined as average of 12 monthly average bills 

A VGKWH Annual average energy use, defined as average of 12 monthly energy usage levels 

A VGKWHI Annual average decremented energy use, defined as average of 12 monthly decremented energy 

usage levels 

4 See Appendix 2 for sample commercial bill calculations. 

5 The explanation of the derivation of the demand decrement appears in Appendix 3: "Demand Decrement Due to 

Standards - The Role of Lighting Coincidence and Diversity." 

J : \marginal\FinalDraftReport 7 -29-99. wpd 9 
Conclusions 

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, given the time constraints of the project. 

C. Weighting Method 

This section describes construction of the weighted distribution of commercial electricity marginal prices. 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 represents a 

unique combination of a building and an applicable tariff, for a total of 29,133 observations. 

The composition of the sample posed a dual weighting problem: distributing weights across buildings and across 

tariffs. The weighting approach solves these problems sequentially. For each building a weight is derived from its 

original MA IS^ building weight and scaling factors. This adjusted building weight is then apportioned across all the 

tariffs that were applied to the building. A composite weighting factor for each building/tariff pair combines both of 

these weighting factors. These weights are all based on energy, average monthly kwh usage at the building level and 

annual MWh sales for each tariff. Appendix 4, Weighting Method for Commercial Sample, shows the details of the 

approach to this dual weighting problem. 

D. Results of Analysis 

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

between marginal electricity prices and average electricity prices. Electricity marginal prices range from 85% below to 

5 1% above the average price for the same customer. At the consumption-weighted mean of the differences, electricity 

marginal prices are 5.2% lower than average price^.^ In the LCC analysis of commercial sector ballasts, this distribution 

of differences was used along with a distribution of average commercial sector electricity prices (which were obtained 

from EIA data on over 3000 utilities serving commercial customers) to generate a distribution of marginal electricity 

prices. In the LCC analysis of industrial sector ballasts, this same distribution of differences was used along with a 

distribution of average industrial sector electricity prices (which were obtained from EIA data on over 2000 utilities 

serving industrial customers) to generate a distribution of marginal electricity prices. 

As a result of the sensitivity analysis mentioned earlier (where additional municipal utility tariffs were included), a 

similar distribution of weighted amounts were found by which average prices exceeded marginal prices, the mean of 

which was -5.6%. 

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

LCC analysis. The weighted mean of the binned distribution was -5.7%. 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

Conclusions 

CT AS was designed as an analysis tool to investigate marginal prices experienced by a range of custqmers 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, given the time constraints of the project. 

C. Weighting Method 

This section describes construction of the weighted distribution of commercial electricity marginal prices. 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 represents a 

unique combination ofa building and an applicable tariff, for a total of29,133 observations. 

The composition of the sample posed a dual weighting problem: distributing weights across buildings and across 

tariffs. The weighting approach solves these problems sequentially. For each building a weight is derived from its 

original MAISY" building weight and scaling factors. This adjusted building weight is then apportioned across all the 

tariffs that were applied to the building. A composite weighting factor for each building/tariff pair combines both of 

these weighting factors. These weights are all based on energy, average monthly kWh usage at the building level and 

annual MWh sales for each tariff. Appendix 4, Weighting Method for Commercial Sample, shows the details of the 

approach to this dual weighting problem. 

D. Results of Analysis 

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

between marginal electricity prices and average electricity prices. Electricity marginal prices range from 85% below to 

51 % above the average price for the same customer. At the consumption-weighted mean of the differences, electricity 

marginal prices are 5.2% lower than average prices.6 In the LCC analysis of commercial sector ballasts, this distribution 

of differences was used along with a distribution of average commercial sector electricity prices (which were obtained 

from EIA data on over 3000 utilities serving commercial customers) to generate a distribution of marginal electricity 

prices. In the LCC analysis of industrial sector ballasts, this same distribution of differences was used along with a 

distribution of average industrial sector electricity prices (which were obtained from EIA data on over 2000 utilities 

serving industrial customers) to generate a distribution of marginal electrieity prices. 

As a result of the sensitivity analysis mentioned earlier (where additional municipal utility tariffs were included), a 

similar distribution of weighted amounts were found by which average prices exceeded marginal priees, the mean of 

which was -5.6%. 

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

LCC analysis. The weighted mean of the binned distribution was -5.7%. 

J :\marginal\FinaIDraftReport7 -29-99. wpd 10 
Figure 1. 

Commercial Electricity Marginal Prices 

Differences from Annual Average 

s s s s s s s s s s s s s 0 m 0 m 0 Y 0 m 0 rn 0 m 0 

9 Y Y F T- F .'- N w m 

% Difference from Average 

Marginal < Average 

Mean = -5.2% 

Minimum = -85% 

Marginal > Average 

Median = -3.3% 

Maximum = +5 1 % 

EE-RM-STD-98-440 COMMENT 241ZZ

(J) 

G> 

n:s 

en 

~ ...... 

(J 

~ 

...... 

(J 

Q) 

-

W 

~ 

0 

0 ~ 

25% 

20% 

15% 

10% 

5% 

0% 

Figure 1. 

Commercial Electricity Marginal Prices 

Differences froln Annual Average 

% Difference from Average 

Marginal < Average 

Mean = -5.2% 

Minimum -85% 

Marginal> Average 

Median = -3.3% 

Maximum = +51 % 

J :\marginal\FinaIDraftReport7 -29-99. wpd 11 
IV. AVALYSIS AND RESULTS - RESIDENTIAL 

Method Used to Derive Residential Electric and Natural Gas Marginal Prices 

Data from the Energy Information Administration's (EIA) 1993 Residential Energy Consumption Survey (RECS) were 

used to calculate marginal energy prices for residential appliance owners. The main advantages of using RECS data 

are: 1) this survey of 7041 U.S. households was designed by EIA to be a nationally representative sample; and 2) the 

data are available to the public. Since RECS includes the exact amounts households paid for utility bills, there is no 

need to derive those bills from a combination of an appropriate tariff and each building's energy consumption. Thus, 

the method for deriving residential marginal energy prices was inherently simpler than our previously described 

method for deriving commercial marginal energy prices. 

EIA had collected utility bills for up to 16 billing cycles for 61 19 of the 7038 RECS households that use electricity, and 

for 3 153 of the 4033 RECS households that use natural gas. EIA has made a public use version of these data available; 

it includes some error inoculation to protect the identity of individual utility customers. 

Marginal energy prices were calculated by performing a separate regression analysis for each household for which 

billing data were available.' Energy consumption was plotted on the x-axis, and the consumer's energy bill was plotted 

on the y-axis. The slope of the regression line for each household (the change in the energy bill divided by the 

change in the energy consumption) equals the marginal energy price for that household. 

Electricitv 

For electricity, the billing data were divided into two seasons, summer and "winter" (actually non-summer), where a bill 

was defined to be a summer bill if the midpoint of the billing period fell between June 1 and September 30. This 

division was done because review of utility tariffs (during the "Analytic" approach described earlier) had found that 

electric utilities often had different rates in summer vs. non-summer, and that most utilities defined summer to fall 

within this range of dates. A linear regression relating electricity bills and usage was performed separately for each 

season. The marginal electricity price for each season was estimated to be equal to the slope of the regression line. 

See Figure 2 below for a sample linear regression on the electricity bills of one of the RECS households. 

For some households, there were either not enough data or the data points were too close together or too scattered to 

determine a slope, particularly in the summer season. After a household-by-household examination of the outliers, 

slopes (marginal prices) that were either less than or equal to zero or greater than $0.22/kWh were also rejected. It was 

possible to calculate slopes for the summer season for 581 8 households, of which 5615 had acceptable fits, yielding 

slopes that fell within the above range of values. It was possible to determine a slope for the "non-summer" season 

for all 61 19 households for which electricity billing data were available. Of these households, 6098 had slopes with 

acceptable fits. There were a total of 5606 households for which both the summer and non-summer marginal electricity 

prices could be determined with acceptable fits. Seasonal prices were used for each of these households in the LCC 

analysis. 

For households for which it was not possible to determine a seasonal rate for both seasons, or for which these rates 

fell outside the acceptable range, a single linear regression was performed from the data from all billing cycles. There 

were an additional 502 households for which a single annual marginal price could be determined. This marginal price 

was used in the LCC analysis for these households. Thus, of the 7038 RECS households with electricity, a marginal 

electricity price or prices were available for 6108 (5606 with seasonal rates, plus 502 with annual marginal prices only), 

or 87%. Most (919) of the households for which a marginal price was not available were those for which EIA did not 

collect billing data, with only 11 additional households being dropped due to an indeterminate or unacceptable 

regression slope. 

' Source: Robert Latta of EIA proposed this approach. 

J :\marginal\FinalDraftReport7-29-99 .wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

IV. At~ALYSIS AND RESULTS - RESIDENTIAL 

Method Used to Derive Residential Electric and Natural Gas Marginal Prices 

Data from the Energy Information Administration's (EIA) 1993 Residential Energy Consumption Survey (RECS) were 

used to calculate marginal energy prices for residential appliance owners. The main advantages of using RECS data 

are: I) this survey of7041 U.S. households was designed by EIA to be a nationally representative sample; and 2) the 

data are available to the public. Since RECS includes the exact amounts households paid for utility bills, there is no 

need to derive those bills from a combination of an appropriate tariff and each building's energy consumption. Thus, 

the method for deriving residential marginal energy prices was inherently simpler than our previously described 

method for deriving commercial marginal energy prices. 

EIA had collected utility bills for up to 16 billing cycles for 6119 ofthe 7038 RECS households that use electricity, and 

for 3153 of the 4033 RECS households that use natural gas. EIA has made a public use version of these data available; 

it includes some error inoculation to protect the identity of individual utility customers. 

Marginal energy prices were calculated by performing a separate regression analysis for each household for which 

billing data were available.7 Energy consumption was plotted on the x-axis, and the consumer's energy bill was plotted 

on the y-axis. The slope of the regression line for each household (the change in the energy bill divided by the 

change in the energy consumption) equals the marginal energy price for that household. 

Electricity 

For electricity, the billing data were divided into two seasons, summer and "winter" (actually non-summer), where a bill 

was defined to be a summer bill ifthe midpoint ofthe billing period fell between June 1 and September 30. This 

division was done because review of utility tariffs (during the "Analytic" approach described earlier) had found that 

electric utilities often had different rates in summer vs. non-summer, and that most utilities defined summer to fall 

within this range of dates. A linear regression relating electricity bills and usage was performed separately for each 

season. The marginal electricity price for each season was estimated to be equal to the slope of the regression line. 

See Figure 2 below for a sample linear regression on the electricity bills of one ofthe RECS households. 

For some households, there were either not enough data or the data points were too close together or too scattered to 

determine a slope, particularly in the summer season. After a household-by-household examination of the outliers, 

slopes (marginal prices) that were either less than or equal to zero or greater than $0.22/kWh were also rejected. It was 

possible to calculate slopes for the summer season for 5818 households, of which 5615 had acceptable fits, yielding 

slopes that fell within the above range of values. It was possible to determine a slope for the "non-summer" season 

for all 6119 households for which electricity billing data were available. Of these households, 6098 had slopes with 

acceptable fits. There were a total of 5606 households for which both the summer and non-summer marginal electricity 

prices could be determined with acceptable fits. Seasonal prices were used for each ofthese households in the LCC 

analysis. 

For households for which it was not possible to determine a seasonal rate for both seasons, or for which these rates 

fell outside the acceptable range, a single linear regression was performed from the data from all billing cycles. There 

were an additional 502 households for which a single annual marginal price could be determined. This marginal price 

was used in the LCC analysis for these households. Thus, of the 7038 RECS households with electricity, a marginal 

electricity price or prices were available for 6108 (5606 with seasonal rates, plus 502 with annual marginal prices only), 

or 87%. Most (919) of the households for which a marginal price was not available were those for which EIA did not 

collect billing data, with only 11 additional households being dropped due to an indeterminate or unacceptable 

regression slope. 

7 Source: Robert Latta ofEIA proposed this approach. 

J : \marginal\F inalDraftReport 7 -29-99. wpd 12 
Figure 2. 

RECS 1993 

Linear Regression on Electricity Bills, Household 4164 

Winter Bills 

- 'Summer Regression 

Marginal Prices 

Summer: 9.886lkWh 

Winter: 4.85$/kWh 

The r-squared values for the regressions of RECS electricity bills were generally very high! Examination of the 

summer regressions and the winter regressions reveals excellent fits in both cases, with r-squared values of 0.95 or 

above 90% of the time for the summer bills and 75% of the time for the winter bills. (See the distribution of the rsquared values shown in Table 2.) 

Natural Gas 

For natural gas, the data were not divided into seasons because review of gas utility rates (during the "Analytic" 

approach described earlier) found that most (21 out of 27) utilities surveyed did not have seasonal rates. Also, many 

households have little or no gas consumption in the summer, making determination of a marginal price for this season 

difficult. Instead, a single regression relating natural gas bills and usage was performed using the data for all of the 

billing cycles. It was possible to determine a slope of the regression line for all 3 153 households for which billing data 

were available. Slopes were rejected as unreasonable if they were less than or equal to zero, or greater than 

$3.20/therm. This left 3 139 households, or 78% of the total of 4033 households that use natural gas. Again, most (880) 

of the households for which a marginal price was not available were those for which EIA did not collect billing data, 

with only 14 additional households dropped due to unacceptable regression slopes. See Figure 3 below for a sample 

linear regression on the gas bills of one of the RECS households. 

' In a regression with a single independent variable, r-squared is the square of the correlation coefficient "r". It 

represents the percent of variation in the dependent variable (in this case, the household's energy bill) that can be 

explained by the variation in the independent variable (in this case, the household's energy consumption). 

J :\marginal\FinalDraftReport7-29-99. wpd 13 

EE-RM-STD-98-440 COMMENT 241ZZ

Figure 2. 

RECS 1993 

Linear Regression on Electricity Bills, Household 4164 

250 

y = 0.0988x + 0.0862 

R2= 1.0000 j. 

/' 

200 /-' 

;I' 

K" 

150 

K";I' 

A Summer Bills 

~ II Winter Bills 

III 

~ 

~" 0.0<8"'.33.64 

R2 = 0.8177 

III 

- -Summer Regression 

--Winter Regression 

Marginal Prices 

Summer: 9.88¢/kWh 

Winter: 4.85¢/kWh 

iii 

100 

50 

o 

o 500 1000 1500 2000 2500 

kWh 

The r-squared values for the regressions ofRECS electricity bills were generally very high.8 Examination of the 

summer regressions and the winter regressions reveals excellent fits in both cases, with r-squared values of 0.95 or 

above 90% of the time for the summer bills and 75% of the time for the winter bills. (See the distribution of the rsquared values shown in Table 2.) 

Natural Gas 

For natural gas, the data were not divided into seasons because review of gas utility rates (during the "Analytic" 

approach described earlier) found that most (21 out of27) utilities surveyed did not have seasonal rates. Also, many 

households have little or no gas consumption in the summer, making determination of a marginal price for this season 

difficult. Instead, a single regression relating natural gas bills and usage was performed using the data for all of the 

billing cycles. It was possible to determine a slope ofthe regression line for all 3153 households for which billing data 

were available. Slopes were rejected as unreasonable if they were less than or equal to zero, or greater than 

$3.20/therm. This left 3139 households, or 78% of the total of 4033 households that use natural gas. Again, most (880) 

ofthe households for which a marginal price was not available were those for which EIA did not collect billing data, 

with only 14 additional households dropped due to unacceptable regression slopes. See Figure 3 below for a sample 

linear regression on the gas bills of one of the RECS households. 

