1. DRAFT
5-24-11 Version
(Only some recommendation changes from 5-17 version)
A National Perspective on
Using Time-Differentiated Rates to Control Power
System Costs
By
Robert J. Procter, Ph.D.
_____________________________
The views expressed in this paper are solely the professional views of the author. Nothing in this paper
should be viewed as representing staff or the commissioners of the Oregon Public Utility Commission. All
rights reserved.
1

2. TABLE OF CONTENTS
I. Overview of this Report 1
II. Power Needs and SG – A Very Broad Overview 2
III. Use of Dynamic Pricing to Manage Costs 5
IV. Defining Dynamic Pricing 7
V. Dynamic Pricing and TOU Rate Design Issues 9
VI. Winners and Losers 13
VII. Concerns about Vulnerable Populations 15
VIII. Dynamic Pricing is Demand Response 19
IX. Opt In, Opt Out, Mandatory Participation 20
X. Time Sensitive Rates and Supporting End-User Technology 21
XI. Social Cost Arguments Supporting Mandatory Program
Participation (or setting DP as the default with an Opt out) 22
XII. Transitioning from Fixed Rates to Dynamic Rates 23
Policy Recommendations 26
Appendix - Summary of Selected Pricing Experiments 28
1

3. I. Overview of this Report
Electricity pricing has resulted in consumption patterns that poorly match electric
production and delivery cost patterns. One result is the looming national problem
of massive spending to build power plants to meet growing demands. We‟ve
been selling the proverbial Cadillac at Yugo prices. Given that the electric
industry is characterized by increasing costs to meet growing needs, it will be
difficult to continue these historical pricing policies. The subsidies embedded in
fixed rates (flat, or inverted, or TOU) will likely become increasingly untenable.
For example, one subsidy of particular interest is how flat fixed rates under-price
on-peak consumption. This subsidy to on-peak consumption leads to even
higher peak capacity needs when that same installed capacity goes unused for
many hours of the day, and in some cases, used for only a few hours a year.
One key question is how much longer can we continue to afford the hidden costs
embedded in the existing regime of fixed power rates?
In the future, the current structure of fixed rates (flat, slightly inclining rates, or
Time-of-Use (TOU)) will become increasingly untenable in many but probably not
all parts of the U.S. Individual utilities, or states, or regions will face different
circumstances (e.g. may not face near-term capacity constraints or have very
small cost differences in meeting peak versus off-peak consumption). These
varying circumstances explain some of the differences in approach taken by
utility commissions, legislatures, and utilities in different states and regions
towards Dynamic Pricing (DP), Demand Response (DR), and Smart Grid (SG).
Considering the potential efficiency benefits of DP, why has its adoption been so
slow in coming? What is DP, and what is its role in SG and DR? What does the
literature on DP studies indicate about the potential effectiveness and important
issues in DP and Peak-Time Rebates (PTR) designs? What are the different
ways DP is defined? What are the consumer-level impacts of DP and PTR?
How do system level benefits argue for mandatory program participation, or at
least setting DP as the default with an opt-out option?
Experiments indicate that DP and DR can significantly reduce peak consumption.
Reducing peak consumption with cost-effective DR will lower future electric costs
1

4. and therefore lower electric rates (these approaches primarily address
consumption on peak and have only a small impact on total electric
consumption).
Turing to the distribution of benefits of DP, experiments indicate that DP tends to
reward customers with relatively flat load curves, those whose consumption is
lower than average, and those who are more able to shift their consumption.
Losers from DP are generally those with peaky loads, higher overall
consumption, and less ability to shift consumption. Successful DP and DR
programs must account for these distributional impacts in their design.
A great deal has been written on this topic on a theoretical level, an applied level,
and summarizing various time-based pricing pilots and programs. The goal of
this paper is to provide an overview of these issues from a national perspective
at the ‟40,000 - 60,000 foot level.‟
II. Power Needs and SG – A Very Broad Overview
Beyond the desires and spending priorities of the Obama Administration, are
there systemic issues underlying SG initiatives? One report suggesting the
answer is an emphatic yes is titled “The Power of Five-Percent, How Dynamic
Pricing Can Save $35 Billion in Electricity Costs.”1 This paper was written prior to
2007, and focusing on the national situation, they projected electric demand to
grow by 19 percent over the next decade while capacity was projected to grow
only by six percent. Also on a national level, the Energy Information
Administration (EIA) projects the average growth rate of grid-based electric
demand to exceed the average growth of electric supply to the grid over the
period 2009-2035. 2
It is highly unlikely that we can afford to continue building plants and power lines
to solve the peak demand – peak supply imbalance. Rather, a consensus has
1
Ahmad Faruqui, Ryan Hledik, Sam Newell, Johannes Pfeifenberger Principal, “The Power of Five-
Percent, How Dynamic Pricing Can Save $35 Billion in Electricity Costs,” by - The Brattle Group, May
16, 2007.
2
Energy Information Administration, “Electricity Supply, Disposition, Prices, and Emissions, AEO2011
Reference Case.”
2

5. been forming around an integrated approach combining peak capacity expansion
with ways to manage peak demand that enhance customer‟s ability to better
control their electric consumption.3 DP is seen as an important piece of the
overall approach to peak demand management, and one element to help bridge
this peak demand – peak supply imbalance and also help manage overall power
supply costs.4
DP does not require that the utility invest in an expensive AMI system. Getting
price signals to customers with no AMI is fairly easy. In Illinois, part of the
answer is the belief that for the vast majority of customers, it is counter-
productive to send them prices every hour. Rather, their approach is to post
hourly prices on a web-site, educate customers about the general price pattern,
and only send them alerts when prices are going to be exceptionally high5.
Turning to other elements of SG, a short article by Pike Research issued in
December 2009, argued that smart meters, while the most visible part of SG,
aren‟t where the real benefits exist. They argue that grid infrastructure projects
(transmission upgrades, substation automation, and distribution automation) will
likely find the best return on investment.6
They further predict that grid automation (i.e., the actions noted in the prior
paragraph) will capture 84 percent of global SG investment through 2015,
compared to just 14 percent for AMI, and 2 percent for electric vehicle (EV)
management systems.”7 According to their research, “…revenues [from the sale
of SG equipment] will peak in 2013 after several years of a strong push by key
3
Ibid.
4
It should be noted that AMI isn‟t necessary for DP. If AMI has been installed, and is operational, then it
is relatively easy to implement DP. DP can still be implemented in the absence of AMI. Doing so does
require targeted meter exchanges. For example, the RTP program in Illinois does not rely on AMI.
ComEd uses interval meters read once per month and Ameren uses a one-way Automated Meter Reading
(AMR) for some customers. Implementing meter data management systems may however be as important
as the technology choices for the meters themselves.
5
When there is one or more hours the following day over a certain threshold (currently $0.13/kWh, energy
only, but this may be lowered to $0.10/kWh), customers are notified automatically via email and telephone
calls.
6
“Smart Grid Investment to Total $200 Billion Worldwide by 2015,” Pike Research, See:
http://www.pikeresearch.com/newsroom/smart-grid-investment-to-total-200-billion-worldwide-by-2015
7
Pike Research.
3

6. governments, and will thereafter be a smaller, albeit still very substantial,
market.”8 Finally, they predict that grid automation upgrades and smart metering
will be in the range of $200 billion in worldwide investment between 2008 and
2015.9
There is no lack of competing definitions for what is and is not SG. Or, what SG
is supposed to enable or accomplish. There isn‟t any reason to summarize that
material here. The Pike Research report takes a different approach and looks at
the key factors driving SG investment. They identify investment as SG if they fall
into one or more of the following four categories based on results or goals:
1. Improved reliability and security,
2. Improved operating efficiencies (with associated lower costs),
3. Balancing power generation supply and demand, and
4. Reducing the overall electrical system‟s impact on climate change.10
These four rationales for SG all relate to reduced future spending to meet
customer needs for electricity.
If cost savings is the primary driver behind SG, why isn‟t SG being implemented
faster? Barriers to this transformation go well beyond pure technical and
economic issues. They note that the slow progress can also be attributed to a
lack of common vision and standards, outdated and fragmented business and
regulatory models, and lack of awareness, and often the trust, of the consuming
public.11
Later in this paper, the reader will see that rates are an integral part of SG.
Turning to rates alternatives using SG, one study that included an examination of
the Net Present Value (NPV) of utility cost savings compared various forms of
variable pricing, concluded that TOU rates produced the lowest overall savings.
8
Ibid.
9
“Smart Grid Technologies,” Pike Research, See: http://www.pikeresearch.com/research/smart-grid-
technologies.
10
Ibid.
11
Ibid.
4

