Efficient Rates: Design
and Customer Response
Steven Braithwait
Christensen Associates Energy Consulting
EEI Advanced Rate Course
July 25, 2012
Agenda


Traditional retail rates
Efficient electricity pricing
 Features
 Examples
 Designing efficient rates

How do customers respond to dynamic
pricing?
July 2012
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1
Historical Background

Traditional retail rates satisfy most accepted
rate design objectives
 Revenue recovery
 Rate & bill stability
 Simplicity

Efficient pricing addresses an historically
overlooked goal –
 Economic efficiency in allocating resources (e.g.,
retail prices reflect marginal costs)
July 2012
3
Traditional Retail Rates

Pre-set rates that reflect categories of
average historical embedded costs –
 Connection charges ($/month) – Recover nonusage-related costs such as Metering, Billing
and Customer service
 Energy prices ($/kWh) – Recover fuel & variable
operating costs
 Demand charges ($/kW of maximum demand) –
Recover fixed capacity costs
July 2012
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2
Traditional Retail Rates (2)

“One-size-fits-all” rates apply to broad classes of
customers (Residential, Small & Large Businesses)

Implications:
 Rates don’t reflect differences in cost to serve individual
customers in a rate class, because:
– Energy costs vary hourly, and
– Customers in a rate class have different load profiles
 Implication: Low-cost customers subsidize high-cost
customers (cross subsidization)
– Creates inertia against changing from status quo rates, since
any rate change will produce winners & losers
July 2012
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Efficient (Smart) Electricity Pricing

Reflects forward-looking, time-varying marginal,
or market costs

Reflects relevant risks to energy providers
 Larger risk premium for products that offer greater
price certainty – e.g., fixed vs. varying prices

Offers optional price structures that acknowledge
diverse customer risk preferences
 I.e., given a choice, some consumers will select a
riskier, more volatile pricing option if it has a lower
expected price; others will pay a premium for less risk
July 2012
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3
Effects of Efficient Pricing

Gives consumers an incentive to use electricity
efficiently and an opportunity to manage their
energy costs –
 Use less when energy costs are high (e.g., summer
afternoons)
 Use more when cost is low (e.g., recharge EVs at night)

Implications – utilities/generators will invest
more efficiently
 Reduces the need for extra peaking generation to meet
peak demand that is insensitive to price because prices
don’t reflect high market costs
July 2012
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Basis for Efficient Pricing

Marginal costs (market costs) vary by time





Hourly
Daily
Seasonally
Distributions of hourly MC are typically
highly skewed, with relatively few very highcost hours
Efficient prices reflect marginal costs
July 2012
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4
Example of Variability in Hourly
Wholesale Energy Costs (Summer)
$500
Averages (per MWh):
Overall
= $51
Excl. top 90 hours = $43
Top 90 hours
= $178
$450
Wholesale Price ($/MWh)
$400
$350
$300
$250
Overall
average
= $51/MWh
($0.051 /kWh)
$200
$150
$100
$50
$1
169
337
505
673
841
1,009
1,177
1,345
1,513
1,681
1,849
2,017
2,185
Hours -- mid-June - mid-September
July 2012
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Basis for Efficient Pricing (2)

Efficient prices reflect marginal costs
 Nearly exactly – Hourly pricing
 On average –Time-of-Use (TOU) rates reflect
average differences by time period
 On important high-cost days – Critical-peak
(CPP) and Variable-Peak (VPP) pricing reflect
high peak costs on limited number of days
July 2012
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5
Time-Based Pricing – Static

