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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

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This document was prepared for the Department of Energy

by staff members of the 

Lawrence Berkeley National Laboratory (LBNL)

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MARGINAL ENERGY PRICES

Final Report

Page

Outline i

Executive Summary 1

I. Background 2

II. Methods 4

III. Analysis and Results – Commercial 6

IV. Analysis and Results – Residential 12

V. Residential Heating Oil and Propane 19

VI. Taxes 22

Appendices

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 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 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 11

Figure 2. RECS 1993 Linear Regression on Electricity Bills, Household 4164 13

Figure 3. RECS 1993 Linear Regression on Gas Bills, Household 1001 14

Figure 4. Residential Summer Electricity Marginal Prices 16

Figure 5. Residential Winter Electricity Marginal Prices 17

Figure 6. Residential Gas Marginal Prices 18

Contributors

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

LBNL Report Contributors: Peter Biermayer, 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 Wiel

Acknowledgments

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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.

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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 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 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.

- Used 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.

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

Committee members, July 28, 1998.

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- 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.

I. 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, DOE1 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 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 (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,

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 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 

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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 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.

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 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

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.

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II. 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

commercial 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.

“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 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.

“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

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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 of the 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 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 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.

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Utility

StateType

Cust.

MWh 

Sales Cust.

MWh 

Sales

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Sample Total 389,251,831 2,802,684 145,990,348 20.7% 15.7% 84% 38%

US TOTAL 928,440,265

Percent of US in 

Modeled Tariffs

Our Sample as 

Percent of Utility By Utility By Modeled Tariffs 

Cust.

Megawatt-hour 

Sales

3,329,782

13,540,374

Total Number 

of Customers

Total Megawatthour Sales

III. ANALYSIS 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 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 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

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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 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 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 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 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 MAISY® 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 MAISY®, 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 MAISY® 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

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

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

primarily because the MAISY® database does not include industrial buildings.

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

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

thus have been ignored in CTAS. 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 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 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 CTAS. The hourly load profiles for these buildings were then extracted from MAISY® and summed up

over a month. Then these monthly loads and demand peaks were inserted into CTAS. For each state, 189 buildings,

with their MAISY® building weights, were included, for a total of 1323 buildings. The 1323 buildings were apportioned

to the main types of commercial buildings used in 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%

Other/Vacant 3.0% 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:

 EE-RM-STD-98-440 COMMENT 312
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.”

9

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

• Each customer/building is considered in turn.

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

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

The output from a CTAS run is written to a comma-separated-values, or “csv” file, with a record for each building-tariff

match. This file, containing the fields shown below, is loaded into a FoxPro® table where additional processing occurs

to produce various charts and distributions.

Field Name Field Description

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

ACCTNO MAISY® customer / building ID number

STATE State from which sample customer was drawn

ANPKKW Building annual peak demand in kW measured over 1 hour

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-1

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 MAISY® weight

BLDGOPHRWeekly hours of operation of building

ANAVGRAT Annual average electricity price

ANMR1 Annual average electricity marginal price

ANE1 Annual percent difference between marginal and average electricity price

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

AVGBILL1 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

AVGKWH1 Annual average decremented energy use, defined as average of 12 monthly decremented energy

usage levels

 EE-RM-STD-98-440 COMMENT 312
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%.

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

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 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 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%.

 EE-RM-STD-98-440 COMMENT 312
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0%

5%

10%

15%

20%

25%

-30%-25%-20%-15%-10%

-5%

0%5%

10%15%20%25%30%

% Difference from Average

% of Electricity Sales

Figure 1.

Commercial Electricity Marginal Prices

Differences from Annual Average

 Marginal < Average Marginal > Average

Mean = -5.2% Median = -3.3%

Minimum = -85% Maximum = +51%

 EE-RM-STD-98-440 COMMENT 312
7

 Source: Robert Latta of EIA proposed this approach.

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IV. ANALYSIS 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 6119 of the 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 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 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 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.

 EE-RM-STD-98-440 COMMENT 312
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).

