CHAPTER 9. SHIPMENTS ANALYSIS 

TABLE OF CONTENTS 

9.1 INTRODUCTION ................................................... 9-1


9.2 MODEL OVERVIEW ................................................ 9-1


9.3 MODEL INPUTS .................................................... 9-3


9.3.1 Shipments Data ............................................... 9-4


9.3.2 Shipments Backcast ............................................ 9-8


9.3.3 Shipments Forecast ............................................ 9-9


9.3.4 Long-Term Purchase Elasticity ................................... 9-10


9.3.4.1	 Elasticity Estimates for Liquid-Immersed and Dry-Type Transformers


.................................................... 9-11


9.3.5 Liquid-Immersed/Dry-Type Market Shares .......................... 9-11


9.3.6 Stock Accounting ............................................ 9-12


9.3.7 Retirement Function .......................................... 9-13


9.3.8 Refurbishments and Rewinds .................................... 9-15


9.3.9 Initial Stock ................................................. 9-16


9.3.10 Effective Date of Standard ...................................... 9-16


9.3.11 Affected Stock ............................................... 9-16


9.4 RESULTS ......................................................... 9-17


Table 9.3.1
 Distribution Transformer Shipment Estimates for 2001 .................. 9-5


...... 9-6


2001

Table 9.3.2
 Liquid-Immersed Distribution Transformer Shipment Estimates, 2001 

Table 9.3.3
 Dry-Type, Medium-Voltage Distribution Transformer Shipment Estimates, 

Table 9.4.1 Cumulative Shipments of Transformers by Trial Standard Level (TSL), 2010–2038


Figure 9.2.1
 Shipments Model Flowchart ...................................... 9-2


Figure 9.3.1
 Age Distribution of Liquid-Immersed In-Service Transformer Stock in 2004

Figure 9.3.2
 Fraction of Original Shipment Retiring ............................. 9-15


LIST OF TABLES 

........................................................... 9-7


LIST OF FIGURES 

........................................................... 9-9


9-i 

9-17 

 EE-RM/STD-00-550 COMMENT 196
CHAPTER 9. SHIPMENTS ANALYSIS


9.1 INTRODUCTION

 The shipments model provides an estimate of the rate at which an in-service stock of 

existing transformers may be replaced by new, more-efficient transformers an energy conservation 

standard. The core of the shipments analysis is an accounting model that DOE developed to 

simulate how existing and future purchases are incorporated into an in-service stock of aging 

transformers that are gradually replaced. An estimate of the rate at which transformers are 

purchased and replaced is a key input to DOE’s estimate of the national impacts of standards for 

distribution transformers. 

For this rulemaking, in addition to using estimates of distribution transformer shipments as 

an input to the national energy savings (NES) analysis (see Chapter 10 of this technical support 

document, or TSD), DOE used the shipments estimates as input to the manufacturer impact 

analysis (MIA). The MIA estimates the impact of potential efficiency standards on manufacturers 

of distribution transformers and assesses the direct impact of such standards on employment and 

manufacturing capacity. 

9.2 MODEL OVERVIEW 

DOE used forecasts of shipments for a base case and a standards case to provide an 

estimate of the annual sales and in-service stock of transformers for the forecast period. The 

estimate included the age distribution of transformers for each transformer type (classified 

according to product classes) and each transformer size. DOE chose an accounting model 

method to prepare shipment scenarios for the base case and several trial standard cases. The 

model uses annual transformer sales and the age distribution of the in-service stock to calculate 

equipment costs for the net present value (NPV) and energy use for the NES, respectively. The 

model keeps track of the age and replacement of transformer capacity given a projection of future 

transformer sales growth. 

Figure 9.2.1 presents a graphical flow diagram of the shipments model portion of the 

distribution transformer national impact (NES and NPV) model and spreadsheet. In the diagram, 

the arrows show the interconnectivity of data exchanges between calculations. Inputs are shown 

as parallelograms. As data flow from these inputs, they may be integrated into intermediate 

results (shown as rectangles) or, through integrating sums or differences (shown as circles), into 

major outputs (shown as boxes with curved bottom edges). 

