# 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 

# DRAFT

Marginal Energy Prices Report

July 1999

U.S. Department ofEnergy Assistant Secretary, Energy Efficiency & 

## Renewable Energy Office ofCodes and Standards Washington, DC 20585

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

## This documentwasprepared forthe DepartmentofEnergy by staff

## members of the Lawrence BerkeleyNational Laboratory (LBNL) This document was prepared for the Department ofEnergy by staffmembers ofthe Lawrence Berkeley National Laboratory (LBNL)

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

### Executive Summary 

## MARGINAL ENERGY PRICES
Final 

## Report

Outline



### I. Background 

### II. Methods 

### IE Analysis and Results -

### Commercial W. Analysis and Results -Residential V. Residential Heating

### Oil and Propane 

### VI. Taxes Appendices Appendix 

### 1. TariffsUsed in the Commercial Analysis Appendix 

### 2. Examples of CommercialBill Calculations 

### 27 Appendix 3. Demand Decrement Due to Standards

### - The Role ofLighting Coincidence and Diversity Appendix 

### 4. Weighting Method for Commercial Sample Appendix 

### 5. Tax Tables


### Tables Table 

### 1. Characterization and SummaryofCommercial Electric Utility Sample Table 

### 2. Distribution of r-squared Values forElectricity (Summer and Winter) and Gas Regressions 14 Table 3. Number ofHouseholds in Each Appliance Data Set, and in Subsetswith Marginal Prices Table4. 

### Heating Oil Prices 19 Table 

### 5. Propane Prices Table 6. State Sales Taxes 3

### 2 Table 

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

### 8. kwhTaxes on Electricity 

### Figures Figure 

### 1. Commercial Electricity Marginal Prices Figure 

### 2. RECS 1993 Linear Regression on Electricity Bills, Household 

### 41

### 64 Figure 3. 

### RECS 1993 Linear Regression on Gas Bills, Household 1001 Figure 

### 4. Residential Summer Electricity Marginal Prices Figure 

### 5. Residential Winter Electricity Marginal Prices Figure 6. Residential GasMarginal Prices


### Contributors Lawrence Berkeley National Laboratory (LBNL) Report Coordinator: Stuart Chaitkin LBNL Report Contributors: Peter Biennayer, Sarah Bretz, SteveBrown, Stuart Chaitkin, Sachu Constantine,

Diane Fisher, SajidHakim, Lucy Liew,Jim Lutz, ChrisMamay, James 

### E. McMahon,Mithra Moezzi, Julie Osborn, Esther Rawner, Judy Roberson, Greg Rosenquist, Nancy Ryan, Isaac Turiel, StephenWiel



### Acknowledgments 

## MARGINAL ENERGY PRICES

Outline

Executive Summary
L IL ill. IV. 

## v. VI. Background Methods Analysis and Results
- Commercial Analysis and Results
- Residential Residential Heating Oil and Propane Taxes Appendices 

### Final Report

4

12 

### 1

2

6

19 22

Appendix 1. Tariffs Used in the Commercial Analysis 26 Appendix 2. Examples ofCommercial Bill Calculations 27 Appendix 3. Demand DecrementDue to Standards

- The Role ofLighting Coincidence and Diversity 28 Appendix 4. Weighting Method for Commercial Sample 29 Appendix 5. Tax Tables 32

Tables Table 1. Characterization and Summary ofCommercial Electric Utility Sample 6 Table 2. Distribution ofr-squared Values for Electricity (Summer and Winter) and Gas Regressions 14 Table 3. Number ofHouseholds in Each Appliance Data Set, and in Subsets with Marginal Prices 15 Table 4. Heating Oil Prices 19 Table 5. Propane Prices 21 Table 6. State Sales Taxes 32 Table 7. Gas and Electric Utility Regulatory Assessments by States 38 Table 8. kWh Taxes on Electricity 38 Figures Figure 1. Commercial Electricity Marginal Prices Figure 2. RECS 1993 Linear Regression on Electricity Bills, Household 4

### J64 Figure 3. RECS 1993 Linear Regression on Gas Bills, Household 1001 Figure 4. Residential SummerElectricity Marginal Prices Figure 5. Residential Winter Electricity Marginal Prices Figure 6. Residential Gas Marginal Prices

Contributors Lawrence Berkeley National Laboratory (LBNL) Report Coordinator: Stuart Chaitkin 14

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

Diane Fisher, Sajid Hakim, Lucy Liew, Jim Lutz, Chris Marnay, James E. McMahon, Mithra Moezzi, Julie Osborn, Esther Rawner, Judy Roberson, Greg Rosenquist, NancyRyan, Isaac Turiel, Stephen WieJ

Acknowledgments



### J:\marginal\FinalDraftReport7-29-99.wpd 11 13

16 

### 17 18

111

### This work was prepared forthe Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Codes and Standards, of the U.S. Department of Energy, under Contract No. DE-AC03-76SF0098. Data and advice were graciously provided by Robert Latta and Steve Wade ofthe Energy Information Administration. This work was prepared for the Assistant Secretary for Energy Efficiency and Renewable Energy, Office ofCodes and Standards, ofthe U.S. Department ofEnergy, under Contract No. DE-AC03-76SF0098. Data and advice were graciously provided by Robert Latta and Steve Wade ofthe Energy Information Administration.



### J:\marginal\FinalDraftReport7

-29-99.wpd IV

### MARGINAL ENERGYPRICES-

### FINAL

### REPORT

Thisreport responds to a recommendation from the Department ofEnergy'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 

### ofthe high priority appliances' energyefficiency standards rulemakings -

### clothes washers, water heaters, fluorescent lampballasts, and central air conditionerslheatpumps. 

### Marginal prices as discussed here are those prices consumerspay (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) dividedby the corresponding change in the amount of energy the consumerused. 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 ora 

### distribution ofaverageprices 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 costsby annual energy consumption. At the utility level, average energyprices are derived by dividing annual revenues by annual energy sales. Section I below provides background on the work developed for marginal energy prices. Section 

### I1 describes the methods used to derive marginal energy prices. Other credible methods to calculate or estimate consumers' marginal energy prices were not found. Because ofthe complexity inherent in utility tariffs, methods that simply subtract fixed costsfrom average costs do not constitute a credible method for calculating marginal prices.
Section 111 showsthe analysis forthe commercial sector (for application to fluorescent lamp ballasts). The analysis centered on the calculation of electricitybills for a distribution of sample buildings in conjunction with a distribution ofcommercial tariffs. Jackson Associates' Market Analysis and Information System (MAISPIdatabase provided the
source ofinformation forthe building energy and demand levels among buildings of different types. Commercial tariffs from a sampleofutilities were collected and modeled..
Section IV describesthe analysis for the residential sector (for application to three products: clothes washers, water heaters, and central air conditionerslheat pumps). The analysis centered on use ofconsumer 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 ofresidential 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 ofour examination ofresidential heating oil and propane prices.
Section VI discusses the effect oftaxes on marginal prices. A summary ofthe results ofthe analyses to derive commercial and residential marginal prices follows:
Commercial

- 

### Analysis based on the LBNL/MAISYa approach.

- 

### Used tariffs for 

### 2 1%of national commercial electricity customers.


### - Electricity marginal prices forindividual customers range from 8.5% 

### below to 

### 51%

### above the average price for the same customer. At the consumption-weighted mean of the differences, electricitymarginal prices
are 

### 5.2% lower than average prices.
Residential

- 

### Analysis based on the RECS national sample.



### J 

### :\marginal\FinalDraftReport7-29-99. wpd MARGINAL ENERGY PRICES
- FINAL REPORT
EXECUTIVE SUMMARY

This report responds to a recommendation from the Department ofEnergy's (DOE) Advisory Committee on Appliance Energy Efficiency Standards. Itpresents the derivation ofestimated consumer marginal energy prices for the commercial and residential sectors for use in the life-cycle cost (LCC) analyses for four ofthe high priority appliances' energy efficiency standards rulemakings

- clothes washers, water heaters, fluorescent lamp ballasts, and central air conditioners/heatpumps. Marginal prices as discussed here are those prices consumers pay (or save) for their last units ofenergy used (or saved). Marginal prices reflect a change in a consumer's bill (that might be associated with new energy efficiency standards) divided bythe corresponding change in the amount ofenergy 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 ofaverage 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 belowprovides background on the work developed for marginal energy prices. Section II describes the methods used to derive marginal energy prices. Other credible methods to calculate or estimate consumers' marginal energy prices were not found. Because ofthe complexity inherent in utility tariffs, methods that simply subtract fixed costs from average costs do not constitute a credible method for calculating marginal prices.

Section III shows the analysis for the commercial sector (for application to fluorescent lamp ballasts). The analysis centered on the calculation ofelectricity bills for a distribution ofsample buildings in conjunction with a distribution ofcommercial tariffs. Jackson Associates' Market Analysis and Information System (MAISY®) database provided the
source ofinformation for the building energy and demand levels among buildings ofdifferent types. Commercial tariffs from a sample ofutilities 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/heatpumps). The analysis centered on use ofconsumerbills from the RECS 1993 database. Seasonal marginal electricityprices and annual marginal natural gas prices were derived by calculating the slope ofthe regression lines associated with monthly bill and consumption data.

Prices ofresidential 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 ofour examination ofresidential heating oil and propane prices.

Section VI discusses the effect oftaxes on marginal prices.

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

Commercial Analysis based on the**LBNL!MAISY~** approach. Used tariffs for 21 % ofnational 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 ofthe differences, electricity marginal prices
are 5.2% lower than average prices.
Residential Analysis based on the RECS national sample.


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

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

### 98% below to 175%above the average price for the same customer. At the consumption-weighted mean of the differences, electricitymarginal prices are 2.5% higher than averageprices in the summer.

- 

### Electricity marginal prices for individual customers in the winter (the remaining eight months) range from 100%below to 130% above the average price for the same customer. At the consumption-weighted mean of the differences, electricity marginal prices are 5.3% lower than average prices in the winter.

- 

### Gas marginal prices range for individual customers from 98%below to 248% above the average price for the same customer. At the consumption-weighted mean ofthe differences, gas marginal prices are 7.8% lower than averageprices.



### L BACKGROUND
The Issue In the past, DOE'S analyses ofthe life-cycle costs ofand 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, atthe price they pay for their last unit ofenergy), not at the average price they pay for their energy. Unfortunately, neither published nor readily available data existed for consumer marginal energy prices. Indeed, a major research effort was required to derive consumer marginal energy prices.
DOE Advisorv Committee In its April 2 1, 1998 letter to Secretary of Energy Pena, the Advisory Committee on ApplianceEnergy Efficiency Standardsdelivered recon~mendations to the Department of Energy regarding, anlong other things, theuse of energy prices in future appliance standards rulemakings. For life-cycle cost analyses the Committeerecommended that DOE should replace the use ofnational average energyprices with the full range of consumer marginal energy rates. Absent consumer marginal energy rate information, the Committee recommended DOEuse a range ofnet energy rates, calculated by removing all fixed charges (such asmonthly customer charges that consumers incur regardless of their monthly energy usage). In response to the Committee's recommendations, DOE' agreed that the use of marginal energy rates would improve the theoretical soundness ofthe analysis and decided to determine marginal rates using either RECS or comn~ercially availabledatabases. While the Department believed at that time that it was unknown if removing fixed costs is more or less reflective ofmarginal rates, it did not intend to take that intermediate step without evidencethat the result would improve the accuracy ofthe 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 63million 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 ofthese 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 ofkilowatthour (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 ofutilities.
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. Ofthe 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. Ofthe 100utilities, only 16 met this test within an assumed onepercent reporting and rounding error. Thus, 84percent ofthe utilities had non-flat rates or a mix ofrate scheduleswhich greatly complicatesthe determinationofthe marginal price. Ofthese 84 utilities,


# ' Letter from Dan Reicher, DOE Assistant Secretary,Energy Efficiency and Renewable Energy, to the Advisory Committee members,July 28, 1998. 

### J 

### :\marginal\FinalDraftReport7-29-99. wpd 2 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 ofthe differences, electricity marginal prices are 5.3% lower than average prices in the winter. Gas marginal prices range for individual customers from 98% below to 248% above the average price for the same customer. At the consumption-weighted mean ofthe differences, gas marginal prices are 7.8% lower than average prices.

L BACKGROUND

The Issue In the past, DOE's analyses ofthe life-cycle costs ofand 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 ofenergy), not at the average price they pay for their energy. Unfortunately, neither publishednorreadily 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 ofEnergy Pena, the Advisory Committee on Appliance Energy Efficiency Standards delivered recommendations to the Department ofEnergy regarding, among otherthings, the use ofenergy prices in future appliance standards rulemakings. For life-cycle cost analyses the Committee recommended that DOE should replace the use ofnational average energyprices with the full range ofconsumer marginal energyrates. Absent consumer marginal energy rate information, the Committee recommended DOE use a range ofnet energy rates, calculated by removing all fixed charges (such as monthly customer charges that consumers incur regardless oftheir monthly energy usage). In response to the Committee's recommendations, DOEi agreed that the use ofmarginal energy rates would improve the theoretical soundness ofthe analysis and decided to determine marginal rates using either RECS or commercially available databases. While the Department believed at that time that it was unknown if removing fixed costs is more or less reflective ofmarginal rates, it did not intend to take that intermediate step without evidence that the result would improve the accuracy ofthe analysis.

Fixed Charges In considering the recommendation to subtract fixed charges, the Department asked LBNL to examine the data provided by the Edison Electric Institute (EEl) concerning electric rates for 104 utilities, serving over 63 million customers. The data included average prices, tail block rates and fixed charges, but not marginal prices. EEl reported that fixed charges as a weighted percentage ofaverage prices represent 7.5 percent. However, in examining the actual rate schedules ofa few ofthese 104 utilities, it was found that some ofthe reported fixed charges were actually minimum charges, not customer charges. Thus the actual fixed charges, which are independent ofkilowatthour (kWh) usage, would be somewhat lower than the reported 7.5 percent depending upon how often minimum charges were considered to be customer charges within the whole EEl sample ofutilities.

The Department agreed that for flat rate schedules, the removal ofthese adjusted fixed charges would yield the marginal rate. However, upon examining the EEl data, it was determined that most rates are not flat. Ofthe 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. Ofthe 100 utilities, only 16 met this test within an assumed one percent reporting and rounding error. Thus, 84 percent ofthe utilities had non-flat rates or a mix ofrate schedules which greatly complicates the determination ofthe marginal price. Ofthese 84 utilities,

i Letter from Dan Reicher, DOE Assistant Secretary, Energy Efficiency and Renewable Energy, to the Advisory Committee members, July 28,1998. 

### J:\marginal\FinaIDraftReport7-29-99.wpd 2

### roughly halfhad 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 gasutilities serving 46 million residential customers. AGA found that fixed customer charges constituted approximately 13.5% ofthose utilities' total gasrevenues from theirresidential consumers. Beyond noticing that the AGA analysis included only two ofCalifornia's gasutilities fe.g.,it did not include data for either Pacific Gas & 

### Electric or SanDiego Gas & 

### Electric), AGA's analysis was not closely evaluated. Marginal Prices With inclining or declining block rates, the actual value of consumer energy savingsis the product ofthe marginal price, plus any applicable taxes orfees, times the energy units saved. While simple in theory, the accurate derivation ofthis saving is very difficult. Consumers are typically billed monthly for their energy usage and will face different marginal rates during the year depending on where their energy consumption places them in the rate schedule in any given month and how that rate may change with the time ofyear. It should be noted that just examining the last ortail block of a rate schedule is not sufficient sincemany, if not most, consumers donot progress to the last block ofthe rate schedule each and every month ofthe year. The seasonal variability of appliance usage ismost obvious for space conditioning products such as air conditioners, but, in reality, almost all appliances undergo some seasonal variation in their usage. Given thepossible wide range ofmarginal 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 savingshigher or lower than the past method ofusing 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 savingsusing marginal prices. Therefore, removing fixed costs, which only guarantees lower estimates of energy prices, would then be a correction in the wrong direction. Restructurinp, Given restructuring ofparts ofthe energy supply sector, customers may soon have more than onebill (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 futurebills. 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 1993survey. Commercial customers' utility bills could be approximated from monthly usage and likely utility tariff. Monthly usage for a large sampleof commercial buildings was available from a commercial product, the MAISPcommercial databa~e.~ Utility tariff sheets were available from utility web sites, commercial services, or the utilities 

### themselves. Data on utility sales and number of customers on various tariffs were available for investorowned utilities from the Federal Energy Regulatory Commission (FERC) Form 1filings made by those utilities. For municipal utilities and co-ops, equivalent tariff-level data were obtained from either the utility itself or were available from the Rural Utility Service.



### MATSF is a proprietary product that was acquired by LBNL from Jackson Associates, Suite 200,4825 Creekstone Drive, Durham,NC 27703; 919-967-90001919-967-8040

### (fax);e-maiI:jjackson@ntrnet.net I

roughly halfhad tail blocks higherthan the average price, indicating inclining block rates, and halfhad 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% ofthose utilities' total gas revenues from theirresidential consumers. Beyond noticing that the AGA analysis included only two ofCalifornia'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 ofconsumer energy savings is the product ofthe marginal price, plus any applicable taxes or fees, times the energy units saved. While simple in theory, the accurate derivation ofthis saving is very difficult. Consumers are typically billed monthly for their energy usage and will face different marginal rates during the year depending on where their energy consumption places them in the rate schedule in any given month and how that rate may change with the time ofyear. It should be noted thatjust examining the last ortail block ofa rate schedule is not sufficient since many, ifnot most, consumers do not progress to the last block ofthe rate schedule each and every month ofthe year. The seasonal variability ofappliance 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 ofmarginal prices between winter and summermonths, 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 ofusing average costs based on total revenue. Given the prevalence ofsummer peaking utilities and seasonal rates, it seems likely that appliances with heavier summerusage, particularly central air conditioners, would most probably produce a greater consumervalue ofsavings using marginal prices. Therefore, removing fixed costs, which only guarantees lower estimates ofenergy prices, would then be a correction in the wrong direction. Restructuring Given restructuring ofparts ofthe energy supply sector, customers may soon have more than one bill (e.g., one from the distribution company, and one or more from generators or suppliers). To capture complete information, future surveys would best gather energy pricing infonnation directly from the customers, rather than from utilities or local distribution companies. The most efficient means to collect energy pricing information in the future involves including consumption by month and pricing information in future bills. The pricing information would include for each customer, the rate schedule: marginal rates, fixed charges, demand charges for commercial and industrial customers, or time-of-use rates where applicable.

Data Sources In the near term (before new surveys can be crafted and implemented), several sources ofinformation 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 ofcommercial buildings was available from a commercial product, the MAISY® commercial database. 2 Utility tariffsheets were available from utility web sites, commercial services, or the utilities themselves. Data on utility sales and number ofcustomers on various tariffs were available for investorowned utilities from the Federal Energy Regulatory Commission (FERC) Form 1filings made by those utilities. For municipal utilities and co-ops, equivalent tariff-level data were obtained from either the utility itselfor were available from the Rural Utility Service.

