The NEMS Commercial Sector Demand Module generates forecasts of commercial
sector energy demand through 2030. The definition of the commercial sector
is consistent with EIA’s State Energy Data System (SEDS). That is, the
commercial sector includes business establishments that are not engaged
in transportation or in manufacturing or other types of industrial activity
(e.g., agriculture, mining or construction). The bulk of commercial sector
energy is consumed within buildings; however, street lights, pumps, bridges,
and public services are also included if the establishment operating them
is considered commercial. Since most of commercial energy consumption occurs
in buildings, the commercial module relies on the data from the EIA Commercial
Buildings Energy Consumption Survey (CBECS) for characterizing the commercial
sector activity mix as well as the equipment stock and fuels consumed to
provide end use services.14
The commercial module forecasts consumption by fuel15 at the Census division
level using prices from the NEMS energy supply modules, and macroeconomic
variables from the NEMS Macroeconomic Activity Module (MAM), as well as
external data sources (technology characterizations, for example). Energy
demands are forecast for ten end-use services16 for eleven building categories17 in each of the nine Census divisions (see Figure 5). The model begins by
developing forecasts of floorspace for the 99 building category and Census
division combinations. Next, the ten end-use service demands required for
the projected floorspace are developed. The electricity generation and
water and space heating supplied by distributed generation and combined
heat and power technologies are projected. Technologies are then chosen
to meet the projected service demands for the seven major end uses.18 Once
technologies are chosen, the energy consumed by the equipment stock (both
existing and purchased equipment) is developed to meet the projected end-use
service demands.19
Key Assumptions
The key assumptions made by the commercial module are presented in terms
of the flow of the calculations described above. The sections below summarize
the assumptions in each of the commercial module submodules: floorspace,
service demand, distributed generation, technology choice, and end-use
consumption. The submodules are executed sequentially in the order presented,
and the outputs of each submodule become the inputs to subsequently executed
submodules. As a result, key forecast drivers for the floorspace submodule
are also key drivers for the service demand submodule, and so on.
Floorspace Submodule
Floorspace is forecast by starting with the previous year's stock of floorspace
and eliminating a portion to represent the age-related removal of buildings.
Total floorspace is the sum of the surviving floorspace plus new additions
to the stock derived from the MAM floorspace growth projection.20
Existing Floorspace and Attrition
Existing floorspace is based on the estimated floorspace reported in the
Commercial Buildings Energy Consumption Survey 1999 (Table 11). Over time,
the 1999 stock is projected to decline as buildings are removed from service
(floorspace attrition). Floorspace attrition is estimated by a logistic
decay function, the shape of which is dependent upon the values of two
parameters: average building lifetime and gamma. The average building lifetime
refers to the median expected lifetime of a particular building type.
The gamma parameter corresponds to the rate at which buildings retire near
their median expected lifetime. The current values for the average building
lifetime and gamma vary by building type as presented in Table 12.21
New Construction Additions to Floorspace
The commercial module develops estimates of projected commercial floorspace
additions by combining the surviving floorspace estimates with the total
floorspace forecast from MAM. A total NEMS floorspace projection is calculated
by applying the MAM assumed floorspace growth rate within each Census division
and MAM building type to the corresponding NEMS Commercial Demand Module’s
building types based on the CBECS building type shares. The NEMS surviving
floorspace from the previous year is then subtracted from the total NEMS
floorspace projection for the current year to yield new floorspace additions.22
Service Demand Submodule
Once the building stock is projected, the Commercial Demand module develops
a forecast of demand for energy-consuming services required for the projected
floorspace. The module projects service demands for the following explicit
end-use services: space heating, space cooling, ventilation, water heating,
lighting, cooking, refrigeration, personal computer office equipment, and
other office equipment.23 The service demand intensity (SDI) is measured
in thousand Btu of end-use service demand per square foot and differs across
service, Census division and building type. The SDIs are based on a hybrid
engineering and statistical approach of CBECS consumption data.24 Projected
service demand is the product of square feet and SDI for all end uses across
the eleven building categories with adjustments for changes in shell efficiency
for space heating and cooling.
Shell Efficiency
The shell integrity of the building envelope is an important determinant
of the heating and cooling loads for each type of building. In the NEMS
Commercial Demand Module, the shell efficiency is represented by an index,
which changes over time to reflect improvements in the building shell.