8 In a regression with a single independent variable, r-squared is the square of the correlation coefficient "r". It 

represents the percent of variation in the dependent variable (in this case, the household's energy bill) that can be 

explained by the variation in the independent variable (in this case, the household's energy consumption). 

J : \marginaJ\F inalDraftReport 7 -29-99. wpd 13 
Figure 3. 

RECS 1993 

Linear Regression on Gas Bills, Household 1001 

y =O 5119x +49798 

R~ = 0 9994 

50 - 

- 

2 

= 40 .- 

m Marginal Price 

$0.5l/therm 

30 - 

I I 

Therms 

The r-squared values for the regressions of RECS natural gas bills were also very high. On an annual basis, it was 

found that the regression line fits were excellent, with r-squared values of 0.95 more than 90% of the time. The 

distribution of the r-squared values for both the electricity and natural gas regressions is shown in Table 2 below. 

Table 2. Distribution of r-sauared Values for Electricitv (Summer and Winter) and Gas Regressions 

Percentile 

9gth 

951h 

90th 

751h 

5Oth 

251h 

1 0th 

5'h 

1 

Mean 

Number of Cases* 

Electricity Nahral Gas 

Summer Winter 

1 1 1 

1 I 1 

1 0.999 0.999 

1 0.997 0.997 

0.999 0.987 0.993 

0.99 0.956 0.984 

0.947 0.874 0.965 

0.868 0.755 0.912 

0.449 0.217 0.6 

0.973 0.946 0.975 

5667 6116 3150 

Total Number of Cases 581 8 61 19 3 153 

* Cases where r-squared values could be calculated. 

J:\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

Figure 3. 

RECS 1993 

Linear Regression on Gas Bills, Household 1001 

80 

70 

60 

50 

€ 

40 

iii 

30 

20 

10 

./ 

/ 

/ y = 0.5119x + 4.9798 

R2 = 0.9994 

/ 

/ Marginal Price 

$0.5I1therrn 

/ 

/ 

~ 

0 

o 20 40 60 80 100 120 140 

Therms 

The r-squared values for the regressions ofRECS natural gas bills were also very high. On an annual basis, it was 

found that the regression line fits were excellent, with r-squared valucs of 0.95 more than 90% ofthe time. The 

distribution of the r-squared values for both the electricity and natural gas regressions is shown in Table 2 below. 

Table 2. Distribution ofr-sguared Values for Electricity (Summer and Winter) and Gas Regressions 

Percentile Electricity Natural Gas 

Summer Winter 

99th 1 

95th 1 

90th 0.999 0.999 

75th 0.997 0.997 

50th 0.999 0.987 0.993 

25th 0.99 0.956 0.984 

10th 0.947 0.874 0.965 

5th 0.868 0.755 0.912 

l" 0.449 0.217 0.6 

Mean 0.973 0.946 0.975 

Number ofCases* 5667 6116 3150 

Total Number of Cases 5818 6119 3153 

* Cases where r-squared values could be calculated. 

J: \marginal\FinalDraftReport7 -29-99. wpd 14 

160 
Electricitv and Natural Gas 

A statistical analysis was performed comparing the data sets for each fuel before and after dropping the households 

for which it was unable to calculate marginal energy prices in order to determine if any bias was being introduced by 

dropping those households. It was found that the subset of households that were dropped were those whose utility 

bills were included in the household's rent; presumably ETA could not collect billing data for those households. 

Looking at just those subsets of RECS households that own the particular appliance of interest, it was found that the 

differences between all households and the subset for which it was able to calculate a marginal price were smaller than 

the differences described above for the set of all RECS households. These results may occur because households 

which own clothes washers, or water heaters, or central air conditioners, tend to have a relatively lower proportion of 

renters in the first place, so fewer households of that type were dropped. For the key parameters, differences were 

found to be negligible (generally 1 % or less) between the appliance data set and the subset with marginal prices. 

Table 3 below shows the number and percent of households used in the analysis for each appliance subset after 

households with no marginal price were dropped. 

Table 3. Number of Households in Each Avuliance Data Set, and in Subsets with Marginal Prices 

* Sum of weights of households. The weight of each household is the number of US households represented by each 

RECS household. 

** "Marginal Price Subset" represents the households for which an electricity marginal price is available. Fuel oil 

marginal price was assumed to be the same as the fuel oil average price, an assumption supported by the research 

reported in Section V below. 

Appliance Subsets 

(HH = household) 

HHs with washer and 

dryer and electricity 

HHs with washer and 

dryer and natural gas 

HHs with central air 

conditioning 

HHs with heat pumps 

HHs with electric water 

heaters 

HHs with natural gas 

water heaters 

HHs with fuel oil water 

heaters** 

Results of Analvsis 

Figures 4 and 5 below display the results of the residential electricity price analysis by showing the relationship 

between marginal electricity prices and average electricity prices for the residential sector in the summer and the 

winter. Figure 6 below displays the results (on an annual basis) of the residential gas analysis. 

In the LCC analysis of three residential products (clothes washers, water heaters, and central air conditionerslheat 

pumps), the marginal electricity and natural gas prices used were derived directly from RECS. Figures 4, 5, and 6 below 

show how different these marginal prices are from average prices. Those marginal electricity prices in the summer 

(June-September) range from 98% below to 175% above the average price for the same customer. At the consumptionAll Households with 

appliance 

Marginal Price Subset 

Survey 

Responses 

4998 

2759 

2414 

651 

2559 

2805 

176 

Survey 

R~~~~~~~~ 

4574 

2439 

2164 

613 

2323 

2535 

163 

Marginal Price Subset as 

percent of All Households 

U.S. 

Households 

(millions)* 

67.8 

40.6 

32.1 

7.9 

33.4 

41.0 

1.8 

U.S. 

Households 

(millions)* 

61.9 

35.6 

28.8 

7.6 

30.3 

36.0 

1.7 

% of 

Survey 

Responses 

92% 

88% 

90% 

94% 

91% 

90% 

93% 

% of Relevant 

U.S. 

Households 

91% 

88% 

90% 

96% 

91% 

88% 

94% 

EE-RM-STD-98-440 COMMENT 241ZZ

Electricity and Natural Gas 

A statistical analysis was perfonned comparing the data sets for each fuel before and after dropping the households 

for which it was unable to calculate marginal energy prices in order to determine if any bias was being introduced by 

dropping those households. It was found that the subset of households that were dropped were those whose utility 

bills were included in the household's rent; presumably EIA could not collect billing data for those households. 

Looking at just those subsets ofRECS households that own the particular appliance of interest, it was found that the 

differences between all households and the subset for which it was able to calculate a marginal price were smaller than 

the differences described above for the set of all RECS households. These results may occur because households 

which own clothes washers, or water heaters, or central air conditioners, tend to have a relatively lower proportion of 

renters in the first place, so fewer households of that type were dropped. For the key parameters, differences were 

found to be negligible (generally 1 % or less) between the appliance data set and the subset with marginal prices. 

Table 3 below shows the number and percent of households used in the analysis for each appliance subset after 

households with no marginal price were dropped. 

Table 3. Number of Households in Each Appliance Data Set, and in Subsets with Marginal Prices 

Appliance Subsets All Households with Marginal Price Subset Marginal Price Subset as 

(HH = household) appliance percent of All Households 

Survey U.S. Survey U.S. %of % of Relevant 

Responses Households Responses Households Survey U.S. 

(millions)* (millions )* Responses Households 

HHs with washer and 4998 67.8 4574 61.9 92% 91% 

dryer and electricity 

HHs with washer and 2759 40.6 2439 35.6 88% 88% 

dryer and natural gas 

HHs with central air 2414 32.1 2164 28.8 90% 90% 

conditioning 

HHs with heat pumps 651 7.9 613 7.6 94% 96% 

HHs with electric water 2559 33.4 2323 30.3 91% 91% 

heaters 

HHs with natural gas 2805 41.0 2535 36.0 90% 88% 

water heaters 

HHs with fuel oil water 176 1.8 163 1.7 93% 94% 

heaters** 

* Sum of weights of households. The weight of each household is the number of US households represented by each 

RECS household. 

** "Marginal Price Subset" represents the households for which an electricity marginal price is available. Fuel oil 

marginal price was assumed to be the same as the fuel oil average price, an assumption supported by the research 

reported in Section V below. 

Results of Analysis 

Figures 4 and 5 below display the results of the residential electricity price analysis by showing the relationship 

between marginal electricity prices and average electricity prices for the residential sector in the summer and the 

winter. Figure 6 below displays the results (on an annual basis) of the residential gas analysis. 

In the LCC analysis of three residential products (clothes washers, water heaters, and central air conditioners/heat 

pumps), the marginal electricity and natural gas prices used were derived directly from RECS. Figures 4,5, and 6 below 

show how different these marginal prices are from average prices. Those marginal electricity prices in the summer 

(June-September) range from 98% below to 175% above the average price for the same customer. At the consumptionJ: \marginal\FinalDraftReport7 -29-99. wpd 15 
weighted mean of the differences, marginal electricity prices are 2.5% higher than average electricity prices in the 

summer. Marginal electricity prices in the "winter" (the remainder of the year) range from 100% below to 130% above 

the average price for the same customer. At the consumption-weighted mean of the differences, electiicity marginal 

prices are 5.3% lower than average electricity prices in the "winter." Marginal gas prices range from 98% below to 

248% above the average price for the same customer. At the consumption-weighted mean of the differences, marginal 

gas prices are 7.8% lower than average gas prices. 

Figure 4 below shows, for each household in the summer, the difference between the marginal electricity price for that 

household and that household's annual average electricity price. 

Figure 4. 

Residential Summer Electricity Marginal Prices 

Differences from Annual Averages 

Marginal c Average Marginal > Average 

% Diirence from Average 

Mean = +2.5% 

Minimum = -98% 

Median = +I .4% 

Minumum = +I 75% 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

weighted mean of the differences, marginal electricity prices are 2.5% higher than average electricity prices in the 

summer. Marginal electricity prices in the "winter" (the remainder of the year) range from 100% below to 130% above 

the average price for the same customer. At the consumption-weighted mean of the differences, electricity marginal 

prices are 5.3% lower than average electricity prices in the "winter." Marginal gas prices range from 98% below to 

248% above the average price for the same customer. At the consumption-weighted mean of the differences, marginal 

gas prices are 7.8% lower than average gas prices. 

Figure 4 below shows, for each household in the summer, the difference between the marginal electricity price for that 

household and that household's annual average electricity price. 

3.0 

..... 

I/) 2.5 

c: 

~ 

E 2.0 

--I/) 

:E 

0 1.5 ..c: 

Q) 

I/) 

::::J 

0 1.0 J: 

U) 

:) 0.5 

o.o§ 

I 

Figure 4. 

Residential Summer Electricity Marginal Prices 

Differences from Annual Averages 

Marginal < Average 

Mean = +2.5% 

Minimum = -98% 

Marginal> Average 

% Difference from Average 

Median = +1.4% 

Minumum = +175% 

J :\marginal\FinalDraftReport7 -29-99. wpd 16 
Figure 5 below shows, for each household in the winter, the difference between the marginal electricity price for that 

household and that household's annual average electricity price. 

Figure 5. 

Residential Winter Electricity Marginal Prices 

Differences from Annual Averages 

Marginal < Average Marginal > Average 

Mean = -5.3% 

Minimum = -100% 

Median = -3.8% 

Maximum = +130% 

EE-RM-STD-98-440 COMMENT 241ZZ

Figure 5 below shows, for each household in the winter, the difference between the marginal electricity price for that 

household and that household's annual average electricity price. 

Figure 5. 

Residential Winter Electricity Marginal Prices 

Differences from Annual Averages 

Marginal < Average Marginal> Average 

~ 2.5r-----------------------------~rtr------------------------------~ I/) 

c 

.2 

~ 2.0 

Ui' 

" '0 .c 1.5 

Q) 

I/) 

::J 

~ 1.0 r----------------------

fA 

::j 

0.5 

0.0 lit<! 

8 

"I ~ ~ I ~ 

Mean = -5.3% 

Minimum = -100% 

J :\marginal\FinalDraftReport7 -29-99. wpd 

% Difference from Average 

Median = -3.8% 

Maximum = +130% 

17 
Figure 6 below shows, for each housel~old, the difference between the marginal gas price for that household and that 

household's annual average gas price. 

Figure 6. 

Residential Gas Marginal Prices 

Differences from Annual Averages 

Marginal < Average Marginal > Average 

3.0 

.;; 2.5 

5 

0 .- - - .- E 2.0 

Y 

In 

I! 

0 1.5 C 

a 

In 

3 ; 1.0 

4 

0.5 

0.0 

y y $4 -?4 5 8 !3 E 

% Difference from Average 

Mean = -7.8% 

Minimum = -90% 

Median = -9.0% 

Maximum = +248% 

J:\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

Figure 6 below shows, for each household, the difference between the marginal gas price for that household and that 

household's annual average gas price. 

3.0 

Ii) 2.5 

c: 

0 

g 2.0 

II) 

'0 

'0 1.5 

.t:. 

Q) 

II) 

:l 

0 1.0 1: 

u) 

:;j 0.5 

0.0 

~ 

I 

Figure 6. 

Residential Gas Marginal Prices 

Differences from Annual Averages 

Marginal < Average 

~ ~ a:l 

I '" I 

Mean = -7.8% 

Minimum = -90% 

~ ..... I 

Marginal> Average 

.... 

~ 

I 

~ ~ 

% Difference from Average 

Median = -9.0% 

Maximum = +248% 

J :\marginal\FinalDraftReport7 -29-99. wpd 18 
V. RESIDENTIAL lilEATING OIL AND PROPANE 

As part of the marginal fuel price study for residential utility customers in the US, brief telephone interviews were 

conducted from January 25 to February 5, 1999 in order to understand and characterize regional variations in pricing 

and distribution of residential heating oil and propane. A sample of distributors was selected from relevant internet 

sites, including that of the National Propane Gas Association (NPGA). The goal was to interview fuel oil and propane 

distributors that represent a cross-section of the industry in terms of company size and location, The interview 

questions were designed to identify factors (including wholesale price, annual usage, delivery, tank rental, and taxes) 

that affect the actual cost of heating oil and propane for residential customers and to determine any divergence 

between average and marginal price for these fuels. 

Heating Oil 

Based on telephone conversations with five East Coast distributors of home heating oil, it was learned that (I) home 

heating oil is not taxed, (2) heating oil storage tanks are owned, not rented, and (3) there is no additional charge for 

delivery, except under unusual circumstances. These are all factors likely to make average and marginal prices similar. 

The price per gallon ranged from $0.85 in Virginia to $0.66 in southern Maine; prices appeared to decrease from south 

to north. Discounts of $0.03-$0.10 per gallon are available, depending primarily on promptness of payment (e.g., within 

5-10 days) or "auto-delivery" service, in which the customer allows the distributor to monitor usage and fill the tank at 

the distributor's convenience. See Table 4 below. 

Table 4. Heating Oil Prices 

Because the interviews indicate that the bills paid by residential consumers for heating oil are essentially volume 

driven, with a single block rate, the average prices inherent in those bills, as reported in RECS, were interpreted as 

being equivalent to marginal prices for the purposes of the LCC analyses for water heaters and clothes washers. 