7. Next was RTP. Then, three different forms of CPP each produced even higher
overall cost savings.12 This is consistent with results from other studies
summarized in the appendix to this report.
III. Use of Dynamic Pricing to Manage Costs
Taking a step back and looking more broadly at the use of DP, we see that it‟s
been with us since the beginning of economic exchange, be that barter exchange
or market-based trade. One writer notes that "Dynamic pricing has always been
with us…the classic hagglers in the market of a Middle East bazaar [is one
example]. People will pay very different prices for the same bolt of fabric. This is
more the norm in transactions than fixed pricing. Fixed pricing is a much later
phenomenon and it's an artificial one.”13 It‟s only been since the Industrial
Revolution that DP was replaced by standardized pricing schemes. DP has
come back into favor in various industries over the last 20 – 30 years.
Some applications of DP are e-commerce, eBay being one example. It‟s also
been reported that IBM uses it to establish prices for some of its computers.14
Auto and home purchases are two additional examples where timing affects
prices (as we will see, prices that vary by time are not necessarily dynamic).
Another place where more standardized pricing has been replaced by DP is the
airline industry.15 The authors note that airlines previously used what they refer
to as “…an allocation based fare-class model” and business success was
measured by load factor - the number of passengers per available seat on a
single leg trip.16 They argue that “The primary driving reason behind the
utilization of a variable pricing policy is capacity limitation or “hard constraints.”
12
Ahmed Faruqui and Lisa Wood, “Quantifying the Benefits of Dynamic Pricing in the Mass Market,”
Edison Electric Institute, January 2008, Table 7, p.18.
13
“What Consumers -- and Retailers -- Should Know about Dynamic Pricing,” in Knowledge@Wharton.
See: http://knowledge.wharton.upenn.edu/article.cfm?articleid=1245.
14
Nick Wreden, “Advantages of Dynamic Pricing,” Direct Marketing News, May 13, 2003. See:
http://www.dmnews.com/advantages-of-dynamic-pricing/article/80877/.
15
Leslie Anne Palamar and Victoria Edwards ,“Dynamic Pricing Friend or Foe, A report on the state of
dynamic pricing in the contracted, corporate rate segment of the North American hospitality industry,”
2007, bte tourism training and consulting, Buckhiester Management.
16
Ibid, p.4.
5

8. Hard constraints are defined as ones that cannot be violated by any price.”17 In
this situation, they propose that there are limited ways to essentially ration this
limited supply. These ways are: (1) allow available supply to be sold on a first
come – first serve basis, (2) allocate limited supply to specific customers, and (3)
slowly raise prices until demand falls to meet supply.18
Hard constraints are certainly an issue in the electric industry, at least nationally,
in the short-run. If they weren‟t an issue, the context for SG laid out earlier in this
paper would be different, and SG‟s rationale would shift to other objectives. It is
likely true that in some regions, for example the Pacific Northwest, where
interconnections with California and British Columbia allow for power purchase
contracts to substitute for building more power plants, that demand-supply
imbalances are often more economically met through power purchase contracts.
When a utility relies on the wholesale market to economically meet customer
needs, this exposes customer to the wholesale power market. It often appears
that both the utility and the customer reaction to the potential for higher market
prices overwhelms the potential for lower than expected power costs. At least
with flat rates, power purchase expenses are spread across time and customers
whereas DP pricing schemes potentially exposes customers to the full variability
in those markets. However, it needs to be stressed that there is a wide range of
options between flat rates on one extreme and full RTP on the other extreme.
This anxiety over one side of the distribution of wholesale market prices is one
reason why there‟s a tension between using DP to ration a scare inventory, on
the one hand, and manage revenues and customer relations, on the other hand.
John Burns, president of Hospitality Technology Consulting captures this thusly,
“We tell ourselves that relationships are important but dynamic pricing is being
driven by revenue management at the expense of good customer relationship
management. Sales and marketing is constantly finding themselves in the
middle”19 This conflict may require balancing the competing goals of marketing
17
Ibid.
18
Ibid.
19
Ibid, p. 5.
6

9. and customer relations on the one hand, and revenue management, on the
other.20 They argue that when supply is constrained and demand varies, fixed
prices aren‟t sustainable.
Flat rates (or slightly inclining block rates) help provide both revenue stability for
the utility and bill stability for the customer. This is one reason customers and
utilities like them. However, they also encourage over-building of increasingly
expensive infrastructure. It seems that this over-building is overlooked by at
least some of the stakeholders in the debate over time-based rates. The practice
of average cost pricing helps to bury the cost of new infrastructure additions by
essentially diluting the impact of the higher incremental cost investments. This is
accomplished by adding the higher incremental cost investments to the existing
rate base and spreading the total bundle of costs across both time and sales. In
contrast, both RTP and CPP can help avoid or reduce overbuilding.
Paul Centolella, Commissioner on the Public Utilities Commission of Ohio,
sounds a cautionary note that seems to get lost in the fight over rate impacts
today. His message is clear that customers will face higher electric rates without
innovation in electric rate structures.21 He notes the fear of driving up monthly
power bills in at least some months can be addressed through „work-arounds‟.
IV. Defining Dynamic Pricing
Within economics, dynamic analysis explicitly includes time as a variable. This is
in contrast to a static analysis which excludes time as an explicit variable in the
analysis. A dynamic analysis studies the path between two discrete points in
time. From this vantage point, any approach that includes time as an explicit
variable is a dynamic analysis.
Turning to pricing schemes, some writers use an approach to defining DP that is
consistent with how economics has historically defined dynamic analysis. That
20
The article by Palamar and Edwards points out that these two functions have been the province of
separate part of the organization.
21
Paul Centolella, “The Smart Grid Needs Smart Prices to Succeed,” Harvard Business Review, October
14, 2010. See: http://blogs.hbr.org/cs/2010/10/smart_prices_are_key_to_smart.html.
7

10. is, if the pricing scheme contains prices that are allowed to vary with time, then
that pricing scheme is dynamic.22 One example of this is a report by the Public
Utility Commission of Texas (PUCT) to the Texas Legislature defines “Dynamic
pricing [as] either time-of-use or real-time pricing…”23 In contrast, the National
Action Plan on Demand Response24 explicitly excludes TOU rates from DP.
They reject TOU as one form of DP since the peak period and rates do not
change in response to changes in system conditions.
Yet a third approach is contained in a report out of the Wharton School which
lumps DP in with other forms of flexible pricing.25 One example they use is drug
companies setting lower prices for low-income customers.
In a paper surveying the results of 17 DP programs, the authors define dynamic
pricing as prices that reflect the wholesale market prices.26 They also reject TOU
as a form of dynamic pricing since the peak period and the rate(s) are set in
advance. However, they do include CPP as a form of dynamic pricing even
though the rates are set in advance. They note that the CPP critical days are
called based on wholesale market conditions. Because of this feature, CPP
reflects power system conditions.
Given the confusion about what is and what is not DP, the clearest and cleanest
approach is to define a pricing scheme as dynamic if it reflects system conditions
at the moment the prices are established or the critical day is called. Therefore,
RTP is dynamic as is CPP. PTR may be dynamic if either the rebate amount or
when it is effective varies with system conditions. Day ahead pricing (DAP) is
also a form of DP since prices reflect system conditions at the time they are
posted. TOU rates are not dynamic pricing even though it‟s a scheme with
different prices at different points in time that have some relationship to historical
22
For example, Faruqiu argues that „Dynamic pricing is a form of time-of-use (TOU) pricing.” See:
Ahmad Faruqui, “The Ethics of Dynamic Pricing,” The Energy Journal, July 2010, p. 13.
23
“A Report on Advanced Metering as Required by House Bill 2129 Public Utility Commission of Texas
September 2010,” Public Utility Commission of Texas September 2010
24
National Action Plan on Demand Response, FERC, June 17, 2010, footnote 15 on pg. 4.
25
“What Consumers -- and Retailers -- Should Know about Dynamic Pricing”
26
Ahmad Faruqui and Sanem Sergici, “Household Response to Dynamic Pricing of Electricity a Survey of
Seventeen Pricing Experiments,” November 13, 2008.
8

