CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSES

TABLE OF CONTENTS 8.1 INTRODUCTION

................................................... 8-1 8.1.1 General Approach for Life-Cycle Cost and Payback Period Analyses

....... 8-1 8.1.2 The Baseline Scenario

.......................................... 8-2 8.1.3 Total Owning Cost

............................................ 8-3 8.2 LIFE-CYCLE COST METHOD

........................................ 8-3 8.2.1 Definition

................................................... 8-3 8.2.2 Life-Cycle Cost Spreadsheet Key Steps

............................. 8-4 8.2.2.1 Step 1: Select A & B

................................... 8-4 8.2.2.2 Step 2: Select Designs That Meet a Chosen Candidate Standard Level

..................................................... 8-6 8.2.2.3 Step 3: Load and Price Profile

............................ 8-7 8.2.2.4 Step 4: Calculate Cost of Losses

........................... 8-7 8.2.2.5 Step 5: Project Losses and Costs into the Future

............... 8-7 8.2.2.6 Step 6: Select Discount Rate

............................. 8-8 8.2.2.7 Step 7: Calculate Present Value of Future Cost of Losses

........ 8-8 8.2.2.8 Step 8: Results Reported

................................ 8-8 8.2.2.9 Step 9: Results of Sample of Transformers

................... 8-8 8.2.2.10 Step 10: Results for “Average” Scenario

..................... 8-8 8.2.2.11 Repeat Process

........................................ 8-9 8.3 LIFE-CYCLE COST INPUTS

.......................................... 8-9 8.3.1 A and B Transformer Selection Parameters and Usage Rates

............. 8-9 8.3.2 Database of Transformer Designs

................................ 8-15 8.3.3 Markup and Installation Costs

................................... 8-15 8.3.4 Transformer Loading

.......................................... 8-15 8.3.5 Electricity Price Analysis

....................................... 8-16 8.3.5.1 Hourly Marginal Electricity Price Model for Liquid-Immersed Transformers

......................................... 8-16 8.3.5.2 Monthly Tariff-Based Analysis for Dry-Type Transformers

...... 8-20 8.3.5.3 Data Collection and Modeling

............................ 8-24 8.3.6 Load Growth Trends

.......................................... 8-30 8.3.7 Electricity Cost and Price Trends

................................. 8-30 8.3.8 Discount Rate

............................................... 8-31 8.3.9 Effective Date of Standard

...................................... 8-36 8.3.10 Transformer Service Life

....................................... 8-36 8.3.11 Maintenance Costs

........................................... 8-37 8.3.12 Power Factor

................................................ 8-37 8.3.13 Default Scenario

............................................. 8-37

8-i8.4 LIFE-CYCLE COST RESULTS

....................................... 8-38 8.4.1 Design Line 1 Results

......................................... 8-40 8.4.2 Design Line 2 Results

......................................... 8-41 8.4.3 Design Line 3 Results

......................................... 8-41 8.4.4 Design Line 4 Results

......................................... 8-42 8.4.5 Design Line 5 Results

......................................... 8-42 8.4.6 Design Line 9 Results

......................................... 8-43 8.4.7 Design Line 10 Results

........................................ 8-43 8.4.8 Design Line 11 Results

........................................ 8-44 8.4.9 Design Line 12 Results

........................................ 8-44 8.4.10 Design Line 13 Results

........................................ 8-45 8.5 LIFE-CYCLE COST SENSITIVITY ANALYSIS

.......................... 8-45 8.5.1 Design Line 1 Summary Sensitivity Results

......................... 8-46 8.5.2 Design Line 12 Summary Sensitivity Results

........................ 8-48 8.6 PAYBACK PERIOD

................................................ 8-50 8.6.1 Payback Period

.............................................. 8-50 8.6.2 Inputs

..................................................... 8-51 8.6.3 Baseline Scenario Complications

