9-i CHAPTER

9. SHIPM

ENTS ANALY SIS TABLE OF CO NTENTS 9.1 INTRODUCTI ON

...................................................... 9-1 9.2 MODEL OVERVI EW

................................................... 9-1 9.3 MODEL I

NPUTS

...................................................... 9-3 9.3.1 Shipments Data

................................................. 9-4 9.3.2 Shipments B ackcast

............................................. 9-8 9.3.3 Shipments F orecast

.............................................. 9-8 9.3.4 Long -Term Pur

chase E lasticity

.................................... 9-10 9.3.4.1 El as ti ci ty E st im at es fo r Li qu id -Im me rs ed an d D ryType Tra nsformers

...................................... 9-11 9.3.5 Liquid-I

mmersed/Dry- Type Marke t Shares

........................... 9-11 9.3.6 Stoc k Accou ntin g .............................................. 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 LIST OF TABLE S Table 9.3.1 Distribution Transformer Shipment Estimates for 2001

.................. 9-5 Table 9.3.2 Liquid-I

mmersed Distribution Transformer Shipment Estimates, 2001

...... 9-6 Table 9.3.3 Dry-Ty

pe, Mediu

m-Voltag e Distrib

ution Tra nsforme r Shipment Estimates, 2001

................................................. 9-7 Table 9.4.1 Cumulative Shipments of Transformers by Tr ial Standard Level (TSL

), 2010–2038

.................................................... 9-17

LIST OF F IGURES Figure 9.2.1 Shipments Model Flowchart

....................................... 9-2 Figure 9.3.1 Age Distribution of Liquid-I mmersed In-Service T

ransformer Stock in 2004

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

.............................. 9-159-1 CHAPTER

9. SHIPM

ENTS ANALY SIS 9.1 INTRODUC TION The shipments model provides an estimate of the rate at which an in-ser vice stock of existing transformers may be replaced by

new, more-efficient transformers me eting the trial standard. The core of the shipments analy sis is an accounting model that the Department developed to simulate how existing and future purchases are incorporated into an in-service stock of aging transformers that a re gradually

replaced. An estimate of the rate at whic h transformers are purchased and replac ed is a key input to the Depa rtment’s estimate of the national impacts of trial standards for distribution transformers. For the notice of proposed rulemaking ( NOPR), in addition to using estimates of distribution transformer shipments as an input to the national energy savings (NES) analysis (see Chapter 1 0 of this te chnical support do cument, or TSD), the Depar tment used

the shipme

nts estimates as input to the manufacturer impact analy sis (MIA). That analy

sis estimates the impact of potential efficiency

standards on businesses (see Chapter 12).

9.2 MODEL OVER

VIEW The Depa rtment use d forec asts of sh ipments for a base

case an d a standa rds case to provide a n estimate

of the an nual sale s and in-s ervice

stock of t ransfor mers for the fore cast period. The estimate included the age distribution of transfor mers for each transformer ty pe (classified according to product cla sses) and each transformer size. The Depa rtment chose an accounting model method to prepare shipment scena rios for the base case and severa l trial standard cases. The model uses the annual transformer sales and the age distribution of the inservice stock to calculate equipment costs for the net pre sent value (NPV) and energy use for the NES, resp ectively . The mode l keeps tr ack of the age

and repl acemen

t of trans former c apacity given a projection of future transfor mer sales growth. Figure 9.2.1 presents a g

raphical flow diagram of the shipments model portion of the distributi on transf

ormer na tional impa

ct (NES a nd NPV) mod

el and spr eadshee t. In t he diag ram, the arrows show the interconnectivity of data exchanges between ca lculations. Inputs are shown as para llelogr ams. As da

ta flows f rom these inputs, it ma

y be integr ated into intermed iate res ults (shown as rectangles) or, throug h integrating sums or difference s (shown as circles), into major outputs (shown as boxes with curved bottom edges).9-2