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

 Marginal Prices

Summer: 9.88¢/kWh

Winter: 4.85¢/kWh 

RECS 1993 

Linear Regression on Electricity Bills, Household 4164

y = 0.0988x + 0.0862

R

2

 = 1.0000

y = 0.0485x + 33.64

R

2

 = 0.8177

0

50

100

150

200

250

0 500 1000 1500 2000 2500

kWh 

Bill ($)

Summer Bills

Winter Bills

Summer Regression

Winter Regression

Figure 2.

The r-squared values for the regressions of RECS 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 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 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)

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.

 EE-RM-STD-98-440 COMMENT 312
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RECS 1993

Linear Regression on Gas Bills, Household 1001

y = 0.5119x + 4.9798

R

2

 = 0.9994

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100 120 140 160

Therms

Bill ($)

Marginal Price

$0.51/therm

Figure 3.

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-squared Values for Electricity (Summer and Winter) and Gas Regressions

Percentile Electricity Natural Gas

 Summer Winter 

 99th 1 1 1

 95th 1 1 1

 90th 1 0.999 0.999

 75th 1 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

 1st 0.449 0.217 0.6

 Mean 0.973 0.946 0.975

 Number of Cases* 5667 6116 3150

 Total Number of Cases 5818 6119 3153

 * Cases where r-squared values could be calculated.

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Electricity 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 EIA 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 Appliance Data Set, and in Subsets with Marginal Prices

Appliance Subsets

(HH = household)

All Households with

appliance

Marginal Price Subset Marginal Price Subset as

percent of All Households

Survey

Responses

U.S.

Households

(millions)*

Survey

Responses

U.S.

Households

(millions)*

% of

Survey

Responses

% of Relevant

U.S.

Households 

HHs with washer and

dryer and electricity

4998 67.8 4574 61.9 92% 91%

HHs with washer and

dryer and natural gas

2759 40.6 2439 35.6 88% 88%

HHs with central air

conditioning

2414 32.1 2164 28.8 90% 90%

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

HHs with electric water

heaters

2559 33.4 2323 30.3 91% 91%

HHs with natural gas

water heaters

2805 41.0 2535 36.0 90% 88%

HHs with fuel oil water

heaters**

 176 1.8 163 1.7 93% 94%

* 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 consumption-

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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.

Figure 4.

Residential Summer Electricity Marginal Prices

Differences from Annual Averages

0.0

0.5

1.0

1.5

2.0

2.5

3.0

% Difference from Average

 Mean = +2.5% Median = +1.4%

Minimum = -98% Minumum = +175%

Marginal < Average Marginal > Average

 EE-RM-STD-98-440 COMMENT 312
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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

0.0

0.5

1.0

1.5

2.0

2.5

3.0

% Difference from Average

Marginal < Average Marginal > Average

Median = -3.8%

Maximum = +130%

Mean = -5.3%

Minimum = -100%

 EE-RM-STD-98-440 COMMENT 312
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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.

Figure 6.

Residential Gas Marginal Prices

Differences from Annual Averages

0.0

0.5

1.0

1.5

2.0

2.5

3.0

% Difference from Average

Marginal < Average Marginal > Average

Mean = -7.8%

Minimum = -90%

Median = -9.0%

Maximum = +248%

 EE-RM-STD-98-440 COMMENT 312
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Company Dixie Gas C&W Oil & Heating Powers Oil Co MacDuffie Petroleum Eastern Propane

Location Verona VA Danbury CT Foxboro MA Hudson NH Rochester NH

Territory 3 counties in VA 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 days: $0.03 auto delivery: $0.03 paid in 7 bus days: $0.10 paid in 10 days: $0.10 none

V. RESIDENTIAL HEATING 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 (1) 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.

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 of local 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, 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).

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 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

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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 ($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 (1) 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.