Time-of-Use (TOU) rates
 Vary by time period, but not by day
 Reflect average difference in marginal costs
–Peak period
–Off-peak period
 Can be seasonal or summer-only
July 2012
11
Load-Weighted Average Hourly Prices
by Type and Time Period
Pricing Categories
All hours, June-Sept
All, excluding top 90
Top 90 hours (Critical)
TOU Periods
Off-peak
Peak
CPP/VPP Peak
Non-critical (excl. top 90)
Critical
July 2012
LoadWeighted
Average
Price
($/kWh)
$
0.050
$
0.044
$
0.178
Premium/
Discount
(relative to
$0.050/kWh)
% of
Summer
Hours
-12%
252%
97%
3%
$
$
0.041
0.080
-18%
57%
82%
18%
$
$
0.062
0.178
23%
252%
15%
3%
12
6
Price Variability by Hour and Day
Quintiles of Weekday Average Wholesale Costs
$200
$175
Peak period
$150
$/MWh
$125
Q1
Q2
Q3
Q4
Q5
$100
Average price
$75
$50
$25
$0
1
5
9
13
17
21
Hours Ending
July 2012
13
Value of Demand Response Through Smart Pricing
Replaces the Cost of Extra Peaking Generation
100%
Typical DR target – 5-10% of max. demand;
1% of hours annually
90%
Percent of Maximum Demand
80%
$20 - 40 million/yr cost savings for
10,000 MW system (1% of total cost)
70%
60%
50%
Peaking
generation
40%
30%
Load duration curve
20%
10%
0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percent of Summer Hours
July 2012
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7
Time-Based Pricing – Dynamic

Dynamic pricing
 Dispatchable – day-of or day-ahead

Examples
 Hourly pricing (day-ahead/hour-ahead notice)
 Variable-peak pricing (VPP) – Oklahoma G&E
– Peak price day-types (Low, medium, high, critical)
 Critical-peak pricing (CPP) – numerous pilots
– High peak price (e.g., $1.00/kWh) applies on limited number of
“event” days; lower non-event peak prices
– Typically linked to TOU rate, but can apply to any base rate
 Peak-Time Rebate (PTR)
– Credit for load reductions below baseline load on event days
July 2012
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Designing Efficient Rates

Reflect averages of expected marginal costs of
serving load over relevant time periods
 E.g., CPP reflects expected marginal energy and
capacity costs in top 1 – 2 % of hours (e.g., $0.25 –
$1.50/kWh)
 Non-critical prices are discounted to reflect removal of
highest-cost peak hours from revenue recovery

T&D costs may be recovered separately
(unbundled), or through adjusted time-based
rates
July 2012
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8
Role of Smart Metering/Smart Grid in
Expanding Efficient Pricing

Time-based pricing requires metering by time
period, and software for data management and
billing – all historical barriers to efficient pricing

AMI availability lowers the hurdle
Business case for AMI requires operating cost
savings (e.g., meter reading, outage detection) as
well as benefits from efficient pricing


With AMI installed, California is moving to
default CPP for medium & large C&I customers,
and optional CPP for mass market
July 2012
17
Customer Reactions to Smart Meters

Negative






Perceived bill increases (e.g., Bakersfield)
Meter accuracy? (tests confirm accuracy)
Health impacts?
Privacy concerns; big brother watching energy use
CA and ME allow opt out (but must pay for extra cost
of meter reading)
Positive example
 SCE rolling out smart metering website that allows
customers to:
– view their usage profiles,
– see their monthly bill to date and a bill forecast, and
– request weekly “budget” notifications of progress toward
usage targets
 Positive focus group response
July 2012
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9
Customer Reactions to Dynamic
Pricing

Negative
“Unfair to charge higher prices when electricity is
needed the most”
“Some customers can’t change usage”
“It will hurt elderly and low-income consumers”
“Just another way for utilities to raise profits”

But – pilot participants report high satisfaction





Implications
Need for early customer engagement to demonstrate
value
Focus on price discounts rather than high prices
July 2012
19
Utility Issues with Dynamic Pricing



More complicated & costly
Possible regulator and intervener push-back
Revenue loss from “structural winners,” or if
sales fall
 Can adjust rates in next rate case

Need to engage customers
Potential Upsides:
 Increased customer satisfaction
 More competitive rates
 Avoided peaking capacity costs
July 2012
20
10
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% Peak load Reduction
How Do Customers Respond
to Dynamic Pricing?