................................. 8-51 8.6.4 Payback Period Results

........................................ 8-52 8.6.5 Design Line 1 Results

......................................... 8-53 8.6.6 Design Line 2 Results

......................................... 8-53 8.6.7 Design Line 3 Results

......................................... 8-54 8.6.8 Design Line 4 Results

......................................... 8-54 8.6.9 Design Line 5 Results

......................................... 8-55 8.6.10 Design Line 9 Results

......................................... 8-55 8.6.11 Design Line 10 Results

........................................ 8-55 8.6.12 Design Line 11 Results

........................................ 8-56 8.6.13 Design Line 12 Results

........................................ 8-56 8.6.14 Design Line 13 Results

........................................ 8-57 8.7 REBUTTABLE PRESUMPTION

...................................... 8-57 8.8 USER INSTRUCTIONS FOR SPREADSHEETS

.......................... 8-65 8.8.1 Startup

.................................................... 8-65 8.8.1.1 Sheet Overview

....................................... 8-65 8.8.2 Basic Instructions

............................................ 8-67

LIST OF TABLES Table 8.3.1 Distributions of A Values for Liquid-Immersed Transformers: Three Scenarios with Usage Percentages

........................................ 8-11 Table 8.3.2 Dis tributions of A Values for Small-Capacity, Medium-Voltage, Dry-Type Transformers (DL 9 and DL 11): Three Scenarios with Usage Percentages

.......................................................... 8-12

8-iiTable 8.3.3

Table 8.3.4 Table 8.3.5 Table 8.3.6 Table 8.3.7 Table 8.3.8 Table 8.3.9 Table 8.3.10 Table 8.4.1 Table 8.4.2 Table 8.4.3 Table 8.4.4 Table 8.4.5 Table 8.4.6 Table 8.4.7 Table 8.4.8 Table 8.4.9 Table 8.4.10 Table 8.4.11 Table 8.4.12 Table 8.5.1 Table 8.5.2 Table 8.6.1 Table 8.6.2 Table 8.6.3 Distributions of A Values for Large-Capacity, Medium-Voltage, Dry-Type Transf ormers (DL 10, DL 12, and DL 13): Three Scenarios with Usage Percentages

................................................. 8-13

Summary Life-Cycle Cost Results for Design Line 1 Representative Unit Summary Life-Cycle Cost Results for Design Line 2 Representative Unit Summary Life-Cycle Cost Results for Design Line 3 Representative Unit Summary Life-Cycle Cost Results for Design Line 4 Representative Unit Summary Life-Cycle Cost Results for Design Line 5 Representative Unit Summary Life-Cycle Cost Results for Design Line 9 Representative Unit Summary Life-Cycle Cost Results for Design Line 10 Representative Unit Summary Life-Cycle Cost Results for Design Line 11 Representative Unit Summary Life-Cycle Cost Results for Design Line 12 Representative Unit Summary Life-Cycle Cost Results for Design Line 13 Representative Unit

Summary of Payback Period Results for Design Line 1 Representative Unit Summary of Payback Period Results for Design Line 2 Representative Unit Generation Capacity Costs

...................................... 8-18 Representative Cost Recovery Factors

............................. 8-19 Percentage of All Commercial and Industrial Customers, by Subdivision, in the Utility Sample

................................................ 8-22 Variables Used to Estimate Company Discount Rates

.................. 8-33 Typical Owners of Different Types of Transformers

................... 8-34 Real Discount Rates by Transformer Ownership Category

.............. 8-35 Transformer Ownership by Design Line

............................ 8-36 Trial Standard Levels Evaluated For Each Design Line (TSL 1 through TSL 6)

..................................................... 8-38 Trial Standard Levels Evaluated For Each Design Line (TSL A through TSL D)

..................................................... 8-39

.......................................................... 8-41

.......................................................... 8-41

.......................................................... 8-42

.......................................................... 8-42

.......................................................... 8-43

.......................................................... 8-43

.......................................................... 8-44

.......................................................... 8-44

.......................................................... 8-45

.......................................................... 8-45 Savings ($), Summary Table for Design Line 1 Representative Unit

