CONSUMERS ENERGY’S PERSONAL
POWER PLAN PILOT- SUMMER 2010
IMPACT EVALUATION
January 31, 2011
Ahmad Faruqui, Ph.D.
Sanem Sergici, Ph.D.
Lamine Akaba, M.Sc.
Prepared for
Consumers Energy
Copyright © 2011 The Brattle Group, Inc.
ACKNOWLEDGEMENTS
We would like to thank Ms. Shaltreece Reddick of Consumers Energy for her helpful
suggestions and comments on earlier drafts of this report and for inspiring project
management throughout the course of the project.
Ahmad Faruqui
Sanem Sergici
Lamine Akaba
Suggested Citation:
“Consumers Energy’s Personal Power Plan Pilot- Summer 2010 Impact Evaluation,” by
Ahmad Faruqui, Sanem Sergici and Lamine Akaba, The Brattle Group Inc., January
2011.
TABLE OF CONTENTS
1.
EXECUTIVE SUMMARY ...................................................................... 1
2.
BACKGROUND AND OVERVIEW...................................................... 5
2.1
2.2
2.3
2.4
2.4.1
2.4.2
2.4.3
3.
PPP EXPERIMENTAL DESIGN ....................................................................... 5
RATE DESIGN ............................................................................................... 5
ENABLING TECHNOLOGY ............................................................................. 6
SAMPLE DESIGN........................................................................................... 8
Treatment Group Recruitment .................................................................... 9
Control Group Recruitment ........................................................................ 9
Treatment Group Education ..................................................................... 10
LOAD IMPACT ANALYSIS METHODOLOGY .............................. 12
3.1
3.1.1
3.1.2
3.1.3
3.2
3.3
4.
MODEL SPECIFICATION AND ESTIMATION: PRICING TREATMENTS ............ 12
Substitution Demand Equation ................................................................. 13
Daily Demand Equation ........................................................................... 15
Substitution and Daily Price Elasticities .................................................. 17
MODEL SPECIFICATION AND ESTIMATION: INFORMATION TREATMENTS ... 19
EXPLORING THE HAWTHORNE EFFECT....................................................... 21
LOAD IMPACT ANALYSIS RESULTS ............................................. 23
4.1
4.2
4.3
4.4
4.5
RCPP PROGRAM IMPACTS ........................................................................ 24
RCPP_TECH PROGRAM IMPACTS ............................................................ 24
RCPR PROGRAM IMPACTS ........................................................................ 25
RPIO AND RPIO_TECH PROGRAM IMPACTS ........................................... 26
SUMMARY OF THE PPP PILOT LOAD IMPACTS ........................................... 26
APPENDICES
APPENDIX 1- CE PPP IMPACT EVALUATION PRESENTATION
APPENDIX 2- PPP PILOT CUSTOMER RECRUITMENT
AND SURVEY MATERIALS
APPENDIX 3- PPP PILOT SATISFACTION SURVEY RESULTS
APPENDIX 4- PPP PILOT POST-PILOT FOCUS GROUP RESULTS
1. Executive Summary
The Brattle Group was retained by Consumers Energy (“CE”) in December 2009 to assist
in the design of a dynamic pricing and information pilot program to develop assessments
of the likely impact of a variety of dynamic pricing programs on CE residential customer
loads. The dynamic pricing pilot program, named the Personal Power Plan (PPP), was
successfully implemented in the summer of 2010. This report presents the results from
the impact evaluation of the PPP.
The PPP Pilot featured 921 residential customers and ran from July 2010 through
September 2010. Around 600 customers were placed on pricing or information
treatments tested in the pilot. Consumers Energy tested two dynamic pricing structures in
the PPP: a critical peak pricing (RCPP) tariff and a critical peak rebate (RCPR) rider,
both of which were combined with a three-period time-of-use (TOU) rate. Table ES-1
presents the all-in rates tested in the pilot.
Table ES-1: Rates Tested in the PPP Pilot
Time / Day
Category
RCPP Rate ($/kWh)
RCPR Rate ($/kWh)
2 p.m.-6 p.m. Weekdays
Peak
0.180
0.256
2 p.m.-6 p.m. Weekdays
Critical Peak
0.690
0.756
7 a.m.-2 p.m. & 6 p.m.-11 p.m. Weekdays
Mid-Peak
0.106
0.106
Weekends, Holidays & 11 p.m.-7 a.m. Weekdays
Off-peak
0.088
0.088
Note: 1- The rates are converted into all-in rates by adding an average non-generation charge of
$0.04/kWh. 2- Peak time rebate is added to the peak period rate to reflect the opportunity cost of not
reducing the load by one kWh.
PPP also recruited a group of customers (RPIO) to test the effectiveness of information in
changing the usage behavior of customers without any changes in the prices. These
customers were only given the information that the electricity prices would be higher on a
day-ahead basis and called up to reduce their load during the peak hours on a voluntary
basis. RPIO customers were still subject to the standard tariff.
Finally, PPP tested the effectiveness of an intelligent communicating thermostat (ICT) in
boosting the impacts from prices or information alone through two additional treatment
cells, RCPP_TECH and RPIO_TECH. With this technology, Consumers Energy would
be able to send a remote signal to the ICT during a critical peak event. The signal allows
the thermostat to adjust automatically to a temperature setting previously selected by the
customers for critical peak events.
The remainder of the customers stayed on the standard tariff, which takes the form of an
inclining block rate in the summer months and a flat rate in all other months, and served
as a control group. Hourly usage was recorded for customers in both groups during the
1
pilot to determine if the treatment group used less during the more expensive periods. In
addition, to assess for any pre-existing difference in the groups, hourly usage was also
recorded during a pre-treatment phase. The experimental design is summarized in Table
ES-2.
Table ES-2: Rate and Technology Combinations Tested in the PPP Pilot
Rate Design
Enabling Technology
RCPP
None
RCPP_TECH
Intelligent Communicating
Thermostat
RCPR
None
RPIO
None
RPIO_TECH
Intelligent Communicating
Thermostat
CE called six critical event days during the course of the pilot period. On critical days,
PPP participants had a strong price incentive to either curtail peak usage or to shift it to
less expensive periods. The specific dates on which these events were called are shown in
Table 3.1 along with the weather conditions that prevailed on those dates. Customers
were notified on a day-ahead basis that the next day would be a critical day using one of
several means of communication, including a phone call, email, and text messages. In
addition to the notifications, customers could also view critical peak event information by
logging in to the PPP web portal.
Using data from the pilot participants and the control group customers both before and
during the pilot period, we estimated demand models to determine the load impacts from
the treatments tested in the PPP Pilot. Figure ES-1 summarizes the treatment impacts.