9-1

 EE-RM/STD-00-550 COMMENT 196
Figure 9.2.1 Shipments Model Flowchart 

As illustrated in the flow diagram, DOE organized shipments into two categories: 

replacements and new capacity. Replacements occur when transformers break down, corrode, are 

9-2

 EE-RM/STD-00-550 COMMENT 196
struck by lightning, or otherwise need to be replaced. New capacity purchases occur due to 

increases in electricity use that may be driven by increasing population, commercial and industrial 

activity, or growth in electricity distribution systems. The model starts with an estimate of the 

overall growth in transformer capacity and then estimates shipments for particular design lines 

using estimates of the relative market share for different design and size categories. The steps for 

the shipments calculation are: 

1.	 Collection of available data – Acquisition and processing of available transformer 

shipments data. 

2.	 Construction of aggregate shipments backcast – Provides an annual estimate of 

historical total capacity shipped using available shipments and electricity 

consumption data. 

3.	 Construction of aggregate shipments forecast – Provides a future base case annual 

shipments estimate by applying a shipments growth rate. 

4.	 Development of liquid-immersed and dry-type market shares – Disaggregates the 

total capacity shipped into liquid-immersed and dry-type transformers. 

5.	 Modeling of purchase price elasticity – Models the impact of higher purchase 

prices on future shipments due to a standard. 

6.	 Accounting of sales and in-service stocks – Provides an annual age distribution of 

in-service transformer stock from shipments estimates and the retirement function. 

7.	 Consistency check with purchase and replacement data – Confirms that shipments 

model forecasts are consistent with available data on utility transformer purchases 

and replacements. 

9.3 MODEL INPUTS 

The shipments model inputs parallel the shipments calculation described in the previous 

section. The shipments model takes both internal and external inputs. Internal inputs comprise 

quantities that are calculated from previous steps in the model. External inputs are data sets 

acquired from exogenous sources. The final outputs of the shipments calculation are the annual 

shipments estimates and the age distribution of in-service transformer stock. The list of specific 

inputs is as follows: 

1.	 Shipments data – Include external estimates of transformer shipments and quantity 

index of transformers manufactured. 

2.	 Shipments backcast – Provides an estimate of total transformer capacity shipped 

before 2001. 

3.	 Shipments forecast – Provides an estimate of total transformers shipped after 

2001. 

4.	 Long-term purchase elasticity – Estimates the price sensitivity of transformers 

shipped over the long term. 

9-3

 EE-RM/STD-00-550 COMMENT 196
5. Dry-type/liquid-immersed market shares – Provides the annual capacity-based 

market shares of dry-type and liquid-immersed transformers shipped. 

6.	 Stock accounting – Provides the age distribution of the current year’s in-service 

transformer stock based on the previous year’s in-service stock and shipments. 

7.	 Retirement function – Provides an estimate of the probability that a transformer 

will be replaced as a function of its age. 

8.	 Initial stock – Provides the age distribution of the in-service stock of transformers 

at the start of the stock-accounting calculation (i.e., in 1950). 

9.3.1 Shipments Data 

DOE uses historical transformer shipments data to calibrate a forecast of future shipments 

and in-service stocks. These data are key inputs to the national impact analysis because changes 

in shipments and in-service stock create nearly proportional changes in the estimate of the energy 

savings from a standard. 

DOE obtained an estimate of sales (for the entire market) for the year 2001, disaggregated 

by product class and kilovolt-ampere (kVA) rating.1 The historical series of the power 

distribution and specialty transformer manufacturing (North American Industry Classification 

System (NAICS) code 335311) quantity index for 1977–2003 is available from the U.S. Bureau 

of Economic Affairs (BEA). The BEA transformer manufacturing quantity index provides 

information on changes to aggregate shipments from 1977 to 2003.2 Market shares for both 

liquid-immersed and dry-type distribution transformers, by product type and kVA rating, came 

from the 2001 shipments estimate. Using the sales estimate for 2001 as a reference point and the 

BEA quantity index data, DOE estimated aggregate transformer shipments from 1977 to 2003. 

Table 9.3.1 presents the shipment estimates in both units and megavolt-amperes (MVA) 

shipped, and the approximate value of these shipments. The distribution transformer industry's 

total sales were about $1.6 billion in 2001. 