....... 8-47 LCC Savings ($), Summary Table for Design Line 12

.................. 8-49 Possible )FC and )OC Combinations, for Payback Period Analysis

....... 8-52

.......................................................... 8-53

.......................................................... 8-53

8-iiiTable 8.6.4 Table 8.6.5 Table 8.6.6 Table 8.6.7 Table 8.6.8 Table 8.6.9 Table 8.6.10 Table 8.6.11 Table 8.7.1 Table 8.7.2 Table 8.7.3 Table 8.7.4 Table 8.7.5 Table 8.7.6 Table 8.7.7 Table 8.7.8 Table 8.7.9 Table 8.7.10

Figure 8.2.1 Fi

gure 8.3.1 Figure 8.3.2 Figure 8.3.3 Figure 8.3.4 Figure 8.4.1 Summary of Payback Period Results for Design Line 3 Representative Unit

.......................................................... 8-54 Summary of Payback Period Results for Design Line 4 Representative Unit

.......................................................... 8-54 Summary of Payback Period Results for Design Line 5 Representative Unit

.......................................................... 8-55 Summary of Payback Period Results for Design Line 9 Representative Unit

.......................................................... 8-55 Summary of Payback Period Results for Design Line 10 Representative Unit

.......................................................... 8-56 Summary of Payback Period Results for Design Line 11 Representative Unit

.......................................................... 8-56 Summary of Payback Period Results for Design Line 12 Representative Unit

.......................................................... 8-57 Summary of Payback Period Results for Design Line 13 Representative Unit

.......................................................... 8-57 Average Transformer Efficiency (TSL 1 through TSL 6)

............... 8-60 Average Transformer Efficiency (TSL D through TSL A)

.............. 8-60 Average Transformer Installed Cost (TSL 1 through TSL 6)

............ 8-61 Average Transformer Installed Cost (TSL D through TSL A)

........... 8-61 Average Marginal Electricity Cost (TSL 1 through TSL 6)

............. 8-62 Average Marginal Electricity Cost (TSL D through TSL A)

............ 8-62 First Year Operating Cost for Rebuttable Presumption, Based on DOE Test Procedure (T SL 1 through TSL 6)

................................ 8-63 First Year Operating Cost for Rebuttable Presumption, Based on DOE Test Procedure (TSL D through TSL A)

............................... 8-63 Rebuttable Payback for Rebuttable Presumption Period (TSL 1 through TSL 6)

..................................................... 8-64 Rebuttable Payback for Rebuttable Presumption Period (TSL D through TSL A)

..................................................... 8-64 LIST OF FIGURES Transformer Life-Cycle Cost Spreadsheet Model Flowchart

.............. 8-5 Map Showing the Division of the Continental United States into 17 Subdivisions

................................................ 8-21 Customer-Weighted Average of Utility Revenue Divided by Sales by Subdivision ($/kWh)

.................................................... 8-23 Effect of a Change in Customer Demand (Energy Consumption is Held Fixed) for a Block-By-Demand Tariff

....................................... 8-26 AEO 2007 Electricity Price Forecasts

.............................. 8-31 Design Load Losses (LL) versus No-Load Losses (NL) for TP-1-2002, Design Li

ne 1

...................................................... 8-40

8-ivFigure 8.5.1 Design Line 1 Sensitivity Results by Efficiency Level

.................. 8-48 Figure 8.5.2 Design Line 12 Sensitivity Results by Efficiency Level

................. 8-49

8-vCHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSES 8.1 INTRODUCTION This chapter of the technical support document (TSD) presents DOE’s life-cycle cost (LCC) and payback period (PBP) analyses. It describes the method DOE used for analyzing the economic impacts of possible standards on consumers. The effect of standards on consumers includes a change in operating expense (usually decreased) and a change in purchase price (usually increased). The LCC and PBP analyses produce two basic outputs to describe the effect of standards on consumers:

C LCC is the total (discounted) cost that a consumer pays over the lifetime of the equipment, including purchase price, installation cost, and operating expenses.

