California’s Statewide Pricing Pilot Summer 2003 Impact Evaluation 17 th Annual Western Conference, San Diego, California Ahmad Faruqui and Stephen S

California’s Statewide Pricing Pilot Summer 2003 Impact Evaluation

17th Annual Western Conference, San Diego, California

Ahmad Faruqui and Stephen S. [email protected]

Charles River Associates

June 25, 2004

CHARLES RIVER ASSOCIATESWork in Progress (6/8/04)—Subject to Revision 2

Outline

•Design of the Statewide Pricing Pilot (SPP)

•Methodology and data

•Residential results

• Price elasticity estimates

• Impact simulations


The genesis of the Statewide Pricing Pilot (SPP)

It is an outgrowth of the CPUC OIR (R.02-06-001) on advanced metering and demand response

The first large-scale scientific experiment focused on dynamic pricing for mass-market consumers

Customer enrollment began in April 2003; new rates became effective in July 2003 and will stay in effect through December 2004

SPP addresses several policy issues:

• What is the price elasticity of demand for electricity by time period?

• Does responsiveness vary by rate type, climate zone and customer characteristics?

• Will customers accept time-varying and dynamic rates?

• Are reductions in energy use and coincident peak demand resulting from widespread use of more economically efficient pricing sufficiently large to off-set the metering costs required to implement rate reform?


The SPP is testing several rate options

Time-of-Use (TOU) rate• Traditional two-part TOU rate

• Peak period from 2 pm to 7 pm

• Rates vary seasonally

Critical Peak Pricing-Fixed (CPP-F) rate• TOU rate 350 days a year

• Much higher price during peak period on up to 15 days a year, which are called the previous evening

Critical Peak Pricing-Variable (CPP-V) rate• Similar to CPP-F except they may be called in just 4 hours

• Critical peak period can vary in length from 1 to 5 hours between 2 pm and 7 pm

• Both treatment and control group consumers had volunteered into a smart thermostat pilot program funded by Assembly Bill 970

The above rates are layered on top of a very complex, five-tier, increasing block rate structure


Additional SPP design features

About 2,500 participants drawn from three investor-owned utilities allocated to various treatment and control groups

There are multiple price levels and ratios for each rate type in order to allow for estimation of all own-price and cross-price elasticities

A mandatory pilot was not politically acceptable

• Customers were randomly selected but not required to participate

• The pilot design attempts to mimic a voluntary “opt-out” pricing regime

Residential sample segmented into four climate zones

C&I sample segmented by size

• <20 kW and between 20 kW and 200 kW


The experiment includes four climate zones

Methodology and Data


Two demand models were used to estimate customer price responsiveness

Double Log. This expresses the log of peak and off-peak usage as a function of the log of peak and off-peak prices and cooling degree hours during each period

Constant elasticity-of-substitution (CES). This expresses the log of the ratio of peak to off-peak usage as a function of the log of the ratio of peak to off-peak price and of the difference in cooling degree hours during the two periods; another equation expresses daily energy usage as a function of daily price and cooling degree hours

Both functional forms are estimated using the “fixed-effects” estimation procedure

The appendix contains more methodological information


Data Characteristics

For the CPP-F and CPP-V rates, the demand models were estimated using values that were averaged over all days in the following three time periods

• Pretreatment period (June only)

• Non-CPP days in the treatment period

• CPP days in the treatment period

• Consequently, there are three time-series observations for each customer, with treatment customers facing a different price for each time period and control customers facing the same price each time

The demand models were estimated using pooled time-series, cross-section data

• A binary variable was used to test whether the price elasticity varies between CPP and non-CPP days for each climate zone. No statistically significant difference was found in zones 2, 3 and 4 using the double-log model and in zones 1, 3 and 4 using the CES model

For the TOU models, the regressions were run using values averaged over two time periods

• Pretreatment period (June only)

• All weekdays during the treatment period

Residential Analysis Results


Key findings for the CPP-F rate

Double Log: Own-price elasticities for peak period energy use are statistically significant in zones 2, 3 and 4

• Range from low of –0.08 to high of –0.21

• Higher in the warmer zones 3 and 4 and lower in the cooler zones 1 and 2

Own-price elasticities for off-peak energy use are statistically significant in zones 1 and 2

• Zone 1 elasticity is –0.17 and –0.10

Cross-price elasticities are typically small and often insignificant

CES: All elasticities of substitution are small but statistically significant; they range between –0.04 to –0.16, with higher values being observed in the warmer zones