This index is dimensioned by building type and Census division and applies
directly to heating. For cooling, the effects are computed from the index,
but differ from heating effects, because of different marginal effects
of shell integrity and because of internal building loads. In the AEO2006
reference case, shell improvements for new buildings are up to 22 percent
more efficient than the 1999 stock of similar buildings. Over the forecast
horizon, new building shells improve in efficiency by 8 percent relative
to their efficiency in 1999. For existing buildings, efficiency is assumed
to increase by 6 percent over the 1999 stock average. The shell efficiency
index affects the space heating and cooling service demand intensities
causing changes in fuel consumed for these services as the shell integrity
improves.
Distributed Generation and Combined Heat and Power
Nonutility power production applications within the commercial sector are
currently concentrated in education, health care, office and warehouse
buildings. Program driven installations of solar photovoltaic systems
are based on information from DOE’s Photovoltaic and Million Solar Roofs
programs as well as DOE and industry news releases and the National Renewable
Energy Laboratory’s Renewable Electric Plant Information System. Historical
data from Form EIA-860, Annual Electric Generator Report, are used to derive
electricity generation for 2000 through 2004 by Census division, building
type and fuel. A forecast of distributed generation and combined heat
and power (CHP) of electricity is developed based on the economic returns
projected for distributed generation and CHP technologies. The model uses
a detailed cash-flow approach to estimate the number of years required
to achieve a cumulative positive cash flow (some technologies may never
achieve a cumulative positive cash flow). Penetration assumptions for
distributed generation and CHP technologies are a function of the estimated
number of years required to achieve a positive cash flow. Table 13 provides
the cost and performance parameters for representative distributed generation
and CHP technologies.
The model also incorporates endogenous “learning” for new distributed generation
and CHP technologies, allowing for declining technology costs as shipments
increase. For fuel cell and photovoltaic systems, parameter assumptions
for the AEO2005 reference case result in a 13 percent reduction in capital
costs each time the number of units shipped to the buildings sectors (residential
and commercial) doubles. Doubling the number of microturbines shipped
results in a 10 percent reduction in capital costs.
Technology Choice Submodule
The technology choice submodule develops projections of the results of
the capital purchase decisions for equipment fueled by the three major
fuels (electricity, natural gas, and distillate fuel). Capital purchase
decisions are driven by assumptions concerning behavioral rule proportions
and time preferences, described below, as well as projected fuel prices,
average utilization of equipment (the capacity factors), relative technology
capital costs, and operating and maintenance (O&M) costs.
Decision Types
In each forecast year, equipment is potentially purchased for three “decision
types”. Equipment must be purchased for newly added floorspace and to replace
the portion of equipment in existing floorspace that is projected to wear
out.25 Equipment is also potentially purchased for retrofitting equipment
that has become economically obsolete. The purchase of retrofit equipment
occurs only if the annual operating costs of a current technology exceed
the annualized capital and operating costs of a technology available as
a retrofit candidate.
Behavioral Rules
The commercial module allows the use of three alternate assumptions about
equipment choice behavior. These assumptions constrain the equipment selections
to three choice sets, which are progressively more restrictive. The choice
sets vary by decision type and building type:
•
Unrestricted Choice Behavior - This rule assumes that commercial consumers
consider all types of equipment that meet a given service, across all fuels,
when faced with a capital purchase decision.
•
Same Fuel Behavior - This rule restricts the capital purchase decision
to the set of technologies that consume the same fuel that currently meets
the decision maker’s service demand.
•
Same Technology Behavior - Under this rule, commercial consumers consider
only the available models of the same technology and fuel that currently
meet service demand, when facing a capital stock decision.
Under any of the above three behavior rules, equipment that meets the service
at the lowest annualized lifecycle cost is chosen. Table 14 illustrates
the proportions of floorspace subject to the different behavior rules for
space heating technology choices in large office buildings.
Time Preferences
The time preferences of owners of commercial buildings are assumed to be
distributed among seven alternate time preference premiums (Table 15).
