Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
CA 02471942 2004-06-28
MEASUREMENT AND VERIFICATION PROTOCOL FOR
TRADABLE RESIDENTIAL EMISSIONS REDUCTIONS
CROSS REFERENCE TO RELATED APPLICATIONS
(0001] The present invention relates to, and is entitled to the benefit of the
earlier filing date and priority of United States Provisional Application
Serial
No. 60/342,843, filed December 28, 2001, which is hereby incorporated by
reference. This application also relates to United States Provisional
Application Serial No. 60/342,853, filed December 28, 2001 and entitled
"System and Method for Residential Emissions Trading."
FIELD OF THE INVENTION
[0002] The present invention relates to a system and method of quantifying
tradable residential emission reductions.
BACKGROUND OF THE INO/ENTION
[0003] Various systems and programs for quantifying and trading
emissions credits have evolved in response to environmental legislations
andlor regulations in the United States. For example, the "bubble concept" of
treating an entire industrial complex as a single source, with a single
allowable emission rate, was advanced by the U.S. steel industry in the late
1970s. This approach let companies choose the most cost-effective mix of
controls to achieve the overall environmental goal for the facility. In
contrast,
the prevailing regulatory framework at that time imposed individual emission
limits on each source within the complex. The U.S. Environmental Protection
Agency (EPA) later adopted such a "bubble policy" for both air and water
discharges.
CA 02471942 2004-06-28
[0004] In 1990, the Clean Air Act Amendments formally legislated emission
trading. For the EPA Acid Rain Program, the Chicago Board of Trade has,
since 1998, administered an annual auction of SO2 (sulfur dioxide) allowances
from private allowance holders (utilities or brokers) to regulated companies,
brokers, environmental groups, and the general public. Beginning in 1999,
the EPA Ozone Transport Commission NOX Budget Program has allowed
trading in nitrogen oxides (NOX) credits in a group of U.S. states, to reduce
summer smog.
[0005] The intra-plant bubble concept thereafter evolved to allow for
trading of emission credits between companies. Pursuant to the 1997 Clean
Air Act Amendments, EPA adopted regulations governing new source
construction that permitted companies to offset emissions increases at one
plant with savings at another, or to trade emissions credits between
companies. This created a market for emissions credits. Brokerage
companies typically handled sales between companies having emissions
credits and those wanting to acquire credits.
[0006] Other domestic emission credit programs have been proposed or
implemented on a state or regional level. The RECLAIM Program (Regional
Clean Air Incentives Market) applies to stationary sources in southern
California and is administered by the South Coast Air Quality Management
District (SCAQMD). Trading of RECLAIM Trading Credits (RTCs) in sulfur
oxides (SOX) and nitrogen oxides (NOX) began in 1994 in an effort to reduce
the area's severe smog. If emissions are below the permitted limit, the
excess RTCs may be sold to others or banked for future use.
2
CA 02471942 2004-06-28
[0007] The state of Maine proposed an Ozone Transportation Region in
conjunction with the Maine Auto Emission Inspection Program, swapping NOX
pollution credits from reduced auto emissions to allow increased industrial
expansion. A Utah Division of Air Quality program provided for companies to
earn emissions credits for S02 and carbon dioxide (C02) reductions.
Massachusetts implemented a retail choice pilot program for residential and
small business customers who purchased "green power" from solar and less-
polluting power plants. Depending on the price that customers would pay for
green power, the suppliers would retire a certain amount of S02 emissions
credits.
[0008] The PERT Project (Pilot Emission Reduction Trading), in Ontario,
Canada began in 1996 and comprises members from industry, government,
and public interest organizations. Under PERT, Emission Reduction Credits
(ERCs) are created when the pollution source reduces emissions below its
actual level or regulated level. ERCs may be used by the source to meet
current or future emissions caps, or may be sold. ERCs may be S02, NOX,
COz, greenhouse gases (GHG) or other contaminants.
[0009] The measurement and verification (M&V) system of the present
invention provides a novel system and method for promoting increased
energy savings, which may be an actual reduction in electricity use (kWh),
electric demand (kW), or thermal units (Btu), and reduced energy use at the
level of the individual residential consumer. Increased residential energy
efficiency may reduce energy consumption for electricity, natural gas, oil,
and
other energy sources. Less energy demand may result in reduced energy
generation or on-site combustion by the utilities, and therefore in reduced
3
CA 02471942 2004-06-28
emissions of a variety of pollutants including, but not limited to: nitrogen
oxides (NOX), volatile organic compounds (VOCs), sulfur oxides (SOX),
particulate matter (PM), carbon monoxide (CO), and greenhouse gases
(GHG) such as carbon dioxide (C02) and methane (CH4).
[0010] The SCQAMD's programs provide alternate methods of compliance
with local emission reduction regulations. For example, in 1997, Rule 2506
established a voluntary program that encourages replacement of old, higher-
emitting equipment (area sources) with lower-polluting technology. The Rule
2506 program generates low-cost emissions credits termed Area Source
Credits (ASCs). Area sources include water heaters, home heaters, clothes
dryers, and small boilers.
[0011] In one embodiment, the present invention also contemplates the
replacement of such residential area sources, but in contrast to the Rule 2506
program, does not require the homeowner to submit a complicated plan for
eligibility. The Rule 2506 plain requires, among other components, a Protocol
for Emission Reduction Quantification, Documentation of the Occurrence and
Extent of the Emission Reduction, Credit Calculation, and a Compliance
Verification Report with annual certification signed under penalty of perjury.
The present invention substantially reduces these transaction costs for the
homeowner by taking care of such complexities at an administrative level.
[0012] The various schemes described above provide substantial
incentives for certain industrial sources of pollution, such as utilities and
industrial plants, to reduce their emissions. Notably lacking in these
schemes,
however, are programs for capturing the benefits of potential energy
efficiency
4
CA 02471942 2004-06-28
measures, which are activities designed to increase the energy efFiciency of a
facility, and the resulting emissions reductions by residential consumers.
[0013] Theoretically, residential emissions reductions could be recognized
under a variety of emissions trading programs. However, five hurdles have
historically kept reductions from residential housing sources off the market:
1. Residential emission savings are generated in very small
quantities relative to those sought by the market;
2. Residential emission savings are not yet fully recognized by
prior known regulatory regimes;
3. Residential emission savings are generated by many divergent
homeowners with no means or incentive for collective action;
4. Transaction costs - those associated with certifying, marketing,
selling, and transferring the reductions - have been prohibitive;
and
5. Electricity producers have been reluctant to accept emission
restrictions normally required prior to the regulator's granting of
a utility displacement credit. A utility displacement credit is a
type of emission credit that can be granted by the governing
regulatory agency to entities that take actions that allow the
utility to avoid delivery of power. Precedent is found under
Clean Air Act programs. For example, a residence or industrial
operation that generates its own power removes its demand
from the grid. This reduction allows the utility to incrementally
reduce its power generation which, in turn, results in an
CA 02471942 2004-06-28
incremental emission reduction from power generating sources
at the utility.
[0014] A residential emissions trading program that reduces or eliminates
these hurdles is disclosed in Assignee's co-pending United States Provisional
Patent Application Number 60/342,853, filed December 28, 2001 and entitled
"System and Method for Residential Emissions Trading," which is
incorporated herein by reference. This system and method may employ a
M&V protocol of the present invention. M&V is the process of determining
savings using a quantifying methodology. Alternatively, any other suitable
quantification, measurement, and/or verification means may be employed.
This program may aggregate emissions reductions through a number of
mechanisms, such as direct purchase from homeowners, as a side
transaction to mortgaging energy efficient homes, or by coordinating with
other entities that are already in a role of aggregating customers (i.e.,
multi-
family building owners, energy service companies, and utility companies).
Emissions reductions from individual homes are insignificant when measured
alone but, when aggregated, can have substantial environmental and financial
value. Aggregating can provide individual homeowners with a mechanism to
add value to individual actions through collective action. Aggregating the
emission reductions can also reduce the per pound transaction cost of an
emissions reduction program and improve the potential to secure recognition
for utility reduction credits and residential emissions savings.
[0015] Residential housing units account for approximately one-fifth of
greenhouse gas (GHG) emissions in the U.S. Building more efficient homes,
retrofitting existing ones, making other structural and fuel changes, and/or
6
CA 02471942 2004-06-28
other improvements, can dramatically decrease the amount of energy used.
Energy efficiency improvements are made to residential units in some
instances in response to energy company demand-side management
programs, consumer upgrades, andlor builder incentives.
[0016] Yet, the energy savings from a single individual home has an
insignificant impact at electricity generation plants. The aggregate impact of
energy efficiency upgrades to thousands of homes, however, could have a
significant impact, such as measurable reductions in peak load.
[0017] Decreases in energy consumption naturally lead to reductions in
pollutant emissions (i.e., criteria pollutants and greenhouse gases). Other
measures, such as switching to low-VOC paints, paving driveways, and
improving home design, can also have significant impacts on air pollution.
Although the air quality impact of a single energy efficient home is
relatively
small, the result can be dramatic when the emissions reductions from large
numbers of homes are aggregated. When the individual residential energy
savings are aggregated in sufficient volumes, the program of "System and
Method for Residential Emissions Trading" contemplates that the aggregation
may comprise a tradable commodity in existing and future emissions trading
markets.
[0018] Embodiments of the present invention provide credible monitoring
and verification procedures for various potential energy efficiency programs
in
order to:
~ Define a common M&V language to be used by participants in a
residential emissions trading program;
7
CA 02471942 2004-06-28
~ Define an acceptable methodology for deriving emissions
reductions from energy savings;
~ Define acceptable methods for quantifying energy savings and
emissions reductions;
~ Evaluate the technical rigor of existing M&V techniques for energy
savings and emissions reductions and determine technical
confidence factors ("TCF") for calculating tradable emissions
reductions; and
~ Explain the relationship between technical rigor and economic
feasibility of existing and planned M&V protocols.
[0019] In one embodiment of the present invention, the residential energy
savings may be captured in the emissions reductions realized by utility
companies that generate less power. In another embodiment, upgrades in
residential appliances - for example, changing a fuel oil-powered device to a
solar-powered device - may produce direct emissions reductions. The
residential reductions in SOX, NOX, C02, VOC, etc., emissions may be
captured in tradable credits. In a third embodiment, emissions reductions
may be generated both by the residential upgrade and the utility's generation
of less power.
[0020] In a program for residential emissions trading, utilities, builders,
and
homeowners may cooperate to encourage the improvements in the energy
efficiency of residential properties, in exchange for the SOX, NOX or other
pollutant reductions that the efficiencies generate. Alternatively, an
emissions
trading initiative (ETI) may support a GHG emissions trading market for
emissions reductions from efficient energy use and fuel switching in
8
CA 02471942 2004-06-28
residential buildings. The resulting residential emissions reductions may be
bundled into an emissions pool and sold into an emissions trading market.
[0021] As part of a program for residential emissions trading, an M&V
protocol ensures that the energy reductions from an energy efficiency
measure are quantified as accurately as practicable. Quantification protocols
ensure that the emission reductions are reliably ascertained. A rigorous M&V
program provides assurance to potential parties in the emission trading
market that reductions - and most important credits - are both actual and
quantifiable. M&V protocols, therefore, have become an important part of
many emissions trading markets.
[0022] For each energy savings opportunity or energy efficiency program,
the energy consumption with the energy efficiency program may be
subtracted from the energy consumption without the energy efficiency
program, giving the energy savings from the program. Energy consumption is
calculated from a number of measurable variables and their associated
measurement techniques.
[0023] In an embodiment, the present invention contemplates quantifying
the following aspects of a given energy efficiency (or emissions reduction)
project:
1. Annual energy use in the baseline home (without upgrades) for
each year in the life of the project;
2. Annual energy use in the upgraded home (with installed energy
efficiency measures) for each year in the life of the project;
3. Appropriate emission factors for the energy consumed for each
year in the life of the project;
9
CA 02471942 2004-06-28
4. Total emissions reductions from the project; and
5. Tradable portion of these emission reductions.
[0024] For each type of energy efficiency project, specific data types and
analytical procedures may be identified. Entities cooperating in the emissions
trading program may be responsible for data collection (i.e., measurement) for
their energy efficiency programs. Using an M&V procedure of the present
invention, the data are compiled and used to assess the emissions reductions
potential for each residential energy efficiency opportunity.
[0025] The present invention has many potential benefits. Energy costs
are typically the second largest cost for homeowners. The present invention,
when implemented in an emissions trading program such as that disclosed in
Assignee's co-pending application for a "System and Method for Residential
Emissions Trading," provides incentives to invest in energy efficiency that
will
save the homeowner money. It has been estimated, for example, that an
efficient house can save 30% on annual energy bills. In addition, the present
invention improves the stability of the emissions credits - a valuable new
commodity - and also helps to decrease the costs associated with energy
efficiency.
[0026] It is therefore an advantage of some, but not necessarily all,
embodiments of the present invention to provide a system and method for
residential emissions trading.
[0027] It is another advantage of some, but not necessarily all,
embodiments of the present invention to provide a system and method for
determining an emissions reduction resulting from a residential energy
savings.
CA 02471942 2004-06-28
(0028] It is yet another advantage of some, but not necessarily all,
embodiments of the present invention to provide an M&V protocol that
ensures that emissions reductions are reliably ascertained.
[0029] Additional advantages of various embodiments of the invention are
set forth, in part, in the description that follows and, in part, will be
apparent to
one of ordinary skill in the art from the description and/or from the practice
of
the invention.
SUMMARY OF THE INVENTION
[0030] In response to the foregoing challenges, an innovative method for
quantifying residential emissions reductions is provided, comprising the steps
of: measuring an energy savings resulting from one or more energy savings
opportunities in one or more residential properties; calculating an emissions
reduction resulting from the energy savings; and aggregating a plurality of
the
emissions reductions into a tradable commodity.