Provane 

Based on telephone conversations with nine distributors of residential propane gas, it was learned that propane prices 

have much more seasonal and regional variability than home heating oil or natural gas. This volatility is due in part to 

the fact that the price of propane is not regulated by state public utility commissions. The three major factors that 

influence the retail price of residential propane are (1) wholesale price (which is primarily a reflection of local supply 

and demand), (2) customer annual usage, and (3) "pre-pay" agreements, all of which are embedded in the pricelgallon 

of residential propane. State and local taxes, if any, are an additional cost. Tax on residential propane sales varies by 

state, county, and municipality; among our respondents it varies from none (Virginia, Texas, Wisconsin) to 8.25% 

(California). 

Company 

Location 

Territory 

Priceigallon 

Discount 

Residential propane customers usually live in rural or remote areas where either natural gas service is not available or 

electricity service is expensive or not reliable. End uses range from cooking and water heating to pool and space 

heating. To the extent that propane is required to heat homes, demand for propane is highest during the winter. Our 

survey respondents reported winter-to-summer price differentials that range from $0.13-$0.58 per gallon. By far the 

most substantial cost savings are those available to residential customers who pre-pay during summer months for 

propane that will be delivered and used during winter. Distributors who offer these discounts are better able to 

anticipate the amount of propane they will need to supply, and often purchase "price insurance" (futures contract) 

from an energy company that effectively protects them from winter price spikes. 

MacDuffie Petroleum 

Hudson NH 

NH, MA 

$0.72 

paid in 10 days: $0.10 

Because 88%-90% of propane used in the US is produced domestically, propane supplies are rarely disrupted. 

However, winter demand often exceeds local supply because of logistical limitations of transporting propane from 

where it is produced to where it is needed. Two large underground propane storage sites exist, one near Mont 

Dixie Gas 

Verona VA 

3 counties in VA 

$0.85 

cash paid in 5 days: $0.03 

Eastern Propane 

Rochester NH 

southern Maine 

$0.66 

none 

J :\marginal\FinalDraftReport7-29-99. wpd 

C&W Oil & Heating 

Danbury CT 

Fairfield County 

$0.82 

auto delivery: $0.03 

Powers Oil Co 

Foxboro MA 

metro Attleboro MA 

$0.75 

paid in 7 bus days: $0.1(, 

EE-RM-STD-98-440 COMMENT 241ZZ

v. RESIDENTIAL HEATING OIL A,"U) PROPANE 

As part of the marginal fuel price study for residential utility customers in the US, brief telephone interviews were 

conducted from January 25 to February 5, 1999 in order to understand and characterize regional variations in pricing 

and distribution of residential heating oil and propane. A sample of distributors was selected from relevant internet 

sites, including that of the National Propane Gas Association (NPGA). The goal was to interview fuel oil and propane 

distributors that represent a cross-section of the industry in terms of company size and location. The interview 

questions were designed to identify factors (including wholesale price, annual usage, delivery, tank rental, and taxes) 

that affect the actual cost of heating oil and propane for residential customers and to determine any divergence 

between average and marginal price for these fuels. 

Heating Oil 

Based on telephone conversations with five East Coast distributors of home heating oil, it was learned that (I) home 

heating oil is not taxed, (2) heating oil storage tanks are owned, not rented, and (3) there is no additional charge for 

delivery, except under unusual circumstances. These are all factors likely to make average and marginal prices similar. 

The price per gallon ranged from $0.85 in Virginia to $0.66 in southern Maine; prices appeared to decrease from south 

to north. Discounts of$0.03-$0.10 per gallon are available, depending primarily on promptness of payment (e.g., within 

5-10 days) or "auto-delivery" service, in which the customer allows the distributor to monitor usage and fill the tank at 

the distributor's convenience. See Table 4 below. 

Table 4. Heating Oil Prices 

Company Dixie Gas C&W Oil & Heating Powers Oil Co MacDuffie Petroleum Eastern Propane 

Location Verona VA Danbury CT Foxboro MA HudsonNH Rochester NH 

Territory 3 counties in V A Fairfield County metro Attleboro MA NH,MA southern Maine 

Price! gallon $0.85 $0.82 $0.75 $0.72 $0.66 

Discount cash paid in 5 davs: SO.03 auto delivery: $0.03 paid in 7 bus davs: $0.1 ( paid in 10 days: $0.1 0 none 

Because the interviews indicate that the bills paid by residential consumers for heating oil are essentially volume 

driven, with a single block rate, the average prices inherent in those bills, as reported in RECS, were interpreted as 

being equivalent to marginal prices for the purposes of the LCC analyses for water heaters and clothes washers. 

Propane 

Based on telephone conversations with nine distributors of residential propane gas, it was learned that propane prices 

have much more seasonal and regional variability than home heating oil or natural gas. This volatility is due in part to 

the fact that the price of propane is not regulated by state public utility commissions. The three major factors that 

influence the retail price of residential propane are (1) wholesale price (which is primarily a reflection oflocal supply 

and demand), (2) customer annual usage, and (3) "pre-pay" agreements, all of which are embedded in the price/gallon 

of residential propane. State and local taxes, ifany, are an additional cost. Tax on residential propane sales varies by 

state, county, and municipality; among our respondents it varies from none (Virginia, Texas, Wisconsin) to 8.25% 

(California). 

Residential propane customers usually live in rural or remote areas where either natural gas service is not available or 

electricity service is expensive or not reliable. End uses range from cooking and water heating to pool and space 

heating. To the extent that propane is required to heat homes, demand for propane is highest during the winter. Our 

survey respondents reported winter-to-summer price differentials that range from $0.13-$0.58 per gallon. By far the 

most substantial cost savings are those available to residential customers who pre-pay during summer months for 

propane that will be delivered and used during winter. Distributors who offer these discounts are better able to 

anticipate the amount of propane they will need to supply, and often purchase "price insurance" (futures contract) 

from an energy company that effectively protects them from winter price spikes. 

Because 88%-90% of propane used in the US is produced domestically, propane supplies are rarely disrupted. 

However, winter demand often exceeds local supply because oflogisticallimitations of transporting propane from 

where it is produced to where it is needed. Two large underground propane storage sites exist, one near Mont 

J :\marginal\FinalDraftReport7 -29-99. wpd 19 
Bellvue, Texas, and the second outside of Conway, Kansas. Three pipelines transport propane from these reservoirs 

to southern, eastern, and midwestern states. Bulk storage facilities are located at regional hubs along each pipeline; 

trucks transport propane between these hubs and regional distributors, who distribute the fuel to residential 

customers. Two of the three pipelines also carry other petroleum products, so propane delivery is often interrupted. 

The cost of distributing propane (from the refinery or port to a bulk storage facility) and delivering it to the residential 

customer is incorporated in the price per gallon that each customer is chaiged. The lowest price per gallon ($0.65) was 

reported in northern Wisconsin, while the highest price ($1.41) was reported in Galveston, near Mont Bellvue, Texas, 

and adjacent to several refineries. One possible explanation for this apparent anomaly is that the price of propane in 

Wisconsin (where winters are harsh) is driven down by high demand and fierce competition, while the price in coastal 

Texas (where winters are mild) is buoyed up by relatively low demand and little competition. In fact, our results 

suggest that local competition (which increases along with local demand) is more a determinant of the wholesale price 

than is the distance from the propane source. 

Next to local wholesale price, total annual usage has the largest effect on residential propane prices. The price per 

gallon reported in Table 5 below represents the retail price to a customer who uses a typical amount of propane 

compared to residential customers in that particular service territory. Volume discounts are usually available to those 

who use more than the typical amount of propane; volume discounts reported by our contacts range from $0.02/gal 

(Utah) to $0.19/gal (Florida). Similarly, customers who use less than the typical amount of propane pay more than the 

regular price per gallon; distributors reported additional costs of $0.10 (Wisconsin) to $0.13 (Virginia). Propane 

distributors often offer discounts to new residential customers because (I) they want new customers and (2) it takes a 

year to determine the usage of each new customer. Contacts reported new customer discounts of none (Georgia) to 

$0.15 (Florida) to $0.3 1 (Texas). 

Residential customers store propane in on-site tanks that range in size from 100 to 1,000 gallons; tank size increases 

with the number of end uses, severity of the climate, and difficulty of site access. Some customers lease (rent) tanks 

from their propane distributor. Renting relieves the customer of responsibility for tank maintenance and provides 

flexibility for temporary or mobile home owners, but prevents the customer from shopping around for the lowest fuel 

price among local distributors. Customers who purchase their own tanks do so for several reasons. Permanent, yearround homes in rural areas tend to have a larger number of propane end uses and bigger tanks, which, because they 

are unsightly and take up space, tend to be installed underground. Distributors are reluctant to lease underground 

tanks because leak detection and maintenance is more difficult, so buried tanks are usually owned. Owning a storage 

tank also allows a customer to shop around for the best available propane price. Among our respondents, percentage 

of tanks leased from a distributor ranges from none (Maine, Virginia) to virtually all (Utah, California). Tank rental fees 

depend on tank size and/or annual usage; annual fees range from none (Wisconsin) to $100 (Utah), but generally they 

are $50-1 00. 

Because the interviews with propane distributors indicate that the bills paid by residential consumers for propane are 

essentially volume driven, with a single block rate, the average prices inherent in those bills, as reported in RECS, were 

interpreted as being equivalent to marginal prices for the purposes of the LCC analyses for water heaters and clothes 

washers. 

EE-RM-STD-98-440 COMMENT 241ZZ

Bellvue, Texas, and the second outside of Conway, Kansas. Three pipelines transport propane from these reservoirs 

to southern, eastern, and midwestern states. Bulk storage facilities are located at regional hubs along each pipeline; 

trucks transport propane between these hubs and regional distributors, who distribute the fuel to residential 

customers. Two of the three pipelines also carry other petroleum products, so propane delivery is often interrupted. 

The cost of distributing propane (from the refinery or port to a bulk storage facility) and delivering it to the residential 

customer is incorporated in the price per gallon that each customer is charged. The lowest price per gallon ($0.65) was 

reported in northern Wisconsin, while the highest price ($1041) was reported in Galveston, near Mont Bellvue, Texas, 

and adjacent to several refineries. One possible explanation for this apparent anomaly is that the price of propane in 

Wisconsin (where winters are harsh) is driven down by high demand and fierce competition, while the price in coastal 

Texas (where winters are mild) is buoyed up by relatively low demand and little competition. In fact, our results 

suggest that local competition (which increases along with local demand) is more a determinant ofthe wholesale price 

than is the distance from the propane source. 

Next to local wholesale price, total annual usage has the largest effect on residential propane prices. The price per 

gallon reported in Table 5 below represents the retail price to a customer who uses a typical amount of propane 

compared to residential customers in that particular service territory. Volume discounts are usually available to those 

who use more than the typical amount of propane; volume discounts reported by our contacts range from $0.02/gal 

(Utah) to $0.19/gal (Florida). Similarly, customers who use less than the typical amount of propane pay more than the 

regular price per gallon; distributors reported additional costs of $0.10 (Wisconsin) to $0.13 (Virginia). Propane 

distributors often offer discounts to new residential customers because (I) they want new customers and (2) it takes a 

year to determine the usage of each new customer. Contacts reported new customer discounts of none (Georgia) to 

$0.15 (Florida) to $0.31 (Texas). 

Residential customers store propane in on-site tanks that range in size from 100 to 1,000 gallons; tank size increases 

with the number of end uses, severity of the climate, and difficulty of site access. Some customers lease (rent) tanks 

from their propane distributor. Renting relieves the customer of responsibility for tank maintenance and provides 

flexibility for temporary or mobile home owners, but prevents the customer from shopping around for the lowest fuel 

price among local distributors. Customers who purchase their own tanks do so for several reasons. Permanent, yearround homes in rural areas tend to have a larger number of propane end uses and bigger tanks, which, because they 

are unsightly and take up space, tend to be installed underground. Distributors are reluctant to lease underground 

tanks because leak detection and maintenance is more difficult, so buried tanks are usually owned. Owning a storage 

tank also allows a customer to shop around for the best available propane price. Among our respondents, percentage 

of tanks leased from a distributor ranges from none (Maine, Virginia) to virtually all (Utah, California). Tank rental fees 

depend on tank size and/or annual usage; annual fees range from none (Wisconsin) to $100 (Utah), but generally they 

are $50-100. 

Because the interviews with propane distributors indicate that the bills paid by residential consumers for propane are 

essentially volume driven, with a single block rate, the average prices inherent in those bills, as reported in RECS, were 

interpreted as being equivalent to marginal prices for the purposes of the LCC analyses for water heaters and clothes 

washers. 

J :\marginal\FinalDraftReport7 -29-99. wpd 20 
Table 5. Propane Prices 

Company 

Location 

Territory 

Typical tank size 

Typical Usage 

$/gal, regular 

Tax 

$/gal, discount 

$/gal, addtl 

Tank rentals 

Rental fees 

new customer: $1 .1 0 $0.08igal if tank is owned > 800 galiyr: $1.02 

< 400 galiyr: $1.18 

50-60% rented 

Company 

Location 

Territory 

Typical tank size 

Typical Usage 

$/gal, regular 

Tax 

$/gal, discount 

$/gal, addtl 

Tank rentals 

Rental fees 

J :\marginal\FinalDraftReport7-29-99. wpd 

Eastern Propane 

Rochester NH 

southem ME 

500 gal 

$0.98-1.01 

unsure 

$0.05/gal if paid in 15 days 

no rentals 

Suburban Propane 

Gilroy CA 

3 counties 

250,500 gal 

4 people, 2000 SF home: 500 gaVyr 

$1.45 winter, $1.30 summer 

7.25-8.25%, depends on locality 

new customer: $1.16 winter 

new customer: $1.03 summer 

will-call delivery: $0.1 Olga1 

most are rented 

$65/yr for 250 gal 

$90ivr for 500 pa1 

River Country Co-op (Cenex) 

Chippewa Falls WI 

northwest WI 

500 gal 

$0.65 

none in W 

$0.05 off for cash if tank is owned 

prepay price guarantee: $0.50igal 

$0.1 Olga1 if tank rented 

< 250 galiyr: $0.1 Oigal more 

40% rented 

no rental fees 

Amerigas 

Salt Lake City UT 

Salt Lake City 

500 gal 

$0.86 

6% home heat 

volume: $0.02/gal 

virtually all 

500 gal: $80iyr 

1.000 gal: $1 0Slvr 

Blossman Gas 

Pensacola FL 

MS, FL, NC 

< 250 gal 

< 250 gallyear 

$1.14 

none 

new customer: $0.99 

for volume: if > 100 gal. $1.06; 

