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MARGINAL ENERGY PRICES DraftFinal Report Page Outline i Executive Summary I
I. Background 2 II. Methods 4 III. Analysis and Results - Commercial 6 IV. Analysis and Results - Residential 12 V. Residential Heating Oil and Propane 19 VI. Taxes 22 Appendices Appendix 1. Tariffs Used in the Commercial Analysis 26 Appendix 2. Examples ofCommercial Bill Calculations 27 Appendix 3. Demand Decrement Due 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. Ga; and Electric Utility Regulatory Assessments by States 38 Table 8. kWh Taxes on Electricity 38 Figures Figure 1. Commercial Electricity Marginal Prices 11 Figure 2. RECS 1993 Linear Regression on Electricity Bills, Household 4164 13 Figure 3. RECS 1993 Linear Regression on Gas Bills, Household 1001 14 Figure 4. Residential Summer Electricity Marginal Prices 16 Figure 5. Residential Winter Electricity Marginal Prices 17 Figure 6. Residential Gas Marginal Prices 18
Contributors LBNL Report Coordinator: Stuart Chaitkin LBNL Report Contributors: Peter Biermayer, Sarah Bretz, Steve Brown, Stuart Chaitkin, Sachu Constantine,

Diane Fisher, Sajid Hakim, Lucy Liew, Jim Lutz, Chris Marnay, James E. McMahon, Mithra Moezzi, Julie Osborn, Esther Rawner, Judy Roberson, Greg Rosenquist, Nancy Ryan, Isaac Turiel, Stephen Wiel
Acknowledgments This work was supported bythe Assistant Secretary for Energy Efficiency and Renewable Energy, Office ofCodes and Standards, ofthe U.S. Department ofEnergy, underContract No. DE-AC03-76SF0098. Data and advice were graciously provided by Robert Latta and Steve Wade oftheEnergy Information Administration.

J:\marginal\finalreport\FinalReport.wpd iMARGINAL ENERGY PRICES
- DRAFT FINAL REPORT EXECUTIVE SUMMARY This report responds to a recommendation from DOE's Advisory Committee on Appliance Energy Efficiency Standards. It presents the derivation ofestimated consumer marginal energy prices for the commercial and residential Osectors for use in the life-cycle cost (LCC) analyses for four appliances

- clothes washers, water heaters, fluorescent lamp ballasts, and central air conditioners/heat pumps. Marginal prices as discussed here are those prices consumers pay (or save) for their last units ofenergy used (or saved). Marginal prices reflect a change in a consumer's bill (that might be associated with new energy efficiency standards) divided by the corresponding change in the amount 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 fromresidential sector energy efficiency standards ora 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 below provides background on DOE's work on marginal energy prices. Section II describes our methods. We are unaware ofany other credible methods to calculate or estimate consumers' marginal energy prices. 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 of commercial tariffs. The MAISY® database provided the source ofinformation for the building energy and demand levels amongbuildings ofdifferent types. We collected and modeled the commercial tariffs from a sample ofutilities.
Section IV describes the analysis for the residential sector (for application to three products: clothes washers, water heaters, and central air conditioners/heat pumps). The analysis centered on use ofconsumer bills from the RECS 1993 database. We derived seasonal marginal electricity prices and annual marginal gas prices by calculating the slope ofthe regression lines associated with monthly bill and consumption data.
We also examined prices ofresidential heating oil and propane. Because bills paid by residential consumers for these fuels are determined almost wholly by the volume purchased, quotedprices are essentially marginal prices. Section V presents theresults ofour examination ofresidential heating oil and propane prices.
We also investigated the effect oftaxes on marginal prices. Section VI discusses this issue.
We summarize the results ofthe analyses to derive commercial andresidential marginal prices as 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.

- 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 inthe summer.
J:\marginal\finalreport\FinalReport.wpd 1Electricity marginal prices for individual customers in the winter (the remaining eight months) range from 100% below to 130% above the average price forthe same customer. At the consumptionweighted 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 forthe same customer. Atthe consumption-weighted mean ofthe differences, gas marginal prices are 7.8% lower than average prices.

I. 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. Usingmarginal energy prices inthese analyses is more theoretically soundbecause consumers would save energy onthe margin (that is, atthe price they pay fortheir last unit ofenergy), notat 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 energyprices. DOE Advisory Committee In itsApril 21, 1998 letter to Secretary ofEnergy Pena,the Advisory Committee onAppliance Energy Efficiency Standards delivered recommendations tothe Department ofEnergy regarding, among other things, the use ofenergy prices in future appliance standards rulemakings. The Committee recommended that for life-cycle cost analyses DOE should replace the use ofnational average energy prices withthe full range ofconsumer marginal energy rates. Absent consumer marginal energy rate information, the Committee recommended DOE use a range ofnet energy rates, calculated byremoving 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 ofmarginal energy rateswould improve the theoretical soundness ofthe analysis and decided to determine marginal rates using either RECS or commercially available databases. While the Department believed atthat time that itwas unknown if removing fixed costs is more or less reflective ofmarginal rates, it did not intend to take that intermediate step without evidence that theresult 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 104utilities, serving over 63 million customers. The data included average prices, tail block rates and fixed charges, but not marginal prices. EEI reported that fixed charges as a weighted percentage ofaverage prices represent 7.5 percent. However, in examining the actual rate schedules ofa few ofthese 104utilities, we found that some ofthe reported fixed charges were actually minimum charges, not customer charges. Thus the actual fixed charges, which are independent ofkWh 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 ofthese adjusted fixed charges would yield the marginal rate. However, upon examining the EEI data, we determined that most rates arenot flat. Ofthe 104 utilities represented in the data, 100 contained average price, tail blockrate and fixed charges. Forutilities with a single flat rate schedule, the average price absent the fixed charges should equal thetail blockrate. Ofthe 100 utilities, only 16 metthis test within an assumed one percent reporting and rounding error. Thus, 84 percent ofthe utilities had nonflat rates or a mix ofrate schedules which greatly complicates the determination ofthe marginal price. Ofthese 84 utilities, roughly halfhad tail blockshigher than the average price, indicating inclining block rates, and halfhad lower tail blocks indicating declining blockrates. Seasonal rates furthercomplicate this picture.

'Letter from Dan Reicher, DOE Assistant Secretary, Energy Efficiency and Renewable Energy, to Dan Barzel (representing the Advisory Committee), July 28, 1998. J:\marginal\finalreport\FinalReport.wpd 2The American Gas Association (AGA) had conducted a similar analysis using 1996 data for264 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., itdid not include data foreither Pacific Gas &Electric or San Diego Gas & Electric), we did not closely evaluate AGA's analysis. 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 aretypically billedmonthly for their energy usage and will face different marginal rates during the year depending on wheretheir 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 ismost obvious for space conditioning products such as air conditioners, but, inreality, almost all appliances undergo some seasonal variation in their usage. Given the possible wide range ofmarginal prices between winter and summer months, any significant seasonal variation in usage could have a significant effect on themarginally calculated savings. Using actual marginal pricesmay yield consumer energy savings higher or lower than the past method ofusing average costs based ontotal revenue. Given the prevalence ofsummer peaking utilities and seasonal rates, it seems likely that appliances with heavier summer usage, particularly central air conditioners, wouldmost probably produce a greater consumer value ofsavings using marginal prices. Therefore, removing fixed costs, which only guarantees lower estimates ofenergyprices, would then be a correction inthe wrong direction. Restructuring Given restructuring ofparts ofthe energy supply sector, customers may soon havemore than one bill (e.g., one from the distribution company, and one ormore from generators or suppliers). Tocapture 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 bymonth 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 forthis current research and analysis on marginal prices. Residential consumers' utilitybills 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. Dataon utility sales and number ofcustomers on various tariffs were available for investorowned utilities from the Federal Energy Regulatory Commission (FERC) Form I filings made by those utilities. For municipal utilities and co-ops, equivalent tariff-level data were obtained from eitherthe utility itselfor were available from the Rural Utility Service.