2 MAISY' is a proprietary product that was acquired by LBNL from Jackson Associates, Suite 200, 4825 Creekstone Drive, Durham, NC 27703; 919-967-9000/919-967-8040 (fax);e-mail:jjackson@ntrnet.net 

### J:\marginal\FinalDraftReport7-29-99.wpd 3

# n METHODS Calculating the true bill effects ofproposed appliance energy efficiency standards on diverse U.S. consumers is difficultbecause ofthe many electricity and natural gastariffs in place, and the complexity ofthese tariffs. Since the set of end-uses currently under consideration for new standards includes fluorescent lampballasts, calculationsfor commcrcia! as well asresidential electricity customers must be included, which complicates the task considerably because commercial tariffs are generally more complex.

To address this difficultresearch question, several complementary approacheswere developed. Those approaches
arebriefly outlined below. Each of these approaches was a step in the overall sequence of setting up the modeling capabilityto allow calculation of samples of customer bills.

"Analvtic" Approach This approach was the first-cut analysis in which residential electricity and gas tariffs and commercial electricity tariffs
were examined as they were collected. Comparing each particular tariff's rate blocks to average prices yielded the maximum and nlinirnum differences between marginal and averageprice possible for that particular tariff. Because consumption is unknown, actual customermarginal prices cannotbe obtainedby this method; only the outer bounds to those prices can be obtained. The findingsfrom 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 chargesbased 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 tobe 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 ofthe 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 ofindividual customer bills: the experimental, empirical, and RECS analyses.
"Emvirical" Avvroach LBNL contracted with Jackson Associates for use of and enhancement to its proprietary MAISPdatabase ofU.S. residential and commercial building energy characteristics. This approach involves allocating these residences and 

### commercial buildings, currently only identifiedby state, to electric utility serviceterritories and then toparticular 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 ofbills was developed using two utilities in a southern California test area, and abill calculator function using the actual tariffs of electric and gas utilities was developed. For commercial buildings, this approach evolved into the one explained in Section 111. Since an estimate of the effect on the house's natural gas bill was also required for analysis of the three residential products (clothes washers, water heaters, and central air conditionerslheat pumps), the households were further assigned to a natural gas local distribution company (LDC) that serves a part ofthe territory ofthat 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 asthe foundation for the "experimental" approach in the analysis of commercial sector fluorescent lampballasts. "Exverimental" Approach During the collection oftariffs 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 11 METIIODS Calculating the true bilI effects ofproposed appliance energy efficiency standards on diverse U.S. consumers is difficult because ofthe many electricity and natural gas tariffs in place, and the complexity ofthese tariffs. Since the set ofend-uses currently under consideration for new standards includes fluorescent lamp balIasts, calculations for commercia! as welI as residential electricity customers must be included, which complicates the task considerably because commercial tariffs are generalIy more complex.

To address this difficult research question, several complementary approaches were developed. Those approaches
are briefly outlined below. Each ofthese approaches was a step in the overall sequence ofsetting up the modeling capability to allow calculation ofsamples ofcustomer bilIs. "Analytic" Approach This approach was the first-cut analysis in which residential electricity and gas tariffs and commercial electricity tariffs
were examined as they were collected. Comparing each particular tariff's rate blocks to average prices yielded the maximum and minimum differences between marginal and average price possible for that particular tariff. Because consumption is unknown, actual customer marginal prices cannot be obtained by this method; only the outerbounds to those prices can be obtained. The findings from this approach were: a) Care must be taken to find and account for alI charges, since the tariffs are often complex, with a number ofcharges 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 ofthe 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 ofdifferences 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 ofindividual customer bills: the experimental, empirical, and RECS analyses. "Empirical" Approach LBNL contracted with Jackson Associates for use ofand enhancement to its proprietary MAISY® database ofU.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 ofcustomers and estimation ofbills was developed using two utilities in a southern California test area, and a bill calculator function using the actual tariffs ofelectric and gas utilities was developed. For commercial buildings, this approach evolved into the one explained in Section III. Since an estimate ofthe 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/heatpumps), the households were further assigned to a natural gas local distribution company (LDC) that serves a part ofthe territory ofthat 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 oftariffs and the designing ofthe computer code for the empirical approach, a spreadsheetbill calculator was developed. Exploratory experimental tariffs could be modeled in this spreadsheet, and, together with 

### ]:\marginal\FinalDraftReport7-29-99.wpd 4

### load shapes from the general literature and estimates ofnionthly customer energyuse effects, bills can be estimated. A variation ofthis method that used actual commercial tariffs and load shapesfrom MAISPcommercial buildings provided the technical approachused for the commercial sectoranalysis. SeeSection I11 for the details and results of this method as applied in final form to the analysis ofthe commercial sector. "Big ban^" 

### Ao~roach The initial sample ofelectric utility tariffs that was analyzed had been chosen with consideration ofdiversity in terms ofutility ownership, geography, and utility size in mind. That sample was extended, astimepermitted, but the utilityby-utility detailed approach necessarily covered only a limited fraction of the total customerpopulation. On the other hand, a large fraction ofcustomers in the country are actually served on a rather small number oftariffs from a limited number oflarge utilities. This fact suggested an alternative "big bang" approach. In this approach,the tariffs with the greatest number ofcustomers would be examined. This approach was drawn upon to a limited degree in the expansion ofthe set ofcommercial tariffs used in the final commercial sector analysis.

RECS Amroach Consumer marginal energy prices were estimated directly from household RECS data by calculating the slopes ofthe regression lines that relate customerbills and customer usage. For electricity, the slopes of the regressions for four
summermonths (June-September) and, separately, for the remaining ("winter") months were calculated. For natural gas,the data didnot support calculations beyond the annual level. After exploring the empirical method outlined above for use in the analysis forthe residential sector, the RECS method (because of its advantageswhich 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 ofthis RECS method. Linkage to the Product LCC Analvses To anticipate the impact ofusing marginal prices in place ofaverage prices, a simple sensitivity analysis on the LCC for each product was conducted, using a range ofpossible 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 sensitiveto the value ofmarginal energy prices, but less sofornatural gas water heaters. c) For clothes washers, the efficiency level having the minimum LCC was not affected overthe range -25% to +25% from average energy prices. In addition, payback period was shown tobe affectedby a lower percentage than the change in energy prices, because the operating savings due to water isunaffected by a change in energy price. d) For ballasts, electronic ballasts were shown to reduce LCC at energy prices down to45%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. Seethe relevant Technical Support Documents (TSDs),Notice ofProposed Rulemakings (NOPRs), and the Advanced Notice ofProposed Rulemaking (ANOPR) -

### all in progress -- 

### for information on the application ofthe results ofthe marginal energy price study to specific products. load shapes from the general literature and estimates ofmonthly customer energy use effects, bills can be estimated. A variation ofthis method that used actual commercial tariffs and load shapes from MAISY® commercial buildings provided the technical approach used for the commercial sector analysis. See Section III for the details and results of this method as applied in final form to the analysis ofthe commercial sector. "BigBang" Approach The initial sample ofelectric utility tariffs that was analyzed had been chosen with consideration ofdiversity in terms ofutility 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 ofthe total customerpopulation. On the other hand, a large fraction ofcustomers in the country are actually served on a rather small number oftariffs from a limited number oflarge utilities. This fact suggested an alternative "big bang" approach. In this approach, the tariffs with the greatest number ofcustomers would be examined. This approach was drawn upon to a limited degree in the expansion ofthe set ofcommercial 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 ofthe regression lines that relate customerbills and customer usage. For electricity, the slopes ofthe 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 ofits advantages which are explained further in Section IV) was adopted for the analysis ofresidential marginal prices in this project. See Section IV for the details and the results ofthis RECS method. Linkage to the Product LCC Analyses To anticipate the impact ofusing marginal prices in place ofaverage prices, a simple sensitivity analysis on the LCC for each product was conducted, using a range ofpossible 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 ofmarginal 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 energyprices. 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 ofProposed Rulemakings (NOPRs), and the Advanced Notice ofProposed Rulemaking (ANOPR) all in progress
-- for information on the application ofthe results ofthe marginal energy price study to specific products.



### J :

\marginal\FinalDraftReport7 -29-99.wpd 5

### III. AYALYSIS

### AND RESULTS

### -COMMERCIAL Commercial marginal electricity prices were derived by calculatingmonthly bills for a distribution ofbuildings (with energy usage information) using a set ofmodeled commercial tariffs. This section of the Marginal Energy Prices report first describes the utility tariffs that were chosen and modeled forthis analysis. Then the process for calculating the electricity bills and the marginal electricity prices for the sampled commercial buildings is described. Because commercial tariffs tend to have dernand charges, the adjustment applicd to represent the reduction in demand associated with new energy efficiency standards is described next. The method by which the sampled buildings are weighted in the analysis is also described. Lastly, the results ofthe commercial analysis are presented.
A. TariffCollection and Modeling


### Tariff Collection For a variety ofutilities around the country, commercial tariffs were collected and then modeled for use inthebill 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 ofregulatory oversight, and the scale of operation. Table 1 below is a list ofthe commercial utility sampleused 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 forthe chosen utilities showing the number ofcustomers and the sales for each of autility's tariffs were obtained.
Table 1. Characterizationand Summary of Commercial Electric Utility Sample

ByUtility

USTOTAL 13,540,374 928,440,265 ByModeled Tan'm Percent ofUS in Modeled Tariffs

Cust.

3.0% 3.1% 2.2% 1.0% 1.2% 1.1% 1.1% 1.1% 0.8% 0.9% 0.6% 0.8% 0.6% 0.6% 0.0% 0.5% 0.6% 0.4% 0.5% 0.3% 0.2% 0.1% 0.0% 0.0% 20.7% OurSampleas Percent ofUtility

MWh Sales

1.7% 2.5% 2.8% 0.3% 0.8% 0.9% 0.9% 0.5% 0.5% 0.9% 0.1% 0.6% 0.3% 0.3% 0.7% 0.2% 0.8% 0.1% 0.3% 0.3% 0.1% 0.1% 0.1% 0.0% 15.7% Utility 

# - PG&E SoCal Edison Commonwealth Edison Virginia Power Detroit Edison Alabama Power Penn Power& Light Niagara Mohawk Power NSP (MN) Union Electric Co Appalachian Power Jersey Central P&L Wisconsin Elec Power Boston Edison Ohio PowerCompany Central Power& Light Arizona Public Service PEPCO Cleveland Electric lllum Seattle City Light NSP (WI) Madison Gas & Electric Savannah Elec & Power PoudreVallev REA Sample Total cust. 73% 8Ph Wh 73% 94% 83% Wh QP? 820h Wh 71% Wh 91% Ph 7% 87% 98% 76% 97% 100% 88% 91% 5% 76% 84% a, 

# - CA CA IL VA MI AL PA NY MN MO VA NJ WI A OH TX AZ MD OH WA WI WI GA CO MWh Sales

32% 45% !Ph 13% 42?h 7G% 43% 200h Wh 720h 4% 51% 15% 35% 25% 16% 82% 9% 4Ph 100% Wh Wh 45% 2% 38% Megawatt-hour Sales 16,025,437 23,057,727 25,859,649 2,696,019 7,613,394 7,917,907 8,781,171 4,550,295 4,461,852 8,735,132

674,926 5,358,036 2,705,293 2,820,415 6,348,160 2,099,439 6,989,968 1,317,800 2,765,339 3,081,941

831,739 685,233 517,052 96.424 145,990,348 a, P Total Number Total Megawatt- % IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU IOU Muni IOU IOU IOU Cwo 552,901 481,417 292,709 189,301 177,088 174.602 149,221 148,124 135,183 134,699 110,740 106,157 93,973 87,644 87,314 85,311 79,755 70,909 64,907 43,497 31,236 16,220 14,100 2.774 3,329,782 49,530,911 51,410,408 49,933,859 20,061,476 17,997,214 11,330,312 20,475,206 23,279,622 5,009,755 12,189,235 16,390,313 10,510,309 18,454,563 7,991,349 24,917,126 12,844,712 8,524,882 15,307,001 5,883,328 3,081,941

866,425 1,727,832 1,156,078

377.974 389,251,831 Cust. 401.747 419,163 291,143 137,813 166,003 154,077 143,849 143,590 110,807 120,596 78.554 104,922 85,735 80,255 6,442 74,107 78,530 53,620 63.161 43,497 27,449 14,815

708 2.101 2,802,684 

## m. Al'lALYSIS Al'WRESULTS

- COMMERCIAL

Commercial marginal electricity prices were derived by calculating monthly bills for a distribution ofbuildings (with energy usage information) using a set ofmodeled commercial tariffs. This section ofthe Marginal EnergyPrices 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 electricityprices 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 ofthe commercial analysis are presented.

A. TariffCollection and Modeling

TariffCollection For a variety ofutilities 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 ofownership (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 ofinexpensive sources of energy, the structure ofregulatory oversight, and the scale ofoperation. Table I below is a list ofthe 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 ofcustomers and the sales for each ofa utility's tariffs were obtained.

Table 1. Characterization and Summary ofCommercial Electric Utility Sample

Utility PG&E SoCal Edison Commonwealth Edison Virginia Power Detroit Edison Alabama Power Penn Power & Light Niagara Mohawk Power NSP(MN) Union Electric Co Appalachian Power Jersey Central P&L Wisconsin Elec Power Boston Edison Ohio Power Company Central Power & Light Arizona Public Service PEPCO Cleveland Electric ilium Seattle City Light NSP(WI) Madison Gas & Electric Savannah Elec & Power Poudre Vallev REA Sample Total US TOTAL**~** 

## '" c. 

### m :>. CA 

## I IOU 

## I
CA IOU IL IOU VA IOU MI IOU AL IOU PA IOU NY IOU MN IOU MO IOU VA IOU NJ IOU WI IOU MA IOU OH IOU TX IOU AZ IOU MD IOU OH IOU WA Muni WI IOU WI IOU GA IOU CO (;o.{)p By Utility

Total Number

552.901 481,417 292,709 189,301 177,088 174,602 149,221 148,124 135,183 134,699 110,740 106,157 93,973 87,644 87,314 85,311 79,755 70,909 64,907 43,497 31,236 16,220 14,100 2774 3,329,782 13,540,374 Total Megawatt·

49,530,911 51,410,408 49,933,859 20,061,476 17,997,214 11,330,312 20,475,206 23,279,622 5,009,755 12,189,235 16,390,313 10,510,309 18,454,563 7,991,349 24,917,126 12,844,712 8,524,882 15,307,001 5,883,328 3,081,941

866,425 1,727,832 1,156,078

377974 389,251,831 928,440,265



### J:\marginal\FinalDraftReport7-29-99.wpd ByModeled Tariffs

Megawatt·hour**Cll~t Sale~**  401.747 16,025,437 419,163 23,057.727 291.143 25,859.649 137,813 2,696,019 166,003 7,613,394 154,077 7,917,907 143,849 8,781,171 143,590 4,550,295 110,807 4,461,852 120,596 8,735,132 78,554 674,926 104,922 5,358,036 85,735 2,705,293 80,255 2,820,415 6,442 6,348,160 74,107 2,099,439 78,530 6,989,968 53,620 1,317,800 63,161 2,765,339 43,497 3,081,941 27,449 831,739 14,815 685,233

708 517,052 2101 96424 2,802,664 145,990,348 Percentof US in OurSample as Modeled Tariffs Percent ofUtility

MWh MWh Cust. Sales Cust. Sales

3.0% 1.7% 73% 32"10 3.1% 2.5% 87% 45% 2.2% 2.8% 99% 52% 1.0% 0.3% 73% 13% 1.2% 0.8% 94% 42% 1.1% 0.9% 88% 70% 1.1% 0.9% 96% 43% 1.1% 0.5% 97% 20% 0.8% 0.5% 82% 89% 0.9% 0.9% 90% 72:'1. 0.6% 0.1% 71% 4% 0.8% 0.6% 99% 51% 0.6% 0.3% 91% 15% 0.6% 0.3% 92% 35% 0.0% 0.7% 70;. 25% 0.5% 0.2% 87% 16% 0.6% 0.8% 98% 82% 0.4% 0.1% 76% 9% 0.5% 0.3% 97% 47% 0.3% 0.3% 100% 100% 0.2% 0.1% 88% 96% 0.1% 0.1% 91% 40% 0.0% 0.1% 50;. 45% 0.0% 0.0% 76% 26% 20.7% 15.7% 84% 38%

6

### Because tariff-level information on sales is not as readily available for publicly and cooperatively ownedutilities as it isfor investor-owned utilities that must report such information annually to FERC,the samplecontained only one municipal utility and one co-op. While the co-op incl~tded in the analysis had an average commercial price nearly the
sameasthe average commercial price of all ofthe 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 ofthe municipal utilities in the sample closer to the average commercialprice of all of the municipal utilities in the country. The bill calculator (which is described below) was then re-run using the expanded set oftariffs. See Section D below for the results ofthis sensitivity analysis.

Commercial Tariff Structure Commercial tariffs can be complex. A commercial tariff usually includes the following types ofinformation: Applicability / 

### Availability -

### the type of customer served under this tariff. Character of Service -

### the type ofcurrent, voltage, and single or three-phase. Monthly Rate -

### customer charge, energy charge, demand charge (if applicable), and extra charges. Definition ofdemand charges. Rate periods (if time-of-use) and definition of seasons. Minimum monthly charge. Power factor adjustments and reactive power demand charges.
Commercial Soreadsheet Modeling Generally, the commercial tariffmodeling effort focused on two common categories ofcommercial tariffs: small general service and general service. The collected commercial tariffs were modeled in spreadsheet form, excluding sometariffs and/or componentsthat could not be applied tobuildings in MAISY'. For instance, sometariffs provide charges for both single and three-phase service. BecauseMAISV 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 ofthe types ofbuildings used in MAISYX, only the secondary voltage levels were considered. Power factorand reactive demand charges, which are adjustments based on voltage and demand usage, were ignored. Other factors were ignored ifthe information could notbeused in 

## MAISP or are rarely applicable. The spreadsheet also contains the number of customersin a tariff, total number of customers in a commercial utility, the megawatt-hours sold,kilowatt-hours sold per customer, and averageprice per kilowatt-hour. These data were extracted from FERCForm 

### 1 for IOU's and from comparable sources for municipal utilities and co-ops. 

### B, Calculation ofCommercialBills and Marginal Prices
Overview The Commercial Tariffs Spreadsheet, or CTAS, contains a collection of modeled commercial electric utility tariffs, a collection ofmodeled commercialbuildings, 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 calendaryear. Then, building energyusage and demands are decremented to account for savingsexpected 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 ofeach 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 ofUS electric utilities and modeled for inclusion in CTAS calculations. CTAScontains the specific commercial tariffs for the utilities shown in Appendix 1.



