9

- i CHAPTER 9. SHIPMENTS ANALYSIS

TABLE OF CO NTENTS

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

9.2 MODEL OVERVIEW ..................................................................................................... 9-2

9.3 MODEL IN PUTS ............................................................................................................ 9-4

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

9.3.2 Shipments Backcast ......................................................................................................... 9-6

9.3.3 Shipments Forecast .......................................................................................................... 9-7

9.3.4 Long

- Term Purchase Elasticity ........................................................................................ 9-8

9.3.4.1 Elasticity Estimates for Liquid

- Immersed and Dry

- Type Transformers ....... 9-9

9.3.5 Liquid-Immersed and Dry- Type Market Shares .............................................................. 9-9

9.3.6 Stock Accounting ........................................................................................................... 9-10

9.3.7 Retirement Function ....................................................................................................... 9-11

9.3.8 Refurbishments and Rewinds ........................................................................................ 9-13

9.3.9 Initial Stock .................................................................................................................... 9-13

9.3.10 Eff ective Date of Standard ............................................................................................. 9-14

9.3.11 Affected Stock ............................................................................................................... 9-14

9.4 RESULTS ...................................................................................................................... 9-14

LIST OF TABLES

T able 9.3.1 Distribution Transformer Shipment Estimates for 2009 ...................................... 9-5

Table 9.3.2 Liquid- Immersed Distribution Transformer Shipment Estimates, 2009 ............. 9-5

Table 9.3.3 Dry -Type, Medium-Voltage Distribution Transformer Shipment Estimates, 2009 .................................................................................................... 9-6

Table 9.4.1 Cumulative Shipments of Transformers by Candidate Standard Level (CSL), 2016 - 2045 ............................................................................................ 9-15

LIST OF FIGURE

Figure 9.2.1 Shipments Model Flowchart ................................................................................ 9-3 Figure 9.3.1 Fraction of Original Shipment Retiring ............................................................. 9-129

- 1 CHAPTER 9. SHIPMENTS ANALYSIS

9.1 INTRODUCTION

The U.S. Department of Energy (DOE) analyzes shipments of affected products as a part of every rulemaking regarding new or amended energy efficiency standards for products that impact national energy use. Estimates of product shipments are a necessary inpu t to calculating national energy savings (NES) and net present value (NPV) of the investment in a more efficient product; both of these calculations are required to analyze the impact of proposed new or amended energy efficiency standards. Shipments also are a necessary input to the manufacturer impact analysis (MIA) , which DOE conducts to prepare its notice of proposed rulemaking (NOPR)

. The MIA estimates the impact of potential efficiency standards on manufacturers of the affected products, in this case distribution transformers, and assesses the direct impact of such standards on employment and manufacturing capacity. This chapter describes the method DOE

used to project annual shipments for liquid -immersed and dry-type distribution transformers

under ba se - and standards

- case efficiency levels and the results obtained.

DOE developed a shipments model to predict shipments of distribution transformers. The shipments model provides an estimate of the rate at which an in-service stock of existing

transforme rs may be replaced by new, more efficient transformers after the enactment of 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. In estimating the effects of potential new standard levels on product shipments, the model accounts for the combined effects on the purchase decision of increases in purchase price, decreases in annual operating costs, and

consumer income.

To estimat e total product shipments, the model estimates shipments for specific market

segments and then aggregates those results. DOE accounted for two market segments: (1) shipments to new construction; and (2) replacement shipments going into existing buildings.

The shipments models are prepared as Microsoft Excel spreadsheets that are accessible on the Internet ( http:// www.eere.energy.gov/buildings/appliance_standards/ ). Appendix 10 A discusses how to access the shipments model and other related spreadsheets and provides basic instructions for using them. The rest of this chapter explains the shipments model in more detail. Section 9.2 describes the methodology that underlies development of the model. Section 9.3

describes the da ta inputs and model calibration, the effects on shipments of changes in purchase price, operating costs, and consumer income, and the affected stoc k of products. Section 9.4 presents the model results for both liquid-immersed and dry-type distribution transformers for the seven efficiency levels that were identified as candidate standard levels (CSLs) for this rulemaking.9

- 2 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 t ype (classified according to equipment classes ) and each transformer size. DOE chose an accounting model method to prepare shipment scenarios for the base case and several CSLs . The model uses annual transformer sales and the age distribution of the in-s ervice stock to calculate equipment costs for the 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.

Fig ure 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 s how 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 (show n as circles), into major outputs (shown as boxes with curved bottom edges).

As illustrated in the flow diagram, DOE organized shipments into two categories: replacements and new capacity. Replacements occur when transformers break down, corrode,

are 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 calculati on 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 t he total capacity shipped into liquid-immersed and dry- type transformers.

