Defaulting Customers Defaulting Customers onto CPP – onto CPP –

Lessons from Actual Lessons from Actual ExperienceExperience

Josh Bode, FSC

July 14, 2009, 2009

Page 2

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 3

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 4

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 5

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

Exceptions Direct access

customers Participants in the

several, but not all, existing DR programs

Who Was Defaulted Onto CPP?

Size Category Number of Customers %

Average of Max Summer

On-Peak 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

Total 1,767 100.0% 399.4

Page 6

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%

100 kW or less

100 to 200 kW

200 to 500 kW

500 kW and up

All defaultedcustomers

Opted out of default CPPChanged the default insurance levelUnclear - active or passive decision?

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

About 75 percent of the smallest and the biggest of customers made active decisions

Page 7

Most Customers Remained on Default CPP

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

0.78

0.77

0.52

0.73

0.73

0.81

0.90

0.77

0.74

0.00 0.20 0.40 0.60 0.80 1.00Proportion of Accounts

Institutional/Government

Schools

Hotels and Apartment Buildings

Offices, Finance, Services

Retail Stores

Water Districts

Wholesale, Transport, Other Utilities

Manufacturing

Agriculture, Mining & Construction

by IndustryProportion of Customers Remaining on CPP-D

Page 8

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

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

0.710.65

0.71

0.810.84

0.89

0.0

00

.20

0.4

00

.60

0.8

01

.00

Prop

ort

ion o

f A

ccounts

-5% to -2%-2% to -1% -1% to 1% 1% to 2% 2 to 5% 5% to 10%

by % Structural Wins w/o Capacity ReservationProportion of Customers Remaining on CPP-D

Page 9

Approximately 50% of remaining customers accepted the default insurance levels

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

The acceptance pattern across industries is different for default capacity reservation than it is for default CPP

0.22

0.71

0.32

0.39

0.41

0.11

0.26

0.38

0.45

0.00 0.20 0.40 0.60 0.80 1.00Proportion of Accounts

Institutional/Government

Schools

Hotels and Apartment Buildings

Offices, Finance, Services

Retail Stores

Water Districts

Wholesale, Transport, Other Utilities

Manufacturing

Agriculture, Mining & Construction

by IndustryProportion of Customers Remaining on Default CRC

Page 10

Opt Out Decisions Varied Across Account Reps

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

Includes account reps with more than 20 assigned accounts. Most have over 100 assigned accounts.

0.03

0.08 0.10 0.10

0.160.20 0.20 0.21

0.24 0.24 0.25 0.25

0.29 0.30

0.42

0.48

0.63

0.0

00

.20

0.4

00

.60

Prop

ort

ion o

f A

ccounts

by Account RepProportion of Customers Opting Out

Page 11

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 12

For questions, feel free to contact

Josh Bode, M.P.P.

Freeman, Sullivan & Co.101 Montgomery Street 15th Floor,

San Francisco, CA 94104

joshbode@fscgroup.com

415.777.0707

Page 13

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 disentangle those relationships and assign weights to the factors affecting the decision

Regressions can help guide policy decisions, particularly about expected enrollment and where to focus efforts in transitioning customers 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?

Regressions can customize estimates of opt out rates in other jurisdictions, with some limitations

Regressions can be used by SDG&E to predict likely opt out rates for new customers defaulted onto the rate

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?

Page 14

Summary of Regression Analysis Conducted

Regression for

Share of CPP Period Consumption

Consumption by TOU block

% Structural wins or losses

Acceptance of Default CPP

Based on heuristic decision-making

% of kWh subject to 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

Allows calculation of opt out and capacity reservation decisions based on TOU billing data – does not require manipulating interval data

Sacrifices some precision and accuracy for usability

Predicts opt out and 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

Acceptance of default Capacity Reservation

(Given acceptance of default CPP)

Page 15

Regression results and key drivers of customer decisions

Key Drivers of Default CPP AcceptanceKey Drivers of Default CPP

Acceptance

Relative Structural Wins: For each 1% increase in relative structural wins, the probability of accepting default CPP changes by approximately 0.02 ( 3%)

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%)

NOTE: For choice models, impacts are not linear

Factors that Did Not Influence Acceptance

Customer Size: There were differences, but they were explained by other factors (e.g. load shape)

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.

Page 16

Key Drivers of Default Capacity Reservation Level Acceptance (Given CPP Acceptance)Key Drivers of Acceptance

Ease of access to billing analysis tools and structural wins w/o CR

% of CPP hours where demand exceeds the default capacity reservation

Volatility of load during CPP-like periods

Customer size

Industry type

NOTE: For choice models, impacts are not linear

Factors that Did Not Influence Acceptance

Peak to off-peak average demand ratio: Most of the effect is already captured by structural wins

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.

Page 17

Overview of rate and deployment process

Comparison of CPP-D, Opt Out, and Pre-default Tariffs

An on-peak demand charge

made the opt out tariff

less appealing

Pre-default

T&D charges were the same for

CPP-D and the opt out

tariff

An on-peak demand charge

made the opt out tariff less

appealing

T&D charges were the same for

CPP-D and the opt out

tariff