9-4

 EE-RM/STD-00-550 COMMENT 196
Table 9.3.1 Distribution Transformer Shipment Estimates for 2001 

Product Class (PC) Units MVA Shipment Value 

Shipped Capacity 

Shipped 2001$ million 

Liquid-Immersed, Medium-Voltage, Single-Phase, (PC 1) 977,388 36,633 698.8 

Liquid-Immersed, Medium-Voltage, Three-Phase, (PC 2) 79,367 42,887 540.4 

Dry-Type, Medium-Voltage, Single-Phase, 20-45 kV BIL,* (PC 5) 119 18 0.5 

Dry-Type, Medium-Voltage, Three-Phase, 20-45 kV BIL, (PC 6) 650 776 13.5 

Dry-Type, Medium-Voltage, Single-Phase, 46-95 kV BIL, (PC 7) 121 22 0.6 

Dry-Type, Medium-Voltage, Three-Phase, 46-95 kV BIL, (PC 8) 2,371 3,913 68.1 

Dry-Type, Medium-Voltage, Single-Phase, >96 kV BIL, (PC 9) 20 4 0.1 

Dry-Type, Medium-Voltage, Three-Phase, >96 kV BIL, (PC 10) 187 367 6.4 

Total 1,060,223 84,620 1,328.4 

* BIL = Basic impulse insulation level 

Tables 9.3.2 and 9.3.3 present the disaggregated shipment estimates for 2001 by product 

class and kVA rating. 

9-5

 EE-RM/STD-00-550 COMMENT 196
Table 9.3.2 Liquid-Immersed Distribution Transformer Shipment Estimates, 2001 

Liquid-Immersed, Medium-Voltage, 

Single-Phase 

Liquid-Immersed, Medium-Voltage, 

Three-Phase 

kVA Units Shipped kVA Units Shipped 

10 114,594 15 -

15 167,234 30 -

25 277,581 45 605 

37.5 66,977 75 1,816 

50 242,931 112.5 4,842 

75 44,214 150 9,078 

100 49,696 225 9,078 

167 9,762 300 17,552 

250 2,158 500 17,552 

333 1,188 750 6,219 

500 1,040 1000 4,523 

667 4 1500 3,769 

833 8 2000 1,884 

- - 2500 2,450 

Total Units 977,388 Total Units 79,367 

Total MVA 36,633 Total MVA 42,887 

9-6

 EE-RM/STD-00-550 COMMENT 196
Table 9.3.3 Dry-Type, Medium-Voltage Distribution Transformer Shipment Estimates, 

2001 

Dry-Type, Medium-Voltage, 

Single-Phase 

Dry-Type, Medium-Voltage, 

Three-Phase 

kVA Units Shipped kVA Units Shipped 

20-45kV 

BIL 

46-95kV 

BIL 

>96kV 

BIL 

20-45kV 

BIL 

46-95kV 

BIL 

>96kV 

BIL 

15 3 2 - 15 2 1 -

25 7 3 - 30 4 2 -

37.5 10 5 - 45 4 2 -

50 17 8 - 75 7 3 -

75 20 25 5 112.5 20 11 -

100 20 25 5 150 18 11 -

167 12 15 3 225 20 19 1 

250 6 8 1 300 59 60 2 

333 12 15 3 500 76 150 5 

500 12 15 3 750 80 177 5 

667 - - - 1000 80 320 10 

833 - - - 1500 80 390 32 

- - - - 2000 100 575 56 

- - - - 2500 100 650 76 

Total Units 119 121 20 Total Units 650 2,371 187 

Total MVA 18.4 22.1 4.1 Total MVA 776 3,913 367 

There are two major assumptions inherent in the shipments model. The first is that the 

relative market shares of the different transformer product classes and size categories are constant 

over time. In actuality, there is probably a gradual increase in the average size of transformers as 

the electricity demand per customer increases, but DOE does not have sufficient data to 

characterize such transformer size trends. 

The second assumption concerns the use of the BEA quantity index data. The BEA index 

data actually include shipments of transformers other than those covered by this rulemaking. The 

use of the BEA Standard Industrial Classification (SIC) code 3612 (NAICS code 335311) 

quantity index to estimate shipments for products under this rulemaking assumes that the quantity 

9-7

 EE-RM/STD-00-550 COMMENT 196
market share of distribution transformers relative to all NAICS code 335311 transformers is 

relatively constant over the period 1977–2003. DOE made this assumption because moredisaggregated quantity index data were not available. 

9.3.2 Shipments Backcast 

The shipments backcast is the estimate of previous aggregate transformer shipments based 

on limited historical data. The backcast of transformers shipments is a key element in estimating 

age distributions of future in-service transformer stock. 