2MAISY is a proprietary product that was acquired byLBNL from Jackson Associates. J. iiargiialfmnalreport\FinalReport.wpd 3II. METHODS Calculating the truebill effects ofproposed appliance standards on diverse U.S. consumers is difficult because ofthe many electricity and natural gastariffs in place, and the complexity ofthese tariffs. Since the set ofend-uses currently under consideration for new standards includes fluorescent lamp ballasts, calculations for commercial as well as residential electricity customers must be included, which complicates the task considerably because commercial tariffs are generally more complex. To address this difficult research question, we developed several complementary approaches, which are briefly outlined below. Each ofthese approaches was a step in the overall sequence ofsetting up the modeling capability to allow calculation ofsamples ofcustomer bills. "Analytic" Approach This approach was the first-cut analysis in which residential electricity and gas tariffs and commercial electricity tariffs were examined as they were collected. Comparing each particulartariff's rate blocks to average prices yielded the maximum and minimum differences between marginal and average price possible for that particular tariff. Because consumption is unknown, actual customer marginal prices cannot be obtained by this method; only the outer bounds to those prices can be obtained. The findings from this approach were: a) Care must be taken to find and account for all charges, since the tariffs are often complex, with a number ofcharges based ondemand levels, voltage levels, seasons, etc. Residential tariffs tend, though, tobe 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) Forresidential electricity, individual customer marginal prices can show a very large range, with differences ranging from -79%to +344% compared tothe average price for customers having a particular tariff,although many ofthe extreme results appear ontariffs that cover only a few customers and thuswould be seldom experienced. d) For commercial electricity, individual customer marginal prices also show a large range and no discemable pattern emerged fromthe analysis. Because the range ofdifferences between marginal and average prices from this analysis was large, we concluded that simple patterns in the relationship between average and marginal prices did notseem to exist. Therefore, we focused onthe methods that permit estimation ofindividual customer bills: the experimental, empirical, and RECS analyses. "EmDirical" ADproach LBNL contracted with Jackson Associates for use ofand enhancement to itsproprietary 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 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 ofcustomers and estimation ofbills was developed using twoutilities in a southern California test area, and a bill calculator function using the actual tariffs ofelectric and gas utilities was developed. Forcommercial buildings, this approach evolved into the one explained in Section III. Since an estimate ofthe effect on thehouse's natural gas bill was also required for analysis ofthethree residential products (clothes washers, water heaters, and central air conditioners/heat pumps), the households were further assigned to a natural gas local distribution company (LDC) that serves a part ofthe territory ofthatresidence's electric utility. While forresidential buildings, this "empirical" approach was put aside in favor ofthe RECS approach described below, itwas used as the foundation for the "experimental" approach in the analysis ofcommercial sector fluorescent lamp ballasts. "Experimental" Approach During the collection oftariffs andthe designing ofthe computer code forthe empirical approach, a spreadsheet bill calculator was developed. Exploratory experimental tariffs could be modeled inthis spreadsheet, and, togetherwith 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 J:\marginal\fialreport\FinalReport.wpd 4provided the technical approach used for the commercial sector analysis. See Section III for the details and results of this method as applied in final form to the analysis ofthe commercial sector. "Big Bang" Approach The initial sample ofelectric utility tariffs thatwe analyzed was chosen with consideration ofdiversity in terms of utility ownership, geography, andutility size in mind. That sample was extended, as time permitted, but the utility-byutility detailed approach necessarily covered only a limited fraction ofthe total customer population. On the other hand, a large fraction ofcustomers in the country are actually served on arather 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. We drew on this approach to a limited degree in the expansion of the set ofcommercial tariffs used in the final commercial sector analysis.
RECS Approach We estimated consumer marginal energy prices directly from household RECS data by calculating the slopes ofthe regression lines thatrelate customer bills and customer usage. For electricity, the slopes ofthe regressions for four summer months (June-September) and, separately, for the remaining ("winter") months were calculated. Fornatural 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, we adopted the RECS method (because ofits advantages which are explained further in Section IV) 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, we conducted a simple sensitivity analysis on the LCC for each product, 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 waterheaters 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 theminimum LCC was not affected over the range -25% to +25% from average energy prices. In addition, payback period was shown to be affected by a lower percentage than the change in energy prices, because the operating savings dueto 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). Forcathode 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.

J:u\arginal\finalreport\FinalReport.wpd 5III. ANALYSIS AND RESULTS - 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 Energy Prices report first describes the utility tariffs thatwere chosen and modeled for this analysis. Then we describe the process forcalculating the electricity bills and the marginal electricity prices for the sampled commercial buildings. Because commercial tariffs tend to have demand charges, we next describe the adjustment we applied to represent the reduction in demand associated with new energy efficiency standards. We also describe the method by which the sampled buildings are weighted in the analysis. Lastly, we present the results ofthe commercial analysis.
A. TariffCollection and Modeling
TariffCollection Fora variety ofutilities around the country, we collected commercial tariffs and then modeled them for use inthe bill calculation process. We chose utilities in an attempt to obtain a sample thatis generally representative ofcommercial customers throughout thenation. In choosing utilities, thekey factors we considered were geographic location, type ofownership (e.g. investor owned, publicly owned, or cooperatively owned), and size. We choose those factors to capture regional differences in such issues asclimate, the availability ofinexpensive sources ofenergy, the structure ofregulatory oversight, and the scale ofoperation. Table I below is a listofthe commercial utility sample used in this analysis. We obtained utility tariffs from the following sources: utility web sites, a consulting company, and utilitiesthemselves. In order to be able toproperly weight results, we also obtained FERC Form 1(or equivalent) data for the chosen utilities showing the number ofcustomers andthe sales for each ofa utility's tariffs.
Table 1. Characterization and Summary ofCommercial Electric Utility Sample

Percent ofUS In Our Sample as By Uiity ByModed Tariffs 

### ~Modeled Tariffs PercentofUtility
jiU Total Number Total Megawatt- Megawatt-hour MWh MWh Uttillty ' of Customers hour Sales Cust. Sales Cus. Sales Cust. ales PG&E CA IOU 552,901 49,530,911 401,747 16,025.437 3.0% 1.7% 73% 32% SoCal Edison CA IOU 481,417 51,410,408 419.163 23,057.727 3.1% 2.5% 87% 45% Commonwealth Edison IL IOU 292,709 49,933.859 291.143 25,859,649 2.2% 2.8% 99% 52% Virginia Power VA IOU 189,301 20,061,476 137.813 2,696.019 1.0% 0.3% 73% 13% Detroit Edison Ml IOU 177,088 17,997,214 166.003 7,613,394 1.2% 0.8% 94% 42% Alabama Power AL IOU 174,602 11,330.312 154,077 7,917.907 1.1% 0.9% 88% 70% Penn Power &Light PA IOU 149,221 20,475,206 143,849 8,781,171 1.1% 0.9% 96% 43% Niagara Mohawk Power NY IOU 148,124 23,279,622 143,590 4,550,295 1.1% 0.5% 97% 20% NSP (MN) MN IOU 135,183 5,009.755 110,807 4,461,852 0.8% 0.5% 82% 89% Union Electric Co MO IOU 134,699 12,189,235 120,596 8,735,132 0.9% 0.9% 90% 72% Appalachian Power VA IOU 110,740 16,390,313 78,554 674,926 0.6% 0.1% 71% 4% Jersey Central P&L NJ IOU 106,157 10,510,309 104,922 5.358.036 0.8% 0.6% 99% 51% Wisconsin Elec Power Wl IOU 93,973 18,454,563 85,735 2.705.293 0.6% 0.3% 91% 15% Boston Edison MA IOU 87,644 7.991,349 80,255 2,820.415 0.6% 0.3% 92% 35% Ohio Power Company OH IOU 87,314 24,917,126 6,442 6,348,160 0.0% 0.7% 7% 25% Central Power & Light TX IOU 85,311 12,844,712 74,107 2,099,439 0.5% 0.2% 87% 16% Arizona Public Service AZ IOU 79,755 8,524,882 78,530 6,989,968 0.6% 0.8% 98% 82% PEPCO MD IOU 70,909 15.307.001 53,620 1.317.800 0.4% 0.1% 76% 9% Cleveland Electric Ilium OH IOU 64,907 5,883.328 63,161 2,765.339 0.5% 0.3% 97% 47% Seattle City Light WA Muni 43,497 3,081.941 43,497 3,081,941 0.3% 0.3% 100% 100% NSP (WI) W IOU 31,236 866,425 27,449 831,739 0.2% 0.1% 88% 96% Madison Gas &Electric Wl IOU 16,220 1,727,832 14,815 685.233 0.1% 0.1% 91% 40% Savannah Elec & Power GA IOU 14,100 1,156,078 708 517.052 0.0% 0.1% 5% 45% Poudre Valley REA CO Co-op 2,774 377.974 2,101 96,424 0.0% 0.0% 76% 26% Sample Total 3.329.782 389.251.831 2,802.684 145.990,348 20.7% 15.7% 84% 38% US TOTAL 13,540,374 928,440,265

J:\marginal\finalreport\FinalReport.wpd 6Because tariff-level information on sales isnot as readily available forpublicly and cooperatively owned utilities as it is for investor-owned utilities thatmust report such information annually to FERC, our sample contained only one municipal utility and one co-op. While the co-op we included in our analysis had an average commercial price nearly the same asthe average commercial price ofall ofthe co-ops inthe country, the same was not true for the municipal utility in our sample. As a sensitivity analysis, we later added several more municipal utilities, which brought the weighted average electricity price ofthemunicipal utilities in our sample closerto the average commercial price ofall ofthe municipal utilities in the country. We then re-ran the bill calculator (which is described below) 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 ofinformation: Applicability / Availability