Overall, the load reduction during the critical peak hours varied across all program types,
from a low of 5.8 percent for the information treatments to a high of 19.4 percent for the
pricing treatments. The total monthly energy consumption increases by roughly one
percent for RCPP_TECH and remains unchanged for the other treatments. These
estimated impacts were statistically significant at the five percent level.1 The enabling
technologies were not found to be effective in shifting usage from peak to off-peak
periods. We conjecture that the ease of overrides may have led to this result, combined
1
Statistical significance at the five percent level implies that there is only a five percent probability
of incorrectly rejecting the null hypothesis that the estimated value is equal to zero, i.e., PPP rates
do not lead to load reductions.
2
with the fact that customer preferences were programmed into the ICTs at the time of
installation.2 However, ICTs were still found to be effective in managing overall usage.
It is also important to note that the substitution elasticities for the RCPP and RCPR rates
were found to be statistically indistinguishable from each other. This result has an
important implication since it shows that the PPP customers showed the same change in
their load profiles, whether they were responding to the RCPP rate or the RCPR rate.
The results from the PPP pilot compares favorably to the results from the previous pilots,
and fall mostly in the middle of the distribution. Figure ES-2 presents the comparison of
the demand response impacts across 73 tests of dynamic pricing.
Section 2 of this report describes the experimental design of the PPP. Section 3
summarizes the analytical methods and data used in the estimation of the load impacts
from the PPP treatments. Section 4 summarizes the impact evaluation results.
Figure ES-1: PPP Pilot Demand Response Impact Summary
Average Peak Demand Reduction (% of oringinal consumption)
0.0%
-2.5%
-5.0%
-5.8%
-5.8%
-7.5%
-10.0%
-12.5%
-15.0%
RCPP
-15.2%
-15.9%
RCPP_TECH
-17.5%
RCPR
-20.0%
RPIO
-19.4%
RPIO_TECH
-22.5%
2
Customer Type
As the override data was not captured as part of the pilot data collection, we were not able to
empirically verify this hypothesis. However, based on the post-pilot satisfaction survey, 43
percent of the participants reported that ICTs did not affect their usage behavior at all during
critical peak event days or affected it only minimally. Of the survey respondents, 27 percent
indicated that the ICTs substantially influenced their energy usage behavior during critical peak
events.
3
Figure ES-2: Comparison of the Demand Response Impacts across Pilots
60%
TOU
TOU w/
Tech
PTR
RTP
PTR
w/
Tech
CPP
CPP w/
Tech
RTP w/
Tech
40%
30%
Consumers Energy
20%
10%
0%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
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53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
Percent Reduction in Peak Load
50%
Pricing Pilot
4
2. Background and Overview
2.1 PPP EXPERIMENTAL DESIGN
Consumers Energy (“CE”) conducted its residential dynamic pricing pilot program,
called the Personal Power Plan (PPP) Pilot, in the summer of 2010. It ran from July 2010
through September 2010. Consumers Energy tested two dynamic pricing structures in
the PPP: a critical peak pricing (RCPP) tariff and a critical peak rebate (RCPR) rider,
both of which are combined with a TOU rate. PPP also recruited a group of customers
(RPIO) to test the effectiveness of information in changing the usage behavior of
customers without any changes in the prices. Finally, PPP tested the effectiveness of an
intelligent communicating thermostat (ICT) in boosting the impacts from prices or
information alone through two additional treatment cells, RCPP_TECH and
RPIO_TECH.
Two different pricing structures, an information treatment, and combination of these
treatments with ICTs yielded five different treatment combinations. In the rest of this
report, we will refer to RCPP, RCPP_TECH, and RCPR treatments as pricing treatments
and RPIO and RPIO_TECH rates as information treatments.
2.2 RATE DESIGN
Consumers Energy’s standard residential rates have an “inclining block rate” structure in
the summer months, and a flat structure in the winter months, as presented in Table 2.1.
It is also important to note that any price variable in our analysis is expressed as an “all-in
rate” which includes non-generation charges and generation charges.
Table 2.1: Consumers Energy Standard Rate Design – 2010
EXISTING RATES
June through September
First 600 kWh
Over 600 kWh
Rate ($/kWh)
0.111
0.171
October-May
All kWh
0.111
Customers in the control group and customers on the information treatment continued to
pay the standard rates during the pilot period. The participants in the three remaining
treatment cells were subject to one of the two following dynamic rate designs:
1. Residential Critical Peak Pricing (RCPP): Under the RCPP rate design, there were
three periods on non-holiday weekdays: off-peak, mid-peak, and peak periods. On the
six event days that Consumers Energy called, the peak hours would become critical
peak hours. The RCPP rates are presented in Table 2.2.
5
Table 2.2: RCPP Rate Design (July 2010 – September 2010)
Time / Day
Category
Rate ($/kWh)
2 p.m.-6 p.m. Weekdays
Peak
0.180
2 p.m.-6 p.m. Weekdays
Critical Peak
0.690
7 a.m.-2 p.m. & 6 p.m.-11 p.m. Weekdays
Mid-Peak
0.106
Weekends, Holidays & 11 p.m.-7 a.m. Weekdays
Off-peak
0.088
Note: The rates are converted into all-in rates by adding an average non-generation charge
of $0.04/kWh.
2. Residential Critical Peak Rebate (RCPR): Under the RCPR rate design, customers
have the opportunity to receive a rebate if they reduce their consumption below their
“typical” usage, during the peak hours of the critical peak event days. More
specifically, the participants received $0.50 for every kWh of load reduction below
their baseline usage. On non-event days, customers were charged the TOU rates, as
presented in Table 2.3. It is important to note that the RCPR tariff is different from a
typical CPR tariff. Under a typical CPR tariff, participants continue to pay the
standard tariff on non-event days, whereas under the RCPR they paid the TOU rates
on the non-event days.
Table 2.3: RCPR Rate Design (July 2010 – September 2010)
Time / Day
Category
Rate ($/kWh)
2 p.m.-6 p.m. Weekdays
Peak
0.256
2 p.m.-6 p.m. Weekdays
Critical Peak
0.756
7 a.m.-2 p.m. & 6 p.m.-11 p.m. Weekdays
Mid-Peak
0.106
Weekends, Holidays & 11 p.m.-7 a.m. Weekdays
Off-peak
0.088
Note: 1- The rates are converted into all-in rates by adding an average non-generation charge of
$0.04/kWh. 2- Peak time rebate is added to the peak period rate to reflect the opportunity cost of not
reducing the load by one kWh.
Consumers Energy called six critical peak event days in the summer of 2010. The PPP
participants were notified of the critical peak days on a day-ahead basis through phone
calls, emails, and text messages. In addition to these notifications, customers could also
view critical peak event information by logging in to the PPP web portal.
2.3 ENABLING TECHNOLOGY
The PPP also tested the effectiveness of an intelligent communicating thermostat (ICT) in
facilitating demand response when dynamic prices and information are offered in
conjunction with enabling technologies. In order to separate the impacts of the enabling
6
technologies from that of prices and information, RCPP and RPIO treatments were tested
with and without the technology options.