11. use patterns. Prices that vary across markets, such as those described in the
Wharton School paper, are also not dynamic.
V. Dynamic Pricing and TOU Rate Design Issues
This section is split into four sub-sections. Each sub-section addresses an issue
that should be addressed in DP and/or TOU rate design.
Length of Time Price is Stable
This issue applies to both DP and TOU. Referring first to RTP, experiments that
I‟ve seen use hourly day-ahead price, sometimes referred to as DAP, as the
price signal. Some, but not all, then use the actual RTP for a given hour for
billing.
One type of DP that has received consideration and that is the focus of a pilot
program development by PGE is CPP. One report argues that CPP is essentially
an alternative approach to pricing peak and off-peak energy differently.27 The
IEEE Whitepaper correctly note that CPP is an attempt to send price signals to
customers that accurately captures the actual cost of providing electricity on a
small number of hours on a few critical days during the year. They suggest that
CPP is “…particularly effective when high wholesale prices are limited to about
100 hours of the year, and their onset is somewhat predictable.”28 CPP is better
than TOU since from an efficiency perspective “…the additional charges are
based on consumption when the [electric] system is actually constrained. Since
CPP effectively allows retail prices to vary with some movements in the
wholesale market, it captures some of the efficiency aspects of RTP.
How far in advance prices are set
Borenstein distinguishes between what he calls the „granularity‟ of prices and
how far in advance prices are set, which he refers to as the „timeliness of
prices.‟29 It makes little sense to set hourly prices a year in advance, for
example. The implication here is that the shorter the timeframe to which a given
27
IEEE Whitepaper, p. 3.
28
Ibid.
29
Ibid, p. 8.
9

12. set of rates apply, the closer to real time those rates should be established. For
example, an hourly price becomes a better signal on cost when the rate is set
nearer to the hour to which the rate applies.
Michael Jaske identifies three ex ante approaches to RTP: (1) day-ahead, (2)
hour-ahead, and (3) near real-time based on ancillary service market.30 At this
point, I‟ve not yet seen a rate design that uses hourly prices set an hour ahead
for price signaling, though that design may exist. Nor have I seen prices set
using the ancillary service market. In the previous section, I referred to a design
employed in Illinois that notifies customers of hourly prices set the day ahead but
bills using actual prices at the hour of use.
Turning to TOU rates, these rates are set far in advance. The regulatory process
determines how far in advance they will be set. For example, in Oregon, when
an IOU submits a rate filing with the Commission, or the Commission proposes
the company make a rate filing, the issue of how far in advance rates are set
does not arise. Rather, the rates that are established are a by-product of using a
test year for determining revenue requirements. Once a set of rates are
approved by the Commission, they are in place until they are changed.
How to Set the Prices for Each Time Period
This section applies to CPP, PTR, and TOU rates only. The smaller the
difference between the rates for on-peak and off-peak use, there is less incentive
to shift consumption. What might not be quite so obvious is the connection
between differences in rate levels at different times and the choice of the default
rate.31 Since we know that many more customers will choose to stay in a
program if the rate is opt out than will choose to opt into the program if the
spread between on and off peak narrows, it will be more important to use RTP as
the default rate and allow opt out.
30
Severin Borenstein, Michael Jaske, and Arthur Rosenfeld, “Dynamic pricing, Advance Metering and
Demand Response in Electricity Markets,” Center for the Study of Energy Markets, October 2002, pp. 33-
34.
31
In this paper, the term „default rate‟ refers to the rate design customers will be on if they do not exercise a
opt out option.
10

13. If the rate design under consideration is TOU, economic theory proposes setting
prices equal to marginal costs. For a period in the middle of the night, theory
suggests using a short-run price of a variable input into generation (SRMC), such
as, off-peak wholesale energy prices with no distribution or transmission costs
included. For on-peak consumption, theory suggests setting it equal to the long-
run incremental fully allocated cost (LRIC) of the marginal resource, usually a
combined-cycle combustion turbine, with marginal distribution and transmission
costs included. Theory doesn‟t provide clear guidance for prices for time periods
between these two periods.
Several caveats to the marginal cost guidelines for a TOU rate are (a) there may
be reasons to use Ramsey pricing,32 and (b) there may be other policy
considerations that support an even larger difference between the on-peak and
off-peak rates than result from applying the aforementioned marginal cost
principles. If this is the case, it is important to set the on-peak rate above LRIC,
rather than lowering the middle of the night rate below SRMC. As for CPP and
PTR, the rate should be set using either the long-run incremental fully allocated
cost of a simple cycle combustion turbine, or using an average of wholesale
energy and capacity purchase expenses during critical hours, whichever is lower.
Equity Considerations
This issue pertains to both DP and TOU. One result of a shift from fixed flat rates
to DP or TOU is some customers will confront the hidden costs they have been
imposing on other parties. When those customers who face higher DP or TOU
rates include vulnerable populations, some, though not all, consumer advocates
call for special consideration of these impacts or oppose any shift from flat fixed
rates33. However, if this concern results in resistance to DP or TOU, we are left
with a rate design that encourages over-investment in generating and distribution
investments (and maybe transmission). Over time, that will result in higher rates
for everyone, including vulnerable populations.
32
This will lead to a divergence between the rates and what they would be using strict MC principles.
33
Two notable exceptions are the legislatively mandated consumer advocates in both Illinois and D.C. who
both went to their respective legislative bodies requesting that utilities be directed to explore and offer DP.
11

14. This raises a thorny design problem of how to design rates to encourage the
needed efficiency improvements and simultaneously address the needs of
vulnerable populations. As a result, it is important to pay close attention to rate
designs and rate levels that both reduce the pressure for expansion of the
existing power system and that also have a greater chance of reducing a
customer‟s monthly bill. Those two goals may be mutually exclusive in the near-
term and compatible only in the long-run. In the near-term this will probably
require some type of „work around‟ that will help to blunt the impact on vulnerable
populations. This issue is addressed more fully in section VII.
Interoperability34 has been identified as a factor affecting DR adoption and it can
also be seen as one aspect of equity. Interoperability eases implementation as it
removes concerns about equipment being able to talk with each other. The
customer needn‟t worry if the software and hardware can communicate
effectively. This removes some risk from investment decisions, especially when
standards are not yet in place. Since more affluent customers will be less
concerned about this issue than will the more vulnerable customers, this
requirement is especially important as one part of managing the impact of DP
(and maybe TOU) on vulnerable populations.
Degree of Market Segmentation
If some rates are optional, one question is how to design of those rates to make
them attractive option relative to current rates. Lewis characterizes rate choice
by customers as a risk management issue. What is striking is the shift in focus
away from focusing solely on cost recovery to rate designs that offer customers
options to individual risk-reward profiles.
This isn‟t a new concept even when applied to regulated electric utilities, though
the industry is on the cusp of a sea-change in product and price offerings.
Mohler put it thusly, "We should have [the] ability to differentially price [EV
charging services],".The fast-charge price could be equivalent to $20 for a gallon
34
Interoperability is the notion that different equipment potentially from different suppliers be able to „talk‟
to each other. For example, the communication protocols allow for information, data, to be passed through
the system. Imagine that you could use your cell phones with any carrier? If that were the case, cell
phones and communications infra-structure would be interoperable.
12