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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 home: 500 gal/yr

$/gal, regular $1.45 winter, $1.30 summer $0.65 $0.98-1.01

Tax 7.25-8.25%, 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: $0.10/gal $0.10/gal if tank rented

< 250 gal/yr: $0.10/gal more

Tank rentals most are rented 40% rented no rentals

Rental fees $65/yr for 250 gal no rental fees

$90/yr for 500 gal

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% home 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

$/gal, 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

100 gal: $36-72/yr, depends on use 1,000 gal: $105/yr

Company All-Star Gas United Propane Dixie Gas

Location Galveston TX Salisbury MD Verona VA

Territory Bolivar Island Maryland VA, some of WV

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 VA, some in WV

$/gal, discount new customer: $1.10 $0.08/gal if tank is owned > 800 gal/yr: $1.02

prepay, >350 gal/yr: $0.83-0.99 2% off if paid within 10 days

$/gal, addtl < 400 gal/yr: $1.18

Tank rentals 50-60% rented 25% rented no rentals

Rental fees all size tanks: $85/yr depends on usage (gallons/year)

ave $12.5/qtr for 500 gal tank

Table 5. Propane Prices

 EE-RM-STD-98-440 COMMENT 312
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 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. 

10 EIA, “End Use Taxes: Current EIA Practices,” August 1994, page 29.

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

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 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 of tax;

indeed, consumers’ marginal 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 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 of the wide

variety of taxes imposed by local governments, the tax adjustment is only practicable at the State level.”10

 EE-RM-STD-98-440 COMMENT 312
11 Ibid, Table B2. The relevant portions of EIA’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 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.

14 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.

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

The EIA report provides a state-by-state list of sales and use taxes on end-use energy consumption.11 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).12 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. 13

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 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.14 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

 EE-RM-STD-98-440 COMMENT 312
15 EIA, “Energy Sales and Revenue 1997,” page 1.

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

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 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 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 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

 EE-RM-STD-98-440 COMMENT 312
25 J:\marginal\FinalDraftReport7-29-99.wpd 

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.

 EE-RM-STD-98-440 COMMENT 312
26 J:\marginal\FinalDraftReport7-29-99.wpd 

Appendix 1. Tariffs Used in the Commercial Analysis

Utility State Tariff Utility State Tariff

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 Illum. Co

Commonwealth Edison Co

Detroit Edison

Jersey Central Power & Light

Madison G&E

Madison G&E

NSP

NSP

NSP

NSP

A L

A L

A L

VA

A Z

A Z

MA

MA

TX

OH

OH

IL

MI

NJ

W I

W I

MN

MN

W I

W I

LPEM

LPM

LPS

SGS

E30

E32

G-1 

G-2

21

Gen Comm

Lg Comm

Rate 6 nonTOU

D3

GS

Cg-1

Cg-5

A10, D12, E13

A14, D16,E15

Cg-2

Cg-2 unmetered

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

W I

NY

NY

OH

DC

CA

CA

PA

PA

CO

GA

W A

W A

CA

CA

MO

MO

VA

W I

Cg-5

SC-2 Demand

SC-2 Non-Demand

GS3-11

GS

A1

A10

GS-1

GS-3

B-97

GS-7

Rate 31

Rate 34

GS-1

GS-2

No. 2

No. 3

GS-1

Cg 1

 

 EE-RM-STD-98-440 COMMENT 312
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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 1: Non-demand tariff

Utility: Alabama Power

Tariff: Commercial, LPS

Customer Base Charge: $8.65/mo.

Summer Energy Charge: $0.06223/kWh

Winter Energy Charge: $0.05843/kWh

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: $0.06183/kWh = $157.47 / 2547

Monthly Marginal Price: $0.05843/kWh = Bill Savings / Energy Savings

 = ($157.47 - $150.03) / (2547 - 2420)

Example 2: Demand Tariff

Utility: Southern California Edison

Tariff: Commercial GS2

Tariff Min/Max kW: 80 - 2000 kW/hr (measured over 15 minutes)

Customer Base Charge: $60.30/mo.