Residential CPP/PTR
 Numerous pilots with voluntary participation
 New evidence on default-like PTR pilots
 Permanent rates/programs (California)

C&I CPP

C&I RTP
July 2012
July 2012
21
Residential Ave. % Peak-Load Reduction
(CPP/RTP & PTR; w/ & w/out technology)
60%
CPP/RTP
Peak-time
Rebate
CPP/PTR w/ enabling technology
50%
40%
30%
20%
10%
0%
Utility/Pilot
22
11
Customer Response in SDG&E DefaultType PTR Pilot
1.6
All Control - Sept 7
All PTR Sept 7
Responders
1) Average participant
reduced usage by 5%
1.4
1.2
kW
2) 11% of “responder”
participants
reduced load by
significant amount;
40% load reduction
Event Hours
1.0
0.8
0.6
0.4
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
July 2012
23
Distribution of Residential CPP Load
Impacts – PG&E SmartRate (2009)

Enrollment in voluntary residential CPP
 Summer average – 16,000 customers

Distribution of load impacts
 12% provide load reductions > 1 kW
 24% provide load reductions of 0.2 to 1 kW
 64% provide load reductions < 0.2 kW
(including estimated load increases)
July 2012
24
12
2009 C&I CPP Load Impact Summary
(Average Hourly Values for Average Event)
Estimated
Reference
Load (MW)
256
130
419
Customer
Accounts
642
476
1,576
Utility
PG&E
SCE
SDG&E
Estimated
Load
Impact
(MW)
8.4
24.6
23.3
Observed
Load (MW)
247
106
396
% Load
Impact
3.3%
18.9%
5.6%
Large % load impact at SCE due largely to some customers facing
$2.00/kWh CPP price. In 2010, average % LI dropped to 3% after
several thousand default customers were added.
July 2012
25
Default CPP Load Impacts, SDG&E
Average Event Day
500,000
90,000
450,000
400,000
80,000
70,000
350,000
60,000
Observed event-day load
300,000
50,000
250,000
40,000
Estimated load impact
200,000
30,000
150,000
20,000
100,000
10,000
50,000
Load Impacts (kW)
Reference and Event-Day Load (kW)
Estimated reference load
Reference
Event Day
Load Impacts
0
0
-10,000
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Hour
July 2012
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13
Distribution of C&I CPP Load
Impacts across Customers

Share of load impacts accounted for by the
top-responding 5% of customers:
 PG&E:
 SCE:
 SDG&E:
64% (16% of load)
55% (15% of load)
74% (13% of load)
July 2012
27
Distributions of RTP Price
Responsiveness by Business Type
(Georgia Power)
5
SIC 32 – Stone, clay & glass
PR Index (5 = very high)
4
0.500
Office Buildings
3
0.450
0.400
2
0.350
1
0.300
Backup generators
0.250
0
1
6
11
16
21
26
31
36
41
0.200
5
0.150
SIC 35-39 – Machinery, etc
0.100
4
41
43
45
47
49
51
67
70
73
76
39
64
37
61
35
33
31
29
27
25
23
21
19
17
15
9
7
13
5
11
3
0.000
1
PR Index (5 = very high)
0.050
3
0.500
2
0.450
Schools & Universities
0.400
1
0.350
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
0.300
0.250
5
SIC 494 – Water supply
0.200
0.150
4
79
58
55
52
49
46
43
40
37
34
31
28
25
22
19
16
13
2
10
7
0.000
4
0.050
1
PR Index (5 = very high)
0.100
3
1
0
1
July 2012
6
11
16
21
26
31
36
41
46
28
14
Conclusions

Nearly all studies of dynamic pricing show
significant price response (though wide
distribution)

Smart meters/grid provide new opportunities
and reduce the cost hurdle

Continued skepticism and inertia on part of
some utilities, regulators and consumers
July 2012
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15