Figure 9.2.1 Shipments Model Flowchart9-3

As illustrated in the flow diagram, the Department org anized shipments into two categories: replacements a nd new capacity . Replacements occur when transformers break down, corrode, are struck by

lightning, or otherwise need to be repla ced. New capacity

purchases occur due to increases in electricity use that may be dr

iven by increa sing population, commercial and industrial activity, or gr

owth in electricity distribution sy stems. The model starts with an estimate of the overall growth in transformer c apacity a nd then estimates shipments for particular design lines using estimates of the relative marke t share for different design a nd size categories. The steps for the shipments c alculati on are: 1. Collection of available data – Acquisition and processing of ava ilable transformer shipments data. 2. Construction of aggrega te shipments backcast – Provides an annual estimate of historica l total ca pacity shipped us ing ava

ilable shi pments an

d electr icity consumption data. 3. Construction of aggrega te shipments forecast – Provides a future base case annual shipments estimate by apply ing a shipments growth rate. 4. Development of liquid-immersed and dry -type marke

t shares – Disaggreg ates the total capacity shipped into liquid-immersed a nd dry-ty pe transformers. 5. Modeling

of purchase pric e elastic ity – Models the

impact of

higher purchas e prices due to a trial standard on future shipments. 6. Accounting of sales and in-service stocks – Provides a n annual age distribution of in-service transformer stock from shipments estimates and the re tirement function. 7. Consistenc

y check with

purchas e and re

placeme nt data – Confirms tha

t shipments model forecasts are consistent with available data on utility transformer purchases and repl acemen

ts.

9.3 MODEL INP UTS The shipments model inputs parallel the shipments calculation described in the previous section. The shipme

nts model ta

kes both in

ternal a nd externa

l inputs. I

nternal inputs com

prise quantitie s that are

calcula ted from p

revious s teps in the

model. Exte

rnal inpu ts are da ta sets acquired from exogenous sources. The fina l outputs of the shipments calculation are the annual shipments e stimates a

nd the ag e distribu

tion of in- service

transformer stoc

k. The list

of speci fic inputs is as

follows: 1. Shipments data – Include external estimates of transfor mer shipments and quantity index of transformers manuf actured. 2. Shipments backcast – Provides an estimate of total transformer capac ity shipped before 2001. 3. Shipments forecast – Provides an estimate of total transformers shipped after 2001. 4. Long-term purc

hase elasticity – Estimates the pr ice sensitivity of transforme rs shipped over the long term.9-4

5. Dry-ty

pe/liquid-immersed market shares – Provides the annual capac ity-based market shares of dry

-type and liquid-immersed tra nsformers shipped. 6. Stock accounting – Provides the age distribution of the curr ent year ’s in-service transformer stoc

k based on the pre

vious y ear’s in-servic e stock a

nd shipmen

ts. 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-ser vice stock of transformers at the start of the stock-accounting calc ulation (i.e., in 1950). 9.3.1 Shipments Data The Department uses historical transformer shipments data to ca librate a forecast of future shipments a

nd in-ser vice stoc ks. These

data ar e key inputs to the

national impact an

aly sis because changes in shipments and in-servic e stock create nearly proportional changes in the estimate of the energy

savings from a trial standard. The Department obtained an estimate of sales (for the entire market) for the y ear 2001, disaggr egated b y product class and ki lovo lt-am pere (k VA) rat ing. 1 The historical series of the power distribution and specialty tra nsformer manufacturing (North Ame rican Industry Classification System (NAICS) code 335311) quantity index for 1977–2003 is available f rom the U.S. Bureau of Economic Affairs (B EA). The BEA transformer manufa cturing quantity inde

x provides information on changes to agg regate shipments from 1977 to 2003. 2 Market shares for both liqu id-im merse d and d ry-type dist ribut ion t ransfo rmers ,by produ ct type an d kVA r ating, came from the 2001 shipments estimate. Using the sales estimate for 2001 a s a reference point and the B EA quanti

ty in dex data, t

he Depa rtment estimated a

ggre gate tr

ansformer shipme

nts from 1977 to 2003. Table 9.3.1 presents the shipment estimates in both units and megavolt-amperes (MVA ) ship ped, a nd th e appro xim ate val ue of t hese s hipm ents. The di stri buti on tra nsfor mer in dust ry's total sales were about $1.6 billion in 2001.9-5

Table 9.3.1 Distribution Transformer Shipment Estimates for 2001 Pro duc t Cl as s ( PC) Units Shipped MVA

