8

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### APPENDIX 8

- E. TECHNICAL ASPECTS OF THE TARIFF

- BASED APPROACH FOR

ROOM AIR CONDITIONER ENERGY PRICES

TABLE OF CONTENTS

8- E.1 REVIEW OF DATASETS A ND CALCULATION METHODS

......................... 8-E-1

8- E.2 THE TARIFF ANALYSIS PROJECT DATABASE AND CALCULATION

TOOLS

................................................................................................................... 8-E-2

LIST OF TABLES

Table 8 -E.1.1 List of Data Sources That Could Be Used To Estimate Electricity

Prices .......................................................................................................... 8-E-1

Table 8 -E.1.2 Residential average prices estimated from EIA Form 861 ........................ 8-E-2

LIST OF FIGURES

Figure 8 -E.2.1 Comparison of average prices for summer (July) calculated using TAP

and EEI data ............................................................................................... 8-E-4 Figure 8 -E.2.2 Comparison of average prices for winter (January) calculated using

TAP and EEI data ...................................................................................... 8-E-48

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### APPENDIX 8

- E. TECHNICAL ASPECTS OF THE TARIFF

- BASED APPROACH

FOR ROOM AIR CONDITIONER ENERGY PRICES

8- E.1 REVIEW OF DATASETS A ND CALCULATION METHO DS

The methodology used to calculate electricity prices is dependent on the data set. Table

8-E.1. 1 lists the data available for this analysis , and the type of methodology that goes with the

data. The t able includes information on the resolution of the data both geographically and in terms of the ability to distinguish customer type, and the degree of complexity and accuracy of

the calculation methods.

Table 8 -E.1.1 List of Data S ources T hat C ould B e U sed To E stimate E lectricity P rices

Method Data Source/Year Resolution Complexity Accuracy Customer Type Geographic

Average price EIA 861 2006 a Low High Low Low

Price by consumption block EEI 2007 Low Medium Low Low

Average or marginal price by household/building RECS/CBECS 2001 High Low Low Low

Tariff

- based prices using existing data (2004) LBNL

- TAP 2004 High Medium Low Medium

Tariff

- based prices using updated data (2008) LBNL

- TAP 2008 High Medium Medium High



### If EIA 861 or EEI data are used, only average prices can be estimated. Residential average prices are likely to be slightly above residential marginal prices due to the relatively large fixed charges on residential bills. However, many areas use ascending block rates, in which

case the marginal price may be higher than the average price. Commercial average prices are typically lower than commercial marginal prices. Due to the complexity and diversity of commercial rate structures, it can be very difficult t o relate an average electricity price to the actual bill savings that may occur under an efficiency scenario in which both the electricity consumption and demand are reduced. This is especially true for peak-coincident end uses. The only method that allows the direct estimation of marginal prices is the tariff

- based approach.

Both the RECS and CBECS datasets provide monthly billing data (consumption and expenditures, and demand for commercial buildings) for some survey years (1992 and 1995 for CBECS, 1997 and 2001 for RECS). While these data can be used to estimate prices in principle, the age of the expenditure data and the size of recent price increases make this approach problematic. In particular, in many areas tariff structures (not just rates) may ha ve changed significantly in the last 5 –10 years. The EEI dataset covers only investor- owned utilities and does not include publicly-owned companies. In some regions, a significant fraction of consumers are served by publicly- owned utilities; EIA data show that on average residential prices tend to be lower, and commercial prices somewhat higher, for publicly-owned versus investor-owned utilities. It is therefore important to include these utilities in the analysis.

a EIA Form 861 data for 2007 became available in May 2009, too late for use in this analysis.8

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### Table 8-E.1. 2 illustrates some of these points using EIA form 861 data 1 

### for the residential sector. In each of the years 2004, 2005 and 2006, the consumer- weighted average revenues divided by sales is calculated over all utilities in a given region, for different market sectors. These numbers are an estimate of the average price paid by a customer in the given sector. The table shows the average price using this estimation for 2006 (in nominal dollars) for publiclyand privately- owned utilities separately. The growth rate is calculated from the price ratios between year 2004 and 2005, 2005 and 2006, and the compound increase between 2004 and 2006. The table illustrates the variability in growth rates by region. Commercial prices show

similar characteristics.

Table 8 -E.1.2 Residential average prices estimated from EIA Form 861

Residential Average Price ¢/kWh (2006) Increase

2004 to

2006

Code Area All Private Public All

1 New England 16.0 16.3 13.1 33.3%

2 Mid

- Atlantic 14.1 13.9 16.6 12.4%

3 ENC 9.3 9.3 9.1 10.2%

4 WNC 8.3 8.3 8.3 7.6%

5 South Atlantic 9.8 9.7 10.0 17.2%

6 ESC 8.2 8.2 8.1 14.3%

7 WSC 11.5 12.4 9.6 26.9%

8 Mountain 9.0 9.1 8.9 8.4%

9 Pacific 12.6 13.8 9.2 15.3%

HI & AK 20.6 22.8 17.2 25.5%



### 8- E.2 THE T ARIFF ANALYSIS PROJE CT DATABASE AND CALCULA TION

TOOLS

The core of the current Tariff Analysis Project ( TAP ) database is a statistically representative sample of approximately 90 electric utilities 2 

### that reflect s the diversity of the geographic region s, ownership types, and company sizes in the industry. The finest level of regional variation is currently a set of seventeen regions, constructed from the intersection of