(0031] The step of calculating an emissions reduction may further
comprise calculating a reduction in emissions of one or more compounds.
The one or more compounds may be selected from the group consisting of:
S02, NOx, and GHGs. The method may further comprise the step of
monitoring the residential energy savings opportunities. The method may
further comprise the step of monitoring the quantification of the emissions
reduction. The method may further comprise the step of verifying the
quantification of the emissions reduction.
[0032] According to another embodiment of the present invention, the
method for quantifying residential emissions reductions comprises the steps
of: estimating an energy savings resulting from one or more energy savings
11
CA 02471942 2004-06-28
opportunities in one or more residential properties; calculating an emissions
reduction resulting from the energy savings; aggregating a plurality of the
emissions reductions into a tradable commodity; monitoring the residential
energy savings opportunity; monitoring the quantification of the emissions
reduction; and verifying the quantification of the emissions reduction.
[0033] The step of estimating an energy savings may further comprise the
step of estimating energy saved by one or more energy efficiency upgrades
selected from the group consisting of: replacement of an appliance; upgrade
of a domestic water heating system; upgrade of a heating system; upgrade of
an air conditioning system; modification to lighting; fuel switching; and
whole
home renovation. The step of aggregating a plurality of the emissions
reductions may further comprise the step of aggregating the emissions
reductions produced by the one or more energy efficiency upgrades into a
tradable commodity.
[0034] The step of aggregating the emissions reductions may further
comprise the step of pooling the emissions reductions, or alternatively,
converting the emissions reductions into one or more emissions trading
credits.
[0035] The step of calculating an emissions reduction resulting from the
energy savings may further comprise the step of calculating a forecasted
emissions reduction. The step of calculating a forecasted emissions reduction
may further comprise the steps of: estimating a forecasted baseline energy
use for the energy savings opportunity; estimating a forecasted baseline
emissions factor for the energy savings opportunity; calculating a forecasted
baseline emissions by multiplying the forecasted baseline energy use with the
12
CA 02471942 2004-06-28
forecasted baseline emissions factor; estimating a forecasted program energy
use for the energy savings opportunity; estimating a forecasted program
emissions factor for the energy savings opportunity; calculating a forecasted
program emissions by multiplying the forecasted program energy use with the
forecasted program emissions factor; and calculating a forecasted emissions
reduction by subtracting the forecasted program emissions from the
forecasted baseline emissions.
[0036] The method may further comprise the step of calculating a tradable
portion of the forecasted emissions reduction. The step of calculating a
tradable portion of the forecasted emissions reduction may further comprise
the step of quantifying a TCF for the energy savings opportunity. The step of
quantifying a TCF may further comprise the steps of: identifying a risk factor
for energy savings estimates; identifying a risk factor for emissions factor
estimates; identifying an adjustment factor; and determining the TCF by its
relationship to the sum of the risk factor for energy savings estimates, the
risk
factor for emissions factor estimates, and the adjustment factor.
[0037] The method may further comprising the steps of: multiplying the
TCF with the emissions reduction to obtain the tradable portion of the
emissions reduction, wherein the remaining portion of the emissions reduction
is non-tradable; and holding the non-tradable portion in reserve for possible
conversion into a tradable commodity. The method may also comprise the
step of converting any portion of the non-tradable portion into a tradable
commodity.
[0038] The step of calculating a forecasted emissions reduction may
further comprise the steps of: calculating a plurality of annual forecasted
13
CA 02471942 2004-06-28
emissions reductions for the residential energy savings opportunities; and
summing the plurality of annual forecasted emissions reductions to determine
a lifetime emissions reduction estimate for the residential savings
opportunities.
[0039] The step of monitoring the residential savings opportunity may
further comprise the steps of: compiling data on the energy savings collected
at a facility; and managing the energy savings data..
(0040] The step of verifying the quantification of the emissions reduction
may further comprise the steps of: calculating a measured emissions
reduction; and comparing the measured emissions reduction to a forecasted
emissions reduction. The step of calculating a measured emissions reduction
may further comprise the step of collecting data for the energy savings
opportunity. The step of calculating a measured emissions reduction may
further comprise the steps of: estimating a measured baseline energy use for
the energy savings opportunity; estimating a measured baseline emissions
factor for the energy savings opportunity; calculating a measured baseline
emissions by multiplying the measured baseline energy use with the
measured baseline emissions factor; estimating a measured program energy
use for the energy savings opportunity; estimating a measured program
emissions factor for the energy savings opportunity; calculating a measured
program emissions by multiplying the measured program energy use with the
measured program emissions factor; and calculating a measured emissions
reduction by subtracting the measured program emissions from the measured
baseline emissions.
14
CA 02471942 2004-06-28
[0041] The steps of estimating a measured baseline energy use and
estimating a measured program energy use may be selected from one or
more of the group consisting of conducting: on-site inspection; metering; sub-
metering; utility bill analysis; and engineering modeling. The step of
conducting engineering modeling may further comprise the step of utilizing
one or more of: engineering calculations and computer simulation. The step
of conducting engineering modeling may further comprise the step of
conducting one or more of: degree day analysis; bin analysis; hourly analysis;
and time-step analysis.
[0042] In accordance with another embodiment of the present invention,
the method for quantifying a tradable emissions commodity comprises the
steps of: offering a plurality of residential energy efficiency programs,
wherein
the energy efficiency programs comprise a plurality of residential energy
savings opportunities; estimating an energy savings resulting from the
plurality of residential energy savings opportunities; calculating emissions
reductions resulting from the energy savings; aggregating the emissions
reductions into a tradable commodity; monitoring the residential energy
savings opportunities; monitoring the quantification of the emissions
reductions; and verifying the quantification of the tradable emissions
reductions to produce a tradable commodity.
[0043] The plurality of residential energy efficiency programs may be
offered by one or more emissions trading partners. The step of verifying the
quantification of the tradable emissions reductions may further comprise the
step of producing a commodity that is tradable on national and international
emissions trading markets. The method may further comprise the~step of
CA 02471942 2004-06-28
offering to a market one or more of the tradable commodities. The step of
offering to a market one or more of the tradable commodities may further
comprise the step of managing one or more transactions of the tradable
commodities in the market.
[0044] It is to be understood that both the foregoing general description
and the following detailed description are exemplary and explanatory only and
are not restrictive of the invention as claimed. The accompanying drawings,
which are incorporated herein by reference and which constitute a part of the
specification, illustrate, certain embodiments of the invention and, together
with the detailed description, serve to explain the principles of the present
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0045] In order to assist the understanding of this invention, reference will
now be made to the appended drawings, in which like reference characters
refer to like elements. The drawings are exemplary only, and should not be
construed as limiting the invention.
[0046] Fig. 1 is a flow chart depicting a method of quantifying reductions in
residential pollution emissions according to an embodiment of the present
invention.
[0047] Fig. 2 is a flow chart depicting a method of estimating an energy
savings, calculating an emissions reduction, aggregating emissions
reductions, monitoring the residential energy savings opportunities, and
monitoring and verifying the quantification of the emissions reductions
according to an another embodiment of the present invention.
16
CA 02471942 2004-06-28
[0048] Fig. 3 is a flow chart depicting the steps of measuring an energy
savings according to an embodiment of the present invention.
[0049] Fig. 4 is a flow chart depicting the steps of calculating an emissions
reduction from an energy savings according to an embodiment of the present
invention.
[0050] Fig. 5 is a graph depicting greenhouse gas add-on sampling versus
creditable emissions according to prior art M&V programs.
(0051] Fig. 6 is a graph depicting baseline and program emissions with
emission reductions according to an embodiment of the present invention.
[0052] Fig. 7 is a flow chart depicting forecasted baseline and program
emissions according to an embodiment of the present invention.
[0053] Fig. 8 is a flow chart depicting measured baseline and program
emissions according to an embodiment of the present invention.
[0054] Fig. 9 is a graph depicting calculated forecast emission reductions
and tradable emissions reductions versus year of program for an embodiment
of the present invention.
[0055] Fig. 10 is a graph depicting calculated forecast and measured
emission reductions and tradable emissions reductions versus year of
program for an embodiment of the present invention.
[0056] Fig. 11 is a graph depicting calculated forecast emission reductions,
measured emission reductions, and tradable emissions reductions versus
year of program for another embodiment of the present invention.
17
CA 02471942 2004-06-28
[0057] Fig. 12 is a graph depicting the correlation between heating degree
days and heating energy consumption according to another embodiment of
the present invention.
DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION
[0058] Reference will now be made in detail to embodiments of the system
and method of the present invention, examples of which are illustrated in the
accompanying drawings.
1J
[0059] With reference to Fig. 1, the method 10 for quantifying reductions in
residential emissions may comprise the steps of measuring an energy savings
resulting from one or more energy savings opportunities in one or more
residential properties 100, calculating an emissions reduction resulting from
the energy savings 200, and aggregating a plurality of the emissions
reductions into a tradable commodity 300. The tradable commodity may
comprise tradable emissions reduction(s), tradable emissions credit(s), or any
other suitable commodity for trading in any emissions trading market.
[0060] According to another embodiment depicted in Fig. 2, the method 20
may comprise the steps of estimating an energy savings resulting from one or
more energy savings opportunities in one or more residential properties 100,
calculating an emissions reduction resulting from the energy savings 200,
aggregating a plurality of the emissions reductions into a tradable commodity
300, monitoring the residential energy savings opportunities 400, monitoring
the quantification of the emissions reduction 500, and verifying the
quantification of the emissions reduction 600.
[0061] As embodied herein and as shown in Fig. 3, the step of measuring
an energy savings resulting from one or more energy savings opportunities in
18
CA 02471942 2004-06-28
one or more residential properties 100 may comprise the steps of quantifying
a baseline energy use 101, quantifying a program energy use 102, calculating
an annual energy savings 103, calculating a lifetime energy savings 104, and
calculating a total program energy savings 105. The equations are shown
below (Equations 1a - 1f).
[0062] Calculating the emissions reduction may comprise calculating a
reduction in emissions of one or more compounds, e.g., pollutants. Such
compounds may include, but are not limited to, S02, NOX, GHGs, and any
other suitable compounds that may be converted into a tradable commodity in
any emissions trading market. As embodied herein and as shown in Fig. 4,
the step of calculating the emissions reduction 200 may further comprise the
steps of calculating a baseline emissions factor 201, calculating a program
emissions factor 202, calculating a baseline emissions 203, calculating a
program emissions 204, calculating an annual emissions reduction 205, and
calculating a lifetime emissions reduction 206. The equations are shown
below (Equations 1 g - 1 I).
[0063] Embodiments of the present invention may also comprise an M&V
protocol for participants in a residential emissions trading program,
including
but not limited to: program partners; program administration staff; third
party
auditors; and program investors.
[0064] In an embodiment of the present invention, the M&V protocol may
focus on the specification of measurement protocols that may be implemented
by the program partners. It also, however, may include monitoring protocols
that may be implemented by program administration staff, and verification
protocols that may be implemented by third party auditors. Monitoring may
19
CA 02471942 2004-06-28
comprise the collection of data at a facility over time, such as, for example,
energy and water consumption, temperature, humidity, and hours of
operation. A purpose of the monitoring protocol may be to compile and
manage the data collected by the program partners. Verification may
comprise the process of examining reports of others to comment on their
suitability for the intended purpose. The verification protocol may act as a
quality assurance mechanism on the data submitted by the utility partners (for
the benefit of the program investors).
[0065] A primary responsibility of program partners may be to carry out the
measurement of emissions reductions from qualifying energy efficiency
programs or improvements. A primary responsibility of program
administration staff may be data collection and management. A primary
responsibility of third party auditors may be quality assurance and quality
control (on data supplied by program partners) for program investors. A
primary responsibility of program investors may be to provide the primary
source of funding for the emission trading program.
[0066] As embodied herein, the M&V protocol may be modified for several
types of projects aimed at improving energy efficiency in residential
buildings.
An embodiment of the present invention may comprise a sequence of steps
that typically are followed in establishing estimated savings and emissions
reductions and verifying the actual savings and emissions reductions from any
given energy efficiency program:
1. Measurement of the energy savings;
2. Quantification of the emissions reductions and assignment of
tradable emission reductions;
CA 02471942 2004-06-28
3. Monitoring of data collection for the energy savings;
4. Monitoring of the quantification of the emissions reductions; and
5. Verification of the quantification of the emissions reductions.
[0067] An embodiment of the present invention may be designed to
address the needs of different participants in a residential emissions trading
program. It is anticipated that as demand for tradable emissions increases in
the marketplace (and the value of tradable emissions increases), that a more
rigid (or less flexible) approach to M&V may be warranted. As shown in Fig.
5, the sampling rigor in existing programs has a direct correlation to the
amount of creditable emissions that are generated (in this example, for a
greenhouse gas program).
[0068] An emissions trading initiative of embodiments of the present
invention is intended to create a marketplace for the trading of emission
reductions that result from energy efficiency programs. Energy efficiency
programs may reduce household energy consumption through the
implementation of more efficient technologies or the maintenance of existing
devices within the home.
[0069] To calculate the emission reductions from an energy efficiency
program, the baseline energy use and the resulting emissions may be
calculated. Baseline emissions are those emissions that would have occurred
if the energy efficiency project had not been undertaken, or if the status quo
had not been altered by the energy efficiency project. This baseline may not
be constant over time, because changes in occupant behavior, weather,
and/or other factors may affect the baseline energy use and emissions.
21
CA 02471942 2004-06-28
[0070] Once the baseline emissions have been calculated, program
emissions may be calculated. Program emissions are those emissions that
occur after the energy efficiency project has been installed or completed.