### In addition totheparameters necessary to calculate customer bills, information was collected for each tariff regarding the number of customers,revenues, and delivered energy. Thistariff-level data on 

### customers and delivered energy
were used to weight the resulting bill and marginal price calculations to represent anational distribution ofmarginal 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 ofthat Because tariff-level infonnation on sales is not as readily available for publicly and cooperatively owned utilities as it is for investor-owned utilities that must report such information annually to FERC, the sample contained only one municipal utility and one co-op. While the co-op included in the analysis had an average commercial price nearly the
same as the average commercial price ofall ofthe 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 ofthe municipal utilities in the sample closer to the average commercial price ofall ofthe municipal utilities in the country. The bill calculator (which is described below) was then re-run using the expanded set oftariffs. See Section D below for the results ofthis sensitivity analysis.

Commercial TariffStructure Commercial tariffs can be complex. A commercial tariffusually includes the following types ofinfonnation: Applicability / Availability

- the type ofcustomer served under this tariff. Character ofService

- the type ofcurrent, voltage, and single or three-phase. Monthly Rate customer charge, energy charge, demand charge (ifapplicable), and extra charges. Definition ofdemand charges. Rate periods (iftime-of-use) and definition ofseasons. Minimum monthly charge. Power factor adjustments and reactive power demand charges.
Commercial Spreadsheet Modeling Generally, the commercial tariffmodeling effort focused on two common categories ofcommercial tariffs: small general service and general service. The collected commercial tariffs were modeled in spreadsheet fonn, excluding some tariffs and/or components that could not be applied to buildings in**MAISY~.** For instance, some tariffs provide charges for both single and three-phase service. Because**MAIS~** does not distinguish between these two types ofservice, only the charges for single phase were modeled. Also, some tariffs charge different prices for primary, secondary, and transmission voltage levels. Because ofthe types ofbuildings used in**MAIS~,** only the secondary voltage levels
were considered. Power factor and reactive demand charges, which are adjustments based on voltage and demand usage, were ignored. Other factors were ignored ifthe information could not be used in**MAIS~** or are rarely applicable. The spreadsheet also contains the number ofcustomers in a tariff, total number ofcustomers in a commercial utility, the megawatt-hours sold, kilowatt-hours sold per customer, and average price per kilowatt-hour. These data were extracted from FERC Fonn 1for IOU's and from comparable sources for municipal utilities and co-ops.
B. Calculation ofCommercial Bills and Marginal Prices
Overview The Commercial Tariffs Spreadsheet, or CTAS, contains a collection ofmodeled commercial electric utility tariffs, a collection ofmodeled commercial buildings, and Visual Basic program modules. Each building is assigned to a group ofapplicable tariffs, based on building demand, and monthly bills are calculated for each tarifffor 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 ofeach 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 ofUS 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 tariffregarding the number ofcustomers, 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 ofmarginal prices. The calculations did not use a tariffunless 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 ofthat



### J:\marginal\FinalDraftReport7-29-99.wpd 7

### utility's tariffs). Industrial tariffs, as contrasted to commercial tariffs, have not been included in CTAS. This is primarily because the MAISPdatabase does not include industrial buildings. Electric utilities typically have many con~mercial tariffs. Southern California Edison, for instance, has over 

### 100 commercial tariffs. Most ofthese tariffs target a very small number of customers, or even individual customers, and thus have been ignored in CTAS. Tomaximize coverage in the limited time available, only the most-used two orthree commercial tariffs for each utility have been included in CTAS. Taken together, these few tariffs cover 

### 84%ofthe customers in our utility sample and 

### 38% ofthe commercial electricity sales by those sampled utilities. Generally speaking, 

### commercial customers are assigned to a particular tariffbased on theirpeak monthly andlor annual demand as measured in kilowatts over a short period oftime, e.g. 

### 15or 

### 30 minutes. This demand "window" was used to determine which ofall the tariffs to apply to a particular building in the CTAS collection. Forinstance the 

### GS-2 tariff carried by Southern California Edison is applied to a customer/building whose peak demand isbetween 

### 20 and 

### 500 kW. This assignment by demand window means that a particular building will have bills calculated using some subset ofall the modeled tariffs, typically onetariff per utility.

CTASBuildings/Customers The collection ofbuildings contained in CTAS comes from 

### MA IS^, aproprietary database containingweighted, representative building collections for each state. A subset ofbuildings was selected fromMAISP, the subsequent weighting ofwhich was intended to create a "prototypical" set ofbuildings that would exist in relatively the same proportions throughout the country, and would thus be representative ofcustomers existing within any of the modeled utility tariffs. Each sampled building in MAISPhas 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 ofbuildings was selected for inclusion in CTAS. The hourly load profiles for these buildings were then extracted from MAISPand summed up over a month. Then these monthly loads and demand peaks were inserted into CTAS. For each state, 

### 189buildings, with their MAISY~ building weights, were included, for a total of 

### 1323buildings. The 

### 1323buildings were apportioned to the main types of commercial buildings used in EIA's Commercial Building Energy Consunlption Survey (CBECS)as follows:
CBECS %

### ofTotal Commercial Building tvoe Buildings in US3 Education 

### 7.6% Food Sales 

### 3.5% Food Service 

### 6.7% Health Care 

### 2.5% Lodging 

### 3.4% Mercantile and Service 

### 30.9% Office 

### 17.9% Public Assembly 

### 7.6% Public Order & 

### Safety 

### 2.1% Religious 

### 5.0% Warehouse & 

### Storage 

### 9.7% Othermacant 

### 3.0% Number of %

### Buildings Buildings in Samole in Sam~le 

### 98 7.4% 63 4.8% 91 6.9% 35 2.6% 42 3.2% 406 30.7% 23 1 17.5%

98 7.4% 28 2.1% 63 4.8% 126 

### 9.5% 42 3.2%



### ----------- --------- ------ --------- 

### TOTAL 

### 100.0% 1323 100.0%
CTAS Calculations CTAS does itswork 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 ofthe following steps,nested and repeated as explained:

Source: CBECS Table

### BC-39:Lighting Equipment, Number ofBuildings, 

### 1995. 

### J 

### :\marginal\FinalDraftReport7-29-99. 

### wpd utility's tariffs). Industrial tariffs, as contrasted to commercial tariffs, have not been included in CTAS. This is primarily because the MAISyt) database does not include industrial buildings.

Electric utilities typically have many commercial tariffs. Southern California Edison, for instanee, has over 100 commercial tariffs. Most ofthese tariffs target a very small number ofcustomers, or even individual customers, and thus have been ignored in CTAS. To maximize coverage in the limited time available, only the most-used two orthree commercial tariffs for each utility have been included in CTAS. Taken together, these few tariffs cover 84% ofthe customers in our utility sample and 38% ofthe commercial electricity sales by those sampled utilities. Generally speaking, commercial customers are assigned to a particular tariffbased on their peakmonthly and/or annual demand as measured in kilowatts over a short period oftime, e.g. 15 or 30 minutes. This demand "window" was used to determine which ofall the tariffs to apply to a particular building in the CT AS collection. Forinstance the GS-2 tariff carried by Southern California Edison is applied to a customerlbuilding whose peak demand is between 20 and 500 kW. This assignmentby demand window means that a particular building will have bills ealculated using some subset ofall the modeled tariffs, typically one tariffperutility.

CTAS Buildings/Customers The collection ofbuildings contained in CTAS comes from MAISY"", a proprietary database containing weighted, representative building collections for each state. A subset ofbuildings was selected from MAISY®, the subsequent weighting ofwhich was intended to create a "prototypical" set ofbuildings that would exist in relatively the same proportions throughout the country, and would thus be representative ofcustomers existing within any ofthe modeled utility tariffs. Each sampled building in MAISY® has a large number ofassociated characteristics, such as size, annual peak demand, etc. Also included are hourly load profiles for an entire year. For each ofseven states (California, Washington, Colorado, Illinois, Texas, New York and Georgia) a subset ofbuildings was selected for inclusion in CT AS. The hourly load profiles for these buildings were then extracted from MAISY® and summed up over a month. Then these monthly loads and demand peaks were inserted into CTAS. For each state, 189 buildings, with their MAISY"" building weights, were included, for a total of 1323 buildings. The 1323 buildings were apportioned to the main types ofcommercial buildings used in EIA's Commercial Building Energy Consumption Survey (CBECS) as follows:

CBECS % ofTotal Commercial Number of % Buildings Building type Buildings in US 3 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% OtherNacant 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 ofthe following steps, nested and repeated as explained:

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

### J:\marginal\FinalDraftReport7-29-99.wpd 8

### Each eustomer/building is considered in 

### turn. For each building a selection is made of applicable tariffs, based on that building's peak annual demand. For eachtariff a calculation of a monthly bill in $/month isperformed." 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 energyprice). The assumed energy use decrement is 5%;the demand decrement is 

### 80% of 5%,that is, 4%.j A monthly marginal price in $/kwh for this month-tariff-building is computedby 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 $/kwhis computed by dividing the original, undecremented bill by the original energy use, e.g. bill $/total kwh. Thepercentage 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 12months 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 havebeen processed.

CTAS OutDut The output from a CTAS run is written to a comma-separated-values, or "csv" file,with a record for each building-tariff match. This file, containing the fields shown below, is loaded into a FoxPro" table where additional processing occurs toproduce various charts and distributions.
FieldName Field Description

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

### ...................... ACCTNO MAISPcustomer/ 

### building ID number STATE Statefrom which sample customer was drawn ANPKKW Building annual peak demand in kW measured over 1hour TARFTYP Tariff group index defined by demand "window" ARFINDX Index used in program TARPTR Index used in program UTILCO Electric utility's name, e.g. Consolidated Edison TARFNAME Name of particular tariff,e.g. GS-I NCUS Number ofcustomers billed under that tariff DELVDMWH Annual megawatthours (MWh) delivered by utility to customers under this tariff BLDGWT Calculated weight of building, derived inpart from MAISPweight BLDGOPHRWeeklyhours of operation ofbuilding ANAVGRAT Annual average electricity price ANMR 

### 1 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 AVGBILLl Annual average decremented bill, defined asaverage of 12 monthly average bills AVGKWH Annual average energy use, defined as averageof 12 monthly energy usage levels AVGKWHl Annual average decremented energyuse, defined asaverage of 12 monthly decremented energy usage levels

SeeAppendix 2 for sample commercial bill calculations. The explanation of the derivation of the demand decrement appears in Appendix 

### 3: "Demand Decrement Due to Standards -

### The Role of Lighting Coincidence and Diversity." Each customerlbuilding is considered in tum. For each building a selection is made ofapplicable tariffs, based on that building's peak annual demand. For each tariffa calculation ofa monthly bill in $/month is performed.4 A second calculation with the same month, tariffand building is performed with decremented monthly energy
use and peak demand to obtain a representative change in the building'sbill (in order to calculate the marginal energy price). The assumed energy use decrement is 5%; the demand decrement is 80% of5%, 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 ofa calendaryear. Annual values are obtained for average price, marginal price, and percent difference by summing each month's values over the year and dividing by 12. The process is repeated for each applicable tarifffor the particular customer/building. The next building is considered and the complete process repeated, until all customer/buildings have been processed.

CTAS Output The output from a CT AS run is written to a comma-separated-values, or "csv" file, with a record for each building-tariff match. This file, containing the fields shown below, is loaded into a FoxPro® table where additional processing occurs to produce various charts and distributions.

Field Name

ACCTNO STATE ANPKKW TARFTYP ARFINDX TARPTR UTILCO TARFNAME NCUS Field Description MAISyID customer/ building ID number State from which sample customerwas drawn Building annual peak demand in kW measured over 1 hour Tariffgroup index defined by demand "window" Index used in program Index used in program Electric utility's name, e.g. Consolidated Edison Name ofparticular tariff, e.g. GS-l Number ofcustomers billed under that tariff DEL VDMWH Annual megawatthours (MWh) delivered by utility to customers underthis tariff BLDGWT Calculated weight ofbuilding, derived inpart from MAISY'"' weight BLDGOPHRWeekly hours ofoperation ofbuilding ANA VGRAT Annual average electricityprice ANMRI Annual average electricity marginal price ANEI Annual percent difference between marginal and average electricity price A

VGBILL Annual average bill, defined as average of 12 monthly bills AVGBILLl Annual average decremented bill, defined as average of 12 monthly average bills AVGKWH Annual average energy use, defined as average of 12 monthly energy usage levels AVGKWHI Annual average decremented energy use, defined as average of 12 monthly decremented energy usage levels

4 See Appendix 2 for sample commercial bill calculations.
5 The explanation ofthe derivation ofthe demand decrement appears in Appendix 3: "Demand Decrement Due to Standards

- The Role ofLighting Coincidence and Diversity." 

### J :

\marginal\FinalDraftReport7 -29-99.wpd 9

### Conclusions CTAS was designed as an analysis tool to investigate marginal prices experienced by a range of customers over a range ofcommercial tariffs throughout the country. It attempts to maximize coverage by running a prototypical set of customers/buildingsagainst as broad a set oftariffs as possible, given the time constraints ofthe project.


### C.Weighting Method
This section describes construction ofthe weighted distribution of commercial electricity marginal prices. The sample ofcommercial buildings consisted of 1323buildings 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 setrepresents a unique combination ofa building and an applicable tariff, for a total of 29,133 observations.
Thecomposition ofthe sample posed a dual weighting problem: distributing weights acrossbuildings and across tariffs. The weighting approach solves these problems sequentially. For each building a weight is derived from its original 

### MA IS^ building weight and scaling factors. This adjusted building weight is then apportioned across all the tariffs that were applied to the building. A composite weighting factor for each building/tariff pair combines both of these weighting factors. These weights are all based on energy, averagemonthly kwhusage at the building level and annual MWh sales for each tariff. Appendix 

### 4, Weighting Method for Commercial Sample, shows the details ofthe approach to this dual weighting problem.

D. 

### ResultsofAnalysis Figure 1 below displaysthe results ofthe commercial analysis by showing the distribution ofthe differences derived between marginal electricity prices and average electricity prices. Electricity marginal prices range from 

### 85%below to 

### 5 1% above the average price for the same customer. At the consumption-weighted mean ofthe differences, electricity marginal prices are 5.2% lower than average

### price^.^ In the LCC analysis of commercial sectorballasts, this distribution of differences was used along with a distribution ofaverage commercial sector electricity prices (which were obtained from EIA data on over 3000 utilities servingcommercial customers) to generate a distribution ofmarginal electricity prices. In the LCC analysis of industrial sector ballasts, this same distribution of differences was used along with a distribution ofaverage industrial sector electricity prices (which were obtained from EIA data on over 2000utilities serving industrial customers) to generate a distribution of marginal electricity prices.
As a result ofthe sensitivity analysismentioned earlier (where additional municipal utility tariffs were included), a similar distribution ofweighted amounts were found by which average prices exceeded marginal prices, the mean of which was -5.6%.

The individual amountsby which average prices exceeded marginal prices had to be binned to prepare inputs forthe LCC analysis. The weighted mean of the binned distribution was -5.7%. 

### J 

### :\marginal\FinalDraftReport7-29-99. wpd Conclusions CTAS was designed as an analysis tool to investigate marginal prices experienced by a range ofcustqmers over a range ofcommercial tariffs throughout the country. It attempts to maximize coverage by running a prototypical set of customers/buildings against as broad a set oftariffs as possible, given the time constraints ofthe project.

C.Weighting Method

This section describes construction ofthe weighted distribution ofcommercial electricity marginal prices. The sample ofcommercial 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 setrepresents a unique combination ofa building and an applicable tariff, for a total of29,133 observations.

The composition ofthe 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/tariffpair 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 ofthe approach to this dual weighting problem.

D. Results ofAnalysis Figure 1below displays the results ofthe commercial analysis by showing the distribution ofthe 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 ofthe differences, electricity marginal prices are 5.2% lower than average prices.6 In the LCC analysis ofcommercial sectorballasts, this distribution ofdifferences was used along with a distribution ofaverage commercial sector electricityprices (which were obtained from EIA data on over 3000 utilities serving commercial customers) to generate a distribution ofmarginal electricity prices. In the LCC analysis ofindustrial sectorballasts, this same distribution ofdifferences was used along with a distribution ofaverage industrial sector electricity prices (which were obtained from EIA data on over 2000 utilities serving industrial customers) to generate a distribution ofmarginal electrieity prices.

As a result ofthe sensitivity analysis mentioned earlier (where additional municipal utility tariffs were included), a similar distribution ofweighted amounts were found by which average prices exceeded marginal priees, the mean of which was -5.6%.

6 The individual amounts by which average prices exceeded marginal prices had to be binned to prepare inputs for the LCC analysis. The weighted mean ofthe binned distribution was -5.7%. 

### J:\marginal\FinaIDraftReport7-29-99.wpd 10

## Figure 1. 

# Commercial Electricity Marginal Prices Differences from Annual Average

s s s s 

## s s s s s 

## s s s s 0 m 0 m 0 0 m 0 rn 0 m 0 

## Y 9 

## Y Y F T- F 

# .'-
N w m % Difference from Average



## Marginal <

## Average Mean =

## -5.2%


## Minimum =

## -85% 

## Marginal >

## Average Median =

## -3.3%
Maximum =

## +5

## 1 % (J) 

### G> 

## n:s 

# en**~** 

# ...... (J**~**


## ...... (J Q)



# - W**~** 

### 0**~** 0 25%

20%

15%

10%

5%

0% 

### Figure 1. 

# Commercial Electricity Marginal Prices Differences froln Annual Average

% Difference from Average



## Marginal 

## < Average Mean = 

### -5.2%

Minimum -85% 

## Marginal> Average Median 

## = -3.3%

Maximum 

## =+51

### %



### J:\marginal\FinaIDraftReport7-29-99.wpd 11

### IV. AVALYSISANDRESULTS -

### RESIDENTIAL

Method Used to Derive Residential Electric and Natural GasMarginal 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 ofusing 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 forutilitybills, there isno 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 commercialmarginal energy prices.

