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 Dis tribution 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-Ty pe, Medium-Voltage Distribution Transformer Shipment Estimates, Cumulative Shipments of Transformers by Trial Standard Level (TSL), 2010–2038 Table 9.4.1

Figure 9.2.1
 Shipments Model Flowchart

...................................... 9-2 2004 Figure 9.3.1 Age Distribution of Liquid-Immersed In-Service Transformer Stock in Figure 9.3.2 Fraction of Original Shipment Retiring

............................. 9-15 LIST OF TABLES

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

LIST OF FIGURES

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

9-i 9-17CHAPTER 9. SHIPMENTS ANALYSIS 9.1 INTRODUCTION The shipments model provides an estimate of the rate at which an in-service stock of ex isting 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 est imate 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).

9-1Figure 9.2.1 Shipments Model Flowchart As illustrated in the flow diagram, DOE organized shipments into two categories: repl acements and new capacity. Replacements occur when transformers break down, corrode, are

9-2struck 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 sh ipments 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 sh ipments 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 i n-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 i ndex of transformers manufactured. 2. Shipments backcast – Provides an estimate of total transformer capacity shipped bef ore 2001. 3. Shipments forecast – Provides an estimate of total transformers shipped after 2001. 4. Long-term purchase elasticity – Estimates the price sensitivity of transformers sh ipped over the long term.

9-35. 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 sto ck based on the previous year’s in-service stock and shipments. 7. Retirement function – Provides an estimate of the probability that a transformer wil l 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 i n-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) shippe d, and the approximate value of these shipments. The distribution transformer industry's total sales were about $1.6 billion in 2001.

9-4Table 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.

9-5Table 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

9-6Table 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 >96 kV BIL 20-45kV BIL 46-95kV BIL >96 kV 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 dat a 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

9-7market share of distribution transformers relative to all NAICS code 335311 transformers isrelatively 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 limitedhistorical 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 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 ywhere 1950 # y < 1977 (MVA), and AllElec(y) = national electricity consumption in year yaccording to EIA's 4 Annual Energy Outlook 2005 (AEO2005) (W-hr)

.

9-89.3.3 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:

9-9Eq. 9.3
where: TotShip(y) = the total capacity of shipments estimated for year ywhere 2003 < y # 2038 (MVA), and AllElec(y) = national electricity consumption forecasted for year yby 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 pot ential 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 sm all 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) = Q 0× (P/P 0 ) 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.

9-10For 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 an d Dry-Type Transformers To model utility and customer decision-making, DOE constructed a model employing a sta ndard 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. 1 5to 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 uni ts 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

9-11CL 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), Dry Ship(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-h r), 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 f ollow 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 re lative 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 AccountingThe stock accounting took transformer shipments, a retirement function, and initial inservi ce 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

9-12because, 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 shipment s, 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 dyn amics 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 num ber 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 yto y+1 , the age is also incremented from ageto 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, Prob Retire (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

9-13Eq. 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 year a, and corrfail = a corrosive failure rate of 0.5 percent per year at age 15 and above. DOE adjusted the parameters of the Weibul l bdistribution 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 div iding 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: Prob Retire (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.

9-14Figure 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 stockacc ounting 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

9-15research 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 whi ch 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 aff ected 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.

9-16Ship (y) = shipments in year y (MVA), and age	 = the a ge 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 shippe d 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 DLiquidImmersed 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.

9-17REFERENCES 1. HVOLT Consultants Inc., P. Hopkinson, and J. Puri. Distribution Transformer Market Shipment Estimates for 2001. February 17, 2003. 370 4 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. Industr y 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. Ja nuary, 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. A rlington, VA. 7. U.S. Department of Labor-Bureau of Labor Statistics. Producer P rice 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,

9-18DC, 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>

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