Program emissions may also change in time, due to the effects of occupant
behavior, weather, and/or other factors.
[0071] After the baseline emissions and the program emissions have been
calculated, the emissions reductions may be calculated as the difference
between the baseline and the program emissions. The emissions reduction,
shown in Fig. 6, is the amount of emissions that are avoided due to the
energy efficiency project.
Measurement of Residential Energy Savings
[0072] Step 100, measuring an energy savings resulting from one or more
energy savings opportunities in one or more residential properties, may
comprise any one or more of a variety of improvements. Examples of energy
efficient upgrades include, but are not limited to: replacing older appliances
with more energy efficient appliances; upgrading domestic hot water (DHW)
heating systems, electric or gas; upgrading heating, ventilation, and/or air
conditioning (HVAC) systems; modifying lighting; fuel switching; renovating
the entire home; and myriad other home improvements. Purchase of new
homes with more energy efficient systems or upgrades from existing systems
to more energy efficient ones are both contemplated by the present invention.
Data Collection
(0073] As embodied herein, measuring an energy savings 100 may
comprise measuring and collecting data for the particular type of energy
efficiency program or energy savings opportunities. Means for measuring an
22
CA 02471942 2004-06-28
energy savings are described below in "Measurement Techniques." For each
type of program, a number of different data collection methods may be used.
The collected data may be used to calculate the energy savings and the
corresponding emissions reductions and, ultimately, the tradable emissions
reductions.
[0074] Before undertaking a data collection effort, it may be advantageous
to identify the type of calculations that will be used. Different methods of
data
collection may comprise different inputs. In some cases, a slight increase in
data collection effort (whether surveying, sub-metering, utility bill
collections,
or other means) may result in a substantial increase in the portion of
emissions reductions that are tradable.
[0075] On-site inspection, metering, sub-metering, utility bill analysis,
engineering modeling, or any combination thereof may be used to assess the
energy savings. On-site inspections may be random, and may comprise
report review, visual inspection, and device rating verification. Metering may
comprise collecting energy and water consumption data over time at a facility
through the use of measurement devices. Utility bill analysis may comprise
analyzing: samples of measured data of the energy savings from the
residential properties; samples of control data of residential energy use; raw
data; data normalized by weather; stratified data; data that are both
stratified
and weather-normalized; or a combination thereof.
[0076] Additional measuring methodologies may include engineering
calculations or computer simulation to assess an energy savings. Computer
simulation may utilize computer-based building energy software. Engineering
23
CA 02471942 2004-06-28
modeling may use heating degree day analysis, bin analysis, hourly analysis,
time-step analysis, or any combination thereof.
Energy Savings
[0077] For a given energy savings opportunity or energy efficiency
improvement program, energy savings may be calculated in step 100, as
shown in Fig. 3, as the difference between baseline energy use and post-
implementation or program energy use. Baseline energy use may be
calculated as the product of instantaneous demand for energy multiplied by
the hours of operation of the relevant energy consuming equipment without
the implementation of any energy efficiency improvements (see Equation 1 a).
Calculations may be for a baseline year, which is a defined period of any
length before implementation of an energy conservation measure. Program
energy use (after completion of the installation of the energy efficiency
improvements) may be calculated in a similar manner (see Equation 1 b). The
annual energy savings may then be calculated as the difference between the
baseline energy use and the program energy use (see Equation 1c).
h
(Eq. 1 a) Baseline Energy Use = ~ KW;
~_,
Where: KW; -
Instantaneous demand for energy at hour "i",
without
implementation of energy efficiency measures,
expressed in kW (kilowatts).
h - Annual number of hours of operation of energy
24
CA 02471942 2004-06-28
consuming equipment without implementation of
energy efficiency measures (hours per year)
h
(Eq. 1 b) Pr ogram Energy Use = ~ KW;p
Where: KW;p - Instantaneous demand for energy in the hour "i", at
completion of the energy efficiency program,
expressed in kW (kilowatts).
h - Annual number of hours of operation of energy
consuming equipment at completion of the energy
efficiency program (hours per year).
(Eq. 1 c) Annual Energy Savings = Baseline Energy Use - Program
Energy Use
[0078] The baseline energy use may be expressed as a series of annual
energy use estimates, one for each year in the anticipated life of the energy
efficiency program. For example, if an energy efficiency program is expected ,
to have a ten-year lifetime, then the baseline energy use can be a series of
ten energy use estimates. Each value in the series represents the expected
annual energy use (without any energy efficiency improvements) for a given
year. Similarly, the program energy use and the annual energy savings may
also be expressed as a time series of values, one for each year in the life of
the program.
CA 02471942 2004-06-28
(Eq. 1 d) Lifetime Energy Savings =
Y
(BaselineEnergyUse~ - ProgramEne~gyUse~ )
Where: Baseline Energy Used - Energy use without the
implementation of energy
efficiency measures, in the
year "j."
Program Energy Used - Energy use with
implementation
of energy efficient measures
(i.e.,
program), in the year "j."
y - Number of years in the life of
the
program.
[0079] Prior to program implementation, an initial estimate (for each year
of the program life) may be made for the baseline energy use, the program
energy use, and the annual energy savings. These initial estimates may be
based on engineering calculations, or any other suitable methodology. After
the energy efficiency program is implemented, these initial estimates may be
updated with monitored data from the field programs.
[0080] The total net energy savings from an energy efficiency program
may be determined by summing the total of energy savings (from Equation
1 d) across all involved households: .
26
CA 02471942 2004-06-28
(Eq. 1 e) Total Program Energy Savings = ~ ES,,
Where: ES - Lifetime Energy Savings from Eq. 1d.
n - Subscript denoting the number of Households.
[0081] In cases where types of households differ, they may be grouped
according to similar characteristics, and summed by group as follows:
(Eq. 1f) Total Program Energy Savings = ~ (HHg * AESg)
Where: g - Subscript denoting a group of households with
similar characteristics.
HH - Number of households in a particular group.
AES - Average energy savings of a home in group 9.
Emission Factors
[0082] Emission factors may be employed in step 200 to correlate
reductions in energy consumption with their associated emission reductions.
Emission factors may indicate the amount of emissions generated per unit of
energy. They are essentially conversion factors, translating energy
measurements (kWh or other appropriate units) to quantifiable emissions
reductions in tonnes per carbon equivalent (TCE) or other pollution emission.
[0083] The residential energy efficiency programs or energy savings
opportunities discussed below may convert fuels into productive energy and
polluting emissions. The amount of emissions and energy generated may be
dependent on the characteristics of the device (device type, efficiency,
pollution reduction, etc.) and on the type of fuel (or source of electricity).
27
CA 02471942 2004-06-28
Through quantifying the efficiency levels and other key variables specific to
the appliances, systems, and devices under consideration in the present
invention, it may be possible to calculate the emissions that result from
their
use and develop a simple factor to use for this conversion.
[0084] EPA has compiled a substantial body of information on emissions
factors in the "Compilation of Air Pollutant Emission Factors" (also known as
AP42), which is incorporated herein by reference. This compilation can be
found on the EPA website at http://www.epa.gov/ttn/chief/index.html. The
data is summarized in EPA's E-Grid database, which contains emissions
factors at the national, state, and utility level. Examples of some of the EPA
factors include:
~ Natural Gas, Fuel Oil, and Coal, which are consumed off-site.
Therefore the emission factors are dependent on the characteristics
of the device that is consuming the fuel and the fuel used. For
example, there are several different kinds of fuel oil. The sulfur
content of coal varies geographically. When these variables have
been compiled, the appropriate emission factors are available from
published references.
~ Electricity emission factors are not calculated with site-based
information. The emissions from electricity generation occur at the
power plants that produce the electricity. Emission factors,
therefore, are based on power plants' emission factors. In many
cases the electricity comes from the grid and consequently the
emission factor is a function of the individual emission factors from
multiple power plants.
CA 02471942 2004-06-28
[0085] In steps 201 and 202 of Fig.4, the following equations may be used
to calculate emission factors.
(Eq. 1 g) Baseline Emission Factors = Average(EF; =~,..n
Where: EF; - Marginal Emission Factor for the baseline, in a
given
hour of the year "i".
;, - Subscript denoting the number of hours of
equipment operation in the year.
(Eq. 1 h) Pr ogram Emission Factors = Average~EF; =,,..n
Where: EF; - Marginal Emission Factor for the program, in a
given
hour of the year "i".
n - Subscript denoting the number of hours of
equipment operation in the year.
[0086] In accordance with an embodiment of the present invention, current
or updated EPA emission factors may be utilized for determining emissions
reductions, or program participants may provide their own emission factors.
Emissions
[0087] In step 203, baseline emissions may be calculated as the product of
baseline energy consumption and emissions factors for the appropriate fuel
source (see Equation 1 i). Similarly, in step 204 program emissions may be
29
CA 02471942 2004-06-28
calculated as the product of the program energy consumption and emissions
factors for the appropriate fuel source (see Equation 1 j).
h
(Eq. 1 i) Baseline Emissions = Baseline Energy Use; * EF;
Where: EF; - Emission Factor for the baseline, in a given hour of
the year "i".
h - Number of hours of equipment operation in the
year.
n
(Eq. 1 j) Pr ogram Emissions = ~ Pr ogram Energy Use; * EF;
.=1 ..
Where: EF; - Emission Factor for the program, in a given hour of
the year "i".
h - Number of hours of equipment operation in the
year
Emissions Reductions
[0088] In step 200, emissions reductions may be calculated as the
difference between baseline pollutant emissions (for a given pollutant) and
program (post-implementation) pollutant emissions. Annual emissions
reductions may be calculated in step 205 (see Equation 1k).
(Eq. 1 k) . Annual Emissions Reductions = Baseline Emissions - Program
Emissions
CA 02471942 2004-06-28
[0089] Baseline emissions may also be expressed as a series of annual
emissions estimates -- one for each year in the anticipated life of the energy
efficiency program (as described above for annual energy savings). Each
value in the series represents the expected annual emissions (without any
energy efficiency improvements) for a given year. Similarly, program
emissions and annual emissions reductions may be expressed as a time
series of values -- one for each year (or other appropriate time period) in
the
life of the project. These annual values may be summed, as shown in the
following equation, to calculate lifetime emissions reductions in step 206.
(Eq. 1 I)
Lifetime Emissions Reductions = ~~Baseline Emissions -Program Emissions
Where: Baseline Emissions = Baseline emissions in the year "j".
Project Emissions - Program emissions in the year °'~11.
y - Number of years in program life.
[0090] Quantifying emissions reductions from measures taken to increase
energy efficiency may require data on -- and is the product of -- energy
savings and emission factors specific to each measure, opportunity, or
program. These estimates may comprise an equation, two variations of which
are shown in Equations 1 i and 1 j. Both equations, as well as those presented
in the following sections, are essentially the same for both future baseline
forecasts and program estimates. The significance of the changes in the
variables may be dependent upon the specific action taken to increase energy
efficiency.
31
CA 02471942 2004-06-28
[0091] As embodied herein, the methodology for quantifying energy
consumption and savings for the energy savings opportunities or energy
efficiency programs may be similar to that for calculating baseline data
above.
Procedures for calculating various areas of potential energy efficiency
upgrades are described in the following sections, including, but not limited
to,
energy efficient appliance, domestic water heating, HVAC, lighting, fuel
switching, and whole house programs. Other suitable energy efficiency
upgrades are considered well within the scope of the present invention.
[0092] As described above under "Data Collection," there are a number of
methods in which to estimate and/or measure energy savings from each of
these program types, including: on-site inspections; engineering calculations;
billing analysis; metering; sub-metering; and any other appropriate means.
[0093] The quality of the overall energy savings assessment may be
dependent on the estimation or (measurement) approach used. A TCF may
assign varying degrees of confidence to an energy savings estimate.
Quantification of TCFs is described below under "Calculation of Technical
Confidence Factors."
Energy Efficient Appliance Programs
[0094] Average household energy efficiency may be increased by
replacing less efficient appliances with more efficient alternatives. Newer
and
more energy efficient appliances generally consume less energy, without
sacrificing performance. Energy efficient products may also provide energy-
saving benefits by working faster, thereby using energy for less time.
Appliance upgrades may include: refrigerators; stoves and ovens; clothes
washers and dryers; dishwashers; and any other appropriate appliances.
32
CA 02471942 2004-06-28
Energy Savings Equations for Appliance Programs
[0095] The energy savings from an appliance upgrade may be calculated
as follows:
(Eq.2a) EnergyConsumption(EC)=~~(kW;*D;)/OBI~
(Eq. 2b) Net Energy Savings = ~ECb - ECp; ~* OBIp;
Where: D - Duration over which energy consumption is
estimated
(hours).
kW - Power demand of the appliance (in kilowatts).
- Subscript denoting the interval during which power
demand remains constant.
- Subscript denoting the baseline scenario.
p. - Subscript denoting the post-implementation
scenario.
OBI - Occupant behavior index.
[0096] Equation 2a determines the area under a graph of kilowatt-hours as
the dependent variable against time. Energy consumption may be calculable
both pre- and post-implementation, and may be useful in quantifying
consumption for a baseline scenario, as well as under an energy efficiency
program scenario. Because appliances generally operate at different power
demands over time, the product of power demand and the duration of time at
that power demand may be summed in order to arrive at the total energy
consumption for a particular appliance. The occupant behavior index (OBI)
33
CA 02471942 2004-06-28
may be useful when additional information is available concerning occupant
behavior over time (due to shifting prices or relocation). OBI is an indicator
variable for the occupant behavior, which may range from 0 to 1. OBI may be
used to normalize energy consumption based on variations in occupants'
behavior or presence, and where occupant behavior directly impacts energy
consumption.
[0097] The total net energy savings from an energy efFiciency program
may comprise the total of energy savings (from Equation 2b) summed across
all households participating in the program.