5. Modeling of purchase price elasticity – Models the impact of higher purchase prices due to a standard on future shipments.

6. Accounting of sales and in- service stocks – Provides an annual age distribution of inservice transformer stock from shipments estimates and the retirement function.9

- 3 Figure 9.2.1 Shipments Model Flowchart9

- 4 9.3 MODEL INPUTS

The shipments model inputs flow from the ship ments calculation steps 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 exog enously. 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 the quantity

index of transformers manufactured. The external estimates of transformer shipments in this analysis include sales data for 2001 and 2009. The quantity index of transformers

manufactured is available for the years from 1977-2008.

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

2009.

3. Shipments forecast – Provides an estimate of total transformers shipped after 2009.

4. Long

- term purchase elasticity

– Estimates the price sen sitivity of transformers shipped over the long term.

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 o f 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 200 9, disaggregated by type and kilovolt- ampere (kVA) rating

. 1 DOE also used a similar sales

estimate for the year 2001, which was completed by the same source. Market shares for both liquid -immersed and dry-type distribution transformers, by product type and kVA rating, came

from the 2001 and 2009 shipments estimate s. In the absence of historical shipments data for

ot her years, DOE explored other means of getting at transformer sales estimates. The historical series of the power distribution and specialty transformer manufacturing (North American Industry Classification System (NAICS) code 335311) quantity index for 1977–2008 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 2008. 2 Using the

sales estimate s for 2001 and 2009 as reference point s and the BEA quantity index data, DOE estimated aggregate transformer shipments from 1977 to 2008.9

- 5 Table 9.3.1 presents the shipment estimates in both units and megavolt- amperes (MVA) shipped, and the approximate value of these shipments. The total sales for the distribution transformer industry were about $1.9 billion in 2009.

Table 9.3.1 Distribution Transformer Shipment Estimates for 2009

Distribution Transformer Equipment Class Units Shipped MVA Capacity Shipped Shipment Value (2009 US$ million) Liquid

- immersed, medium

- voltage, single

- phase 683,726 21,994 714.8 Liquid

- immersed, medium

- voltage, three

- phase 49,739 32,266 786.0 Dry

- type, low

- voltage, single

- phase 17,740 647 22.0 Dry

- type, low

- voltage, three

- phase 206,929 15,778 394.4 Dry

- type, medium

- voltage, single

- phase, 20

- 45 kV BIL 709 23 0.7 Dry

- type, medium

- voltage, three

- phase, 20

- 45 kV BIL 522 257 6.2 Dry

- type, medium

- voltage, single

- phase, 46

- 95 kV BIL 546 23 0.8 Dry

- type, medium

- voltage, three

- phase, 46

- 95 kV BIL 2,074 3,655 98.7 Dry

- type, medium

- voltage, single

- phase, ≥96 kV BIL 202 9 0.3

* BIL = Basic impulse insulation level

Table s 9.3.2 and 9.3.3 present the disaggregated shipment estimates for 2009 by product

type and kVA rating.

Table 9.3.2 Liquid

- Immersed Distribution Transformer Shipment Estimates, 200 9
Liquid

- Immersed, Medium

- Voltage, Single

- Phase Liquid

- Immersed, Medium

- Voltage,

Three

- Phase

kVA Units Shipped kVA Units Shipped

10 58,090 15

- 15 169,083 30

- 25 243,583 45 1,438

37.5 41,755 75 3,753

50 119,455 112.5 790

75 26,338 150 7,426

100 18,679 225 1,969

167 4,357 300 7,340

250 1,905 500 6,650

333 238 750 3,502

500 238 1000 3,171

667 5 1500 2,941

833

- 2000 2,497

- 2500 2,260

Total Units 683,726 Total Units 43,736

Total MVA 21,994 Total MVA 28

, 3729

- 6 Table 9.3.3 Dry -Type

, Medium

- Voltage Distribution Transformer Shipment Estimates, 2009

Dry

- Type, Medium -Voltage, Single- Phase Dry

- Type, Medium -Voltage, Three- Phase

kVA Units Shipped

kVA Units Shipped

20

- 45kVBIL 46

- 95kVBIL >96kVBIL 20

- 45kVBIL 46

- 95kVBIL >96kVBIL

15 242 182 61 15 4

- - 25 42 182 61 30 7

- - 37.5 61 42 18 45 7

- - 50 61 42 18 75 10 2

- 75 30 18 12 112.5 34 4

- 100 30 18 12 150 30 5

- 167 12 18 6 225 36 12

- 250 7 8 2 300 91 30 25

333 12 18 6 500 121 85 74

500 12 18 6 750 121 121 75

667

- - - 1000 61 242 194

833

- - - 1500

- 363 244

- - - - 2000

- 605 280

- 182 61 2500

- 605 394

Total Units 709 546 202 Total Units 522 2,074 1,286

Total MVA 23 23 9 Total MVA 257 3,655 2,206

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 t he 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 a ssumes that the quantity market share of distribution transformers relative to all NAICS code 335311 transformers is relatively constant over the period 1977–2009. DOE made this assumption because quantity index data that are more disaggregated were not a vailable.