Adding the time preference premiums to the 10-year Treasury Bill rate from
MAM results in implicit discount rates, also known as hurdle rates, applicable
to the assumed proportions of commercial floorspace. The effect of the
use of this distribution of discount rates is to prevent a single technology
from dominating purchase decisions in the lifecycle cost comparisons. The
distribution used for AEO2006 assigns some floorspace a very high discount
or hurdle rate to simulate floorspace which will never retrofit existing
equipment and which will only purchase equipment with the lowest capital
cost. Discount rates for the remaining six segments of the distribution
get progressively lower, simulating increased sensitivity to the fuel costs
of the equipment that is purchased. The proportion of floorspace assumed
for the 0.0 time preference premium represents an estimate of the Federally
owned commercial floorspace that is subject to purchase decisions in a
given year. In accordance with Executive Order 13123 signed in June 1999,
the Federal sector uses a rate comparable to the 10-year Treasury Bill
rate when making purchase decisions.
The distribution of hurdle rates used in the commercial module is also
affected by changes in fuel prices. If a fuel’s price rises relative to
its price in the base year (1999), the nonfinancial portion of each hurdle
rate in the distribution decreases to reflect an increase in the relative
importance of fuel costs, expected in an environment of rising prices.
Parameter assumptions for AEO2006 result in a 30 percent reduction in
the nonfinancial portion of a hurdle rate if the fuel price doubles. If
the time preference premium input by the model user results in a hurdle
rate below the assumed financial discount rate for the commercial sector,
15 percent, with base year fuel prices (such as the rate given in Table
15 for the Federal sector), no response to increasing fuel prices is assumed.
Technology Characterization Database
The technology characterization database organizes all relevant technology
data by end use, fuel, and Census division. Equipment is identified in
the database by a technology index as well as a vintage index, the index
of the fuel it consumes, the index of the service it provides, its initial
market share, the Census division index for which the entry under consideration
applies, its efficiency (or coefficient of performance or efficacy in the
case of lighting equipment), installed capital cost per unit of service
demand satisfied, operating and maintenance cost per unit of service demand
satisfied, average service life, year of initial availability, and last
year available for purchase. Equipment may only be selected to satisfy
service demand if the year in which the decision is made falls within the
window of availability. Equipment acquired prior to the lapse of its availability
continues to be treated as part of the existing stock and is subject to
replacement or retrofitting. This flexibility in limiting equipment availability
allows the direct modeling of equipment efficiency standards. Table 16
provides a sample of the technology data for space heating in the New England
Census division.
An option to allow endogenous price-induced technological change has been
included in the determination of equipment costs and availability for the
menu of equipment. This concept allows future technologies faster diffusion
into the market place if fuel prices increase markedly for a sustained
period of time. Although no price-induced change would have been expected
using AEO2006 reference case fuel prices, the option was not exercised
for the AEO2006 model runs.
End-Use Consumption Submodule
The end-use consumption submodule calculates the consumption of each of
the three major fuels for the ten end-use services plus fuel consumption
for combined heat and power and district services. For the ten end-use
services, energy consumption is calculated as the end-use service demand
met by a particular type of equipment divided by its efficiency and summed
over all existing equipment types. This calculation includes dimensions
for Census division, building type, and fuel. Consumption of the five
minor fuels is forecast based on historical trends.
Equipment Efficiency
The average energy consumption of a particular appliance is based initially
on estimates derived from CBECS 1999. As the stock efficiency changes
over the model simulation, energy consumption decreases nearly, but not
quite proportionally to the efficiency increase. The difference is due
to the calculation of efficiency using the harmonic average and also the
efficiency rebound effect discussed below. For example, if on average,
electric heat pumps are now 10 percent more efficient than in 1999, then
all else constant (weather, real energy prices, shell efficiency, etc.),
energy consumption per heat pump would now average about 9 percent less.
The Service Demand and Technology Choice Submodules together determine
the average efficiency of the stocks used in adjusting the initial average
energy consumption.
Adjusting for Weather and Climate
Weather in any given year always includes short-term deviations from the
expected longer-term average (or climate). Recognition of the effect
of weather on space heating and air conditioning is necessary to avoid
projecting abnormal weather conditions into the future. In the commercial
module, proportionate adjustments are made to space heating and air conditioning
demand by Census division. These adjustments are based on National Oceanic
and Atmospheric Administration (NOAA) data for Heating Degree Days (HDD)
and Cooling Degree Days (CDD). A 10 percent increase in HDD would increase
space heating consumption by 10 percent over what it would have been otherwise.
The commercial module uses a 30-year average for HDD and CDD by Census
division, adjusted over the projection period by projections for state
population shifts.
Short-Term Price Effect and Efficiency Rebound
It is assumed that energy consumption for a given end-use service is affected
by the marginal cost of providing that service. That is, all else equal,
a change in the price of a fuel will have an inverse, but less than proportional,
effect on fuel consumption. The current value for the short-term price
elasticity parameter is -0.25 for all major end uses except refrigeration.
