Defaulting Customers onto CPP –
Lessons from Actual Experience
Josh Bode, FSC
July 14, 2009
Key Questions We Will Address
 Why does default dynamic pricing matter from a national perspective?
 How did SDG&E transfer customers onto default dynamic pricing?
 Did customer actively decide or were their decisions passive?
 How do customer decisions regarding opt-out CPP vary?
 Do customers who win or lose without changing their behavior make
different decisions?
 How did customers respond to demand reservation options?
 Can account representatives influence customer decisions?
Page 1
Why do default or opt-out dynamic rates matter?

As the FERC National Assessment of DR potential shows, the enrollment approach used
for dynamic pricing fundamentally affects the potential for load reduction
 Under opt-in pricing, the national DR potential is 9 percent of peak demand
 Under opt-out (not mandatory) dynamic pricing, the DR potential is as much as 14 percent
of peak
 Under mandatory dynamic pricing, the DR potential is as much as 20 percent of peak





California is making TOU/CPP the default rate for C&I customer and many states are
deciding whether or not to do the same
Importantly, default dynamic pricing allows customers to choose and can provide them
the opportunity to test new rates if 1st year bill protection is offered
The TOU/CPP rates adopted reflect the not only wholesale market costs, but the value of
capacity - $1.06/kwh or $1060/MWh
The SDG&E results provide data based on customers actual choices
The analysis and subsequent tools developed allows utilities to estimate 1st year
participation under opt-out pricing based on utility specific rates, customer mix, and load
shapes
Page 2
How did SDG&E transfer customers onto default
dynamic pricing?

Bill protection was offered for the first year – customers could test the rate without any
risk

By default, customers who did not opt out had 50% of their summer maximum demand
insured, but could adjust the value up or down, if desired

Customers were given 45 days from the default date to opt out (Customers default dates
rate varied depending on their bill cycle)

In addition, SDG&E made a CPP Analysis Tool available to customers who registered
online that allowed them to assess bill impact under a variety of user defined scenarios

Customers were told to expect an average of 9 events per year with a maximum of 18
events during a season

The event period is from 11 to 6 p.m. and can only occur Monday trough Saturday during
summer months (May 1st–Sept. 30th)—SDG&E filed to allow events to be called year
round
Page 3
Who Was Defaulted Onto CPP?
 All customers with remotely read 15 minute interval meters whose electric
demand exceeded 20 kW, with a few exceptions
 Approximately 400 Customers below 200 kW were defaulted onto CPP in
2008, allowing estimation of the response by medium (20-200 kW)
customers
Size Category
Number of
Customers
Average of
Max
Summer OnPeak
Demand
%
100 kW or below
175
9.9%
48.2
100 to 200 kW
225
12.7%
157.3
200 to 500 kW
871
49.3%
315.3
500 kW and above
386
21.8%
1,003.3
Unclassified
110
6.2%
~400.0
1,767
100.0%
399.4
Total
Page 4
Exceptions


Direct access customers
Participants in the several,
but not all, existing DR
programs
Did customer actively decide or were their decisions
passive?
Type of Decision by Customer Size
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
 At minimum, 64 percent
of customers made
active decisions
 Of the customers that
accepted the default
TOU/CPP tariff and
insurance levels, some
unknown share of them
made an active decision
100 kW or less
100 to 200 kW
200 to 500 kW
500 kW and up
All defaulted
customers
Opted out of default CPP
Changed the default insurance level
Unclear - active or passive decision?
Page 5
 About 75 percent of the
smallest and the biggest
of customers made
active decisions
Most Customers Remained on Default CPP
Proportion of Customers Remaining on CPP-D
by Industry
Agriculture, Mining & Construction
0.74
Manufacturing
0.77
Wholesale, Transport, Other Utilities
0.90
Water Districts
0.81
Retail Stores
0.73
Offices, Finance, Services
0.73
Hotels and Apartment Buildings
0.52
Schools
0.77
Institutional/Government
0.78
0.00
0.20
0.40
0.60
0.80
Proportion of Accounts
Page 6
1.00
 Except for hotels,
acceptance rates
exceeded 70%
across industries
 Acceptance rates
vary depending on
expected structural
wins, industry, the
share of the annual
consumption that
occurs during CPP
hours, and direct
access to billing
analysis tools
The Higher the Structural Wins, the Higher the
Likelihood of Customers Remaining on Default CPP
 Structural wins vary by
industry because of
differences in the load shapes
Proportion of Customers Remaining on CPP-D
0.80
1.00
by % Structural Wins w/o Capacity Reservation
0.81
0.84
0.89
0.71
0.71
0.00
0.20
0.40
0.60
0.65
-5% to -2%
-2% to -1%
-1% to 1%
1% to 2%
2 to 5%
Page 7
5% to 10%
 The share of total consumption
that occurs during high price
CPP or TOU hours is closely
related to structural wins
without insurance
 Models that relied on the
share of consumption during
CPP or TOU hours (heuristics)
were stronger predictors than
those based strictly on
structural wins and losses
Approximately 50% of remaining customers
accepted the default insurance levels
Proportion of Customers Remaining on Default CRC
by Industry
Agriculture, Mining & Construction
0.45
Manufacturing
0.38
Wholesale, Transport, Other Utilities
Water Districts
0.26
0.11
Retail Stores
0.41
Offices, Finance, Services
0.39
Hotels and Apartment Buildings
0.32
Schools
Institutional/Government
0.00
 Almost all customers
who declined the
default capacity
reservation value
chose no capacity
reservation – they
preferred to face the
risk rather than pay
for the insurance
0.71
0.22
0.20
0.40
0.60
Proportion of Accounts
Page 8
0.80
1.00
 The acceptance
pattern across
industries is different
for default capacity
reservation than it is
for default CPP
Opt Out Decisions Varied Across Account Reps
Proportion of Customers Opting Out
by Account Rep
0.60
0.63
0.48
0.40
0.42
0.29 0.30
0.24 0.24 0.25 0.25
0.20
0.20 0.20 0.21
0.16
0.10
0.08 0.10
0.00
0.03
Includes account reps with more than 20 assigned accounts. Most have
over 100 assigned accounts.
Page 9
 Some, but not all, of
the variation is due to
differences in account
rep assignments
 Account reps influence
the decision even after
controlling for industry,
size, and customer
load shapes
Conclusions