- the type ofcustomer served underthis tariff. Character ofService

- the type ofcurrent, voltage, and single orthree-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 ontwo common categories ofcommercial tariffs: small general service and general service. We modeled the collected commercial tariffs in spreadsheet form, excluding some tariffs and/or components that could notbe applied tobuildings in MAISY. For instance, ifatariffprovides both single and three-phase service, only the charges for single phase are modeled. Also, sometariffs charge different prices forprimary, secondary, and transmission voltage levels. Because ofthetypes ofbuildings used in MAISY, we considered onlythe secondary voltage levels. We ignored power factor and reactive demand charges, which are an adjustment based onvoltage and demand usage. We ignored other factors ifthe information couldnot be used in MAISY or are rarely applicable. The spreadsheet also contains the number ofcustomers in a tariff,total number of customers in a commercial utility, the megawatt-hours sold, kilowatt-hours sold percustomer, and average price per kilowatt-hour. These data are extracted from FERC Form 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. Eachbuilding is assigned to a group ofapplicable tariffs, based onbuilding demand, and monthlybills arecalculated foreach tariff for a calendar year. Then,building energy usage and demands are decremented to account for savings expected froma new energy efficiency standard, monthly bills arerecalculated, and the differences inbills and energy use areused to calculate marginal prices. Commercial Tariffs Electric utilities calculate customer bills from tariffsthat define thetype and amount ofeach charge. These charges aregenerally levied on a monthly basis, and generally have three main components: fixed charges, usage charges, and demand charges. We collectedthese tariffs for a number ofUS electric utilities and modeled them for inclusion in CTAS calculations. CTAS contains the specific commercial tariffs forthe utilities shown in Appendix 1. In additionto the parameters necessaryto calculate customer bills, we collected and included information for each tariffregarding the number ofcustomers, revenues, and delivered energy. We used this tariff-level data on customers and delivered energy toweight the resulting bill and marginal price calculations torepresent a national distribution of marginal prices. Our calculations did notuse atariffunless it was alsopossible tocollect usage and delivered energy data forthat individual tariff(as opposed tobeing able to collect such information only for the entire utility or a group ofthat utility's tariffs). Industrial tariffs, as contrasted to commercial tariffs, have notbeen included in CTAS. This is primarily because the MAISY database does notinclude industrial buildings.
J:\marginal\finalreport\FinalReport.wpd 7Electric utilities typically have many commercial tariffs. Southern California Edison, forinstance, has over 100 commercial tariffs. Most ofthese tariffs target a very smallnumber ofcustomers, or even individual customers, and thus havebeen ignored in CTAS. Tomaximize coverage in the limited time available, only the most-used two or three commercial tariffs for each utility havebeen includedin CTAS. Taken together, these few tariffs cover 84% of the customers in our utility sample and 38% ofthe commercial electricity sales bythose sampled utilities. Generally speaking, commercial customers are assigned to a particular tariffbased on their peak monthly and/or annual demand as measured in kilowatts over a short period oftime, e.g. 15 or 30 minutes. We use this demand "window" to determine which ofall the tariffsto apply to a particular building in the CTAS collection. For instance the GS-2 tariffcarried by Southern California Edison is applied to acustomer/building whose peak demand is between 20 and 500kW. This assignment bydemand window means that aparticular building will have bills calculated 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. We selected a subset ofbuildings from MAISY, the subsequent weighting ofwhich was intended to create a "prototypical" set ofbuildings that would exist inrelatively the same proportions throughout the country, and would thusbe 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 loadprofiles for an entire year. For each ofseven states (California, Washington, Colorado, Illinois, Texas, New York and Georgia) a subset ofbuildings was selected forinclusion in CTAS. The hourly loadprofiles for thesebuildings were then extracted fromMAISY and 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 atotal of 1323 buildings. The 1323 buildings were apportioned to the main types ofcommercial buildings used in CBECS as follows: CBECS % ofTotal Commercial Number of % Buildings Building type Buildings in US3 Buildings in Sample in Sample Education 7.6% 98 7.4% Food Sales 3.5% 63 4.8% Food Service 6.7% 91 6.9% Health Care 2.5% 35 2.6% Lodging 3.4% 42 3.2% Mercantile and Service 30.9% 406 30.7% Office 17.9% 231 17.5% Public Assembly 7.6% 98 7.4% Public Order &Safety 2.1% 28 2.1% Religious 5.0% 63 4.8% Warehouse & Storage 9.7% 126 9.5% Other/Vacant 3.0% 42 3.2% TOTAL 100.0% 1323 100.0% CTAS Calculations CTAS does its work inbatch 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: * Each customer/building is considered in turn. * For each building a selection is made ofapplicable tariffs, based on thatbuilding's peak annual demand. * For each tariffa calculation ofa monthly bill in $/month is performed.4

3Source: CBECS Table BC-39: Lighting Equipment, Number ofBuildings, 1995. 4See Appendix 2 for sample commercial bill calculations. J:\marginal\finalreport\FinalReport.wpd 8* A second calculation with the same month,tariffand building is performed with decremented monthly energy use andpeak demand to obtain a representative change in the building's bill (in order to calculate the marginal energy price). The assumed energy use decrement is 5%; the demand decrement is 80% 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 bythe difference between the two energy consumption levels, e.g., change in $/change in kWh. An average price in $/kWh is computed by dividingthe original, undecremented bill by the original energy use, e.g. bill S/total kWh. * The percentage difference between the average and marginal price is calculated by subtracting the average price from the marginal price and dividing bythe marginal price. * These calculations are repeated forthe 12 months ofa calendar year. * Annual values are obtained for average price, marginal price, and percent difference by summing each month's values over the year and dividing by 12. * The process is repeated for each applicable tariff for the particular customer/building. * The next building is considered and the complete process repeated, until all customer/buildings have been processed. CTAS Output The output from a CTAS run is written to a comma-separated-values, or "csv" file, with a record for each buildingtariffmatch. This file, containing the fields shownbelow, is loaded into a FoxPro table where additional processing occurs toproduce various charts and distributions. Field Name Field Description ACCTNO MAISY customer /building ID number STATE State from which sample customer was drawn ANPKKW Building annual peak demand in kWmeasured over 1hour TARFTYP Tariffgroup index defined by demand "window" ARFINDX Index used in program TARPTR Index used inprogram UTILCO Electric utility's name, e.g. Consolidated Edison TARFNAME Name ofparticular tariff, e.g. GS-1 NCUS Number ofcustomers billed under thattariff DELVDMWH Annual megawatt-hours delivered by utility tocustomers under this tariff BLDGWT Calculated weightofbuilding, derived in partfrom MAISY weight BLDGOPHR Weekly hours ofoperation ofbuilding ANAVGRAT Annual average electricity price ANMR1 Annual average electricity marginal price ANE 1 Annual percent difference between marginal and average electricity price AVGBILL Annual average bill, defined as average of 12 monthlybills AVGBILL1 Annual average decremented bill, defined as average of 12 monthly average bills AVGKWH Annual average energyuse, defined as average of 12 monthly energyusage levels AVGKWH1 Annual average decremented energy use, defined as average of 12 monthly decremented energyusage levels Conclusions CTAS was designed as an analysis toolto investigate marginal prices experienced bya range ofcustomers over a range ofcommercial tariffs throughout the country. It attempts to maximize coverage byrunning a prototypical set of customers/buildings against as broad a set oftariffs aspossible, given the time constraints ofthe project.

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

- The Role ofLighting Coincidence and Diversity." J:\marginal\finalreport\FinalReport.wpd 9C. Weighting Method This section describes construction ofthe weighted distribution ofcommercial electricity marginal prices. The sample ofcommercial buildings consisted of1323 buildings from seven states with each building supplying multiple observations to the final data set, one for each applicable tariff. Each observation in the final data set represents a unique combination ofa building and an applicable tariff,for a total of29,133 observations. The composition ofour sample posed a dual weighting problem: distributing weights across buildings and across tariffs. Our 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 allthe tariffs that were applied tothe building. A composite weighting factor for each building/tariffpair combines both of these weighting factors. These weights are allbased on energy, average monthly kWh usage atthe building level and annual MWh sales for eachtariff. Appendix 4, Weighting Method for Commercial Sample, showsthe details ofour approach tothis dual weighting problem. D. Results ofAnalysis Figure 1below displays the results ofthe commercial analysis by showing the distribution ofthe differences we derived between marginal electricity prices and average electricity prices. Electricity marginal prices range from 85% belowto 51% above the average price forthe same customer. At the consumption-weighted mean ofthe differences, electricity marginal prices are 5.2% lower than average prices.6 Inour LCC analysis ofcommercial sectorballasts, we used this distribution ofdifferences along with a distribution ofaverage commercial sector electricity prices (which we obtained from EIA data on over 3000 utilities serving commercial customers) togenerate a distribution of marginal electricity prices. Inour LCC analysis ofindustrial sector ballasts, we used this same distribution of differences along with adistribution ofaverage industrial sector electricity prices (which we obtained from EIA data on over 2000 utilities serving industrial customers) to generate adistribution ofmarginal electricity prices. As a result ofthe sensitivity analysis mentioned earlier (where we included additional municipal utility tariffs), we found a similar distribution ofweighted amounts bywhich average prices exceeded marginal prices, the mean of which was -5.6%.