> 150 gal, $1.00; >500 gal, $0.95 

500 gal tanks are owned 

250 gal: $25-48/yr, depends on use 

I 00 e al: $36 - 7 2 lvr. denends on use 

Corbin Gas 

Atlanta GA 

Metro Atlanta 

250,330 gal 

$0.99 

-7%, depends on county 

no new customer discount 

60% rented 

$30-80lyr, depends on usage 

EE-RM-STD-98-440 COMMENT 241ZZ

Table 5. Propane Prices 

Company Suburban Propane River Country Co-op (Cenex) Eastern Propane 

Location Gilroy CA Chippewa Falls WI Rochester NH 

Territory 3 counties northwest WI southern ME 

Typical tank size 250,500 gal 500 gal 500 gal 

Typical Usage 4 people, 2000 SF horne: 500 gal/yr 

$/gal, regular $1.45 winter, $1.30 summer $0.65 $0.98-1.01 

Tax 7.25-S.25%, depends on locality none in WI unsure 

$/gal, discount new customer: $1.16 winter $0.05 off for cash if tank is owned $0.05/gal if paid in 15 days 

new customer: $1.03 summer prepay price guarantee: $0.50/gal 

$/gal, addtl will-call delivery: $O.IO/gal $O.lO/gal if tank rented 

< 250 gal/yr: $0.1 O/gal more 

Tank rentals most are rented 40% rented no rentals 

Rental fees $65/yr for 250 gal no rental fees 

$90/vr for 500 £!al 

Company Blossman Gas Corbin Gas Amerigas 

Location Pensacola FL Atlanta GA Salt Lake City UT 

Territory MS,FL,NC Metro Atlanta Salt Lake City 

Typical tank size < 250 gal 250,330 gal 500 gal 

Typical Usage < 250 gal/year 

$/gal, regular $1.14 $0.99 $0.86 

Tax none ~ 7%, depends on county 6% horne heat 

$/gal, discount new customer: $0.99 no new customer discount volume: $0.02/gal 

for volume: if> 100 gal. $1.06; 

> 150 gal, $1.00; >500 gal, $0.95 

Sigal, addtl 

Tank rentals 500 gal tanks are owned 60% rented virtually all 

Rental fees 250 gal: $25-48/yr, depends on use $30-80/yr, depends on usage 500 gal: $80/yr 

JOO £!al: $36-72/vr. tlenentl~ nn use LOOO £!al' $105/vr 

Company All-Star Gas United Propane Dixie Gas 

Location Galveston TX Salisbury MD Verona VA 

Territory Bolivar Island Maryland VA, some ofWV 

Typical tank size 120 gal, 250 gal 500 gal 325,500 gal 

Typical Usage 

$/gal, regular $1.41 $1.15 $1.05 

Tax none depends on county; no state tax none in V A, some in WV 

$/gal, discount new customer: $1.10 $O.OS/gal iftank is owned > 800 gal/yr: $1. 02 

prepay, >350 gal/yr: $0.83-0.99 2% offifpaid within 10 days 

$/gal, addtl < 400 gal/yr: $1.18 

Tank rentals 50-60% rented 25% rented no rentals 

Rental fees all size tanks: $S5/yr depends on usage (gallons/year) 

ave $12.5/atr for 500 gal tank 

]: \marginal\FinalDraftReport7 -29-99. wpd 21 
VI. TAXES 

Overview 

The marginal energy prices that consumers experience can be affected by the taxes that those consumers pay as part 

of their energy bills. Those energy bills can include taxes in two fundamentally different ways. First, tax obligations 

that an energy utility itself incurs (such as federal income taxes and property taxes) are most often considered to be 

part of the utility's revenue requirements. Consequently, such taxes are embedded within approved utility rates; they 

are not itemized on utility bills (even the unbundled bills that are becoming more prevalent in the utility industry, 

particularly as electricity restructuring occurs in more states). Prices paid by consumers are higher (than they 

otherwise would be) to the degree that these sorts of taxes are included in their utilities' base rates. But, because 

consumer energy savings (from using appliances that are more energy efficient) would not directly and immediately 

reduce the level of these embedded taxes, the existence of these taxes does not affect the calculation of consumers' 

marginal energy prices in a manner that is relevant to this research project. 

The second, and more directly visible, way in which consumers pay taxes when they pay their energy bills is through 

paying various sorts of taxes (or fees) as direct add-ons to their energy bills. These taxes (such as utility users taxes, 

franchise fees, and regulatory fees) are often called "pass-through" taxes. They are taxes assessed by state andlor 

local jurisdictions. They are collected by utilities on behalf of consumers and then are passed on by those utilities to 

the jurisdictions that imposed them. Since these taxes are not considered to be part of a utility's revenue 

requirements, they are not embedded within utility rates. Rather, such taxes are listed as line items on consumers' 

bills. The marginal prices that consumers pay for energy are directly affected by the existence of this type of tax; 

indeed, consun~ers' rllarginal energy prices are higher than they would be if these taxes and fees did not exist. This 

type of tax is usually assessed as either a fixed percentage of the pre-tax energy bill or as a fixed dollar amount per 

energy unit. In some instances, pass-through taxes using both types of assessment mechanisms are added to 

consumers' bills. For either type of pass-through tax assessment, the consumption of less energy (due to using 

appliances that are more energy-efficient) will reduce (on a direct and immediate basis) the tax portion of the energy bill 

as well as the non-tax portion of that bill. For that reason, this research project is interested in the degree of those 

savings. 

Range and Magnitude of Pass-Through Taxes 

Because pass-through taxes are assessed by individual states and local jurisdictions, they can vary widely in their 

magnitude, even within the same utility service territory (see below). There are four main types of pass-through taxes: 

1) state sales taxes (see Appendix 5, Table 6) 

2) state utility regulatory assessments (see Appendix 5, Table 7) 

3) state taxes on units of energy (see Appendix 5, Table 8) 

4) local utility users taxes 

These taxes individually range in size from being non-existent in some areas to being as much as 10% in other areas9. 

In addition, because these sorts of taxes are set by various government agencies, they rarely appear on published 

utility tariffs. They appear as line item charges on consumers' actual bills. Therefore, determining what these taxes 

(especially those that are locally imposed) really are for particular customers of particular utilities would be a very 

difficult task. As EIA said in its 1994 report "End-Use Taxes: Current EIA Practices," 

"Adding statutory taxes to pre-tax prices is a tedious, error-prone process. . . . Because of the wide 

variety of taxes imposed by local governments, the tax adjustment is only practicable at the State level."'0 

The highest single tax rate for energy that the research uncovered was for one small city in California that imposes a 

utility users tax of 9.9%. Otherwise, the highest utility users tax rate the research uncovered is 8%. State-level sales 

taxes and other state-level taxes can raise the effective tax to consumers beyond these levels, though. 

"' EIA, "End Use Taxes: Current EIA Practices," August 1994, page 29. 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

VI. TAXES 

Overview 

The marginal energy prices that consumers experience can be affected by the taxes that those consumers pay as part 

of their energy bills. Those energy bills can include taxes in two fundamentally different ways. First, tax obligations 

that an energy utility itself incurs (such as federal income taxes and property taxes) are most often considered to be 

part of the utility's revenue requirements. Consequently, such taxes are embedded within approved utility rates; they 

are not itemized on utility bills (even the unbundled bills that are becoming more prevalent in the utility industry, 

particularly as electricity restructuring occurs in more states). Prices paid by consumers are higher (than they 

otherwise would be) to the degree that these sorts of taxes are included in their utilities' base rates. But, because 

consumer energy savings (from using appliances that are more energy efficient) would not directly and immediately 

reduce the level of these embeddcd taxes, the existence of these taxes does not affect the calculation of consumers' 

marginal energy prices in a manner that is relevant to this research project. 

The second, and more directly visible, way in which consumers pay taxes when they pay their energy bills is through 

paying various sorts of taxes (or fees) as direct add-ons to their energy bills. These taxes (such as utility users taxes, 

franchise fees, and regulatory fecs) are often called "pass-through" taxes. They are taxes assessed by state and/or 

local jurisdictions. They are collected by utilities on behalf of consumers and then are passed on by those utilities to 

the jurisdictions that imposed them. Since these taxes are not considered to be part of a utility's revenue 

requirements, they are not embedded within utility rates. Rather, such taxes are listed as line items on consumers' 

bills. The marginal prices that consumers pay for energy are directly affected by the existence of this type oftax; 

indeed, consumers' marginal energy prices are higher tllan they would be if these taxes and fees did not exist. This 

type of tax is usually assessed as either a fixed percentage of the pre-tax energy bill or as a fixed dollar amount per 

energy unit. In some instances, pass-through taxes using both types of assessment mechanisms are added to 

consumers' bills. For either type of pass-through tax assessment, the consumption ofless energy (due to using 

appliances that are more energy-efficient) will reduce (on a direct and immediate basis) the tax portion ofthe energy bill 

as well as the non-tax portion of that bill. For that reason, this research project is interested in the degree of those 

savings. 

Range and Magnitude of Pass-Through Taxes 

Because pass-through taxes are assessed by individual states and local jurisdictions, they can vary widely in their 

magnitude, even within the same utility service territory (see below). There are four main types of pass-through taxes: 

1) state sales taxes (see Appendix 5, Table 6) 

2) state utility regulatory assessments (see Appendix 5, Table 7) 

3) state taxes on units of energy (see Appendix 5, Table 8) 

4) local utility users taxes 

These taxes individually range in size from being non-existent in some areas to being as much as 10% in other areas 9. 

In addition, because these sorts of taxes are set by various government agencies, they rarely appear on published 

utility tariffs. They appear as line item charges on consumers' actual bills. Therefore, determining what these taxes 

(especially those that are locally imposed) really are for particular customers of particular utilities would be a very 

difficult task. As EIA said in its 1994 report "End-Use Taxes: Current EIA Practices," 

"Adding statutory taxes to pre-tax prices is a tedious, error-prone process. . .. Because ofthe wide 

variety of taxes imposed by local governments, the tax adjustment is only practicable at the State level."l0 

9 The highest single tax rate for energy that the research uncovered was for one small city in California that imposes a 

utility users tax of9.9%. Otherwise, the highest utility users tax rate the research uncovered is 8%. State-level sales 

taxes and other state-level taxes can raise the effective tax to consumers beyond these levels, though. 

10 EIA, "End Use Taxes: Current ErA Practices," August 1994, page 29. 

J :\marginal\FinalDraftReport7 -29-99. wpd 22 
The EIA report provides a state-by-state list of sales and use taxcs on end-use energy consumption." It also indicates 

the states in which energy consumption (by energy fuel type) is exempt from state sales and use taxes. In addition, 

this report includes an estimate of the magnitude of the possible effect of including State sales taxes in energy prices. 

For electricity prices, EIA's report shows that on average electricity prices for the residential sector rise only 0.9% with 

the inclusion of state sales taxes. The range of increase in individual states is from 2% to 6.5% for those states where 

electricity consumption in the residential sector is not exempt from sales tax. For the commercial sector, the rise in 

electricity prices due to the inclusion of the applicable state sales taxes would be 4.2% on average for the nation, with 

a range by state of from 3% to 8% for those states where electricity consumption is not exempt from sales tax. (EIA's 

report does not include similar estimates of the effect of including state sales taxes on the consumption of natural gas.) 

To get a sense for the magnitude and variation of other state and local-level taxes, various sources were consulted. 

The most comprehensive survey of other taxes on a state level was found in a report on state and local taxation by the 

Alliance to Save Energy (ASE).I2 This report reveals that percentage taxes for regulatory assessments in the 39 states 

that impose them (see Appendix 5, Table 7; taken from ASE's report) range in size from 0.02% to 0.50%. In other 

states, such as California, cities can charge consumers a utility users tax. One city imposes a 9.9% utility users tax. l3 

Several other cities set this tax as high as 7.5% or 8.0%. 

Taxes or fees on units of energy consumed range in size from 0.01 5 cents per kwh to 0.26 cents per kWh in the nine 

states (see Appendix 5, Table 8; taken from ASE's report) that impose that type of tax on energy used by consumers. 

In California, for instance, while PG&E customers pay a $0.0002/kWh in support of the California Energy Commission, 

customers of SDG&E also pay regulatory fees amounting to $0.00012/kWh on the electricity portion of their bills and 

$0.00078/therm on the gas portion of their bills. States that have no sales taxes are not prohibited from imposing 

electric and gas regulatory assessments and kWh taxes on electricity. Variations and anomalies such as these make an 

exhaustive study of the actual taxes and fees associated with utility energy bills around the country, and even those 

associated with this project's modest utility sample, beyond our scope. Indeed, it is unlikely that anyone has ever 

conducted a survey of state and local taxes comprehensive enough to capture the type of utility users taxes that can 

occur in California alone.I4 As a result, the effect of taxes on marginal prices was addressed in the manner outlined 

below. 

Imvact of Taxes on Marginal Prices 

Pass-through taxes increase consumers' energy bills; they also increase consumers' marginal prices. For a 

percentage-based pass-through tax (like the utility users tax described above), which is applied to the whole energy 

bill (including any fixed charges, such as customer charges), the consumer's marginal price is increased by the 

percentage size of the tax itself. Thus, an 8% tax would increase a consumer's marginal price by 8%. Consumers taxed 

at the 2% level would have a marginal price 2% higher than if they paid no pass-through taxes. Because taxes on units 

of energy (like the per kwh regulatory fees described above) do not apply to fixed charges, they raise marginal prices 

in a non-linear, hut still minor and easily calculated, manner. 

Imuact of Taxes on Savin~s 

Since consumers both pay and save at a rate equal to the marginal price, other things being equal, reductions in 

energy consumption due to new appliance energy efficiency standards will cause reductions in the size of the passthrough 

'I Ibid, Table B2. The relevant portions of EIA's Table B2 are shown in Appendix 5, Table 6 of this report. 

I* The Alliance to Save Energy, "State and Local Taxation: Energy Policy by Accident," 1994. 

l3 For example, in the Pacific Gas and Electric Company (PG&E) service territory of northern California, the size of the 

percentage-based utility users tax, which is applied to both the electricity and natural gas components of a PG&E 

customer's bill, varies from 0% in many of the cities and counties served by PG&E to as much as 9.9% in one city, 

8.0% in five cities and 7.5% in eight cities. A total of 68 cities in PG&E's service area currently impose this type of tax 

on energy consumption by their residents. While higher utility users tax rates may exist in California or in other states, 

no evidence of them was found. 

l4 Andrew Hoerner, Center for a Sustainable Economy, personal communication, February 10, 1999. Additional 

sources whose web sites were consulted for information on state and local energy taxcs included The Federation of 

Tax Administrators, EPRI, and The Multistate Tax Commission. 

EE-RM-STD-98-440 COMMENT 241ZZ

The ElA report provides a state-by-state list of sales and use taxes on end-use energy consumption.!! It also indicates 

the states in which energy consumption (by energy fuel type) is exempt from state sales and use taxes. In addition, 

this report includes an estimate of the magnitude of the possible effect of including State sales taxes in energy prices. 

For electricity prices, ElA's report shows that on average electricity prices for the residential sector rise only 0.9% with 

the inclusion of state sales taxes. The range of increase in individual states is from 2% to 6.5% for those states where 

electricity consumption in the residential sector is not exempt from sales tax. For the commercial sector, the rise in 

electricity prices due to the inclusion ofthe applicable state sales taxes would be 4.2% on average for the nation, with 

a range by state offrom 3% to 8% for those states where electricity consumption is not exempt from sales tax. (EIA's 

report does not include similar estimates ofthe effect of including state sales taxes on the consumption of natural gas.) 

To get a sense for the magnitude and variation of other state and local-level taxes, various sources were consulted. 

The most comprehensive survey of other taxes on a state level was found in a report on state and local taxation by the 

Alliance to Save Energy (ASE).!2 This report reveals that percentage taxes for regulatory assessments in the 39 states 

that impose them (see Appendix 5, Table 7; taken from ASE's report) range in size from 0.02% to 0.50%. In other 

states, such as California, cities can charge consumers a utility users tax. One city imposes a 9.9% utility users tax. J3 

Several other cities set this tax as high as 7.5% or 8.0%. 

