Dissertation Outline
Derek Ryan Strong
May 2015
Department of Economics
University of North Carolina at Greensboro
The Emergence and Early Diffusion of Smart Meter Technology
in the US Electric Power Industry
Scope of Research
My dissertation will examine the emergence and early diffusion of smart meter technology in
the US electric power industry. Smart meters refer to a specific type of electric power meter,
which in general measures the consumption of electric power. A smart meter is an advanced
power meter based on digital electronic technology and capable of two-way communication between the electric power utility and the consumer. Smart meters are a product innovation from
the meter manufacturer’s perspective, but they are a process innovation for the utility because
the adoption of this technology affects their production process. Smart meters are considered
an enabling technology critical for the development of a more intelligent electric power grid
that efficiently matches supply and demand. These meters provide capabilities for dynamic,
real-time pricing of electricity as well as outage management among other uses (Edison Electric Institute 2011). Smart meters are one component of an advanced metering infrastructure
(AMI) that also includes communication networks and data management systems (US Department of Energy 2015).
This research fits broadly within the innovation economics literature and more specifically
within studies of the generation and diffusion of technology. This research will also draw from
economic history, political economy, energy economics, and environmental economics. The
specific research questions that I plan to investigate include the following:
– How and why were smart meters developed?
– What factors have influenced the diffusion of smart meters?
– What policy implications result from this analysis?
The first part of my dissertation will study the initial invention of smart meters and key innovators in their use. I will explore the R&D process behind the development of smart meters and
relate this history to their economic rationale and the evolving electric power industry. I will
emphasize the history of electricity rate structures and their relation to market structure, the
competitive environment, and policy changes over time. Furthermore, I will connect this history with the debate surrounding the theories of technology-push and demand-pull, two general
sources of innovation. The second part of my dissertation will combine an understanding of
the historical and institutional context of the development and adoption of smart meters with
an analytical description of their diffusion in the United States. I will analyze the determinants
of both the temporal and spatial diffusion of smart meters, drawing on models of technology
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diffusion found in the literature. The third part of my dissertation will explore policy implications stemming from the preceding analysis. In particular, I will focus on policy related to
innovation in the electric power sector. I will examine the role of government support for smart
meters as well as the impact of regulation.
This research is interesting and worthy because it studies a potentially transformational
technology in that smart meters provide enhanced opportunities for consumer engagement
around electricity consumption and support further technological and institutional innovation
in the electric power industry. For example, smart meters can enable more efficient consumption of electricity through real-time monitoring and they can aid in the integration of renewable
energy sources and electric vehicles onto the power grid. Importantly, these factors also affect
the environmental externalities associated with the production, distribution, and consumption
of electricity. At the same time, smart meters also have drawbacks. They are more expensive
than other types of meters and they raise privacy, security, and health concerns (Edison Electric
Institute 2011).
Furthermore, the diffusion of smart meters is a policy-relevant topic. The federal government has supported the adoption of smart meters by electric power utilities. For instance,
through the Smart Grid Investment Grant (SGIG) program of the American Recovery and Reinvestment Act (ARRA) of 2009, approximately $1 billion of public funds has been disbursed
to support smart meter installations (US Department of Energy 2015). My research will help
assess the effectiveness of this federal support. In addition, studying the history and diffusion
of smart meters will add to our understanding of the current and future technological capabilities of the electric power industry, which in turn may influence related innovation, energy, and
environmental policies. Finally, my research will also assess the international standing of the
United States in smart meter adoption.
A Brief Literature Review
There is relatively little literature covering the innovation aspects of smart meters. Most of
the literature on smart meters revolves around the effects of dynamic pricing on consumer
behavior (for an example see Gans, Alberini, and Longo 2013). Similarly, I have found little
literature on the history of smart meters, although there is a rich literature on the history of the
electric power industry in general. Neufeld (forthcoming) and Hughes (1983) provide histories
of the early electric power industry from an economic and technical perspective, respectively.
However, there is a less comprehensive literature on the history of the electric power industry
in the second half of the twentieth century. A notable exception is the literature detailing
the liberalization of electricity markets in the US during the 1990s. For example, Borenstein
(2002) describes the California experience and the electricity crisis of 2000-01.
Understanding this history will aid in understanding the emergence and diffusion of smart
meters. I expect market structure and utility ownership to be important factors in their diffusion. There is an extensive literature relating market structure and innovation as well as a
literature relating regulation and innovation. Furthermore, there is some literature specifically
covering innovation in the electric power industry, with market structure playing an important
theoretical role. For example, Kiesling (2009) studies the relationship between deregulation
and innovation in the power industry.