(Eq. 2c) Total Program Energy Savings =~ESh
Where: ES - Energy Savings.
n - Subscript denoting the number of households
participating in the program.
[0098] In cases where types of households differ, they may be grouped
according to similar characteristics, and summed by group as follows:
(Eq. 2d) Total Program Energy Savings =~~HHg ~AES9
Where: g - Subscript denoting a group of households with
similar characteristics.
HH - Number of households in a particular group.
AES - Average energy savings of a home in group g.
Data Collection, Testing, and End Use Metering
For Appliance Programs
34
CA 02471942 2004-06-28
[0099] Depending on the calculation methodology used, different sets of
information may be required. The data collection methodology, therefore, may
be based on the calculations' input requirements. The key input variables may
include:
1. Energy: the energy consumption of the device may be measured with
energy consumption meter (to spot test or sub-meter), may be
collected from utility bills, or may be derived from other appropriate
source(s).
2. Wattage: the power demand (kW) of the device for a given unit of time
and use may be measured with watt meters (to either spot test or sub-
meter the appliance), from inspecting the device's nameplate capacity,
or other appropriate means.
3. Usage: the number of hours the device is "on" may be measured with
time of use loggers, or other appropriate means.
[00100] Measurements may be taken according to industry-accepted
standards/practices. Records may be maintained, indicating the method of
test or measurement standard used. Relevant standards and codes may
include older, current, more recent or replacement versions of:
~ Household Refrigerators, Combinations Refrigerator-Freezers, and
Household Freezers (ARAM, American National Standards
Institute(ANSI)/AHAM; HRF 1);
~ Household Refrigerators and Freezers (Canadian Standards
Association (CSA) C22.2 No. 63- M1987); and
CA 02471942 2004-06-28
~ Capacity Measurement and Energy Consumption Test Methods for
Refrigerators, Combination Refrigerator-Freezers, and Freezers
(CSA, CAN/CSA C3 00-M91 );
each of which is incorporated herein by reference.
Energy Efficient Domestic Water Heating Programs
[00101] Domestic hot water (DHW), such as electric or gas, consumes
energy by heating water for showers, baths, and other household uses.
Improvements in domestic hot water systems of homes may result in
substantial energy savings. For example, an oil-fired boiler could be replaced
with a natural gas hot water heater.
(Eq. 3a) Household Energy Consumption = ~WC * SpH * ~T)/EfF
Where: WC - Amount of water consumed (in kg) during the
period
under consideration.
SpH - Specific heat capacity of water (4.184 J g'~ °C'~)
~T - Difference between the inlet and outlet water
temperature (in degrees Celsius).
Eff - Overall operating efficiency of the water heating
device.
[00102] The net energy savings from a whole home DHW upgrade may be
calculated as in Equation 1 d. In particular, household energy consumption for
a baseline and for post-implementation may be calculated. Net energy
savings may be calculated as the difference between the two. The program-
36
CA 02471942 2004-06-28
wide energy savings may be determined by summing savings in each
household, as represented in Equation 1e or 1f.
Data Collection, Testing, and End Use Metering
For Domestic Hot Water Heating Programs
[00103] Depending on the calculation methodology used, different sets of
information may be required. Consequently, the data collection methodology
may be based on the calculations' input requirements. The key input
variables may include:
1. Energy: the energy consumption of the installation may be measured
with kWh meter (to spot test or sub-meter), utility bill records, sub-
system consumption monitoring, or other appropriate means.
2. Efficiency: the system efficiency may be found from manufacturer's
specifications, tested according to the appropriate American Society of
Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE)
standards indicated below, or other appropriate means.
3. Consumption: the household water consumption may be monitored
using flow meters, may be based on ASHRAE estimates, or other
appropriate means.
4. Temperature: water temperature may be measured using
thermometers, may be based on assumptions found in the ASHRAE
Fundamentals Handbook, or other appropriate means.
[00104] Measurements may be taken according to industry-accepted
standards/practices. Records may be maintained comprising the method of
test or measurement standard used. Relevant standards and codes may
include older, current, more recent, or replacement versions of:
37
CA 02471942 2004-06-28
~ Oil-fired Steam and Hot-Water Boilers for Residential Use (CSA.
B140.7.1-1976 (R 1991);
~ Gas Appliance Thermostats (AGA, ANSI 221.23-1989; Z21.23a-
1991 );
~ Hot Water Immersion Controls (NEMA, NEMA DC-12-1985 (R
1991 ));
~ Method of Testing to Determine the Thermal Performance of Solar
Collectors (ASHRAE, ANSI/ASHRAE 93-1986 (RA 91));
~ Methods of Testing to Determine the Thermal Performance of Solar
Domestic Water Heating Systems (ASHRAE, ASHRAE 95-198 1
(RA 87));
~ Methods of Testing for Rating Residential Water Heaters (ASHRAE,
ANSI/ASHRAE 118.1-1993); and
~ Methods of Testing for Rating Combination Space Heating and
Water Heating Appliances (ASHRAE, ANSI/ASHRAE 124-1991);
each of which is incorporated herein by reference.
Energy Efficient HVAC Programs
[00105] Residential heating, ventilation, and/or air conditioning (HVAC)
systems maintain comfortable temperatures. The demands placed on a
particular HVAC system may be dependent not only on the weather but also
on how well the home is insulated and the demands of the occupants. In
geographic regions where the exterior environment is uncomfortable for much
of the year (whether for heating or cooling), improvements in HVAC systems
may have the potential for substantial energy savings.
38
CA 02471942 2004-06-28
Energy Savings Equations for HVAC Programs
[00106] In cases where HVAC energy end use consumption is metered,
energy savings may be calculated from the following equation:
(Eq. 4a) Household Energy Savings =
(ECb/ (Wlb*OBIb) - ECp; / (Wlp;*OBIp;)) *OBIp;* Wlpi
Where: EC - Household energy consumption (as measured in
kWh).
WI - Weather index.
OBI - Occupant behavior index.
- Subscript denoting the baseline (without EE
program)
scenario.
p; - Subscript denoting the post-implementation (with
EE
program) scenario.
[00107] In cases where sub-metered energy consumption is not available,
energy consumption and household energy savings may be alternatively
calculated using the two equations below:
(Eq. 4b) Household Energy Consumption =
DD * 24 * 1 /Eff * RC / (DT;"doors - DToutdoors)
(Eq. 4c) Household Energy Savings = ECb- ECP;
39
CA 02471942 2004-06-28
Where: DD - Heating degree days (HDD) or cooling
degree
days (CDD), as appropriate.
Eff - Overall device efficiency rating.
RC - Rated capacity of the device.
DT - Design temperature.
EC - Household energy consumption (as
measured
in kWh).
- Subscript denoting the baseline (without EE
program) scenario.
p; - Subscript denoting the post-implementation
(with EE program) scenario.
[00108] The total net energy savings from the energy efficiency program
may be determined by summing savings in each household, calculated as
shown in Equations 1e and 1f.
Data Collection, Testing and End Use Metering
For HVAC Programs
[00109] Depending on the calculation methodology used, different sets of
information may be required. Consequently, the data collection methodology
may be based on the calculations' input requirements. The key input
variables may include:
1. Energyo the energy consumption of the device may be measured with
kWh meter (to spot test or sub-meter), or may be collected from utility
bills, or other appropriate means.
CA 02471942 2004-06-28
2. Wattage: the power demand (kW) of the device for a given unit of time
and use may be measured with watt meters (to either spot test or sub-
meter the appliance), or from inspecting the device's nameplate
capacity, or other appropriate means.
3. Usage: the number of hours the device is "on" may be measured with
time of use loggers, or other appropriate means.
4. Heating Degree Days and Cooling Degree Days: a measure of
heating or cooling load on a facility created by an outdoor temperature.
When the mean daily outdoor temperature is one degree below a
stated reference temperature such as 1 °C, for one day, it is defined
that there is one heating degree day. If this temperature difference
prevailed for ten days there would be ten heating degree days counted
for the total period. If the temperature difference were to be 12° for
10
days, 120 heating degree days would be counted. When ambient
temperature is below the reference temperature, heating degree days
are counted; when ambient temperatures are above the reference,
cooling degree days are counted. Any reference temperature may be
used for recording degree days, usually chosen to reflect the
temperature at which heating or cooling is no longer needed. Many
utilities operate weather stations that record this information. The
National Oceanographic and Atmospheric Agency also gathers this
information (http://www.ncdc .noaa.gov/).
5. Rated Capacity (Btu/hr): the rated capacity may be found from
manufacturer's specifications, or tested according to the appropriate
ASHRAE standards indicated below, or other appropriate means.
41
CA 02471942 2004-06-28
6. Efficiency: the system efficiency (whether AFUE or SEER) may be
found from manufacturer's specifications, or may be tested according
to the appropriate ASHRAE standards indicated below, or other
appropriate means.
7. Design Temperature (Tdesign,indoor and Tdesign,outdoor). design
temperatures may be specified in the ASHRAE Fundamentals
Handbook or by local code organization (state building codes, etc.), or
from other appropriate means.
[00110] Measurements may be taken according to generally-accepted
standards and/or practices. Records may be maintained comprising the
method of test or measurement standard used. Relevant standards and
codes may include older, current, more recent, or replacement versions of:
Air Conditioning:
~ HVAC Systems - Testing, Adjusting and Balancing (1993) (Sheet
Metal and Air Conditioning Contractors' National Association
(SMACNA));
~ Determining the Required Capacity of Residential Space Heating
and Cooling Appliances (CSA, CAN/CSA-F280-M90);
~ Load Calculation for Residential Winter and Summer Air
Conditioning, 7th Ed (1986) (ACCA, ACCA Manual J);
~ Methods of Testing for Seasonal Efficiency of Unitary Air
Conditioners and Heat Pumps (ASHRAE, ANSI/ASHRAE 116-
1983);
~ Heat Pump Systems: Principles and Applications (Commercial and
Residence) (ACCA, Manual H);
42
CA 02471942 2004-06-28
~ Method of Testing for Rating Room Air Conditioners and Packaged
Terminal Air Conditioners (ASHRAE, ANS1/ASHRAE 16-1983 (RA
88));
~ Method of Testing For Rating Room Air Conditioners and Packaged
Terminal Air Conditioner Heating Capacity (ASHRAE,
ANSI/ASHRAE 58-1986 (RA 90));
~ Methods of Testing for Rating Room Fan-Coil Air Conditioners
(ASHRAE, ANSI/ASHRAE, 79-1984 (RA 91));
~ Methods of Testing for Rating Unitary Air-Conditioning (ASHRAE,
ANSI/ASHRAE 37-1988);
~ Room Air Conditioners (Underwriters' Laboratories (UL), UL 484);
Ducts:
~ Duct Design for Residential Winter and Summer Air Conditioning
(ACCA. Manual D);
~ HVAC Air Duct Leakage Test Manual (1985) (SMACNA, SMACNA);
~ Pipes, Ducts and Fittings for Residential Type Air Conditioning
Systems (CSA, B228.1-1968);
Heating:
~ HVAC Systems - Testing, Adjusting and Balancing (1993)
(SMACNA, SMACNA);
~ Installation Standards for Residential Heating and Air Conditioning
Systems (1988) (SMACNA, SMACNA);
~ Residential Equipment Selection (ACCA, Manual S);
43
CA 02471942 2004-06-28
~ Determining the Required Capacity of Residential Space Heating
and Cooling Appliances (CSA, CAN/CSA-F280-M90);
~ Oil-fired Steam and Hot-Water Boilers for Residential Use (CSA,
B140.7.1-1976 (R 1991);
~ Gas Appliance Thermostats (AGA, ANSI 221.23-1989; Z21.23a-
1991 );
~ Heat Pump Systems: Principles and Applications (Commercial and
Residence) (ACCA, Manual H);
~ Methods of Testing for Annual Fuel Utilization Efficiency of
Residential Central Furnaces and Boilers (ASHRAE,
ANSI/ASHRAE 103 -1993);
~ Methods of Testing for Rating Unitary Air-Conditioning and Heat
Pump Equipment) (ASHRAE, ANSI/ASHRAE 37-1988);
~ Requirements for Residential Radiant Tube Heaters (AGA, 7-89);
~ Installation Guide for Residential Hydronic Heating Systems, 6th ed.
(1988) (HYDI, IBR 200); and
~ Methods of Testing for Performance Rating of Wood burning
Appliances (ASHRAE, ANSI/ASHRAE 106-1984);
each of which is incorporated herein by reference.
Energy Efficient Lighting Programs
[00111] Adequate lighting typically is a necessity in living and working
environments. Many spaces, such as hallways, may require twenty-four hour
illumination. Lighting upgrades, therefore, may have substantial potential to
reduce energy consumption, especially in situations where lights are on for
extended periods of time. Improvements in lighting efficiencies also may lead
44
CA 02471942 2004-06-28
to reduced cooling loads, because inefficient lights cause electrical energy
to
be converted to heat instead of light.
[00112] In cases where wattage is constant (i.e., non-variable light
systems), the energy consumption may be calculated from the following
equation:
(Eq. 5a) Household Energy Consumption = (kWb - kWp~) * t
Where: kW - reported energy demand (in kilowatts).
- Subscript denoting the baseline scenario.
p; - Subscript denoting the post-implementation
scenario.
t - duration of time over which the lighting system is
active.
[00113] The baseline scenario for lighting upgrade programs may comprise
the continued use of a current lighting system or comparable standard
replacement systems (assuming no energy efficiency program is in place).
Post-implementation energy consumption may be calculated from accurate
on-site metering, by multiplying the duration of usage by an accepted
standard rate of energy consumption for a particular system, or by other
appropriate means. Equation 5a is calculable only when the wattage of the
lights is fixed (the lights are not dimmable) and the number of hours is
known.