Taxes or fees on units of energy consumed range in size from 0.015 cents per kWh to 0.26 cents per kWh in the nine 

states (see Appendix 5, Table 8; taken from ASE's report) that impose that type of tax on energy used by consumers. 

In California, for instance, while PG&E customers pay a $0.0002/kWh in support of the California Energy Commission, 

customers ofSDG&E also pay regulatory fees amounting to $0.000 12/kWh on the electricity portion of their bills and 

$O.00078/therm on the gas portion of their bills. States that have no sales taxes are not prohibited from imposing 

electric and gas regulatory assessments and kWh taxes on electricity. Variations and anomalies such as these make an 

exhaustive study of the actual taxes and fees associated with utility energy bills around the country, and even those 

associated with this project's modest utility sample, beyond our scope. Indeed, it is unlikely that anyone has ever 

conducted a survey of state and local taxes comprehensive enough to capture the type of utility users taxes that can 

occur in California alone.!4 As a result, the effect of taxes on marginal prices was addressed in the manner outlined 

below. 

Impact of Taxes on Marginal Prices 

Pass-through taxes increase consumers' energy bills; they also increase consumers' marginal prices. For a 

percentage-based pass-through tax (like the utility users tax described above), which is applied to the whole energy 

bill (including any fixed charges, such as customer charges), the consumer's marginal price is increased by the 

percentage size of the tax itself. Thus, an 8% tax would increase a consumer's marginal price by 8%. Consumers taxed 

at the 2% level would have a marginal price 2% higher than if they paid no pass-through taxes. Because taxes on units 

of energy (like the per kWh regulatory fees described above) do not apply to fixed charges, they raise marginal prices 

in a non-linear, but still minor and easily calculated, manner. 

Impact of Taxes on Savings 

Since consumers both pay and save at a rate equal to the marginal price, other things being equal, reductions in 

energy consumption due to new appliance energy efficiency standards will cause reductions in the size of the passthrough 

II Ibid, Table B2. The relevant portions ofElA's Table B2 are shown in Appendix 5, Table 6 of this report. 

12 The Alliance to Save Energy, "State and Local Taxation: Energy Policy by Accident," 1994. 

13 For example, in the Pacific Gas and Electric Company (PG&E) service territory of north em California, the size of the 

percentage-based utility users tax, which is applied to both the electricity and natural gas components of a PG&E 

customer's bill, varies from 0% in many of the cities and counties served by PG&E to as much as 9.9% in one city, 

8.0% in five cities and 7.5% in eight cities. A total of68 cities in PG&E's service area currently impose this type of tax 

on energy consumption by their residents. While higher utility users tax rates may exist in California or in other states, 

no evidence of them was found. 

!4 Andrew Hoerner, Center for a Sustainable Economy, personal communication, February 10, 1999. Additional 

sources whose web sites were consulted for information on state and local energy taxes included The Federation of 

Tax Administrators, EPRI, and The Multistate Tax Commission. 

J : \marginal\FinalDraftReport 7 -29-99. wpd 23 
taxes consumers pay, whether those taxes are percentage taxes or per-unit taxes. The resulting reductions in tax 

payments are savings to the consumer directly associated with the more efficient appliances. Consumers facing 8% 

tax prices will save an additional 8% beyond what they would have saved if they did not have to pay such taxes in the 

first place. Consumers with 2% taxes will save an additional 2% of their (pre-tax) bill when their energy consumption is 

reduced by using appliances that are more energy-efficient. 

Treatment of Taxes in This Research Project 

This project used two main approaches to derive marginal prices. The RECS-based approach used actual consumer 

bills that already include all taxes (both those embedded in utility rates and pass-through taxes). Therefore, the 

marginal prices derived from those bills "automatically" take into account the effects of changes in taxes due to 

changes in energy consumption. For use in life-cycle cost or savings analyses, no further adjustments are needed to 

reflect the existence of taxes in the marginal prices derived from the RECS-based approach. 

The MAISP-based approach involved using distributions of both building energy usage levels (from the MAISP 

database) and utility tariffs to derive commercial electricity hills. Because those utility tariffs do not already include 

pass-through taxes, the marginal prices derived by this approach are smaller than they otherwise would be to the 

degree that pass-through taxes apply to the sampled buildings. Therefore, when analyzing the results of this research 

effort, consideration should be given to the applicability of the four types of pass-through taxes mentioned here 

(state-level sales and use taxes, other state-level % taxes, $ per energy unit taxes, and locally imposed utility users 

taxes) to derive marginal electricity prices for commercial buildings that include taxes. While adding the pass-through 

taxes applicable at the state level (from the EIA and ASE report tables shown below) would involve a non-trivial and 

very time-consuming, but still relatively straightforward exercise, there is no clear way to obtain the level of the locally 

determined utility users tax that might apply to the sampled buildings. Therefore, because of the lack of 

comprehensive information on the magnitude of the locally imposed utility users taxes, one must be aware that the 

marginal prices so calculated may not be as high as they really should be wherever those taxes would apply. Thus, 

marginal prices and savings for an individual consumer may still be understated by from 0% to 10%. 

Taxes and Average Prices 

Once marginal prices are calculated and taxes are considered, a comparison with regularly published average prices, 

such as those issued by EIA, may be desired. Those published average prices are calculated by dividing utility 

revenues by sales. With regard to whether or not these published average prices include taxes, the underlying 

question is which taxes are included in utility revenues. It is clear that embedded taxes are included within the utility 

revenue requirements that form the basis for utility revenues through the traditional rate-setting process; however, it is 

not clear whether pass-through taxes are considered to be part of utility revenues. In response to the request for 

clarity on this issue, EIA referred us to a page in EIA's "Electric Sales and Revenue 1997" report that contains the 

following statement: 

"Taxing authorities differ on whether a specific tax is assessed to the utility or to the consumer, a difference 

that in turn determines whether or not the tax is included in the electric utility's operating revenue^."'^ 

Therefore, it is concluded that one cannot know for sure whether pass-through taxes, especially those imposed by 

local government agencies, are included in utility revenues and thus included in average prices. While this lack of 

certainty about the inclusion of pass-through taxes in published average prices jeopardizes any comparison of 

average and marginal prices, it is believed that comparing RECS-based marginal prices with published average prices 

would be more sound, for example, than comparing marginal prices derived by the MAISP-based approach (where 

taxes must be added in manually) with published average prices. 

Tax Feedback Loons 

Some might argue that local governments (and perhaps even state governments) might raise their locally-imposed 

utility tax rates if the revenues they obtain from consumers' energy bills decline because consumers use less energy as 

a consequence of improved appliance energy-efficiency standards. This project did not attempt to analyze such 

" EIA, "Energy Sales and Revenue 1997," page 1 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

taxes consumers pay, whether those taxes are percentage taxes or per-unit taxes. The resulting reductions in tax 

payments are savings to the consumer directly associated with the more efficient appliances. Consumers facing 8% 

tax prices will save an additional 8% beyond what they would have saved ifthey did not have to pay such taxes in the 

first place. Consumers with 2% taxes will save an additional 2% of their (pre-tax) bill when their energy consumption is 

reduced by using appliances that are more energy-efficient. 

Treatment of Taxes in This Research Project 

This project used two main approaches to derive marginal prices. The RECS-based approach used actual consumer 

bills that already include all taxes (both those embedded in utility rates and pass-through taxes). Therefore, the 

marginal prices derived from those bills "automatically" take into account the effects of changes in taxes due to 

changes in energy consumption. For use in life-cycle cost or savings analyses, no further adjustments are needed to 

reflect the existence of taxes in the marginal prices derived from the RECS-based approach. 

The MAISY"'-based approach involved using distributions of both building energy usage levels (from the MAISY® 

database) and utility tariffs to derive commercial electricity bills. Because those utility tariffs do not already include 

pass-through taxes, the marginal prices derived by this approach are smaller than they otherwise would be to the 

degree that pass-through taxes apply to the sampled buildings. Therefore, when analyzing the results of this research 

effort, consideration should be given to the applicability ofthe four types of pass-through taxes mentioned here 

(state-level sales and use taxes, other state-level % taxes, $ per energy unit taxes, and locally imposed utility users 

taxes) to derive marginal electricity prices for commercial buildings that include taxes. While adding the pass-through 

taxes applicable at the state level (from the EIA and ASE report tables shown below) would involve a non-trivial and 

very time-consuming, but still relatively straightforward exercise, there is no clear way to obtain the level of the locally 

determined utility users tax that might apply to the sampled buildings. Therefore, because ofthe lack of 

comprehensive information on the magnitude of the locally imposed utility users taxes, one must be aware that the 

marginal prices so calculated may not be as high as they really should be wherever those taxes would apply. Thus, 

marginal prices and savings for an individual consumer may still be understated by from 0% to 10%. 

Taxes and Average Prices 

Once marginal prices are calculated and taxes are considered, a comparison with regularly published average prices, 

such as those issued by EIA, may be desired. Those published average prices are calculated by dividing utility 

revenues by sales. With regard to whether or not these published average prices include taxes, the underlying 

question is which taxes are included in utility revenues. It is clear that embedded taxes are included within the utility 

revenue requirements that form the basis for utility revenues through the traditional rate-setting process; however, it is 

not clear whether pass-through taxes are considered to be part of utility revenues. In response to the request for 

clarity on this issue, EIA referred us to a page in EIA's "Electric Sales and Revenue 1997" report that contains the 

following statement: 

"Taxing authorities differ on whether a specific tax is assessed to the utility or to the consumer, a difference 

that in turn determines whether or not the tax is included in the electric utility's operating revenues.,,15 

Therefore, it is concluded that one cannot know for sure whether pass-through taxes, especially those imposed by 

local government agencies, are included in utility revenues and thus included in average prices. While this lack of 

certainty about the inclusion of pass-through taxes in published average prices jeopardizes any comparison of 

average and marginal prices, it is believed that comparing RECS-based marginal prices with published average prices 

would be more sound, for example, than comparing marginal prices derived by the MAISY®-based approach (where 

taxes must be added in manually) with published average prices. 

Tax Feedback Loops 

Some might argue that local governments (and perhaps even state governments) might raise their locally-imposed 

utility tax rates if the revenues they obtain from consumers' energy bills decline because consumers use less energy as 

a consequence of improved appliance energy-efficiency standards. This project did not attempt to analyze such 

15 EIA, "Energy Sales and Revenue 1997," page I. 

J :\marginal\FinalDraftReport7 -29-99. wpd 24 
positions. It is noted that while similar arguments have been made during debates on electric industry restructuring 

occurring around the country, there is no widespread agreement on their validity. 

Conclusion 

This report has not attempted to account for all appropriate taxes in the calculation of marginal energy prices in the 

analysis because it is essentially impossible to do so. Comparisons between published average energy prices and 

calculated marginal energy prices must be made with care because they are subject to error for the same reasons. This 

report indicates the degree of error that may be present. 

The derivation of commercial consumer marginal electricity prices excludes pass-through taxes and thus 

underestimates consumers' marginal bill savings by 0% to as much as lo%, depending upon the level of state and 

locally imposed taxes. In contrast, the derivation of marginal electricity and gas prices for the residential sector from 

actual consumer bills from RECS includes pass-through taxes; thus, the underestimation of consumers' marginal bill 

savings inherent in the commercial analysis does not exist in the residential analysis. 

EE-RM-STD-98-440 COMMENT 241ZZ

positions. It is noted that while similar arguments have been made during debates on electric industry restructuring 

occurring around the country, there is no widespread agreement on their validity. 

Conclusion 

This report has not attempted to account for all appropriate taxes in the calculation of marginal energy prices in the 

analysis because it is essentially impossible to do so. Comparisons between published average energy prices and 

calculated marginal energy prices must be made with care because they are subject to error for the same reasons. This 

report indicates the degree of error that may be present. 

The derivation of commercial consumer marginal electricity prices excludes pass-through taxes and thus 

underestimates consumers' marginal bill savings by 0% to as much as 10%, depending upon the level of state and 

locally imposed taxes. In contrast, the derivation of marginal electricity and gas prices for the residential sector from 

actual consumer bills from RECS includes pass-through taxes; thus, the underestimation of consumers' marginal bill 

savings inherent in the commercial analysis does not exist in the residential analysis. 

J: \marginaJ\FinalDraftReport7 -29-99. wpd 25 
A~~endix 1. Tariffs Used in the Commercial Analvsis 

Utility 

Alabama Power 

Alabama Power 

Alabama Power 

Appalachian Power Co 

Arizona Public Service 

Arizona Public Service 

Boston Edison 

Boston Edison 

Central Power and Light 

Cleveland Electric Illum. Co 

Cleveland Electric Illurn. Co 

Commonwealth Edison Co 

Detroit Edison 

Jersey Central Power & Ligh 

Madison G&E 

Madison G&E 

NSP 

NSP 

NSP 

NSP 

State 

AL 

AL 

AL 

VA 

AZ 

AZ 

MA 

MA 

TX 

OH 

OH 

IL 

MI 

NJ 

WI 

WI 

MN 

MN 

WI 

WI 

Tariff 

LPEM 

LPM 

LPS 

SGS 

E30 

E32 

G-l 

G-2 

21 

Gen Comm 

Lg Comm 

Rate 6 nonTOU 

D3 

GS 

Cg-l 

Cg-5 

AIO, D12, El3 

A14, D16,E15 

Cg-2 

Cg-2 unmetered 

Utility 

NSP 

Niagara Mohawk Power Corp 

Niagara Mohawk Power Corp 

Ohio Power Company 

PEPCO 

Pacific Gas and Electric 

Pacific Gas and Electric 

Pennsylvania Power & Light 

Pennsylvania Power & Light 

Poudre Valley 

Savannah Electric and Power 

Seattle City Light 

Seattle City Light 

SoCal Edison 

SoCal Edison 

Union Electric Co. 

Union Electric Co. 

Virginia Power Company 

Wisconsin Electric Power 

State 

WI 

NY 

NY 

OH 

DC 

CA 

CA 

PA 

PA 

CO 

GA 

WA 

WA 

CA 

CA 

MO 

MO 

VA 

WI 

Tariff 

Cg-5 

SC-2 Demand 

SC-2 Non-Demand 

GS3-11 

GS 

Al 

A10 

GS-I 

GS-3 

B-97 

GS-7 

Rate31 

Rate 34 

GS-1 

GS-2 

No.2 

No.3 

GS-1 

Cg 1 

EE-RM-STD-98-440 COMMENT 241ZZ

Appendix I. 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 Corj: NY SC-2 Demand 

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

Appalachian Power Co VA SGS Ohio Power Company OR GS3-11 

Arizona Public Service AZ E30 PEPCO DC OS 

Arizona Public Service AZ E32 Pacific Gas and Electric CA Al 

Boston Edison MA G-l Pacific Gas and Electric CA AIO 

Boston Edison MA G-2 Pennsylvania Power & Light PA OS-1 

Central Power and Light TX 21 Pennsylvania Power & Light PA OS-3 

Cleveland Electric IlIum. Co OR GenComm Poudre VaHey CO B-97 

Cleveland Electric IlIum. Co OR LgComm Savannah Electric and Powe GA OS-7 

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

Detroit Edison MI TOU Seattle City Light WA Rate 34 

Jersey Central Power & Ligh NJ D3 SoCal Edison CA OS-I 

Madison G&E WI GS SoCal Edison CA OS-2 

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

NSP MN Cg-5 Union Electric Co. MO No.3 

NSP MN AIO, D12, E13 Virginia Power Company VA GS-l 

NSP WI A14, D16,E15 Wisconsin Electric Power WI Cg 1 

NSP WI Cg-2 

Cg-2 unmetered 

J :\marginal\FinalDraftReport7 -29-99. wpd 26 
Appendix 2. Examples of Commercial Bill Calculations 

Commercial tariffs generally fall into two categories, non-demand and demand. Demand is measured in kilowatts (kW) 

required by the customer over a relatively short time period, often 30 minutes. Meters are installed at the customer site 

to measure monthly and sometimes annual peak demand, and these are used to compute the demand component of the 

customer's bill. 