Taking into account these and other factors that I think have influenced the adoption of
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smart meters, there are different ways that I could go about modeling their diffusion. Geroski
(2000) provides a broad overview of theoretical models of technology diffusion, contrasting
probit with epidemic models. Stoneman (2002) also provides a broad overview of diffusion
models used in economics and provides several empirical examples. He also attempts to build
a general empirical model that can be used to assess the importance of a variety of theoretical
factors that may influence the diffusion of technology. Additionally, Thirtle and Ruttan (1987)
explore the specific supply and demand factors that influence the emergence and diffusion
of innovations. Rogers (1995) examines diffusion from a multidisciplinary perspective and
Hägerstrand (1967) emphasizes the spatial aspects of diffusion.
Some literature on the diffusion and impact of smart meters does exist. From an institutional and policy perspective, Rose (2014) describes the role of the US federal government
in promoting smart meters. Zhang and Nuttall (2011) use an agent-based model to evaluate
government policy in the UK supporting the diffusion of smart meters. The authors mostly
engage in exploratory exercises in building agent-based models of electricity markets. Rixen
and Weigand (2014) also model the impact of policy on smart meter diffusion using agentbased simulation. The authors find that market liberalization positively impacts the adoption
of smart meters. Additionally, they find that monetary grants increase both the speed and level
of diffusion. Lastly, Cook et al. (2012) perform an estimated cost-benefit analysis of installing
smart meters in the US and find positive net benefits.
Research Design
Data
Data on the history of smart meter development will come from a variety of sources. An
important source will be patents, which provide information about who was involved in an
invention as well as substantial technical details. Patent citations also provide data on the
flow of knowledge and technical advancement. Other sources will include relevant reports and
documents from meter manufacturers, utilities, industry associations, and federal agencies.
The principal dataset on smart meter adoption that I will use is collected by the US Energy Information Administration (EIA). This publicly available dataset is derived from Form
EIA-861, which is an annual survey that is required of all entities generating, transmitting, or
distributing electric energy in the United States. The survey is conducted at the business level,
not the holding company level, and records electric power sales, revenue, generation, and energy efficiency data among other items. In 2007 a section was added to the survey on the use
of advanced meters. As a result, data is available on the number of smart meters installed by
each utility in the US, which is broken down into residential, commercial, industrial, and transportation categories. The data is currently available through 2013. Data for 2014 is expected
to be released later this year. This dataset allows me to describe both the temporal and spatial
diffusion of smart meters in the US. Moreover, it is a panel dataset, allowing me to track the deployment of smart meters over time for individual utilities. However, the panel is unbalanced;
the number of observations varies each year, starting at 639 in 2007 and increasing to 1,864 in
2013.
Additionally, as mentioned above, the ARRA has provided substantial funding for the installation of smart meters by utilities. The ARRA website detailing the SGIG program, ad3
ministered by the US Department of Energy (DOE), also provides data on the number of smart
meters installed from this funding. This dataset begins in 2010 and continues to the present.
Recipients of this funding are required to submit monthly reports through the SmartGrid Integrated Project Reporting Information System (SIPRIS) to the DOE. Thus, this data is more
granular than the EIA data, although it has fewer utilities reporting (81 total). Subsequently,
I can use this data to assess the impact of the ARRA on the diffusion of smart meters. I will
also need to collect and/or find additional data on other factors that I identify as potentially
important determinants of the diffusion process. This data will come from the EIA or industry
sources.
Models of Diffusion
I will need to select the most appropriate model of diffusion based on the unique characteristics
of smart meter technology and the adoption environment. Alternatively, as Stoneman (2002)
recommends, I could pursue a comparative approach whereby I use multiple models and assess
which one fits the observed pattern of diffusion best based on some criteria.
Conventionally, many empirical diffusion studies use an epidemic model that involves fitting data to a type of sigmoid function, which generates an S-curve. For example, the logistic
function or the Gompertz function can be used. There are critiques of this approach, however.
For one, they often do not include enough economic content, focusing more on awareness,
communication, and learning. Grübler (1991) argues that conventional diffusion modeling approaches are inadequate. His critique is that these models analyze “diffusion into a vacuum”.
Generally speaking new technologies replace old ones. In this case, smart meters are replacing
older digital or analog meters. Models of diffusion-substitution may be more appropriate, such
as the substitution model developed by Fisher and Pry (1971). Grübler proposes a multivariate
modeling approach that classifies the diffusion process into distinct stages, each of which may
have different adoption environments.