The daily price elasticities in zones 3 and 4 are statistically significant and equal –0.06; those in zones 1 and 2 are not significant


Key findings for the CPP-V rate

Double Log: The own price elasticity for peak period energy use is –0.21 and statistically significant

The own price elasticity for off-peak energy use is not statistically significant

Cross-price elasticities are typically small or not statistically significant

CES: The elasticity of substitution is –0.21

The daily price elasticity is not statistically significant

It is important to note that these elasticities pertain to customers who had already volunteered into an earlier pilot program involving smart thermostats and therefore cannot be generalized to the population as a whole


Key findings for the TOU rate

Double Log: None of the own-price or cross-price elasticities are statistically significant; Interestingly, CPP-F customers on non-CPP days,who face a TOU rate, display significant price elasticitiesCES: The elasticity of substitution for zones 2 and 3 is statistically significant and ranges between –0.11 and –0.28

None of the daily price elasticities are significant

There are a couple of reasons why the TOU price elasticities are not as statistically significant as the CPP-F price elasticities

• The sample sizes of the TOU customers are smaller than those for the CPP-F rate

• Without a CPP rate that is exercised a few days each month, customers may forget they are on a time-varying rate


Rate impacts can be simulated by using the estimated demand models

The estimated demand models can simulate the impact for the rates used in the experiment and for a variety of other rates that are generally similar to the ones used in the experiment

They should NOT be made using the point elasticities in the previous slides, since point elasticities will tend to exaggerate the impact of large price increases and underestimate the impact of large price decreases

It is important to note that impact simulations require both own and cross-price effects, another reason why relying on just the point estimates of the own-price elasticity will mislead rather than inform policy analysis


A Double-Log example of how point elasticities over-state the impact of large price increases

0

10

20

30

40

50

60

70

80

0 0.2 0.4 0.6 0.8 1

Coincident Peak Demand (kW)

Cri

tica

l P

eak

Pri

ce

(¢/k

Wh

)

Non-linear Function Linear Function Perfectly Inelastic Demand


Impact analysis is based on the weighted average prices for treatment customers in each climate zone

71.5

23.7

7.5

51.7

21.3

11.0

61.0

22.5

9.4

0

10

20

30

40

50

60

70

80

ce

nts

/kW

h

CPP-F High Ratio CPP-F Low Ratio Average

Residential Price For Consumer At Midpoint of Tier 3(Weighted Average, Climate Zone 2)

CPP Period Peak Period Off-Peak Period

Control Group Average Price 13.3 cents/kWh


Peak period impacts are larger in the hotter climate zones than in the cooler zones

Percent Change In Energy Use By Rate Period

For Average Experimental Prices On CPP Days

-20.8

-12

4.32.2

4.4

-24

-7.1

0.3

9.55.3

-30

-25

-20

-15

-10

-5

0

5

10

15

Zone 1 Zone 2 Zone 3 Zone 4 All

% C

han

ge

In k

Wh

Peak Period Off-Peak Period

These estimates come from a sub-sample of customers who volunteered into a smart thermostat pilot and may not be generalizable to the general population of residential customers


Impacts are considerably smaller on non-CPP Days, which have lower peak prices

Percent Change In Energy Use By Rate Period

For Average Experimental Prices On Non-CPP Days

-9.3

-2.3

3.82.2

3.8

-6.2

1.2

4.3

7.3

4.2

-12-10-8-6-4-202468

10


% C

han

ge

In k

Wh

Peak Period Off-Peak Period



Impacts vary somewhat by experimental rate

Percent Change In Energy Use During

The Peak Period On CPP Days By Price Ratio

-23.5

-11.5-8.1

-17.8

-11.7

-24.5

-5.1

3.4

-1.9

-22.8

-30

-25

-20

-15

-10

-5

0

5


% C

han

ge

In k

Wh

High Ratio Low Ratio



Impacts also vary somewhat by model specification

Percent Change In Energy Use During Peak Period

For Average Experimental Prices On CPP Days

-20.8

-12

-7

-18.2

-11.8

-24

-7.1

0.3

-5.7

-22.6

-30

-25

-20

-15

-10

-5

0

5


% C

han

ge

In k

Wh

Log-Log Specification CES Specification



Conclusions

Customers show significant response to both the CPP-F and CPP-V rates

Impacts are higher in the hotter zones for both CPP and non CPP days

Responses are substantially higher on CPP days than on non-CPP days

• For all zones, the CPP day impact is -12% and the non-CPP day impact is –2.3%

CPP day impacts differ slightly between the two experimental rates within the CPP-F rate