[00114] When lights are dimmable or when it is possible to monitor the
system-specific energy consumption, the energy consumption, (pre- or post-
implementation) may be calculated as presented in Equation 1c. Net
CA 02471942 2004-06-28
household energy savings may be calculated as shown in Equation 1 d, and
program-wide energy savings may be calculated as in Equations 1e and 1f.
Data Collection, Testing, and Sub-Metering
For Lighting Programs
[00115] Depending on the calculation methodology used, different sets of
information may be required. Consequently, the data collection methodology
may be based on the calculations' input requirements. The key input
variables may include:
1. Energy: the energy consumption of the installation may be measured
with kWh meter (to spot test or sub-meter), or sub-system consumption
monitoring, or other appropriate means.
2. Wattage: the power demand (kW) of the device for a given unit of time
and use may be measured with watt meters (to either spot test or sub-
meter the installations), or from inspecting the rating on the installed
bulb and the ballast's nameplate capacity, or from other appropriate
means.
3. Usage: the number of hours the installation is "on" may be measured
with time of use loggers, or other appropriate means.
[00116] Measurements may be taken according to generally-accepted
standards and/or practices. Records may be maintained comprising the
method of test or measurement standard used. Relevant standards and
codes may include older, current, more recent, or replacement versions of:
~ Illuminating Engineering Society Lighting Handbook, 8th Edition,
Illuminating Engineering Society of North America, 1993;
46
CA 02471942 2004-06-28
~ Economic Analysis of Lighting, Illuminating Engineering Society of
North America;
~ ASHRAE/IES Standard 90.1-1989, American Society of Heating
Refrigerating and Air-Conditioning Engineers (ASHRAE) and
Illuminating Engineering Society (IES), 1989;
~ Advanced Lighting Guidelines: 1993, Electric Power Research
Institute (EPRI)/California Energy Commission (CEC)/United States
Department of Energy (DOE), May 1993;
~ Lighting Upgrade Manual. US EPA Office of Air and Radiation
6202J. EPA 430-B-95-003 January 1995;
~ Calculation Procedures and Specification of Criteria for Lighting
Calculations, Illuminating Engineering Society of North America;
~ Determination of Average Luminance of Indoor Luminaires,
Illuminating Engineering Society of North America;
~ Design Criteria for Interior Living Spaces ANSI Approved,
Illuminating Engineering Society of North America; and
~ Lighting Fundamentals Handboole, Electric Power Research
Institute, TR- 101710, March 1993;
each of which is incorporated herein by reference.
Fuel Switching Programs
[00117] Fuel switching may include changing from a more-polluting to a
less-polluting fuel. Most combustible fuels, while producing energy, result in
a
range of air pollutants. Increasing the efficiency of a device or system may
reduce emissions, so too changing to a °'cleaner" fuel may reduce
emissions.
Fuel switching improvements may include use of a specific fuel (e.g.,
47
CA 02471942 2004-06-28
switching from coal with a high sulfur content to coal with a low sulfur
content)
or switching to a different fuel type (e.g., switching from fuel oil to
natural gas).
Other cleaner fuel sources may include solar, heat pump, geothermal,
methane, and a variety of others. Fuel switching changes the emission
factors for the device and may also result in a greater operating efficiency.
Maintenance may also be done on the device while doing the fuel conversion.
[00118] Fuel switching emissions reductions may be calculated from the
following equation:
(Eq. 6a) Emission Reduction = ECb; ~ EFb; - ECp; ~ EFp
Where: ECb; - energy consumption for the baseline.
ECp; - energy consumption after the program.
EFb; - Marginal Emission Factor during the baseline.
EFp; - Marginal Emission Factor after the program.
[00119] Emission factors may be calculated for both the baseline case and
the upgrade, due to the different operating efficiencies and pollution
emission
rates.
Data Collection, Testing, and End Use Metering
For Fuel Switching Programs
[00120] Changing fuel sources typically impacts a home's space heating
and cooling systems (HVAC), and related emissions factors. The emissions
factors may be calculated as previously described under "Emissions Factors."
Energy Efficient Whole House Programs
[00121] Whole home upgrades may increase home insulation and decrease
both infiltration of outside air (cold air in winter and hot air in summer)
and
48
CA 02471942 2004-06-28
leakage of inside air (warm air in winter and cool air in summer). Such
renovations may include, but are not limited to: installing insulation in
attics
and exterior walls; installing more efficient windows and/or doors; reducing
infiltration; and any other appropriate improvements. Whole home energy
consumption may be highly dependent on the exterior environment and
therefore, it may be advantageous to normalize the result using a weather
index for the local environment, when. possible.
[00122 The net energy savings from a whole home upgrade may be
calculated as in Equation 7a. The program-wide energy savings may be
determined by summing savings in each household, as presented in Equation
7b.
(Eq. 7a) Net Energy Savings = ~ECb /OBIb -ECP; /OBIP; ~~ OBIp;
Where: EC - Energy Consumption.
- Subscript denoting the baseline scenario.
p; - Subscript denoting the post-implementation
scenario.
OBI - Occupant behavior index.
(Eq. 7b) Total Program Energy Savings = ~~HH9 ~ AES9
Where: 9 - Subscript denoting a group of households with
similar
characteristics.
HH - Number of households in a particular group.
49
CA 02471942 2004-06-28
AES - Average energy savings of a home in group g.
Data Collection, Testing, and Sub-Metering
For Whole House Programs
[00123] Depending on the calculation methodology used, different sets of
information may be required. Consequently, the data collection methodology
may be based on the calculations' input requirements. The key input
variables may include:
1. Energy: the energy consumption of the installation may be measured
with kWh meter (to spot test or sub-meter); utility bill records; sub-
system consumption monitoring; or other appropriate means.
2. Building Insulation: insulation levels may be gathered from
construction records or may be estimated based on the building's age,
building type, or other appropriate means.
3. Infiltration: testing for infiltration may be conducted with a Minneapolis
blower door or other suitable product. Testing may be undertaken by a
trained and experienced technician, according to the relevant
standards.
[00124] Modification of a building's thermal envelope may impact primarily
on the home's space heating and space cooling loads.
[00125] Measurements may be taken according to generally-accepted
standards and/or practices. Records may be maintained comprising the
method of test or measurement standard used. Relevant standards and
codes may include older, current, more recent, or replacement versions of:
~ Air leakage Performance for Detached Single-Family Residential
Buildings (ASHRAE, ANSI/ASHRAE 119-1988);
CA 02471942 2004-06-28
~ Methods of Determining Air Change Rates in Detached Dwellings
(ASHRAE, ANSI/ASHRAE 136-1993);
~ Methods of Testing for Room Air Diffusion (ASHRAE,
ANSI/ASHRAE 113-1990);
~ Ventilation for Acceptable Indoor Air Quality (ASHRAE,
ANSI/ASHRAE 62-1989);
~ Model Energy Code (1992) (Council of American Building Officials
(CABO));
~ Thermal Environmental Conditions for Human Occupancy
(ASHRAE, ANSI/ASHRAE 55-192); and
~ Energy Conservation in New Building Design Residential only
(ASHRAE, ANSI/ASHRAE/IES 90A-1980);
each of which is incorporated by reference. Other energy efficient upgrade or
improvements are considered to be well within the scope of the present
invention.
Quantification of Emissions Reductions
[00126 Emission reductions are a function of their associated emission
factors and energy savings. Reductions in emissions of a gas may be
calculated from the following equation:
(Eq. 8a) Reduction in Emissions of gas g = ~ (ESp,9 ~ EFp,9
p=1
Where: p - Subscript denoting the implemented project, or
specific efficiency-improving measure.
51
CA 02471942 2004-06-28
- Number of contributing energy efficiency
programs.
ES - Energy saved from project p, expressed in kWh
(kilowatt-hours).
EF - Emission factor associated with g, expressed as
tons
carbon equivalent (TCE) per kWh.
g - Gas.
[00127] The relevant emission factors may vary over time. Embodiments of
the present invention also contemplate incorporating a changing emission
factor into the above equation.
Quantification of Tradable Emissions Reductions
[00128] Emissions reductions from an energy efficiency program may be
calculated in step 200 based on the predicted energy savings and relevant
emission factors. Uncertainties are associated with both the energy savings
and the emission factor estimates. Embodiments of the present invention
include a set of procedures for assessing the level of uncertainty in these
estimates and the assignment of TCFs to each (see below). A purpose of the
TCFs is to determine a portion of the calculated emissions reduction that is
certain (or tradable) from the portion that is uncertain (or untradable). The
.uncertain portion of the emissions reductions may be held in reserve and may
be released in future years, if verified.
[00129] Although it is possible to offer tradable emissions reductions within
the scope of the present invention with a specified degree of uncertainty
(e.g.
1.,000 metric tonnes of C02 ~ 10%), embodiments also contemplate offering
52
CA 02471942 2004-06-28
tradable emissions reductions without uncertainty (e.g. 1,000 metric tonnes of
C02). It may be desirable to calculate the emissions reductions that are
guaranteed to occur, despite any uncertainty in the calculations (or
estimation
process). For example, if the calculated emissions reductions for a given
energy efficiency program were 1,000 metric tonnes with an uncertainty of
t10%, only 900 metric tonnes may be considered tradable. According to an
embodiment of the present invention, a method for calculating a tradable
portion of the emissions is presented in Equation 9a.
(Eq. 9a) Tradable Emissions Reductions = Emissions Reductions ~ TCF
Where: TCF - Technical Confidence Factor
[00130] TCF may be a number from 0 to 1 (or other appropriate scale) that
captures the uncertainty in both the energy savings and emissions factor
estimates. A high TCF (approaching 1) indicates that there is very little
uncertainty in the calculated emission reductions and, therefore, the size of
the tradable emissions reductions pool is almost the same size as the
calculated emissions reductions. A low TCF (approaching 0) indicates that
there is substantial uncertainty and the tradable emissions reductions,
therefore, are only a small portion of the calculated emissions reductions.
[00131] The graph in Fig. 9 presents an example of predicted emissions
reductions from the calculations (Equations 2-7 above) and tradable
emissions reductions. The vertical error bars show the uncertainty. A TCF
may be identified and used on the calculated emissions reductions to produce.
the tradable emissions reductions (the horizontal dashed line in Fig. 9).
53
CA 02471942 2004-06-28
[00132] In a forecasting phase of the M&V process, the emissions reduction
potential may be predicted, or estimated. This is shown as the solid
horizontal line in Fig. 9. Based on the anticipated measurement approach to
be used in the program phase of an M&V process, uncertainty of the
measured emissions reduction results may be estimated. This uncertainty is
shown by the vertical error bars. The uncertainty bars indicate the portion of
the estimated emissions reduction that is certain (i.e., the region below the
error bars) and uncertain (the region within the error bars). This general
approach may be used to determine a TCF for each of several M&V
approaches.
[00133] As data are collected on the emissions reductions from a given
energy efficiency program during the program phase of the M&V process, the
measured data are expected to agree with forecasted emissions reductions
predicted in the forecasting phase, albeit with some degree of variability. A
purpose of TCFs is to ensure that the measured emissions reductions (the
fluctuating dotted line in Fig. 10) always exceed the "tradable emissions
reduction" (i.e., are reliable estimates).
[00134] In an embodiment of the present invention, data may be entered by
a program participant (e.g., program partner) into electronic spreadsheets
that
automatically calculate emissions reductions and tradable emissions
reductions for a program. Data entered into the electronic spreadsheets)
may include, but is not limited to: energy consumption; emissions factors; and
M&V options. The spreadsheets) may be adapted to provide a number of
options to the participant, allowing the participant to select the most
relevant
options. For example, a participant may select a default emissions factor or
54
CA 02471942 2004-06-28
may enter its own emissions factor. Once the applicable data is entered, the
spreadsheet may automatically perform the various calculations through
linked algorithms. Electronic spreadsheets may be provided by suitable
software, such as, for example, Excel spreadsheets. Alternatively, data may
be entered into hardcopy versions of spreadsheets without automatic
calculations of emissions reductions and tradable emissions reductions.
Future Options
[00135] At the mid-point, or any other appropriate point, in the "lifetime" of
a
set of energy efficiency programs, the actual emissions reductions may
consistently exceed the tradable emissions. In this case, emissions
reductions forecasts and TCFs may be overly conservative. Consequently,
greater emissions reductions were realized than were offered in the pool of
tradable emissions reductions. Fig. 11 shows how a new pool of tradable
emissions reductions (depicted as Tradable Emissions Reduction 2) may be
formed from the un-traded (or untapped) emissions reductions from these
energy efficiency programs. The new pool may be formed from actual field
measurements of energy savings and resulting emissions reductions.
Calculation of TCFs
[00136] A method for assessing tradable emissions is provided in Equation
9a. The TCF may be determined based on the sum of three other factors, as
in the following equation.
(Eq. 9b) TCF = Technical Confidence Factor
TCF =1-(RFES+RFEF+AF)
CA 02471942 2004-06-28
Where: RFES - Risk Factor for Energy Savings Estimates
REEF - Risk Factor for Emission Factors Estimates
AF - Adjustment Factor
These factors are defined below.
Identification of Risk Factors
For Energy Consumption (RFES)
[00137] Risk factors factor in uncertainty in the calculations used to derive
the calculated emissions reductions. A risk factor is, therefore, a function
of
the type of program (such as HVAC or lighting), and the rigor used to verify
the energy savings and emission factors. The rigor of an energy savings
program is dependent on the type of measurement approach method used,
and the scale at which these methods are undertaken. Possible
measurement approaches include: Energy Star; engineering
calculations/modeling; billing analysis; metering/sub-metering, and/or other
appropriate means.