The enabling technology tested in the PPP pilot was a programmable thermostat which
could also receive wireless signals from the utility. On the event days, Consumers
Energy sent a wireless signal to these ICTs to increase the set-back temperatures to preprogrammed levels. For instance, if an ICT was programmed to have a setback
temperature of 79 degrees with a wireless signal from Consumers Energy, the
temperature setting was automatically raised to 79 degrees. Consumers Energy followed
a unique approach which involved pre-programming the ICTs to customers’ preferred
levels. In the previous example, the set-back temperature of 79 degrees would have been
selected by the customer to reflect their preferences. In the previous pilots, the customers
were not given this option and their thermostats increased the temperature settings by a
given increment, e.g., two or four degrees, determined by the utility.
Another unique feature of the Consumers Energy’s ICT deployment was the ease of overrides by the customers. The customers could simply turn their units off, and then back on
to override the event-day settings. Unfortunately, customer specific data on overrides
through this method, which would allow us to investigate this phenomenon, was not
captured during the data compilation process.
Customers could also call the
implementation vendor to have them override the setting.
With dynamic prices, information treatments and enabling technologies tested in the
pilot, PPP involved five different treatment cells. The number of combinations is five
rather than six since the RCPR rate with ICT combination was not tested in the pilot.
These combinations are shown in Table 2.4.
Table 2.4: Rate and Technology Combinations Tested in the PPP Pilot
Rate Design
Enabling Technology
RCPP
None
RCPP_TECH
Intelligent Communicating
Thermostat
RCPR
None
RPIO
None
RPIO_TECH
Intelligent Communicating
Thermostat
7
2.4 SAMPLE DESIGN
The PPP Pilot featured approximately 600 program participants. The pilot participants
were randomly selected and recruited from the AMI population, which largely resided in
the Jackson area. The pilot design included two control groups. The first group,
consisting of 228 customers, is the GCON group which was randomly selected from the
same AMI population. These customers were unaware of the pilot program and
represented what the treatment customers would behave like absent the pilot treatments.
The second group, composed of 92 customers, is the RCON group which was also
randomly selected from a sample representative of the population residing in the
Consumers Energy service territory.3 RCON customers were told that Consumers
Energy would observe their everyday usage patterns during the summer of 2010, from
early to mid June through the end of September.
Having two control groups of which one does not know it is a control group and the other
knows that it is a control group (and is aware of being observed) allows for the
measurement of any “Hawthorne effect” in the pilot. The Hawthorne effect refers to
human subjects changing their behavior simply due to the awareness of being observed.
This issue is discussed later in the report.
Table 2.5 presents the design of the PPP Pilot sample as of September 2010.
Table 2.5: The PPP Pilot Sample Design (as of September 2010)
Group
RCPP
Control Group
Total
122
-
122
98
-
98
RCPR
152
-
152
RPIO
155
-
155
RPIO_TECH
74
-
74
GCON
-
228
228
RCON
-
92
92
Total
601
320
921
RCPP_TECH
3
Treatment Group
The number of the control group customers in the original design was 115. However, due to
problems with incomplete data, only 92 control customers were included in our analysis dataset.
8
2.4.1 Treatment Group Recruitment
Approximately 3,200 customers out of the 4,600 customers in the greater Jackson area,
that had received AMI meters, represented the participant population for this pilot. Pilot
customers were randomly selected and then recruited from this sample through direct
mailing and follow-up calls.
In the recruitment process, small groups of customers were randomly selected for
participation in one of the pilot groups. Once a treatment group was filled, another
random group of customers was selected and sent a direct mail piece as appropriate for
the next category of treatment. Customers who received the mailings could contact CE’s
hot line by telephone. CE also used outbound calls to contact customers who did not
respond. Once a treatment group was filled and the next wave of mailings began,
customers who did not respond were no longer contacted. Ample information was
provided in the mailing to clearly describe the purpose of the pilot which was to evaluate
new ways for customers to manage their energy costs with information, technology, and
advanced tools. The information also explained the importance of knowing how and
when when we use energy as it relates to managing costs. Furthermore, the materials
provided a brief description of the program and explained what actions customers can
take to reduce their usages when critical peak periods occurred. These materials are
provided in Appendix 2.
In order to enroll in the PPP pilot program, customers had to speak to a representative,
either by calling the toll free number provided, or they could have received an outbound
call from the implementation vendor. Prior to marketing, a unique identifier was
recorded to indicate the treatment group to which the customer was randomly assigned.
All customers with the exception of those targeted for the RCON group, received the
same letter. To preserve anonymity of the various treatment groups, there was no
mention of a specific rate or treatment group in the marketing materials. Upon
enrollment, the unique identifier enabled the representatives to know which information
to share with the enrollee. It was at enrollment that the customer would have had initial
exposure to the rate and applicable treatments. They were also asked to respond to a
survey to gather information on customers’ socio-demographic and appliance
characteristics, at the time of the enrollment. Customers were offered appreciation
payments totaling $150 (for RCPP, RCPR, and RPIO treatments) or $175 (for
RCPP_TECH and RPIO_TECH) upon their completion of all requirements of the
programs. Enrollment surveys and customer recruitment materials are provided in
Appendix 2.
2.4.2 Control Group Recruitment
The PPP pilot involved two different control groups. The first group, GCON, was a
random sample of customers drawn from the same AMI population that the treatment
group customers were drawn from. These customers were intended to serve as a proxy
for the behavior of the treatment group customers had they not been in the treatment
group, i.e., to help define conditions in the “but-for” world. They were also surveyed at
the beginning of the pilot to be able to construct a comparable dataset of socio-
9
demographic and appliance characteristics to those collected for the treatment customers.
It is important to note that the GCON customers were not aware of their involvement in
the PPP Pilot.
We use the hourly data together with the survey data to ascertain the degree of
comparability between the treatment and control group customers before the pilot begins.
If the control and treatment groups are comparable and balanced in the pre-treatment
period, we can feel reasonably comfortable that the control group provides a good proxy
for the usage of the treatment customers (absent the program treatments) in the treatment
period and ensures the internal validity of the pilot results.
Our analyses revealed that the GCON and treatment groups were largely comparable in
the pre-treatment and treatment periods in terms of their usage characteristics and load
profiles. Any remaining differences between the two groups are accounted for through
the use of a procedure known as the “difference-in-differences” method which essentially
subtracts any pre-existing difference between the treatment and control groups from the
observed difference between the two groups in the treatment period. Details are provided
in Appendix 1.
The second group, RCON, is also a random sample of customers but it is drawn from a
larger sample representing the entire Consumers Energy population, not just those with
AMI who were located in the vicinity of Jackson. Unlike the GCON group, these
customers were told that Consumers Energy would observe their everyday usage patterns
in the summer beginning in June until the end of September. As we mentioned
previously, this group allows for estimating the size of the Hawthorne effect.