EIA had collected utilitybills for up to 16billing cycles for 61 19ofthe 7038 RECS households that use electricity, and for 3 153 of the 4033RECShouseholds that usenatural gas. EIA has made a public use version ofthese 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 eachhousehold forwhich billing data were available.' Energy consumption was plotted on the x-axis, and the consumer's energy bill was plotted
on the y-axis. The slope ofthe regression line for each household (the change in the energybill divided bythe change in the energy consumption) equals the marginal energy price for that household.
Electricitv For electricity, the billing data were divided into two seasons, summer and "winter" (actually non-summer), where a bill
was defined to be a summerbill if the midpoint of the billing period fell between June 1and September 30. This division was done because review ofutility 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 summerto 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 theregression line. SeeFigure 2 below for a sample linearregression on the electricity bills of one ofthe RECS households.
For some households, there were either not enough data or the data points were too close together or too scattered to determine a slope, particularly in the summer season. After a household-by-household examination ofthe outliers, slopes (marginal prices) that were either less than or equal to zero or greater than $0.22/kWh were alsorejected. It was possible to calculate slopes for the summer season for 5818 households, ofwhich 5615 had acceptable fits, yielding slopesthat fell within the above range ofvalues. It was possible to determine a slope forthe "non-summer" season for all 6119households for which electricity billing data were available. Of these households, 6098 had slopeswith acceptable fits. There were a total of 5606 households for which both the summer and non-summer marginal electricity prices could be determined with acceptable fits. Seasonal prices were used for each ofthese households in the LCC analysis.

For households for which it was not possible to determine a seasonal rate for both seasons, or for which these rates fell outside the acceptablerange, a single linearregression was performed from the data from all billing cycles. There
were an additional 502 households forwhich a single annual marginal price could be determined. This marginal price was used in the LCC analysis for these households. Thus, ofthe 7038 RECS households with electricity, a marginal electricity price orprices were available for 6108 (5606with 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 forwhich EIA did not collect billing data, with only 11 additional households being dropped dueto an indeterminate orunacceptable regression slope.



# 'Source: Robert Latta ofEIA proposed this approach. 

### J 

### :\marginal\FinalDraftReport7-29-99 .wpd IV.**At~ALYSIS** 

### AND RESULTS
- RESIDENTIAL

Method Used to Derive Residential Electric and Natural Gas Marginal Prices Data from the Energy Information Administration's (EIA) 1993 Residential Energy Consumption Survey (RECS) were used to calculate marginal energy prices for residential appliance owners. The main advantages ofusing RECS data
are: I) this survey of7041 U.S. households was designed by EIA to be a nationally representative sample; and 2) the data are available to the public. Since RECS includes the exact amounts households paid for utility bills, there is no need to derive those bills from a combination ofan appropriate tariffand each building's energy consumption. Thus, the method for deriving residential marginal energy prices was inherently simplerthan ourpreviously described method for deriving commercial marginal energy prices.

EIA had collected utility bills for up to 16 billing cycles for 6119 ofthe 7038 RECS households that use electricity, and for 3153 ofthe 4033 RECS households that use natural gas. EIA has made a public use version ofthese data available; it includes some error inoculation to protect the identity ofindividual 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 energybill was plotted
on the y-axis. The slope ofthe regression line for each household (the change in the energy bill divided bythe change in the energy consumption) equals the marginal energyprice 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 summerbill ifthe midpoint ofthe billing period fell between June 1 and September 30. This division was done because review ofutility 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 ofdates. 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 ofthe regression line. See Figure 2 below for a sample linear regression on the electricity bills ofone ofthe RECS households.

For some households, there were eithernot 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 ofthe outliers, slopes (marginal prices) that were either less than or equal to zero or greater than $0.22/kWh were also rejected. Itwas possible to calculate slopes for the summer season for 5818 households, ofwhich 5615 had acceptable fits, yielding slopes that fell within the above range ofvalues. It was possible to determine a slope for the "non-summer" season for all 6119 households for which electricity billing data were available. Ofthese households, 6098 had slopes with acceptable fits. There were a total of5606 households for which both the summer and non-summer marginal electricity prices could be determined with acceptable fits. Seasonal prices were used for each ofthese households in the LCC analysis.

For households for which it was not possible to determine a seasonal rate for both seasons, or for which these rates fell outside the acceptable range, a single linear regression was performed from the data from all billing cycles. There
were an additional 502 households for which a single annual marginal price could be determined. This marginal price
was used in the LCC analysis for these households. Thus, ofthe 7038 RECS households with electricity, a marginal electricity price orprices were available for 6108 (5606 with seasonal rates, plus 502 with annual marginal prices only),
or 87%. Most (919) ofthe 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 orunacceptable regression slope.

7 Source: Robert Latta ofEIA proposed this approach. 

### J :

\marginal\FinalDraftReport7 -29-99.wpd 12

### Figure 2.

RECS 1993 Linear Regression on ElectricityBills, Household 4164

Winter Bills 

# - 'Summer Regression



### Marginal Prices Summer: 9.886lkWh Winter: 4.85$/kWh

Ther-squared values fortheregressions ofRECS electricity bills were generally very high! Examination ofthe summer regressions and the winter regressions reveals excellent fits in both cases, with r-squared values of 0.95 or above90%of the time forthe summer bills and 

### 75%of the time for the winter bills. (See the distribution ofthe rsquared values shown in Table 2.)

Natural Gas Fornatural gas, the data were not divided into seasons because review ofgasutility rates (during the "Analytic" approach described earlier)found that most (21 out of27)utilities surveyed did not have seasonal rates. Also, many households have little or no gasconsumption in the summer,making determination ofa marginal price for this season difficult. Instead, a single regression relating natural gasbills and usage wasperformed using the data for all ofthe billing cycles. It waspossible to determine a slopeofthe regression line for all 3153households forwhich billing data were available. Slopeswere rejected asunreasonable if they were less than or equal to zero, or greater than $3.20/therm. This left 3139 households, or 78%ofthe total of 4033 households thatuse natural gas. Again, most (880) ofthe households forwhich a marginal price was not available were those forwhich EIA did not collect billing data, with only 

### 14additional households dropped duetounacceptable regression slopes. See Figure 

### 3below for a sample linear regression onthe gasbills ofone ofthe RECS households.


# 'In a regression with a single independent variable, r-squared is the squareofthe correlation coefficient "r". It represents the percent ofvariation in the dependent variable (in this case, the household's energybill) that can be explained by the variation in the independent variable (in this case, the household's energy consumption). 

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

### wpd 13 Figure 2.

RECS 1993 Linear Regression on Electricity Bills, Household 4164

250

y = 0.0988x + 0.0862 R2= 1.0000 j. 

### /' 200 

# /-' ;I' 

### K"

150 

### K";I'

A Summer Bills II Winter Bills**~** III

# ~

# ~" 0.0<8"'.33.64 R2 = 0.8177

III - -Summer Regression --Winter Regression

Marginal Prices Summer: 9.88¢/kWh Winter: 4.85¢/kWh iii

100

50



### oo 500 1000 1500 2000 2500 kWh

The r-squared values for the regressions ofRECS electricity bills were generally very high.8 Examination ofthe
summerregressions and the winter regressions reveals excellent fits in both cases, with r-squared values of0.95 or above 90% ofthe time for the summer bills and 75% ofthe time for the winter bills. (See the distribution ofthe rsquared values shown in Table 2.)

Natural Gas Fornatural gas, the data were not divided into seasons because review ofgas utility rates (during the "Analytic" approach described earlier) found that most (21 out of27)utilities surveyed did not have seasonal rates. Also, many households have little or no gas consumption in the summer, making determination ofa marginal price for this season difficult. Instead, a single regression relating natural gas bills and usage was performed using the data for all ofthe billing cycles. Itwas possible to determine a slope ofthe regression line for all 3153 households for which billing data
were available. Slopes were rejected as unreasonable ifthey were less than or equal to zero, or greater than $3.20/therm. This left 3139 households, or 78% ofthe total of4033 households that use natural gas. Again, most (880) ofthe households for which a marginal price was not available were those for which EIA did not collect billing data, with only 14 additional households dropped due to unacceptable regression slopes. See Figure 3 below for a sample linear regression on the gas bills ofone ofthe RECS households.

8 In a regression with a single independent variable, r-squared is the square ofthe correlation coefficient "r". It represents the percent ofvariation in the dependent variable (in this case, the household's energy bill) that can be explained by the variation in the independent variable (in this case, the household's energy consumption). 

### J :

\marginaJ\FinalDraftReport7 -29-99.wpd 13

### Figure 

### 3.

RECS 1993 Linear Regression on Gas Bills, Household 1001

y =O 5119x +49798 R~= 0 9994 50 

### - 

# - 2 

### = 40 

### .- m 

### Marginal Price $0.5l/therm 30 

### -



# I I

Therms



### Ther-squared values for the regressions ofRECS natural gas bills were also very high. On an annual basis, it was found that the regression line fits were excellent,with r-squared values of 

### 0.95 more than 

### 90% ofthe time. The distribution ofthe r-squared values forboth the electricity and natural gasregressions is shown in Table

### 2 below.
Table 

### 2. Distribution ofr-sauared Values forElectricitv (Summer and Winter) and GasRegressions
Percentile



### 9gth 951h 90th 751h 5Oth 251h 1 0th 5'h 1 Mean Number of Cases* Electricity Nahral Gas Summer Winter 

### 1 1 1 1 I 

### 1 1 0.999 0.999 1 0.997 0.997 0.999 0.987 0.993 0.99 0.956 0.984 0.947 0.874 0.965 0.868 0.755 0.912

0.449 0.217 0.6 0.973 0.946 0.975

5667 6116 3150

Total Number of Cases 

### 581 8 61 19 3

### 153 

## * Cases where r-squared values could be calculated. 

### J:\marginal\FinalDraftReport7-29-99. wpd Figure 3.

RECS 1993 Linear Regression on Gas Bills, Household 1001

80

70

60

50 

### € 40 iii

30

20

10 

# ./ 

# / / y = 0.5119x + 4.9798 R2 = 0.9994 

# / / Marginal Price $0.5I1therrn 

# / /**~**

0 

### o 20 40 60 80 100 120 140 Therms

The r-squared values for the regressions ofRECS natural gas bills were also very high. On an annual basis, it was found that the regression line fits were excellent, with r-squared valucs of0.95 more than 90% ofthe time. The distribution ofthe r-squared values for both the electricity and natural gas regressions is shown in Table 2 below.

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

Percentile Electricity Natural Gas Summer Winter 99th 1 95 th 1 90th 0.999 0.999 75th 0.997 0.997 50th 0.999 0.987 0.993 25th 0.99 0.956 0.984 10

th 0.947 0.874 0.965 5th 0.868 0.755 0.912 

### l" 0.449 0.217 0.6 Mean 0.973 0.946 0.975 Number ofCases* 5667 6116 3150

Total Number ofCases 5818 6119 3153

* Cases where r-squared values could be calculated.


### J:\marginal\FinalDraftReport7-29-99.wpd 14 160

### Electricitv andNatural Gas A statistical analysis was performed comparing the data setsfor each fuelbefore 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 whoseutility bills were included in the household's rent;presumably ETA could not collect billing data forthose households. Looking at just those subsets of RECS householdsthat own the particular appliance of interest, it was found that the differences between all households and the subset for which it was ableto calculate a marginal price were smaller than the differences described above for the set of all RECShouseholds. Theseresults may occurbecause 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 fewerhouseholds ofthat type were dropped. For the key parameters, differences were found tobe negligible (generally 1%

### or less) between the appliance data set and the subset with marginal prices. Table 3 below showsthe number and percent of households used in the analysis for each appliance subset after households with no marginal price were dropped.
Table 3. Number ofHouseholds in EachAvuliance Data Set, and in Subsetswith Marginal Prices



## * Sum ofweights ofhouseholds. Theweight of each household isthe number ofUShouseholdsrepresented by each RECShousehold. 

## ** "Marginal Price Subset" represents the households forwhich an electricity marginal price is available. Fuel oil marginal price was assumed to be the same asthe fuel oil average price, an assumption supported by the research reported in Section V below.

Appliance Subsets (HH =

### household)

HHswith washer and dryer and electricity HHs with washer and dryer and natural gas HHs with central air conditioning HHswith heat pumps HHs with electric water heaters HHs with natural gas water heaters HHswith fuel oil water heaters**

Results ofAnalvsis Figures 4 and 5 below display the results ofthe 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) ofthe residential gas analysis.

In the LCC analysis ofthree residential products (clotheswashers, water heaters, and central air conditionerslheat pumps), the marginal electricity and natural gas prices used were derived directly from RECS. Figures4, 5, and 6below showhow differentthese marginal prices are from average prices. Those marginal electricity prices in the summer (June-September) range from 98%below to 175%above the average price for the same customer. At the consumptionAll Households with appliance Marginal Price Subset
Survey Responses

4998

2759

2414

651 2559

2805

176 Survey 

### R~~~~~~~~
4574

2439

2164

613 2323

2535

163 Marginal Price Subset as percent ofAll Households U.S. Households (millions)* 67.8

40.6

32.1

7.9 33.4

41.0

1.8 U.S. Households (millions)* 61.9

35.6

28.8

7.6 30.3

36.0

1.7 % 

### of Survey Responses 92%
88%

90%

94% 91%
90%

93% %

### ofRelevant U.S. Households 91%
88%

90%

96% 91%
88%

94% Electricity and Natural Gas A statistical analysis was perfonned comparing the data sets for each fuel before and after dropping the households for which it was unable to calculate marginal energy prices in order to determine ifany bias was being introduced by dropping those households. It was found that the subset ofhouseholds 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 atjustthose subsets ofRECS households that own the particular appliance ofinterest, 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 ofall RECS households. These results may occurbecause 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 ofthat 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 ofhouseholds used in the analysis for each appliance subset after households with no marginal price were dropped.

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

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

## =household) appliance percent ofAll Households Survey U.S. Survey U.S. %of % ofRelevant Responses Households Responses Households Survey U.S. (millions)* (millions)* Responses Households HHs with washer and 4998 67.8 4574 61.9 92% 91% dryer and electricity HHs with washer and 2759 40.6 2439 35.6 88% 88% dryer and natural gas HHs with central air 2414 32.1 2164 28.8 90% 90% conditioning HHs with heat pumps 651 7.9 613 7.6 94% 96% HHs with electric water 2559 33.4 2323 30.3 91% 91% heaters HHs with natural gas 2805 41.0 2535 36.0 90% 88% water heaters HHs with fuel oil water 176 1.8 163 1.7 93% 94% heaters**

* Sum ofweights ofhouseholds. The weight ofeach household is the number ofUS 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 ofAnalysis Figures 4 and 5 below display the results ofthe 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) ofthe residential gas analysis.

In the LCC analysis ofthree 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

### J:\marginal\FinalDraftReport7-29-99.wpd 15

### weighted mean ofthe differences,marginal electricity prices are 2.5% higher than average electricity prices in the
summer. Marginal electricity prices in the "winter" (the remainder ofthe year) range from 100%

### below to 

### 130%above the average price for the same customer. At the consumption-weighted mean ofthe differences, electiicity marginal prices are 5.3% lower than average electricity prices in the "winter." Marginal gasprices range from

### 98%below to 248% above the average price for the samecustomer. At the consumption-weighted mean ofthe differences, marginal gasprices are 7.8% lower than average gasprices.
Figure 4 below shows, for each household in the summer, the difference between the marginal electricity price forthat household and that household's annual average electricity price.


## Figure4. 

# Residential Summer Electricity Marginal Prices Differences from Annual Averages
Marginal c 

### Average Marginal 

## >Average

% Diirencefrom

### Average



## Mean 

## = 

## +2.5% Minimum 

## = -98% 

## Median 

## = +I .4% 

## Minumum = 

## +I 75%

J :\marginal\FinalDraftReport7-29-99.

### wpd

weighted mean ofthe differences, marginal electricity prices are 2.5% higher than average electricityprices in the
summer. Marginal electricity prices in the "winter" (the remainder ofthe year) range from 100% below to 130% above the average price for the same customer. At the consumption-weighted mean ofthe differences, electricitymarginal 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 ofthe 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 electricityprice for that household and that household's annual average electricity price.

3.0



### ..... 2.5 I/) 

### c:

### ~ 

## E 2.0



# -- I/) 

## :E 0 1.5 ..c: Q) I/) ::::J 0 1.0 

### J: U) 

### :) 0.5


### o.o§

I 

### Figure 4. 

# Residential Summer Electricity Marginal Prices Differences from Annual Averages
Marginal < 

### Average

Mean 

## = +2.5% Minimum 

# =-98% 

### Marginal> Average

% 

### Differencefrom Average

Median 

# = +1.4% Minumum 

# =+175%



### J:\marginal\FinalDraftReport7-29-99.wpd 16

### Figure 

### 5below shows, for each household in the winter, the difference between the marginal electricityprice forthat household and that household's annual average electricityprice.



## Figure 5. 

# Residential Winter Electricity Marginal Prices Differences from Annual Averages
Marginal 

## <Average Marginal 

## >Average



## Mean 

## = -5.3% Minimum = 

## -100% Median = 

## -3.8% Maximum 

## = +130% Figure 5 below shows, for each household in the winter, the difference between the marginal electricity price for that household and that household's annual average electricity price.



## Figure 5. 

# Residential Winter Electricity Marginal Prices Differences from Annual Averages
Marginal < 

### Average Marginal> Average**~ 2.5r-----------------------------~rtr------------------------------~**  I/) 

### c 

### .2**~** 2.0 

### Ui' 

# " '0 .c 1.5 Q) I/) ::J

### ~ 

# 1.0 r---------------------- fA ::j 0.5

0.0 lit<! 8 "I

# ~**~**

# ~ I



### Mean 

## = -5.3% 

### Minimum = 

### -100%

J:\marginal\FinalDraftReport7-29-99.wpd % Difference from Average



### Median 

# =-3.8% 

### Maximum = 

### +130%

17

### Figure 

### 6below shows, for each housel~old, the difference between themarginal gasprice forthat household 

### and that household's annual average gasprice.



## Figure 6. 

# Residential Gas Marginal Prices Differences from Annual Averages
Marginal

## <Average Marginal

## >Average

3.0



## .;; 2.5 5 0 

# .- 

# - - .- E 2.0

Y In 

## I! 0 1.5 C 

### a In 3 

# ;

1.0 

# 4
0.5

0.0 

# y 

# y $4 

# -?4 

# 5 

# 8 !3 

# E

% Difference

### from Average



## Mean 

## = -7.8% Minimum = 

## -90% Median

## = -9.0% Maximum 

## = +248%

J:\marginal\FinalDraftReport7-29-99. wpd Figure 6 below shows, for each household, the difference between the marginal gas price for that household and that household's annual average gas price.