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

2001 1 and uses BEA’s NAICS code 335311 quantity index to estimate total shipments in the period 1977 to 2008. 3 Specifically, DOE used the following equation to backcast shipments

from 2008 to 1977:9

- 7 TotShip(y) = TotShip(2001) × BEA(y)/BEA(2001) Eq. 9.1

where:

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

where 1977 ≤ y < 200 8 (MVA), TotShip(2001) = the total transformer capacity shipped, from the shipments estimate (MVA), and BEA(y) = th e BEA quantity index for year y

.

Annual shipments of transformer capacity for the 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 2009, a proxy for growth of transformer sales during this period. 4 Using this method, the shipments for the years 1950 to 1977 are given by the following equation:

TotShip(y) = TotShip(1977) × AllElec(y) / AllElec(1977) 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 Annual Energy Outlook 2010 (AEO20 10 ) (W

- hr). 5

9.3.3 S hipments Forecast

After constructing a shipments backcast and calibrating it with shipments data, DOE constructed a forecast of transformer 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 in

which 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 decoupled the overall shipments and retirements in the construction of the shipments

forecast and maintained t he age distribution of the in-service transformer stock using a

retirement function.9

- 8 DOE constructed the transformer shipments forecast assuming that transformer shipments growth is equal to forecasted growth in electricity consumption, as given by the

AEO2010 forecast up to the year 2035. 5 For the years 2036–2045 , DOE extrapolated the AEO 2010 forecast, using its growth rate of electricity consumption between 2025 and 2035. Specifically, DOE used the following equation for the shipments forecast:

TotShip(y) = TotShip(2009 ) × AllElec(y) / AllElec(2001 ) Eq. 9.3

wh ere:

TotShi p(y) = the total capacity of shipments estimated for year y where 2011 < y

≤ 2045 (MVA), and AllElec(y) = national electricity consumption forecasted for year y by AEO20 10 (or by an extrapolation of AEO20 10 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 incre ases 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 pric e (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 whe n the elasticity is constant:

Q(P) = Q0 × (P/P0) e Eq. 9.5

where:

Q(P) = the quantity purchased as a function of price, Q 0 = a reference quantity at a reference price P 0 , and9

- 9 e = the elasticity, which is almost always negative or zero (i.e., non-positive) with respect to price.

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 p urchased, 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 pu rchase 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. DOE fitted the model to the

Air -Conditioning and Refrigeration Institute's sales data and real Producer Price Index (PPI) of unitary air conditioners. 6

9.3.5 Liquid

- Immersed and Dry

- Type Market Shares 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.

The shipments forecast and backcast described above provided an aggregate estimate of the total capacity of transformers shipped from 1950 to 2045 . To disaggregate the total capacity into the capacity for different types of transformers, DOE applied capacity market share

estimates. To disti nguish between liquid- immersed and dry -type transformers, DOE applied a liquid -immersed and dry-type market share, respectively. To distinguish between different equipment classes and size categories within each equipment 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. 4 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:

LiqShip(y) = CL × AllElec( y );

where CL = LiqShip(2001) / AllElec(2001) **∀** y ≤2008

CL = LiqShip(2009) / AllElec(2009) **∀** y ≥ 2009 Eq. 9.6

DryShip(y) = CD × CIElec( y );

where CD = DryShip(2001) / CIElec(2001) for all y ≤2008

C

D = DryShip(2009) / CIElec(2009) for all y ≥ 2009 Eq. 9.79

- 10 DryMS( y) = CD × CIElec( y) / (CL × AllElec( y) + CD × CIElec( y )) Eq. 9.8

LiqMS(y) = 1

− DryMS(y) Eq. 9.9

where:

CL = 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 transf ormers 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), LiqShip(2009) = the capacity of liquid -immersed transformers shipped in 2009 (MVA), DryShip(2009) = the capacity of dry -type transformers shipped in 2009 (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 ca pacity 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 equipment classes, DOE assumed that

the relative market share wi thin 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 equipment 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 th e 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. Th e age distribution of in- service transformer stocks i s a key input to both the NES and NPV calculations, because the operating costs for any year depend on9