15. of gasoline, he suggested. Motorists content to charge overnight, when power
prices are lowest, might pay the equivalent of 75 cents a gallon -- a bargain price.
"Until we can give them a way to painlessly respond to that price signal, I don't
know how we get to where we need to go,"35
VI. Winners and Losers
In an article titled “Dynamic Pricing is Smart Grid’s Secret Sauce,” Kiesling
argues that DP is one of the most valuable direct consumer benefits provided by
SG. It provides these benefits by making the customer aware of the cost of their
energy use and thereby helping the customer compare that to his/her value.36
Kiesling further argues that DP benefits consumers whose consumption is
flexible while not harming customers with less flexibility. She argues that less
price responsive customers can benefit from DP since it reduce the quantity of
peak power demanded, thereby reducing system costs and average prices paid
by these customers.
Using economic jargon, an opportunity cost of not adopting DP (and perhaps
TOU) are the higher system costs that otherwise could have been avoided.
There will be customers in vulnerable populations who will be disadvantaged by
DP and TOU, at least in the near-term. However, rather than lose the potential
cost savings to all customers, we need to find tools that work to cushion these
impacts.
Commissions and legislatures seem reluctant to adopt DP and TOU that return
benefits to many, but not all, at least partly to „protect‟ non-price responsive
customers. The irony is that all customers will likely face even higher future
electricity rates due to higher system costs absent better managing of peak
usage. In turn, money that could have gone to supporting other businesses goes
to paying electric bills. This suggests there are benefits from DP and TOU that
accrue to the economy generally as money that would have been spent on even
35
“Consumer Response a Lingering Riddle for Backers of 'Smart Grid',” by Peter Behr of ClimateWire,
October 23, 2009, as published in The New York Times, See:
http://www.nytimes.com/cwire/2009/10/23/23climatewire-consumer-response-a-lingering-riddle-for-bac-
52276.html
36
“Dynamic Pricing is Smart Grid’s Secret Sauce,” by Lynne Kiesling, May 13, 2008. See:
http://www.smartgridnews.com/artman/publish/article_441.html.
13

16. higher electric utility bills gets reallocated to spending on cloths, food,
entertainment and the like. Analysis of the magnitude of foregone economic
growth should be counted as a cost of maintaining fixed electric rates.
Turning to RTP, the benefits of RTP have been discussed in various forums.37
Borenstein argues that while RTP can reduce peak consumption by smoothing
out the peak, there will also be some energy savings, but not much.38 Its clearer
what the benefits are to the utility instituting a CPP or RTP rate structure, for
example, but it isn‟t so clear what the benefits are to the individual customer.39
Studies of DP and TOU generally indicate that DP returns benefits to customers
as a group.40 It is harder to reach conclusions about individual customers since
numerous factors combine to determine how DP affects an individual customer.
However, these same studies do show that there are benefits for all customer
classes.41
Customers with flatter load profiles and those who are more able to shift their
consumption will tend to benefit from DP. Customers with peakier loads, those
less able to shift consumption to lower cost periods, and those with higher overall
consumption levels tend to be disadvantaged by DP.
Some other categories of benefits include, but are not necessarily limited to,
reduced disruptions associated with the permitting, siting, and actual construction
of generation, distribution, and transmission facilities, and less demand pressure
on wholesale power market prices. While state statutes sometimes limits the
environmental benefits and costs that a commission may consider in decision-
making, some of these impacts do indirectly filter into Commission decisions.
For example, DP that achieves greater use of existing power system will reduce
the need for, and the size of, any increments to that system. This reduces
37
One example is the work of Severin Borenstein. For example see his presentation titled “Issues in
Implementing Dynamic Electricity Prices,” CITRIS Research Exchange, April 2007. See:
http://www.youtube.com/watch?v=LdD4sYvDa08.
38
He‟s used the number 10 percent for the amount of total energy savings.
39
For example, see a short piece by Chris Lewis, “The Evolution of Dynamic Pricing,” Cognera
Corporation. See: http://www.electricenergyonline.com/?page=show_article&mag=64&article=502.
40
The Appendix contains summaries of some DP and TOU pricing experiments.
41
This „composition problem‟ is common in economics and is partly addressed in section X below.
14

17. resources devoted to electric production and thereby reduces the environmental
impacts associated of expansions.
Faruqui suggests targeting those most likely to benefit while avoiding those most
likely to be harmed by DP. He argues that the benefits of DP can be achieved
without having all customers participate. While there‟s isn‟t adequate room in
this paper to layout his argument with the accompanying graphs, suffice it to say
that a customer with a load profile flatter than the class average will benefit
immediately from DP and should enroll.
VII. Concerns about Vulnerable Populations
Earlier, I touched on the very hot issue of how a move from fixed rates impact
vulnerable populations. While there is no lack of opinion on this issue, there is a
lack of solid data upon which to reach conclusions about how best to protect
vulnerable populations while also designing prices to better reflect costs.
This concern about harming some ratepayers has its roots in a belief about what
constitutes a fair price. Faruqui references Vickery who, in a paper on
responsive pricing, proposed there was a sense of a just price as an ethical
norm. This belief in a fair price on an ethical basis was echoed by Eric Hirst who
wrote [regarding DP], “The greatest barriers are legislative and regulatory,
deriving from state efforts to protect retail customers from the vagaries of
competitive markets.42 It goes without saying (but I‟ll say it) that flat electric rates
while a time-honored design for residential customers in particular, result in
higher system costs and higher overall rates than need be the case.
Another issue is who to include as part of a vulnerable population. Often these
populations are identified as the elderly, or low-income customers, or the
medically fragile. As Alexander points out, it can be very difficult to find these
customers. She argues that utilities don‟t typically gather demographic date,
such as, a customer age and household income.43 She also argues that
reliance on the Low Income Home Energy Assistance Program doesn‟t solve this
42
Eric Hirst, “Price Responsive Demand in Wholesale Markets: Why is so Little Happening?”
43
Ibid, p. 44.
15

18. identification problem since that program reaches only about 40 percent of those
who are eligible.44 Finally, she argues that the elderly seldom seek out or apply
for low-income programs, and they usually aren‟t well represented when these
rate decisions are made.45
Alexander also argues that high cooling and heating costs contribute to “food
insecurity.”46 For example, she quotes from a U.S. Department of Agriculture
study that concluded that the odds of food insecurity are 43 percent lower in the
summer than in the winter in those states that have relatively high-heating
requirements.47
Regarding low-income customers, there are two basic hypotheses about how DP
affects these customers. One approach argues that low income customers
benefit immediately since they use relatively less energy during air conditioning
peaks than more affluent customers with larger dwellings. In turn, it‟s argued that
the low-income customer has a less peaky load profile than the class average.
The second hypothesis is that low income customers generally use less energy
than more affluent customers (smaller dwellings, fewer electric consuming
appliances etc.) and they are much less able to shift load from peak to off-peak
periods and/or to curtail peak period usage. Hence, they would be harmed by
dynamic pricing.48
In an effort to determine which of these two hypotheses are closer to the truth, a
study was designed using data from a load research sample of a large urban
utility. Next, they reviewed the empirical evidence from five recent utility projects.
They concluded that a majority of low income customers do benefit from DP
because they use relatively less energy during the peak hours compared to the
average residential customer. They determined that between 65 percent and 79
44
Ibid.
45
Ibid.
46
Ibid, p. 41.
47
Ibid.
48
Ahmad Faruqui and Lisa Wood, “Dynamic Pricing and Low Income Customers -- Can they Co-Exist?”
July 9, 2010. See: http://www.smartmeters.com/the-news/1079-dynamic-pricing-and-low-income-
customers-can-they-co-exist.html.
16

19. percent of low-income customers would benefit depending on rate design.49
They also concluded that low income customers do shift their load in response to
price signals.50 Faruqui separately argues that 80 percent of low-income
customers would benefit from DP and that increases to 92 percent with a
“…modest amount of …” of DR achieved.51
Faruqui‟s conclusion that low-income consumers benefit because they have
flatter consumption profiles and use less electricity on average than wealthier
customers may not persuade some consumer advocates to support a move to
some form of time-variable rates. However, those opposing these moves seem
to believe that rates will otherwise not go up as a result of keeping the existing
rate structure. This implicit assumption in arguments opposing DP or TOU is
likely to be false, at a minimum for the country as a whole, for reasons that were
discussed earlier.
Alexander summarizes the political side of this argument about rate design and
vulnerable populations indicating that the American Association Retired People
and the National Association of State Utility Consumer Advocates are opposed to
mandatory DP programs and call for cost-effective DP programs with voluntary
participation.52 She also summarized the experience of various utilities with TOU
rate structures. According to her, Central Maine Power implemented a
mandatory TOU rate structure but abandoned it after a few years in the face of
what she termed „vociferous‟ opposition especially from the elderly. She also
notes that Puget Sound Energy implemented a mandatory TOU rate in 2001 but
had abandoned it late in 2002.53 She argues that while there is a dearth of
analysis of DP‟s impacts on low-income populations, they indicate that this
49
Ibid.
50
Faruqui and Wood noted that two studies, carried out by Connecticut Light & Power Company (CL&P)
and BGE, find that low income customers were equally price responsive to the average customers, while
the California Statewide Pricing Pilot (SPP) carried out jointly by the state‟s three investor-owned utilities
and the SmartRate program offered by Pacific Gas & Electric Company (PG&E) found that they were less
responsive. The Pepco DC results, on the other hand, showed that low income customers were much more
responsive than other customers.
51
Ibid.
52
Alexander, p. 42.
53
Ibid.
17

