8-E-i

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 AND 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-4
8-E-1

APPENDIX 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 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 table 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 Sources That Could Be Used To Estimate Electricity Prices

Method Data Source/Year

Resolution

Customer Type Geographic Complexity Accuracy

Average price EIA 861 2006a 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 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 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 have 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-E-2

Table 8-E.1.2 illustrates some of these points using EIA form 861 data1 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 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 utilities2 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 Division level. To facilitate the use of these data in the tariff-based 

electricity price analysis, the TAP regions are defined 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 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 separate 
8-E-3

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 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 and special-use tariffs such as street-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. 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 market. Given the importance of residential tariffs 

for the current 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 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 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 detailed 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 summer 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 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-4

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

AKALARAZ

CA

CO

CT

DC

DE

FL

GA

HI

IA

ID

IL

IN

KSKYLA

MA

MD

ME

MI

MN

MO

MS

MT

NCND

NE

NH

NJ

NM

NVNY

OHOKORPA

RI

SC

SD

TNTXUT

VA

VT

WA

WI

WVWY 0

5

10

15

20

25

30

EEI 2008S SumHi SumLo SumMed

Winter Avg Prices for 750kwh

0

5

10

15

20

25

30

AKALARAZCACO

CT

DC

DE

FL

GA

HI

IA

ID

IL

IN

KSKYLA

MA

MDME

MI

MN

MO

MSMTNCND

NE

NH

NJ

NMNVNYOHOKORPA

RI

SCSD

TN

TXUT

VA

VT

WA

WI

WVWY 0

5

10

15

20

25

30

EEI 2008W WinHi WinLo WinMed
8-E-5

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.