A value of -0.1 is currently used for commercial refrigeration. A value
of -0.05 is currently used for PC and non-PC office equipment and other
minor uses of electricity. For example, for lighting this value implies
that for a 1 percent increase in the price of a fuel, there will be a corresponding
decrease in energy consumption of 0.25 percent. Another way of affecting
the marginal cost of providing a service is through equipment efficiency.
As equipment efficiency changes over time, so will the marginal cost of providing the end-use service. For
example, a 10 percent increase in efficiency will reduce the cost of providing
the service by 10 percent. The short-term elasticity parameter for efficiency
rebound effects is -0.15 for affected end uses; therefore, the demand for
the service will rise by 1.5 percent (-10 percent x -0.15). Currently,
all services are affected by the short-term price effect and services affected
by efficiency rebound are space heating and cooling, water heating, ventilation
and lighting.
Legislation and Other Federal Programs
Energy Policy Act of 1992 (EPACT92)
A key assumption incorporated in the technology selection process is that
the equipment efficiency standards described in the EPACT92 constrain minimum
equipment efficiencies. The effects of standards are modeled by modifying
the technology database to eliminate equipment that no longer meets minimum
efficiency requirements. For standards effective January 1, 1994, affected
equipment includes electric heat pumps—minimum heating system performance
factor of 6.8, gas and oil-fired boilers—minimum combustion efficiency
of 0.8 and 0.83, respectively, gas and oil-fired furnaces—minimum thermal
efficiency of 0.8 and 0.81, respectively, fluorescent lighting—minimum
efficacy of 75 lumens per watt, incandescent lighting—minimum efficacy
of 16.9 lumens per watt, air-cooled air conditioners—minimum energy efficiency
ratio of 8.9, electric water heaters—minimum energy factor of 0.85, and
gas and oil water heaters—minimum thermal efficiency of 0.78. Updated standards
are effective October 29, 2003 for gas water heaters—minimum thermal efficiency
of 0.8. An additional standard affecting fluorescent lamp ballasts becomes
effective April 1, 2005. The standard mandates electronic ballasts with
a minimum ballast efficacy factor of 1.17 for 4-foot, 2-lamp ballasts and
0.63 for 8-foot, 2-lamp ballasts.
The 10 percent Business Investment Tax Credit for solar energy property
included in EPACT92 is directly incorporated into the cash-flow approach
for projecting distributed generation by commercial photovoltaic systems.
For solar hot water heaters, the tax credit is factored into the installed
capital cost assumptions used in the technology choice submodule.
Energy Policy Act of 2005 (EPACT05)
The passage of the EPACT05 in August 2005 provides additional minimum efficiency
standards for commercial equipment. Some of the standards for explicitly
modeled equipment, effective January 1, 2010, include: an Energy Efficiency
Rating (EER) ranging from 10.8 to 11.2 for small package air conditioning
and heating equipment; daily electricity consumption limits by volume for
commercial refrigerators, freezers, and refrigerator-freezers; and electricity
consumption limits per 100 pounds of ice produced based on equipment type
and capacity for automatic ice makers. The EPACT05 adds standards for medium
base compact fluorescent lamps effective January 1, 2006, for ballasts
for Energy Saver fluorescent lamps effective in 2009 and 2010, and bans
the manufacture or import of mercury vapor lamp ballasts effective January
1, 2008.
Several efficiency standards in the EPACT05 pertain to equipment not explicitly
represented in the NEMS Commercial Demand Module. For illuminated exit
signs, traffic signals, low voltage dry-type transformers, and commercial
prerinse spray valves, assumed energy reductions are calculated based on
per-unit savings relative to a baseline unit and the estimated share of
installed units and sales that already meet the standard. Total projected
reductions are phased in over time to account for stock turnover. Under
the EPACT05 standards, illuminated exit signs and traffic signal modules
must meet ENERGY STAR program requirements as of January 1, 2006. The requirements
limit input power demand to 5 watts or less per face for exit signs. Nominal
wattages for traffic signal modules are limited to 8 to 15 watts, based
on module type. Effective January 1, 2007, low voltage dry-type distribution
transformers are required to meet the National Electrical Manufacturers
Association Class I Efficiency Levels with minimum efficiency levels ranging
from 97 percent to 98.9 percent based on output. Commercial prerinse spray
valves26 must have a maximum flow rate of 1.6 gallons per minute, effective
January 1, 2006 with energy reductions attributed to hot water use.