Capa city Shipped Shipment

Value 2001$ milli

on Liqui d-Imme rsed, Medium-Vol tage , Si ngl e-Pha se, (PC 1) 977,388 36,633 698 .8 Liqui d-Imme rsed, Medium-Vol tage , Three -Phas e, ( PC 2) 79,367 42,887 540 .4 Dry- Type, Medium-Volt age, Sing le- Phase, 20-45 kV BIL, * (PC 5) 119 18 0.5 Dry- Type, Medium-Volt age, Three- Phase, 20-45 kV BIL, (PC 6) 650 776 13.5 Dry- Type, Medium-Volt age, Sing le- Phase, 46-95 kV BIL, (PC 7) 121 22 0.6 Dry- Type, Medium-Volt age, Three- Phase, 46-95 kV BIL, (PC 8) 2,371 3,913 68.1 Dry-T ype, M edium -Vo ltage, Sin gle-P hase, > 96 kV BIL, ( PC 9) 20 4 0.1 Dry-T ype, M edium -Vo ltage, T hree-P hase, > 96 kV BIL, ( PC 10) 187 367 6.4 Total 1,060, 223 84,620 1,32 8.4 * BIL = Basic impuls e insu lati on level Tables 9.3.2 and 9.3.3 present the disaggr egated shipment estimates for 2001 by product class and kVA rating.9-6

Table 9.3.2 Liquid-Immersed Distribution Transformer Shipment Estimate s, 2001

Liquid-Immers ed, Medium-Voltage,

Single-P hase Liquid-Immers ed, Medium-Voltage, Thre e-Ph ase 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 To tal Un its 977,388 To tal Un its 79,367 Tot al MVA 36,633 Tot al MVA 42,8879-7

Table 9.3.3 Dry-Type , Medium- Voltage Distribut

ion Transfor mer Ship ment Es timat es, 2001 Dry-Type, M edium-Voltage, Single-P hase Dry-Type, M edium-Voltage, Thre e-Ph ase 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 To tal Un its 119 121 20 To tal Un its 650 2,371 187 Tot al MVA 18.4 22.1 4.1 Tot al 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 differe nt transformer product classes and size categ ories are constant over time. In actuality , there is probably a

gradual increase in the ave rage size of transformers as the electricity demand per customer increases, but the Depa rtment does not have sufficient data to characterize such transfor mer size trends. The second assumption concerns the use of the BEA quantity index data. The BEA inde x data actually

include shipments of transformers other than those covered by this rulemaking. The use of the BEA Standard I ndustrial Classification (SIC) code 3612 (NAICS code 335311) quantity index to estimate shipments for products under this rulemaking assumes that the quantity market share

of distribution transformers relative to all NAI CS code 335311 transformers is r

elativel y c onstant ov er the pe

riod 1977–

2003. The

Department made

this assumption because more-disaggr egated quantity

index data were not available.9-8

9.3.2 Shipments Backcast The shipme

nts backc ast is the e stimate of previous aggr egate transformer shipments based on limited hist

orical d ata. The backca st of tran sformers shipments is a key element i n estimating age distributions of future in-service tra nsformer stock. The shipments backcast begins with the estimate of transforme r shipments in the year 2001 1 and uses BEA’s NAI CS code 335311 quantity index to estimate total shipments in the period 1977 to 2003. 2 Specific ally , the Dep artment u sed the f ollowing equation to backc ast shipments f

rom 2003 to

1977: Eq. 9.1
where: TotShip(y) = the total capacity of transformer shipments estimated for y ear y where 1977 # y< 2003 (MVA), TotS hip(20 01) = the total transformer capacity shipped, from the shipment estimate, (MVA), and BEA(y) = the BEA quantity inde x for year y . Annual shipments of transformer capacity for yea rs prior to 1977 are backcast to 1950 using annual growth of electric ity consumption from Table 8.9 of DOE ’s Energy Information Administra

tion (EI A)’s Annual Energy Review 2003 , a proxy for g rowth of transformer sales during this period. 3 Using this method, the shipments for the ye ars 1950 to 1977 are given by the following equation : Eq. 9.2
where: TotShip(y) = the total capacity of shipments estimated for ye ar ywhere 1950 # y < 1977 (MVA), and AllE lec(y) = national electricity c onsumption in year yaccord ing to EIA's

Annual Energy Outlook 2005 (AEO20 05) (W-hr)

. 4 9.3.3 Shipments Forecast After constructing a shipments backcast and c alibrating it with shipments data, the Department constructed a foreca st of transformers shipments. This forecast provided the necess ary input for equ ipmen t exp ense an d in-s ervice trans forme r stoc k accou ntin g.9-9