- 11 the age distribution. The dependence of operating cost on the transformer age distribution occurs because, under a candidate standard scenario that produces increasing efficiency over time, older, less efficient t ransformers will have higher operating costs, while younger, more efficient

transformers tend to 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:

Stock ( y, age = 1) = Ship ( y

− 1) 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:

Stock(y+1,age+1) = Stock(y,age) × [1

− Prob Retire (age)] 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 a Weibull distribution and is adapted from an earlier study for DOE, ORNL 6804/R1, p. D-1. 7

r ( age ) = exp �− � age

d � d � (( 1

− constfail ) age × ( 1 − constfail ) age

− 15 ) Eq. 9.12

where:9

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

DOE adjusted the parameters of the Weibull 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.

Prob Retire (age) = [r(age

− 1)

− r(age)] / r(age) Eq. 9.13

where:

Prob Retire (age) = the probability that a transfor mer of a particular age will be

retired

.

Figure 9.3.1 shows the retirement rate of distribution transformers.

Figure 9.3.1 Fraction of Original Shipment Retiring

a Constant failure could be due to lightning or other random events. 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0%

0 10 20 30 40 50 60Probability of Failure in Year t to t+1

Age of Unit9

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

temperatures

, which could alter the retirement function for these transformers. Upon review of the engineering data , DOE found that the more efficient transformers that are ma de with an amorphous core material were the transformer s 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 fail ures of these transformers as they

age . However, because amorphous core technology is a relatively recent technology (within the last couple of decades), and because use of this technology can result in larger, bulkier transformers, this type of transformer may have features that contribute 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 transformer lifetime function should be largely independent of transformer efficie ncy.

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 following

section s.

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 performe d 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

research , including discussions with owners of transformers t o 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 e fficiency 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. 4

For simplicity, DOE set the in

- service transformer stock in the first year at zero. a

a Note that transformer stocks in 1950 were quite small compared to transformer stocks in 2001. This number does not affect the analysis because most of the transformer stock from the year 1950 would no longer be in service after 2001.9

- 14 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 201 5 . The exact effective date of the standard is January 1, 2015, so all distribution transformers manufactured or imported starting on the first day of the

year 2015 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 tran sformer 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:

AffStock ( y ) = S h ip ( y ) + ∑ Stock ( age ) y− StdYear

age =1 Eq. 9.14

where :

AffStock(y) = stock of transformers of all vintages that are operational in year y (MVA), 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 ; these

calculations are described in c hapter 10.

9.4 RESULTS

The main output of the shipments model is the total capacity of distribution transformers shipped annually from 2016 through 2045. 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 CSLs for liquid -immersed and dry-type distribution transformers

are shown in Table 9.4.1

.9

- 15 Table 9.4.1 Cumulative Shipments of Transformers by Candidate Standard Level (C SL), 2016 - 2045

Candidate Standard Level

Base

- case 1 2 3 4 5 6 7
Liquid

- Immersed 1.98 1.97 1.97 1.97 1.96 1.95 1.95 1.95

Low

- Voltage Dry

- Type 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63

Medium

- Voltage Dry

- Type 0.23 0.23 0.23 0.23 0.23 0.23 0.23 0.23

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, found in c hapter 12 of the NOPR

.

The national impact spreadsheet is available as an Excel file on the DOE website: http://www.eere.energy.gov/buildings/appliance_standards/commercial/distribution_transformer

s.html . Instructions for using the spreadsheet are in Appendix 10-A of this technical support

document.9

- 16 REFERENCES

1. Hopkinson, P. & Puri, J. Distribution Transformer Market Shipment Estimates for 2001

. (HVOLT Consultants Inc.: 2003).

2. U.S. Department of Commerce. Bureau of Economic Analysis U.S. Department of Commerce. Bureau of Economic Analys is. at <http://www.bea.gov/national/nipaweb/DownSS2.asp>

3. Bureau of Economic Analysis Industry Economic Accounts Information Guide. Annual Industry Accounts. Gross Domestic Product (GDP) by Industry. (2005).

4. U.S. Department of Energy- Energy Informati on Administration Annual Energy Review 2009, Chapter 8: Electricity, Table 8.9 Electricity End Use, 1949 -2009 . (U.S. Department of Energy: Washington DC, 2010).

5. U.S. Department of Energy – Energy Information Administration Annual Energy Outlook

2010: W ith Projections Through 2035 . (U.S. Department of Energy: Washington DC, 2010).

6. 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).

7. Barnes, P., Van Dyke, J.W., McConnell, B.W. & Das, S. Determination Analysis of Energy Conservation Standards for Distribution Transformers . (Oak Ridge National Laboratory: 1996).