20. group‟s price responsiveness is much less than that for higher-income customers
(pilot results appear more mixed to me that she suggests).54 55
Turning to the issue of ways to provide some bill protection for vulnerable
populations, there are a number of options, including but not limited to,
1. Capping their power bills for some period of time and gradually removing
that cap.
2. Using a tiered-pricing scheme where a fixed and flat rate applies to an
amount of energy purchases that are considered „essential‟ and allowing
consumption above that amount priced at some higher rate (note that there
are many ways to design this tiered- rate approach).
3. Create an account where bill reductions are added to the account and they
are used to balance bill increases which are debits to that account, and any
remaining savings at the end of a year is refunded to the customer.
4. Placing these customers on fixed and flat rates.
A recently released report on PG&E‟s various time based pricing tariffs includes
evidence on the impacts on vulnerable populations of bill protection to reduce the
risk of higher monthly bills. They found that bill protection reduced peak energy
savings induced by DR by about 25 percent, and that it reduced program
attrition. They report that the average load impact for customers under bill
protection was around 12.8 percent, compared to 18.1percent for customers not
under bill protection.56 They also report not having data to assess how bill
protection affects the decision to enroll in the program.57
54
Barbara A. Alexander, p. 44.
55
The PowerCentsDC experiment (summarized in the appendix) shows similar results for low-income and
all other participants.
56
Stephen S. George, Josh L. Bode, Elizabeth Hartmann, 2010 Load Impact Evaluation of Pacific Gas and
Electric Company's Time-Based Pricing Tariffs, Final Report, April 1, 2011, p. 66.
57
Ibid.
18

21. VIII. Dynamic Pricing is Demand Response58
All DP is DR. Some of that DR may not be realized for any number of reasons;
but, that doesn‟t diminish the fact that DP is DR.
The National Action Plan on Demand Response defines DR as “…the ability of
customer to rely on a reliability trigger or a price trigger…to lower their energy
use.”59 They further differentiate between dispatchable and non-dispatchable
DR.60 Dispatchable DR is defined as planned DR that is not controlled by the
customer which includes, but is not limited to, direct load control.61 They define
non-dispatchable DR as DR that the customer controls62. With non-dispatchable
DR, the customer may or may not respond to price changes. They note that this
latter form of DR is also referred to as price-responsive DR.6364
One design challenge of non-dispatchable DR programs is the need to determine
the baseline from which demand reductions are measured. Borenstein argues
they are difficult to set for several reasons.65 The issue of baselines also arises
with the PTR. He argues that PTR is a poor substitute for either CPP or RTP.66
67
He rightly points out that if the PTR payments come from other customers,
then those costs will be reflected in rates.
One reason DR is attractive is it offers the potential to meet the need for peak
energy faster and at lower cost than building more generation. A short and
58
One demand management program omitted from this discussion is the use of interruptible contracts that
allow the system operator to curtail loads and provide for very large penalty payments if the customer does
not curtail loads.
59
National Action Plan on Demand Response, FERC, June 17, 2010, p. 3.
60
Ibid.
61
Ibid.
62
Ibid.
63
Ibid.
64
PTR is one form of non-dispatchable DR.
65
For his argument, see: Severin Borenstein, “Time-Varying Retail Electricity Prices: Theory and
Practice,” p. 17
66
In a separate paper, Borenstein argues that PTR, which he refers to as Real-Time Demand Reduction
Programs, are fairly blunt instruments in which the system operator announces the program is in effect and
the rate offered for voluntary curtailment is usually set in advance. See: Severin Borenstein, Michael Jaske,
and Arthur Rosenfeld, “Dynamic pricing, Advance Metering and Demand Response in Electricity
Markets,” Center for the Study of Energy Markets, October 2002, p. 16.
67
Several of the studies summarized in the appendix to this paper show that PTR are not as effective as
RTP or CPP but are more effective than TOU.
19

22. concise overview of this linkage was provided by Rick Bush, where he notes that
over 70 utilities offer RTP as either a pilot or a permanent program. In a
cautionary note from the telecom industry, Bush comments that consumers
appear to desire choice until a large bill arrives, and then choice isn‟t seen in
such favorable light.
Realizing DR requires more than just adopting some form of DP. It also requires
that enabling technology be installed that can take the price signals and
automatically change consumption (as pre-determined by the building
tenant/owner). One report on enabling technology, based on a review of 57
different residential sector initiatives performed between 1974 and 2010,
concludes that to realize higher program savings, smart meters must be used in
conjunction with real-time (or near-real time) web-based or in-home devices and
enhanced billing approaches and well-designed programs that successfully
inform, engage, empower, and motivate customers.68
Numerous studies demonstrate that DR potential varies from modest to
substantial, largely depending on the data used in the experiments and the
availability of enabling technologies. Across the range of experiments examined,
TOU rates induced a drop in peak demand that ranged between three to six
percent. By comparison, CPP tariffs led to a drop in peak demand of 13 to 20
percent. When enabling technologies were employed, reductions in peak
demand from CPP rates range from 27 to 44 percent.69
IX. Opt In, Opt Out, Mandatory Participation
Earlier in this paper, I made reference to a report titled “The Five Percent
Solution, How Dynamic Pricing Can Save $35 Billion in Electricity Costs.” The
authors of that paper used Monte Carlo simulation to estimate participation rates
of opt out versus opt in program designs. They concluded that “…about 80
percent [of customers] would stay on dynamic pricing if it is offered as the default
rate and that a substantially smaller number, perhaps 20 percent, would select in
68
Ehrhardt-Martinez, Karen, Kat A. Donelly and John A. “Skip” Laitner, “Advanced Metering Initiatives
and Residential Feedback Programs: A Meta-Review for Household Electricity-Saving Opportunities,”
June 2010, available at http://www.aceee.org/pubs/e105.htm.
69
Faruqui and Sergici.
20

23. on a voluntary basis.”70 These huge differences do underscore the importance of
this question – is program participation mandatory or voluntary and if voluntary,
what is the default rate structure?
Michael Godorov, the manager of smart meter operations for Pennsylvania
Power and Light (PPL), indirectly addresses the question of opt in, opt out, or
mandatory participation arguing that the peak-off peak difference must be high
enough to induce the consumer to change their behavior.71 For example, if opt-in
is chosen, and the alternative is flat rates, we can expect customers to opt in who
expect to benefit, even if they don‟t shift their use pattern. This likely also
increases the revenue recovery risk the utility faces since those who opt in are
more likely to be those who expect to benefit.
The customer‟s existing use pattern will play a large role in determining who wins
and who loses from DP. If program participation is voluntary, this raises a
concern about Adverse Selection. Borenstein argues that a RTP can be
implemented using opt in as long as there is no cross subsidization between
customers who select the RTP and those who choose to remain on the flat rate.72
While he may be right as a matter of theory, there probably isn‟t a rate design in
existence at a real-world utility that has no cross-subsidization in it. Anyone with
actual rate case experience knows that rate design and setting rates is a
sausage-making process. By definition, rate design is all about who pays how
much.
X. Time Sensitive Rates and Supporting End-User Technology
Another policy question is what requirement, if any, to include that require energy
management devices at the point of end-use. This is a policy question since
studies show that automatic control devices, even in the residential sector,
substantially increase peak savings.
70
“The Five Percent Solution…,” p. 4.
71
“Consumer Response a Lingering Riddle for Backers of 'Smart Grid',” p.2.
72
Severin Borenstein, “Time-Varying Retail Electricity Prices: Theory and Practice,” p. 30.
21