3.0

Ii) 2.5 

### c: 0 

# g 2.0

II) '0 

### '0 1.5 .t:. Q) II) :l 0 1.0 1: u) :;j 0.5

0.0

### ~ I 

### Figure 6. 

# Residential Gas Marginal Prices Differences from Annual Averages

Marginal < 

### Average**~ ~**  a:l 

## '" I I



### Mean 

# = -7.8% 

### Minimum 

# =-90%**~** ..... I 

### Marginal> Average**~ ~** 

### ~ 

## .... I 

### % 

### Difference from Average



### Median 

# = -9.0% 

### Maximum 

## = +248%

J:\marginal\FinalDraftReport7-29-99.wpd 18

### V. 

### RESIDENTIALlilEATING

### OILAND 

### PROPANE

Aspart ofthe marginal fuel price study for residential utility customers in the US, brief telephone interviews were conducted from January 25to February 5, 1999in order to understand and characterize regional variations in pricing and distribution ofresidential heating oil and propane. A sample of distributors was selected from relevant internet sites, including that ofthe National Propane GasAssociation (NPGA). The goal was to interview fuel oil and propane distributorsthat 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 ofheating oil and propane forresidential customers and to determine any divergence between average and marginal price for these fuels. Heating Oil Based on telephone conversations with five East Coast distributors of home heating oil, it was learned that (I)home heating oil isnot 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, dependingprimarily on promptness ofpayment (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. SeeTable 

### 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 ofthe LCC analyses for water heaters and clotheswashers.

Provane Based on telephone conversationswith nine distributors ofresidential 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 duein part to the fact that the price ofpropane is not regulated by state public utility commissions. The three major factors that influence the retail price ofresidential propane are (1)wholesale price (which isprimarily areflection oflocal supply and demand), (2) customer annual usage, and (3) "pre-pay" agreements, all of which are embedded in the pricelgallon ofresidential propane. State and local taxes, ifany, are an additional cost. Tax on residential propane sales varies by state, county, and municipality; among our respondents it varies from none (Virginia, Texas, Wisconsin) to 8.25% (California). Company Location Territory Priceigallon

Discount



### Residential propane customers usually live in rural orremote areas where either natural gas service is not available or electricity service is expensive ornot reliable. End uses range from cooking and water heating to pool and space heating. To the extentthat propane is required to heat homes, demand for propane is highest during the winter. Our survey respondents reported winter-to-summer price differentialsthat range from $0.13-$0.58 per gallon. By far the most substantial cost savings are those available toresidential customers who pre-pay during summermonths for propane that will be delivered and used duringwinter. Distributors who offer these discounts arebetter able to anticipatethe amount ofpropane they will need to supply, and often purchase "price insurance" (futures contract) from an energy company that effectively protects them from winter price spikes. MacDuffie Petroleum HudsonNH

NH, MA

$0.72 paid in 10days: $0.10



### Because 88%-90% ofpropane used in the US is produced domestically, propane supplies are rarely disrupted. However, winter demand often exceeds local supply because of logistical limitations oftransporting propane from where it is produced to where it is needed. Twolarge underground propane storage sites exist, onenearMont Dixie Gas Verona VA 3 counties in VA $0.85 cash paid in 5 days: $0.03 Eastern Propane Rochester NH southern Maine $0.66
none



### J :\marginal\FinalDraftReport7-29-99. wpd C&W Oil & Heating Danbury CT Fairfield County $0.82 auto delivery: $0.03 Powers Oil Co Foxboro MA metro Attleboro MA $0.75 paid in 7bus days: $0.1(, 

## v. RESIDENTIAL HEATING OIL A,"U) 

### PROPANE

As part ofthe marginal fuel price study for residential utility customers in the US, brieftelephone interviews were conducted from January 25 to February 5, 1999 in order to understand and characterize regional variations in pricing and distribution ofresidential heating oil and propane. A sample ofdistributors was selected from relevant internet sites, including that ofthe National Propane Gas Association (NPGA). The goal was to interview fuel oil and propane distributors that represent a cross-section ofthe industry in terms ofcompany 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 ofheating 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 ofhome heating oil, it was learned that (I) home heating oil is not taxed, (2) heating oil storage tanks are owned, not rented, and (3) there is no additional charge for delivery, except under unusual circumstances. These are all factors likely to make average and marginal prices similar. The price pergallon 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 ofpayment (e.g., within 5-10 days) or "auto-delivery" service, in which the customer allows the distributor to monitorusage and fill the tank at the distributor's convenience. See Table 4 below.

Table 4. Heating Oil Prices

Company Dixie Gas C&W Oil & Heating Powers Oil Co MacDuffie Petroleum EasternPropane Location Verona VA Danbury CT Foxboro MA HudsonNH Rochester NH Territory 3 counties in V A Fairfield County metro Attleboro MA NH,MA southern Maine Price!gallon $0.85 $0.82 $0.75 $0.72 $0.66

Discount cash paid in 5 davs: SO.03 auto delivery: $0.03 paid in 7 bus davs: $0.1 ( paid in 10 days: $0.1 0 none

Because the interviews indicate that the bills paid by residential consumers for heating oil are essentially volume driven, with a single blockrate, the average prices inherent in those bills, as reported in RECS, were interpreted as being equivalent to marginal prices for the purposes ofthe LCC analyses for water heaters and clothes washers. Propane Based on telephone conversations with nine distributors ofresidential 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 ofpropane is not regulated by state public utility commissions. The three major factors that influence the retail price ofresidential propane are (1) wholesale price (which is primarily a reflection oflocal supply and demand), (2) customer annual usage, and (3) "pre-pay" agreements, all ofwhich are embedded in the price/gallon ofresidential propane. State and local taxes, ifany, are an additional cost. Tax on residential propane sales varies by state, county, and municipality; among our respondents it varies from none (Virginia, Texas, Wisconsin) to 8.25% (California).

Residential propane customers usually live in rural or remote areas where either natural gas service is not available or electricity service is expensive or not reliable. End uses range from cooking and water heating to pool and space heating. To the extent that propane is required to heat homes, demand for propane is highest during the winter. Our survey respondents reported winter-to-summer price differentials that range from $0.13-$0.58 pergallon. 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 ofpropane 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% ofpropane used in the US is produced domestically, propane supplies are rarely disrupted. However, winter demand often exceeds local supply because oflogisticallimitations oftransporting propane from where it is produced to where it is needed. Two large underground propane storage sites exist, one nearMont


### J:\marginal\FinalDraftReport7-29-99.wpd 19

### Bellvue, Texas, and the second outside ofConway, Kansas. Threepipelines transport propane fromthese reservoirs to southern, eastern, and midwestern states. Bulk storage facilities are located at regional hubs along eachpipeline; truckstransport propane between these hubs and regional distributors, who distributethe fuel toresidential customers. Two of thethree pipelines also carry other petroleum products, sopropane deliveryis often interrupted.
The cost of distributing propane (from the refinery or portto a bulk storage facility) and delivering it totheresidential customer is incorporated in the price per gallon that each customer is chaiged. The lowest price per gallon ($0.65) was reported in northern Wisconsin, while the highest price ($1.41)was reported in Galveston, near Mont Bellvue, Texas, and adjacent to several refineries. Onepossible explanation forthis apparent anomaly is that theprice ofpropane in Wisconsin (wherewinters are harsh) is driven down by high demand and fierce competition, while the price in coastal Texas (where winters are mild) is buoyed up by relatively low demand and little competition. In fact, our results suggest that local competition (which increases along with local demand) is more a determinant ofthe wholesale price than is the distance from the propane source. Next to local wholesale price, total annual usage has the largest effect on residential propane prices. Theprice per gallon reported in Table 5 below represents the retail price to a customer who uses a typical amountofpropane compared to residential customers in that particular service territory. Volume discounts are usually available to those who use more than the typical amount ofpropane; volume discountsreported by our contacts range from $0.02/gal (Utah) to $0.19/gal (Florida). Similarly, customers who use less than the typical amount ofpropane pay more than the regular price per gallon; distributorsreported additional costs of $0.10 (Wisconsin) to $0.13 (Virginia). Propane distributors often offer discountsto new residential customers because (I)they want new customers and (2) ittakes a year to determinethe usage of each new customer. Contacts reported new customer discountsofnone (Georgia)to $0.15 (Florida)to $0.31(Texas). Residential customers store propane in on-site tanks that range in size from 100to 1,000 gallons;tank size increases with the number of end uses, severity ofthe climate, and difficulty of site access. Somecustomers lease (rent)tanks from their propane distributor. Renting relieves the customer ofresponsibility fortank maintenance and provides flexibility fortemporary ormobile home owners, but prevents the customer from shopping around forthe lowest fuel price among local distributors. Customers who purchase their own tanks do sofor several reasons. Permanent, yearround homes in rural areastend to have a larger number of propane end uses and bigger tanks, which, because they
areunsightly and take up space,tend to be installed underground. Distributors are reluctantto lease underground tanks because leak detection and maintenance is more difficult, soburied tanks are usually owned. Owning a storage tank also allows a customer to shop around for the best availablepropane price. Among our respondents, percentage of tanks leased from a distributor ranges from none (Maine, Virginia) to virtually all (Utah, California). Tankrental fees depend on tank size and/or annual usage; annual feesrange 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 forpropane are essentially volume driven,with a single block rate, the average prices inherent in those bills, as reported in RECS, were interpreted asbeing equivalent to marginal prices for the purposes ofthe LCC analysesfor water heaters and clothes washers. Bellvue, Texas, and the second outside ofConway, 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 ofthe three pipelines also carry other petroleum products, so propane delivery is often interrupted.

The cost ofdistributing propane (from the refinery orportto a bulk storage facility) and delivering 

### it to the residential customer is incorporated in the price per gallon that each customer is charged. The lowest price per gallon ($0.65) was reported in northern Wisconsin, while the highest price ($1041) was reported in Galveston, nearMontBellvue, Texas, and adjacent to several refineries. One possible explanation for this apparent anomaly is that the price ofpropane 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, ourresults suggest that local competition (which increases along with local demand) is more a determinant ofthe wholesale price than is the distance from the propane source.
Next to local wholesale price, total annual usage has the largest effect on residential propane prices. The price per gallon reported in Table 5 below represents the retail price to a customer who uses a typical amount ofpropane compared to residential customers in that particular service territory. Volume discounts are usually available to those who use more than the typical amount ofpropane; 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 ofpropane paymore than the regular price per gallon; distributors reported additional costs of$0.10 (Wisconsin) to $0.13 (Virginia). Propane distributors often offer discounts to new residential customers because (I) they want new customers and (2) it takes a year to determine the usage ofeach new customer. Contacts reported new customer discounts ofnone (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 ofend uses, severity ofthe climate, and difficulty ofsite access. Some customers lease (rent) tanks from their propane distributor. Renting relieves the customer ofresponsibility for tank maintenance and provides flexibility for temporary or mobile home owners, butprevents 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 ofpropane 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 ourrespondents, percentage oftanks leased from a distributor ranges from none (Maine, Virginia) to virtually all (Utah, California). Tankrental 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 ofthe LCC analyses for waterheaters and clothes washers.



### J:\marginal\FinalDraftReport7-29-99.wpd 20Table

### 5. Propane Prices
Company Location Territory Typical tank size Typical Usage $/gal, regular Tax $/gal,discount $/gal, addtl

Tank rentals

Rental fees

new customer:$1.10 $0.08igaliftank is owned > 800 galiyr: $1.02

<400 galiyr: $1.18
50-60% rented Company Location Territory Typical tank size Typical Usage $/gal, regular Tax $/gal, discount

$/gal, addtl Tank rentals

Rental fees



### J 

### :\marginal\FinalDraftReport7-29-99. wpd Eastern Propane RochesterNH southemME

500 gal $0.98-1.01

unsure $0.05/galif paid in 15days

no rentals Suburban Propane Gilroy CA 3 counties 250,500 gal 4 people, 2000 SFhome: 500 gaVyr $1.45 winter,$1.30 summer 7.25-8.25%, depends on locality

new customer: $1.16 winter
new customer: $1.03 summer will-call delivery:$0.1Olga1
most are rented $65/yr for 250 gal $90ivrfor 500 pa1 River Country Co-op (Cenex) Chippewa FallsWI northwest WI 500gal $0.65
none in W $0.05 off for cash iftank is owned prepay price guarantee: $0.50igal $0.1Olga1 iftank rented
<250 galiyr: $0.1Oigal more 40% rented
no rental fees

Amerigas Salt Lake City UT

Salt Lake City 500 gal $0.86 6%homeheat volume: $0.02/gal

virtually all 500 gal: $80iyr 1.000 gal: $10Slvr Blossman Gas Pensacola FL MS, FL, NC

<250 gal <250 gallyear $1.14
none new customer: $0.99 for volume:if> 100 gal. $1.06; > 150 gal, $1.00; >500 gal, $0.95
500 gal tanks are owned 250 gal: $25-48/yr,depends on use I00 

### eal: $36

### -7 2 lvr. denends on use Corbin Gas Atlanta GA Metro Atlanta 250,330 gal $0.99 -7%, depends on county
no new customer discount

60%rented $30-80lyr, depends on usage 

### Table 5. Propane Prices
Company Suburban Propane River Country Co-op (Cenex) Eastern Propane Location Gilroy CA Chippewa Falls WI Rochester NH Territory 3 counties northwest WI southern ME Typical tank size 250,500 gal 500 gal 500 gal Typical Usage 4 people, 2000 SF horne: 500 gal/yr $/gal,regular $1.45 winter, $1.30 summer $0.65 $0.98-1.01 Tax 7.25-S.25%, depends on locality none in WI unsure $/gal,discount new customer: $1.16 winter $0.05 offfor cash iftank is owned $0.05/gal ifpaid in 15 days
new customer: $1.03 summer prepay price guarantee: $0.50/gal $/gal,addtl will-call delivery: $O.IO/gal $O.lO/gal iftank rented
< 250 gal/yr: $0.1 O/gal more Tank rentals most are rented 40% rented no rentals

Rental fees $65/yr for 250 gal no rental fees $90/vr for 500 £!al
Company Blossman Gas Corbin Gas Amerigas Location Pensacola FL Atlanta GA Salt Lake City UT Territory MS,FL,NC Metro Atlanta Salt Lake City Typical tank size < 250 gal 250,330 gal 500 gal Typical Usage < 250 gal/year $/gal, regular $1.14 $0.99 $0.86 Tax none**~**7%, depends on county 6% horne heat $/gal,discount new customer: $0.99 no new customer discount volume: $0.02/gal for volume: if> 100 gal. $1.06; > 150 gal, $1.00; >500 gal, $0.95 Sigal,addtl Tank rentals 500 gal tanks are owned 60% rented virtually all

Rental fees 250 gal: $25-48/yr, depends on use $30-80/yr, depends on usage 500 gal: $80/yr JOO £!al: $36-72/vr.**tlenentl~** nn use LOOO £!al' $105/vr
Company All-Star Gas United Propane Dixie Gas Location Galveston TX Salisbury MD Verona VA Territory Bolivar Island Maryland VA, some ofWV Typical tank size 120 gal, 250 gal 500 gal 325,500 gal Typical Usage $/gal, regular $1.41 $1.15 $1.05 Tax none depends on county; no state tax none in VA, some in WV $/gal,discount new customer: $1.10 $O.OS/gal iftank is owned > 800 gal/yr: $1.02 prepay, >350 gal/yr: $0.83-0.99 2% offifpaidwithin 10 days $/gal, addtl < 400 gal/yr: $1.18

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

Rental fees all size tanks: $S5/yr depends on usage (gallons/year)
ave $12.5/atr for 500 gal tank



### ]:\marginal\FinalDraftReport7-29-99.wpd 21

### VI. TAXES

Overview The marginal energy prices that consumers experience can be affected by the taxesthat those consumers pay aspart oftheir energybills. Those energy bills can include taxes in two fundamentally differentways. First, tax obligations that an energy utility itself incurs (such as federal income taxes and property taxes) are most often considered tobe part ofthe utility's revenue requirements. Consequently, such taxes are embedded within approvedutility rates; they
are not itemized on utility bills (even the unbundled bills that are becoming more prevalent in theutility industry, particularly as electricity restructuring occursin more states). Prices paid by consumers are higher (than they otherwisewould be) to the degree that these sorts of taxes are included in theirutilities' base rates. But, because
consumer energy savings (from using appliances that are more energy efficient)would not directly and immediately reduce the level ofthese embedded taxes, the existence ofthese 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 energybills isthrough paying various sorts of taxes (or fees) as direct add-ons totheir energy bills. These taxes (such asutility users taxes, franchise fees, and regulatory fees) are often called "pass-through" taxes. They are taxes assessedby state andlor local jurisdictions. They are collected by utilities on behalf of consumers and then are passed on by thoseutilities to thejurisdictions that imposed them. Sincethese taxes are not considered tobe 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 ofthis type oftax; indeed, consun~ers' rllarginal energy prices arehigher 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 ofpass-through tax assessment, the consumption of less energy (dueto using appliances that are more energy-efficient) will reduce (on a direct and immediatebasis) the tax portion ofthe energy bill
aswell asthe non-tax portion of that bill. For that reason, this research project is interested in the degree of those savings. Range and Magnitude ofPass-Through Taxes Because pass-through taxes are assessed by individual states and localjurisdictions, they can vary widely in their magnitude, even within the sameutility service territory (seebelow). There are four main types ofpass-through taxes:


### 1) state sales taxes (see Appendix 

### 5,Table 6) 2) state utility regulatory assessments (seeAppendix 

### 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 asmuch as 

### 10%in other areas9. In addition,because these sorts of taxes are setby various government agencies, they rarely appear onpublished 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 forparticular customers ofparticular 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 oftaxes imposed by local governments, the tax adjustment is only practicable atthe State level."'0

Thehighest single tax rate for energy that the research uncovered was for one small city in California that imposes a utility users tax of 9.9%. Otherwise, the highest utility users tax rate the research uncovered is 8%. State-level sales taxes and other state-level taxes can raise the effective tax to consumers beyond these levels,though. 

# "' EIA, "End Use Taxes: Current EIA Practices," August 1994, page 29. 

### J :\marginal\FinalDraftReport7-29-99. wpd VI. TAXES

Overview The marginal energy prices that consumers experience can be affected by the taxes that those consumers pay as part oftheir energy bills. Those energy bills can include taxes in two fundamentally different ways. First, tax obligations that an energy utility itselfincurs (such as federal income taxes and property taxes) are most often considered to be part ofthe utility's revenue requirements. Consequently, such taxes are embedded within approvedutility 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 oftaxes are included in theirutilities' base rates. But, because
consumer energy savings (from using appliances that are more energy efficient) would not directly and immediately reduce the level ofthese embeddcd taxes, the existence ofthese taxes does not affect the calculation ofconsumers' 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 oftaxes (or fees) as direct add-ons to their energy bills. These taxes (such as utility users taxes, franchise fees, and regulatory fecs) are often called "pass-through" taxes. They are taxes assessed by state and/or localjurisdictions. They are collected by utilities on behalfofconsumers and then are passed on by those utilities to thejurisdictions that imposed them. Since these taxes are not considered to be part ofa 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 ofthis type oftax; indeed, consumers' marginal energy prices are higher tllan they would be ifthese taxes and fees did not exist. This type oftax is usually assessed as either a fixed percentage ofthe pre-tax energy bill or as a fixed dollar amount per energy unit. In some instances, pass-through taxes using both types ofassessment mechanisms are added to consumers' bills. For either type ofpass-through tax assessment, the consumption ofless energy (due to using appliances that are more energy-efficient) will reduce (on a direct and immediate basis) the tax portion ofthe energybill
as well as the non-tax portion ofthat bill. For that reason, this research project is interested in the degree ofthose savings. Range and Magnitude ofPass-Through Taxes Because pass-through taxes are assessed by individual states and localjurisdictions, they can vary widely in their magnitude, even within the same utility service territory (see below). There are four main types ofpass-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 ofenergy (see Appendix 5, Table 8) 4) local utility users taxes

These taxes individually range in size from being non-existent in some areas to being as much as 10% in other areas9. In addition, because these sorts oftaxes 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 ofparticular utilities would be a very difficult task. As EIA said in its 1994 report "End-Use Taxes: Current EIA Practices," "Adding statutory taxes to pre-tax prices is a tedious, error-prone process.
... Because ofthe wide variety oftaxes imposed by local governments, the tax adjustment is only practicable at the State level."l0

9 The highest single tax rate for energy that the research uncovered was for one small city in California that imposes a utility users tax of9.9%. Otherwise, the highest utility users tax rate the research uncovered is 8%. State-level sales taxes and other state-level taxes can raise the effective tax to consumers beyond these levels, though.