Figure 9.3.1 Age Distribution of Liquid-Immersed In-Service Transformer Stock in 2004 The Depa rtment constructe d a simplif ied fore cast of tr

ansformer shipme

nts for the

base case sc enario b ased on the assumpt ion that lon

g-ter m growth in electr icity consumpti on will drive transformer shipments. The detailed dy namics of transformer shipments is 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. The Department examined the possibility of modeling the fluctuations in transfor mers shipped using a bottom-up model where the shipments are trigge red by re tirements and new capacity additions, but found insufficient data to calibrate model parameters within an a cceptable margin of err

or. Hence, the Department de-coupled the overall shipments and retire ments in the construction of the ship ments foreca st and maint ained the age d istr ibut ion o f the i n-serv ice tr ansfo rmer s tock u sing a retirement function. Figure 9.3.1 shows the estimated a ge distribution of the in-service stock of liquid-immersed transformers in 2004. The Department developed similar age distributions for medium-vo ltage, dry-ty

pe trans formers

.

The Department constructed the transformer shipments forecast assuming that transformer shipments growth is equal to foreca sted growth in electricity consumption, as given by the AEO2005 forecast up to the y ear 2025. 4 For the ye ars 2026–2038, DOE extrapolated the AEO 2005 forecast, using its growth rate of electricity c onsumption between 2015 and 2025. Specific ally , the Dep artment u sed the f ollowing equation for the shipments f

orecas t: Eq. 9.39-10

where: TotShip(y) = the total capacity of shipments estimated for ye ar ywhere 2003 < y # 2038 (MVA), and AllE lec(y) = national electricity c onsumption forecasted for y ear yby AEO2005 (or by an e

xtrapolation of AEO2005 data) (W-hr). The next se ction des cribes how the De partmen t adjusted its base c ase sce

nario forecast

to account for price increases a rising from a trial standard. 9.3.4 Long-Term Purchase Elasticity The Department used purchase elasticity to adjust the base case shipments forecasts for potentia l transformer pri ce incr

eases du e to a tria l standar d. The lon

g-ter m purcha se elasti city

is a measur e of how s ensitive t

ransfor mer shipme

nts are to potentia l increa ses in pric e. Elasti city 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 u sed to det

ermine price elasticity

is: e= (dQ/Q ) / (dP/P ) Eq. 9.4 where: dQ/Q = a small percentage change in quantity purchase d ( Q ), and dP/P = a small percentage change in price. If the elasticity

is constant, then the quantity purcha sed can be written in terms of the price, a reference

price, a reference

quantity, and the elasticity . Specifically, the f ollowing equation holds true

when the elastici ty is constant : Q(P) = Q 0 × (P/P 0 ) e Eq. 9.5

where: Q(P) = the quantity purchase d as a function of price, Q 0 = a reference quantity at a reference price P 0 , and e = the elasticity, which is almost alway s negative or zero (i.e., non-positive) with respect to price. For the f orecast, the re ferenc e price and the r efere nce qua ntity are the price a nd quantit

y from the base case scenario. A c hange in price due to a trial standard the n has an impact on the quantity purchase d, Q(P) , as described by the above equation.9-11

9.3.4.1 Elasticit

y Estima tes for Liqui d-Imm ersed and Dry-Type Transformers The Department constructed a model employ ing a standard econometric logit equa tion, used for gener al applic ations of

market r esponse t o costs an d perce ived utilit

y, t o model util ity and customer dec ision-mak ing. For liquid-i

mmersed t

ransfor mers, DO E fitted the model to transformer purchase data from U.S. F ederal Energy Regulatory

Commission (FERC) Form No. 1 5 to determine the parameters of the logit equa tion. This resulted in a value of -0.04 for price elasticity. The De

partment assigned -0.04 as the medium scenar io and incremente d the ela sticity to -0.2 to implement a high sensitivity to price chang e. The low sc en ar io as su me s z er o e la st ic it y, or no impact on purchase decisions from a price c hange. For dry-type transf

ormers, historical purchase data were not available. Since dry-type units are primaril y us ed in commercial a

nd industr

ial applic ations, a s are uni tary air cond itioners, th e D ep ar tm en t u se d s al es an d p ri ce da ta fo r a ir co nd it io ne rs to es ti ma te pr ic e e la st ic it y fo r d rytype transfor

mers. It fitted the model to the Air-Conditioning and Refr igeration Institute' s sales data 6 and rea