Typically demand tariffs are assigned to large customers, such as office buildings, hospitals, and malls. Non-demand 

tariffs are assigned to smaller customers, such as restaurants and small grocery stores. An example of each is 

illustrated below. 

Example I: Non-demand tariff 

Utility: Alabama Power 

Tariff: Commercial, LPS 

Customer Base Charge: $8.65lmo. 

Summer Energy Charge: $O.O6223kWh 

Winter Energy Charge: $O.O5843kWh 

Customer: Usage: 2547 kwh 

Month: January 

Baseline Bill: $157.47 = 8.65 + ($0.05843 * 2547) 

Bill with 5% Usage Decrement: (Figures below include rounding) 

Usage: 2420 kwh = 2547 kWh - (0.05 * 2547 kwh) 

Bill: $150.03 = 8.65 + ($0.05843 * 2420) 

Monthly Average Price: $0.06183kWh = $157.47 / 2547 

Monthly Marginal Price: $0.05843lkWh = Bill Savings I Energy Savings 

= ($157.47 - $150.03) 1 (2547 - 2420) 

Example 2: Demand Tariff 

Utility: 

Tariff: 

Tariff MinIMax kW: 

Customer Base Charge: 

Energy Block Cutoffs: 

Energy Charge, Block 1 : 

Energy Charge, Block 2: 

Demand Charge Component 1 : 

Demand Charge Component 2: 

Customer Usage: 

Month: 

Monthly Peak kW: 

Baseline Bill: 

Bill with 5% Energy and 

4% Demand Decrement: 

Monthly Average Price: 

Monthly Marginal Price: 

Southern California Edison 

Commercial GS2 

80 - 2000 kW/hr (measured over 15 minutes) 

$60.30/mo. 

Block 1 : 300 kWh/kW (based on monthly peak kW) 

$O.O7692kWh 

$0.04391kWh 

$5.40 (Summer), $5.40 (Winter) 

$7.75 (Summer), $0.00 (Winter) 

150388 kwh 

May 

7 1 kW (over 15 minutes) 

$7750.35 = $60.30 + ((300*71)*$0.07692) + ((150388 - (300*71))*$0.04391) 

+ (71 *$5.40) 

$7376.71 = $60.30 + ((300*(71*0.96))*$0.07692) 

+ (((150388*0.95) - (300*(71*0.96)))*$0.04391) 

+ ((71 *0.96)*$5.40) 

$0.05 163kWh = 7376.7 1 1 (1 50388*0.95) 

$0.04969/kWh = Bill Savings 1 Energy Savings 

= ($7750.35 - $7376.71) /(I50388 - (150388 * 0.95)) 

EE-RM-STD-98-440 COMMENT 241ZZ

Appendix 2. Examples of Commercial BiII Calculations 

Commercial tariffs generally fall into two categories, non-demand and demand. Demand is measured in kilowatts (kW) 

required by the customer over a relatively short time period, often 30 minutes. Meters are installed at the customer site 

to measure monthly and sometimes annual peak demand, and these are used to compute the demand component of the 

customer's bill. 

Typically demand tariffs are assigned to large customers, such as office buildings, hospitals, and malls. Non-demand 

tariffs are assigned to smaller customers, such as restaurants and small grocery stores. An example of each is 

illustrated below. 

Example 1: Non-demand tariff 

Utility: 

Tariff: 

Customer Base Charge: 

Summer Energy Charge: 

Winter Energy Charge: 

Customer: Usage: 

Month: 

Baseline Bill: 

Bill with 5% Usage Decrement: 

Usage: 

Bill: 

Monthly Average Price: 

Monthly Marginal Price: 

Example 2: Demand Tariff 

Utility: 

Tariff: 

Tariff MinIMax kW: 

Customer Base Charge: 

Energy Block Cutoffs: 

Energy Charge, Block I: 

Energy Charge, Block 2: 

Demand Charge Component 1: 

Demand Charge Component 2: 

Customer Usage: 

Month: 

Monthly Peak kW: 

Baseline Bill: 

Bill with 5% Energy and 

4% Demand Decrement: 

Monthly Average Price: 

Monthly Marginal Price: 

Alabama Power 

Commercial, LPS 

$8.65/mo. 

$0.06223IkWh 

$0.05843IkWh 

2547 kWh 

January 

$157.47 = 8.65 + ($0.05843 * 2547) 

(Figures below include rounding) 

2420 kWh = 2547 kWh - (0.05 * 2547 kWh) 

$ 150.Q3 = 8.65 + ($0.05843 * 2420) 

$0.06183IkWh = $157.47 I 2547 

$0.05843/kWh = Bill Savings I Energy Savings 

= ($157.47 - $150.03) I (2547 - 2420) 

Southern California Edison 

Commercial GS2 

80 - 2000 kW/hr (measured over 15 minutes) 

$60.30/mo. 

Block I: 300 kWhlkW (based on monthly peak kW) 

$0.07692IkWh 

$0.04391IkWh 

$5.40 (Summer), $5.40 (Winter) 

$7.75 (Summer), $0.00 (Winter) 

150388 kWh 

May 

71 kW (over 15 minutes) 

$7750.35 = $60.30 + «300*71)*$0.07692) + «(150388 - (300*71»*$0.04391) 

+ (71 *$5.40) 

$7376.71 $60.30 + «300*(71 *0.96»*$0.07692) 

+ «((150388*0.95) - (300*(71 *0.96»)*$0.04391) 

+ «71 *0.96)*$5.40) 

$0.05163IkWh = 7376.71 1(150388*0.95) 

$0.04969/kWh = Bill Savings I Energy Savings 

= ($7750.35 - $7376.71) I (150388 - (150388 * 0.95» 

J : \marginal\FinalDraftReport 7 -29-99. wpd 27 
Aunendix 3. Demand Decrement Due to Standards - The Role of Lighting Coincidence and Diversitv 

In the process of marginal price estimation, it is 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) are 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 fcot T12 systems 

would save about 15% of lighting energy used by fluorescent lamps and since about 30-35% of building energy use is 

for lighting, about 5% of building energy use will be saved if EEM ballasts are converted to electronic ballasts. The 

5% reduction was assumed to occur every month. 

It is also necessary to estimate the reduction in peak demand if building energy use is reduced by 5% 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, the coincidence factor was expected to be close 

to 1.0 since, whatever the maximum number of lamps that operate during the lighting peak, a very similar number of 

lamps can be expected to operate during the building peak. EEI has suggested using 0.9 for the default coincidence 

factor. '' 

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

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

Diversity is already accounted for because the actual building energy usage (which comes from the MAIST 

database) already excludes those lamps not in use during some parts of the day. Instead the concern is with lighting 

average kW because that is, in essence, reduced by 5% in the bill calculator when monthly energy use is reduced by 

5%. It is assumed that the lighting hours do not change when more efficient ballasts are installed. 

Diversity (D) = lighting peak kwllighting kW installed 

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, demand reduction is assumed to be 80% of energy savings. For 5% energy savings, it is 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. The 80% value relative to the average 

value is used because the monthly lighting energy use can be considered to be equal to some average kW times the 

nionthly lighting hours and the monthly lighting energy use is reduced by a fixed percentage. 

Ih 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. 

J:\marginal\FinalDraftReport7-29-99. wpd 28 

EE-RM-STD-98-440 COMMENT 241ZZ

Appendix 3. Demand Decrement Due to Standards - The Role of Lighting Coincidence and Diversity 

In the process of marginal price estimation, it is 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) are 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· f('ot TI2 systems 

would save about 15% of lighting energy used by fluorescent lamps and since about 30-35% of building energy use is 

for lighting, about 5% of building energy use will be saved ifEEM ballasts are converted to electronic ballasts. The 

5% reduction was assumed to occur every month. 

It is also necessary to estimate the reduction in peak demand if building energy use is reduced by 5% 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, the coincidence factor was expected to be close 

to 1.0 since, whatever the maximum number of lamps that operate during the lighting peak, a very similar number of 

lamps can be expected to operate during the building peak. EEl has suggested using 0.9 for the default coincidence 

factor. 16 

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 is already accounted for because the actual building energy usage (which comes from the MAISY® 

database) already excludes those lamps not in use during some parts of the day. Instead the concern is with lighting 

average kW because that is, in essence, reduced by 5% in the bill calculator when monthly energy use is reduced by 

5%. It is assumed that the lighting hours do not change when more efficient ballasts are installed. 

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

Some analysts might multiply coincidence (C) times diversity (D); that would not yield the desired ratio oflighting kW 

during the building peak to lighting average kW. 

In relative terms, demand reduction is assumed to be 80% of energy savings. For 5% energy savings, it is assumed 

that the demand reduction would be 4% of peak kW. This means that if500 identical fixtures are operating on average, 

that only 400 (or 80%) are operating during the building peak demand period. The 80% value relative to the average 

value is used because the monthly lighting energy use can be considered to be equal to some average kW times the 

monthly lighting hours and the monthly lighting energy use is reduced by a fixed percentage. 

16 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. 

J :\marginal\FinalDraftReport7 -29-99. wpd 28 
Aooendix 4: Weighting Method for Commercial Sam~le 

The composition of the commercial sample posed a dual weighting problem: distributing weights across buildings and 

across the approximately 20 tariffs that were applied to each building. The weighting approach solves these problems 

sequentially. The first step is to develop building weights, derived from the original MAISP building weight and 

scaling factors (based on kwh usage), as discussed below. This adjusted building weight is then apportioned across 

all the tariffs (weighted by annual MWh sales) that were applied to the building. The composite weighting factor for 

each buildingttariff pair combines both of these weighting factors. 

Calculation of Adiusted Building Weights 

Each building in the sample is viewed as representing a share of the commercial buildings in its region of the US. State 

and regional scaling factors were developed and used to rescale the individual buildings' MAISP weights to 

represent national usage, as described below. 

The first step was to account for the fact that the sample was a subset of the MAISY@ buildings for each state. For 

each of the seven states a state factor was computed: 

8. Sum of MAISY weights for all buildings in the state 

State Factor = 

Sum of MAISY 8. weights for sampled buildings in the state 

The statewide energy usage represented by each sampled building was then computed by multiplying the building's 

MAISP weight by its average annual energy usage and the state factor. For each sampled building i: 

Building i's Adjusted State Energy Use = Annual kwh, * MAISP Weight, * State Factor 

The next step was to scale state usage to regional usage. US Census Divisions were assigned to one of seven 

regions, each represented by a state in our sample. Census Divisions not directly represented by a state in our 

sample were assigned to a state representing the most similar adjacent division. The table below shows the mapping 

of sampled states to Census Divisions. 

Region Factors were then computed based on state and regional commercial retail electricity sales derived from 1998 

EIA data (see the table below). The Region Factors are the ratio of commercial sector electricity sales in the division(s) 

corresponding with each of the sampled states to the conlmercial sector electricity sales in each of those individual 

states. 

Assignment of Sampled States to Census Divisions 

for Weighting Calculations 

Regional Retail Commercial Sales Region Factor = State Retail Commercial Sales 

Sampled State 

New York 

Illinois 

Georgia 

Texas 

Colorado 

Washington 

California 

Applying the Region Factors was the next step in computing the Adjusted Building Weights for each building: 

Census Division(s) Represented 

Middle Atlantic, New England 

East North Cenhal, West North Central 

South Atlantic, East South Central 

West South Central 

Mountain 

Pacific Contiguous (excluding California), Pacific Noncontiguous 

California 

Building i's Adjusted Region Energy Use = Region Factor * Building i's Adjusted State Energy Use 

EE-RM-STD-98-440 COMMENT 241ZZ

Appendix 4: Weighting Method for Commercial Sample 

The composition of the commercial sample posed a dual weighting problem: distributing weights across buildings and 

across the approximately 20 tariffs that were applied to each building. The weighting approach solves these problems 

sequentially. The first step is to develop building weights, derived from the original MAISY® building weight and 

scaling factors (based on kWh usage), as discussed below. This adjusted building weight is then apportioned across 

all the tariffs (weighted by annual MWh sales) that were applied to the building. The composite weighting factor for 

each building/tariff pair combines both of these weighting factors. 

Calculation of Adjusted Building Weights 

Each building in the sample is viewed as representing a share of the commercial buildings in its region of the US. State 

and regional scaling factors were developed and used to rescale the individual buildings' MAISY® weights to 

represent national usage, as described below. 

The first step was to account for the fact that the sample was a subset of the MAISY® buildings for each state. 

each of the seven states a state factor was computed: 

Sum of MAISY ® weights for all buildings in the state 

State Factor=-----------'=-------"'-------

Sum of MAISY ® weights for sampled buildings in the state 

For 

The statewide energy usage represented by each sampled building was then computed by multiplying the building's 

MAISY® weight by its average annual energy usage and the state factor. For each sampled building i: 

Building i's Adjusted State Energy Use = Annual kWh; * MAISY® Weight; * State Factor 

The next step was to scale state usage to regional usage. US Census Divisions were assigned to one of seven 

regions, each represented by a state in our sample. Census Divisions not directly represented by a state in our 

sample were assigned to a state representing the most similar adjacent division. The table below shows the mapping 

of sampled states to Census Divisions. 

Assignment of Sampled States to Census Divisions 

for Weighting Calculations 

Sampled State Census Division(s) Represented 

New York Middle Atlantic, New England 

Illinois East North Central West North Central 

Georgia South Atlantic, East South Central 

Texas West South Central 

Colorado Mountain 

Washington Pacific Contiguous (excluding California). Pacific Noncontiguous 

California California 

Region Factors were then computed based on state and regional commercial retail electricity sales derived from 1998 

EIA data (see the table below). The Region Factors are the ratio of commercial sector electricity sales in the division(s) 

corresponding with each of the sampled states to the commercial sector electricity sales in each of those individual 

states. 

Regional Retail Commercial Sales 

Region Factor = State Retail Commercial Sales 

Applying the Region Factors was the next step in computing the Adjusted Building Weights for each building: 

Building i's Adjusted Region Energy Use = Region Factor * Building i's Adjusted State Energy Use 

J : \marginal\F inalDraftReport 7 -29-99. wpd 29 
The final step in calculating the Adjusted Building Weight takes each building as a share of the national total. For 

each sampled building i: 

Adjusted Region Energy Use; Adjusted Building Weight = 

Sum of Adjusted Region Energy Use for all sampled buildmgs 

For each sampled building, the Adjusted Building Weight is the percentage of national energy use represented by that 

building. 

Derivation of Weights for BuildindTariff Pairs 

In the second phase of the weighting process, the weight for each building was apportioned across the applicable 

tariffs that had been used to compute marginal prices for it. These buildingltariff weights sum to one for the entire 

sample and to the Adjusted Building Weight for each sampled building. 