24. For example, in the PowerCents DC study in the Appendix, the addition of a
smart thermostat programmed to respond to price signals about doubled the
percentage savings from both CPP and PTR (called CPR in that study). One
example of the impact that automatic controls have on peak savings is indicated
by how much higher peak savings are when an automatic thermostat is present.
For example, participants with all electric homes reduced peak use 22 percent
without an automatic thermostat and by 51 percent with an automatic thermostat.
Another study that showed significant savings increase when automatic control is
introduced was California‟s experiment with various rates designs. One result of
that experiment showed a residential peak load reduction for the average critical
peak day of 23.5 percent without automatic controls and 34.5 percent with
automatic controls.73
XI. Social Cost Arguments Supporting Mandatory Program Participation (or
setting DP as the default with an Opt out)
In light of the peak savings achieved by the experiments in the appendix, it‟s safe
to say that customers who either opt out of (or fail to opt in to) DP rates, also
benefit from those customers who do participate. These non-participating
customers are known as „free riders‟ since they benefit without participating in the
program.
The FRP is a classic issue in the Natural Resources literature within economics.
Basically, a FRP arises when it is prohibitively costly to exclude people from
program benefits if they are not program participants. This type of problem
overlaps with the economic discussion of externalities, Public Goods, and the
property right theory. Some common examples of Public Goods are clean air
and traffic congestion. Public Goods type problems arise when property rights
are either not well defined (e.g., clean air), or they are well defined but very costly
to enforce (e.g., illegal downloading).
73
“California Statewide Pricing Pilot (SPP) Overview and Results 2003-2004,”p. 21. See:
http://sites.energetics.com/madri/toolbox/pdfs/pricing/pricing_pilot.pdf. Also see “Retail Rate Options for
Small Customers, The California Statewide Pricing Pilot,”
www.raabassociates.org/Articles/Levy_10.28.05.ppt
22

25. These types of problems are important because they illustrate cases where
individual customers acting in their own self interest make decisions that are not
the best for the customer base or the utility as a whole. When there is a Public
Good present, the policy conclusion is that there is too little of that good being
produced and consumed.
There are more than economic arguments at stake in this decision. There are
equity issues involved and perceptions of what individual choice means. Choice
for whom? Under what circumstances? There are political issues involved
arising out of perceptions about the sanctity of individual choice, among other
factors, including but not limited to, how to define fairness. As we‟ve seen in
California lately, some communities have gone so far as to try and criminalize
smart meter installations.
XII. Transitioning from Fixed Rates to Dynamic Rates
Its one thing to talk about some far-off destination, and it‟s often quite another to
plot the route and actually take the journey. A journey from a vanilla-type
electricity rate designs, especially for residential customers, to one with different
flavors is likely to have fits and starts. If the benefits are sufficient for that
journey, what are some important considerations in planning our route?
The IEEE whitepaper proposes a five-step process that makes general sense,74
1. Create customer buy-in by educating them about why their rates are
changing, how DP helps the community (improve reliability, prevent even
higher rates, help the environment), how they can use them to reduce their
monthly bills.
2. Offer Supporting Technology, such as in-home displays that information
such as, (a) how much electricity is being used by various end-uses, (b) real-
time consumption information, and (c) that are married to enabling
technologies like programmable thermostats, appliances, and home area
networks,
74
IEE Whitepaper, pp. 29-30.
23

26. 3. Design two-part rates with a fixed and uniform rate for a fixed amount of
consumption followed by a second step with DP.
4. Provide bill protection and bill comparisons by guaranteeing that bill would
be no higher than what it would have been under the pre-existing rates (this
shifts some risk to the utility) and phase the bill protection out over future
years.
5. Give customers choices by allowing customers to shift between different
time varying rates and/or between time varying and fixed rates.
These five steps do provide a starting point for designing such a transition.
Clearly, they shouldn‟t be read as being sequential. Rather, they are key steps in
the transition.
The two-part rate proposal is consistent with one of several possible approaches
to managing monthly electric bills for vulnerable populations. Two-part rates and
bill protection may help gain the support of consumer advocates wary of the
equity impacts of time-varying rates on vulnerable populations. As long as there
are still net benefits after offering that protection, the overall system cost will be
lower than would have otherwise been the case. In the language of economics,
that set of policies would be Pareto optimal and better than what currently exists.
Few, if any, would be harmed and many would benefit substantially compared to
the status quo.
There are a variety of ways to provide protection against higher monthly bills.
Four different approaches were identified in Section VII above. When designing a
bill protection strategy, preference should be given to an approach that includes
some exposure to price variability since doing otherwise also limits the customer‟s
ability to take advantage of price reductions.
Allowing customers to switch between fixed and time-varying rates, warrants close
examination to better gauge (a) how this fits into the other risk management
24

27. strategies in this list, (b) the potential for cost shifting, (c) it raises the issue of
Adverse Selection since those most likely to switch believe they will be
advantaged, and (d) if it is allowed, how to define the rules to offer meaningful
choice with an eye towards how this choice impacts other customers.
25

28. Policy Recommendations
1. Since at least one time-based rates experiment showed that CPP provided
savings that exceeded RTP, there is a question about the need to adopt RTP
to achieve peak use reductions. This raises a question about the rate design
to start with that customers may opt out of (opt out is preferred to opt in for
reasons discussed in the paper), or that a utility mandates. Three options are
(a) flat rates with CPP, (b) TOU rates with CPP, or (c) full RTP. The selection
should reflect your expectation of which of these alternatives better correlates
with your overall policy goals.
2. If RTP with opt out option is the rate option, TOU with CPP should be
preferred to flat rates with or without CPP for customers who opt out of the
RTP rate.
3. Considering that (a) studies indicate CPP produces at least the same amount
of reduction in peak use as Peak-Time Rebates (PTR), and (b) problems in
setting PTR baselines, CPP should be preferred to PTR, perhaps except as
part of monthly bill risk management for vulnerable populations.
4. Off-peak rates should only reflect fuel cost of the marginal resource. On-
peak power rates should reflect the fully allocated cost of the incremental
resource. If wholesale power market prices are used, the on-peak price
should include both energy and capacity. If there is a shoulder period,
moving it closer to the off-peak rate will allow for a larger difference between
the off-peak rate and the on-peak rate and avoid exceeding the revenue
requirement constraint. Transmission and distribution costs should only
appear in the on-peak rate. If other policy goals warrant a larger difference
between on-peak and off-peak rates, increase the on-peak rate rather than
lowering the off-peak rate.
26

29. 5. A smart-meter roll out should be coupled with AMI, or some other way to
implement DP.75 Since studies indicate that an energy management system
significantly increases savings, a rollout of some type of energy management
system should be evaluated as part of the AMI (or its substitute) roll-out. The
burden of proof for not including it should rest with the utility.
6. Moving to time-based rates discussed in recommendations 1-4, should
include strategies to mitigate at least some of the very near-term customer bill
risk (and utility revenue recovery risk). There are numerous ways to structure
that mitigation and several different approaches were described in this paper.
75
Smart-meters are the digital meters that are installed at the point of end-use. AMI requires that Smart
Meters be installed but it also includes the hardware and software that operationalizes two-way
communications between the utility and the Smart Meter and links with the record keeping system used in
generating customer bills. As noted in the paper, there are alternatives to an AMI roll-out that can support
hourly pricing even in the residential sector.
27

30. Appendix - Summary of Selected Pricing Experiments
This appendix is a survey of DP experiments. There are at least 70 DP pilots and
programs nationwide. I made no attempt to draft a comprehensive list of all these
efforts. Instead, this appendix represents an overview of the more widely cited
experiments. This summary is a broad-brush overview. It is not designed to be a
thorough and detailed summary of any specific effort. Nor does it compare the efficacy
of various efforts.
A. Faruqui and Sergici Report76
The report by Faruqui and Sergici referenced in the above provides a survey of
seventeen U.S. pricing experiments. The report provides an excellent, and
concise, overview of a variety of pricing experiments in the U.S. and other
countries. Those seventeen experiments used different pricing strategies (TOU
and CPP), were conducted for varying lengths of time, with different number of
participants, with and without enabling technology. The overarching conclusion
is these pricing schemes can substantially reduce consumption at critical periods.
People do respond to the price signals. Table 1 summarizes features of the
experiments summarized in that report. Figure 1 illustrates the range of DR
impacts from each of those experiments.77 Notes for Table 1 follow that table.
76
Rather than replicate their entire report here, you will find the details in their report. What is included
here is a table summarizing the experiments studied and the percentage reduction in peak load of each
experiment.
77
Both Figure 1 and Table 1 are form the report summarized.
28