C PBP measures the amount of time it takes consumers to recover the estimated higher purchase expense of more energy efficient equipment through lower operating costs. This chapter presents inputs and results for the LCC and PBP analyses, as well as key variables, current assumptions, and computational equations. DOE performed the calculations discussed here using a series of Microsoft Excel spreadsheets, which are accessible on DOE's website (http://www.eere.energy.gov/buildings/appliance_standards/commercial/ distribution_transformers.html). Section 8.8 of this document contains details and instructions for usin g the spreadsheets. There are five appendices to this chapter: 1) Appendix 8A presents a complete set of results; 2) Appendix 8B discusses uncertainty and variability; 3) the utility sample is fully detailed in Appendix 8C; 4) Appendix 8D contains a complete set of sensitivity results for design lines 1and 12; and 5) Appendix 8E presents average baseline transformer design properties.
8.1.1 General Approach for Life-Cycle Cost and Payback Period Analyses Recognizing that each transformer customer is unique, DOE calculated the LCC and PBP for a representative sample (i.e., a distribution) of individual transformers. In this manner, DOE’s analysis explicitly recognized that there is both variability and uncertainty in its inputs. DOE used Monte Carlo simulations to model the distributions of inputs. The Monte Carlo process statistically captures input variability and distribution without testi ng all possible input combinations. The results are expressed as the number of transformers experiencing economic impacts of varying magnitudes. DOE developed the LCC model using Excel spreadsheets combined with Crystal Ball, a commercially available add-in program. A detailed explanation of the Monte Carlo simulation process and the use of probability distributions in this analysis is contained in Appendix 8B. The LCC results are displayed as distributions of impacts compared to the baseline condi tions. DOE presents tabular results at the end of this chapter; they are based on 10,000

8-1samples per Monte Carlo simulation run. In addition, Appendix 8A contains graphs that illustrate the following analysis results for each standard level of each design line:

C a cumulative probability distribution showing the percentage of transformers that would have net savings due to standards, and

C a frequency chart depicting variation in LCC for each standard level considered in the analysis. DOE developed two approaches for the LCC calculations: one for liquid-immersed transformers and one for medi um-voltage, dry-type transformers. aBecause the large majority of liquid-immersed transformer owners are utilities, the liquid-immersed LCC calculations used utility marginal costs and distribution markups that do not include a wholesaler and contractor markup. In contrast, because the majority of dry-type transformer owners are commercial and industrial enterprises, DOE used monthly marginal electricity costs and complete distribution markups for the dry-type transformer LCC calculation. For simplicity, DOE used only one type of LCC calculation for each design line of transformer, based on the majority owner type of that category.
8.1.2 The Baseline Scenario In developing appliance standards, DOE has traditionally used an existing standard level as a baseline from which it calculates the impact of any candidate standard level. Because distribution transformers are not currently subject to a national energy efficiency standard, DOE developed an alternative approach to determine an appropriate baseline against which to compare various candidate standard levels. That alternative approach focused on the mix of selection criteria customers are known to use when purchasing transformers. Those criteria include first costs, as well as what is known in the transformer industry as total owning cost (TOC), used by some customers as an alternative criterion to first costs. The TOC method combines first costs with the cost of losses. Purchasers of distribution transformers, especially in the utility sector, have long used the TOC method to determine which transformers to purchase. 1, 2 To establish the LCC baseline scenario, DOE used distributions of efficiencies and an estimated percent of transformers currently being purchased using the TOC method. That scenario represents the range of transformer costs and efficiencies that transformer purchasers would likely experience without national energy efficiency standards in place.