Derivation of State and Regional Weights Used in Computed Tariff Average Rates 

A different set of weights was computed for each sampled building because a unique set of tariffs was applied to each 

sampled building based on its particular pattern of energy usage. The building-specific tariff weights were computed 

using the FERC data on MWh sales for each tariff.17 

This process was implemented in two steps. 

State 

California 

Colorado 

Georgia 

Illinois 

New York 

Texas 

Washington 

Total 

The first step was to calculate the relative weight of each of the tariffs that were applied to a building. For each 

building (denoted by i) and tariff (denoted by j): 

State 

Factor 

22.75 

6.64 

9.99 

13.16 

11.54 

7.06 

6.62 

MIA% Sales for Tariff j Share of Tariff j for Building i = Sum of MWh Sales for AD Tariffs Applicable to Building i 

Sum of Bldg Wts 

" The annual reports that IOUs submit to FERC, "Form 1 - Annual Report for Major Electric Utilities, Licensees, and 

Others", include inforination on the utilities' sales, number of customers, and average rate by tariff or rate schedule on 

"page" 304 of that form ("Sales of Electricity by Rate Schedules"). An electronic database of the FERC Form 1 filings 

(that included page 304 data) was obtained from a commercial service known as Energy Central News. 

Sampled 

Buildings 

24506 

12698 

14022 

15645 

28734 

46881 

18221 

160707 

Region 

Factor 

1 .OO4.10 

8.64 

4.04 

3.22 

1.45 

1.80 

Commercial kwh (millions) 

State 

557548 

84339 

140103 

205813 

331675 

331176 

120664 

1771 31 8 

State 

671 0 

1203 

2369 

431 2 

3895 

6142 

1833 

Region 

671 0 

4933 

20465 

1 7431 

12526 

891 1 

3306 

74282 

EE-RM-STD-98-440 COMMENT 241ZZ

The final step in calculating the Adjusted Building Weight takes each building as a share of the national total. For 

each sampled building i: 

W . h Adjusted Region Energy Usei Adjusted Building elg t . = -------"-----:::.-----=::.:......--'------- 1 Sum of Adjusted Region Energy Use for all sampled buildings 

For each sampled building, the Adjusted Building Weight is the percentage of national energy use represented by that 

building. 

Derivation of State and Regional Weights Used in Computed Tariff Average Rates 

Sum of Bldg Wts Commercial kWh (millions) 

Sampled State Region 

State Buildings State Factor State Region Factor 

California 24506 557548 22.75 6710 6710 1.00 

Colorado 12698 84339 6.64 1203 4933 4.10 

Georgia 14022 140103 9.99 2369 20465 8.64 

Illinois 15645 205813 13.16 4312 17431 4.04 

New York 287341 331675 11.54 3895 12526 3.22 

Texas 46881 331176 7.06 6142 8911 1.45 

Washington 18221 120664 6.62 1833 3306 1.80 

Total 160707 1771318 74282 

Derivation of Weights for Building/TariffPairs 

In the second phase of the weighting process, the weight for each building was apportioned across the applicable 

tariffs that had been used to compute marginal prices for it. These building/tariffweights sum to one for the entire 

sample and to the Adjusted Building Weight for each sampled building. 

A different set of weights was computed for each sampled building because a unique set oftariffs was applied to each 

sampled building based on its particular pattern of energy usage. The building-specific tariff weights were computed 

using the FERC data on MWh sales for each tariff. 17 

This process was implemented in two steps. 

The first step was to calculate the relative weight of each ofthe tariffs that were applied to a building. For each 

building (denoted by i) and tariff (denoted by j): 

MWh Sales for Tariff j Share of Tariff j for Building 1 = ----------------'::.....------

Sum of MWh Sales for All Tariffs Applicable to Building 

17 The annual reports that IOUs submit to FERC, "Form 1 - Annual Report for Major Electric Utilities, Licensees, and 

Others", include information on the utilities' sales, number of customers, and average rate by tariff or rate schedule on 

"page" 304 of that form ("Sales of Electricity by Rate Schedules"). An electronic database of the FERC Form 1 filings 

(that included page 304 data) was obtained from a commercial service known as Energy Central News. 

J : \marginal\F inaIDraftReport7 -29-99. wpd 30 
The building-specific tariff weights were then combined with the adjusted building weights to obtain a unique weight 

for each buildingltariff pair: 

Weight for Building i on Tariff j = Adjusted Building Weight, * Share of Tariff j for Building i 

These are the weights that were used to calculate weighted averages for the sample and for binning the data to derive 

the distribution of percent differences between average and marginal prices (called epsilons) for use in the LCC 

analysis. 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ ( . 

The building-specific tariff weights were then combined with the adjusted building weights to obtain a unique weight 

for each building/tariff pair: 

Weight for Building i on Tariffj = Adjusted Building Weighti * Share of Tariffj for Building i 

These are the weights that were used to calculate weighted averages for the sample and for binning the data to derive 

the distribution of percent differences between average and marginal prices (called epsilons) for use in the LCC 

analysis. 

J :\marginal\FinaIDraftReport7 -29-99. wpd 31 
Avvendix 5. Tax Tables. 

Table 6. Sales and Use Taxes on End-Use Energy Consumvtion (with date of last effective change in'~arentheses1 

(Source: EIA End-Use Taxes, Current EIA Practices, 1994. Table B2) EIA's source for this table is: State 

TaxationIRevenue Offices and Research Institute of America, All States Tax Guide, March 29, 1993. 

State Electric Natural Gas 

AL Tax Rate Schedule: 

0-40 kwh - 4% 

>40-60 kwh - 3% 

>60 kwh - 2% 

(71 1163) 

Tax Rate: 4% 

(71 1163) 

AK No Sales Tax No Sales Tax 

AZ Tax Rate: 5% 

(611183) 

Same as Electric 

AR Tax Rate: 4.5% Tax Rate: 4.5% 

(51719 1) (51719 1 ) 

Exemption(s): Exemption(s): 

Residents with income less than Qualified steel manufacturers. 

$12,000 on first 500 kwh per month, 

manufacturing aluminum by electrolytic 

reduction, & qualified steel manufacturers. 

C A Tax Rate: 6% Tax Rate: 6% 

(711 5191) (711 5/91) 

Exemption(s): Exemption(s): 

Residential Energy Surcharge-- Residential, commercial, and industrial. 

excise tax (0.lrnill per kwh). 

CO Tax Rate: 3% 

(811 184) 

Exemption(s): 

Residential and industrial. 

Tax Rate: 3% 

(811184) 

Exemption(s): 

Residential heating, manufacturing 

processing, mining, irrigation, 

communications, and transportation. 

CT Tax Rate: 6% Tax Rate: 6% 

(1011191) (1011191) 

Exemption(s): Exemption(s): 

Residential - first $l5Olmonth; Residential - 75% for powering industrial 

75% for powering industrial manufacturing plant or directly used in 

manufacturing plant or directly used farming, fabrication or manufacturing. 

in farming, fabrication or manufacturing. Includes bottled propane. 

DE No Sales Tax No Sales Tax 

DC Tax Rate: 6% Same as Electric 

(611180) 

Exemption(s): 

Residential and used in manufacturing. 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

• 

Appendix 5. Tax Tables. 

Table 6. Sales and Use Taxes on End-Use Energy Consumption (with date oflast effective change in' parentheses) 

(Source: EIA End-Use Taxes, Current EIA Practices, 1994. Table B2) EIA's source for this table is: State 

TaxationlRevenue Offices and Research Institute of America, All States Tax Guide, March 29, 1993. 

State 

AL 

AK 

AZ 

AR 

CA 

CO 

CT 

DE 

DC 

Electric 

Tax Rate Schedule: 

0-40kWh-4% 

>40-60 kWh - 3% 

>60kWh-2% 

(7/1/63) 

No Sales Tax 

Tax Rate: 5% 

(6/1/83) 

Tax Rate: 4.5% 

(5/7/91) 

Exemption(s): 

Residents with income less than 

$12,000 on first 500 kWh per month, 

manufacturing aluminum by electrolytic 

reduction, & qualified steel manufacturers. 

Natural Gas 

Tax Rate: 4% 

(7/1/63) 

No Sales Tax 

Same as Electric 

Tax Rate: 4.5% 

(5/7/91) 

Exemption(s): 

Qualified steel manufacturers. 

Tax Rate: 6% 

(7/15/91) 

Exemption(s): 

Tax Rate: 6% 

0/15/91) 

Residential Energy Surcharge--

excise tax (O.lmiII per kWh). 

Tax Rate: 3% 

(8/1/84) 

Exemption(s): 

Residential and industrial. 

Tax Rate: 6% 

Exemption(s): 

Residential, commercial, and industrial. 

Tax Rate: 3% 

(8/1/84) 

Exemption(s): 

Residential heating, manufacturing 

processing, mining, irrigation, 

communications, and transportation. 

Tax Rate: 6% 

(10/1/91) 

Exemption(s): 

(10/1/91) 

Exemption(s): 

Residential first $150/month; 

75% for powering industrial 

manufacturing plant or directly used 

in farming, fabrication or manufacturing. 

No Sales Tax 

Tax Rate: 6% 

(6/1/80) 

Exemption(s): 

Residential and used in manufacturing. 

Residential - 75% for powering industrial 

manufacturing plant or directly used in 

farming, fabrication or manufacturing. 

Includcs bottled propane. 

No Sales Tax 

Same as Electric 

J: \marginal\FinalDraftReport7 -29-99. wpd 32 
Tax Rate: 6% Tax Rate: 6% 

(211 188) (211188) 

Exemption(s): Exemption(s): 

Residential and by qualified businesses Residential, used in manufacturing at a fixed 

In enterprise zones. location in the State, boiler fuel, and space flights. 

Gi4 Tax Rate: 4% 

(411 189) 

Tax Rate: 4% 

(711191) 

Exemption(s): 

Utility services and fuels. 

Tax Rate: 4% 

(41 1 189) 

Exemption(s): 

Heating structures for poultry raising. 

Same as Electric 

Tax Rate: 4% Tax Rate: 4% 

(411 186) (411 186) 

Exemption(s): Exemption(s): 

Residential, commercial and industrial. Residential, commercial, and industrial fuels. 

Subject to utility tax: Subject to Gas Tax under Gas Tax Revenue Act. 

Rate: 32 one-hundredth $/kwh, or 5% of Rate: 2.461therm or 5% of gross receipts, 

gross receipts, whichever is less. whichever is less. 

Tax Rate: 5% Same as Electric 

(111183) 

Exemption(s): 

Manufacturing for production, farmers, 

and restaurants. 

Public Utilities: gross revenue tax of 

0.15% 

Tax Rate: 5% 

(71 1 192) 

Exemption(s): 

Manufacturing processing. 

Tax Rate: 5% 

(71 1/92) 

Exemption(s): 

Manufacturing processing, fuels for agriculture, 

creameries, dairies and ice cream factories. 

KS Tax Rate: 4.25% Tax Rate: 4.25% 

(711 189) (711 189) 

Exemption(s): Exemption(s): 

Residential, farm use (no irrigation), Residential, farm use, manufacturing, and mining. 

manufacturing, and mining. 

ICY Tax Rate: 6% 

(71 1190) 

Exemption(s): 

Residential sector. 

Tax Rate: 6% 

(419190) 

Exemption(s): 

Residential sector. 

LA Tax Rate: 4% Tax Rate: 4% 

(71 1/84) (71 1 184) 

Exemption(s): Exemption(s): 

Chloralkali Manufacturers. Agriculture, and fuel for boilers. 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

" . 

FL 

GA 

m 

ID 

IL 

IN 

IA 

KS 

KY 

LA 

Tax Rate: 6% 

(2/1188) 

Exemption(s): 

Residential and by qualified businesses 

In enterprise zones. 

Tax Rate: 4% 

(4/1/89) 

Tax Rate: 4% 

(7/1/91) 

Exemption(s): 

Utility services and fuels. 

Tax Rate: 4% 

(4/1/86) 

Exemption(s): 

Residential, commercial and industrial. 

Subject to utility tax: 

Rate: 32 one-hundredth ¢/kWh, or 5% of 

gross receipts, whichever is less. 

Tax Rate: 5% 

(1/1/83) 

Exemption(s): 

Manufacturing for production, farmers, 

and restaurants. 

Public Utilities: gross revenue tax of 

0.15% 

Tax Rate: 5% 

(711192) 

Exemption( s): 

Manufacturing processing. 

Tax Rate: 6% 

(2/1/88) 

Exemption(s): 

Residential, used in manufacturing at a fixed 

location in the State, boiler fuel, and space flights. 

Tax Rate: 4% 

(411/89) 

Exemption(s): 

Heating structures for poultry raising. 

Same as Electric 

Tax Rate: 4% 

(411/86) 

Exemption(s): 

Residential, commercial, and industrial fuels. 

Subject to Gas Tax under Gas Tax Revenue Act. 

Rate: 2.4¢/therm or 5% of gross receipts, 

whichever is less. 

Same as Electric 

Tax Rate: 5% 

(711192) 

Exemption(s): 

Manufacturing processing, fuels for agriculture, 

creameries, dairies and ice cream factories. 

Tax Rate: 4.25% Tax Rate: 4.25% 

(7/1/89) (711 /89) 

Exemption(s): Exemption(s): 

Residential, farm use (no irrigation), Residential, fann use, manufacturing, and mining. 

manufacturing, and mining. 

Tax Rate: 6% 

(7/1/90) 

Exemption(s): 

Residential sector. 

Tax Rate: 4% 

(711/84) 

Exemption( s): 

Chloralkali Manufacturers. 

Tax Rate: 6% 

(4/9190) 

Exemption(s): 

Residential sector. 

Tax Rate: 4% 

(711/84) 

Exemption(s): 

Agriculture, and fuel for boilers. 

J :\marginal\FinaIDraftReport7 -29-99. wpd 33 
Tax Rate: 6% Tax Rate: 6% 

(81119 1) (811191) 

Rate is 1 % on 95% of electricity used Rate is 1% on 95% of gas used in 

in manufacturing facility. manufacturing facility. 

Exemption(s): Exemption(s): 

0-750 kwh in residential sector. Residential. 

Tax Rate: 5% Tax Rate: 5% 

(61 1 177) (61 1 177) 

Exemption(s): Exemption(s): 

Residential, four units or fewer. Residential, four units or fewer, and 

manufacturing. 

Tax Rate: 5% Tax Rate: 5% 

(1 1/12/75) (1 1/12/75) 

Exemption(s): Exemption(s): 

Residential and manufacturing when Residential and Industrial. 

used for production andlor the company 

has fewer than six employees. 

Tax Rate: 4% 

(111161) 

Exemption(s): 

Agriculture 

Tax Rate: 6% 

(611 8191) 

Exempt from sales tax. 

Same as Electric 

Same as Electric 

Tax Rate: 6% Same as Electric 

(1211183) 

Manufacturers and some agricultural 

activities pay at 1.5%. 

Exemption(s): 

Residential. 

5.725% Sales Tax 5.725% Sales Tax 

(71 1/92) (71 1/92) 

Exemption(s): Exemption(s): 

Residential, drying farm crops. Residential and drying farm crops. 

Manufacturers are exempt from paying 

the tax for the amount that exceeds 10% 

of the cost of production. 

No Taxes No Taxes 

Tax Rate: 5% Same as Electric 

(711 0190) 

Exempt if >50% is used for processing, 

manufacturing, refining, farming and 

irrigation, or for hospital use. 