[00138] The Energy Star label may be employed to provide credible
monitoring and verification procedures for each of the various programs it
covers (e.g., appliances, homes). Default values for different programs may
be provided. If a participant's program is based on Energy Star, the default
values and associated risk factors may be used.
[00139] Energy savings values may be based on other sources, such as, for
example, previously published studies or statistics. These estimates may be
regional or local and may be from a number of different sources, whether
governmental, academic, private, or other sources. Risk factors associated
with several types of outside sources are presented in Table 1.
56
CA 02471942 2004-06-28
[00140] Energy savings and emissions reductions may also be quantified
using engineering estimates, or computer models, or other appropriate
means. This may include simple degree day analysis, bin analysis, hourly
modeling, and/or time-step analysis with building energy software (such as
DOE-2, EnergyPlus, or any other suitable software). Sample risk factors for
different engineering calculation methods at different scales of measurement
(the number of homes and weather scenarios examined) are shown in Table
2.
[00141] Billing analysis may be performed by analyzing large samples of
measured data from program participants and control groups to quantify the
shift in energy consumption due to program participation. This analytical
methodology may be performed on raw data or on data that is normalized and
stratified by relevant factors (such as weather and group characteristics).
Sample risk factors, for different billing analysis methods, at different
scales of
inspection (the percentage of homes examined), are presented in Table 3.
[00142] Metering and sub-metering may be used to measure the
consumption in those end-uses affected by a given energy efficiency program.
Sample risk factors for different metering and sub-metering analysis methods,
at different scales of inspection (the percentage of homes examined), are
shown in Table 4.
57
CA 02471942 2004-06-28
Table 1
Risk Factors
For Other Sources (Published)
Methodology Risk Factors
Utility Estimates (based. on 0.25
previous
published studies)
Energy Star Labeled Homes 0.07
Table 2
Risk Factors
For Engineering Estimates and Modeling
Risk Factors
Methodology
No.
of
BuildingsNVeather
Scenarios
Considered
1-5 6-10 11-20
Simplified Energy Calculations0.25 0.21 0.11
Simplified Energy Calculations0.21 0.14 0.07
with
field inspection
Detailed Energy Calculations 0.21 0.14 0.07
Detailed Energy Calculations 0.11 0.07 0.04
with
field inspection
Calculations on Home Characteristics 0.20
(defaults)
58
CA 02471942 2004-06-28
Table 3
Risk Factors
For Billing Analysis
Risk Factors
Methodology
Sampling
5% 10% 25%
100%
Raw data analyzed 0.25 0.21 0.11
Data normalized by weather 0.07
Data are stratified (grouped 0.21 0.14 0.07
by
appropriate characteristics
before
analysis) 0.04
Stratified and weather normalized0.21 0.14 0.07
0.04
0.11 0.07 0.04
0.02
59
CA 02471942 2004-06-28
Table 4
Risk Factors
For MeteringlSub-Metering
Emission Factor Source Risk Factors
Regional/multi-state average 0.2
(published)
State historical average 0.15
Utility 5-year forecast 0.1
Third party analysis of utility 0.05
(including
5-year forecast)
Identification of Risk Factors
For Emission Factors (RFEF)
[00143] Once energy savings are calculated, emission factors may be used
to convert these savings into emissions reductions. Emission factors typically
have some uncertainty, based on the method of measurement and the
resolution of the data (national, state, utility, or plant specific). Sample
risk
factors for emission factors based on difFerent quantification methodologies
are presented in Table 5.
Table 5
Risk Factors
For Emission Factors
~ TYPe of Plan
Methodology
3 year Historical2-4 Year Plan'6-8 Year Plan
Trend
Default/E-Grid4 0.45 -- --
CA 02471942 2004-06-28
Utility Estimate'0.55 0.65 0.75
3rd Party6 0.65 0.75 0.85
Notes:
~ Historical emission factors are used to predict future emissions.
2 The utility's plans for generation capacity are used to develop a 2-4 year
estimate of emissions.
3 The utility's plans for generation capacity are used to develop a 6-8 year
estimate of emissions.
4 EPA's emission factor database (E-grid) is used to estimate emission
factors.
The utility estimates emission factors.
6 Outside consultants are used to calculate the utility's emission factors.
Identification of Adjustment Factors (AF)
[00144] Uncertainty may be related to future energy use patterns (e.g., due
to unexpected changes in energy costs or weather) and emission factors
(e.g., due to unexpected changes in regulations). Such changes may be
difficult to anticipate and could affect emissions reductions achieved in a
given
year. To provide a buffer for these future possibilities, an Adjustment Factor
(AF) may be incorporated into a TCF. An AF may be assigned a value
corresponding to the total emissions reductions available, such as, for
example 15%. An assigned value may be periodically revisited and updated.
An AF ensures that the tradable emissions reductions do not exceed the
actual emissions reductions achieved by a program. If an overall TCF is
shown to be too conservative, the excess emissions reductions may be
included in future emission pools. Alternatively, if the actual emissions
reductions are shown to align with the tradable emissions reductions, the
61
CA 02471942 2004-06-28
overall TCF has effectively performed its function of protecting the financial
interests of an ETI's participants.
Monitoring of Energy Savings
And Quantification of
Emissions Reductions
[00145] In the early stages of an energy savings program, emissions
reductions may be predicted years into the future. This involves making a
number of assumptions about energy consumption and emission factors.
This forecasting phase is outlined in Fig. 7.
[00146] Once one or more energy savings opportunities have been
implemented, actual energy consumption and emission factors may be
measured, providing estimates of actual emissions reductions. This
measurement phase is shown in Fig. 8. In the steps of monitoring the
residential energy savings opportunities 400 and monitoring the quantification
of the emissions reduction 500, as depicted in Fig. 2, program participants,
such as program administration staff, may compile and manage the energy
savings and emissions reductions data measured and collected by program
partners.
Verification of Energy Savings
[00147] In step 600, as depicted in Fig. 2, quantification of the emissions
reduction may be verified. As described above, an initial estimate of energy
savings may be calculated based on an assessment of the difference
between baseline energy use and post-implementation or measured energy
use.
[00148] Baseline forecasts may be constructed from historical records of
energy consumption and use. When historical information is not available,
62
CA 02471942 2004-06-28
field monitoring or other appropriate means may be employed. Post-
implementation energy use may be measured, or may be estimated through
engineering calculations, deemed savings estimates, or other appropriate
means. Deemed savings estimates may be used for energy efficient
technologies that are well-understood and on which there is general
agreement on the energy use and savings that can be achieved (e.g., many
electric appliances). Deemed savings may be calculated by using a device's
power output and length of use. Deemed savings may be used when a
device is used for predictable time periods and energy consumption does not
vary. For example, deemed savings could be used with lights that are on 24
hours a day, 365 days a year (the energy consumption may be calculated with
reasonable certainty due to the consistent demand and length of use).
[00149] After installation of the measures, baseline energy use and post-
implementation energy use may be verified through field monitoring, deemed
savings estimates, or other appropriate means. Net energy savings may be
calculated by subtracting post-implementation energy use from baseline
energy use. In cases where energy consumption is highly dependent on
external variables (such as an HVAC system's dependence on weather),
energy consumption may be normalized for such variables.
Verification of Emissions Reductions
(00150] Step 600 may further comprise verifying the emissions reductions
for energy savings opportunities or energy efficiency programs. Baseline
emissions and emission reductions that result from implementation of a
project may be calculated from energy consumption and savings data. The
translation from energy use/savings to emissions/reductions may be based on
63
CA 02471942 2004-06-28
emission factors appropriate to the device and fuel source (e.g., gas, oil,
electric) being examined. In an embodiment of the present invention, a
methodology is used to determine emission factors based on U.S. EPA's
"Compilation of Air Pollutant Emission Factors" (AP-42), or any subsequent
revision or replacement. After energy consumption has been calculated for
the baseline and upgrade scenarios, an emission factor database may be
used to calculate the emissions reductions of the program.
[00151] In step 600, calculations and estimates undertaken in the
measurement phase may be used to verify that the emissions reductions
predicted in the forecasting phase are achieved. Verification may afford the
emissions reduction purchaser confirmation that the reductions are genuine.
This process may support the value of the emissions reductions in the
marketplace. Self-verification by program participants and/or third party
verification may be employed. If measured emissions reductions are
significantly different from forecasted emissions reductions, then
reconciliation
may be needed. For example, a program partner may recalculate and
resubmit new estimations of its tradable emissions reductions.
[00152] Energy savings may be calculated from analysis of historical energy
consumption and modeling of future consumption. These calculations will
have a degree of uncertainty and may be verified after the program has been
in place for a length of time, thereby allowing actual consumption to be
measured from utility bills, metering devices, and/or other appropriate means.
Uncertainty
[00153] As described above, a degree of uncertainty is involved in energy
savings and thus emissions reductions calculations. Statistical methods may
64
CA 02471942 2004-06-28
be used in calculating energy savings in step 200 to determine the results of
a
particular residential energy saving program and to help secure confidence
and financing for a residential emission trading credit program embodying the
present invention. The M&V protocol of the present invention may further
comprise statistical means, such as confidence levels and sampling. Methods
for applying the following statistical equations are known in the art of error
and
risk analysis. Uncertainty analysis may also employ methods described in the
International Performance Measurement & Verification Protocol, Appendix B,
which is incorporated herein by reference.
[00154] A certain degree of uncertainty is inherent in many measurements,
estimations, and forecasts. Sources of uncertainty include, for example,
instrumentation error, modeling error, sampling error, and other systematic
and/or random errors. The magnitude of errors typically is given by
manufacturer's specifications. Typically, instrumentation errors are small,
and
are not believed to be a major source of error in estimating savings.
Nonetheless, they too may be considered where appropriate.
[00155] Modeling error refers to errors in the models used to estimate
parameters of interest. Biases may arise from model miss-specification,
including, but not limited to: omitting important terms from the model;
assigning incorrect values for "known" factors; and extrapolation of the model
results outside their range of validity. Random effects of factors not
accounted for by the model variables are non-systematic errors.
[00156] Various regression (linear and/or non-linear) and/or correlation
functions may be employed in the models of the present invention.
Regression models are inverse mathematical models that describe the
CA 02471942 2004-06-28
correlation of independent and dependent variables. Linear regressions may
be employed of the form:
(Eq. 10a) Y=bo +blxl +b2x2 +...+bPxP +e
Where:
y and xk, k - 1, 2, 3,..., p observed variables.
bk, k - 0, 1, 2,..., p coefficients estimated by the
regression.
a - Residual error not accounted for by the
regression equation.
[00157] Methods for applying this and the following equations, and the
variables used therein, are known by those of ordinary skill in the art.
Models
of this type may be used in two~ways:
1. To estimate the value of y for a given set of x values. An
example of this application is the use of a model estimated from
data for a particular year or portion of a year to estimate
consumption for a normalized year.
2. To estimate one or more of the individual coefficients bk.
[00158] In the first case, where the model is used to predict the value of y
given the values of the xk's, the accuracy of the estimate may be measured by
the root mean squared error (RMSE) of the predicted mean. This accuracy
measure is provided by most standard regression packages. The MSE of
prediction is the expected value of the following equation and the RMSE of
prediction is the square root of the MSE.
66
CA 02471942 2004-06-28
(Eq. 1 Ob) ° ~Y ~X - Y Ix, line JZ
Where:
y ~ X - True mean value of y at the given value of
x.
Y ~ X, line - Value estimated by the fitted regression
line. '
[00159] In the second case, where the model is used to estimate a
particular coefficient bk, the accuracy of the estimate may be measured by the
standard error of the estimated coefficient. This standard error is also
provided by standard regression packages. The variance of the estimate b is
the expected value of:
(Eq. 10c) ~b...b')z
Where:
b - True value of the coefficient.
b' - Value estimated by the regression.
The standard error is the square root of the variance.
(00160] Three statistical indices may be used to evaluate regression models
in embodiments of the present invention, as defined below (SAS 1990).
1. The Coefficient of Determination, R2 (%)
(Eq. 10d)
67
CA 02471942 2004-06-28
r 2
\ypred, i y data, i
RZ 1- i=' x 100
( 2
~, 'y data y data, i
i=1
2. The Coefficient of Variation, CV (%):
(Eq 10e)
tt
( _ z
l.v pred,i .Ydata,i
i=1
CV = ~ ~ p x 100
y data
3. Mean Bias Error, MBE (%)
(Eq. 1 Of)
tt
/ _ z
l.Ypred,i .Ydata,i
MBE = i=1 x100
~-B
y data
[00161] Another form of error taken into consideration in embodiments of
the present invention is sampling error. Sampling error refers to errors
resulting from the fact that a sample of units was observed, rather than
observing the entire set of units under study. The simplest form of sampling
error is random error. A fixed number n of units is selected at random from a
total population of N units. Each unit has the same probability of being
included in the sample.
(Eq. 1 Og)
6~
CA 02471942 2004-06-28
j, 2
SE(Y) = O- ~ )~ ~ (YmY) ~
[00162] Methods for applying these equations and the variables used
therein are known by those of ordinary skill in the art. For more complicated
random samples, more complex formulas of the type well-known in the art
may be employed. In general, however, the standard error is proportional to
(1/n°'S). That is, increasing the sample size by a factor "f' will
reduce the
standard error (improve the precision of the estimate) by a factor of
f°'5
Combining Components of Uncertainty
[00163 If the savings (S) estimate is a sum of several independently
estimated components (C):
(Eq. 10h) S =CI +CZ +C3 +...Cp
then, the standard error of the estimate is given by:
(Eq. 1 Oi)
SE~S~= ~SE~C1)z +SE~CZ~2 +SE~C3~z +...SE~CP~Z~o.s
If the savings (S) estimate is a product of several independently estimated
components (C):
(Eq. 1 Oj) S = C1 ~ Cz ~ C3 ~ ... ~ CP
then, the relative standard error of the estimate is approximated by:
(Eq. 1 Ok)
CA 02471942 2004-06-28
SE(S) SE(C ) 2 SE(C ) 2 SE(C ) 2 SE(C ) 2
~Ci ~ + ~Cz ~ + ~Cs ~ + . . . +
[00164] Methods for applying such equations and the variables used therein
would be known by one of ordinary skill in the art.