The shipments backcast begins with the estimate of transformer shipments in the year 

20011 and uses BEA’s NAICS code 335311 quantity index to estimate total shipments in the 

period 1977 to 2003.2 Specifically, DOE used the following equation to backcast shipments from 

2003 to 1977: 

Eq. 9.1 

where: 

TotShip(y) = the total capacity of transformer shipments estimated for year y 

where 1977 # y < 2003 (MVA), 

TotShip(2001) = the total transformer capacity shipped, from the shipments estimate 

(MVA), and 

BEA(y) = the BEA quantity index for year y. 

Annual shipments of transformer capacity for years prior to 1977 are backcast to 1950 

using annual growth of electricity consumption from Table 8.9 of DOE’s Energy Information 

Administration (EIA)’s Annual Energy Review 2003, a proxy for growth of transformer sales 

during this period.3 Using this method, the shipments for the years 1950 to 1977 are given by the 

following equation: 

Eq. 9.2 

where: 

TotShip(y) = the total capacity of shipments estimated for year y where 1950 # y 

< 1977 (MVA), and 

AllElec(y) = national electricity consumption in year y according to EIA's 

4 Annual Energy Outlook 2005 (AEO2005) (W-hr). 

9-8

 EE-RM/STD-00-550 COMMENT 196
9.3.3 Shipments Forecast 

After constructing a shipments backcast and calibrating it with shipments data, DOE 

constructed a forecast of transformers shipments. This forecast provided the necessary input for 

equipment expense and in-service transformer stock accounting. 

DOE constructed a simplified forecast of transformer shipments for the base case scenario 

based on the assumption that long-term growth in electricity consumption will drive transformer 

shipments. The detailed dynamics of transformer shipments are highly complex. This complexity 

can be seen in the fluctuations in the total quantity of transformers manufactured, as expressed by 

the BEA transformer quantity index. DOE examined the possibility of modeling the fluctuations 

in transformers shipped using a bottom-up model where the shipments are triggered by 

retirements and new capacity additions, but found insufficient data to calibrate model parameters 

within an acceptable margin of error. Hence, DOE de-coupled the overall shipments and 

retirements in the construction of the shipments forecast and maintained the age distribution of the 

in-service transformer stock using a retirement function. Figure 9.3.1 shows the estimated age 

distribution of the in-service stock of liquid-immersed transformers in 2004. DOE developed 

similar age distributions for medium-voltage, dry-type transformers. 

Figure 9.3.1	 Age Distribution of Liquid-Immersed In-Service 

Transformer Stock in 2004 

DOE constructed the transformer shipments forecast assuming that transformer shipments 

growth is equal to forecasted growth in electricity consumption, as given by the AEO2005 

forecast up to the year 2025.4 For the years 2026–2038, DOE extrapolated the AEO 2005 

forecast, using its growth rate of electricity consumption between 2015 and 2025. Specifically, 

DOE used the following equation for the shipments forecast: 

9-9

 EE-RM/STD-00-550 COMMENT 196
Eq. 9.3 

where: 

TotShip(y) = the total capacity of shipments estimated for year y where 2003 < y 

# 2038 (MVA), and 

AllElec(y) = national electricity consumption forecasted for year y by AEO2005 

(or by an extrapolation of AEO2005 data) (W-hr). 

The next section describes how DOE adjusted its base case scenario forecast to account 

for price increases arising from a standard. 

9.3.4 Long-Term Purchase Elasticity 

DOE used purchase elasticity to adjust the base case shipments forecasts for potential 

transformer price increases due to a standard. The long-term purchase elasticity is a measure of 

how sensitive transformer shipments are to potential increases in price. Elasticity is defined as the 

percentage change in quantity purchased divided by the percentage change in price (or some other 

factor that influences purchase behavior). 

The basic formula DOE used to determine price elasticity is: 

e= (dQ/Q) / (dP/P) Eq. 9.4 

where: 

dQ/Q = a small percentage change in quantity purchased (Q), and


dP/P = a small percentage change in price. 