An alternative to an epidemic model is a duration model, which models the time (i.e., the
duration) until adoption of a technology (i.e., the event or transition). I plan to use a duration model found in Stoneman (2002) that incorporates various theories of diffusion, including
rank, stock, order, and epidemic effects. Rank effects refer to heterogeneity in firm characteristics that result in differential benefits from the adoption of technology. Stock effects refer
to decreasing marginal benefits from adoption with increases in previous adopters at a given
cost of adoption. Order effects refer to the differential benefits from the positional ordering of
adoption. Epidemic effects refer to awareness about a technology, learning by using, imitation,
and/or reductions in uncertainty related to technological expectations. An advantage of using
a duration model is that it is relatively easier to account for censoring as well as time-varying
exogenous factors. One difficulty, in my case, is defining the adoption of smart meters. Certainly the use of one smart meter does not equate to adoption; perhaps something akin to a 10%
ratio of smart meters installed to total meters installed for a utility can be considered adoption.
This approach focuses on the hazard function, which predicts the probability that a firm
that has not adopted a technology will do so in some time period 𝑡. A proportional hazard
function can be specified as
𝑥(𝑡), 𝛽 ) = ℎ0 (𝑡)𝑒𝑥𝑝{𝑥
𝑥(𝑡)′𝛽 }Φ(𝑡)
ℎ(𝑡|𝑥
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where ℎ0 (𝑡) denotes the baseline hazard, 𝑥 (𝑡) is a vector of explanatory variables that may
change over time including those associated with rank, stock, and order effects, 𝛽 is a vector of
parameters, and Φ(𝑡) is a function representing epidemic effects. One then assumes a distributional form for the hazard function, such as the Weibull distribution, although nonparametric
estimation techniques exist as well. Note that empirically the epidemic effect in this model
cannot be separately identified from the baseline hazard. A diffusion process that can be explained primarily by epidemic effects will result in a hazard rate that increases over time. In
other words, one would expect to find positive duration dependence of the baseline hazard.
A duration model, though, studies interfirm diffusion (i.e., the adoption of technology
across firms) and not intrafirm diffusion (i.e., the extent or intensity of use of a technology
within a firm). I may also want to consider another model that examines intrafirm diffusion.
Additionally, I could use panel data models to examine the diffusion process, such as a probit
panel data model or count panel data model. However, it is not clear if these models accurately
portray the dynamics underlying the diffusion process.
Some Descriptive Statistics
Based on data from Form EIA-861, I generated some descriptive statistics of the diffusion of
smart meters in the United States by smart meters installed. Figure 1 depicts the aggregate
pattern of smart meter diffusion in the US, which corresponds to an S-curve. In the first year
of data, 2007, I would describe some utilities as early adopters. PPL Electric Utilities Corp,
based in Pennsylvania (a liberalized electricity market), had more than 1 million smart meters
installed. Other utilities with relatively large numbers of smart meters installed (greater than
100,000) included Arizona Public Service Co, Pacific Gas & Electric Co, and Great Lakes
Energy Coop. Figure 2 depicts the same pattern disaggregated by customer class. As the
figure shows, residential smart meters make up the vast majority of the installations, around
90% for each year of data. I excluded the transportation category because these installations
are negligible. Additionally, Figure 3 compares the aggregate diffusion pattern of digital nonsmart meters (automatic meter reading, or AMR) to smart meters (AMI). The figure suggests
that a substitution process is underway.
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Figure 1: Diffusion of smart meters in the US.
Figure 2: Diffusion of smart meters in the US by customer class.
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Figure 3: Diffusion of AMR vs. AMI in the US.
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Tentative Outline
I. INTRODUCTION
Definitions
Research Questions
Hypotheses
II. LITERATURE REVIEW
The Generation of Technology
The Diffusion of Technology
Innovation, Market Structure, and Regulation
The Evolution of the Electric Power Industry
Smart Meter Technology
III. THE EMERGENCE OF SMART METER TECHNOLOGY
A History of Electric Power Meters
The Invention of Smart Meters
The Economic Rationale of Smart Meters
Smart Meter Innovators
IV. THE EARLY DIFFUSION OF SMART METERS IN THE US
Data
Descriptive Statistics
Models of Diffusion
Empirical Results
V. POLICY IMPLICATIONS FOR ELECTRIC POWER INNOVATION
Electricity Policy
Environmental Policy
Innovation Policy
VI. CONCLUSION
Key Findings
International Comparisons
Future Research
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