APPENDIX 8B. TECHNICAL ASPECTS OF THE TARIFF-BASED APPROACH

TABLE OF CONTENTS

8B.1 REVIEW OF DATASETS AND CALCULATION METHODS...........................8B-1

8B.2 THE TAP DATABASE AND CALCULATION TOOLS......................................8B-2

LIST OF TABLES

Table 8B.1.1 List of data sources that could be used to estimate electricity prices ..........8B-1

Table 8B.1.2 Residential average prices es timated from EIA Form 861 ..........................8B-2

LIST OF FIGURES

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

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

TAP and EEI data ........................................................................................8B-4

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

8B.1 REVIEW OF DATASETS AND CALCULATION METHODS

The methodology used to calcula te electricity prices is depe ndent on the data set. Table

8B.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 geographically and in terms of the ability to distinguish customer type, and the degree of complexity and accuracy of the calculation methods.

Table 8B.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 re sidential 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 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 ma rginal 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 in principle be used to estimate prices, the age of the expenditure data and the size of recent price increase s make this approach problematic. In particular, in many areas tariff structures (not just rates) may have changed

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.
8B-1significantly 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 vs. investor-owned utilities. It is therefore important to include these utilities in the analysis.

Table 8B.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 the given sector. The table shows the average price estimated in this way 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 8B.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%

8B.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 vari ation 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 useof these data in the tariff-based electricity price analysis, the TAP regions are defined as subsectors 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 wi th the nine climate regions defined by the

8B-2National Climatic Data Center. (3) Since the onset of dere gulation, 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 out into separate 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 ofcustomers served by privately- vs. publiclyowned companies. With the current database, variability in prices is well represented at the level of these seventeen regions. For the current an alysis, regional variation is actually somewhat coarser, consisting of nine census divisions pl us four large states as is used in RECS.

Electricity tariffs are typically classed as residential or non-reside ntial. 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 most do not. There are usually several general-service tariffs for different customer sizes, where si ze 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 we re 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 wh en they are the default tariff. Tariffs have been collected for both primary and secondary delivery voltage. As commercial users of residential-style equipment are a small portion of the total custom er base, it was not considered necessary to update the commercial data for this analysis. Hence, the commercial tariffs used 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 TA P 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 standardized data format, it is relatively straightforward to develop bill calculation tools. These tools take as input th e monthly electricity use data, consisting of consumption and demand, by TOU period if n ecessary. 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 in formation 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 aver age prices can then be directly compared to the EEI data. The results are presented gra phically in Figures 8B.2.1 and 8B.2.2, for bills

8B-3calculated with 750 kWh of consumption. In these plots, the horizontal axis shows the state and 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 data include publicly-owned utilities, which introduces more variability than is in the EEI dataset.

Summer Avg Prices for 750kwh

0 5 10 15 20 25 30

A K AL A R AZ C A C O CT D C DE F L GA HI IA I D IL IN KS K Y LA MA MD ME MI MN MO MS MT N C N D NE N H NJ N M N V N Y OH OK OR PA R I SC S D TN TX UT VA V T W A WI W V WY 0 5 10 15 20 25 30 E E I 2008 S S um H i S u m L o S u mMe d

Figure 8B.2.1 Comparison of average prices for summer (J uly) calculated using TAP and EEI data

Winter Avg Prices for 750kwh

0 5 10 15 20 25 30

A K AL AR AZ CA CO C T DC D E FL G A H I I

A ID I L I

N K

S K Y L

A MA MD M E MI MN MO M S M T NC N D NE NH NJ N M N V NY OH OK O R PA R I S

C S D TN TX U T V

A VT WA WI WV WY 0 5 10 15 20 25 30 E E I 200 8W W i n H i W i n Lo Wi n M e d

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

8B-4REFERENCES

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 Cond itioners and Heat Pumps Technical Support Document. Including: 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. Report No. LBNL-55551.

4 U.S. Department of Energy -- Office of Energy Efficiency and Renewable Energy. Commercial Unitary Air Cond itioners 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 Bo lduc, 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. For data updates see the website <tariffs.lbl.gov>.

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