10 EIA, "End Use Taxes: Current ErA Practices," August 1994, page 29. 

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

### The EIA report provides a state-by-state list of sales and use taxcs on end-use energy consumption." It also indicates the states in which energy consumption (by energy fuel type) is exempt from state sales and use taxes. In addition, thisreport includes an estimate ofthe 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 sectorrise only 0.9% with the inclusion of state sales taxes. Therange ofincrease in individual statesis from 2% to 6.5% for those states where electricity consumption in the residential sector is not exempt from salestax. For the commercial sector,the rise in electricity prices dueto the inclusion ofthe 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 similarestimates ofthe effect ofincluding state sales taxes on the consumption ofnatural gas.)

To get a sense for the magnitude and variation of other state and local-level taxes, various sourceswere 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 SaveEnergy (ASE).I2 This report revealsthat percentage taxes forregulatory 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 asCalifornia, cities can charge consumers a utility users tax. One city imposes a 9.9% utility users tax. l3 

### Several other cities setthis tax ashigh as 7.5% or 8.0%.

Taxes or fees onunits of energy consumed range in size from 0.015 cents per kwhto 0.26 cents per kWh in the nine states (seeAppendix 5, Table 8;taken from ASE's report) that imposethat type oftax 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 oftheir bills and $0.00078/therm on the gas portion oftheir bills. Statesthat have no sales taxes are not prohibited from imposing electric and gasregulatory 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 thisproject's modest utility sample, beyond our scope. Indeed, it isunlikely that anyone has ever conducted a survey of state and local taxes comprehensive enough to capture the type of utility users taxes that can occur in California alone.I4 As a result, the effect oftaxes on marginal prices was addressed in the manner outlined below.

Imvact ofTaxes 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 increasedby the percentage size ofthe tax itself. Thus, an 8%tax would increase a consumer's marginal price by 

### 8%. Consumerstaxed atthe 

### 2%level would have a marginal price 2% higher than ifthey paid nopass-through taxes. Because taxes on units ofenergy (likethe per kwhregulatory fees described above) donot apply to fixed charges, they raise marginal prices in a non-linear, hut still minor and easily calculated, manner.

Imuact of Taxes on Savin~s Since consumers both pay and save at a rate equal to the marginal price, other thingsbeing equal, reductions in energy consumption due to new appliance energy efficiency standardswill cause reductions in the size ofthe passthrough

'I 

### Ibid, Table B2. Therelevant portions ofEIA's Table B2 are shown in Appendix 5,Table 6 of this report.
I* 

### The Alliance to SaveEnergy, "State and Local Taxation: Energy Policy by Accident," 1994.

l3

### For example,in the Pacific Gas and Electric Company (PG&E) service territory of northern California, the sizeof 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 onecity, 8.0% in five cities and 7.5% in eight cities. A total of 68 cities in PG&E's service area currently imposethis 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 ofthem was found.
l4

### Andrew Hoerner, Center for a Sustainable Economy, personal communication,February 10, 1999. Additional
sources whose web siteswere consulted for information on state and local energy taxcs included The Federation of Tax Administrators, EPRI, and The Multistate Tax Commission. The ElA report provides a state-by-state list ofsales and use taxes on end-use energy consumption.!! It also indicates the states in which energy consumption (by energy fuel type) is exempt from state sales and use taxes. In addition, this report includes an estimate ofthe magnitude ofthe possible effect ofincluding State sales taxes in energy prices. For electricityprices, ElA's report shows that on average electricity prices for the residential sectorrise only 0.9% with the inclusion ofstate sales taxes. The range ofincrease 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 electricityprices due to the inclusion ofthe applicable state sales taxes would be 4.2% on average for the nation, with
a range by state offrom 3% to 8% for those states where electricity consumption is not exempt from sales tax. (EIA's report does not include similar estimates ofthe effect ofincluding state sales taxes on the consumption ofnatural gas.)

To get a sense for the magnitude and variation ofother state and local-level taxes, various sources were consulted. The most comprehensive survey ofother taxes on a state level was found in a report on state and local taxation by the Alliance to Save Energy (ASE).!2 This report reveals that percentage taxes for regulatory assessments in the 39 states that impose them (see Appendix 5, Table 7; taken from ASE's report) range in size from 0.02% to 0.50%. In other states, such as California, cities can charge consumers a utility users tax. One city imposes a 9.9% utility users tax. J3 Several other cities set this tax as high as 7.5% or 8.0%.

Taxes or fees on units ofenergy 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 oftax on energy used by consumers. In California, for instance, while PG&E customers pay a $0.0002/kWh in support ofthe California Energy Commission, customers ofSDG&E also pay regulatory fees amounting to $0.000 12/kWh on the electricityportion oftheir bills and $O.00078/therm on the gas portion oftheir 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 ofthe 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 ofstate and local taxes comprehensive enough to capture the type ofutility users taxes that can occur in California alone.!4 As a result, the effect oftaxes on marginal prices was addressed in the manner outlined below. Impact ofTaxes 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 ofthe 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 ifthey paid no pass-through taxes. Because taxes on units ofenergy (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 ofTaxes 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 ofthe passthrough

II Ibid, Table B2. The relevant portions ofElA's Table B2 are shown in Appendix 5, Table 6 ofthis 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 ofnorthem California, the size ofthe percentage-based utility users tax, which is applied to both the electricity and natural gas components ofa PG&E customer's bill, varies from 0% in many ofthe cities and counties served by PG&E to as much as 9.9% in one city, 8.0% in five cities and 7.5% in eight cities. A total of68 cities in PG&E's service area currently impose this type oftax on energy consumption by their residents. While higher utility users tax rates may exist in California or in other states,
no evidence ofthem was found. !4 Andrew Hoerner, Center for a Sustainable Economy, personal communication, February 10, 1999. Additional
sources whose web sites were consulted for information on state and local energy taxes included The Federation of Tax Administrators, EPRI, and The Multistate Tax Commission. 

### J :

\marginal\FinalDraftReport7 -29-99.wpd 23

### taxes consumers pay, whether those taxes arepercentage taxes orper-unit taxes. The resulting reductions in tax payments are savingstothe consumer directly associated with the more efficient appliances. Consumersfacing 

### 8% tax prices will save an additional 

### 8%beyond what they would have saved if they did not have to pay suchtaxes in the first place. Consumers with 2%taxeswill save an additional 2% oftheir (pre-tax) bill when their energy consumption is reduced by using appliances that are more energy-efficient.
Treatment of Taxes in This Research Project Thisproject used two main approaches to derivemarginal prices. The RECS-based approachused actual consumer bills that already include all taxes (both those embedded in utility rates and pass-through taxes). Therefore, the marginal prices derived from thosebills "automatically" take into account the effects of changes in taxes dueto changesin energy consumption. For use in life-cycle cost or savings analyses, no further adjustments are needed to reflect the existence oftaxes in the marginal prices derived from the RECS-based approach.

The MAISP-based approach involved using distributions ofboth building energy usage levels (from theMAISP database) and utility tariffs to derive commercial electricity hills. Because those utility tariffs donot already include pass-through taxes, the marginal prices derived by this approach are smaller than they otherwise would be tothe degree that pass-through taxes apply to the sampledbuildings. Therefore, when analyzing the results of thisresearch effort, consideration should be given to the applicability of the four types ofpass-through taxes mentioned here (state-level sales and use taxes, other state-level %

### taxes, $

### per energyunit taxes, and locally imposed utility users taxes) to derivemarginal electricityprices for commercial buildings that include taxes. While adding thepass-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 ofthe locally determined utility users tax that might apply tothe sampled buildings. Therefore, because ofthe lack of comprehensive information on the magnitude ofthe locally imposed utility users taxes, onemust be awarethat the marginal prices so calculated may not be as high asthey 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. Thosepublished average prices are calculatedby 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 theutility revenue requirements that form the basis for utility revenues through the traditional rate-setting process; however, it is not clear whetherpass-through taxes are considered to be part ofutility 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 containsthe following statement: "Taxing authorities differ on whether a specific tax is assessed to the utility orto the consumer, a difference that in turn determines whether or not the tax is included in the electric utility's operating

### revenue^."'^
Therefore, it is concluded that one cannot know for sure whether pass-through taxes, especially those imposed by local government agencies, are included in utility revenues and thus included in averageprices. While this lack of certainty aboutthe inclusion ofpass-through taxes in published average pricesjeopardizes any comparison of average and marginal prices, it is believed that comparingRECS-based marginal prices with published average prices would be more sound, for example, than comparing marginal prices derived by the MAISP-based approach (where taxes must be added in manually) with published average prices.
Tax Feedback Loons Somemight argue that local governments (and perhaps even state governments) might raise their locally-imposed utility tax rates if the revenues they obtain from consumers' energy bills decline because consumers use less energy as a consequence of improved appliance energy-efficiency standards. This project did not attempt to analyze such

"

### EIA, "Energy Sales and Revenue 1997,"page 1 

### J 

### :\marginal\FinalDraftReport7-29-99. wpd taxes consumers pay, whether those taxes are percentage taxes or per-unit taxes. The resulting reductions in tax payments are savings to the consumer directly associated with the more efficient appliances. Consumers facing 8% tax prices will save an additional 8% beyond what they would have saved ifthey did not have to pay suchtaxes in the first place. Consumers with 2% taxes will save an additional 2% oftheir (pre-tax) bill when their energy consumption is reduced by using appliances that are more energy-efficient.

Treatment ofTaxes 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 ofchanges 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 oftaxes in the marginal prices derived from the RECS-based approach.

The MAISY"'-based approach involved using distributions ofboth 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 smallerthan they otherwise would be to the degree that pass-through taxes apply to the sampled buildings. Therefore, when analyzing the results ofthis research effort, consideration should be given to the applicability ofthe four types ofpass-through taxes mentioned here (state-level sales and use taxes, other state-level % taxes, $ per energy unit taxes, and locally imposedutilityusers taxes) to derive marginal electricity prices for commercial buildings that include taxes. While adding thepass-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 ofthe locally determined utility users tax that might apply to the sampled buildings. Therefore, because ofthe lack of comprehensive information on the magnitude ofthe locally imposed utility users taxes, one mustbe 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. Itis clear that embedded taxes are included withinthe utility
revenue requirements that form the basis for utility revenues through the traditional rate-setting process; however, it is not clear whetherpass-through taxes are considered to be part ofutility 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 orto 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 whetherpass-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 ofpass-through taxes in published average pricesjeopardizes 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 ifthe revenues they obtain from consumers' energy bills decline because consumers use less energy as a consequence ofimproved appliance energy-efficiency standards. This project did not attempt to analyze such

15 EIA, "Energy Sales and Revenue 1997," page I. 

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

### positions. It is noted that while similar arguments havebeen made during debates on electric industry restructuring occurring around the country, there isno widespread agreement on their validity.
Conclusion Thisreport has not attempted to account for all appropriate taxes in the calculation ofmarginal 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 carebecause they are subject to error forthe samereasons. This report indicates the degree of error that may be present.
The derivation of commercial consumer marginal electricity prices excludespass-through taxes and thus underestimates consumers' marginal bill savings by 

### 0% to asmuch as 

### lo%, depending upon the level of state and locally imposed taxes. In contrast, the derivation ofmarginal 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 savingsinherent in the commercial analysis does not exist in the residential analysis. 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 ofmarginal 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 oferror that may be present.
The derivation ofcommercial 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 ofstate and locally imposed taxes. 

### In contrast, the derivation ofmarginal electricity and gas prices for the residential sector from actual consumer bills from RECS includes pass-through taxes; thus, the underestimation ofconsumers' marginal bill savings inherent in the commercial analysis does not exist in the residential analysis.



### J:\marginaJ\FinalDraftReport7-29-99.wpd 25A~~endix 1. Tariffs Used in the 

### Commercial Analvsis



### Utility
Alabama Power Alabama Power Alabama Power Appalachian Power Co Arizona Public Service Arizona Public Service Boston Edison Boston Edison Central Power and Light Cleveland Electric Illum. Co Cleveland Electric Illurn. Co Commonwealth Edison Co Detroit Edison Jersey Central Power & Ligh Madison G&E Madison G&E NSP NSP NSP NSP 

### State
AL AL AL VA AZ AZ MA MA TX OH OH IL MI NJ WI WI MN MN WI WI 

### Tariff

LPEM LPM LPS SGS E30 E32 G-l G-2 21 Gen Comm Lg Comm Rate 6 nonTOU D3 GS Cg-l Cg-5 AIO, D12, El3 A14, D16,E15 Cg-2 Cg-2 unmetered 

### Utility
NSP Niagara Mohawk Power Corp Niagara Mohawk Power Corp Ohio Power Company PEPCO Pacific Gas and Electric Pacific Gas and Electric Pennsylvania Power & Light Pennsylvania Power & Light Poudre Valley Savannah Electric and Power Seattle City Light Seattle City Light SoCal Edison SoCal Edison Union Electric Co. Union Electric Co. Virginia Power Company Wisconsin Electric Power 

### State
WI NY NY OH DC CA CA PA PA CO GA WA WA CA CA MO MO VA WI 

### Tariff Cg-5 SC-2 Demand SC-2Non-Demand GS3-11 GS Al A10 GS-I GS-3 B-97 GS-7 Rate31 Rate 34 GS-1 GS-2 No.2 No.3 GS-1 Cg 1 Appendix I. Tariffs Used in the Commercial Analysis

Utility State Tariff Utility State Tariff
Alabama Power AL LPEM NSP WI Cg-5 Alabama Power AL LPM Niagara Mohawk Power Corj: NY SC-2 Demand Alabama Power AL LPS Niagara Mohawk Power Corj: NY SC-2 Non-Demand Appalachian Power Co VA SGS Ohio Power Company OR GS3-11 Arizona Public Service AZ E30 PEPCO DC OS Arizona Public Service AZ E32 Pacific Gas and Electric CA Al Boston Edison MA G-l Pacific Gas and Electric CA AIO Boston Edison MA G-2 Pennsylvania Power & Light PA OS-1 Central Power and Light TX 21 Pennsylvania Power & Light PA OS-3 Cleveland Electric IlIum. Co OR GenComm Poudre VaHey CO B-97 Cleveland Electric IlIum. Co OR LgComm Savannah Electric and Powe GA OS-7 Commonwealth Edison Co IL Rate 6 non- Seattle City Light WA Rate 31 Detroit Edison MI TOU Seattle City Light WA Rate 34 Jersey Central Power & Ligh NJ D3 SoCal Edison CA OS-I Madison G&E WI GS SoCal Edison CA OS-2 Madison G&E WI Cg-I Union Electric Co. MO No.2 NSP MN Cg-5 Union Electric Co. MO No.3 NSP MN AIO, D12, E13 Virginia Power Company VA GS-l NSP WI A14, D16,E15 Wisconsin Electric Power WI Cg 1 NSP WI Cg-2 Cg-2 unmetered



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

### 26

### Appendix 2. Examples of Commercial Bill Calculations

Commercial tariffs generally fall into two categories, non-demand and demand. Demand is measured in kilowatts (kW) required by the customer over a relatively short time period, often 30minutes. Meters are installed at the customer site to measure monthly and sometimesannual peak demand, and these areused to computethe demand component ofthe 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 ofeach is illustrated below. Example 

### I:Non-demand tariff Utility: Alabama Power Tariff: Commercial,LPS CustomerBase Charge: $8.65lmo. Summer Energy Charge: $O.O6223kWh Winter Energy Charge: $O.O5843kWh
Customer: Usage: 2547kwh Month: January Baseline Bill: $157.47=

### 8.65

### +($0.05843 

## * 2547)
Bill with 5% Usage Decrement: (Figures below include rounding) Usage: 2420kwh =

### 2547kWh -

### (0.05 

## * 2547kwh) Bill: $150.03 =

### 8.65

## +($0.05843 

## * 2420) MonthlyAverage Price: $0.06183kWh =

### $157.47/ 

### 2547 Monthly Marginal Price: $0.05843lkWh =

### Bill SavingsI Energy Savings

=

### ($157.47- 

### $150.03) 1(2547

### -2420)

Example 2:Demand Tariff Utility: Tariff: TariffMinIMax kW: Customer Base Charge: Energy Block Cutoffs: Energy Charge, Block 1: 

### Energy Charge, Block 2: Demand Charge Component 1 : 

### Demand Charge Component 2:
Customer Usage: Month: Monthly Peak kW: Baseline Bill:
Bill with 5% Energy and 4% Demand Decrement:

Monthly Average Price: Monthly Marginal Price: Southern California Edison Commercial GS2 80 - 

### 2000 kW/hr (measured over 15 minutes) $60.30/mo. Block 1:

### 300kWh/kW (based on monthly peak kW) $O.O7692kWh $0.04391kWh $5.40 (Summer), $5.40 (Winter) $7.75 (Summer),$0.00 (Winter)
150388kwh May 7 1kW (over 15minutes) $7750.35 =

### $60.30

## +((300*71)*$0.07692) 

### +((150388- 

### (300*71))*$0.04391) 

## +(71*$5.40)
$7376.71 =

### $60.30

## +((300*(71*0.96))*$0.07692) 

## +(((150388*0.95)

### - (300*(71*0.96)))*$0.04391) 

## +((71*0.96)*$5.40) $0.05163kWh =

### 7376.711(150388*0.95) $0.04969/kWh =

### Bill Savings 1

### Energy Savings

=

### ($7750.35- 

### $7376.71)/(I50388

### - (150388

## * 0.95)) Appendix 2. Examples ofCommercial BiII Calculations

Commercial tariffs generally fall into two categories, non-demand and demand. Demand is measured in kilowatts (kW) required by the customer over a relatively short time period, often 30 minutes. Meters are installed at the customer site to measure monthly and sometimes annual peak demand, and these are used to compute the demand component ofthe 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 ofeach is illustratedbelow. Example 

