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.

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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

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

2

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.”

3

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.

4

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.

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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.

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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.

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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.

8

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.

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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.

10

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.

11

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.

12

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.

13

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.

14

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.

15

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.

16

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.

17

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.

18

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.

19

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.

20

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.

21

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.

22

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

23

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.

24

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

25

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.

26

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.

27

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.

28

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.

29

30

31

32

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.

33

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

34

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.

35

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

36

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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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.

38

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

39

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

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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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