a Because the Energy Policy Act of 2005 established energy conservation standards for low-voltage, dry-type distribution transformers, DOE removed this type of transformer (design lines 6, 7, and 8) from the analysis, during preparation of the notice of proposed rulemaking (NOPR).
8-28.1.3 Total Owning Cost The utility industry developed TOC evaluation as an easy-to-use tool to reflect the unique fi

nancial environment faced by each transformer purchaser. To express variation in such factors as the cost of electric energy, and capacity and financing costs, the utility industry developed a range of evaluation factors, called A and B values, to use in their calculations. A and B are the equivalent first costs of the no-load and load losses (in $/watt), respectively. No-load losses refer to the core losses that are roughly constant once the transformer is energized; load losses are the coil losses that vary roughly with the square of the load on the transformer. The TOC transformer purchasers (i.e., those using the TOC method to determine which uni ts to buy) assign an economic value to transformer losses: A and B parameters. Then they add these costs to the first cost of acquiring the transformer, to derive TOC. The equation for calculating transformer TOC is: TOC = FC + (A ×NLL) + ( B ×LL) Eq. 8.1 where: FC = first cost of acquiring the transformer, including purchase price and installation cost (2006$), A = the no-load loss valuation parameter in dollars per watt ($/W), NLL = the no-load loss at nameplate load (W), B = the load loss valuation parameter ($/W), and LL = the load loss at nameplate load (W). Throughout the LCC analysis, DOE expresses monetary values in units of year 2006 real dollars (2006$).

8.2 LIFE-CYCLE COST METHOD 8.2.1 Definition The basis for both the LCC analysis and the LCC spreadsheet model is the LCC equation. TheLCC equation reflects both the first costs of a transformer and the present value of the operating costs over the service life of the transformer. The LCC equation is:
Eq. 8.2 where:

8-3FC = the first cost (2006$), n = the i ndex for the year of operation (yr), Lifetime = the service life of the transformer, OC n = the operating cost in year n, including the value of the losses and maintenance costs (2006$/yr), and Drate = the d iscount rate applied to the calculation (%).
8.2.2 Life-Cycle Cost Spreadsheet Key Steps While the LCC is a simple equation, DOE's LCC spreadsheet model takes into account the dy namic nature of a variety of inputs over the service life of a transformer. A simplified flowchart (Figure 8.2.1) illustrates the key steps implemented in the LCC spreadsheet: the main inputs, the key computational steps, and the important outputs. The LCC spreadsheet is available as an Excel file on the DOE website at http://www.eere.energy.gov/buildings/appliance_standards/commercial/distribution_transformers.ht ml. Sections 8.2.2.1 through 8.2.2.11 describe the computational flow of the LCC model as shown i n the flowchart. Following this explanation of the analytical steps in the model, this chapter presents the specific inputs that DOE developed and then used in the LCC model for this rulemaking. Next, the chapter presents the results of the LCC model runs for the various design lines, and the key sensitivities to those results. The LCC process is a means of determining the financial impact resulting from the imposition of energy efficiency standards for distribution transformers, from the perspective of the consumer, or owner, of the transformer. Several types of information are necessary for this calculation: the first costs of transformers with and without standards, the operating costs of the transformers with and without standards, the year the standard is to become effective, and the lifetime of transformers. For ease of comparison, DOE presents all costs in present values, which requires using discount rates. The section on LCC inputs (section 8.3) explains in more detail DOE’s methods for determining these inputs.
8.2.2.1 Step 1: Select A & B Step 1: Select customer choice A and B parameters from the choices on the “A & B Dist” worksheet. The purpose of this step is to establish the current purchasing decision environment. For liquid-immersed transformers, based on National Electrical Manufacturers Association (NEMA) comments,DOE assumed that 25 percent are purchased based on lowest first cost and 75 percent are purchased using TOC evaluation. 3 Commercial and industrial (C&I) entities can also apply an A- and B-based evaluation process for dry-type transformer purchase evaluation. While the analytic process for determining