6 The individual amounts by which average prices exceeded marginal prices had to be binned toprepare inputs for the Crystal Ball LCC analysis. The weighted mean ofthe binned distribution was -5.7%. J:\marginal\finalreport\FinalReport.wpd 10

### Figure 1. 

# Commercial Electricity Marginal Prices Differences from Annual Average

25%



### ~020%

.2 15%
L_



### 5%1.0%

e.): o C

D ^ ^ Pa P 1 ' N 

### o Avraoe



### % Difference from Average



## Marginal < Average Marginal > Average

Mean = -5.2% Median = -3.3% 

### Minimum = -85% Maximum = +51%

J:',rarginal\fialreport\FinalReport.wpdIV. ANALYSIS AND RESULTS
- RESIDENTIAL Method Used to Derive Residential Electric and Natural Gas Marginal Prices We used data from the Energy Information Administration's (EIA) 1993 Residential Energy Consumption Survey (RECS) to calculate marginal energy prices forresidential appliance owners. The main advantages ofusing RECS data are: 1)this survey of7041 U.S. households was designed byEIA 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 toderive those bills from a combination ofan appropriate tariffand eachbuilding's energy consumption. Thus, our method for deriving residential marginal energy prices was inherently simplerthan our previously described method for deriving commercial marginal energy prices. EIA had collected utility bills forup 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 apublic use version ofthese data available; it includes some error inoculation toprotect the identity ofindividual utility customers. We calculated marginal energy prices byperforming a separate regression analysis foreach 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 by the change in the energy consumption) equals the marginal energy price for that household. Electricity Forelectricity, we divided the billing data into two seasons, summer and "winter" (actually non-summer), where abill was defined to be a summer bill ifthe midpoint ofthe billing period fell between June 1and September 30. This division was done because our review ofutility tariffs (during the "Analytic" approach described earlier) had found that electric utilities oftenhad different rates in summer vs.non-summer, and thatmost utilitiesdefined summer to fall within thisrange ofdates. A linear regression relating electricity bills and usage was performed separately foreach season. The marginal electricity price for each season was estimated to be equal tothe slope ofthe regression line. See Figure 2below for a sample linear regression on the electricity bills ofone ofthe RECS households. For some households, there were either notenough data orthe data points were too close together or too scattered to determine a slope, particularly inthe summer season. After a household-by-household examination ofthe outliers, we also rejected slopes (marginal prices) which were less than or equal to zero, or greater than $0.22/kWh. It was possible to calculate slopes forthe 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 forthe "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 foreach ofthese households in the LCC analysis. For households for which it was not possibleto determine a seasonal rate forboth seasons, or forwhich these rates felloutside 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 pricecould 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 or prices were available for 6108 (5606 with seasonal rates, plus 502 with annual marginal prices only), or 87%. Most (919) ofthe households forwhich a marginal price was not available were those forwhich EIA didnot collectbilling data, with only 11 additional households beingdropped due to an indeterminate or unacceptable regression slope.

7We thank Robert Latta, Energy Information Administration, forfirst proposingthis approach. J:\marginal\finalreport\FinalReport.wpd 12Figure 2.

RECS 1993 Unear Regression on Electricity Bills, Household 4164

250
y=0.0988x +0.0862 R= 1.0000 A 200- 

## A /t
150
- A Summer Bills S E-~~~~~~~~~~~~~~~~~~~~* m Winter Bills m=-I 1I 

### /-

-- Summer Regression Winter Regression 100 ' y=0.0485x +33.64 Fe=2= 0.81 77 7Marginal Prices Summer: 9.88¢/kWh 50 ------------------------- Winter: 4.85¢/kWh

0 0 500 1000 1500 2000 2500 kWh

The r-squared values for the regressions ofRECS electricity bills were generally very high. 8 We examined both the summer regressions and the winter regressions and found that in both case the fits were excellent, 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 r-squared values shown below.)
Natural Gas For natural gas, the data were not divided into seasons because our review ofgasutility rates (during the "Analytic" approach described earlier) found that most (21 out of27)utilities surveyed did nothave 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 gasbills and usage was performed using the data for all ofthe billing cycles. Itwas possibleto determine a slope ofthe regression line for all 3153 households for which billing data were available. We rejected slopes as unreasonable ifthey were less than or equal to zero, or greater than $3.20/therm. This left 3139households, or 78% ofthe total of4033 households that use natural gas. Again, most (880) ofthe households for which a marginal price was not available werethose 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 (inthis case, the household's energy bill) that can be explained by the variation in the independent variable (in this case, the household's energy consumption). J:\marginal\finalreport\FinalReport.wpd 13Figure 3

RECS 1993 Linear Regression on Gas Bills,Household 1001
80

70

60 y= 0.5119x +4.9798 R =0.9994 50

40- mS~~~~~~~ I.^-~~~~~ 

### ~ ~Marginal Price 30| $0.51/therm 30

20 -
10 - 0

0 20 40 60 80 100 120 140 160 Therms

The r-squared values for the regressions ofRECS natural gas billswere also very high. On an annual basis, we found thatthe regression line fits were excellent, with r-squared values 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-squared Values for Electricity (Summer and Winter) and Gas Regressions
Percentile Electricity Natural Gas Summer Winter 99th 1 1 1 95th 1 1 1 90 h 1 0.999 0.999 75th 1 0.997 0.997 50th 0.999 0.987 0.993 25th 0.99 0.956 0.984 10

t" 0.947 0.874 0.965 5th 0.868 0.755 0.912 1"

t 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 couldbe calculated.

J:\marginal\finalreport\FinalReport.wpd 14Electricity and Natural Gas Weperformed a statistical analysis comparing the data sets for each fuel before and after dropping the households for which we were unable to calculate marginal energy prices in orderto determine ifany bias was being introduced by dropping those households. We 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 forthose households. When we looked atjustthose subsets ofRECS households that ownthe particular appliance ofinterest, we found that the differences between all households and the subset for which we were able to calculate a marginal price were smaller than the differences described above for the set ofall RECS households. We believe these results occur because households which own clothes washers, or water heaters, or central air conditioners, tend to have arelatively lowerproportion ofrenters in the first place, so fewer households ofthattype were dropped. For the key parameters, we found differences to be negligible (generally 1%or less) between the appliance data set and the subset with marginal prices. Table 3below shows the number and percent ofhouseholds used in the analysis foreach appliance subset after households with nomarginal pricewere 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 conditioning HHs with heat pumps 651 7.9 613 7.6 94% 96% HHs with electric 2559 33.4 2323 30.3 91% 91% water 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 tobe the same as the fuel oil average price, an assumption supported bythe research reported in Section V below. Results ofAnalysis Figures 4 and 5below display the results ofthe residential electricity price analysis by showing the relationship between marginal electricityprices and average electricity prices forthe residential sector in the summer and the winter. Figure 6below displays the results (on an annual basis) ofthe residential gas analysis. In our LCC analysis ofthree residential products (clothes washers, water heaters, and central air conditioners/heat pumps), we used the marginal electricity and gasprices that we 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 forthe same customer. At the consumption-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
J:\marginal\finalreport\FinalReport.wpd 15to 130% above the average price forthe same customer. Atthe consumption-weighted mean ofthe differences, electricity marginal prices are 5.3% lower than average electricity prices in the "winter." Marginal gas prices range from 98% below to 248% above the average price for the same customer. At the consumption-weighted mean ofthe differences, marginal gas prices are 7.8% lower than average gas prices. Figure4 below shows, foreach household in the summer, the difference between the marginal electricity price for that household and that household's annual average electricity price. 

### Figure 4. 

# Residential Summer Electricity MarginalPrices Differences from Annual Averages Marginal <Average Marginal >Average

3.0

0 

## zo

o 1.0

: 0.5

0.0

-o o co n 0~ 0- 0n 0 0 0 00 

### %

## Dffeene fromAverage

Mean = +2.5% Median = +1.4% Minimum = -98% Minumum = +175%

J:\marginal\finalreport\FinalReport.wpd 16Figure 5below 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 WinterElectricity Marginal Prices Differences from Annual Averages Marginal <Average Marginal >Average

3.0

2.5

- 

### 2o2.0



### o

_

0.5

0.0

_

- I I I_ 

### %Difference from Average

Mean =-5.3% Median = -3.8% Minimum =-100% Maximum = +130%

J:\niarginal\finalreport\FinalReport.wpd 17Figure 6 below shows, for eachhousehold, the difference between themarginal gasprice for that 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
0 2.0

i 1.0

0.5

0.0
o 1 a a ° ° ° 0 0 0-0 

### %Difference from Average

Mean =-7.8% Median = -9.0% Minimum =-90% Maximum = +248%

J:'marginal\finalreport\FinalReport.wpd 18V. RESIDENTIAL HEATING OILAND PROPANE As part ofthe marginal fuel price study forresidential utility customers in the US, we conducted brieftelephone interviews from January 25 to February 5, 1999 in orderto understand and characterize regional variations in pricing and distribution ofresidential heating oil and propane. We selected a sample ofdistributors from relevant internet sites, including that ofthe National Propane Gas Association (NPGA). Our goal was to interview fuel oil and propane distributorsthat represent a cross-section ofthe industry in terms ofcompany size and location. Our 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 forthese fuels. Heating Oil Based on telephone conversations with five East Coast distributors ofhome heating oil, we learned that (1)home heating oil is not taxed, (2) heating oil storage tanks are owned, not rented, and (3)there is no additional charge for delivery, exceptunder unusual circumstances. These are all factors likelyto 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 southto north. Discounts of$0.03-$0.10 per gallon are available, depending primarily on promptness of payment (e.g., within 5-10 days) or "auto-delivery" service, in which the customer allows the distributor to monitor usage and fill the tank atthe distributor's convenience. See Table 4below. Table 4. Heating Oil Prices Company Dixie Gas C&W Oil &Heating Powers Oil Co MacDuffie Petroleum Eastern Propane Location Verona VA Danbury CT Foxboro MA Hudson NH Rochester NH Territory 3 counties in VA Fairfield County metro Attleboro MA NH, MA southern Maine Price/gallon $0.85 $0.82 $0.75 $0.72 $0.66