l Produce r Price I ndex (PPI) of unita

ry air cond itioners. 7 The resulting value of elasticity was -0.02. The

Department assigned -0.02 as the medium scena rio and incremented the elasticity to -0.2 to implement a high se nsitivity to price chang e. The low scenario assumes zero elasticity, or no impact on purc hase decisions from a price change. 9.3.5 Liquid-Immersed/Dry-Type Market Shares The shipments forecast and backcast descr ibed above provided an agg regate estimate of the total c apacity

of trans formers

shipped f rom 1950 to

2038. To di sagg

regat e the tota l capac ity into the capacity f

or different ty

pes of transformers, the Department applied ca pacity marke

t share estimates. To distinguish between liquid-immersed and dry -type transf

ormers, the Department appli ed a liqui d-immers ed and dr y- typ e market share, respectively . To disting uish between different product classes and size ca tegories within each product class, the Depa rtment used 2001 market shares es timates. The Depa rtment use d trends i

n electr icity consumpti on from EI

A’s reta il sales da ta to estimate market share tre

nds for liq

uid-immersed and d

ry -ty pe trans formers

. 3 Assuming that transformer sal es over t

he long term tra ck electricity

sales for the sectors served by those transformers, D OE deriv ed the fo llowing market share mode l: Eq. 9.6 Eq. 9.7 Eq. 9.8 Eq. 9.99-12

where: CL = the constant of proportionality be tween the electricity

consumption and the sales of liquid-immersed transformers in the y ear 2001, CD = the constant of proportionality be tween the electricity

consumption and the sales of dry-type transf ormers in the yea

r 2001, LiqS hip(20 01) = the capacity of liquid-immersed transformers shipped in 2001 (MVA), DrySh ip(200 1) = the capacity of dry-ty pe transformers shipped in 2001 (MVA), AllE lec(y) = the total consumption of electricity in y ear y(W-hr), CIEle c(y) = the consumption of electricity by the commercial and industrial sectors in yea

r y(W-hr) , LiqMS(y) = the capacity market share of liquid-immersed transformers in y ear y(%), and DryMS (y) = the capacity market share of dry

-type transf

ormers in yea

r y(%). The key assumption

behind the

market share equ

ations is that transf

ormer c apacity market shares follow the relative electricity consumption of the end users of the electricity that passes through the transformers. The dy namics of liquid-immersed and dry-type marke t shares are likely to be f

airly complicated, but the D epartment 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 differe nt kVA ratings and product classes, the Depa rtment as su me d t ha t t he re la ti ve ma rk et sh ar e w it hi n e ac h t ra ns fo rm er typ e ( i. e. , l iq ui d- im me rs ed or dr ytype) is constant over

time. Given a lack of detailed, long-term marke t share data, an alternative assumption regarding kVA rating s and product class market shares may not be feasible. Once the shipments backcast, forecast, ela sticity, and market shar es were fully

specified, DOE had c ompletely

specifie d the cha

racter istics of t ransfor mer shipme

nts. The next step wa s 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 e stimate of the age distribution of in-service transformers stocks for all y ears. The age distribution of in-service tr ansformer stocks was a key input to both the NES and NPV calculations, since the operating costs for any year de pend on the age distribution. The dependence of ope rating cost on the transformer ag e distribution occurs because , under a

trial sta ndard sc enario t

hat produ ces incr easing efficie ncy over time , older, l essefficient transformers may have higher operating c osts, while younge r, more-efficient transformers wi ll have lower ope rating costs.9-13

The Department calculated total in-service stoc k of distribution transformers by integrating historical shipments, starting from 1950. As transf ormers are added to the in-service stock, some of the older ones retire and exit the stock. To estimate the shipment forecasts, the Department developed a series of equations that define the dyna

mics and accounting of in-service tra nsformer stocks. For new units, the equation is: Eq. 9.10

where: Stock (y, age) = the po pulat ion o f in-s ervice trans forme rs of a p artic ular age (MVA), y = the year for which the in-service stock is being estimated, a nd Ship (y) = the number of transformers purchased in a pa rticular ye ar (MVA). This equation says that the number of one-yea r-old units is simply equal to the number of new transformer units purchased the previous y ear. 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 equa tion say s that, as time goe s on, only

a fraction of the

in-service stock exists in the next y ear. As the y ear is inc remente d and go es from yto y+1 , the age is also incremented from age to age+1 . Also, as time moves forward, a fraction of the in-ser vice stock is removed, and that fraction is determined by a retirement probability

function, Prob Retire (age) , which is described in the next section. 9.3.7 Retire ment F unction The accounting of in-service tra nsformer stock requires specification of a r etirement probabil ity f

unction. T

he Depa rtment de rived the retirement proba

bility function from a modified version of a transformer reliability function. The reliability func tion for determining the lifetime of the transformer is adapted from an e arlier study for the Department, ORNL 6804/R1, p. D-1. 8
Eq. 9.12a