Energy Block Cutoffs: Block 1: 300 kWh/kW (based on monthly peak kW)

Energy Charge, Block 1: $0.07692/kWh

Energy Charge, Block 2: $0.04391/kWh

Demand Charge Component 1: $5.40 (Summer), $5.40 (Winter)

Demand Charge Component 2: $7.75 (Summer), $0.00 (Winter)

Customer Usage: 150388 kWh

Month: May

 Monthly Peak kW: 71 kW (over 15 minutes)

Baseline Bill: $7750.35 = $60.30 + ((300*71)*$0.07692) + ((150388 - (300*71))*$0.04391)

 + (71*$5.40)

Bill with 5% Energy and

4% Demand Decrement: $7376.71 = $60.30 + ((300*(71*0.96))*$0.07692)

 + (((150388*0.95) - (300*(71*0.96)))*$0.04391)

 + ((71*0.96)*$5.40)

Monthly Average Price: $0.05163/kWh = 7376.71 / (150388*0.95)

Monthly Marginal Price: $0.04969/kWh = Bill Savings / Energy Savings

 = ($7750.35 - $7376.71) / (150388 - (150388 * 0.95))

 EE-RM-STD-98-440 COMMENT 312
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 EEI on August 28, 1997.

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

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-foot 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.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 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

monthly lighting hours and the monthly lighting energy use is reduced by a fixed percentage.

 EE-RM-STD-98-440 COMMENT 312
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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. For

each of the seven states a state factor was computed:

Sum of MAISY weights for sampled buildings in the state

Sum of MAISY weights for all buildings in the state

State Factor 





=

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 i * MAISY® Weight i * 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.

State Retail Commercial Sales

Regional Retail Commercial Sales Region Factor =

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

 EE-RM-STD-98-440 COMMENT 312
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.

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

Derivation of State and Regional Weights Used in Computed Tariff Average Rates

Sum of Bldg Wts Commercial kWh (millions)

State

Sampled 

Buildings State

State 

Factor State Region

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 28734 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

The final step in calculating the Adjusted Building Weight takes each building as a share of the national total. For

each sampled building i:

Sum of Adjusted Region Energy Use for all sampled buildings

Adjusted Region Energy Use Adjusted Building Weight i

i =

For each sampled building, the Adjusted Building Weight is the percentage of national energy use represented by that

building.

Derivation of Weights for Building/Tariff 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 building/tariff weights 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 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.

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):

Sum of MWh Salesfor All Tariffs Applicable to Building i

MWh Salesfor Tariff j

Share of Tariff j for Building i =

 EE-RM-STD-98-440 COMMENT 312
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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 Tariff j = Adjusted Building Weight i * 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. 

 EE-RM-STD-98-440 COMMENT 312
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Appendix 5. Tax Tables.

Table 6. Sales and Use Taxes on End-Use Energy Consumption (with date of last effective change in parentheses) 

(Source: EIA End-Use Taxes, Current EIA Practices, 1994. Table B2) EIA’s source for this table is: State

Taxation/Revenue Offices and Research Institute of America, All States Tax Guide, March 29, 1993.

State Electric Natural Gas

AL Tax Rate Schedule: Tax Rate: 4%

0-40 kWh – 4% (7/1/63)

>40-60 kWh – 3%

>60 kWh – 2%

(7/1/63)

AK No Sales Tax No Sales Tax

AZ Tax Rate: 5% Same as Electric

(6/1/83)

AR Tax Rate: 4.5% Tax Rate: 4.5%

(5/7/91) (5/7/91)

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.

CA Tax Rate: 6% Tax Rate: 6%

(7/15/91) (7/15/91)

Exemption(s): Exemption(s):

Residential Energy Surcharge-- Residential, commercial, and industrial.

excise tax (0.1mill per kWh).

CO Tax Rate: 3% Tax Rate: 3%

(8/1/84) (8/1/84)

Exemption(s): Exemption(s):

Residential and industrial. Residential heating, manufacturing

processing, mining, irrigation,

communications, and transportation.

CT Tax Rate: 6% Tax Rate: 6%

(10/1/91) (10/1/91)

Exemption(s): Exemption(s):

Residential – first $150/month; 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

(6/1/80)

Exemption(s):

Residential and used in manufacturing.