Discount cash paid in 5days: $0.03 auto delivery: $0.03 paidin 7bus days: $0.10 paid in 10 days: $0.10 none
Because our interviews indicate that thebills paid by residential consumers for heating oil are essentially volume driven, with a single block rate, we interpreted the average prices inherent in those bills, as reported in RECS, 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, we learned that propane prices have much more seasonal and regional variability than home heating oil ornatural gas. This volatility is due in part to the factthat the price ofpropane is not regulated by state publicutility commissions. The threemajor factors that influence the retail price ofresidential propane are (1)wholesale price (which isprimarily 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 localtaxes, ifany, are an additional cost. Tax on residential propane sales varies by state, county, and municipality; among our respondents it varies fromnone (Virginia, Texas, Wisconsin) to 8.25% (California). Residential propane customers usually live in rural or remote areas where either natural gas service is notavailable or electricity service is expensive ornot reliable. End uses range from cooking and water heating to pool and space heating. To the extent that propane is required toheat homes, demand forpropane ishighest during the winter. Our survey respondents reported winter-to-summer price differentials that range from $0.13-$0.58 per gallon. By farthe 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 needto 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 inthe US is produced domestically, propane supplies are rarely disrupted. However, winter demand often exceeds local supplybecause oflogistical limitations oftransporting propane from where it is produced to where it is needed. Two large underground propane storage sites exist, one near Mont 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 atregional hubsalong each pipeline; J:\marginal\finalreport\FinalReport.wpd 19trucks 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 or port to a bulk storage facility) and delivering it to the residential customer is incorporated in the price pergallon that each customer is charged. According to one knowledgeable
source at theNPGA, the wholesale price ofpropane increases with the distance ofeach hub along the pipeline (or from a refinery), the distance from thehub to the distributor, and the distance from the distributor to the customer. However, ourrelatively limited information sources failed to confirm this relationship between propane retail price and distance from a source. In fact, by farthe lowest price pergallon ($0.65) was reported innorthern Wisconsin, while the highest price ($1.41) was reported in Galveston, near Mont Bellvue, Texas, and adjacent to several refineries. One possible explanation forthis apparent anomaly isthat 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 byrelatively low demand and little competition. In fact, our results suggest that local competition (which increases along with local demand) ismore 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 onresidential 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 thatparticular service territory. Volume discounts are usually available to those who use more than thetypical 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 thetypical amount ofpropane pay more than the regular price per gallon; distributors reported additional costs of$0.10 (Wisconsin) to $0.13 (Virginia). Propane distributors often offer discounts to new residential customers because (1)they want new customers and (2) it takes a year todetermine the usage ofeach new customer. Our contacts reported new customer discounts ofnone (Georgia) to $0.15 (Florida) to $0.31 (Texas).
Residential customers store propane in on-site tanks thatrange 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 fortemporary or mobile home owners, butprevents the customer from shopping around for the lowest fuel price among local distributors. Customers who purchase their owntanks do so for several reasons. Permanent, yearround homes in rural areas tend tohave a larger number ofpropane end uses and bigger tanks, which, becausethey
are unsightly and take up space, tend tobe installed underground. Distributors are reluctant to lease underground tanks because leak detection and maintenance is more difficult, so buried tanks are usuallyowned. Owning a storage tank also allows a customer to shop around for the best available propane price. Among our respondents, percentage oftanks leased from a distributor ranges fromnone (Maine, Virginia) to virtually all (Utah, California). Tank rental fees depend ontank size and/or annual usage; annual fees range from none (Wisconsin) to $100 (Utah), butgenerally they are $50-100.
Because our interviews with propane distributors indicate that the bills paid by residential consumers for propane are essentially volume driven, with a single blockrate, we interpreted the average prices inherent in those bills, as reported in RECS, as being equivalent to marginal prices for the purposes ofthe LCC analyses for water heaters and clothes washers.

J:\marginal\finalreport\FinalReport.wpd 20Table 5. Propane Prices Company Suburban Propane River Country Co-op (Cenex) Eastern Propane Location Gilroy CA Chippewa Falls WI Rochester NH Territory 3counties northwest WI southern ME Typical tank size 250, 500 gal 500 gal 500 gal Typical Usage 4people, 2000 SF home: 500 gal/yr S/gal, regular S1.45 winter, S130 summer $0.65 S0.98-1.01 Tax 7.25-8.25%, depends on locality none in WI unsure S/gal, discount new customer: $1.16 winter $0.05 offfor cash iftankis owned $0.05/gal ifpaid in 15 days new customer: $1.03 summer prepay price guarantee: $0.50/gal S/gal, addtl will-call delivery: $0.10/gal $0.10/gal iftank rented < 250 gal/yr: $0.10/gal more Tank rentals most are rented 40% rented no rentals

Rental fees $65/yr for 250 gal no rental fees $90/yr for 500 gal

_ Company Blossman Gas Corbin Gas Amerigas Location Pensacola FL AtlantaGA 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 S/gal, regular $1.14 $0.99 $0.86 Tax none -7%, depends on county 6% home heat S/gal, discount new customer: $0.99 no newcustomer discount volume: $0.02/gal for volume: if> 100 gal. $1.06; > 150 gal, $1.00; >500 gal, $0.95 S/gal, addtl Tank rentals 500 gal tanks are owned 60%rented virtually all

Rental fees 250 gal: $25-48/yr, depends on use $30-80/yr, depends on usage 500 gal: $80/yr 100gal: $36-72/yr, depends on use 1,000 gal: $105/yr Company All-StarGas United Propane Dixie Gas Location Galveston TX Salisbury MD Verona VA Territory Bolivar Island Maryland VA, some ofWV Typical tanksize 120 gal, 250 gal 500 gal 325, 500 gal Typical Usage S/gal, regular $1.41 $1.15 $1.05 Tax none depends on county; no state tax none in VA, some in WV $/gal, discount new customer: $1.10 $0.08/gal iftank is owned > 800 gal/yr: $1.02 prepay, >350 gal/yr: $0.83-0.99 2% offifpaid within 10 days S/gal, addtl < 400 gal/yr: $1.18
Tank rentals 50-60% rented 25% rented no rentals

Rentalfees all size tanks: $85/yr depends on usage (gallons/year) ave $12.5/qtr for 500 gal tank

J:\marginal\finalreport\FinalReport.wpd 21VI. TAXES
Overview The marginal energy prices that consumers experience can be affected bythe taxes that those consumers pay as part oftheir energybills. Those energy bills can include taxes in two fundamentally different ways. First, tax obligations that an energy utility itselfincurs (such as federal income taxes andproperty taxes) aremost often considered to be part ofthe utility's revenue requirements. Consequently, such taxes are embedded within approved utility rates; they are not itemized on utilitybills (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) tothe degree that these sorts oftaxes are included in theirutilities' base rates. But, because consumer energy savings (from using appliances that aremore energy efficient) would not directly and immediately reduce the level ofthese embedded taxes, the existence ofthese taxes does notaffect the calculation ofconsumers' marginal energy prices in a mannerthat is relevant tothis 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 fees) are often called "pass-through" taxes. They are taxes assessed by state and/or localjurisdictions. They are collected by utilities onbehalfofconsumers and then are passed on by those utilities to thejurisdictions that imposed them. Since these taxes arenot considered tobepart ofa utility's revenue requirements, they arenot 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 bythe existence ofthis type oftax; indeed, consumers' marginal energy prices are higher than they wouldbe ifthese taxes and fees did notexist. This type oftax is usually assessed as either a fixed percentage ofthe pre-tax energy bill oras a fixed dollar amount per energyunit. In some instances, pass-through taxes using bothtypes 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 energy bill aswell as the non-tax portion ofthat bill. For thatreason, 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 withinthe same utility service territory (seebelow). There are fourmain 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 utilityusers taxes Thesetaxes individually range in size frombeing non-existent in some areas tobeing as much as 10% in other areas9

. In addition,because these sorts oftaxes are set byvarious government agencies, they rarely appear onpublished utilitytariffs. They appear as line item charges on consumers' actual bills. Therefore, determining what these taxes (especially those that are locally imposed) really arefor particular customers ofparticular utilities would be avery difficult task. As EIA said in its 1994report "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 bylocal governments, the taxadjustment is only practicable atthe State level."°1

