Sioshansi F.P. Smart Grid: Integrating Renewable, Distributed & Efficient Energy
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Figure 11.6
Two key smart grid standards are the interfaces between utilities and authorized
third-party data providers (OpenADE) and between smart meters and HAN
devices (OpenHAN).
Demand Response and Smart Grid Coalition, DRSG Comments to DOE Smart Grid RFI:
Addressing Policy and Logistical Challenge, November 1, 2010.
Smart Grid Applications
Utilities implementing the smart grid are faced with also
adding the IT application software that ultimately enables
delivery of the desired functionality. The range of such
software is essentially unlimited. In order to obtain some of
the key, basic functionality described in smart grid business
cases, utilities are implementing software to support meter
data management, dynamic pricing programs, and, for
distributed renewable resources, net metering.
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Meter Data Management
Meter data has traditionally been handled by the billing
system, which was designed to handle a single meter register
read per month from each meter. With quarter-hourly interval
data, that data volume is now being increased by a factor of
nearly 3,000—not to mention outage and voltage data, tamper
alerts, and other data reported by meters. In this new
environment, meter data management requires significantly
more functionality. To achieve the needed results, utilities
implementing smart grids are installing purpose-built meter
data management systems (MDMs).
The MDM portion of a smart meter system has three basic
functions:
1. Collect meter data,
2. Manage that data,
3. Provide a meter data warehouse.
The MDM essentially acts as a buffer between users of the
data and the collection of the data. This allows utilities to use
multiple systems without burdening applications with the
specifics of how data are collected or how commands are
dispatched and, similarly, allows different applications to
view the same data in different ways. The “meter data
warehouse” is simply a database to store meter usage and
other data and is, in fact, the simplest element. A
comprehensive meter data management system will provide
essential services far beyond a meter data warehouse.
MDM functions reveal an additional layer of complexity
when looked at more closely. Collecting meter data involves
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not only retrieving meter reads, but also sending
configuration data to meters and receiving other data such as
outage alerts and meter diagnostic flags. Managing smart
meter data begins with validation, editing, and estimation
(VEE) of meter data. Validation includes various checks such
as ensuring the data are coming from a valid meter address.
Editing is manual intervention to correct data that have been
determined to be incorrect or to insert replacement values for
missing data. Estimation is the automatic correction or
replacement of data based on various calculation algorithms.
VEE is the solution to some of the data reliability and
accuracy issues identified earlier in this chapter. Utilities have
always performed VEE on interval data; the difference with
smart grid is that utilities are now handling interval data for
millions of meters rather than hundreds or perhaps a few
thousand. Such meters have been used for many years but
only for small numbers of large commercial and industrial
customers.
Dynamic Pricing Rates
A key source of benefits in the smart grid is dynamic pricing
rates. Such programs can account for $2 billion in savings for
a utility with less than 1.5 million meters.
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However, as
with the other benefits of smart grid, such savings are
possible only if the software application functionality
properly supports implementation of the pricing plans. In
another example, dynamic pricing benefits in the European
Union have been estimated at EUR 53 billion.
25
See the
Chapter 3 for additional elucidation of dynamic pricing
benefits.
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Baltimore Gas & Electric Company, Application of Baltimore Gas and
Electric Company for Authorization to Deploy a Smart Grid Initiative
and to Establish a Tracker Mechanism for the Recovery of Costs, July
13, 2009.
25
Ahmad Faruqui, Dan Harris, and Ryan Hledik, Unlocking the EUR 53
billion savings from smart meters in the EU: How increasing the
adoption of dynamic tariffs could make or break the EU's smart grid
investment, Energy Policy, October 2010.
Features of Dynamic Pricing Rates
Dynamic pricing rates include several options. The details of
these plans illustrate the numerous capabilities that a smart
grid IT application must have in order to bill such rates
accurately. Time-of-use (TOU) rate schedules assign different
pre-determined rates to pre-defined time periods, and
customers pay those pre-determined rates during each time
period. For example, during the summer, the rate charged
during the afternoon is generally higher than the rate charged
at night. The different rates reflect the fact that it is generally
more expensive to serve customers during some time periods.