If the elasticity is constant, then the quantity purchased can be written in terms of the 

price, a reference price, a reference quantity, and the elasticity. Specifically, the following 

equation holds true when the elasticity is constant: 

e Q(P) = Q0 × (P/P0) Eq. 9.5 

where: 

Q

Q(P) = the quantity purchased as a function of price,


0 = a reference quantity at a reference price P0 , and


e = the elasticity, which is almost always negative or zero (i.e., non-positive)


with respect to price. 

9-10

 EE-RM/STD-00-550 COMMENT 196
For the forecast, the reference price and the reference quantity are the price and quantity 

from the base case scenario. A change in price due to a standard then has an impact on the 

quantity purchased, Q(P), as described by the above equation. 

9.3.4.1 Elasticity Estimates for Liquid-Immersed and Dry-Type Transformers 

To model utility and customer decision-making, DOE constructed a model employing a 

standard econometric logit equation, used for general applications of market response to costs 

and perceived utility. For liquid-immersed transformers, DOE fitted the model to transformer 

purchase data from U.S. Federal Energy Regulatory Commission (FERC) Form No. 15 to 

determine the parameters of the logit equation. This resulted in a value of -0.04 for price 

elasticity. DOE assigned -0.04 as the medium scenario and incremented the elasticity to -0.2 to 

implement a high sensitivity to price change. The low scenario assumes zero elasticity, or no 

impact on purchase decisions from a price change. 

For dry-type transformers, historical purchase data were not available. Since dry-type 

units are primarily used in commercial and industrial applications, as are unitary air conditioners, 

DOE used sales and price data for air conditioners to estimate price elasticity for dry-type 

transformers. It fitted the model to the Air-Conditioning and Refrigeration Institute's sales data6 

and real Producer Price Index (PPI) of unitary air conditioners.7 The resulting value of elasticity 

was -0.02. DOE assigned -0.02 as the medium scenario and incremented the elasticity to -0.2 to 

implement a high sensitivity to price change. The low scenario assumes zero elasticity, or no 

impact on purchase decisions from a price change. 

9.3.5 Liquid-Immersed/Dry-Type Market Shares 

The shipments forecast and backcast described above provided an aggregate estimate of 

the total capacity of transformers shipped from 1950 to 2038. To disaggregate the total capacity 

into the capacity for different types of transformers, DOE applied capacity market share estimates. 

To distinguish between liquid-immersed and dry-type transformers, DOE applied a 

liquid-immersed and dry-type market share, respectively. To distinguish between different 

product classes and size categories within each product class, DOE used 2001 market shares 

estimates. 

DOE used trends in electricity consumption from EIA’s retail sales data to estimate 

market share trends for liquid-immersed and dry-type transformers.3 Assuming that transformer 

sales over the long term track electricity sales for the sectors served by those transformers, DOE 

derived the following market share model: 

Eq. 9.6 

Eq. 9.7 

9-11

 EE-RM/STD-00-550 COMMENT 196
CL 

Eq. 9.8 

Eq. 9.9 

where: 

= the constant of proportionality between the electricity consumption 

and the sales of liquid-immersed transformers in the year 2001, 

CD = the constant of proportionality between the electricity consumption 

and the sales of dry-type transformers in the year 2001, 

LiqShip(2001) = the capacity of liquid-immersed transformers shipped in 2001 

(MVA), 

DryShip(2001) = the capacity of dry-type transformers shipped in 2001 (MVA), 

AllElec(y) = the total consumption of electricity in year y (W-hr), 

CIElec(y) = the consumption of electricity by the commercial and industrial 

sectors in year y (W-hr), 

LiqMS(y) = the capacity market share of liquid-immersed transformers in year y 

(%), and 

DryMS(y) = the capacity market share of dry-type transformers in year y (%). 

The key assumption behind the market share equations is that transformer capacity market 

shares follow the relative electricity consumption of the end users of the electricity that passes 

through the transformers. The dynamics of liquid-immersed and dry-type market shares are likely 

to be fairly complicated, but DOE believes this is the best way to capture long-term average 

trends in market share, given the lack of detailed market share data over long time periods. 

For the market shares of different kVA ratings and product classes, DOE assumed that the 

relative market share within each transformer type (i.e., liquid-immersed or dry-type) is constant 

over time. Given a lack of detailed, long-term market share data, an alternative assumption 

regarding kVA ratings and product class market shares may not be supportable. 

Once it had fully specified the shipments backcast, forecast, elasticity, and market shares, 

DOE had completely specified the characteristics of transformer shipments. The next step was to 

provide an accounting of in-service transformer stocks, as described in the next section. 