31. 29

32. 30

33. 31

34. Below are summaries of a few other experiments that are not
summarized in the Faruqui and Sergici report.
B. PowerCentsDC
One interesting pilot was PowerCentsDC78. Several reasons makes their pilot
unique,
1. It was conceived in part by the official consumer advocate organization
for D.C.
2. It tested three different price structures and various information formats,
and
3. Limited income customers were recruited to test their price
responsiveness.
Three pricing plans were studied, Critical Peak Pricing (CPP), Critical Peak
rebates (CPR), and Real-Time Pricing (HP for Hourly Pricing) that followed the
wholesale electric price. Customers with limited income participated only in the
CPR option. It should be noted that summer peak reduction under CPR for the
low-income group was 11 percent while it was 13 percent for „regular‟ income
customers.79 Among other conclusions, they note that CPP led to the greatest
reductions in peak demand while CPR was the most popular option.80 Regarding
low-income participants, participation rates were higher than for the regular
income group, and the low-income group‟s peak reduction was only slightly less
than that for the regular group.81
C. MyPower Pricing Pilot Program82
Public Service Electric and Gas Company (PSE&G) offered a residential
TOU/CPP pilot pricing program in New Jersey during 2006 and 2007. The
PSE&G pilot had two programs, myPower Sense and myPower Connection.
78
PowerCentsDC Program, Final Report, September 2010. See: http://www.powercentsdc.org/ESC 2010-
09-08 PCDC Final Report - FINAL.pdf
79
Ibid, p. 11.
80
Ibid, p. 5.
81
Ibid.
82
IEE Whitepaper, pp. 17-18.
32

35. myPower Sense educated participants about the TOU/CPP tariff and they were
notified of a CPP event on a day-ahead basis. myPower Connection participants
received a free programmable communicating thermostat (PCT) that received
price signals from PSE&G and adjusted their air conditioning settings based on
previously programmed set points on critical days.
There were 1,148 participants in the pilot program; 450 in the control group, 379
in myPower Sense, and 319 in myPower Connection. The TOU/CPP tariff
consisted of a base rate of $0.09 per kWh. There were three adjustments to this
base rate, (1) a night discount of $0.05 per kWh in both summers, (2) an on-peak
adder of $0.08 per kWh and $0.15 per kWh respectively in the summers of 2006
and 2007, and (3) a critical peak adder for the summer months that resulted in a
critical peak prices of $0.78 per kWh and $1.46 per kWh, respectively, in the
summers of 2006 and 2007.
The results from this experiment were as follows,83
myPower Sense customers with Central A/C reduced peak load
o by three percent on TOU only days.
o by 17 percent on peak days.
myPower Sense customers without Central A/C reduced peak load
o by six percent on TOU-only days, and
o by 20 percent on CPP days.
myPower Connection customers (those with the PCT) reduced their peak
demand
o by 21 percent due to TOU-only pricing
o by 47 percent on CPP days
D. Power Smart Pricing Program
According to discussions with ICC staff, the current ComEd and Ameren Power
Smart Pricing program (PSPP) were legislatively created and are optional rates
open to anyone.84 According to the company web-site for Ameren, the Power
83
Ibid, p. 18.
84
The ICC will be opening a docket soon to review the programs and the net benefits they may or may not
be creating for non-participants
33

36. Smart Pricing program is an hourly pricing program for residential customers. In
this case, the electricity prices are set a day ahead by the hourly wholesale
electricity market run by the Midwest Independent System Operator (MISO).
According to ICC staff, the ComEd RTP uses DAP for advisory purposes but bills
used the RTPs. The Ameren program started that way but reverted to using the
day ahead prices for billing. ICC staff noted that Ameren now has about the
same number of participants as ComEd despite having a customer base one
third the size. Follow the link to learn more about Midwest ISO prices compared
to flat rate prices.85 86
According to the T&D World column, a survey of 600 residential homes “Nearly
60% of residential energy consumers are willing to change their electricity-use
patterns to save money, though many seek savings in return for signing on to a
demand-response program.” One study performed by Frost & Sullivan titled
“U.S. Smart Grid Market – A Customer Perspective on Demand Side
Management,”87 In that study, they noted a significant percent of those surveyed
(78 percent) said they would be interested in adjusting their power usage with a
one-day notice of prices. A smaller fraction (60 percent) expressed an interest in
allowing the utility to cycle their air-conditioner if that resulted in a lower utility bill.
E. Texas88
The Public Utilities Commission of Texas (PUCT) staff wrote a report to the
Texas legislature last year covering AMS deployment in Texas and efforts, to
include DP pilots, outside the state. Among the points made are the following,
Demand response programs that rely on dynamic pricing or TOU rates are
only just beginning to be offered in Texas. Currently, Nations Power offers
prepaid service with RTP. This service is only available to customers with
85
See: http://www.powersmartpricing.org/about-hourly-prices/
86
ICC staff has indicated that in May both ComEd and Ameren will be filing a variety of reports including
four year program evaluations that will contain a significant amount of new information and will be the
basis for a docketed proceeding to review the programs.
87
This report is quite expensive. I‟ve relied on a separate 15 slide presentation for these
comments.
88
Comments are based on correspondence and phone calls with PUCT staff.
34

37. smart meters installed on their premises. The smart meters provide
consumption data in fifteen minute intervals, enabling the company to provide
customers RTP. Customers can see their historical and current consumption
and current prices.
TXU Energy offers a TOU rate that encourages their residential customers to
save money by shifting demand to off-peak hours. Under this plan, customers
pay a higher peak rate during summer afternoons (1-6pm, M-F, May-October)
when demand is highest and a lower rate at all other times of the year. The
lower rate applies to 93% of the hours of the year.
Reliant Energy also offers a TOU plan that rewards the customer for shifting
demand to lower priced off peak periods. Reliant‟s plan divides pricing
periods into three categories, off peak, standard and summer peak. The
higher summer peak hours account for only 3% of the total hours in the year
(4-6pm, M-F, April-October). Standard pricing applies to the other periods of
high demand and varies by season. Reliant‟s TOU plan is available to
customers with smart meters.
Reliant is also piloting the implementation of in-home displays with
consumers in Texas. This product offers consumers the ability to see real
time consumption and projected bill amounts. In addition, Reliant Energy
offers email alerts that utilize the 15-minute interval consumption data to
provide weekly insights into consumption and projected bill amounts.
Gateway Energy Services recently launched the Lifestyle Energy Plan, a
three month pilot program to test two different TOU rates. Under the pilot,
customers will continue to be billed on their current flat rate structure but will
be able to see their monthly bill based on a TOU rate. Customers will have
online access to reports detailing their usage and a side-by-side billing
analysis of the TOU rate plan versus their flat rate plan. At the end of the
pilot, customers who would have saved money with the TOU rate plan will
receive a credit on their monthly bill equal to that savings. Criteria for
35