2.4.3 Treatment Group Education
One of the factors that determines the success of a pilot program is the awareness and
education of the pilot participants about the opportunities presented to them by the
various treatments that are being tested in the pilot. Consumers Energy took several
actions to ensure that the PPP pilot participants were properly informed about the pilot
objectives and also that they were engaged in the pilot. Some of these actions are
summarized below:
1. All PPP participants received a “welcome package” prior to the start of the pilot. The
package was comprised of a main section featuring a welcome letter, refrigerator magnet,
and a quick reference guide. The reference guide provided a general program overview
including potential event hours and ways to reduce electricity consumption in order to
save money on the program. Each package was also customized for the five treatment
groups using program and technology specific inserts. The program inserts included
information about the new pricing design and what customers should expect to see on
their summer bills. The technology inserts provided details on how the intelligent
communicating thermostat operated and would enable the customer to save more on the
program. These materials are presented in Appendix 2.
10
2. When PPP participants logged into their personalized web portal online account, they
could access information about the PPP pilot such as: historical energy consumption in
intervals ranging from 15-60 minutes and up to several weeks at a time, additional tips
for year-round savings, comparison reports to others in the smart meter community, a
breakdown of usage patterns by kWh or cost per kWh, weather information, online
program guide, and more. The website was customized for each of the treatment groups
so customers could receive program information specific to the applicable group. Critical
peak event notifications were also posted on these websites to serve as an additional form
of customer notification.
3. Customers had the opportunity to both receive and retrieve savings feedback during
the pilot. For example, customers in the RCPR group saw a line item on their monthly
utility bill showing the total kWh reduced and corresponding rebate. Customers in all
treatment groups could also retrieve information by logging into the web portal, which
contained a log with links to past event days. The customer could simply click on the
link to see what their usage was during the event period. Usage during critical peak
hours was highlighted in red. Customers were also able to run comparison reports to see
how well they performed compared to other customers with smart meters.
4. Customers had easy access to customer service representatives throughout the pilot
program. They were instructed to use two phone numbers during pilot. The first was the
toll free number to the PPP program support team, which was staffed by the
implementation vendor. Call handling was limited to inquiries regarding customer
enrollment, appointments and scheduling, ICT overrides, and general questions about the
program. The second number customers were provided was simply Consumers Energy’s
toll free number to customer service. A group of CE customer service representatives
identified as the “specialty group,” received additional training and education about the
program, and handled all other calls related to the program.
5. Following the pilot, participants were asked to complete a post-pilot survey
summarizing their experience on the program. A post-pilot focus group was also
conducted with customers who had participated in the pilot. These materials are
respectively presented in Appendices 3 and 4.
11
3. Load Impact Analysis Methodology
Our analytical approach to evaluating the load impacts of the PPP Pilot is based on the
application of econometrics and microeconomic theory to data collected in the PPP. We
first specify electricity demand models that represent the electricity consumption
behavior of the CE customers. Second, we use econometric methods (regression
analysis) to estimate and parameterize the models. Finally, we simulate the impact of the
treatments that were deployed in the pilot as well as intermediate treatments that could be
deployed in the post-pilot phase.
Demand models are used to estimate the demand response impacts of each PPP pricing
treatments, as opposed to alternative methods such as analysis of variance and covariance
(ANCOVA), in part because they allow for estimation of the price elasticities. This
capability is vital to being able to estimate the impact of prices other than those used in
the pilot. However, we still rely on the ANCOVA models to be able to estimate the
impacts of information treatments.
Section 3.1 provides an analytical description of the model specification, estimation and
price elasticities for the RCPP, RCPP_TECH, and RCPR treatments. Section 3.2
provides an analytical description of the ANCOVA model estimated for the RPIO and
RPIO_TECH treatments.
3.1 MODEL SPECIFICATION AND ESTIMATION: PRICING TREATMENTS
To estimate the price elasticities (and eventually the demand response impacts) for
RCPP, RCPP_TECH and RCPR customers, we employ a widely used model, the
constant elasticity of substitution (CES) demand model. For a two-period rate structure,
the CES model consists of two equations. The first equation models the ratio of peak to
off-peak quantities as a function of the ratio of peak to off-peak prices and other terms,
and the second models average daily electricity consumption as a function of average
daily price of electricity and other terms. The two equations constitute a system for
predicting electricity consumption by time period where the first equation essentially
predicts the changes in the load shape caused by changing peak to off-peak price ratios
and the second equation predicts the changes in the level of daily electricity consumption
caused by changing average daily electricity price. New level of daily electricity
consumption implied by the second equation is partitioned between peak and off-peak
periods using the new load shape implied by the first equation.
The RCPP and RCPR rate designs in the PPP pilot include three pricing periods.
However, as can be seen from Tables 2.2 and 2.3, the difference between the mid-peak
and off-peak periods is minimal and roughly corresponds to 2 cents per kWh. Due to the
small price differential between these two periods, we have combined the mid-peak and
off-peak periods and assigned all of these hours to a new “off-peak” period that covers all
12
hours in a day excluding the peak hours.4 This combination effectively allows us to
model the substitution behavior between two periods: peak period and a combined midpeak and off-peak period.
CE metered the hourly usage of the treatment and control group customers both before
and during the pilot period. This data compilation yielded a data set of 921 customers
starting in May 2010 and extending through September 01, 2010. This cross-sectional
time series data set of the PPP participants and control group customers is employed to
estimate our demand models. We employ a “fixed-effects” estimation routine to estimate
this demand system. Fixed effects estimation uses a data transformation method that
removes any unobserved time-invariant effect that has a potential impact on the
dependent variable. By estimating a fixed effects model, we effectively control for all
customer specific characteristics that don’t vary over time and isolate their impact on the
dependent variable. This approach is equivalent to a “dummy variable regression”
approach where one introduces individual dummy variables for all the customers that are
included in the regression.5 However, there are also several observed variables that may
affect the level of the dependent variable and therefore need to be explicitly controlled for
in the model. In the following, we discuss these variables and more generally the
econometric specifications of our models.
3.1.1 Substitution Demand Equation
As stated earlier, the substitution equation captures the consumption substitution behavior
of the customers between peak (or critical peak on the event days) and off-peak periods.
The substitution equation takes the following functional form:
3
Peak _ kWh
)it = α 0 + α1THI _ DIFFit + ∑ δ k (THI _ DIFFxD _ Monthk )it + α 3 D _ TreatPeriod t +
OffPeak _ kWh
k =1
Peak _ Pr ice
)it xTHI _ DIFFit +
α 4TreatCustomer + α 5 D _ TreatPeriodxTreatCustomerit + α 6 ln(
OffPeak _ Pr ice
Peak _ Pr ice
Peak _ Pr ice
α 7 ln(
)it xTHI _ DIFFit xTECH + α 8 ln(
)it xTHI _ DIFFit xCPR +
OffPeak _ Pr ice
OffPeak _ Pr ice
α 9 D _ WEEKEND + vi + uit
ln(
4
5
We also did some exploratory analyses with three periods. These analyses confirmed that there
was no substitution between mid-peak and off-peak periods and customers did not respond to the
slight price difference between these two periods.