TOU rates do not change based on current market conditions.
Different TOU rates are set for the summer and the winter
seasons.
Critical-peak pricing (CPP) generally describes rates where a
very high rate will apply to a customer's usage during CPP
events, typically 60 hours per year. In return, the customer
gets a small discount during the remaining hours of the year.
The CPP event is triggered based on system conditions, such
as high temperature. CPP events are for specific hours and are
called on a day-ahead basis, and must be tracked in the IT
system to enable billing. There are a limited number of CPP
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events a year. Another name for CPP is peak-day pricing
(PDP)
Real-time pricing (RTP) rates are based on prices in the
wholesale energy market, for example the PJM day-ahead
market. RTP rates apply to every hour of every day, and are
subject to change from hour to hour. RTP rates are usually
communicated to customers a day ahead. Such rates are also
called “hourly pricing.”
Peak-time rebate (PTR) is a program that provides the
customer a rebate on a per kWh basis for reductions in the
customer's usage below a threshold level on days when a PTR
event is called. The baseline is specific to each customer and
is based on the customer's prior usage for specific days prior
to the day of the PTR event. The PTR event is like a CPP
event, is called based on system conditions, and is called on a
day-ahead basis. The number of events per year is either
specified or can be within a range, usually up to 15 days, four
hours per event. Chapter 12 includes a description of the
specific experience in California with CPP pricing.
Software to Support Dynamic Pricing Rates
The IT systems needed to support dynamic pricing vary by
specific application. All of the options require bill calculation,
of course. All but TOU require some means of notifying
customers of prices or events. Such notification systems
typically must support automated phone calls, text messaging,
and email. PTR requires continuous calculation and tracking
of customer baselines for use in bill calculation. RTP requires
a link to the wholesale market system to obtain market prices,
plus a means of sending prices to customers. This usually
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involves the internet to display the hourly prices on the
utility's website, plus sometimes text messaging and email.
The billing involves very different calculations from those in
historical utility billing systems. In some cases, utilities
implement software applications outside of their billing
systems to perform part of these new calculations. The key
such function is framing of interval data into billing
determinants. This involves adding up individual interval data
values into pre-determined billing periods. For example, in a
time-of-use rate, the interval data may be framed into peak,
mid-peak, and off-peak values. For a CPP program, the
framing may be for critical peak usage quantities. Once the
interval data are simplified into a small number of these
values—called “billing determinants”—the billing system can
then calculate the bill.
Net Metering
Net metering is a method of simplifying the measurement of
energy produced by a renewable or distributed energy
generator when it is connected to an electric utility
distribution system. Net metering is permitted by law for
solar, wind, and biomass generators that are generally
intended to supply no more than the customer's annual energy
usage. The regulation of rates for buying and selling varies
significantly from jurisdiction to jurisdiction, so practitioners
should take care to review the specific tariffs in the relevant
locale. The term “net metering” refers to the measurement of
the difference between the electricity supplied by an electric
company and the electricity that is generated by an eligible
customer-generator and fed back to the electric company
during a customer-generator's billing period. In other words,
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during a single reading period, such as one month, it is the net
of the energy supplied by the electric company and used by
the customer and the energy produced by a customer and sent
back to the electric company through the company's
distribution system.
As with dynamic pricing, net metering introduces new
functionality requirements for utility IT systems to calculate
bills and credits. The terms of utility tariffs typically require a
customer to pay the monthly customer charge, regardless of
the net energy used. However, for energy billed, the customer
only pays for energy that is used, netted against any
generation produced by the customer. Any excess generation
balance is carried forward to the next month and used in the
bill calculation. If any excess generation balance exists at the
end of 12 months, the customer is paid a per-kWh amount
usually based on the wholesale generation cost, and the cycle
restarts. The per-kWh rate paid for the annual true-up is
typically about half the total retail price of power, because it
excludes transmission, distribution, customer, and other costs
outside of generation. The practical effect of this policy is to
allow customers to use the utility grid as storage, so that
excess energy produced at any given instant can be captured
for later use.
