8

- F

- i APPENDIX 8

- F. TECHNICAL ASPECTS OF THE TARIFF

- BASED APPROAC H

TABLE OF CO NTENTS

8

- F.1 REVIEW OF DATASETS A ND CALCULATION METHO DS

.......................... 8

- F

- 1

8

- F.2 THE TAP DATABASE AND CALCULATION TOOLS

................................ ..... 8

- F

- 2

LIST OF TABLES

Table 8

- F.1.1 List of data sources that could be used to estimate electricity prices

......... 8

- F

- 1

Table 8

- F.1.2 Residential average prices estimated from EIA Form 861

......................... 8

- F

- 2

LIST OF FIGURE S

Figure 8

- F.2.1 Comparison of average prices for summer (July) calculated using TAP

and EEI data

................................ ................................ ................................ 8

- F

- 4 Figure 8

- F.2.2 Comparison of average prices for winter (January) calculated using

TAP and EEI data

................................ ................................ ....................... 8

- F

- 48

- F

- 1 APPENDIX 8

- F. TECHNICAL ASPECTS OF THE TARIFF

- BASED APPROACH

8

- F.1 REVIEW OF DATASETS A ND CALCULATION METHO DS

The methodology used to calculate electricity prices is d ep endent on the data set. Table

8

- F .1.1 lists the data that was available as this analysis was being completed, and the type of methodology that goes with the data. The table includes information on the resolution of the data both geographicall y and in terms of the ability to distinguish customer type, and the degree of complexity and accuracy of the calculation methods.

Table 8

- F.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 b . 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, and in these cases 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 i n 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 pro vide 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 in principle be used to estimate prices, 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

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

b The average price is defined as the ratio of expenditure to energy use.8

- F

- 2 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 vs. investor

- owned utilities. It is therefore important to include these utilities in the analysis.

Table 8

- F .1.2 illustrates some of these points using EIA form 861 data for the residential

sector. 1 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 t he given sector. The table shows the average price estimated in this way for 2006 (in nominal dollars) for publicly

and privately

- owned utilities separately. The growth rate is calculated from the price ratios between year 2004 and 2005, 2005 and 2006, a nd the compound increase between 2004 and 2006. The table illustrates the variability in growth rates by region. Commercial prices show

similar characteristics.

Table 8

- F.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

- F.2 THE TAP DATABASE AND CALCULATION TOOLS

The core of the current TAP database is a statistically representative sample of approximately 90 electric utilities that reflect industry diversity in geographic region, ownership type, and company size. 2 The finest level of regional variation is currently a set of seventeen regions, constructed from the intersection of census divisions with major climate zon es. 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 pri ce 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 b ehaviors 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, differen t areas of the country can have very different electricity market structures, which can affect prices and billing8

- F

- 3 practices. To capture this variability, states or regions with independent markets have been broken out into separate regions. In practice thi s 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

- vs. publicly

owned companies. With the current database, variability in prices is well represented 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, where size is defined here 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

time

- of

- use (TOU) residential tariffs that 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 bo th primary and secondary delivery voltage. As 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. Hence, the commercial tariffs use d here were collected in 2004. 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 (a complete discussion of the level of detail in the data can be found in published

work 5 ,3 ). Given a standardize d data format, it is relatively straightforward to develop bill calculation tools. These tools take as input the monthly electricity use data, consisting of consumption and demand, by TOU period if necessary. The bill calculation then produces the monthly bill.

To validate the tariff data and bill calculation tools, we perform a series of diagnostic tests which make use of the electricity price information described above, particularly the EEI data. We present a detailed discussion here of the validation for the residential data; the commercial data has been validated in a similar manner through previous work. 5,3 EEI provides a “typical” bill at three monthly consumption levels (500 kWh, 750 kWh and 1000 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 da ta. The results are pr esented graphically in Figures 8B.2.1 and 8B.2. 2, for bills calculated with 750 kWh of consumption. In these plots, the horizontal axis shows the state and8

- F

- 4 data for all the utilities within a given state are used in the determination of the values plotted along the vertical axis. 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. All prices are given in cents per kWh. Note the TAP d ata include publicly

- owned utilities, which introduces more variability than is in the EEI dataset.

Figure 8

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

Figure 8

- F.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

- F

- 5 REFERENCES

1 Regulatory Research Associates. Average Regulated Retail Price of Electricity, 2007 & Comparative Historical Data. 2008. Jersey City NJ.

2 U .S. Department of Energy

-- Office of Energy Efficiency and Renewable Energy. Commercial Unitary Air Conditioners and Heat Pumps Technical Support Document. Including: Appendix M: Sample Utilities. July, 2004. Washington, D.C. http://www1.eere.ener gy.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. Report No. LBNL

- 55551.

4 U.S. Department of Energy

-- Office of Energy Efficiency and Renewable Energy. Commercial Unitary Air Conditioners and Heat Pumps Technical Support Document. Including: Life

- Cycle Cost and Pay

- back Period Analysis. July, 2004. Washington, D.C. http://www1.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/cuac_tsd_chp_ 8.pdf

5 Coughlin, Katie, 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. Fo r data updates see the website <tariffs.lbl.gov>.