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FL Tax Rate: 6% Tax Rate: 6%

(2/1/88) (2/1/88)

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.

GA Tax Rate: 4% Tax Rate: 4%

(4/1/89) (4/1/89)

Exemption(s):

Heating structures for poultry raising.

HI Tax Rate: 4% Same as Electric

(7/1/91)

Exemption(s):

Utility services and fuels.

ID Tax Rate: 4% Tax Rate: 4%

(4/1/86) (4/1/86)

Exemption(s): Exemption(s):

Residential, commercial and industrial. Residential, commercial, and industrial fuels.

IL Subject to utility tax: Subject to Gas Tax under Gas Tax Revenue Act.

Rate: 32 one-hundredth ¢/kWh, or 5% of Rate: 2.4¢/therm or 5% of gross receipts, 

gross receipts, whichever is less. whichever is less.

IN Tax Rate: 5% Same as Electric

(1/1/83)

Exemption(s):

Manufacturing for production, farmers,

and restaurants.

Public Utilities: gross revenue tax of

0.15%

IA Tax Rate: 5% Tax Rate: 5%

(7/1/92) (7/1/92)

Exemption(s): Exemption(s):

Manufacturing processing. Manufacturing processing, fuels for agriculture,

creameries, dairies and ice cream factories.

KS Tax Rate: 4.25% Tax Rate: 4.25%

(7/1/89) (7/1/89)

Exemption(s): Exemption(s):

Residential, farm use (no irrigation), Residential, farm use, manufacturing, and mining.

manufacturing, and mining.

KY Tax Rate: 6% Tax Rate: 6%

(7/1/90) (4/9/90)

Exemption(s): Exemption(s):

Residential sector. Residential sector.

LA Tax Rate: 4% Tax Rate: 4%

(7/1/84) (7/1/84)

Exemption(s): Exemption(s):

Chloralkali Manufacturers. Agriculture, and fuel for boilers.

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ME Tax Rate: 6% Tax Rate: 6%

(8/1/91) (8/1/91)

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.

MD Tax Rate: 5% Tax Rate: 5%

(6/1/77) (6/1/77)

Exemption(s): Exemption(s):

Residential, four units or fewer. Residential, four units or fewer, and

manufacturing.

MA Tax Rate: 5% Tax Rate: 5%

(11/12/75) (11/12/75)

Exemption(s): Exemption(s):

Residential and manufacturing when Residential and Industrial.

used for production and/or the company

has fewer than six employees.

MI Tax Rate: 4% Same as Electric

(1/1/61)

Exemption(s):

Agriculture

MN Tax Rate: 6% Same as Electric

(6/18/91)

Exempt from sales tax.

MS Tax Rate: 6% Same as Electric

(12/1/83)

Manufacturers and some agricultural

activities pay at 1.5%.

Exemption(s):

Residential.

MO 5.725% Sales Tax 5.725% Sales Tax

(7/1/92) (7/1/92)

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.

MT No Taxes No Taxes

NE Tax Rate: 5% Same as Electric

(7/10/90)

Exempt if >50% is used for processing,

manufacturing, refining, farming and

irrigation, or for hospital use.

NV Tax Rate: 6.5% Tax Rate: 6.5%

(10/1/91) (10/1/91)

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Exempt if delivered through lines. Exempt if delivered to customers through mains, lines,

or pipes and residential heating.

NH No Sales Tax No Sales Tax

NJ Tax Rate: 7% Tax Rate: 7%

(7/1/90) (7/1/90)

Electric utility sales are exempt. Natural gas utilities sales are exempt.

NM Tax Rate: 5% Tax Rate: 5%

(7/9/91) (7/9/91)

Exemption(s):

Warehouse and offices.

NY Tax Rate: 4% Tax Rate: 4%

(7/1/71) (7/1/71)

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).