9The highest single tax rate for energy that our research uncovered was forone small city in California that imposes a utility userstax of9.9%. Otherwise, the highest utility users tax rate we know ofis 8%. State-level salestaxes and other state-level taxes can raise the effective tax to consumers beyond these levels, though. 10 EIA, "End UseTaxes: Current EIA Practices," August 1994, page 29. J:'marginal\finalreport\FinalReport.wpd 22That EIA report provides a state-by-state listof sales and use taxes on end-use energy consumption." It also indicates the states in which energy consumption (byenergy fuel type) is exempt from state sales and use taxes. In addition, this report includes an estimate ofthe magnitude ofthepossible effect ofincluding State sales taxes in energy prices. For electricity prices, EIA's report shows thaton average electricity prices for the residential sector rise only 0.9% with the inclusion of state 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 notexempt from sales tax. 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 offrom 3% to 8% for those states where electricity consumption isnot exempt from sales tax. (EIA's report does not include similar estimates ofthe effect ofincluding state sales taxes on the consumption of natural gas.) To get a sense for the magnitude and variation ofother state and local-level taxes, we consulted various sources. The most comprehensive survey ofother taxes on a state level was found in areport on state and local taxation by the Alliance to Save Energy (ASE).'2 This report reveals that 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 as California, cities can charge consumers a utility users tax. One city imposes a 9.9% utility users tax.' Several other cities set this tax as high as 7.5% or 8.0%.
Taxes or fees onunits ofenergy consumed range in size from 0.015 cents per kWh to 0.26 cents perkWh in the nine states (see Appendix 5, Table 8; taken fromASE'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 $0.00012/kWh on the electricity portion of their bills and $0.00078/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, we have learned that it is unlikely that anyone has ever conducted a survey ofstate and local taxes comprehensive enough to capture thetype of utilityusers taxes that can occur in California alone. 14 As aresult, we have addressed the effect oftaxes on marginal prices 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 tothe whole energy bill (including any fixed charges, such as customer charges), the consumer's marginal price is increased bythe percentage size ofthe tax itself. Thus, an 8% tax would increase a consumer's marginal price by 8%. Consumers taxed atthe 2% level would have amarginal price 2% higher than ifthey paid nopass-through taxes. Because taxes on units ofenergy (like theper 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 bothpay and save at a rate equal to the marginal price, other things being equal, reductions in energy consumption due tonew appliance energy efficiency standards will cause reductions in the size ofthe pass-through

" 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 ofnorthern California, the size of the 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 of tax on energy consumption by their residents. While higher utility users tax rates may exist in California or in other states, we found no evidence ofthem. 14 Andrew Hoerner, Center for a Sustainable Economy, personal communication, February 10, 1999. Additional
sources whose web sites were consulted for information on state and local energy taxes included The Federation of Tax Administrators, EPRI, and The Multistate Tax Commission. J:\marginal\finalreport\FinalReport.wpd 23taxes consumers pay, whether those taxes arepercentage taxes or per-unit taxes. The resulting reductions in tax payments are savings to the consumer directly associated with the more efficient appliances. Consumers facing 8% tax prices will save an additional 8%beyond what they would have saved ifthey did not have to pay such taxes in the first place. Consumers with 2% taxes will save an additional 2% oftheir (pre-tax) bill when theirenergy consumption is reduced by usingappliances that aremore energy-efficient. Treatment ofTaxes in This Research Project Thisproject used two main approaches to derive marginal prices. The RECS-based approach used actual consumer bills that already include all taxes (boththose embedded in utility rates and pass-through taxes). Therefore, the marginal prices derived from those bills "automatically" take intoaccount the effects ofchanges in taxes due to changes in energy consumption. Foruse in life-cycle cost or savings analyses, no further adjustments are needed to reflectthe 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 utilitytariffs do notalready include pass-through taxes, the marginal prices derived bythis approach are smaller than they otherwise would be tothe degree that pass-through taxes apply tothe sampled buildings. Therefore, when analyzing the results ofthis research effort, consideration should be given tothe applicability ofthe fourtypes ofpass-through taxes mentioned here (statelevel sales and use taxes, other state-level % taxes, $ perenergy unittaxes, and locally imposed utilityusers taxes) to derive marginal electricity prices forcommercial buildings that includetaxes. While adding the pass-through taxes applicable at the state level (from the EIA and ASEreport tables shownbelow) would involve anon-trivial and very time-consuming, butstill relatively straightforward exercise, there is no clearway to obtain the level ofthe locally determined utility users tax that might apply to the sampledbuildings. Therefore, because ofthe lack of comprehensive information on the magnitude ofthe locally imposed utility users taxes, one must be aware that the marginal prices socalculated 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 issuedby EIA, may be desired. Those published average prices are calculated by dividing utility revenues by sales. With regard towhether ornot these published average prices include taxes, the underlying question is which taxes are included in utilityrevenues. While we are confident that embedded taxes are included withinthe utility revenue requirements that formthe basis for utility revenues through the rate-setting process, we are not surewhether pass-through taxes are considered to be part ofutility revenues. In response to our request for clarity onthis issue, EIAreferred us toa page in EIA's "Electric Sales and Revenue 199T'report that contains the following statement: "Taxing authorities differ onwhether a specific tax is assessed tothe utility or to the consumer, a difference that in turn determines whether or notthe tax is included inthe electric utility's operating revenues."'5 Therefore, we conclude that one cannot know for surewhether pass-through taxes, especially those imposed by local government agencies, are included in utility revenues and thus included in average prices. While this lack ofcertainty about the inclusion ofpass-through taxes inpublished average pricesjeopardizes any comparison ofaverage and marginal prices, we believe that comparing RECS-based marginal prices with published average prices would be more sound, for example, than comparing marginal prices derived bythe 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 utilitytax rates ifthe revenues they obtain from consumers' energy billsdecline because consumers use less energy as a consequence ofimproved appliance energy-efficiency standards. This project did notattempt to analyze such positions. We do note that while similar arguments have been made during debates on electric industry restructuring occurring around the country, we know ofno widespread agreement ontheir validity.

15EIA, "Energy Sales and Revenue 1997," page 1. J:\marginal\tinalreport\FinalReport.wpd 24Conclusion We have not attempted to account for all appropriate taxes in the calculation ofmarginal energy prices in our analysis because, as we have explained, 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 paper indicates the degree oferror that may be present. Our 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 uponthe level ofstate and locally imposed taxes. In contrast, our 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:\marginal\fnalreport\FinalReport.wpd 25Appendix 1. Tariffs Used in the Commercial Analysis

Utility State Tariff Utility State Tariff Alabama Power AL LPEM NSP WI Cg-5 Alabama Power AL LPM Niagara Mohawk Power Corp WI SC-2 Demand Alabama Power AL LPS Niagara Mohawk Power Corp NY SC-2 Non-Demand Appalachian Power Co VA SGS Ohio Power Company OH GS3-11 Arizona Public Service AZ E30 PEPCO DC GS Arizona Public Service AZ E32 Pacific Gas and Electric CA Al Boston Edison MA G-l Pacific Gas and Electric CA A10 Boston Edison MA G-2 Pennsylvania Power & Light PA GS-I Central Power and Light TX 21 Pennsylvania Power & Light PA GS-3 Cleveland Electric Illum. Co OH Gen Comm Poudre Valley CO B-97 Cleveland Electric Illum. Co OH Lg Comm Savannah Electric and Power GA GS-7 Commonwealth Edison Co IL Rate 6 non-TOU Seattle City Light WA Rate 31 Detroit Edison MI D3 Seattle City Light WA Rate 34 Jersey Central Power & Light NJ GS SoCal Edison CA GS-1 Madison G&E WI Cg-l SoCal Edison CA GS-2 Madison G&E WI Cg-5 Union Electric Co. MO No. 2 NSP MN A10, D12, E13 Union Electric Co. MO No. 3 NSP MN A14, D16,E15 Virginia Power Company VA GS-I NSP WI Cg-2 Wisconsin Electric Power WI Cg 1 NSP WI Cg-2 unmetered

J:\marginal\finalreport\FinalReport.wpd 26Appendix 2. Examples ofCommercial Bill Calculations
Commercial tariffs generally fall intotwo categories, non-demand and demand. Demand is measured in kilowatts required by the customer over a relatively short time period, often 30 minutes. Meters are installed at the customer site to measure monthly and sometimes annual peak demand, and these are used to compute the demand component of the customer's bill. Typically demand tariffs are assigned to large customers, such as office buildings, hospitals, and malls. Non-demand tariffs are assigned to smaller customers, such as restaurants and small grocery stores. An example ofeach is illustrated below. Example 1:Non-demandtariff Utility: Alabama Power Tariff: Commercial, LPS Customer Base Charge: $8.65/mo. Summer Energy Charge: $0.06223/kWh Winter Energy Charge: $0.05843/kWh
Customer: Usage: 2547 kWh Month: January Baseline Bill: $157.47 = 8.65 + ($0.05843 *2547)
Bill with 5% Usage Decrement: (Figures below include rounding) Usage: 2420 kWh = 2547 kWh

- (0.05 *2547kWh) Bill: $150.03 = 8.65 + ($0.05843 * 2420) Monthly Average Price: $0.06183/kWh = $157.47 / 2547 Monthly Marginal Price: $0.05843/kWh = Bill Savings /Energy Savings
= ($157.47

- $150.03) / (2547

- 2420)
Example2: DemandTariff Utility: Southern California Edison Tariff: Commercial GS2 TariffMin/Max kW: 80

-2000 kW/hr (measured over 15 minutes) Customer Base Charge: $60.30/mo. Energy Block Cutoffs: Block 1: 300 kWh/kW (based on monthly peak kW) Energy Charge, Block 1: $0.07692/kWh Energy Charge, Block 2: $0.04391/kWh Demand Charge Component 1: $5.40 (Summer), $5.40 (Winter) Demand Charge Component 2: $7.75 (Summer), $0.00 (Winter)
Customer Usage: 150388 kWh Month: May Monthly Peak kW: 71 kW (over 15 minutes) Baseline Bill: $7750.35 = $60.30 + ((300*71)*$0.07692) + ((150388