Constant failure cou ld be du e to lightning or other rand om eve nts.

b The We ibull dist

ribut ion is one of the most wi dely us ed lif etime distr ibutions i n reliabil ity engi neering. It is used to mod el mate rial streng th, or time s-to-failure o f electro nic and mec hanica l com pon ents, eq uipm ent, or sys tems.
9-14 where: r(age) = the relia bility of a tra nsforme r of a ce rtain ag e, wher

e reliab ility

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 perce nt per yea r a , and corrfail = a corrosive failure rate of 0.5 per cent per year at age 15 and above

. The Department adjusted the parameters of the Weib ull b dist ribut ion t o main tain a n avera ge lifetime of 32 yea

rs. It adapted the fa

ilure rates and the lifetime from ORNL 6804/R1, p. D-1. The Depa rtment converted

the relia bility function into an an

nual ret irement probabilit

y function by dividing the incr emental reliability at a g

iven 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 distributi on transf

ormers.

Eq. 9.13 where: Prob Retire (age) = the probability that a transf ormer of a particular age

will be retired.9-15

Figure 9.3.2 Fract

ion of Original

Shipment

Retiring

Once the retirement probabilityfunction is specified, the remaining input to the stockacc ounti ng e quat ion is the in itial in-se rvic e stoc k of tr ansf orme rs, a s des crib ed in

the n ext section. 9.3.8 Refurbishm ents and Re winds Transformers that are not retired ca n be refurbished and returned to the stock. Minor refurbishments may inc lude replacement of connectors, bushings, or the oil. Major re fu rb is hm en ts co ul d i nc lu de th e r ew in di ng of th e t ra ns fo rm er , a fu nc ti on of te n p er fo rm ed by a rewindin g firm. ORNL -6804/R1 r eported annual r efurbis hed cap acity , includin

g rewo und units, to be approximately one perc ent of the in-service transformer ca pacity. The De

partment carried out further research and had discussions with owners of transformers to ascertain the estimated annual r efurbishments. The finding s were in conclusi ve: Major

refurb ishment ap

pears to repres ent a sma

ll fract ion of the distributi on transf

ormer ma rket; however, that share

could potentially grow

with the imposition of an energy-efficiency standard. Transformer users expr essed hesit ancy regardi ng wide spread adopt ion o f rewou nd tra nsfor mers. Not fi ndin g a consensus regarding tra

nsformer refurbishment, the Department did not include major refurb ishments in

the curr ent anal ysi s. Minor r

efurbishment is wi desprea d and alr eady is captured in the retirement function.a

Note t hat tra nsf

ormer s tocks in 1950 w ere qu it e smal l compar ed to t rans forme r st ocks i n 2001.
9-16 9.3.9 Initial Stoc k The Department initialized the stock-accounting model in the y ear 1950, which was the first year electricity c onsumption data were available. 3 For simplicity, the Department set the inservice transformer stock in the first y ear at zero. a This numbe

r does not affect the ana

lys is because most of the transformer stock from the y ear 1950 will no longer be in service af ter 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 ‘affec ted stock,’ the effective date of the trial standa rd must be defined. The Department assumed for this analy sis that any new ene

rgy-efficiency standard for distribution transformers will become effec tive 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 yea

r 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-ser vice transformer stock that is potentially impacted by a trial standard level. It ther efore consists of those in-service transformers that are purchased in or a fter the ye ar the trial standard level has taken effe ct, as described by

the following equation:
Eq. 9.14

where: Aff_Stock(y) = stock of transformers of all vintages that are operational in yea

r 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, a nd affected stocks of transformers, it was able to proceed to the NES and NPV models and c alculations, as described in Chapter 10.9-17

9.4 RESULTS The main o

utput of th e shipmen

ts model is t

he total c apacity

of distrib ution tran sformers shipped annually from 2010 throug h 2038. Total shipments depend on transformer lifetime and growth in new electricity

demand. The Department does not expect any direct effect on total shipments in the long term from energy -efficiency

standards. Total shipments for all trial standar d levels f or liquid- immersed

and dry -ty pe distrib ution tran sformers are shown in Tabl e 9.4.1. Table 9.4.1 Cumulative Shipments of Transformers by Trial Standard Level (TSL),