NC Tax Rate: 4% Tax Rate: 4%

(7/16/91) (7/16/91)

Manufacturers pay 1% for use in Farmers, manufacturers, and commercial

production. laundries and dry cleaners pay 1%.

ND Tax Rate: 5% Tax Rate: 5%

(12/6/89) (12/6/89)

Electricity is exempt.

OH Tax Rate: 5% Tax Rate: 5%

(1/24/81) (1/24/81)

Electricity and utility services are exempt. Natural gas is exempt from sales tax.

OK Tax Rate: 4.5% 4.5% Sales Tax

(5/1/90) (5/1/90)

Exemption(s): Exemption(s):

Residential and manufacturing. Residential and manufacturing.

OR No Sales Tax No Sales Tax

PA Tax Rate: 6% Tax Rate: 6%

(11/1/68) (11/5/68)

Exemption(s): Exemption(s):

Residential. Residential.

RI Tax Rate: 7% Tax Rate: 7%

(7/1/90) (7/1/90)

Exemption(s): Exemption(s):

Residential. Residential, and fuel for plants and flowers.

SC Tax Rate: 5% Tax Rate: 5%

(7/1/84) (7/1/84)

Exemption(s): Exemption(s):

Residential, crop irrigation, and radio Residential, manufacturing, agricultural curing,

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broadcasting. poultry production, and motor fuel, except aviation

fuels.

SD Tax Rate: 4% Same as Electric

(3/1/88)

Exemption(s):

Farm use.

TN Tax Rate: 6% Same as Electric

(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

TX 6.25% Sales Tax Same as Electric

(7/1/90)

Exemption(s):

Non-commercial or non-business use.

UT Tax Rate: 5% Tax Rate: 5%

(1/1/90) (1/1/90)

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.

VT Tax Rate: 5% Tax Rate: 5%

(6/1/91) (6/1/91)

Exemption(s): Exemption(s):

Taxpayer-generated and 60% used in Residential and for agricultural use. Motor fuels

own business. otherwise taxed except jet fuel.

VA Tax Rate: 3.5% Tax Rate: 3.5%

(1/1/87) (1/1/87)

Exemption(s): Exemption(s):

Electricity through lines for manufacturing Residential and manufacturing for direct

for direct processing, making feed for sale processing, making feed for sale or resale,

or resale, commercial leasing or renting of commercial leasing or renting of laundered

laundered textile products sold to textile products sold to watermen for 

watermen for extracting fish, bivalves, or extracting fish, bivalves, or crustaceans

crustaceans from water. from water and for drying or curing crops.

WA Tax Rate: 6.5% Same as Electric

(1/1/87)

WV Tax Rate: 6% Tax Rate 6%

(6/1/88) (6/1/88)

Electricity sales are exempt. Natural gas sales are exempt.

WI Tax Rate: 5% Tax Rate: 5%

(5/1/88) (5/1/88)

Exemption(s): Exemption(s):

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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

(7/1/67)

Exemption(s):

Manufacturing process, and agricultural

use.

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18 ASE Report, Table 5 on page 8.

19 ASE Report, Table 6 on page 8.

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Table 7. Gas and Electric Utility Regulatory Assessments by States 18

Tax Rate Tax Rate

State (% of Retail Price) State (% of Retail Price)

AK rate unavailable MN rate unavailable

AL 0.15% MS 0.16%

AZ 0.20% MT 0.28%

AR 0.40% NV 0.43%

CO 0.20% NJ 0.25%

DE 0.20% NM 0.50%

FL 0.13% NY 0.33%

GA Fee based on property value OR 0.25%

HA 0.13% PA 0.30%

ID 0.30% SC rate unavailable

IL 0.19% SD 0.15%

IN 0.15% TN 0.08%

IA 0.10% TX 0.17%

KS 0.20% UT 0.30%

KY 0.20% VT 0.50%

LA 0.04% VA 0.20%

ME 0.25% WA 0.20%

MD 0.17% WV 0.29%

MA 0.25% WY 0.30%

MI 0.02%

Table 8. kWh Taxes on Electricity19

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

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