9.3.6 Stock Accounting 

The stock accounting took transformer shipments, a retirement function, and initial inservice transformer stock as inputs and provided an estimate of the age distribution of in-service 

transformer stocks for all years. The age distribution of in-service transformer stocks was a key 

input to both the NES and NPV calculations, since the operating costs for any year depend on the 

age distribution. The dependence of operating cost on the transformer age distribution occurs 

9-12

 EE-RM/STD-00-550 COMMENT 196
because, under a trial standard scenario that produces increasing efficiency over time, older, lessefficient transformers may have higher operating costs, while younger, more-efficient transformers 

will have lower operating costs. 

DOE calculated total in-service stock of distribution transformers by integrating historical 

shipments, starting from 1950. As transformers are added to the in-service stock, some of the 

older ones retire and exit the stock. 

To estimate the shipment forecasts, DOE developed a series of equations that define the 

dynamics and accounting of in-service transformer stocks. For new units, the equation is: 

Eq. 9.10 

where: 

Stock (y, age) = the population of in-service transformers of a particular age 

(MVA), 

y = the year for which the in-service stock is being estimated, and 

Ship (y) = the number of transformers purchased in a particular year (MVA). 

This equation indicates that the number of one-year-old units is simply equal to the 

number of new transformer units purchased the previous year. The slightly more complicated 

accounting equations are those that describe the accounting of the existing in-service stock of 

transformer units: 

Eq. 9.11 

This equation says that, as time goes on, only a fraction of the in-service stock exists in the 

next year. As the year is incremented and goes from y to y+1, the age is also incremented from 

age to age+1. Also, as time moves forward, a fraction of the in-service stock is removed, and 

that fraction is determined by a retirement probability function, ProbRetire(age), which is described 

in the next section. 

9.3.7 Retirement Function 

The accounting of in-service transformer stock requires specification of a retirement 

probability function. DOE derived the retirement probability function from a modified version of 

a transformer reliability function. The reliability function for determining the lifetime of the 

transformer is adapted from an earlier study for DOE, ORNL 6804/R1, p. D-1.8 

9-13

 EE-RM/STD-00-550 COMMENT 196
Eq. 9.12 

where: 

r(age) = the reliability of a transformer of a certain age, where reliability is 

defined as the probability that a transformer will last to a particular 

age, 

d and e = parameters used for fitting the reliability data, 

constfail = a constant failure rate of 0.5 percent per yeara

, and 

corrfail = a corrosive failure rate of 0.5 percent per year at age 15 and above. 

DOE adjusted the parameters of the Weibull

b

 distribution to maintain an average lifetime 

of 32 years. It adapted the failure rates and the lifetime from ORNL 6804/R1, p. D-1. 

DOE converted the reliability function into an annual retirement probability function by 

dividing the incremental reliability at a given age by the fraction of transformers that last to that 

age, as shown in Equation 9.13 below. Figure 9.3.2 shows the retirement rate of distribution 

transformers. 

Eq. 9.13 

where: 

ProbRetire (age) = the probability that a transformer of a particular age will be retired. 

a Constant failure could be due to lightning or other random events. 

b The Weibull distr ibution is one of the most widely used lifetime distributions in reliability engineering. It is used to 

model material strength, or times-to-failure of electronic and mechanical components, equipment, or systems. 

9-14

 EE-RM/STD-00-550 COMMENT 196
Figure 9.3.2 Fraction of Original Shipment Retiring 

DOE considered the possibility that more-efficient transformers may operate at lower 

temperatures that could alter the retirement function for transformers. Upon review of the 

engineering data, DOE found that the more-efficient transformers made with amorphous core 

material were the transformer designs that had a significant drop in operating temperatures. 

Theoretically, lower operating temperatures should lower the degradation rate of electrical 

insulation in the transformer and lead to fewer failures due to insulation degradation for older 

transformers. However, because amorphous core technology is a relatively recent technology 

(within the last couple of decades), which can result in larger, bulkier transformers, it may have 

features that may lead to more frequent replacements over the long term. Such features may 

include larger size and weight, which can lead to increased failures during storms or replacement 

due to size or space constraints. DOE did not have enough information to determine if such 

transformers would have longer or shorter system lifetimes relative to baseline efficiency 

transformers and therefore estimated that the transformers lifetime function should be largely 

independent of transformer efficiency. 