38. customer participation included having a smart meter installed and enrollment
in Gateway‟s variable rate plan.75
F. Baltimore Gas & Electric Company (BGE)
BGE recently tested customer price responsiveness to different dynamic pricing
options through a Smart Energy Pricing (SEP) pilot. The rates were tested in
combination with two enabling technologies: an IHD known as the energy orb, a
sphere that emits different colors to signal off-peak, peak, and critical peak hours,
and a switch for cycling central air conditioners. Without enabling technologies,
the reduction in critical peak period usage ranged from 18 to 21%. When the
energy orb was paired with dynamic prices, critical peak period load reduction
impacts ranged from 23 to 27%. The ORB boosted DR approximately by 5%.
BGE repeated the SEP pilot for the second time in the summer of 2009. Results
revealed that the customers were persistent in their price responsiveness across
the period. The average customer reduced peak demand by 23% due to dynamic
prices only. When the ORB was paired with dynamic prices, the impact was
27%.89
G. The Connecticut Light and Power Company/ Plan-It Wise Pilot
Another full scale pilot taking advantage of smart meters and three types of
dynamic pricing was recently carried out by Connecticut Light and Power
(CL&P). The Plan-It Wise Energy Pilot was designed as both a smart metering
and rate plan pilot before the further deployment of smart meters to the 1.2
million metered electric customers in the CL&P service territory.90 Consumers
who participated received a smart meter, along with an enabling technology such
as a smart thermostat, energy orb or appliance smart switch. Residential
customers enrolled in the Peak-Time Price (PTP) rate plan reduced peak
demand by 23.3% if supplied with an efficiency enabling device, and 16.1%
without such a device. Commercial and industrial (C&I) PTP customers reduced
peak demand 7.2% with a device and 2.8% without. On average, Plan-it Wise
89
Faruqui, Ahmad, Sanem Sergichi, Effects of In-Home Displays on Energy Consumption: A Summary of
Pilot Results, Peak Load Management Alliance Webinar, April 6, 2010.
90
Connecticut Department of Public Utility Control‟s Docket No. 05-10-03RE01 Compliance Order No. 4,
Results of CL&P Plan-It Wise Energy Pilot, available at
http://nuwnotes1.nu.com/apps/clp/clpwebcontent.nsf/AR/PlanItWise/$File/Planit%20Wise%20Pilot%20Re
sults.pdf.
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39. residential participants saved $15.21 over the three-month pilot span, while C&I
customers averaged $15.45 in savings.99 In an exit survey, 92% of the
residential and 74% of the C&I participants said they would be open to further
programs.91
H. Report on PG&E’s Opt In CPP Experiment92
The report contains ex post and ex ante load impact estimates for PG&E‟s
residential time-based pricing tariffs. In 2010, PG&E had three time-based tariffs
in effect: (1) SmartRateTM1 is a dynamic rate that is an overlay on other
available tariffs. SmartRate has a high price during the peak period on event
days, referred to as Smart Days, and slightly lower prices at all other times during
the summer. Prices vary by time of day only on Smart Days; (2) Rate E-7 is a
two-period, static time-of-use (TOU) rate with a peak period from 12 PM to 6 PM.
This rate is closed to new enrollment; and (3) Rate E-6 is a three-period TOU
rate with a peak period from 1 PM to 7 PM in the summer and from 5 PM to 8 PM
in the winter (when partial peak prices are in effect).
The report contains ex post load impact estimates for the above rates. It also
examines the incremental impact of enabling technology on SmartRate demand
response for customers that are enrolled in both PG&E‟s SmartRate
and SmartAC programs. Load impact estimates for the SmartAC program are
contained in a separate report.
PG&E began offering SmartRate to residential customers in the Bakersfield and
greater Kern County area in May 2008. This region was the first in PG&E‟s
service territory to receive SmartMeters. By the end of the 2008 program year,
enrollment in the Kern County area exceeded 10,000 customers. At the
start of the 2010 summer season, enrollment had grown to around 24,500
customers and was extremely stable over the summer. In light of the pending
termination of SmartRate, PG&E stopped actively marketing the rate in 2010,
although enrollment remained open to new customers.
91
Ibid.
92
This summary is based on the Executive Summary of a report by Stephen S. George, Josh L. Bode,
Elizabeth Hartmann, 2010 Load Impact Evaluation of Pacific Gas and Electric Company's Time-Based
Pricing Tariffs, Final Report, April 1, 2011.
37

40. Under SmartRate, there can be up to 15 event days during the summer season,
which runs from May 1st through October 31st. Prices only vary by time of day on
SmartDays, unless a customer‟s underlying rate is a time-of-use (TOU) rate. The
peak period on SmartDays is from 2 PM to 7 PM and customers are notified that
the next day will be a SmartDay by 3 PM on the preceding day. The SmartRate
pricing structure is an overlay on top of PG&E‟s other tariff offerings. SmartRate
pricing consists of an incremental charge that applies during the peak period on
Smart Days and a per kilowatt-hour credit that applies for all other hours from
June through September. For residential customers, the additional peak period
charge on Smart Days is 60¢/kWh.
There were 13 event days in 2010. The average load reduction across the five
hour event window provided by residential SmartRate customers on each event
day was 0.26 kW, or 14.1%, which is similar in percentage terms to the 2009
impact estimate of 15%. The average percent reduction ranged from a low of
5.7%6 on June 29th, the first event of the summer, to a high of 22.8% on
September 10th. The average load reduction per participant ranged from a low of
0.11 kW on the first event day to a high of 0.47 kW on PG&E‟s system peak day,
August 24, 2010. On that day, SmartRate participants reduced electricity use by
21.3% across the 2 PM to 7 PM event period.
Aggregate reductions in peak demand on Smart Days ranged from a low of 2.6
MW on the first event day, June 29th, to a high of 11.5 MW on PG&E‟s system
peak day, August 24, 2010. Aggregate load reduction for the summer averaged
6.5 MW per event.
Due to a notification problem, slightly less than half of all participants were
notified on June 29th, which largely explains the low impact estimate for that day.
In addition to meeting the basic load impact protocol requirements, detailed
analysis has been conducted to understand how load impacts vary across
several factors, including: (1) Local capacity area; (2) CARE status; (3) Number
of successful notifications; and (4) Central air conditioning saturation and
temperature (Note: CARE stands for California Alternate Rates for Energy, and is
38

41. a program through which enrolled, low income consumers receive lower rates
than do non-CARE customers). The analysis also investigates several important
policy questions, including: (1) Attrition rates and the pattern of attrition for
SmartRate participants; (2) Persistence of load impacts across multiple years;
(3) Whether bill protection affects customer load impacts; (4) Whether load
impacts vary between structural winners and losers; and (5) The extent to which
automated load response via thermostats or direct load control switches
produce incremental impacts over and above what customers with central air
conditioning (AC) provide on their own.
Key findings from this detailed analysis include, but are not limited to, the
following:
Consumers do not appear to increase energy use in response to the slightly
lower prices afforded on non-event days, nor do demand reductions on
Smart Days carry over to other weekdays.
CARE customers in aggregate responded less to price signals than other
customers. However, after controlling for variations in underlying
characteristics, such as air conditioning ownership, event notification and
other factors, percent reductions for CARE customers are not significantly
different from those of non-CARE customers.
Event notification is highly correlated with load reductions. Comparative
statistics show that both the average and percentage load reduction roughly
triple between customers who are successfully notified through one option
and those that receive four successful notifications.
Customers that are enrolled in both SmartRate and SmartAC provide
significantly greater demand response than those who are on SmartRate
alone.
There is a very wide range of demand response across customers. 36% of
customers provide no load reduction at all, although one quarter of these
participants (9% overall) did not receive event notifications. On the other
hand, more than one third of all customers provided impacts of 0.2 kW or
greater and 9% of all customers provided load reductions exceeding 1 kW.
Load reductions do not decline over the course of multiple day event
periods. Indeed, demand response on the second day of a two or three-day
39

42. event sequence is higher than on the first day. Response on the third day is
about the same as on the first day.
Results indicate that (1) average load reductions appear to persist over time
for customers that have been on the program for multiple years; (2) There is
evidence that first year bill protection mutes price signals to some extent
(regression analysis indicates that average load impacts are roughly 25%
less when customers are under first year bill protection than when they are
not.); (3) Load impacts for customers on a balanced payment plan are not
statistically significantly different from those of customers who are not on
such a plan.
The vast majority of customers who sign up for SmartRate have stayed on
the program. Attrition is quite low after adjusting for customer turnover that is
unrelated to the program (e.g., account closures). The attrition rate is highest
during the first two months a customer is on SmartRate. CARE customers
have marginally higher drop-out rates than non-CARE customers, all other
things being equal. Drop-out rates differ only marginally in months that have a
large number of events compared with months in which fewer events were
called. On the other hand, high bills are correlated with higher drop-out rates,
although less so for CARE customers than for non-CARE customers (whose
bills fluctuate much more due to the much steeper increasing block rate
structure faced by non-CARE customers.
Load reductions are greater during summer months than in the winter, both
in absolute and percentage terms. The average peak period reduction
across the year is 0.16 kW or roughly 11%. In summer, the average is 0.21
kW and 12.7%. Percentage impacts range from a low of roughly 6% to a
high of approximately 14%. The 14% impact occurs in May, right after the
higher summer rates go into effect.
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43. 41