### 1: Non-demand tariff
Utility: Tariff: CustomerBase Charge: Summer Energy Charge: Winter Energy Charge:

Customer: Usage: Month: Baseline Bill:

Bill with 5% Usage Decrement: Usage: Bill: Monthly Average Price: MonthlyMarginal Price:

Example 

### 2: Demand Tariff
Utility: Tariff: TariffMinIMax kW: CustomerBase Charge: Energy Block Cutoffs: Energy Charge, Block I: Energy Charge, Block 2: Demand Charge Component 1: Demand Charge Component 2:

Customer Usage: Month: Monthly Peak kW: Baseline Bill:

Bill with 5% Energy and 4% Demand Decrement:

Monthly Average Price: Monthly Marginal Price: Alabama Power Commercial, LPS $8.65/mo. $0.06223IkWh $0.05843IkWh

2547 kWh January $157.47 = 8.65 

### + ($0.05843 * 2547)
(Figures below include rounding) 2420 kWh = 2547 kWh

- (0.05 * 2547 kWh) $150.Q3 = 8.65 

### + ($0.05843 *2420) $0.06183IkWh = $157.47 I 2547 $0.05843/kWh = Bill Savings I Energy Savings

= ($157.47

- $150.03) I (2547

- 2420)

Southern California Edison Commercial GS2 80

- 2000 kW/hr (measured over 15 minutes) $60.30/mo. Block I: 300 kWhlkW (based on monthly peak kW) $0.07692IkWh $0.04391IkWh $5.40 (Summer), $5.40 (Winter) $7.75 (Summer), $0.00 (Winter)

150388 kWh May 71 kW (over 15 minutes) $7750.35 = $60.30 

### + «300*71)*$0.07692) 

### +«(150388

- (300*71»*$0.04391) 

### + (71 *$5.40)
$7376.71 $60.30 

### + «300*(71 *0.96»*$0.07692) 

### +«((150388*0.95)

- (300*(71 *0.96»)*$0.04391) 

### + «71 *0.96)*$5.40) $0.05163IkWh = 7376.71 1(150388*0.95) $0.04969/kWh = Bill Savings I Energy Savings

= ($7750.35

- $7376.71) I (150388

- (150388 * 0.95» 

### J :

\marginal\FinalDraftReport7 -29-99.wpd 27

### Aunendix 3. Demand Decrement Due to Standards - 

### The Role ofLighting Coincidence and Diversitv

In the process ofmarginal 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) arebased 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 fourfcotT12 systems would save about 15%of lighting energyused by fluorescent lamps and since about 30-35% of building energy use is for lighting, about 5% ofbuilding 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 ifbuilding energy use isreduced by 

### 5% onaverage. Some analystsprefer to discuss this issue in terms ofdiversity and coincidence. Coincidence is the lightingkW during the building peak divided by the lighting peak kW. In this situation, the coincidence factorwas expectedtobe 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 duringthe building peak. EEI has suggested using 0.9 for the default coincidence factor.

# ''

Coincidence factor (C) =

### lighting kW during building peakllighting peak kW
Thediversity factoris a measure of thepercent ofthe installed kW for lighting used during the lighting peak. Diversity is already accounted for because the actual building energy usage (which comes fromthe MAIST database)already excludes those lamps not inuse during some parts ofthe day. Instead the concerniswith lighting averagekW because that is, in essence, reduced by 5% in the bill calculator when monthly energyuse is reduced by 5%. It is assumed that the lighting hours donot changewhen more efficient ballasts are installed. Diversity (D) =

### lighting peak kwllighting kW installed
Someanalystsmight multiply coincidence 

### (C)times diversity (D); 

### that would not yield the desired ratio oflightingkW during the building peak to lighting average kW.

In relative terms, demand reduction is assumed to be 80%ofenergy 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 tothe average value is used because the monthly lighting energyuse can be consideredtobe equal to some averagekW times the nionthly lighting hours and the monthly lighting energyuse is reduced by a fixed percentage.

Ih 

### Seecomment #12 on the Draft Report on Potential Impact of Possible Energy Efficiency Levels for Fluorescent Lamp Ballasts,,received by DOE from Steve Rosenstock ofEEI on August 28, 1997. 

### J:\marginal\FinalDraftReport7-29-99. wpd 28 Appendix 3. Demand Decrement Due to Standards

- The Role ofLighting Coincidence and Diversity

In the process ofmarginal price estimation, it is necessary to account for demand charges in the calculation ofsome commercial sectorbills. 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 winterpeak kW and summer and winter average peak kW. Since a change from energy-efficient magnetic (EEM) ballasts meeting the current standard to electronic ballasts for four· f('ot TI2 systems would save about 15% oflighting energy used by fluorescent lamps and since about 30-35% ofbuilding energyuse is for lighting, about 5% ofbuilding energy use will be saved ifEEM ballasts are converted to electronic ballasts. The 5% reduction was assumed to occur every month.

It is also necessary to estimate the reduction in peak demand ifbuilding energy use is reduced by 5% on average. Some analysts prefer to discuss this issue in terms ofdiversity 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, whateverthe maximum number oflamps that operate during the lighting peak, a very similarnumber of lamps can be expected to operate during the building peak. EEl has suggested using 0.9 for the default coincidence factor. 16
Coincidence factor (C) = lighting kW during building peak/lighting peak kW

The diversity factor is a measure ofthe percent ofthe 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 ofthe day. Instead the concern is with lighting average kW because that is, in essence, reduced by 5% in the bill calculator when monthly energyuse is reduced by 5%. It is assumed that the lighting hours do not change when more efficient ballasts are installed.
Diversity (D) = lighting peak kW/lighting kW installed

Some analysts might multiply coincidence (C) times diversity (D); that would not yield the desired ratio oflighting kW during the building peak to lighting average kW.

In relative terms, demand reduction is assumed to be 80% ofenergy savings. For 5% energy savings, it is assumed that the demand reduction would be 4% ofpeak kW. This means that if500 identical fixtures are operating on average, that only 400 (or 80%) are operating during the building peak demand period. The 80% value relative to the average value is used because the monthly lighting energy use can be considered to be equal to some average kW times the monthly lighting hours and the monthly lighting energy use is reduced by a fixed percentage.

16 See comment #12 on the Draft Report on Potential Impact ofPossible Energy Efficiency Levels for Fluorescent Lamp Ballasts" received by DOE from Steve Rosenstock ofEEl on August 28, 1997. 

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

### Aooendix

### 4: Weighting Method for Commercial Sam~le The composition ofthe commercial sample posed a dual weightingproblem: distributing weights across buildings and across the approximately 

### 20 tariffs that were applied to each building. The weighting approach solvesthese problems sequentially. The first stepis to develop building weights, derived from the original MAISPbuilding weight and scaling factors (based on kwhusage), as discussed below. This adjusted building weight isthen apportioned across all the tariffs (weighted by annual MWh sales)that were applied to the building. The compositeweighting factor for each buildingttariff pair combinesboth ofthese weighting factors.
Calculation ofAdiusted Building Weights Each building in the sampleisviewed as representing a share ofthe commercial buildings in its region ofthe US. State and regional scaling factors were developed and used to rescale the individual buildings' MAISPweightsto represent national usage, asdescribed below.

The first step was to account forthe factthat the sample was a subset of the MAISY@ buildings for each state. For each of the seven states a state factor was computed: 

## 8. Sumof MAISY weightsfor

### all buildings in the state State Factor

### = 

## 8. SumofMAISY weightsforsampled buildings in the state

The statewide energyusage represented by each sampled building was then computed by multiplyingthe building's MAISPweight by its average annual energy usage and the state factor. For each sampled building i: Building i's Adjusted State Energy Use =

### Annual kwh,

## * MAISPWeight, 

## * State Factor The next step was to scale state usage toregional usage. US Census Divisions were assigned to one of seven regions, eachrepresented by a state in our sample. Census Divisions not directlyrepresented 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 CensusDivisions.

Region Factors were then computed based on state and regional commercialretail electricity sales derived from 1998 EIA data (seethe tablebelow). The Region Factors are the ratio of commercial sector electricity sales in the division(s) correspondingwith each ofthe sampled states to the conlmercial sector electricity sales in each ofthose individual states. Assignment of Sampled Statesto Census Divisions for Weighting Calculations

Regional Retail Commercial Sales Region Factor 

### = State Retail Commercial Sales Sampled State NewYork Illinois Georgia Texas Colorado Washington California

Applying the Region Factorswas the next step in computing the Adjusted Building Weights for each building: CensusDivision(s) Represented Middle Atlantic, New England East North Cenhal, West North Central South Atlantic, East South Central West South Central Mountain Pacific Contiguous (excluding California), Pacific Noncontiguous California

Building i's Adjusted Region Energy Use =

### Region Factor 

## * Building i's Adjusted State Energy Use Appendix 4: Weighting Method for Commercial Sample

The composition ofthe 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/tariffpair combines both ofthese weighting factors.
Calculation ofAdjusted Building Weights Each building in the sample is viewed as representing a share ofthe commercial buildings in its region ofthe 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 ofthe MAISY® buildings for each state. each ofthe seven states a state factor was computed:
Sum ofMAISY

## ® weights forall buildings in the state State Factor=-----------'=-------"'------- Sum ofMAISY

## ® weights for sampled buildings in the state For

The statewide energy usage represented by each sampled building was then computed by multiplying the building's MAISY® weight by its average annual energy usage and the state factor. For each sampled building i: Building i's Adjusted State Energy Use = 

### Annual kWh; 

## *MAISY® Weight; 

### * State Factor
The next step was to scale state usage to regional usage. US Census Divisions were assigned to one ofseven 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 ofsampled states to Census Divisions. Assignment ofSampled 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 ofcommercial sector electricity sales in the division(s) corresponding with each ofthe sampled states to the commercial sector electricity sales in each ofthose individual states.
Regional Retail Commercial Sales Region Factor = State Retail Commercial Sales Applying the Region Factors was the next step in computing the Adjusted Building Weights for each building: Building i's Adjusted Region Energy Use = 

### Region Factor 

## *Building i's Adjusted State Energy Use



### J :

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### 29-99.wpd 29

### The final step in calculating the Adjusted Building Weight takes each building as a shareofthe national total. For each sampledbuilding i: 

### Adjusted Region Energy Use; Adjusted Building Weight = 

### Sum of Adjusted Region Energy Use for all sampled buildmgs

For each sampled building, the Adjusted Building Weight is the percentage ofnational energyuserepresentedby that building.

Derivation of Weights for BuildindTariffPairs In the second phase ofthe weighting process, the weight for each building was apportioned across the applicable tariffs that had been used to compute marginal prices for it. These buildingltariff weights sum to one for the entire sample and to the Adjusted Building Weight for each sampled building. 

### Derivationof State and Regional Weights Used in Computed Tariff Average Rates

A different set ofweights was computed for each sampled building because a unique set oftariffs was applied to each sampled building based on its particular pattern ofenergy usage. The building-specific tariff weights were computed using the FERC data on MWh sales for each tariff.17 This process was implemented in two steps. 

### State California Colorado Georgia Illinois New York Texas Washington
Total

The first stepwas to calculate the relative weight of each ofthe tariffs that were applied to a building. For each building (denoted by i) and tariff (denoted by

### j): 

### State Factor 22.75

6.64 9.99 13.16 11.54

7.06 6.62



### MIA% Sales for Tariff j 

### Share of Tariffj 

### for Building i = 

### Sum of MWh 

### Sales for

## AD Tariffs Applicable toBuilding 

### i Sum of Bldg Wts

"

### The annual reports that IOUs submit to FERC, "Form 

### 1 - 

### Annual Report for Major Electric Utilities, Licensees, and Others", include inforination on the utilities' sales, number ofcustomers, and average rate by tariff orrate schedule on "page" 304 of that form ("Sales ofElectricity by Rate Schedules"). An electronic database ofthe FERC Form 

### 1 filings (that included page 304 data) was obtained from a commercial service known as Energy Central News. 

### Sampled Buildings 24506 12698 14022 15645 28734 46881 18221
160707 Region Factor 1

### .OO4.10 8.64 4.04 3.22 1.45 1.80 Commercial kwh(millions)
State 557548

84339 140103 205813 331675 331176 120664
1771 31 8 State 6710 1203 2369 431 2 3895 6142 1833 Region 6710 4933 20465 1

7431 12526

891 1 3306
74282 The final step in calculating the Adjusted Building Weight takes each building as a share ofthe national total. For each sampled building i:



### W . h Adjusted Region Energy Usei Adjusted Building elg t . 

# =-------"-----:::.-----=::.:......--'-------

1 

### Sum of Adjusted Region Energy Use for all sampled buildings

For each sampled building, the Adjusted Building Weight is the percentage ofnational energyuse represented by that building.



### Derivation of State and Regional Weights Used in Computed TariffAverage Rates Sum of Bldg Wts Commercial kWh (millions) Sampled State Region State Buildings State Factor State Region Factor California 

### 24506 557548 22.75 6710 6710 1.00 Colorado 

### 12698 84339 6.64 1203 4933 4.10 Georgia 

### 14022 140103 9.99 2369 20465 8.64 Illinois 

### 15645 205813 13.16 4312 17431 4.04 New York 

### 287341 331675 11.54 3895 12526 3.22 Texas 

### 46881 331176 7.06 6142 8911 1.45 Washington 

### 18221 120664 6.62 1833 3306 1.80

Total 

### 160707 1771318 74282

Derivation ofWeights for Building/TariffPairs In the second phase ofthe weighting process, the weight for each building was apportioned across the applicable tariffs that had been used to compute marginal prices for it. These building/tariffweights sum to one for the entire sample and to the Adjusted Building Weight for each sampled building.

A different set ofweights was computed for each sampled building because a unique set oftariffs was applied to each sampled building based on its particularpattern ofenergy usage. The building-specific tariffweights 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 ofeach ofthe tariffs that were applied to a building. For each building (denoted by i) and tariff(denoted byj):



### MWh Sales for Tariff j Share of Tariffj for Building 1

# =----------------'::.....------ Sum of MWh Sales for All Tariffs Applicable to Building

17 The annual reports that IOUs submit to FERC, "Form 1 - Annual Report for Major Electric Utilities, Licensees, and Others", include information on the utilities' sales, number ofcustomers, and average rate by tariffor rate schedule on "page" 304 ofthat form ("Sales ofElectricity by Rate Schedules"). An electronic database ofthe FERC Form 1filings (that included page 304 data) was obtained from a commercial service known as Energy Central News.



### J :

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### Thebuilding-specific tariff weights were then combined with the adjusted building weights to obtain a unique weight for eachbuildingltariffpair: Weight for Building i on Tariff j =

### Adjusted Building Weight,

## * Share of Tariffj for Building i

These are the weightsthat were used to calculate weighted averages for the sample and forbinning the data to derive the distribution ofpercent differences between average and marginal prices (called epsilons) for use in the 

### LCC analysis.



### J 

### :\marginal\FinalDraftReport7-29-99. wpd ( 

# .

The building-specific tariffweights were then combined with the adjusted building weights to obtain a unique weight for each building/tariffpair: Weight for Building i on Tariffj = Adjusted Building Weighti 

## * Share ofTariffj 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 ofpercent differences between average and marginal prices (called epsilons) for use in the LCC analysis.



### J:\marginal\FinaIDraftReport7-29-99.wpd 31

### Avvendix 5.Tax Tables.

Table 6. Sales and Use Taxes on End-Use Energy Consumvtion (with date of last effective change in'~arentheses1 (Source: EIA End-Use Taxes, Current EIA Practices, 1994.TableB2) EIA's source for this table is: State TaxationIRevenue Offices and Research Institute ofAmerica, All StatesTax Guide, March 29, 1993.


### State Electric Natural 

### Gas

AL Tax Rate Schedule: 0-40kwh -

### 4% >40-60 kwh -

### 3% >60kwh -

### 2% (711163) Tax Rate: 4% (711163)

AK No Sales Tax No Sales Tax

AZ Tax Rate: 5% (611183) Same as Electric

AR Tax Rate: 4.5% Tax Rate: 4.5% (517191) (517191 ) 

### Exemption(s): Exemption(s): Residents with income less than Qualified steel manufacturers. $12,000 on first 500kwhper month, manufacturing aluminum by electrolytic reduction, & 

### qualified steel manufacturers.

C



### A Tax Rate: 6% Tax Rate: 6% (7115191) (7115/91) Exemption(s): Exemption(s): Residential Energy Surcharge-- Residential, commercial, and industrial. excisetax (0.lrnill per kwh).
CO Tax Rate: 3% (811184) Exemption(s): Residential and industrial. Tax Rate: 3% (811184) Exemption(s): Residential heating, manufacturing processing, mining, irrigation, communications, and transportation.



### CT Tax Rate: 6% Tax Rate: 6% (1011191) (1011191) Exemption(s): Exemption(s): Residential -

### first $l5Olmonth; Residential 

### - 75% forpowering industrial 75% for powering industrial manufacturingplant or directly used in manufacturing plant or directly used farming,fabrication ormanufacturing. in farming, fabrication or manufacturing. Includes bottled propane.

DE No Sales Tax No Sales Tax
DC Tax Rate: 6% Same as Electric (611180) Exemption(s): Residential and used in manufacturing.



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

# •

Appendix 5. Tax Tables.

Table 6. Sales and Use Taxes on End-Use Energy Consumption (with date oflast effective change in'parentheses) (Source: EIA End-Use Taxes, Current EIA Practices, 1994. Table B2) EIA's source for this table is: State TaxationlRevenue Offices and Research Institute ofAmerica, All States Tax Guide, March 29, 1993.

State



### AL

AK

AZ

AR

CA

CO



### CT

DE

DC Electric

Tax Rate Schedule: 0-40kWh-4% >40-60 kWh

- 3% >60kWh-2% (7/1/63)

No Sales Tax

Tax Rate: 5% (6/1/83)

Tax Rate: 4.5% (5/7/91) Exemption(s): Residents with income less than $12,000 on first 500 kWh permonth, manufacturing aluminum by electrolytic reduction, & qualified steel manufacturers. Natural Gas

Tax Rate: 4% (7/1/63)

No Sales Tax

Same as Electric

Tax Rate: 4.5% (5/7/91) Exemption(s): Qualified steel manufacturers.

Tax Rate: 6% (7/15/91) Exemption(s): Tax Rate: 6% 0/15/91)

Residential Energy Surcharge-- excise tax (O.lmiII per kWh).

Tax Rate: 3% (8/1/84) Exemption(s): Residential and industrial.

Tax Rate: 6% Exemption(s): Residential, commercial, and industrial.

Tax Rate: 3% (8/1/84) Exemption(s): Residential heating, manufacturing processing, mining, irrigation, communications, and transportation.

Tax Rate: 6% (10/1/91) Exemption(s): (10/1/91)Exemption(s): Residential first $150/month; 75% for powering industrial manufacturing plant or directly used in farming, fabrication or manufacturing.