Once DOE specified the retirement probability function, the remaining input to the stockaccounting equation was the initial in-service stock of transformers, as described in the next 

section. 

9.3.8 Refurbishments and Rewinds 

Transformers that are not retired can be refurbished and returned to the stock. Minor 

refurbishments may include replacement of connectors, bushings, or the oil. Major refurbishments 

could include the rewinding of the transformer, a function often performed by a rewinding firm. 

ORNL-6804/R1 reported annual refurbished capacity, including rewound units, to be 

approximately one percent of the in-service transformer capacity. DOE carried out further 

9-15

 EE-RM/STD-00-550 COMMENT 196
research and had discussions with owners of transformers to ascertain the estimated annual 

refurbishments. The findings were inconclusive: Major refurbishment appears to represent a 

small fraction of the distribution transformer market; however, that share could potentially grow 

with the imposition of an energy efficiency standard. Transformer users expressed hesitancy 

regarding widespread adoption of rewound transformers. Not finding a consensus regarding 

transformer refurbishment, DOE did not include major refurbishments in the current analysis. 

Minor refurbishment is widespread and already is captured in the retirement function. 

9.3.9 Initial Stock 

DOE initialized the stock-accounting model in the year 1950, which was the first year for 

which electricity consumption data were available.3 For simplicity, DOE set the in-service 

transformer stock in the first year at zero.a This number does not affect the analysis because most 

of the transformer stock from the year 1950 will no longer be in service after 2001. 

9.3.10 Effective Date of Standard 

A key output of the shipments model is the in-service stock of transformers that may be 

affected by a standard. To calculate this ‘affected stock,’ the effective date of the standard must 

be defined. DOE assumed for this analysis that any new energy efficiency standard for 

distribution transformers will become effective in 2010. The exact effective date of the standard 

is January 1, 2010, so all distribution transformers manufactured or imported starting on the first 

day of the year 2010 are affected by the standard. 

9.3.11 Affected Stock 

The affected stock is an output of the shipments model and a key input for the NES and 

NPV calculations. The affected stock consists of that portion of the in-service transformer stock 

that is potentially impacted by a standard. It therefore consists of those in-service transformers 

that are purchased in or after the year the standard has taken effect, as described by the following 

equation: 

Eq. 9.14 

where: 

Aff_Stock(y) =	 stock of transformers of all vintages that are operational in year y 

(MVA), 

a Note that transformer stocks in 1950 were quite small compared to transformer stocks in 2001. 

9-16

 EE-RM/STD-00-550 COMMENT 196
Ship (y)	 = shipments in year y (MVA), and 

age	 = the age of the transformer (years). 

Once DOE had specified the shipments, in-service stocks, and affected stocks of 

transformers, it was able to proceed to the NES and NPV models and calculations, as described in 

Chapter 10. 

9.4 RESULTS 

The main output of the shipments model is the total capacity of distribution transformers 

shipped annually from 2010 through 2038. Total shipments depend on transformer lifetime, the 

market elasticity of transformer purchases with respect to price, and growth in new electricity 

demand. Total shipments for all trial standard levels for liquid-immersed and dry-type distribution 

transformers are shown in Table 9.4.1. 

Table 9.4.1	 Cumulative Shipments of Transformers by Trial Standard Level (TSL), 

2010–2038 

Distribution 

Transformers 

Transformer Capacity Shipments in Thousand MVA 

Base 

Case 

TSL 1 

(TP 1) 

TSL 2 TSL 3 TSL 4 TSL 5 TSL 6 TSL 

A 

TSL 

B 

TSL 

C 

TSL 

D 

LiquidImmersed 

3256 3252 3249 3245 3241 3204 3174 3225 3242 3244 3247 

Dry-Type 222.5 222.4 222.2 222.0 221.7 220.2 220.2 

An important factor that influences the size of the potential standards-induced change in 

shipments is the actual equipment price increase due to standards. If price increases are large, the 

shipments volume will decrease almost proportionally to the price increase, but because the price 

elasticity of transformers is less than one, price increases will result in increased gross sales dollar 

volume to the transformer manufacturer. The net financial impact of these opposing effects is 

examined in the MIA (see Chapter 12). 