Both approaches will produce the same coefficient estimates and all the other statistical estimates
will be the same. The only difference between two approaches will be in level of the R-squares.
Fixed-effects estimation only represents the amount of time variation in the dependent variable
that is explained by the time variation in the explanatory variables (Wooldridge, 2003). In other
words, while fixed-effects estimation doesn’t take into account the explained variation by the
individual customer dummies, the dummy variable regression does take into the explanatory
power of the individual dummies. For that reason, R-squared obtained from the dummy variable
regression will be larger.
13
where:
ln(
Peak _ kWh
)it
OffPeak _ kWh
: Logarithm of the ratio of peak to off-peak load for a given
day.
THI _ DIFFt
: The difference between average peak and average off-peak
THI.
THI= 0.55 x Drybulb Temperature + 0.20 x Dewpoint + 17.5
THI _ DIFFt xD _ Monthk
ln(
: Interaction of THI_DIFF variable with monthly dummies.
D _ TreatPeriod t
: Dummy variable is equal to 1 when the period is July 2010
through September 2010.
TreatCusto mer
: Dummy variable is equal to 1 for a treatment customer.
D _ TreatPeriodt xTreatCustomeri
: Interaction of D_TreatPeriod with treatment customer
dummy TreatCustomer.
Peak_ Price
ln(
)it xTHI_ DIFFt
OffPeak_ Price
: Interaction of ratio of peak to off-peak prices of RCPP
customers and THI_DIFF for a given day.
Peak _ Pr ice
)it xTHI _ DIFFit xD _ TECH
OffPeak_ Pr ice
Peak _ Pr ice
ln(
)it xTHI _ DIFFITi xD _ CPR
OffPeak_ Pr ice
D _ WEEKENDt
: Interaction of ratio of peak to off-peak prices of RCPP
customers, THI_DIFF and TECH for a given day.
: Interaction of ratio of peak to off-peak prices of RCPR
customers, THI_DIFF and TECH for a given day.
: Dummy variable that is equal to 1 on weekends.
vi
: Time invariant fixed effects for customers.
u it
: Normally distributed error term.
It is important to note that this equation is estimated using data on both treatment and
control customers and that this data involves both the pre-treatment and treatment
periods. Such a “panel” database allows one to isolate the true impact of the experiment
by controlling for any potential biases due to (i) differences between control and
treatment customers in the pre-treatment period (ii) any changes in the consumption
behavior of the treatment customers between the pre-treatment and treatment periods that
are not related to the treatment per se. These potential confounding factors are controlled
for by introducing dummy variables pertaining to the customer type and the analysis
period. We also control for several other variables that are conjectured to affect the
consumption choice between peak and off-peak periods.
14
This equation is estimated to determine the substitution elasticity of the PPP pricing
customers. The substitution elasticity indicates the percent change in the ratio of peak to
off-peak consumption due to a one percent change in the ratio of peak to off-peak prices.
Normally, if our model did not have any interactions of the price ratio with the weather
term, (THI_DIFF), α 6 would represent the substitution elasticity estimated from this
model for the RCPP price only customers. However, the specification of the PPP
substitution model implied that the substitution elasticity of the PPP customers increased
with the hotter weather. Therefore, we included an interaction term between the price
ratio and the weather term in the model to capture this relationship in the elasticity term.
We also introduced the interaction terms between the price ratios and dummy variables
for the RCPP_TECH and RCPR customers to capture the incremental impact of
technology and critical peak rebates on the price responsiveness of the customers. The
estimation results for the substitution demand model are provided in Appendix 1.
Once the model is estimated and its parameters are identified, the substitution elasticities
can be derived using the following equations:
Subst _ Elasticity price _ RCPP
= α 6 * THI _ DIFFt
(1)
Subst _ Elasticity price _ RCPP +TECH
= (α 6 + α 7 ) * THI _ DIFFt
(2)
Subst _ Elasticity price _ RCPR
= (α 6 + α 8 ) * THI _ DIFFt
(3)
These equations make it possible to determine a substitution elasticity implied by a
specific weather condition and the existence of an enabling technology.
3.1.2 Daily Demand Equation
The daily demand equation captures the change in the level of overall consumption due to
the changes in the average daily price. Similar to the substitution equation, the daily
equation also relies on the pre-treatment and the treatment period data on both treatment
and control group customers. This practice allows the elasticity estimates to be free from
biases concerning any pre-existing differences between the control and treatment group
customers as well as the changes in the consumption patterns of the treatment customers
between the pre-treatment and treatment periods due to factors other than the treatment.
As in the case of substitution equations, we also control for other independent variables
that can affect the average daily consumption and use the fixed effects routine to estimate
the model. The specification of the daily demand model is provided below:
3
ln(kW ) it = α 0 + α 1 ln(THI ) it + ∑ δ k (ln(THI ) xD _ Monthk ) it + α 3TreatCustomer +
k =1
α 4 D _ TreatPeriod t + α 5 D _ TreatPeriodxTreatCustomerit + α 6 ln(Pr ice) it x ln(THI ) it +
α 7 ln(Pr ice) it x ln(THI ) it xTECH + α 8 ln(Pr ice) it x ln(THI ) it xCPR + α 9 D _ WEEKEND + vi + u it
where:
ln(kW)it
: Logarithm of the daily average of the hourly load.
ln(THI )it
: Logarithm of the daily average of the hourly THI.
15
ln(THI )t xD _ Monthk
: Interaction of ln(THI) variable with monthly dummies.
TreatCusto mer
: Dummy variable is equal to 1 for the treatment customers.
D _ TreatPeriodt
: Dummy variable is equal to 1 when the period is July
2010 through September 2010.
D _ TreatPeriodt xTreatCustomeri
: Interaction of D _ TreatPeriod t with treatment customer
dummy TreatCustomeri .
ln(Pr ice)it x ln(THI )t
: Interaction of ln(price) for RCPP customers with ln(THI).
ln(Pr ice) it x ln(THI ) t xTECH
: Interaction of ln(price) for RCPP_TECH customers, with
ln(THI).
ln(Pr ice)it x ln(THI )t xCPR
: Interaction of ln(price) for RCPR customers with ln(THI).
D _ WEEKENDt
: Dummy variable that is equal to 1 on weekends.
vi
: Time invariant fixed effects for customers.
u it
: Normally distributed error term.
The daily equation is estimated to determine the daily price elasticity of the PPP pricing
customers. The daily price elasticity indicates the percent change in the daily average
consumption due to a one percent change in the daily average price. Similar to the
substitution elasticities, the daily price elasticity also increases with the warmer weather.
In order to capture this relationship, we introduced an interaction term between the
average daily price and the weather term (ln(THI)). We also introduced interaction terms
between the daily average price term and dummy variables for the RCPP_TECH and
RCPR customers to capture the incremental impact of technology and critical peak
rebates on the price responsiveness of the customers. The estimation results for the daily
demand equation are also provided in Appendix 1.