Results are generally similar across the two functional forms tested in this study

Customers respond to TOU rates in zones 2 and 3; and they respond to the TOU portion of the CPP-F rate in zones 3 and 4


Next Steps

Assess whether price responsiveness varies with weather conditions

Assess whether price responsiveness varies with customer characteristics such as the ownership of central air-conditioners

Analyze the response of residential customers in the Information Only treatment cells of Track A (Zones 2 and 3)

Analyze the response of residential customers in Track B who face an enhanced community-based information treatment

Evaluate price responsiveness during Winter 2003-04

Evaluate the response of small C&I customers

These results will become available by the end of July

Appendix


Double-Log demand model specification

This model is estimated with a “fixed-effects” estimation procedure that includes customer-specific constant terms.

ln(Qi) = + idi + iln(Pi) + jln(Pj) + (CDHi) +

where

Qi = average daily energy use in the ith period

Pi = average price during the ith period

CDHi = cooling degree hours during the ith period

di = a binary variable equal to 1 for the ith customer, 0 otherwise; these are the fixed effects that don’t vary over time and are customer specific

ii = the own price elasticity of demand for energy during the ith period

ij = the cross price elasticity of demand for energy during the ith period with respect to the price during the jth period

= regression error term


Constant Elasticity-Of-Substitution (CES) demand model specification

The CES demand model involves two equations, one for assessing the rate of substitution between peak and off-peak energy use and the other for measuring the impact of price changes on daily energy use. The substitution equation has the following form:

ln(Qi/Qj) = + iDi + ln(Pi/Pj) + (CDHi - CDHj) +

where

Qi = average daily energy use per hour in the ith period

Pi = average price during the ith period

CDHi = cooling degree hours per hour during the ith period

Di = a binary variable equal to 1 for the ith customer, 0 otherwise

= the elasticity of substitution between peak and off-peak energy use



CES demand model specification (continued)

The daily demand model specification included in the CES system has the following form.

ln(Qd) = + iDi + pln(Pd) + (CDHd) +

where

Qd = average daily energy use per hour

Pd = average daily price

CDHd = average daily cooling degree hours

di = a binary variable equal to 1 for the ith customer, 0 otherwise

p = the own price elasticity of demand for daily energy use



The price elasticities of the CES function

ii =dzi + wj

ij= dzj - wi


Price elasticities and elasticities of substitution for residential energy use for CPP-F tariff

Log-Log

CES

Climate Zone

Price Term

Peak Usage Off-Peak Usage

Elasticity of Substitution

Daily

Peak -0.04 +0.02 Zone 1 Off-Peak -0.19 -0.17

-0.06 +0.06


-0.04 -0.03

Peak -0.13 -0.00 Zone 3 Off-Peak +0.08 -0.06

-0.12 -0.06


-0.16 -0.06

Values in bold type are statistically significant at the 95% confidence level ; these point

estimates should not be used for making impact simulations

Ahmad Faruqui:

Footnote elasticities

Ahmad Faruqui:

Footnote elasticities


Price elasticities and elasticities of substitution for residential energy use for CPP-V tariff

Log-Log

CES

Climate Zone

Price Term



Daily


-0.21 +0.37

Values in bold type are statistically significant at the 95% confidence level; these point

estimates should not be used for making impact simulations; these estimates come from a sub-sample of customers who volunteered into a smart thermostat pilot and may not be

generalizable to the general population of residential customers

Ahmad Faruqui:

Two footnotes

Ahmad Faruqui:

Two footnotes


Price elasticities and elasticities of substitution for residential energy use for TOU tariff

Log-Log

CES

Climate Zone

Price Term



Daily

Peak +0.01 -0.09 Zone 1 Off-Peak -0.08 -0.27

+0.00 -0.38

Peak -0.03 -0.00 Zone 2 Off-Peak +0.27 -0.06

-0.11 +0.06

Peak -0.30 +0.07 Zone 3 Off-Peak +0.01 -0.11

-0.28 +0.07

Peak +0.02 +0.06 Zone 4 Off-Peak +0.03 -0.00

-0.03 +0.07

Values in bold type are statistically significant at the 95% confidence level; these point

estimates should not be used for making impact simulations

Documents

California’s Statewide Pricing Pilot Summer 2003 Impact Evaluation 17 th Annual Western Conference, San Diego, California Ahmad Faruqui and Stephen S