Sioshansi F.P. Smart Grid: Integrating Renewable, Distributed & Efficient Energy

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to be the smart metering entity responsible providing the

provincial meter data repository (MDM/R) service.

The MDM/R collects consumption data from each

distribution company, validates it, and then converts the

hourly data into total consumption for each TOU billing

period. These TOU billing determinants are then returned to

the distribution company for customer billing. Once

introduction of TOU rates is complete across the province, the

MDM/R will collect and process hourly interval data from

approximately 4.5 million smart meters.

Ontario recently introduced a robust feed-in-tariff that is

driving significant investments in renewable generation. One

of the province's targets is that by 2030 nearly 13% of

electricity generated is from wind, solar, and bioenergy.

Much of this new renewable generation is expected to come

from several hundreds or thousands of small generation

facilities at homes and farms, each requiring bidirectional

meters that measure and report both energy consumed and

generated.

Government of Ontario, Ontario's Long-Term Energy Plan, Queen's

Printer for Ontario, 2010.

Smart Meter Texas

In 2007, the state of Texas adopted rules to introduce smart

meters in support of a number of policy objectives, including

improved system reliability (particularly after storms), energy

conservation, and enhanced retail services. The smart meter

technology standards in Texas include 15-minute interval data

collection and ensuring the meter has the communication

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module necessary to connect to a home area network (also

used in businesses).

Facing IT challenges in managing and distributing smart

meter data to competitive energy retailers and customers, the

four investor-owned utilities in the state (CenterPoint, Oncor,

American Electric Power-Texas, and Texas-New Mexico

Power) formed Smart Meter Texas.

Smart Meter Texas provides a centralized repository of smart

meter data. Distribution companies collect and send smart

meter data to the repository, while Electric Reliability

Council of Texas (ERCOT) and power retailers receive data

from the repository.

Smart Meter Texas (Figure 11.7) also provides centralized

services for provision of home area network devices and a

default website for customers to access their meter data.

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Figure 11.7

In Texas, the distribution companies maintain electricity usage data, though the

Smart Meter Texas portal also stores the data for retrieval by other authorized

users (“HAN” signifies “home area network”).

Source: Texas PUC Advanced Metering Implementation Team, November 2009.

The Smart Meter Texas portal has several consumer benefits.

It is compliant with the Americans with Disabilities Act

(ADA). As with the Ontario MDM/R, it standardizes

functionalities and data access across different distribution

utilities within the state. This means consistent content,

availability and data format. Moreover, the web portal allows

retail electric providers to take full advantage of the smart

meters deployed. This includes the ability to develop retail

electric services that incorporate dynamic pricing and demand

response for residential and small commercial consumers.

This standardization ensures that the retailers, customers, and

their agents have access to the same basic information in

every territory. It also ensures that retailers do not have to

develop different systems and processes for providing

customer products in each utility territory. In this way, Texas,

like Ontario, captures the scale economies inherent in

centralized IT approaches.

Conclusions

Overcoming the IT challenges of the smart grid turns out to

be even more difficult than deploying communication

networks and millions of meters and other devices. Success

requires a transformation of traditional approaches, with the

emphasis on sharing data and communications networks and

avoiding point solutions that support only a single department

or business process. Where utilities have surmounted the

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obstacles, such as in Ontario and Texas, consumers are

receiving the benefits of online and real-time data, pricing

choices, and device automation through utility-level and HAN

interfaces to service providers or devices. In other cases,

consumers are not yet seeing the benefits of smart meters and

smart grids, with occasional negative consequences.

The utility industry has been one of the slowest to adopt new

IT technology, with many good reasons. Yet the arrival of the

smart grid, distributed renewable generation, and electric

vehicles is strongly and rapidly increasing the pressure on

utilities to modernize and transform their IT systems and

strategies.

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Chapter 12. How Large Commercial and

Industrial Customers Respond to Dynamic

Pricing—The California Experience

Steven D. Braithwait and Daniel G. Hansen

Chapter Outline

Introduction 289

Dynamic Pricing and DR Programs in California 291

CPP Customers, Tariffs, and Event Characteristics 293

Customer Enrollment by Industry Type 293

CPP Tariffs 295

CPP Events 296

Analysis Methods 296

Estimated CPP Load Impacts 298

Overall Program Load Impacts 299

Estimated Load Impacts by Utility 299

Special Analyses of SDG&E Default CPP 306

Concentrations of CPP Load Impacts 308

Perspectives on CPP Participation and Load Impacts 309

Looking Forward 311

CPP in California 311

Price-Response Potential Outside of California 312

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Dynamic Pricing versus DR Programs 312

Conclusions 314

References 314

Appendix 315

Many smart grid business plans cite the benefits from more efficient pricing that will be facilitated by

the presence of smart meters To support quantification of those benefits, a number of utilities have

conducted residential smart pricing pilot studies designed to measure customer price response Similar

studies for large commercial and industrial (C&I) customers have not been available until recently

This chapter describes customer enrollment and estimated load impacts for critical peak pricing (CPP)

tariffs, as well as several demand response (DR) programs for large C&I customers in California CPP

began in 2004 with voluntary tariffs, but has transitioned to default tariffs, with the option to opt out to

a time-of-use (TOU) rate The chapter concludes that CPP customers on average respond in significant

ways, but that load reductions tend to be concentrated in a relatively small fraction of the customers,

and some price-responsive customers appear to participate in DR programs rather than CPP

Dynamic pricing, customer response to dynamic pricing, California

Introduction

Economists have long favored time-varying retail electricity

prices, such as time-of-use (TOU) rates, because they reflect

correspondingly time-varying marginal costs of generating

and delivering electric power, as reflected in wholesale

market costs, and thus improve economic efficiency.