The daily price elasticities from the estimated model can be derived using the following
equation:
Daily _ Elasticity Pr ice _ RCPP
= α 6 * ln(THI ) t
Daily _ Elasticity Pr ice _ RCPP +TECH
=
(α 6 + α 7 ) * ln(THI ) t
(5)
Daily _ Elasticity Pr ice _ RCPR
=
(α 6 + α 8 ) * ln(THI ) t
(6)
(4)
It is possible to estimate a daily price elasticity implied by a specific weather condition
using this equation.
16
3.1.3 Substitution and Daily Price Elasticities
After estimating the parameters of the substitution and daily equations, we next calculate
the substitution and daily price elasticities for the pricing treatments using the
methodology described above. These elasticities are then used in the CE-PRISM model
to determine the impacts from the PPP pilot. The PRISM model generates several
metrics including percent change in peak and off-peak consumption on critical and noncritical days and percent change in total monthly consumption. These metrics are
generated by solving the estimated substitution and daily demand equations. Section 4
provides a detailed description of the PRISM model.
The estimation results from the substitution equation revealed that the incremental
impacts of ICTs and CPRs on substitution elasticity over and above that of the CPP rates
were not statistically significant at the five percent level. This implies that the treatment
customers in the RCPP, RCPP_TECH and RCPR cells demonstrated similar substitution
price elasticities. We have seen the similarity of the substitution elasticities for the CPP
and CPR rates in some of the previous pilots. However, the similarity of the RCPP and
RCPP_TECH elasticities was a surprising result based on the evidence from the earlier
pilots. In those pilots, customers with enabling technologies always demonstrated higher
price responsiveness compared to customers without enabling technologies. We
conjecture that the ease of overrides on the event days and CE’s allowing customers to
choose their set-back temperatures at the time of the initial installations may have led to
this result. Possibly the set-back levels simply equaled the values that were normally set
by the customers. Unfortunately, neither the data on overrides nor customers’ default
temperature settings before the installation of the ICTs were recorded during the pilot
implementation stage. Therefore, these potential explanations remain as hypotheses.
However, one piece of data regarding the customers’ experience with the ICTs is offered
by the post-pilot satisfaction survey. Based on the survey results, 43 percent of the
participants reported that ICTs did not affect their usage behavior at all or only affected it
minimally during critical peak events. Of the survey respondents, 27 percent indicated
that the ICTs substantially influenced their energy usage behavior during critical peak
events. These findings imply that the customers may have not utilized their ICTs for
peak load management purposes.
The estimation results from the daily demand equations revealed that the daily price
elasticities for the RCPP and RCPR customers were similar in statistical terms, as they
were in the substitution equations. However, unlike the result from the substitution
equation, the RCPP_TECH customers were more price-responsive in terms of their daily
usages compared to the RCPP and RCPR customers. This implies that even though the
RCPP_TECH customers did not statistically differ from the RCPP customers in terms of
their load shifting behavior from peak to off-peak periods, they clearly utilized their ICTs
to manage their overall electricity usage in a way to differentiate themselves from the
non-ICT enabled RCPP customers.
As mentioned earlier, the PPP price elasticities are weather dependent, i.e., they take on
different values for different weather conditions. For example, the impact of weather on
the substitution elasticity for the RCPP price only customers is captured through the
17
THI_DIFF variable in Equation 1 and ln (THI) variable in Equation 4. In order to
calculate the price elasticities and eventually to be able to quantify the load impacts from
the PPP pilot, we calculated the THI_DIFF and ln (THI) variables based on the “average
event day weather.” We calculated the average event day weather by taking the average
values of THI_DIFF and THI variables for the six event days. Table 3.1 presents the
weather information for each of the six CPP event days as well as the average critical
event day weather.
Table 3.1: Weather Information on the Critical Event Days
Event Date
Minimum
THI
Maximum
THI
Average
Peak THI
Average
THI
LN(THI)
THI_DIFF
Event Day 1
7/29/2010
61.1
71.4
70.5
67.5
4.2
3.6
Event Day 2
8/12/2010
69.7
77.3
76.3
72.7
4.3
4.3
Event Day 3
8/20/2010
66.0
79.0
78.1
72.2
4.3
7.0
Event Day 4
8/24/2010
63.3
72.3
71.4
67.6
4.2
4.6
Event Day 5
8/31/2010
67.1
78.9
78.2
73.1
4.3
6.1
Event Day 6
9/1/2010
69.3
78.8
75.5
72.5
4.3
3.6
-
-
-
75.0
70.9
4.3
4.9
ID
Average (Event 1 -Event 6)
Using the average CPP day weather information, we find that the substitution elasticity
for the RCPP, RCPP_TECH, and RCPR rates is -0.1076. This implies that a one percent
change in the ratio of peak to off-peak prices leads to a -0.107 percent change in the ratio
of peak to off-peak consumption. The daily price elasticity for the RCPP and RCPR rates
is statistically insignificant, hence is set equal to zero for the purposes of our analysis.
However, when the RCPP rate is paired with the ICT, the daily price elasticity is
calculated as -0.089. This implies that for one percent change in the average daily price,
the average daily consumption changes by -0.089 percent. Table 3.2 presents the
substitution and daily price elasticities implied by the average event day weather
conditions.
The substitution elasticity from the PPP pilot is favorably comparable to those estimated
in recent pilot programs. Table 3.3 presents the pricing treatments tested in these pilots,
estimated substitution elasticities, load impacts and implied arc elasticities.
6
Substitution elasticity parameters are normally presented as positive values. However, since we
are relating how much the peak/off-peak usage ratio changes as the peak/off-peak price ratio
changes by one percent, our substitution elasticity parameters take on a negative sign.
18
Table 3.2: Substitution and Daily Price Elasticities Estimated
from the PPP Pilot
Substitution/Daily
Program
Type
Based on
Average Weather
Substitution Elasticity
RCPP
Price Only
-0.107
Substitution Elasticity
RCPP
Price + TECH
-0.107
Substitution Elasticity
RCPR
Price Only
-0.107
Daily Elasticity
RCPP
Price Only
0.000
Daily Elasticity
RCPP
Price + TECH
-0.089
Daily Elasticity
RCPR
Price Only
0.000
Table 3.3: Comparison of the PPP Elasticities to those of the Other Utility Pilots
Pilot
Rate
Original Price
($/kWh)
Critical Price
($/kWh)
% Change in Substitution
% Peak
Implied ARC
the Price
Elasticity Demand Impact
Elasticity
BGE SEP 2008
DPP
PTRL
PTRH
0.153
0.153
0.153
1.309
1.313
1.903
756%
758%
1144%
-0.096
-0.096
-0.096
-20%
-18%
-21%
-0.026
-0.024
-0.018
BGE SEP 2009
PTR
0.164
1.664
915%
-0.120
-23%
-0.025
CL&P PWEP 2009
CPP_L
CPP_H
PTR_L
PTR_H
0.201
0.201
0.201
0.201
0.856
1.815
0.856
1.815
326%
803%
326%
803%
-0.080
-0.080
-0.052
-0.052
-10%
-16%
-7%
-11%
-0.031
-0.020
-0.021
-0.014
CA SPP 2003-2004
CPP
0.130
0.590
354%
-0.120
-13%
-0.037
CMS PPP 2010
CPP
CPR
0.132
0.132
0.690
0.756
423%
473%
-0.107
-0.107
-15%
-16%
-0.036
-0.034
As the RPIO and RPIO_TECH customers were only subject to the information treatment
and not the pricing treatment, it is not possible to estimate price elasticities for these
customers. Instead, their impacts are directly derived from the peak demand equations
that are discussed below.