The EPACT05 expands the Business Investment Tax Credit to 30 percent for
solar property installed in 2006 and 2007. Business Investment Tax Credits
of 30 percent for fuel cells and 10 percent for microturbine power plants
are also available for property installed in 2006 and 2007. These credits
are directly incorporated into the cash-flow approach for distributed generation
systems and factored into the installed capital cost assumptions for solar
hot water heaters.
Energy Efficiency Programs
Several energy efficiency programs affect the commercial sector. These
programs are designed to stimulate investment in more efficient building
shells and equipment for heating, cooling, lighting, and other end uses.
The commercial module includes several features that allow projected efficiency
to increase in response to voluntary programs (e.g., the distribution of
time preference premiums and shell efficiency parameters). Retrofits of
equipment for space heating, air conditioning and lighting are incorporated
in the distribution of premiums given in Table 14. Also the shell efficiency
of new and existing buildings is assumed to increase from 1999 through
2025. Shells for new buildings increase in efficiency by 7 percent over
this period, while shells for existing buildings increase in efficiency
by 5 percent.
Commercial Technology Cases and Alternative Renewables Cases
In addition to the AEO2006 reference case, three side cases were developed
to examine the effect of equipment and building standards on commercial
energy use—a 2005 technology case, a high technology case, and a best available
technology case. These side cases were analyzed in stand-alone (not integrated
with the NEMS demand and supply modules) buildings (residential and commercial)
modules runs and thus do not include supply-responses to the altered commercial
consumption patterns of the three cases. AEO2006 also analyzed an integrated
high technology case, which combines the high technology cases of the four
end-use demand sectors, the electricity high fossil technology case, the
advanced nuclear cost case, and the high renewables case, and an integrated
2005 technology case, which combines the 2005 technology cases of the four
end-use demand sectors, the electricity low fossil technology case, and
the low renewables case.
The 2005 technology case assumes that all future equipment purchases are
made based only on equipment available in 2005. This case assumes building
shell efficiency to be fixed at 2005 levels. In the reference case, existing
building shells are allowed to increase in efficiency by 6 percent over
1999 levels, and new building shells improve by 8 percent by 2030 relative
to new buildings in 1999.
The high technology case assumes earlier availability, lower costs, and/or
higher efficiencies for more advanced equipment than the reference case.
Equipment assumptions were developed by engineering technology experts,
considering the potential impact on technology given increased research
and development into more advanced technologies. In the high technology
case, building shell efficiencies are assumed to improve 25 percent more
than in the reference case after 2005. Existing building shells, therefore,
increase by 7.4 percent relative to 1999 levels and new building shells
by 10.4 percent relative to their efficiency in 1999 by 2030.
The best available technology case assumes that all equipment purchases
after 2005 are based on the highest available efficiency in the high technology
case in a particular simulation year, disregarding the economic costs of
such a case. It is designed to show how much the choice of the highest-efficiency
equipment could affect energy consumption. Shell efficiencies in this
case are assumed to improve 50 percent more than in the reference case
after 2005, i.e., existing shells increase by 8.9 percent relative to 1999
levels and new building shells by 12.4 percent relative to their efficiency
in 1999 by 2030.
Fuel shares, where appropriate for a given end use, are allowed to change
in the technology cases as the available technologies from each technology
type compete to serve certain segments of the commercial floorspace market.
For example, in the best available technology case, the most efficient
gas furnace technology competes with the most efficient electric heat pump
technology. This contrasts with the reference case, in which, a greater
number of technologies for each fuel with varying efficiencies all compete
to serve the heating end use. In general, the fuel choice will be affected
as the available choices are constrained or expanded, and will thus differ
across the cases.
Two integrated cases that focus on electricity generation incorporate alternative
assumptions for non-hydro renewable energy technologies, including residential
and commercial photovoltaic systems. In each of these cases, assumptions
regarding non-renewable technologies are not changed from the reference
case.
The low renewables case assumes that the cost and performance characteristics
for residential and commercial photovoltaic systems remain fixed at 2005
levels through the forecast horizon.
The high renewables case assumes that costs for residential and commercial
photovoltaic systems are 10 percent lower than reference case cost estimates
by 2030.
Commercial Tables
Commercial Notes |