The national impact spreadsheet is available as an Excel file on the DOE website: 

http://www.eere.energy.gov/buildings/appliance_standards/commercial/distribution_transformers. 

html. Instructions for using the spreadsheet are in Appendix 10A of this TSD. 

9-17

 EE-RM/STD-00-550 COMMENT 196
REFERENCES 

1.	 HVOLT Consultants Inc., P. Hopkinson, and J. Puri. Distribution Transformer Market 

Shipment Estimates for 2001. February 17, 2003. 3704 High Ridge Road, Charlotte, NC, 

28270, tel: (704) 846-3290, e-mail: P.Hopkinson@hotmail.com. Prepared for Navigant 

Consulting, Inc., Washington DC. 

2.	 U.S. Department of Commerce - Bureau of Economic Analysis. Industry Economic 

Accounts Information Guide. Annual Industry Accounts. Gross Domestic Product (GDP) 

by Industry. 2005. (Last accessed May 11, 2005.) This material is available in Docket 

#86. Contact Ms. Brenda Edwards-Jones, U.S. Department of Energy, Building 

Technologies Program, Mailstop EE-2J, 1000 Independence Avenue, SW, Washington, 

DC, 20585-0121, telephone (202) 586-2945 for more information. <For download page, 

see http://www.bea.gov/bea/dn2/iedguide.htm#gpo . For 1977-1997 data, go to 

http://bea.gov/bea/pn/GDPbyInd_SHIP_SIC.xls . For 1998-2003 data, go to 

http://bea.gov/bea/pn/GDPbyInd_SHIP_NAICS.xls .>> 

3.	 U.S. Department of Energy-Energy Information Administration. Annual Energy Review 

2003, Chapter 8: Electricity, Table 8.9 Electricity End Use, 1949-2003. 2004. (Last 

accessed May 9, 2005.) This material is available in Docket #86. Contact Ms. Brenda 

Edwards-Jones, U.S. Department of Energy, Building Technologies Program, Mailstop 

EE-2J, 1000 Independence Avenue, SW, Washington, DC, 20585-0121, telephone (202) 

586-2945 for more information. <http://www.eia.doe.gov/emeu/aer/txt/stb0809.xls> 

4.	 U.S. Department of Energy - Energy Information Administration. Annual Energy Outlook 

2005: With Projections Through 2025. January, 2005. Washington, DC. Report No. 

DOE/EIA-0383(2005). <http://www.eia.doe.gov/oiaf/aeo/index.html> 

5.	 U.S. Department of Energy - Federal Energy Regulatory Commission. Form No. 1 ­

Electric Utility Annual Report. 2002. (Last accessed February 11, 2005.) This material is 

available in Docket #86. Contact Ms. Brenda Edwards-Jones, U.S. Department of 

Energy, Building Technologies Program, Mailstop EE-2J, 1000 Independence Avenue, 

SW, Washington, DC, 20585-0121, telephone (202) 586-2945 for more information. 

<http://www.ferc.gov/docs-filing/eforms/form-1/form1_blank.pdf> 

6.	 Air-Conditioning and Refrigeration Institute. 1999 Statistical Profile of the AirConditioning, Refrigeration, and Heating Industry. 1999. Arlington, VA. 

7.	 U.S. Department of Labor-Bureau of Labor Statistics. Producer Price Index. Industry: 

Refrigeration and Heating Equipment - Product: Unitary Air Conditioners - Series ID: 

PCU3585#2 (N). 2005. (Last accessed April 11, 2005.) This material is available in 

Docket #86. Contact Ms. Brenda Edwards-Jones, U.S. Department of Energy, Building 

Technologies Program, Mailstop EE-2J, 1000 Independence Avenue, SW, Washington, 

9-18

 EE-RM/STD-00-550 COMMENT 196
DC, 20585-0121, telephone (202) 586-2945 for more information.


<http://bls.gov/ppi/home.htm>


8.	 Barnes, P. R., J. W. Van Dyke, B. W. McConnell, S. M. Cohn, and S. L. Purucker. The 

Feasibility of Replacing or Upgrading Utility Distribution Transformers During Routine 

Maintenance. 1995. Oak Ridge National Laboratory. Oak Ridge, TN. Report No. ORNL6804/R1. < http://www.ornl.gov/~webworks/cpr/v823/rpt/78562.pdf> 

9-19

 EE-RM/STD-00-550 COMMENT 196