Tax Rate: 6.5% 

(1011191) 

Tax Rate: 6.5% 

(1011191) 

EE-RM-STD-98-440 COMMENT 241ZZ

I. • 

ME 

MD 

MA 

MI 

MN 

MS 

MO 

MT 

NE 

NY 

Tax Rate: 6% 

(811/91) 

Rate is 1 % on 95% of electricity used 

in manufacturing facility. 

Exemption(s): 

0-750 kWh in residential sector. 

Tax Rate: 5% 

(611177) 

Exemption(s): 

Residential, four units or fewer. 

Tax Rate: 5% 

(11112175) 

Exemption(s): 

Residential and manufacturing when 

used for production andlor the company 

has fewer than six employees. 

Tax Rate: 4% 

(111161) 

Exemption(s): 

Agriculture 

Tax Rate: 6% 

(6118/91 ) 

Exempt from sales tax. 

Tax Rate: 6% 

(12/1/83) 

Manufacturers and some agricultural 

activities pay at 1.5%. 

Exemption(s): 

Residential. 

Tax Rate: 6% 

(811191) 

Rate is 1 % on 95% of gas used in 

manufacturing facility. 

Exemption(s): 

Residential. 

Tax Rate: 5% 

(611/77) 

Exemption(s): 

Residential, four units or fewer, and 

manufacturing. 

Tax Rate: 5% 

(11112175) 

Exemption( s): 

Residential and Industrial. 

Same as Electric 

Same as Electric 

Same as Electric 

5.725% Sales Tax 

(711192) 

5.725% Sales Tax 

(711/92) 

Exemption( s): 

Residential, drying fann crops. 

Manufacturers are exempt from paying 

the tax for the amount that exceeds 10% 

of the cost of production. 

No Taxes 

Tax Rate: 5% 

(7/10/90) 

Exempt if>50% is used for processing, 

manufacturing, refining, fanning and 

irrigation, or for hospital use. 

Exemption(s): 

Residential and drying fann crops. 

No Taxes 

Same as Electric 

Tax Rate: 6.5% 

(1011191 ) 

Tax Rate: 6.5% 

(1011/91 ) 

J :\marginal\FinalDraftReport7 -29-99. wpd 34 
Exempt if delivered through lines. Exempt if delivered to customers through mains, lines, 

or pipes and residential heating. 

No Sales Tax No Sales Tax 

Tax Rate: 7% Tax Rate: 7% 

(711 190) (71 1 190) 

Electric utility sales are exempt. Natural gas utilities sales are exempt. 

Tax Rate: 5% 

(71919 1 ) 

Exemption(s): 

Warehouse and offices, 

Tax Rate: 5% 

(71919 1 ) 

Tax Rate: 4% Tax Rate: 4% 

(71117 1) (71117 1) 

Exemption(s): Exemption(s): 

Residential, agricultural use, and Residential, agricultural use, and fuel for 

manufacturing assembling, mining, and manufacturing assembling, mining, and 

research (direct-predominant use). research (direct and predominant use). 

Tax Rate: 4% Tax Rate: 4% 

(7116191) (7114191) 

Manufacturers pay 1 % for use in Farmers, manufacturers, and commercial 

production. laundries and dry cleaners pay 1 %. 

Tax Rate: 5% Tax Rate: 5% 

(1 2/6/89) (1 2/6/89) 

Electricity is exempt. 

Tax Rate: 5% Tax Rate: 5% 

(1124181) (1124181) 

Electricity and utility services are exempt. Natural gas is exempt from sales tax. 

Tax Rate: 4.5% 4.5% Sales Tax 

(511 190) (511 190) 

Exemption(s): Exemption(s): 

Residential and manufacturing. Residential and manufacturing. 

No Sales Tax No Sales Tax 

Tax Rate: 6% 

(1 1/1/68) 

Exemption(s): 

Residential. 

Tax Rate: 7% 

(71 1190) 

Exemption(s): 

Residential. 

Tax Rate: 6% 

(1 115168) 

Exemption(s): 

Residential. 

Tax Rate: 7% 

(711 190) 

Exemption(s): 

Residential, and fuel for plants and flowers. 

Tax Rate: 5% Tax Rate: 5% 

(711 184) (71 1/84) 

Exemption(s): Exemption(s): 

Residential, crop irrigation, and radio Residential, manufacturing, agricultural curing, 

EE-RM-STD-98-440 COMMENT 241ZZ

.. 4. • 

NH 

NJ 

NM 

NY 

NC 

ND 

OR 

OK 

OR 

PA 

RI 

SC 

Exempt if delivered through lines. 

No Sales Tax 

Tax Rate: 7% 

(7/1/90) 

Electric utility sales are exempt. 

Tax Rate: 5% 

(7/9/91) 

Exemption(s): 

Warehouse and offices. 

Tax Rate: 4% 

(7/1171) 

Exemption(s): 

Exempt if delivered to customers through mains, lines, 

or pipes and residential heating. 

No Sales Tax 

Tax Rate: 7% 

(711/90) 

Natural gas utilities sales are exempt. 

Tax Rate: 5% 

(7/9/91) 

Tax Rate: 4% 

(7/1171) 

Exemption(s): 

Residential, agricultural use, and 

manufacturing assembling, mining, and 

research (direct-predominant use). 

Residential, agricultural use, and fuel for 

manufacturing assembling, mining, and 

research (direct and predominant use). 

Tax Rate: 4% 

(7/1 6/91) 

Manufacturers pay 1 % for use in 

production. 

Tax Rate: 5% 

(12/6/89) 

Electricity is exempt. 

Tax Rate: 5% 

(1/24/81) 

Tax Rate: 4% 

(7116/91 ) 

Farmers, manufacturers, and commercial 

laundries and dry cleaners pay 1%. 

Tax Rate: 5% 

(12/6/89) 

Tax Rate: 5% 

(1/24/81) 

Electricity and utility services are exempt. Natural gas is exempt from sales tax. 

Tax Rate: 4.5% 

(5/1/90) 

Exemption(s): 

Residential and manufacturing. 

No Sales Tax 

Tax Rate: 6% 

(11/1/68) 

Exemption(s): 

Residential. 

Tax Rate: 7% 

(7/1/90) 

Exemption(s): 

Residential. 

Tax Rate: 5% 

(7/1/84) 

Exemption( s): 

4.5% Sales Tax 

(5/1/90) 

Exemption( s): 

Residential and manufacturing. 

No Sales Tax 

Tax Rate: 6% 

(11/5/68) 

Exemption( s): 

Residential. 

Tax Rate: 7% 

(7/1/90) 

Exemption(s): 

Residential, and fuel for plants and flowers. 

Tax Rate: 5% 

(7/1/84) 

Exemption(s): 

Residential, crop irrigation, and radio Residential, manufacturing, agricultural curing, 

J ;\marginal\FinaIDraftReport7 -29-99. wpd 35 
broadcasting. 

Tax Rate: 4% 

(311188) 

Exemption(s): 

Farm use. 

poultry production, and motor fuel, except aviation 

fuels. 

Same as Electric 

Tax Rate: 6% Same as Electric 

(41 1/92) 

Rate is 1.5% for manufacturers, and for 

farmers for use in food production, and 

for nurserymen for use in growing 

horticultural products. 

Exemption(s): 

Residential 

6.25% Sales Tax 

(71 1/90) 

Exemption(s): 

Non-commercial or non-business use 

Same as Electric 

Tax Rate: 5% Tax Rate: 5% 

(111190) (111190) 

Rate is 2% for Residential. Rate is 2 percent for Residential. 

Exemption(s): Exemption(s): 

All other businesses are exempt from Greenhouses (wholesale), irrigation, and orchards. 

sales tax except commercial business. 

Tax Rate: 5% Tax Rate: 5% 

(611191) (611191) 

Exemption(s): Exemption(s): 

Taxpayer-generated and 60% used in Residential and for agricultural use. Motor fkels 

own business. otherwise taxed except jet fuel. 

Tax Rate: 3.5% 

(111187) 

Exemption(s): 

Electricity through lines for manufacturing 

for direct processing, making feed for sale 

or resale, com~nercialeasing or renting of 

laundered textile products sold to 

watermen for extracting fish, bivalves, or 

crustaceans from water. 

Tax Rate: 6.5% 

(111187) 

Tax Rate: 6% 

(611188) 

Electricity sales are exempt 

Tax Rate: 5% 

(511188) 

Exemption(s): 

Tax Rate: 3.5% 

(111187) 

Exemption(s): 

Residential and manufacturing for direct 

processing, making feed for sale or resale, 

commercial leasing or renting of laundered 

textile products sold to watermen for 

extracting fish, bivalves, or crustaceans 

from water and for drying or curing crops. 

Same as Electric 

Tax Rate 6% 

(611188) 

Natural gas sales are exempt. 

Tax Rate: 5% 

(511188) 

Exemption(s): 

J :\marginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

,.. J. .. 

SD 

TN 

TX 

UT 

vr 

VA 

WA 

WV 

WI 

broadcasting. 

Tax Rate: 4% 

(3/1/88) 

Exemption(s): 

Farm use. 

Tax Rate: 6% 

(4/1/92) 

Rate is 1.5% for manufacturers, and for 

farmers for use in food production, and 

for nurserymen for use in growing 

horticultural products. 

Exemption(s): 

Residential 

6.25% Sales Tax 

(7/1/90) 

Exemption(s): 

Non-commercial or non-business use. 

Tax Rate: 5% 

(1/1/90) 

Rate is 2% for Residential. 

Exemption(s): 

All other businesses are exempt from 

sales tax except commercial business. 

Tax Rate: 5% 

(6/1/91) 

Exemption(s): 

Taxpayer-generated and 60% used in 

own business. 

Tax Rate: 3.5% 

(1/1/87) 

Exemption(s): 

Electricity through lines for manufacturing 

for direct processing, making feed for sale 

or resale, commercial leasing or renting of 

laundered textile products sold to 

watermen for extracting fish, bivalves, or 

crustaceans from water. 

Tax Rate: 6.5% 

(1/1/87) 

Tax Rate: 6% 

(6/1/88) 

Electricity sales are exempt. 

Tax Rate: 5% 

(5/1/88) 

Exemption(s): 

J :\marginal\FinalDraftReport7 -29-99. wpd 

poultry production, and motor fuel, except aviation 

fuels. 

Same as Electric 

Same as Electric 

Same as Electric 

Tax Rate: 5% 

(1/1/90) 

Rate is 2 percent for Residential. 

Exemption(s): 

Greenhouses (wholesale), irrigation, and orchards. 

Tax Rate: 5% 

(6/1/91) 

Exemption(s): 

Residential and for agricultural use. Motor fuels 

otherwise taxed except jet fuel. 

Tax Rate: 3.5% 

(1/1/87) 

Exemption(s): 

Residential and manufacturing for direct 

processing, making feed for sale or resale, 

commercial leasing or renting oflaundered 

textile products sold to watermen for 

extracting fish, bivalves, or crustaceans 

from water and for drying or curing crops. 

Same as Electric 

Tax Rate 6% 

(6/1/88) 

Natural gas sales are exempt. 

Tax Rate: 5% 

(5/1/88) 

Exemption(s): 

36 
Residential and farming: November Residential and farming: November through April; 

through April. commercial fishing, interstate and foreign commerce, 

and railroads. 

WY Tax Rate: 3% Same as Electric 

(711 /67) 

Exemption(s): 

Manufacturing process, and agricultural 

use. 

EE-RM-STD-98-440 COMMENT 241ZZ

'" I. Ii' 

Wy 

Residential and farming: November 

through April. 

Tax Rate: 3% 

(711/67) 

Exemption(s): 

Manufacturing process, and agricultural 

use. 

J ;\marginal\FinalDraftReport7 -29-99. wpd 

Residential and farming: November through April; 

commercial fishing, interstate and foreign commerce, 

and railroads. 

Same as Electric 

37 
Table 7. Gas and Electric Utilitv Regulatory Assessments bv States" 

- State 

AK 

AL 

AZ 

AR 

co 

DE 

FL 

GA 

HA 

ID 

IL 

IN 

I A 

KS 

KY 

LA 

ME 

MD 

MA 

MI 

Tax Rate 

I% of Retail Price) 

rate unavailable 

0.15% 

0.20% 

0.40% 

0.20% 

0.20% 

0.13% 

Fee based on property value 

0.13% 

0.30% 

0.19% 

0.15% 

0.10% 

0.20% 

0.20% 

0.04% 

0.25% 

0.17% 

0.25% 

0.02% 

Table 8. kwh Taxes on Electricityi9 

- State 

AL 

AK 

CA 

ID 

MD 

MT 

ND 

SC 

WV 

Tax Rate 

0.04 cents/kWh hydroelectricity 

0.025-0.05 centsikwh electric co-ops 

0.02 centslkW11 electricity consumed 

0.05 centsikwh hydroelectricity 

0.01 5 cents/kWh electricity consumed 

0.02 centslkWh electricity produced 

0.025 centstkwh electricity generated from 

0.05 centslkwh electricity produced 

0.26 centslkwh electricity produced 

l8 ASE Report, Table 5 on page 8. 

l9 ASE Report, Table 6 on page 8. 

Tax Rate 

- State I% of Retail Price) 

MN rate unavailable 

MS 0.16% 

MT 028% 

NV 0.43% 

NJ 0.25% 

NM 0.50% 

NY 0.33% 

OR 0.25% 

PA 0.30% 

SC rate unavailable 

SD 0.15% 

TN 0.08% 

TX 0.17% 

UT 0.30% 

LT 0.50% 

VA 0.20% 

WA 0.20% 

WV 0.29% 

WY 0.30% 

coal 

J:\rnarginal\FinalDraftReport7-29-99. wpd 

EE-RM-STD-98-440 COMMENT 241ZZ

· {. ... 

Table 7. Gas and Electric Utility Regulatory Assessments by States 18 

Tax Rate 

State (% of Retail Price) State 

AK rate unavailable 

AL 0.15% 

AZ 0.20% 

AR 0.40% 

CO 0.20% 

DE 0.20% 

FL 0.13% 

GA Fce based on property value 

HA 0.l3% 

ID 0.30% 

IL 0.19% 

IN 0.l5% 

IA 0.l0% 

KS 0.20% 

KY 0.20% 

LA 0.04% 

ME 0.25% 

MD 0.l7% 

MA 0.25% 

MI 0.02% 

Table 8. kWh Taxes on ElectricityI9 

State Tax Rate 

AL 0.04 cents/kWh hydroelectricity 

AK 0.025-0.05 cents/kWh electric co-ops 

CA 0.02 cents/kWh electricity consumed 

ID 0.05 cents/kWh hydroelectricity 

MD 0.015 cents/kWh electricity consumed 

MT 0.02 cents/kWh electricity produced 

ND 0.025 cents/kWh electricity generated from coal 

SC 0.05 cents/kWh electricity produced 

WV 0.26 cents/kWh electricity produced 

18 ASE Report, Table 5 on page 8. 

19 ASE Report, Table 6 on page 8. 

J :\marginal\FinalDraftReport7 -29-99. wpd 

Tax Rate 

(% of Retail Price) 

MN rate unavailable 

MS 0.16% 

MT 0.28% 

NV 0.43% 

NJ 0.25% 

NM 0.50% 

NY 0.33% 

OR 0.25% 

PA 0.30% 

SC rate unavailable 

SD 0.l5% 

TN 0.08% 

TX 0.l7% 

UT 0.30% 

VT 0.50% 

VA 0.20% 

WA 0.20% 

WV 0.29% 

WY 0.30% 

38