Uncertainty Propagation for Different Mathematical Operations
Operati Z=x+y Z=x~y L=xwyw
on
Simple pz=ICI+~yl+... ~_~+~Y+ ~=Iml~+nl~y+
... ..
Error z x y z x y
Standar z z - - z 2 2 z
~z = (fix) + ~z ax ~z ynax
(~y) Dy n0y
d + +
C ~
~
Deviatio
z x
xJ Y Y
z
n
Error
[00165] Components may be estimated independently. Independence
means that whatever random errors affect one of the components are
unrelated to errors affecting the other components. In particular, different
components would not be estimated by the same regression fit, or from the
same sample of observations.
[00166] Methods for applying the above formulae and the variables used
therein would be known by those of ordinary skill in the art. The above
formulae for combining error estimates from different components may serve
as the basis for a propagation of error analysis. This type of analysis may be
used to estimate how errors in one component may affect the accuracy of the
overall estimate. Monitoring resources may then be designed cost-effectively
CA 02471942 2004-06-28
to reduce error in the final savings estimate. This assessment may take into
account:
~ the effect on savings estimate accuracy of an improvement in the
accuracy of each component; and
~ the cost of improving the accuracy of each component.
Establishing a Level of Quantifiable Uncertainty
[00167] Determining savings may comprise estimating a difference in level
rather than measuring the level of consumption , directly. In general,
calculating a difference with a given relative precision requires greater
absolute precision than for measuring a level of consumption. Therefore, a
larger sample would be needed than for measuring a level with the same
relative precision. For example, suppose an average load is around 500 kW,
and the anticipated savings is around 100 kW. A 10% error with 90%
confidence (90/10) criterion applied to the load would require absolute
precision of 50 kW at 90 percent confidence. The 90/10 criterion applied to
the savings would require absolute precision of 10 kW, at the same
confidence level.
[00168] Precision criterion may be applied not only to demand or energy
savings but also to parameters that determine savings. For example, a
savings amount could comprise the product of number (N) of units, hours (H)
of operation, and change (C) in watts:
(Eq. 1 OI) Savings Amount = N ~ H ~ C
Where:
N - Number of units
71
CA 02471942 2004-06-28
H - Number of hours of operation
C - Change in watts
[00169] The 90/10 criterion could be applied separately to each of these
parameters. Achieving 90/10 precision for each of these parameters
separately does not imply that 90/10 is achieved for the savings. On the other
hand, if number of units and change in watts are assumed to be known
without error, 90/10 precision for hours implies 90/10 precision for savings.
[00170] The precision standard may be imposed at various levels in an
M&V protocol of the present invention. The choice of level of disaggregation
may affect the desired sample size and associated monitoring costs. Possible
level choices include any one or more of the following:
~ For individual sites, where sampling is conducted within each site;
~ For all savings associated with a particular type of technology,
across several sites for a given project, where both sites and units
within sites may be sampled;
~ For all savings associated with a particular type of technology in a
particular type of usage, across several sites for a project; and
~ For all savings associated with all technologies and sites for a given
energy savings opportunity.
[00171] In general, the higher the precision, the higher the data collection
requirements. If the primary goal is to ensure savings accuracy for a project
or group of projects as a whole, the same precision requirement may not be
imposed on each subset. A uniform relative precision target for each subset
may conflict with the goal of obtaining the best precision possible for the
project as a whole.
72
CA 02471942 2004-06-28
Use of Normalization Factors
[00172] Normalization may be further used in measuring and calculating
energy savings to compensate for dependence on environmental variables
such as occupant behavior, weather, and other factors. This may be
conducted only when dependence on these factors is strong.
Weather Index
[00173] Energy consumption is sometimes dependent on the exterior
environment. Due to this dependence, it may be preferable to take into
account the weather when trying to calculate the energy efficiency of a
system. This process is called normalization. Weather normalization may be
used for those programs that have weather sensitive energy consumption
(such as, for example, HVAC systems, fuel switching, and whole home
upgrades). The first step in normalization is to quantify the weather. For
example, predicted energy savings from HVAC may be based on the number
of annual heating degree days (HDD) or cooling degree days (CDD). By
comparing the relationship between energy consumption and HDD, it may be
possible to establish what the energy consumption of an upgraded building
would be in the same weather that was used to calculate the baseline energy
consumption.
[00174] The effects of weather may also be considered in analyzing historic
energy consumption patterns. For example, a home may have higher energy
consumption after an energy efficiency upgrade if the weather is more severe,
yet energy consumption would have been even higher had there been no
upgrade.
73
CA 02471942 2004-06-28
[00175] Weather normalization may comprise modeling energy
consumption of a home under a number of different weather scenarios. This
modeling may be accomplished using software supplied by the U.S.
Department of Energy or other appropriate building energy modeling software.
Engineering estimates also may be used to estimate energy consumption but
this method typically has lower accuracy.
(00176] Based on the modeling or engineering estimates, a correlation
between Heating Degree Days (HDD) and Cooling Degree Days (CDD) and
energy consumption may be developed. For example, Fig. 12 shows the
results of modeling the same home under different total number of HDD
assumptions.
[00177] After a relationship is developed, future weather may be calculated
in terms of annual heating degree days. This prediction could be the thirty-
year mean temperature, or alternatively, another estimation based on recent
historical weather trends. Correlation calculations and assumptions about
future weather patterns may be explicitly defined. For example, the graph
depicted in Fig. 12 shows heating energy consumption (in MMBtu) equal to
0.0159 (HDD) - 10.6.
[00178] By including weather normalization in energy consumption
calculations, future energy consumption may be calculated and historic
energy savings may be analyzed more accurately, than had the effects of
weather been ignored.
[00179] For the geographic area of a given energy efficiency program, it
may be preferable to calculate the historical average and standard deviation
of heating degree days (HDD)/cooling degree days (CDD) for various time
74
CA 02471942 2004-06-28
horizons. These calculations may provide an understanding of the uncertainty
induced by weather. For example, the following criteria may be used:
~ 5 year average HDD
~ 5 year standard deviation HDD
~ 5 year average CDD
~ 5 year standard deviation CDD
~ 10 year average HDD
~ 10 year standard deviation HDD
~ 10 year average CDD
~ 10 year standard deviation CDD.
Occupant Behavior Index
[00180] The number and behavior of occupants in a home can substantially
affect the energy consumption of a home. Energy conscious people may turn
off lights when they leave the room, whereas other inhabitants may not. A
two person family may use much less energy than a six person family, all
other factors being equal. As a result, energy consumption may shift if the
occupants of a home change, regardless of the upgrades undertaken. To
compensate for this effect, characteristics of inhabitants may be gathered and
used to normalize the model where possible. This additional analysis may be
employed when the sample size is small. If there are 'thousands of homes
participating in a given program, the change of inhabitants in one house will
likely be balanced by changes elsewhere in the program.
[00181] Indices for occupant behavior may be developed by modeling a
prototypical house under a number of occupant scenarios. For example, a
single home's energy consumption may be determined for a couple, a family
CA 02471942 2004-06-28
of three, and a family of seven. This analysis may be used to develop a
relationship (such as a formula) between occupants and energy consumption.
Consequently, this' relationship may be used to compensate for occupant
changes by normalizing raw consumption data for a given household or sets
of households.
[00182] For example, domestic hot water consumption is highly correlated
to the number of inhabitants and therefore a formula may be developed to
normalize the hot water consumption for the number of inhabitants.
[00183] In addition, household energy consumption is often sensitive to
energy prices. As a result, calculations on energy consumption may account
for significant price shifts. A formula expressing the relationship between
consumer behavior and energy price may be developed for normalization of
energy consumption data based on the changes in occupant behavior due to
shifts in prices.
[00184] It will be apparent to those skilled in the art that various
modifications and variations can be made in the construction, configuration,
steps, and/or operation of the present invention without departing from the
scope or spirit of the invention. .
[00185] The present invention contemplates participation in existing new
source review, open market, and area source emissions trading markets
where other pollutants such as NOX, VOCs, SOX, PM, and CO and C02 .
emission reductions are traded. Further, a four pollutants - N02, SOX, C02
and mercury - approach to emissions regulation is currently under
consideration in legislative arenas. It is expressly contemplated that these -
76
CA 02471942 2004-06-28
and other pollutants yet to be determined - are within the scope of the
present
invention.
(00186] Furthermore, the method steps of various embodiments of the
present invention may be disclosed in participant guidelines, which directives
are followed by all program participants in an ETI. The method steps may
further be implemented via data processing means. In particular, a system for
quantifying residential emissions reductions may comprise client devices) for
inputting energy savings data and other data relating to residential energy
savings opportunities. Client devices) may comprise, but are not limited to,
one or more computers or any other suitable hardware device. Client
devices) may communicate with one or more servers via a network, such as,
but not .limited to, the Internet. One or more databases may reside on
servers) for storing inputted energy savings data and other relevant data.
Data stored on databases) may be processed in accordance with the various
calculations disclosed herein for quantifying and aggregating emissions
reductions. Software contained on databases) may comprise program
instructions for carrying out the various calculations.
(00187] Thus, it is intended that the present invention cover the
modifications and variations of the invention, provided they come within the
scope of the appended claims and their equivalents.
77
CA 02471942 2004-06-28
Appendix A - Measurement Techniques
Electricity
A number of different means for measuring energy savings may be
employed by the present invention. A method of sensing alternating electrical
current (AC) for energy efficiency and savings applications may comprise
sensing current with a current transformer or current transducer (CT). CTs
may be placed on wires connected to specific loads, such as motors, pumps,
or lights, and may be connected to an ammeter, power meter, or other
suitable meter device. CTs may have split core or solid torroid configuration.
Torroids are typically more economical than split-core CTs, but require a load
to be disconnected for a short period while they are installed. Split-core CTs
allow installation without disconnecting the load. Both types of CTs may have
accuracies better than one percent.
Voltage may be sensed by a direct connection to the power source. In
an embodiment of the present invention, voltmeters and power measuring
equipment are directly connected to voltage leads. Alternatively, voltmeters
and power measuring equipment may utilize an intermediate device, such as
a potential transducer (PT), to lower the voltage to safer levels at the
meter.
In an embodiment of the present invention, true RMS power digital
sampling meters are used for inductive loads such as motors or magnetic
ballasts. Though electrical load is the product of voltage and current,
separate voltage and current measurements are not preferred for these loads.
Such meters are particularly important if variable frequency drives or other
harmonic-producing devices are on the same circuit, resulting in the
likelihood
of harmonic voltages at the motor terminals. True RMS power and energy
78
CA 02471942 2004-06-28
metering technology, based on digital sampling principles, may be preferred,
because of its ability accurately to measure distorted waveforms and properly
to record load shapes.
Power measurement equipment meeting the IEEE Standard 519-1992
sampling rate of 3 kHz may be used where harmonic issues are present.
Most metering equipment of the type known in the art comprises sampling
strategies to address this issue. It may be preferable to obtain documentation
from meter manufacturers in order to ascertain that the equipment is
accurately measuring electricity use under waveform distortion.
Power may also be measured directly using watt transducers. Watt-
hour energy transducers that integrate power over time eliminate the error
inherent in assuming or ignoring variations in load over time. Watt-hour
transducer pulses may be recorded by a pulse-counting data logger for
storage and subsequent retrieval and analysis. An alternative technology
comprises combining metering and data logging functions into a single piece
of hardware.
In an embodiment of the present invention, hand-held wattmeters,
rather than ammeters, are used for spot measurements of watts, volts, amps,
power factor, or waveforms. Regardless of the type of solid-state electrical
metering device used, the device should meet the minimum performance
requirements for accuracy of the American National Standards Institute
standard for solid state electricity meters, ANSI C12.16-1991, published by
the Institute of Electrical and Electronics Engineers (IEEE). This standard
applies to solid-state electricity meters that are primarily used as watt-hour
79
CA 02471942 2004-06-28
meters, typically requiring accuracies of one to two percent based on
variations of load, power factor, and voltage.
Runtime
Some equipment may not be continuously metered with recording watt-
hour meters to establish energy consumption, such as, for example, constant
load motors and lights. For such equipment, determination of energy savings
may comprise measuring the time that a piece of equipment is on, and then
multiplying it by a short term power measurement. Self-contained battery-
powered monitoring devices may be utilized to record equipment runtime and,
in some cases, time-of use information, providing a reasonably priced, simple
to install, approach for energy savings calculations.
Temperature
Computerized temperature measurement devices may comprise
resistance temperature detectors (RTDs), thermocouples, thermistors,
integrated circuit (IC) temperature sensors, and any other suitable devices
for
measuring temperature.
Resistance Temperature Detectors (RTDs) are known means in the
energy management field for measuring air and water temperature. An RTD
measures the change in electrical resistance in materials. RTDs are generally
considered accurate, reproducible, stable, and sensitive.
RTDs are economical and readily available in various configurations to
measure indoor and outdoor air temperatures, as well as fluid temperatures in
chilled water or heating systems. RTDs may comprise 100 and 1,000 Ohm
platinum devices in various packaging configurations, further comprising
ceramic chips, flexible strips, and thermowell installations.