Customer generators benefit from net metering by less
expensive interconnection with the utility (e.g., only a single
standard meter without additional switches). In this way,
electricity needs in excess of the renewable output can be
obtained from the grid without having to disconnect or shut
down a renewable generator. The ease of interconnection
allows the customer to use the renewable generator in a
grid-connected manner without significant installation or
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operating expense, thus improving the benefit of having the
renewable generator on the grid.
As additional customer generator equipment is installed,
power quality problems can emerge. For example, excess
solar generation on local distribution grid node can cause
voltage problems.
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Utilities and others are looking to IT
applications to solve or prevent such issues. Hawaii is
conducting a pilot program. The goal of this demonstration is
to solve various issues, including voltage issues related to
reverse power flow that stem from photovoltaic solar power
generation linked with distribution system circuits.
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Collaborating on Hawaii Smart Grid, Today's Energy Solutions, May
19, 2011. Available at http://www.onlinetes.com/
renewable-energy-manufacturing-Japan-United-States-NEDO-tes-051911.aspx.
Case Studies
This section provides case studies of the province of Ontario
and the state of Texas. The authors are personally familiar
with the implementation of major smart grid IT projects there:
the MDM/R in Ontario and the Smart Meter Texas
centralized data portal in Texas. These two jurisdictions
probably lead the globe in functionality provided via smart
meters at commercial scale. Functions available to millions of
customers with smart meters in Ontario and/or Texas include
next-day online and real-time in-premise energy usage
information, dynamic pricing, remote service connect and
disconnect, handling of outage alarms from smart meters, and
prepayment service.
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Ontario MDM/R
Facing significant shortfalls in generation capacity in coming
years, in 2004 the Province of Ontario launched an ambitious
program to reduce peak electricity demand through a variety
of programs. Central to that objective was the introduction of
smart meters and associated time-of-use rates across the entire
province by the end of 2010. The deployment of smart meters
in Ontario is complicated by extremes in customer density,
with very congested urban areas contrasting with rural and
extremely remote communities throughout large expanses in
the north of the province. Ontario also has over 80 different
electricity distributors, ranging in size from 1.2 million
customers to several dozen with fewer than 10,000 customers.
After an extensive consultation period, the provincial
government announced a two-tiered IT model with some
functions remaining the responsibility of the local distribution
companies and others being centralized into a new
province-wide service. This model achieves three key policy
objectives:
• Scale economies. The province was faced with having up
to 80 separate MDM implementations, one for every local
distribution company. As noted, some of these have fewer
than 10,000 customers. By having a centralized repository,
the province is able to capture the scale economies
associated with having a single system and single
implementation.
• Highest service levels. By concentrating the capability in
a single operation, Ontario is able to provide higher levels
of service and functionality than individual utilities could
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afford—especially the smaller utilities. For example, for
disaster recovery, Ontario has a full back-up system up and
running—at a separate geographic location—to take over
operations in the event of an event damaging or destroying
the primary facility. Another example is expertise; the
centralized operator can afford to hire the best experts,
because any additional cost is de minimus when allocated
across the many users of the system.
• Consistent and reliable data. Finally, by having all the
utilities use a single system, the province ensures that all
customers, regardless of the utility serving them, receive
the same, consistent accuracy and reliability of smart meter
data.
These benefits of centralization occur as a matter of course in
competitive industries. For cellular telephone billing in the
United States, for instance, two companies handle almost all
of the billing. In contrast, there are hundreds, perhaps over a
thousand, billing systems for the 3,000 electric utilities in the
United States.
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Jon Brock, Business Process Outsourcing: Is It for the Utility Market,
CISWorld.com, August 2003. Available at http://www.cisworld.com/
articles/0308_brock3.htm.
Distribution companies are responsible for installing and
maintaining smart meters, ensuring hourly consumption data
are collected daily, handling customer billing and collecting,
answering customer queries, and generally being the agent for
conservation programs with the customer. The Independent
Electricity System Operator (IESO), the province's electricity
grid and market operator, was designated by the government
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