- (300*71))*$0.04391) + (71*$5.40) Bill with 5% Energy and 4% Demand Decrement: $7376.71 = $60.30 + ((300*(71*0.96))*$0.07692) + (((150388*0.95)

- (300*(71*0.96)))*$0.04391) + ((71*0.96)*$5.40) Monthly Average Price: $0.05163/kWh = 7376.71 / (150388*0.95) Monthly Marginal Price: $0.04969/kWh = Bill Savings /Energy Savings = ($7750.35

- $7376.71) / (150388

- (150388 *0.95))
J:\marginal\finalreport\FinalReport.wpd 27Appendix 3. DemandDecrement 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 sector bills. Demand charges depend upon thepeak building demand (kW) each month. Foreach customer or building analyzed, the monthly energy use (kWh) and the monthly demand (kW) are based on actual annualkWh 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-foot T12 systems would save about 15% oflighting energy used by fluorescent lamps and since about 30-35% ofbuilding energy use is forlighting, about 5% ofbuilding energy use will be saved ifEEMballasts are converted to electronic ballasts. We assumed that the 5% reduction occurs every month. Itis alsonecessary to estimate the reduction in peak demand ifbuilding energy use is reduced by 5%on average. Some analysts prefer to discuss this issuein terms ofdiversity and coincidence. Coincidence is the lighting kW during the building peak divided bythe lighting peak kW. Inthis situation, we expect the coincidence factor to be close to 1.0 since, whatever the maximum number oflamps that operate during the lightingpeak, a very similar number of lamps can be expected tooperate duringthe building peak. EEI has suggested using 0.9 forthe default coincidence factor. 6 Coincidence factor (C) = lighting kW during building peak/lighting peakkW The diversity factor is a measure ofthe percent ofthe installed kW for lighting used duringthe lighting peak. Diversity is already accounted forbecause the actual building energy usage (which we have from the MAISY database) already excludes those lamps not in use during some parts ofthe day. We are instead concerned with lighting average kWbecause that is, in essence, reduced by 5%in the bill calculator when monthly energy use is reduced by 5%. We assume that the lighting hours donot change when more efficient ballasts are installed. Diversity (D) = lightingpeak kW/lighting kW installed Some analysts might multiply coincidence (C) times diversity (D); that would not yieldthe desired ratio oflighting kW during the building peak to lighting average kW. In relativeterms, we assumed demand reduction to be 80% ofenergy savings. For 5%energy savings, we assumed thatthe demand reduction would be 4% ofpeak kW. This meansthat if500 identical fixtures are operating on average, that only 400 (or 80%) are operating during the building peak demand period. We use the 80% value relative tothe average value because themonthly lightingenergy use canbe considered to be equal to some average kW times the monthly lighting hours and we are reducing monthly lighting energy use by a fixed percentage.

16 See comment #12 onthe Draft Report onPotential Impact ofPossible Energy Efficiency Levels for Fluorescent Lamp Ballasts,, received by DOE from Steve Rosenstock ofEEI onAugust 28, 1997. J:\marginal\finalreport\FinalReport.wpd 28Appendix 4: Weighting Method for Commercial Sample The composition ofthe commercial sampleposed a dual weighting problem: distributing weights across buildings and across the approximately 20 tariffs that were applied to eachbuilding. Our weighting approach solves these problems sequentially. The first step is to develop building weights, derived fromthe 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 ofAdiusted 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 torescale the individual buildings' MAISY weights to represent national usage, as described below. The first step was to account forthe fact thatwe had sampled a subset ofthe MAISY buildings for each state. For each ofthe seven states we computed a state factor:
State Facto = Sum of MAISY weights for allbuildings in the state State Factor = Sum of MAISY weights for sampled buildings inthe state We then computed the statewide energy usage represented by each sampledbuilding by multiplying the building's MAISY weight by its average annual energyusage and the state factor. Foreach sampled building i: Building i'sAdjusted State Energy Use = Annual kWhi*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 notdirectly represented by a state in our sample were assigned to a state representing the most similar adjacent division. The table below showsthe mapping of sampled states to Census Divisions. Assignment ofSampled States toCensus Divisions for Weighting Calculations Sampled State Census Division(s) Represented New York Middle Atlantic, NewEngland Illinois EastNorth Central, WestNorth Central Georgia South Atlantic, East SouthCentral 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 (seethe table below). The Region Factors are the ratio ofcommercial sector electricity sales in the division(s) corresponding with each ofthe sampled states tothe commercial sector electricity sales in each ofthose individual states. Region Factor Regional Retail Commercial Sales Region Factor = State Retail Commercial Sales Applying the Region Factors was the next stepin computing the Adjusted Building Weights foreach building: Building i'sAdjusted Region Energy Use = Region Factor * Building i's Adjusted State Energy Use

J:\marginal\finalreport\FinalReport.wpd 29The final step in calculating the Adjusted Building Weight takes eachbuilding as a share ofthe national total. For each sampled building i: Adjusted Building Weight. Adjusted Region Energy Usei Adjusted Building Weight, = Sum ofAdjusted Region Energy Usefor allsampledbuildings For each sampled building, the Adjusted BuildingWeight is thepercentage ofnational energy use represented bythat building.


### Derivation of State and Regional Weights Used inComputed TariffAverage Rates Sum of Bldg Wts Commercial kWh (millions) i Sampled StateRegion State Buildings State Factor State Region Factor California 45 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 T3.16t 4312 174341 404 NewYork 2873411. 3895 12526 3.22 Texas 46881 331176 7.06 6142 8911T 1.45 Washington i 18221 120664 6.62 1833 3306 1.80 Total a 160707 1771318

_ 74282i
Derivation ofWeights forBuilding/Tariff Pairs In the second phase ofthe weighting process, the weight for each building was apportioned across the applicable tariffs that hadbeen used to compute marginal prices for it. These building/tariff weights sum toone forthe entire sample and to the Adjusted Building Weight for each sampledbuilding. A different set ofweights was computed for each sampled building because a unique set oftariffs was applied toeach sampled building basedon its particular pattern ofenergy usage. The building-specific tariffweights were computed using the FERC data on MWh sales for each tariff.' 7 This process was implemented in two steps. The firststep was to calculate the relative weight ofeach ofthe tariffs thatwere applied to a building. Foreach building (denoted by i)and tariff(denoted byj):
MWh Sales for Tariffj Share ofTariffj for Building i = Sum ofMWh Sales for All Tariffs Applicable to Building i

'7The annual reports that IOUs submitto FERC, "Form 1

- Annual Report for Major Electric Utilities, Licensees, and Others", include information on the utilities' sales, number ofcustomers, and average rate bytariffor rate schedule on "page" 304 ofthat form ("Sales ofElectricityby Rate Schedules"). We obtained an electronic database ofthe FERC Form 1filings (that included page 304 data) from a commercial service known as Energy Central News.

:'arginal\finalreport\FinalReport.wpd 30The building-specific tariffweights were then combined with the adjusted building weights to obtain a unique weight for each building/tariffpair: Weight for Building ion Tariffj = Adjusted Building Weight i * Share ofTariffj for Building i
These arethe weights thatwere used to calculate weighted averages forthe 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:'narginal\finalreport\FinalReport.wpd 31Appendix 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 forthis table is: State Taxation/Revenue Offices and Research Institute ofAmerica, All States Tax Guide, March 29, 1993. State Electric NaturalGas AL Tax Rate Schedule: Tax Rate: 4% 0-40 kWh

- 4% (7/1/63) >40-60 kWh

- 3% >60 kWh

- 2% (7/1/63) AK No Sales Tax No Sales Tax AZ Tax Rate: 5% Same as Electric (6/1/83) AR Tax Rate: 4.5% Tax Rate: 4.5% (5/7/91) (5/7/91) Exemption(s): Exemption(s): Residents with income lessthan Qualified steel manufacturers. $12,000 on first 500kWh per month, manufacturing aluminum by electrolytic reduction, & qualified steelmanufacturers. CA Tax Rate: 6% Tax Rate: 6% (7/15/91) (7/15/91) Exemption(s): Exemption(s): Residential Energy Surcharge-- Residential, commercial, and industrial. excise tax (0.Imill per kWh). CO Tax Rate: 3% Tax Rate: 3% (8/1/84) (8/1/84) Exemption(s): Exemption(s): Residential and industrial. Residential heating, manufacturing processing, mining, irrigation, communications, and transportation. CT Tax Rate: 6% Tax Rate: 6% (10/1/91) (10/1/91) Exemption(s): Exemption(s): Residential

- first $150/month; Residential

-75% for powering industrial 75% for powering industrial manufacturing plant or directly used in manufacturing plant or directly used farming, fabrication or manufacturing. in farming, fabrication or manufacturing. Includes bottled propane. DE No Sales Tax No Sales Tax DC Tax Rate: 6% Same as Electric (6/1/80) Exemption(s): Residential and used in manufacturing.