The regression models developed can help customize estimates of default
CPP acceptance for utilities with different tariff designs, business mix, and
load shapes

The models cannot cannot, however, control for differences in the process
employed in defaulting customers onto dynamic prices, nor do they predict
customer decision after they have tried dynamic pricing
So far, we have learned that

 A substantial share of customer actively engage in the decision of whether to accept default
dynamic rates and demand insurance levels
 The majority of them choose to remain on default CPP
 A large proportion of customers preferred to face the risk rather than pay for insurance that reduces
bill volatility
 Accounts representatives can influence customer decisions - but unless a clear recommendation or
approach is adopted, the influence may not be uniform

Much more will be learned over the next several years concerning customer
decisions associated with fundamental shifts in pricing strategy, including:
Page 10
For questions, feel free to contact
Josh Bode, M.P.P.
Freeman, Sullivan & Co.
101 Montgomery Street 15th Floor,
San Francisco, CA 94104
[email protected]
415.777.0707
Page 11
Regression results and key drivers of customer decisions
How And Why Choice Regression Models are Useful

Graphs and tables do not disentangle
the relationships between potential
drivers/predictors

Regressions can customize estimates of
opt out rates in other jurisdictions, with
some limitations

Regressions disentangle those
relationships and assign weights to the
factors affecting the decision

Regressions can be used by SDG&E to
predict likely opt out rates for new
customers defaulted onto the rate

Regressions can help guide policy
decisions, particularly about expected
enrollment and where to focus efforts in
transitioning customers

Key factors in assessing and shaping the
model:
 Does it make sense?
 Are the estimates unbiased?
 Do the results change substantially if we
add or exclude other potential drivers?
 How does it perform when compared with
the actual decisions?
 What is the effect of structural wins and
losses on decision to stay or opt out?
 What share of customers will accept
default CPP and provide load reduction
potential?
 How much do customers value insurance
against the price spikes in CPP?
Page 12
Summary of Regression Analysis Conducted
Regression for
Acceptance of
Default CPP
Share of CPP Period
Consumption
Consumption by TOU
block
% Structural wins or
losses
 Based on heuristic
 Allows calculation of
 Predicts opt out and
decision-making
 % of kWh subject to
Acceptance of
default Capacity
Reservation
(Given acceptance
of default CPP)
CPP charges is highly
correlated with
structural wins/losses
 Can draw conclusions
w/o full bill analysis
 Designed to assess
influence of account
reps after controlling
for other factors
opt out and capacity
reservation decisions
based on TOU billing
data – does not require
manipulating interval
data
 Sacrifices some
precision and accuracy
for usability
Page 13
CRC decision as a
function of wins/losses
with CPP, both with and
without a capacity
reservation charge
 % of kWh subject to
CPP dominates the
structural win/loss
variables when included
Regression results and key drivers of customer decisions
Key Drivers of Default CPP Acceptance
Key Drivers of Default CPP Acceptance
Factors that Did Not Influence Acceptance
 Relative Structural Wins: For each 1%
increase in relative structural wins, the
probability of accepting default CPP
changes by approximately 0.02 ( 3%)
 Customer Size: There were differences,
but they were explained by other factors
(e.g. load shape)
 Industry Type: Hotels are more likely to
reject default CPP and wholesale and
transportation are more likely to accept it
 Ease of Access to Billing Analysis Tools:
Providing direct access to tools decreases
the probability of acceptance by
approximately 0.056 (7.5%)
 Load Factor: There were differences, but
they are explained by other factors
 The load factor does not necessarily
reflect the coincidence of high customer
load with high system load
 Climate Zone: There were no statistically
significant differences based on SDG&E
climate zone
 There is also little variation in SDG&E and
much of the climate zone differences are
captured in the structural wins or loses.
NOTE: For choice models, impacts are not linear
Page 14
Key Drivers of Default Capacity Reservation Level
Acceptance (Given CPP Acceptance)
Key Drivers of Acceptance
Factors that Did Not Influence Acceptance
 Ease of access to billing analysis
tools and structural wins w/o CR
 Peak to off-peak average demand ratio:
Most of the effect is already captured
by structural wins
 % of CPP hours where demand
exceeds the default capacity
reservation
 Volatility of load during CPP-like
periods
 Customer size
 Industry type
 Load Factor: Again, there are
differences, but they are explained by
other factors
 Climate Zone: The conclusion applies
to SDG&E, but may not apply to IOU’s
because of SDG&E’s limited variation in
weather
 However, weather is related to load
shapes and structural wins.
NOTE: For choice models, impacts are not linear
Page 15
Overview of rate and deployment process
Comparison of CPP-D, Opt Out, and Pre-default Tariffs
Pre-default
An
Anon-peak
on-peak
demand
demandcharge
charge
made
made
thethe
optopt
out
out
tariff
tariff
less
less
appealing
appealing
T&DT&D
charges
charges
were
thewere
same
theforsame
CPPD and
for CPP-D
the optand
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tariff
out tariff
Page 16