3.2 MODEL SPECIFICATION AND ESTIMATION: INFORMATION
TREATMENTS
We are required to employ a different model for the RPIO and RPIO_TECH customers,
as these customers were not subject to any pricing treatments, but only to information
only or information plus technology treatments. Therefore, it is not possible to estimate
the demand impacts for these customers using the substitution and daily equations.
Instead, we model the changes in the peak electricity consumption of these customers on
19
the event days using an ANCOVA approach. This approach is based on flagging the
event days and comparing the changes in the average hourly peak usage on those days,
caused by event notification, to the average hourly peak usage on non-event days. The
estimation equation takes the following functional form:
3
ln(kW) it = α 0 + α1THIit + ∑δ k (THIxD_ Monthk ) it + α 3 D _ TreatPeriod t + α 4TreatCustomer +
k =1
α 5 D _ TreatPeriodxTreatCustomerit + α 6 D _ Event_ Dayt xTHIit + α 7 D _ Event_ Dayt xTHIit xD _ TECH +
α 8 D _ WEEKEND+ vi + uit
where:
ln(kW )it
: Logarithm of the average peak load for a given day.
THI t
: THI= 0.55 x Drybulb Temperature + 0.20 x Dewpoint + 17.5
THIxD _ Monthk
: Interaction of the average peak THI with monthly dummies.
D _ TreatPeriod t
: Dummy variable is equal to 1 when the period is July 2010
through September 2010.
TreatCusto mer
: Dummy variable is equal to 1 for a treatment customer.
D _ TreatPeriodt xTreatCustomeri
D _ Event_ Dayt xTHIit
D _ Event_ Dayt xTHIit xD_ TECH
: Interaction of D_TreatPeriod with TreatCustomer.
: Interaction of D_Event_Day and THI for RPIO customers.
: Interaction of D_Event_DayxTHI and dummy variable for
RPIO_TECH customers.
D _ WEEKENDt
: Dummy variable that is equal to 1 on weekends.
vt
: Time invariant fixed effects for customers
ut
: Normally distributed error term.
As in the case of the substitution and daily equations, the peak demand equation relies on
the pre-treatment and the treatment period data on both treatment and control group
customers. This methodology eliminates potential confounding factors that are
associated with any pre-existing differences between the treatment and control groups
and pre-treatment and treatment periods. We also control for other independent variables
such as weather characteristics that can affect the peak consumption. We also employ the
fixed effects estimation routine mentioned earlier for the demand model to estimate this
ANCOVA model.
Similar to the pricing treatments, the initial specifications of the PPP demand model
implied that the peak consumption of the PPP information customers increased with the
20
hotter weather. Therefore, we include an interaction term between the event day and THI
variables in the model to capture this relationship. We also introduce an interaction term
between the event day variable and the RPIO TECH customers to capture the incremental
impact of technology above and beyond that of the information alone. The estimation
results for the peak demand model are provided in Appendix 1.
Once the model is estimated and the parameters are identified, the peak impacts can be
derived using the following equations:
I mpacts RPIO
I mpacts RPIO _ TECH
= exp ( α 6 * THI t ) - 1
= exp ( (α 6 + α 7 ) * THI t ) - 1
(7)
(8)
As we will discuss in the next section, the estimation results indicate that the event day
notification induced information customers to reduce their peak demand usage.
However, the ICT did not yield an additional impact above and beyond that of the
information only treatment.
3.3 EXPLORING THE HAWTHORNE EFFECT
As discussed earlier in the report, PPP design included two control groups. GCON group
is a traditional control group which involved customers that are uninformed of the pilot
program and used to construct a but-for case for the usages of the treatment group
customers had they not been on the program. We utilized the GCON customer data in
our analyses for the impact evaluation of the PPP program.
RCON group was formed to test the existence of a Hawthorne effect which refers to
human subjects changing their behavior simply due to the awareness of being observed.
To be able to test this effect, RCON customers were told that their usages would be
recorded and examined for the pilot duration. We have conducted several analyses to
find out whether the RCON customers were behaving differently than the GCON
customers during the pilot period. First was the comparison of typical day load profiles
between GCON and RCON customers in the pre-pilot and pilot periods. The second was
the comparison of the distribution of the pilot period usages by pricing period. Neither of
these comparisons revealed any systematical differences between the GCON and RCON
group usages.
We also re-estimated all of the impact evaluation models this time utilizing the RCON
group as the control group and compared these results to those estimated using the
GCON control group. We found that the substitution elasticities were the same for both
estimations involving GCON and RCON groups. However, the daily elasticity for the
RCPP_TECH customers was higher when estimated using the RCON group, compared to
the original model (estimated using the GCON group). This implies that the RCON
customers increased their average daily usages during the treatment period more than the
GCON customers did. However, if there was indeed a Hawthorne effect, we would
expect the RCON customer to reduce their loads due to the pure knowledge that they
were being observed. Therefore, this finding is contrary to what we would expect to see
21
if there was a Hawthorne effect. Finally, the load reduction impact estimated from the
ANCOVA model using the RCON group is slightly lower compared to that using the
GCON group, however the difference between these two coefficients is not statistically
significant.
Based on these findings, we conclude that there is not a strong evidence for a Hawthorne
effect in the PPP pilot.
22
4. Load Impact Analysis Results
After estimating the substitution and daily demand equations for the pricing treatments
and the peak demand model for the information treatments, the next step in our impact
evaluation study is to determine the load impacts from the rates tested in the PPP pilot.
The load impacts for the pricing treatments are determined through the Pricing Impact
Simulation Model (PRISM) software. The PRISM software emerged from the California
Statewide Pricing Pilot (SPP). Although the PRISM was originally developed for
California, it can be adapted to conditions in other parts of North America after
adjustments have been made for weather, customer price responsiveness (price
elasticities), rate, and load shape characteristics. We calibrated the PRISM model to the
CE conditions by updating the model with CE’s weather dependent price elasticity terms,
using CE’s standard and PPP rates and inserting the average pre-PPP CE customer load
profile.
Figure 4.1 shows the simplified structure of the PRISM Model.
