APPENDIX 8-E. TECHNICAL ASPECTS OF THE TARIFF-BASED APPROACH
FOR ROOM AIR CONDITIONER ENERGY PRICES

TABLE OF CONTENTS

8-E.1
8-E.2 REVIEW OF DATASETS AND CALCULATION METHODS .................................1
THE TARIFF ANALYSIS PROJECT DATABASE AND CALCULATION
TOOLS.......................................................................................................................... .2

LIST OF TABLES

Table 8-E.1.1 List of Data Sources That Could Be Used To Estimate Electricity
Prices..................................................................................................................1 Table 8-E.1.2 Residential average pr ices estimated from EIA Form 861 ................................2

LIST OF FIGURES

Figure 8-E.2.1 Comparison of average prices for summer (July) calculated using TAP
and EEI data.......................................................................................................4 Figure 8-E.2.2 Comparison of average prices for winter (January) calculated using
TAP and EEI data ..............................................................................................4

8-E-iAPPENDIX 8-E. TECHNICAL ASPECTS OF THE TARIFF-BASED APPROACH

FOR ROOM AIR CONDITIONER ENERGY PRICES

8-E.1 REVIEW OF DATASETS AND CALCULATION METHODS

The methodology used to calcula te electricity prices is depe ndent 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 table includes information on the reso lution 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 Sources That Could Be Used To Estimate Electricity Prices

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 re sidential 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 to 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 esp ecially 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 ca n 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 have changed significantly in the last 5–10 years. The EEI dataset covers only inve stor-owned utilities and does not include publicly-owned companies. In so me 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-E-1Table 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. Thetable 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, an d 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 TARIFF ANALYSIS PROJECT DATABASE AND CALCULATION
TOOLS

The core of the current Tariff Analysis Project (TAP) database is a statistically representative sample of approximately 90 electric utilities 2 that reflects the diversity of the geographic regions, 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 Census Divisions 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 Divisi on level. To facilitate the use of these data in the tariff-based electricity price analysis, the TAP regions are de fined as sub-sectors of Census Divisions. 2) Weather variability plays an important role in determining load characteristics, which in turn can influence prices, as utilities will try to encourage or discourage certain behaviors to make their system operations more efficient. Therefore, the Census Divisions ar e 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 separate

8-E-2regions. In practice this means that California, New York, Texas and Florida are defined as separate regions. Within each region, the numbe r of utilities chosen was determined by the relative population living in that area, and the pr oportion of customers served by privatelyversus 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, consisting of nine Census Divisions plus four large States as is used
in RECS.

Electricity tariffs are typically classed as residential or non-residential. Non-residential may be further subdivided into general service an d special-use tariffs such as street-lighting, agricultural, etc. For the general service category, some utilities explicitly distinguish between commercial and industrial, but mo st do not. There are usually seve ral general-service tariffs for different customer sizes. Size is defined by the 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 ma rket. Given the importance of residential tariffs for the current analysis, these data were updat ed 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 custom er base, it was not cons idered 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 normalized 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 format, it is relatively straightforward to develop bill calculation tools. These tools use monthly electrici ty use data by TOU period if necessary for input, which consists of consumption and demand. The bill calcula tion 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 pri ce information described above, particularly the EEI data. A detailed discussion is presented here of the validation for the residential data; the
commercial data has been validated in a simila r 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 summer and winter separately. It is straightforward
to use TAP to calculate bills at each of thes e consumption levels, for the summer and winter seasonal rates as appropriate. The corresponding aver age 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 State. The figures show the median average price calculated from the EEI data, and the range (high, low and median) of average 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-E-3**Summer Avg Prices for 750kwh**

0 5 10 15 20 25 30

A

K A

L A

R A

Z CA C O CT DC D E FL GA HI IA ID I L I

N KS K Y L

A M

A M

D M

E MI MN MO M S M

T NC ND NE N H NJ N M NV N Y O

H O

K O

R P

A RI S C SD T N T

X UT V A V

T W

A WI WV WY 0 5 10 15 20 25 30 E E I 20 08 S S u m H i S u m L o Su m M e d

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

**Winter Avg Prices for 750kwh**

0 5 10 15 20 25 30

AK A L AR A Z CA C O CT D C D

E FL G A HI I A I

D I

L IN KS K Y LA M A M

D ME MI MN MO MS MT NC N D NE N H NJ N M N

V NY OH O K OR P A RI S C SD T N T

X UT V A VT W A WI WV W Y 0 5 10 15 20 25 30 E E I 20 08 W W i n H i W i n L o Wi n M e d

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

8-E-4REFERENCES

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

< http://www.eia.doe.gov/cneaf/e lectricity/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/applia nce_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/applia nce_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.

8-E-5