Ce nsus D ivisions with major climate zones

. 3 4 

### The region definitions resolve three sources of variability: 1) For datasets such as CBECS and RECS, general demographic variability is represented at the Census D ivision level. To facilitate the use of these data in th e tariff

- based electricity price analysis, the TAP regions are defined as sub

- sectors of C ensus Divisions. 2) Weather variability plays an important role in determining load characteristics, which in turn can influence prices, as utilities will try to enco urage or discourage certain behaviors to make their system operations more efficient. Therefore, the Census Divisions are subdivided according to how they overlap with the nine climate regions defined by the National Climatic Data Center. (3) Since the onset of deregulation, different areas of the country can have very different electricity market structures, which can affect prices and billing practices. To capture this variability, States or regions with independent markets have been broken down into separate8

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### regions. In practice this means that California, New York, Texas and Florida are defined as separate regions. Within each region, the number of utilities chosen was determined by the relative population living in that area, and the proportion of customers served by privately

v ersus publicly- owned companies. With the current database, variability in prices is wellrepresented at the level of these seventeen regions. For the current analysis, regional variation is actually somewhat coarser, co nsisting of nine Census Divisions plus four large S tates as is used

in RECS.

Electricity tariffs are typically classed as residential or non

- residential. Non

- residential may be further subdivided into general service and special

- use tariffs such as stree t-lighting, agricultural, etc. For the general service category, some utilities explicitly distinguish between commercial and industrial, but most do not. There are usually several general- service tariffs for

different customer sizes . S ize is defined by th e value of the annual peak load, and in each size class there is generally a default tariff. TAP currently contains the default residential and commercial tariffs for each customer size and market. Given the importance of residential tariffs

for the curren t analysis, these data were updated in 2008. TAP also includes any optional timeof- use (TOU) residential tariffs that are offered by the utilities in the sample. On the commercial side, TOU tariffs are always included when they are the default tariff. Tariffs have been collected for both primary and secondary delivery voltage. Since commercial users of residential

- style equipment are a small portion of the total customer base, it was not considered necessary to update the commercial data for this analysis. Therefore , the commercial tariffs collected in 2004 were used for analysis. The resulting prices are rescaled to reflect recent price increases.

Tariff data are collected using a series of spreadsheet forms which automatically populate

the set of norm alized tables that comprise the TAP database. Most of the tariff features (various types of block rates, TOU rates, seasonal rates, demand charges, etc.) are captured without approximation (see 5



### , 3 

### for a complete discussion of the level of detail in the data). Given a

standardized data fo rmat, it is relatively straightforward to develop bill calculation tools. These

tools use monthly electricity use data by TOU period if necessary for input, which consists of consumption and demand. The bill calculation then produces the monthly bill.

To validate the tariff data and bill calculation tools, a series of diagnostic tests were performed which make use of the electricity price information described above, particularly the

EEI data. A d etailed discussion is presented here of the validation for the residential data; the commercial data has been validated in a similar manner through previous work. EEI provides a “typical” bill at three monthly consumption levels (500 kWh, 750 kWh and 1,000 kWh for residential). These are reported by utility for su mmer and winter separately. It is straightforward to use TAP to calculate bills at each of these consumption levels, for the summer and winter seasonal rates as appropriate. The corresponding average prices can then be directly compared to

the EEI data. The results for bills calculated at 750 kWh consumption are presented graphically in Figures 8- E. 2. 1 and 8- E. 2.2, and are organized by S tate. The figures show the median average price calculated from the EEI data, and the range (high, low and median) of aver age prices for all the tariffs in TAP. Note the TAP data include publicly- owned utilities, making the data more

variable than the EEI dataset.8

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### Figure 8 -E.2.1 Comparison of average prices for summer (July) calculated using TAP and EEI data

Figure 8 -E.2.2 Comparison of average prices for winter (January) calculated using TAP and EEI data Summer Avg Prices for 750kwh

0 5 10 15 20 25 30

AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY 0 5 10 15 20 25 30 EEI 2008S SumHi SumLo SumMed

Winter Avg Prices for 750kwh

0 5 10 15 20 25 30

AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY 0 5 10 15 20 25 30 EEI 2008W WinHi WinLo WinMed8

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### REFERENCES

1. U.S. Department of Energy - Energy Information Administration, Form 861, 2006.

< http:// www.eia.doe.gov/cneaf/electricity/page/eia861.html >

2. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Commercial Unitary Air Conditioners and Heat Pumps Technical Support Document Appendix M: Sample Utilities, July 2004. Washington, D.C.

< http://www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/cuac_tsd_ app_m.pdf >

3. Coughlin, K., C. Bolduc, R. Van Buskirk, G. Rosenquist & J. E. McMahon, Tariff -based Analysis of Commercial Building Electricity Prices , 2008. Lawrence Berkeley National Laboratory. Berkeley, CA.

4. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Commercial Unitary Air Conditioners and Heat Pumps Technical Support Document, July, 2004. Washington, DC.

< http://www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/cuac_tsd_ chp_8.pdf >

5. Coughlin, K., Richard White, Chris Bolduc, Diane Fisher & Greg Rosenquist, The Tariff Analysis Project: A database and analysis platform for electricity tariffs , 2006. Lawrence Berkeley National Laboratory. Berkeley, CA. Report No. LBNL-55680.