CA 02471942 2004-06-28
Depending on the application, two, three, and four-wire RTDs may be
employed. Accuracy, distance, and routing between the RTD and the data
logging device may determine the specific type of RTD for a project. Four-
wire RTDs may offer a high level of precision. Three-wire RTDs may
compensate for applications where an RTD requires a long wire lead,
exposed to varying ambient conditions. Wires of identical length and material
exhibit similar resistance-temperature characteristics and can be used to
cancel the effect of the long leads in an appropriately designed bridge
circuit.
Two-wire RTDs may be field-calibrated to compensate for lead length and
may not have lead wires exposed to conditions that vary significantly from
those being measured.
For Installation of RTDs, conventional copper lead wire may be used
as opposed to the more expensive thermocouple wire. Metering equipment
may allow for direct connection of RTDs by providing internal signal
conditioning and the ability to establish offsets and calibration
coefficients.
Thermocouples measure temperature using two dissimilar metals,
joined together at one end, which produce a small unique voltage at a given
temperature. The voltage may be measured and interpreted by a
thermocouple thermometer. Thermocouples may comprise different
combinations of metals, for different temperature ranges. In addition to
temperature range, chemical abrasion, vibration resistance, and installation
requirements may be considered when selecting a thermocouple.
Thermocouples may be employed when reasonably accurate
temperature data are required, such as for thermal energy metering. The
main disadvantage of thermocouples is their weak output signal. As a result,
81
CA 02471942 2004-06-28
thermocouples are sensitive to electrical noise and may require amplifiers.
Few energy savings determinations warrant the accuracy and complexity of
current thermocouple technology, although improvements in thermocouple
technology may make it attractive for a wider variety of applications.
Thermistors are semiconductor temperature sensors comprising an
oxide of manganese, nickel, cobalt, or one of several other suitable
materials.
One difference between thermistors and RTDs is that thermistors exhibit a
relatively large resistance change with temperature. Thermistors are not
interchangeable, and their temperature-resistance relationship is non-linear.
Thermistors may include shielded power lines, filters, or DC voltage, as they
are relatively fragile. Thermistors are infrequently used in savings
determinations.
Integrated Circuit Temperature Sensors may comprise semiconductor
diodes and transistors that exhibit reproducible temperature sensitivities. IC
sensors may further comprise an external power source. These devices are
occasionally found in HVAC applications where low cost and a strong linear
output are required. IC sensors have a fairly good absolute error, but they
are
fragile and are subject to errors due to self-heating.
Humidity
Accurate, affordable, and reliable humidity measurement has always
been difficult and time-consuming. Equipment to measure relative humidity is
commercially available and installation is relatively straightforward.
Calibration of humidity sensors may be a concern and may be documented in
reporting in conjunction with M&V protocols of the present invention.
82
CA 02471942 2004-06-28
Flow
Flow may be measured for natural gas, oil, steam, condensate, water,
and compressed air, among others. Liquid flow measurement devices are
well-known prior to the present invention. Flow sensors may be grouped into
two general types: intrusive flow meters (using differential pressure and
obstruction sensors), and non-intrusive flow meters (using ultrasonic and
magnetic sensors).
The appropriate flow meter for a particular application may depend on
the type of fluid being measured; how dirty or clean it is; the highest and
lowest expected flow velocities; and the budget.
Differential Pressure Flow Meters calculate fluid flow rate by measuring
pressure loss across a restriction. This technique is commonly used in
building and industrial applications. Pressure drops generated by various
shaped restrictions have been well-characterized over the years, and would
be known by those of ordinary skill in the art. These "head" flow elements
come in a wide variety of configurations, each with strengths and
weaknesses. Examples of flow meters utilizing the concept of differential
pressure flow measurement include Orifice Plate meter, Venturimeter, and
Pitot Tube meter. The accuracy of differential pressure flow meters that may
be employed in the present invention is typically from about one to about five
percent of the maximum flow for which each meter is calibrated.
Obstruction Flow Meters may provide a linear output signal over a wide
range of flow rates, often without the pressure loss penalty incurred with an
orifice plate or venturi meter. These meters may comprise a small target,
weight, or spinning wheel placed in the flow stream. Fluid velocity may be
83
CA 02471942 2004-06-28
determined by the rotational speed of the meter (turbine) or by the force on
the meter body (vortex).
Turbine meters may measure fluid flow by counting the rotations of a
rotor that is placed in a flow stream, providing an output that is linear with
flow
rate. Turbine meters may comprise an axial-type or insertion-type. Axial
turbine meters may have an axial rotor and a housing that is sized for an
appropriate installation. Insertion turbine meters may allow the axial turbine
to
be inserted into the fluid stream and use existing pipe as the meter body.
Insertion turbine meters may measure fluid velocity at a single point in the
cross-sectional area of the pipe. Total volumetric flow rate for the pipe may
be inferred from the measurement. Insertion turbine meters may be installed
in straight sections of pipe away from internal flow turbulence.
Vortex meters utilize oscillating instabilities in a low pressure field after
it splits into two flow streams around a blunt object to measure flow. Vortex
meters require minimal maintenance. and have high accuracy and long-term
repeatability. Vortex meters may provide a linear output signal that is
captured by meter/monitoring equipment. .
Non-Interfering Flow Meters may be employed in applications where
the pressure drop of an intrusive flow meter is of critical concern, or where
the
fluid is dirty, such as in sewage, slurries, crude oils, chemicals, some
acids,
process water, and other similar fluids.
Ultrasonic flow meters may be employed to measure clean fluid
velocities by detecting small differences in the transit time of sound waves
that are shot at an angle across a fluid stream. Ultrasonic flow meters
facilitate rapid measurement of fluid velocities in pipes of varying sizes.
84
CA 02471942 2004-06-28
Accuracies may range from one percent of actual flow to two percent of full
scale. In alternative embodiments, an ultrasound meter that uses the Doppler
principle in place of transit time may be employed. In such meters, a certain
amount of particles and air are necessary in order for the signal to bounce
off
and be detected by a receiver. Doppler-effect meters are available with an
accuracy between about two percent and about five percent of full scale and
cost somewhat less than standard transit time-effect ultrasonic devices.
Meter cost is independent of pipe size.
Magnetic flow meters may measure the disturbance that a moving
liquid causes in a strong magnetic field. Magnetic flow meters are usually
more expensive than other types of meters. Such meters have no moving
parts, and are accurate to about one to about two percent range of actual
flow.
Pressure
Mechanical methods of measuring pressure are well-known. U-tube
manometers were among the first pressure indicators. Manometers are large,
cumbersome, and not well suited for integration into automatic control loops.
Manometers are usually found in the laboratory or used as local indicators.
Depending on the reference pressure used, they may indicate absolute,
gauge, or differential pressure. Pressure measurement devices may be
selected based on their accuracy, pressure range, temperature effects,
outputs (millivolt, voltage, or current signal), and application environment.
Modern pressure transmitters have been developed from the
differential pressure transducers used in flow meters. They may be used in
CA 02471942 2004-06-28
building energy management systems, which are computers programmed to
control and/or monitor the operations of energy consuming equipment in a
facility, and measure pressure with the necessary accuracy for proper building
pressurization and air flow control.
Thermal Energy
The measurement of thermal energy flow may comprise flow and
temperature difference. For example, cooling provided by a chiller is recorded
in Btus and is calculated by measuring chilled water flow and the temperature
difference between the chilled water supply and return lines. An energy flow
meter may perform an internal Btu calculation in real time based on input from
a flow meter and temperature sensors. Electronic energy flow meters
typically are accurate to better than one percent. They may also provide other
useful data on flow rate and temperature (both supply and return).
When a heating or cooling plant is under light load relative to its
capacity, there may be as little as a 5~F difference between the two flowing
streams. To avoid significant error in thermal energy measurements, the two
temperature sensors may be matched or calibrated. The sensors may be
matched or calibrated with respect to one another, rather than to a standard.
Suppliers of RTDs provide sets of matched devices.
Typical purchasing specifications may be for a matched set of RTD
assemblies (each consisting of an RTD probe, holder, connection head with
terminal strip, and a stainless steel thermowell), calibrated to indicate the
same temperature, for example within a tolerance of 0.1~F over the range of
25~F to 75~F. A calibration data sheet typically is provided with each set.
Design and installation of temperature sensors used for thermal energy
86
CA 02471942 2004-06-28
measurements may consider the error caused by: sensor placement in the
pipe; conduction of the thermowell; and any transmitter, power supply, or
analog-to-digital converter. Complete error analysis through the
measurement system may be preferred.
Thermal energy measurements for steam may require steam flow
measurements (e.g., steam flow or condensate flow), steam pressure;
temperature, and feedwater temperature where the energy content of the
steam is then calculated using steam tables. In instances where steam
production is constant, measurements may be reduced to measurement of
steam flow or condensate flow (i.e., assumes a constant steam temperature-
pressure and feedwater temperature-pressure) along with either temperature
or pressure of steam or condensate flow.
Relevant standards and codes for measurement include older, current,
more recent, or replacement versions of:
~ Standard Method for Temperature Measurement (ASHRAE,
ANSI/ASHRAE 41.1986 (RA 91));
~ Standard Method for Pressure Measurement (ASHRAE,
ANSI/ASHRAE 41.3 -1989 (RA 91)); and
~ Measurement Uncertainty (American Society for Mechanical
Engineers (ASME), ANSI/ASME PTC 19.1-1 985 (R 1990));
each of which is incorporated herein by reference.
87
CA 02471942 2004-06-28
Appendix B - Glossary
The following abbreviations and definitions are used herein:
ACCA - Air Conditioning Contractors of America.
AGA - American Gas Association.
ANSI - American National Standards Institute.
ASHRAE - American Society of Heating, Refrigerating, and Air-Conditioning
Engineers.
ASME - American Society for Mechanical Engineers.
Baseline Adjustments - Non-routine adjustments arising during a post-
retrofit period that cannot be anticipated and which require custom
engineering analysis.
Baseline year Conditions - Set of conditions which gave rise to the energy
use/demand of the baseline year:
Baseline year Energy Data - The energy consumption or demand during the
base year.
Baseline year - A defined period of any length before implementation of an
energy conservation measure (ECM).
CABO - Council of American Building Officials.
CSA - Canadian Standards Association.
CV (RMSE) - Coefficient of Variation of the RMSE.
Degree Day - A measure of heating or cooling load on a facility created by
outdoor temperature. When the mean daily outdoor temperature is one
degree below a stated reference temperature such as 1 °C, for one day,
it is
defined that there is one heating degree day. If this temperature difference
prevailed for ten days there would be ten heating degree days counted for the
88
CA 02471942 2004-06-28
total period. If the temperature difference were to be 12° for 10 days,
120
heating degree days would be counted. When ambient temperature is below
the reference temperature, heating degree days are counted; when ambient
temperatures are above the reference, cooling degree days are counted. Any
reference temperature may be used for recording degree days, usually
chosen to reflect the temperature at which heating or cooling is no longer
needed.
Deemed savings - The energy consumption calculated by using a device's
power output and length of use. Deemed savings are used when a device is
used for predictable time periods and energy consumption does not vary. For
example, deemed savings could be used with lights that are on 24 hours a
day, 365 days a year (the energy consumption can be calculated with
reasonable certainty due to the consistent demand and length of use).
Energy ConservationlEfficiency Measure (ECM or EEM) - A set of
activities designed to increase the energy efficiency of a facility. Several
ECMs may be carried out in a facility at one time, each for a different
purpose.
An ECM may involve. one or more of: physical changes to facility equipment;
revisions to operating and maintenance procedures; software changes; or
new means of training or managing users of the space or operations and
maintenance staff.
EMS or Energy Management System - A computer that can be
programmed to control and/or monitor the operations of energy consuming
equipment in a facility.
89
CA 02471942 2004-06-28
Energy Performance Contract - A contract between two or more parties
where payment is based on achieving specified results, typically, guaranteed
reductions in energy consumption and/or operating costs.
Energy Savings - Actual reduction in electricity use (kWh), electric demand
(kW), or thermal units (Btu).
M&V or Measurement & Verification - Process of determining savings
using a quantifying methodology.
Metering - Collection of energy and water consumption data over time at a
facility through the use of measurement devices.
Monitoring - Collection of data at a facility over time for the purpose of
savings analysis (i.e., energy and water consumption, temperature, humidity,
hours of operation, etc.).
Occupant Behavior Index (OBI) - Indicator variable for the occupant
behavior (should range from 0 to 1 ). This index is used to normalize the
energy consumption based on variations in the occupants° behavior or
presence. For example, more occupants will place greater demand on HVAC
systems. This is used where occupant behavior directly impacts energy
consumption.
Post-Retrofit Period - Any period of time following completion of an energy
efficient program.
Regression Model - Inverse mathematical model that describes the
correlation of independent and dependent variables.
Reserve Coefficient - Ratio of the amount of emission credits held in reserve
to the total calculated emission reductions. This factor is used to compensate
CA 02471942 2004-06-28
for the uncertainties in calculating and monitoring energy reductions and
emission factors.
RMSE - Root mean square error.
Simulation Model - Assembly of algorithms that calculates energy use
based on engineering equations and user-defined parameters.
SMACNA - Sheet Metal and Air Conditioning Contractors' National
Association.
UL - Underwriters' Laboratories.
Verification - Process of examining the report of others to comment on its
suitability for the intended purpose.
Weather Index - Energy consumption can be heavily dependent on the
exterior environment. For example, less heating energy is used during mild
winters than in severe winters. Due to this dependence, it is often important
to take into account the weather when trying to calculate the energy
efFiciency
of a system. This process is called normalization. The first step in
normalization is to quantify the weather. Indicator variables such as heating
degree days (HDD) and cooling degree days (CDD) are frequently used for
this purpose. By comparing the relationship between energy consumption
and HDD, it is possible to establish what the energy consumption of the
upgraded building would be in the same weather that was used to calculate
the baseline energy consumption.
91