Electricity costs vary for two primary reasons: 1) consumers'

demands for power vary substantially by season, day, and

hour due to a combination of normal cycles of personal

behavior and business operations, as well as weather

differences that affect weather-sensitive uses such as air

conditioning; and 2) electricity cannot be easily stored, but

must instead be generated at the exact time that it is

demanded. These two factors combine to cause electricity

suppliers to rely on a mix of generation technologies,

including extra reserve capacity, each of which has different

capacity and operating costs, to meet these varying demands

at reasonable levels of reliability.

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Questions are often raised about the extent to which

consumers will actually change their electricity usage

behavior if they were to face time-varying prices. After all, if

usage patterns do not change, then TOU rates will simply

change the timing of revenue recovery but leave system costs

unchanged. However, numerous studies, beginning in the

1970s, have demonstrated that consumers in general will

respond to time-varying electricity prices.

Early economic

assessments often concluded that even if consumers did

respond, the economic benefits to be derived from

time-varying pricing would likely fall short of the cost of the

more complex metering that is required to bill consumers for

consumption at different prices and different times of day.

This pessimistic benefit-cost calculus has been altered

recently by a combination of falling costs of advanced

metering devices, and perceived operational cost savings that

may be achieved from widespread installation of advanced or

smart metering and smart grid systems, regardless of whether

smart pricing is adopted.

See Faruqui and Malko [1], Braithwait [2] and Braithwait and O'Sheasy

[3].

If smart meters are in fact installed in the larger context of

smart grid development, then the cost barrier to dynamic,

time-varying pricing is reduced substantially, opening the

way to achieving benefits from consumers' price response, as

well as from grid operation savings. Dynamic pricing gives

customers access to low-cost power at correspondingly low

prices, when available, and occasionally sends high-price

signals in hours when generation and transmission costs are

highest. Demand response (DR) in the form of load

reductions that represent consumers' response to dynamic

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pricing can provide a reliable and valuable resource at times

of generation, transmission, or distribution system capacity

constraints, and can thus improve reliability and reduce costs.

However, considerable uncertainty still exists about how

consumers of various types will accept and respond to smart

pricing.

As a result of this uncertainty, a number of electric utilities

around the country have initiated pilot programs to test a

variety of pricing designs. Numerous papers and reports have

summarized the performance of several such pilots targeted at

residential customers, involving dynamic pricing such as

critical-peak pricing (CPP), real-time pricing (RTP), and

peak-time rebates (PTR).

However, information on the

performance of such rates and programs for larger

commercial and industrial customers is much less widely

available. Fortunately, several years of load impact

evaluations of non-residential DR programs for the three large

investor-owned utilities in California—Pacific Gas and

Electric (PG&E), Southern California Edison (SCE), and San

Diego Gas and Electric (SDG&E)—have produced a wealth

of information on how those customers respond to dynamic

pricing, such as CPP, as well as to DR program incentives.

This chapter summarizes key findings and insights from the

most recent statewide evaluations that we have conducted for

the California utilities.

Dynamic pricing is characterized by electricity prices that can vary at

short notice, such as with notification of a day ahead or a few hours

ahead of the occurrence of high prices. This is in contrast, for example,

to basic TOU rates, whose static prices vary by time period, but remain

the same for every weekday during a season. In Chapter 3, Faruqui

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describes several examples of dynamic pricing. Faruqui and Sergici [4]

survey a number of residential pilots.

See Braithwait et al. [5], [6] and [7] for complete descriptions of the

CPP, demand bidding, and aggregator DR program evaluations,

respectively.

The chapter begins with a brief history of the rationale for

smart pricing in California, an overview of the dynamic

pricing and DR programs offered to large commercial and

industrial customers at the California utilities, along with

details on the participating customers. It then describes the

analysis approach that was used to estimate hourly load

impacts, summarizes the empirical findings on customer price

response, and offers some perspective on the meaning of the

results and future trends for CPP in California. The chapter

concludes by commenting more broadly on the potential for

DR outside of California, discussing some elements of the

tradeoffs between dynamic pricing and DR programs offered

by regional ISOs or RTOs.

Dynamic Pricing and DR Programs in California

California's push toward smart metering and dynamic pricing

originated in the state's electricity crisis of 2000/2001, which

was characterized by periodic supply disruptions,

skyrocketing wholesale prices, and frequent load curtailments

and rotating outages. At the time, it was generally recognized

that if some form of dynamic, time-varying pricing had been

in place and customers had reduced load during periods of

unusually high prices, then the resulting demand reductions

would have gone a long way toward limiting the wild

gyrations in wholesale prices and reducing the high electricity

costs that ultimately occurred. However, the advanced

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Sioshansi F.P. Smart Grid: Integrating Renewable, Distributed &amp; Efficient Energy

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Sioshansi F.P. Smart Grid: Integrating Renewable, Distributed & Efficient Energy