2010–2038
Distr ibut ion Transformers Tra ns fo rme r Ca pac it y Sh ip men ts in Tho us and MVA
Ba se Ca se TSL 1

(TP 1) TSL 2 TSL 3 TSL 4 TSL 5 TSL 6
Liquid- Immersed 350 1.3 349 8.7 349 4.7 348 7.2 348 4.4 345 2.1 342 0.7 Dry- Type 246 .9 246 .8 246 .8 246 .7 246 .3 244 .9 244 .7 An importa

nt factor that inf

luences the size of

the poten tial stand ards-induced change

in shipments is the actual equipment price increase due to standards. If price increases are lar ge, the shipments volume will decrease almost proportionally to the price increase, but because the pr ice elasticity of transfor

mers is less than one, price increases will result in increase d 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 D OE website: http://www.eere.energy .gov/buildings/appliance_standards/commercial/distribution_transformers .html. Instructions for using the spreadshe et are in Appendix 10A of this TSD.9-18 REFEREN CES 1. HVOLT Consultants I nc., P. Hopkinson, and J. Puri. Distribution Transformer Market Shipment Estimates for 2001. Februa ry 17, 2003. 3 704 High

Ridge Roa

d, Charlo tte, NC, 28270, tel: (704) 846-3290, e-mail: P.Hopkinson@hotmail.com. Prepared for Naviga nt 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 accesse d May 11, 2005.) This materia l is available in Docket #86. Contact Ms. Brenda Edwards-Jones, U.S. Department of Energ y, Building Technologies Program, Mailstop EE-2J, 1000 I ndependence Avenue, SW, Washington, DC, 20585-0121, telephone (202) 586-2945 for more information. <F or download page, see http:/ /www.be a.gov/

bea/dn2/ iedguid e.htm#g po . For 1 977-1997data, g o to http://bea .gov/be a/pn/GDPbyI nd_SHI P_SIC. xls . For 199 8-2003 da ta, go to http://bea.gov/bea/pn/GDPbyI nd_SHIP_NAI CS.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. (La st accessed May

9, 2005.) This material is available in Docket #86. Contact Ms. Brenda Edwards-Jones, U.S. Department of Energy , Building Technologies Progra m, Mailstop EE-2J, 1000 Independence Ave nue, SW, Washington, DC, 20585-0121, telephone (202) 586-2945 for more

informat ion. <http ://www.e ia.doe.gov/emeu/ aer/txt/stb 0809.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/EI A-0383( 2005). <

http://www .eia.doe .gov/oia f/aeo/in dex.html> 5. U.S. Department of Energy - Federal Energy

Regulatory

Commission. Form No. 1 - Electric Utility Annual Report. 2002. (La st acce ssed Fe bruary 11, 2005.)

This mate

rial is available in Docket #86. Contact Ms. Brenda Edwar ds-Jones, U.S. Department of Energy , Building Technologies Progra m, Mailstop EE-2J, 1000 Independence Avenue, SW, Washington, DC, 20585-0121, telephone (202) 586-2945 for more information. <http://www.ferc.gov/docs-filing/efor ms/form-1/form1_blank.pdf> 6. Air-Conditioning and Refrigeration I nstitute. 1999 Statis tical Profile of the A irConditioning, Refrigeration, and Heating Industry. 1999. Arlington, VA. 7. U.S. Department of Labor- Bureau of L abor Statistics. Producer Price Index. Industry: Refrigeration and Heating Equipment - Product: Unitary Air Conditioners - Series ID: PCU3585#2 (N). 2005. (La st acce ssed Apr il 11, 2005. ) This mate

rial is available i

n Docket #86. Contact Ms. Brenda Edwards-Jones, U.S. Department of E nergy , Building9-19

Technologies Program, Mailstop EE-2J, 1000 I ndependence Avenue, SW, Washington, DC, 20585-0121, telephone (202) 586-2945 for more information. <http://bl s.gov/p

pi/home.h

tm> 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. 199 5. O ak R id ge N ati ona l Lab ora to ry. O ak R id ge, TN. Rep ort No. ORN L6804/R1. < http://www.ornl.gov/~webworks/cpr/v823/rpt/78562.pdf>