Figure 4.1: PRISM Impacts Model- Inputs and Outputs
Model Inputs
Customer’s peak
period usage
Customer’s off-peak
period usage
All-in peak price of
new rate
All-in off-peak price of
new rate
Central air-conditioning
saturation
Weather
Basic Drivers
of Impacts
Load Shape Effects
Peak-to-off-peak
usage ratio
Peak-to-off-peak price
ratio
Substitution effect
(i.e. load shifting)
Overall change in
load shape
(peak and off-peak
by day)
Elasticity of
substitution
Geographic location
Customer class
(e.g. residential, C&I)
Load-wtd avg daily allin price of new rate
Existing flat rate
Aggregate Load
Shape and Energy
Consumption
Impact
Daily price elasticity
Daily effect
(i.e. conservation or
load building)
Difference between
new rate (daily
average) and existing
flat rate
23
The PRISM model generates several metrics including percent change in peak and offpeak consumption on critical and non-critical days and percent change in total monthly
consumption. These metrics are generated by solving the estimated substitution and daily
demand equations. In this process, we plug in the standard rates, PPP rates, and the
beginning load values in the estimated daily and substitution equations and solve for the
new load values using the estimated elasticities. First, the new daily demand is solved
using the daily price elasticities, and then this new level of daily demand is partitioned
between peak and off-peak periods using the substitution elasticities.
After estimating the demand equations and calibrating the PRISM to CE conditions, we
calculate the load impacts associated with each of the pricing treatments tested in the
PPP. The impacts for the information treatments are readily given by equations (7) and
(8).
4.1 RCPP PROGRAM IMPACTS
The RCPP rate alone yield a 15.2 percent reduction in load during peak hours on critical
peak days and 5.0 percent reduction in peak period load on non-critical days, on average.
The RCPP rate does not lead to any changes in the monthly usage, as the daily price
elasticity is not found to be statistically significant for the RCPP customers. This implies
that the RCPP rates only induced customers to shift their loads from peak to off-peak
periods, and do not lead to any statistically detectable load building or load conservation
impacts. The load impacts from the RCPP rates are presented in Table 4.1.
Table 4.1: Load Impacts from RCPP Treatment
Impact Type
RCPP
Critical Days - Peak (% of original consumption)
-15.2%
Critical Days - Off-Peak (% of original consumption)
4.4%
Non-Critical Days - Peak (% of original consumption)
-5.0%
Non-Critical Days - Off-Peak (% of original consumption)
1.3%
Total Consumption Change (%/Month)
0.0%
4.2 RCPP_TECH PROGRAM IMPACTS
When the RCPP rates are paired with the ICT (RCPP_TECH), the peak period load
reductions reach 19.4 percent on critical event days and 3.9 percent on non-critical days.
In addition to the peak period load impacts, the RCPP_TECH rates yield an increase in
the total monthly consumption by 0.8 percent. As we have reported earlier, the daily
price elasticity for the RCPP_TECH customers is sizable and statistically significant.
24
This implies that the RCPP_TECH customers were responsive to the changes in the daily
price levels. During the pilot period, the average daily price reached to $0.232/kWh on
critical event days, whereas it fell to $0.099/kWh on the non-critical days. Given that the
number of event days were far less than the number of non-event days in a given month;
the load building effect overweighed the load conservation effect, and average monthly
usage has increased by 0.8 percent. The load impacts from the RCPP rates are presented
in Table 4.2.
Table 4.2: Load Impacts from RCPP_TECH Treatment
Impact Type
RCPP_TECH
Critical Days - Peak (% of original consumption)
-19.4%
Critical Days - Off-Peak (% of original consumption)
-0.7%
Non-Critical Days - Peak (% of original consumption)
-3.9%
Non-Critical Days - Off-Peak (% of original consumption)
2.5%
Total Consumption Change (%/Month)
0.8%
4.3 RCPR PROGRAM IMPACTS
The RCPR rates alone yield an average peak load reduction of 15.9 percent on the critical
peak days and 7.8 percent reduction in peak period load on non-critical days. Similar to
the RCPP rates, the total monthly consumption of RCPR rates remain unchanged as the
daily elasticity is statistically insignificant. The load impacts from the RCPP rates are
presented in Table 4.3.
Table 4.3: Load Impacts from RCPR Treatment
Impact Type
Critical Days - Peak (% of original consumption)
RCPR
-15.9%
Critical Days - Off-Peak (% of original consumption)
4.6%
Non-Critical Days - Peak (% of original consumption)
-7.8%
Non-Critical Days - Off-Peak (% of original consumption)
2.1%
Total Consumption Change (%/Month)
0.0%
25
4.4 RPIO AND RPIO_TECH PROGRAM IMPACTS
The RPIO program, which involved provision of information to the customers regarding
the event days, yields an average peak load reduction of 5.8 percent on the critical peak
days. When the RPIO rate is paired with the ICT, the enabling technology does not yield
additional impacts above and beyond that of the information only treatment. Table 4.4
presents the impacts from the RPIO and RPIO_TECH programs.
Table 4.4: Load Impacts from RPIO and RPIO_TECH Treatments
Impact Type
RPIO
RPIO_TECH
Critical Days - Peak (% of original consumption)
-5.8%
-5.8%
Critical Days - Off-Peak (% of original consumption)
0.0%
0.0%
Non-Critical Days - Peak (% of original consumption)
0.0%
0.0%
Non-Critical Days - Off-Peak (% of original consumption)
0.0%
0.0%
Total Consumption Change (%/Month)
0.0%
0.0%
4.5 SUMMARY OF THE PPP PILOT LOAD IMPACTS
The average reduction in critical peak period usage ranges from 5.8 percent to 19.4
percent from the pricing and information treatments tested in the PPP Pilot. The presence
of ICT with RCPP rates increases the impact achieved from the RCPP rates alone and
yields an impact 19.4 percent. The total monthly consumption increases by roughly one
percent for RCPP_TECH and remains unchanged for the RCPP and RCPR rates alone.
Figure 4.2 presents the average peak load impacts on critical peak days across the PPP
treatments.
26
Figure 4.2: The PPP Pilot Demand Response Impact Summary
Average Peak Demand Reduction (% of oringinal consumption)
0.0%
-2.5%
-5.0%
-5.8%
-5.8%
-7.5%
-10.0%
-12.5%
-15.0%
RCPP
-15.2%
-15.9%
RCPP_TECH
-17.5%
RCPR
-20.0%
RPIO
-19.4%
RPIO_TECH
-22.5%
Customer Type
The results from the PPP pilot compares favorably to the results from the previous pilots,
and mostly fall in the middle of the distribution. Figure 4.3 presents the demand response
impacts from 73 tests of dynamic pricing.
Figure 4.3: Comparison of the Demand Response Impacts across the Pilots
60%
TOU
TOU w/
Tech
PTR
RTP
PTR
w/
Tech
CPP
CPP w/
Tech
RTP w/
Tech
40%
30%
Consumers Energy
20%
10%
0%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
Percent Reduction in Peak Load
50%
Pricing Pilot
27