J:I .\arginal.finalreport\FinalReport.wpd 32FL Tax Rate: 6% Tax Rate: 6% (2/1/88) (2/1/88) Exemption(s): Exemption(s): Residential and by qualified businesses Residential, used in manufacturing ata fixed In enterprise zones. location inthe State, boiler fuel, and space flights. GA Tax Rate: 4% Tax Rate: 4% (4/1/89) (4/1/89) Exemption(s): Heating structures forpoultry raising. HI Tax Rate: 4% Same as Electric (7/1/91) Exemption(s): Utility services and fuels. ID Tax Rate: 4% Tax Rate: 4% (4/1/86) (4/1/86) Exemption(s): Exemption(s): Residential, commercial and industrial. Residential, commercial, and industrial fuels. IL Subjectto utilitytax: Subject to Gas Tax underGas Tax Revenue Act. Rate: 32 one-hundredth 0/kWh, or 5%of Rate: 2.4¢/therm or 5% ofgross receipts, gross receipts, whichever is less. whichever is less. IN Tax Rate: 5% Same as Electric (1/1/83) Exemption(s): Manufacturing forproduction, farmers, and restaurants. Public Utilities: gross revenue tax of 0.15% IA Tax Rate: 5% Tax Rate: 5% (7/1/92) (7/1/92) Exemption(s): Exemption(s): Manufacturing processing. Manufacturing processing, fuels for agriculture, creameries, dairies and ice cream factories. KS Tax Rate: 4.25% Tax Rate: 4.25% (7/1/89) (7/1/89) Exemption(s): Exemption(s): Residential, farm use (no irrigation), Residential, farm use, manufacturing, and mining. manufacturing, and mining. KY Tax Rate: 6% Tax Rate: 6% (7/1/90) (4/9/90) Exemption(s): Exemption(s): Residential sector. Residential sector. LA TaxRate: 4% Tax Rate: 4% (7/1/84) (7/1/84) Exemption(s): Exemption(s): Chloralkali Manufacturers. Agriculture, and fuel for boilers.

J:\marginal\fmalreport\FinalReport.wpd 33ME Tax Rate: 6% Tax Rate: 6% (8/1/91) (8/1/91) Rate is 1%on 95% ofelectricity used Rate is 1%on 95% ofgas used in in manufacturing facility. manufacturing facility. Exemption(s): Exemption(s): 0-750 kWh in residential sector. Residential. MD Tax Rate: 5% Tax Rate: 5% (6/1/77) (6/1/77) Exemption(s): Exemption(s): Residential, fourunits or fewer. Residential, four units or fewer, and manufacturing. MA Tax Rate: 5% Tax Rate: 5% (11/12/75) (11/12/75) Exemption(s): Exemption(s): Residential and manufacturing when Residential and Industrial. used for production and/or the company has fewer than six employees. MI Tax Rate: 4% Same as Electric (1/1/61) Exemption(s): Agriculture MN Tax Rate: 6% Same as Electric (6/18/91) Exempt from sales tax. MS Tax Rate: 6% Same as Electric (12/1/83) Manufacturers and some agricultural activities pay at 1.5%. Exemption(s): Residential. MO 5.725% Sales Tax 5.725% Sales Tax (7/1/92) (7/1/92) Exemption(s): Exemption(s): Residential, drying farm crops. Residential and drying farm crops. Manufacturers are exempt from paying the tax forthe amountthat exceeds 10% ofthe cost ofproduction. MT No Taxes No Taxes NE Tax Rate: 5% Same as Electric (7/10/90) Exempt if>50% is used for processing, manufacturing, refining, farming and irrigation, or for hospital use. NV Tax Rate: 6.5% Tax Rate: 6.5% (10/1/91) (10/1/91) Exempt ifdelivered through lines. Exempt ifdelivered to customers through mains, lines, orpipes and residential heating. J:\marginal\finalreport\FinalReport.wpd 34NH No Sales Tax No Sales Tax NJ Tax Rate: 7% Tax Rate: 7% (7/1/90) (7/1/90) Electric utility sales are exempt. Natural gas utilities sales are exempt. NM Tax Rate: 5% Tax Rate: 5% (7/9/91) (7/9/91) Exemption(s): Warehouse and offices. NY Tax Rate: 4% Tax Rate: 4% (7/1/71) (7/1/71) Exemption(s): Exemption(s): Residential, agricultural use, and Residential, agricultural use, and fuel for manufacturing assembling, mining, and manufacturing assembling, mining, and research (direct-predominant use). research (direct and predominant use). NC Tax Rate: 4% Tax Rate: 4% (7/16/91) (7/16/91) Manufacturers pay 1% foruse in Farmers, manufacturers, and commercial production. laundries and drycleaners pay 1%. ND TaxRate: 5% Tax Rate: 5% (12/6/89) (12/6/89) Electricity is exempt. OH Tax Rate: 5% Tax Rate: 5% (1/24/81) (1/24/81) Electricity and utility services are exempt. Natural gas is exempt from sales tax. OK Tax Rate: 4.5% 4.5% Sales Tax (5/1/90) (5/1/90) Exemption(s): Exemption(s): Residential and manufacturing. Residential and manufacturing. OR No Sales Tax No Sales Tax PA Tax Rate: 6% Tax Rate: 6% (11/1/68) (11/5/68) Exemption(s): Exemption(s): Residential. Residential.
RI Tax Rate: 7% Tax Rate: 7% (7/1/90) (7/1/90) Exemption(s): Exemption(s): Residential. Residential, and fuel forplants and flowers. SC Tax Rate: 5% Tax Rate: 5% (7/1/84) (7/1/84) Exemption(s): Exemption(s): Residential, cropirrigation, and radio Residential, manufacturing, agricultural curing, broadcasting. poultry production, and motorfuel, except aviation fuels.

J :\marginal\finalreport\FinalReport.wpd 35SD Tax Rate: 4% Same asElectric (3/1/88) Exemption(s): Farm use. TN Tax Rate: 6% Same asElectric (4/1/92) Rate is 1.5% formanufacturers, and for farmers for use in foodproduction, and fornurserymen for use in growing horticultural products. Exemption(s): Residential TX 6.25% Sales Tax Same asElectric (7/1/90) Exemption(s): Non-commercial ornon-business use. UT Tax Rate: 5% Tax Rate: 5% (1/1/90) (1/1/90) Rate is 2% forResidential. Rate is 2 percent for Residential. Exemption(s): Exemption(s): All other businesses are exempt from Greenhouses (wholesale), irrigation, and orchards. sales tax except commercial business. VT Tax Rate: 5% Tax Rate: 5% (6/1/91) (6/1/91) Exemption(s): Exemption(s): Taxpayer-generated and 60% used in Residential and for agricultural use. Motor fuels own business. otherwise taxed exceptjet fuel. VA Tax Rate: 3.5% Tax Rate: 3.5% (1/1/87) (1/1/87) Exemption(s): Exemption(s): Electricity through lines formanufacturing Residential and manufacturing for direct for directprocessing, making feed for sale processing, making feed for sale or resale, orresale, commercial leasing orrenting of commercial leasing orrenting oflaundered laundered textile products sold to textile products sold to watermen for watermen for extracting fish,bivalves, or extracting fish, bivalves, or crustaceans crustaceans fromwater. fromwater and for drying or curing crops. WA Tax Rate: 6.5% Same as Electric (1/1/87) WV Tax Rate: 6% Tax Rate 6% (6/1/88) (6/1/88) Electricity sales are exempt. Natural gas sales are exempt. WI Tax Rate: 5% Tax Rate: 5% (5/1/88) (5/1/88) Exemption(s): Exemption(s): Residential and farming: November Residential and farming: November through April; through April. commercial fishing, interstate and foreign commerce, and railroads.

J:\marginal\finalreport\FinalReport.wpd 36WY Tax Rate: 3% Same as Electric (7/1/67) Exemption(s): Manufacturing process, and agricultural use.

J: unarginal\fmalreport\FinalReport.wpd 37Table 7. Gas and Electric Utility Regulatorv Assessments by States'8
Tax Rate Tax Rate State (% ofRetail Price) State (% ofRetail Price) AK rate unavailable MN rate unavailable AL 0.15% MS 0.16% AZ 0.20% MT 0.28% AR 0.40% NV 0.43% CO 0.20% NJ 0.25% DE 0.20% NM 0.50% FL 0.13% NY 0.33% GA Fee based on property value OR 0.25% HA 0.13% PA 0.30% ID 0.30% SC rate unavailable IL 0.19% SD 0.15% IN 0.15% TN 0.08% IA 0.10% TX 0.17% KS 0.20% UT 0.30% KY 0.20% VT 0.50% LA 0.04% VA 0.20% ME 0.25% WA 0.20% MD 0.17% WV 0.29% MA 0.25% WY 0.30% MI 0.02%

Table 8. kWh Taxes on Electricity' 9
State Tax Rate AL 0.04 cents/kWh hydroelectricity AK 0.025-0.05 cents/kWh electric co-ops CA 0.02 cents/kWh electricity consumed ID 0.05 cents/kWh hydroelectricity MD 0.015 cents/kWh electricity consumed MT 0.02 cents/kWh electricity produced ND 0.025 cents/kWh electricity generated from coal SC 0.05 cents/kWh electricity produced WV 0.26 cents/kWh electricity produced

18ASE Report, Table 5 on page 8. 1 9 ASE Report, Table 6 on page 8.

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