No Sales Tax

Tax Rate: 6% (6/1/80) Exemption(s): Residential and used in manufacturing. Residential

- 75% for powering industrial manufacturing plant or directly used in farming, fabrication ormanufacturing. Includcs bottled propane.

No Sales Tax

Same as Electric



### J:\marginal\FinalDraftReport7

-29-99.wpd 32

### Tax Rate: 6% Tax Rate: 6% (211188) (211188) Exemption(s): Exemption(s): Residential and by qualified businesses Residential,used in manufacturing at a fixed In enterprise zones. location in the State,boiler fuel, and space flights.

Gi4 

### Tax Rate: 4% (411189)

Tax Rate: 4% (711191) Exemption(s): Utility services and fuels. TaxRate: 4% (41

### 1189) Exemption(s): Heating structures forpoultry raising.
Same as Electric

Tax Rate: 4% Tax Rate: 4% (411186) (411186) Exemption(s): Exemption(s): Residential, commercial and industrial. Residential, commercial,and industrial fuels.
Subject to utility tax: Subject to Gas Tax under Gas Tax Revenue Act. Rate: 32 one-hundredth $/kwh,or 5%of Rate: 2.461therm or 5% ofgrossreceipts, gross receipts, whichever is less. whichever is less.

Tax Rate: 5% Same as Electric (111183) Exemption(s): Manufacturing forproduction, farmers, and restaurants. Public Utilities: gross revenuetax of 0.15%

Tax Rate: 5% (711 192) Exemption(s): Manufacturing processing. Tax Rate: 5% (711/92) Exemption(s): Manufacturing processing, fuels for agriculture, creameries, dairies and ice cream factories.



### KS Tax Rate: 4.25% Tax Rate: 4.25% (711189) (711189) Exemption(s): Exemption(s): Residential, farm use (no irrigation), Residential, farm use, manufacturing, and mining. manufacturing, and mining.
ICY 

### Tax Rate: 

### 6% (711190) Exemption(s): Residential sector. TaxRate: 6% (419190) Exemption(s): Residential sector.



### LA TaxRate: 4% Tax Rate: 4% (711/84) (711 184) Exemption(s): Exemption(s): Chloralkali Manufacturers. Agriculture, and fuel for boilers.



### J 

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

### " 

# .

FL

GA



## m

ID



### IL

IN



### IA

KS



### KY

LA Tax Rate: 6% (2/1188) Exemption(s): Residential and by qualified businesses In enterprise zones.
Tax Rate: 4% (4/1/89)

Tax Rate: 4% (7/1/91) Exemption(s): Utility services and fuels.

Tax Rate: 4% (4/1/86) Exemption(s): Residential, commercial and industrial.
Subjectto utility tax: Rate: 32 one-hundredth ¢/kWh, or 5% of gross receipts, whichever is less.

Tax Rate: 5% (1/1/83) Exemption(s): Manufacturing for production, farmers, and restaurants. Public Utilities: gross revenue tax of 0.15%

Tax Rate: 5% (711192) Exemption(s): Manufacturing processing. Tax Rate: 6% (2/1/88) Exemption(s): Residential, used in manufacturing at a fixed location in the State, boiler fuel, and space flights.

Tax Rate: 4% (411/89) Exemption(s): Heating structures for poultry raising.

Same as Electric

Tax Rate: 4% (411/86) Exemption(s): Residential, commercial, and industrial fuels.
Subject to Gas Tax under Gas Tax Revenue Act. Rate: 2.4¢/therm or 5% ofgross receipts, whichever is less.

Same as Electric

Tax Rate: 5% (711192) Exemption(s): Manufacturing processing, fuels for agriculture, creameries, dairies and ice cream factories.

Tax Rate: 4.25% Tax Rate: 4.25% (7/1/89) (711/89) Exemption(s): Exemption(s): Residential, farm use (no irrigation), Residential, fann use, manufacturing, and mining. manufacturing, and mining.

Tax Rate: 6% (7/1/90) Exemption(s): Residential sector.

Tax Rate: 4% (711/84) Exemption(s): Chloralkali Manufacturers. Tax Rate: 6% (4/9190) Exemption(s): Residential sector.

Tax Rate: 4% (711/84) Exemption(s): Agriculture, and fuel for boilers.



### J:\marginal\FinaIDraftReport7-29-99.wpd 33

### Tax Rate: 6% Tax Rate: 6% (811191) (811191) Rate is 1 %

### on 95% ofelectricity used Rate is 1%on 95% of gasused in in manufacturing facility. manufacturingfacility. Exemption(s): Exemption(s): 0-750kwhin residential sector. Residential.

Tax Rate: 5% Tax Rate: 5% (611 177) (611 177) Exemption(s): Exemption(s): Residential, four units or fewer. Residential, four units or fewer, and manufacturing.

Tax Rate: 5% Tax Rate: 5% (1 1/12/75) (1 1/12/75) Exemption(s): Exemption(s): Residential and manufacturing when Residential and Industrial. used for production andlor the company has fewer than six employees.

Tax Rate:4% 

### (111161) Exemption(s): Agriculture

Tax Rate: 6% (6118191) Exempt from sales tax. Same asElectric

Same as Electric

Tax Rate: 6% Same asElectric (1211183) Manufacturers and some agricultural activities pay at 1.5%. Exemption(s): Residential.
5.725% Sales Tax 5.725% SalesTax (711/92) (711/92) Exemption(s): Exemption(s): Residential, drying farm crops. Residential and drying farm crops. Manufacturers are exempt from paying the tax for the amount that exceeds 10% of the cost of production.

No Taxes No Taxes

Tax Rate: 5% Same asElectric (7110190) Exempt if>50% is used forprocessing, manufacturing, refining, farming and irrigation,or for hospital use.
Tax Rate: 6.5% (1011191) Tax Rate: 6.5% (1011191) I. •

ME



### MD

MA

MI

MN

MS

MO

MT

NE

NY Tax Rate: 6% (811/91) Rate is 1 % on 95% ofelectricity used in manufacturing facility. Exemption(s): 0-750 kWh in residential sector.

Tax Rate: 5% (611177) Exemption(s): Residential, four units or fewer.

Tax Rate: 5% (11112175) Exemption(s): Residential and manufacturing when used for production andlor the company has fewer than six employees.

Tax Rate: 4% (111161) Exemption(s): Agriculture

Tax Rate: 6% (6118/91 ) Exempt from sales tax.

Tax Rate: 6% (12/1/83) Manufacturers and some agricultural activities pay at 1.5%. Exemption(s): Residential. Tax Rate: 6% (811191) Rate is 1 % on 95% ofgas used in manufacturing facility. Exemption(s): Residential.

Tax Rate: 5% (611/77) Exemption(s): Residential, four units or fewer, and manufacturing.

Tax Rate: 5% (11112175) Exemption(s): Residential and Industrial.

Same as Electric

Same as Electric

Same as Electric

5.725% Sales Tax (711192) 5.725% Sales Tax (711/92) Exemption(s): Residential, drying fann crops. Manufacturers are exempt from paying the tax for the amount that exceeds 10% ofthe cost ofproduction.

No Taxes

Tax Rate: 5% (7/10/90) Exempt if>50% is used for processing, manufacturing, refining, fanning and irrigation, or for hospital use. Exemption(s): Residential and drying fann crops.

No Taxes

Same as Electric

Tax Rate: 6.5% (1011191 ) Tax Rate: 6.5% (1011/91 )



### J:\marginal\FinalDraftReport7-29-99.wpd 34

### Exempt ifdelivered through lines. Exempt ifdelivered to customers through mains, lines,
orpipes and residential heating.
No Sales Tax No Sales Tax

TaxRate: 7% Tax Rate: 7% (711190) (711 190) Electric utility sales are exempt. Natural gas utilities sales are exempt.

TaxRate: 5% (719191 ) 

### Exemption(s): Warehouse and offices, Tax Rate: 5% (719191 )



### Tax Rate: 4% Tax Rate: 4% (711171) (71117 1) Exemption(s): Exemption(s): Residential, agriculturaluse, and Residential, agricultural use, and fuel for manufacturing assembling, mining, and manufacturing assembling, mining, and research (direct-predominant use). research (direct and predominant use).

Tax Rate: 4% TaxRate: 4% (7116191) (7114191) Manufacturers pay 1 %

### foruse in Farmers, manufacturers, and commercial production. laundries and dry cleaners pay 1%.



### TaxRate: 5% Tax Rate: 5% (12/6/89) (12/6/89) Electricity is exempt.

Tax Rate: 5% Tax Rate: 

### 5% (1124181) (1124181) Electricity and utility services are exempt. Natural gas is exempt from sales tax.

Tax Rate: 4.5% 4.5% Sales Tax (511190) (511190) Exemption(s): Exemption(s): Residential and manufacturing. Residential and manufacturing.

No Sales Tax No Sales Tax
Tax Rate: 6% (1 1/1/68) Exemption(s): Residential.
Tax Rate: 7% (711190) Exemption(s): Residential. Tax Rate: 

### 6% (1 115168)Exemption(s): Residential.

Tax Rate: 7% (711190) Exemption(s): Residential, and fuel for plants and flowers.

Tax Rate: 

### 5% Tax Rate: 5% (711184) (711/84) Exemption(s): Exemption(s): Residential, cropirrigation, and radio Residential, manufacturing, agricultural curing, .. 4. •



### NH

NJ

NM

NY

NC

ND



### OR

OK



### OR


### PA

RI

SC Exempt ifdelivered through lines.

No Sales Tax

Tax Rate: 7% (7/1/90) Electric utility sales are exempt.

Tax Rate: 5% (7/9/91) Exemption(s): Warehouse and offices.

Tax Rate: 4% (7/1171) Exemption(s): Exempt ifdelivered to customers through mains, lines,
orpipes and residential heating.

No Sales Tax

Tax Rate: 7% (711/90) Natural gas utilities sales are exempt.

Tax Rate: 5% (7/9/91)

Tax Rate: 4% (7/1171) Exemption(s): Residential, agricultural use, and manufacturing assembling, mining, and research (direct-predominant use). Residential, agricultural use, and fuel for manufacturing assembling, mining, and research (direct and predominant use).

Tax Rate: 4% (7/1 6/91) Manufacturers pay 1 % for use in production.

Tax Rate: 5% (12/6/89) Electricity is exempt.

Tax Rate: 5% (1/24/81) Tax Rate: 4% (7116/91 )Farmers, manufacturers, and commercial laundries and dry cleaners pay 1%.

Tax Rate: 5% (12/6/89)

Tax Rate: 5% (1/24/81) Electricity and utility services are exempt. Natural gas is exempt from sales tax.

Tax Rate: 4.5% (5/1/90) Exemption(s): Residential and manufacturing.

No Sales Tax

Tax Rate: 6% (11/1/68) Exemption(s): Residential.

Tax Rate: 7% (7/1/90) Exemption(s): Residential.

Tax Rate: 5% (7/1/84) Exemption(s): 4.5% Sales Tax (5/1/90) Exemption(s): Residential and manufacturing.

No Sales Tax

Tax Rate: 6% (11/5/68) Exemption(s): Residential.

Tax Rate: 7% (7/1/90) Exemption(s): Residential, and fuel for plants and flowers.

Tax Rate: 5% (7/1/84) Exemption(s): Residential, crop irrigation, and radio Residential, manufacturing, agricultural curing, 

### J;\marginal\FinaIDraftReport7-29-99.wpd 35

### broadcasting.

Tax Rate:4% (311188) Exemption(s): Farm use. poultry production, and motor fuel, except aviation fuels.
Same as Electric

TaxRate: 6% Same as Electric (411/92) Rate is 1.5% for manufacturers, and for farmers foruse in food production, and fornurserymen for use in growing horticultural products. Exemption(s): Residential
6.25% Sales Tax (711/90) Exemption(s): Non-commercial ornon-business use Same as Electric

Tax Rate: 5% Tax Rate: 

### 5% (111190) (111190) Rate is 2%for Residential. Rate is 2percent for Residential. Exemption(s): Exemption(s): All otherbusinesses are exempt from Greenhouses (wholesale), irrigation, and orchards. salestax except commercial business.
Tax Rate: 5% Tax Rate: 5% (611191) (611191) Exemption(s): Exemption(s): Taxpayer-generated and 60%used in Residential and for agricultural use. Motor fkels
own business. otherwise taxed exceptjet fuel.

TaxRate: 3.5% (111187) Exemption(s): Electricity through lines for manufacturing for direct processing, making feed for sale or resale, com~nercial easing orrenting of laundered textile products sold to watermen for extracting fish, bivalves, or crustaceans from water.
Tax Rate: 6.5% (111187)

Tax Rate: 6% (611188) Electricity sales are exempt

Tax Rate: 5% (511188) Exemption(s): TaxRate: 3.5% (111187) Exemption(s): Residential and manufacturing for direct processing, making feed for sale orresale, commercial leasing orrenting oflaundered textile products sold to watermen for extracting fish, bivalves, or crustaceans from water and for drying or curing crops.
Same as Electric

Tax Rate 6% (611188) Natural gas sales are exempt.

Tax Rate: 

### 5% (511188) Exemption(s):


### J 

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

..

SD

TN



### TX

UT



## vr

VA

WA

WV

WI broadcasting.

Tax Rate: 4% (3/1/88) Exemption(s): Farm use.
Tax Rate: 6% (4/1/92) Rate is 1.5% for manufacturers, and for farmers for use in food production, and for nurserymen for use in growing horticultural products. Exemption(s): Residential

6.25% Sales Tax (7/1/90) Exemption(s): Non-commercial or non-business use.
Tax Rate: 5% (1/1/90) Rate is 2% for Residential. Exemption(s): All otherbusinesses are exempt from sales tax except commercial business.

Tax Rate: 5% (6/1/91) Exemption(s): Taxpayer-generated and 60% used in
own business.

TaxRate: 3.5% (1/1/87) Exemption(s): Electricity through lines for manufacturing for direct processing, making feed for sale
or resale, commercial leasing orrenting of laundered textile products sold to watermen for extracting fish, bivalves, or crustaceans from water.

Tax Rate: 6.5% (1/1/87)

Tax Rate: 6% (6/1/88) Electricity sales are exempt.

Tax Rate: 5% (5/1/88) Exemption(s): 

### J:\marginal\FinalDraftReport7-29-99.wpd poultry production, and motor fuel, except aviation fuels.

Same as Electric

Same as Electric

Same as Electric

Tax Rate: 5% (1/1/90) Rate is 2 percent for Residential. Exemption(s): Greenhouses (wholesale), irrigation, and orchards.

Tax Rate: 5% (6/1/91) Exemption(s): Residential and for agricultural use. Motor fuels otherwise taxed exceptjetfuel.

Tax Rate: 3.5% (1/1/87) Exemption(s): Residential and manufacturing for direct processing, making feed for sale orresale, commercial leasing orrenting oflaundered textile products sold to watermen for extracting fish, bivalves, or crustaceans from water and for drying or curing crops.
Same as Electric

Tax Rate 6% (6/1/88) Natural gas sales are exempt.

Tax Rate: 5% (5/1/88) Exemption(s):

36

### Residential and farming: November Residential and farming: November through April; through April. commercial fishing, interstate and foreign commerce, and railroads.



### WY TaxRate: 

### 3% Same as Electric (711 

### /67) 

### Exemption(s): Manufacturingprocess, and agricultural
use. '" I. Ii'



### Wy Residential and farming: November through April.

Tax Rate: 3% (711/67) Exemption(s): Manufacturing process, and agricultural
use.



### J;\marginal\FinalDraftReport7-29-99.wpd Residential and farming: Novemberthrough April; commercial fishing, interstate and foreign commerce, and railroads.

Same as Electric

37

### Table 

### 7. Gas and Electric Utilitv Regulatory Assessments bv States"

State 

# - AK AL AZ AR 

# co DE 

### FL GA HA 

### ID IL IN 

### IA KS KY LA ME MD MA MI Tax Rate 

## I% ofRetail Price) rate unavailable 

### 0.15% 0.20% 0.40% 0.20% 0.20% 0.13% Fee based on property value 

### 0.13% 0.30% 0.19% 0.15% 0.10% 0.20% 0.20% 0.04% 0.25% 0.17% 0.25% 0.02%

Table 

### 8. kwhTaxes on Electricityi9
State 

# - AL AK CA 

### ID MD MT 

### ND SC WV Tax Rate 

### 0.04 cents/kWh hydroelectricity 

### 0.025-0.05centsikwh electric co-ops 

### 0.02 centslkW11electricity consumed 

### 0.05 centsikwh hydroelectricity 

### 0.015 cents/kWh electricity consumed 

### 0.02 centslkWh electricity produced 

### 0.025 centstkwh electricity generated from 

### 0.05 centslkwh electricity produced 

### 0.26 centslkwh electricity produced

l8 

### ASE Report, Table 

### 5 on page 

### 8.

l9 

### ASE Report, Table 6 on page 

### 8. Tax Rate State 

## I% ofRetail Price) 

# - MN rate unavailable 

### MS 0.16% MT 

### 028% 

### NV 0.43% 

### NJ 0.25% 

### NM 0.50% NY 0.33% OR 

### 0.25% PA 

### 0.30% SC rate unavailable 

### SD 0.15% 

### TN 0.08% 

### TX 0.17% 

### UT 0.30% 

## LT 0.50% VA 

### 0.20% WA 

### 0.20% WV 

### 0.29% WY 

### 0.30%

coal



### J:\rnarginal\FinalDraftReport7-29-99. wpd 

# · {. 

# ...

Table 7. Gas and Electric Utility Regulatory Assessments by States 18
Tax Rate State (% ofRetail Price) State AK rate unavailable AL 0.15% AZ 0.20% AR 0.40% CO 0.20% DE 0.20% FL 0.13% GA Fce based on property value HA 0.l3% ID 0.30% IL 0.19% IN 0.l5% IA 0.l0% KS 0.20% KY 0.20% LA 0.04% ME 0.25% MD 0.l7% MA 0.25% MI 0.02%

Table 8. kWh Taxes on Electricity I9
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 electricityproduced ND 0.025 cents/kWh electricity generated from coal SC 0.05 cents/kWh electricity produced WV 0.26 cents/kWh electricity produced

18 ASE Report, Table 5 on page 8.

19 ASE Report, Table 6 on page 8.



### J:\marginal\FinalDraftReport7-29-99.wpd Tax Rate (% ofRetail Price) MN rate unavailable MS 0.16% MT 0.28% NV 0.43% 

### NJ 0.25% 

### NM 0.50% NY 0.33% OR 0.25% PA 0.30% SC rate unavailable SD 0.l5% TN 0.08% TX 0.l7% UT 0.30% 

### VT 0.50% VA 0.20% WA 0.20% WV 0.29% WY 0.30%

38