8-4Figure 8.2.1 Transformer Life-Cycle Cost Spreadsheet Model Flowchart

8-5A and B values used by C&I purchasers is different from that used by electric utilities, the fundamental meanings of A and B are the same for both groups of transformer purchasers. The LCC spreadsheet simulates different transformer purchase evaluation scenarios with twodif ferent A and B distributions. All liquid-immersed transformer design lines use a common set of scenarios, and medium-voltage, dry-type design lines use a different but common set of scenarios. The specific inputs used in these two scenarios are shown in section 8.3 of this TSD chapter, as well as in the “A & B Dist” worksheet of the LCC spreadsheets. These scenarios can be selected using the “Transformer Customer A’s and B’s” pull-down menu on the “Summary” worksheet. Step 2 below explains the application of these A & B distributions when actual transformer designs are selected.
8.2.2.2 Step 2: Select Designs That Meet a Chosen Candidate Standard Level Step 2: Select designs and a candidate standard level. The purpose of this step is to select a candidate standard level and its associated transformer designs to evaluate with the LCC spreadsheet. DOE used NEMA’s TP 1-2002 1as the first possible candidate standard level to evaluate, and developed five additional candidate standard levels based on information obtained from the engineering analysis for the notice of proposed rulemaking (NOPR). In response to comments received on the NOPR, DOE added four additional standard levels. The engineering analysis yielded a cost-efficiency relationship in the form of manufacturer selling prices, no-load losses, and load losses for a wide range of realistic transformer designs. This set of data provided the LCC model with a distribution of transformer design choices. (See the “Design Table” worksheet, a condensed version of the engineering analysis output.) After the user chooses a candidate standard level, the spreadsheet selects from its database of designs the subset of designs that satisfy the selected candidate standard level and another set of designs that satisfy the baseline scenario. In addition to the economic value of losses, other factors may affect design selection. DOE accounted for such factors by adding a random cost factor to the transformer’s first costs. Using this factor, DOE captured the range of typical real-world cost variation in the first cost of the transformer. DOE modeled this random cost factor as a uniformly distributed random number that can either raise or lower the first cost of the transformer by up to 15 percent. The spreadsheet selects the transformer design that has the lowest TOC (including the random cost factor) for the customer. For each iteration cycle, a design is chosen based on A and B distributions from Step 1. Step 2a: Calculate markup and installation costs. For liquid-immersed transformers, which are typically purchased directly by utilities from manufacturer representatives, DOE considered the transformer purchase price to be the manufacturer selling price plus a distributor markup, the shipping cost, and sales tax. It added installation costs separately. For dry-type transformers, the distribution channel includes various intermediaries who add their own costs to the manufacturer selling price. These costs include a manufacturer markup, distribution markup, contractor markup, shipping cost, installation costs, and sales tax.

8-6Key inputs for this step include markup and installation costs. DOE presents its specific values for these inputs in Chapter 7.
8.2.2.3 Step 3: Load and Price Profile Step 3: The spreadsheet model dynamically selects a sample transformer load profile from distributions derived from available data. To estimate the impact of transformer losses on C&I companies’ electricity bills, DOE modeled the relationship between monthly transformer load characteristics and customer demand and usage. DOE derived distributions of load parameters from available hourly load data. For liquid-immersed transformers, DOE developed an hourly transformer load simulation model to capture the dynamics and economics of transformer loads. The load profiles and characteristics are provided in the “Load & Price Parameters” worksheet of the LCC spreads heet for liquid-immersed transformers and in the “Demand & Usage” worksheet of the LCC spreadsheet for dry-type transformers. For the cost of electricity, DOE used the marginal cost of electricity for liquid-immersed transformers. For dry-type transformers, it developed a method to calculate customer monthly bills. For both transformer types, DOE calculated the total cost of electricity both with and without transformer losses and took the difference to calculate the incremental electricity costs. Section 8.3.5provides a detailed discussion of the electricity price analysis.
8.2.2.4 Step 4: Calculate Cost of Losses Step 4: The spreadsh eet model calculates the cost of losses by estimating the incremental impacts of no-load and load losses from the loss coefficients of the design, the monthly customer load characteristics (demand and usage), and the cost of electricity. In this step, the spreadsheet combines the no-load losses, load losses, and electricity price information for each transformer in the baseline scenario and in the standards scenario. Subsequent steps project the costs of losses into the future and then convert them back to present values.
8.2.2.5 Step 5: Project Losses and Costs into the Future Step 5: The spreadsh eet model projects losses and costs into the future, based on load growth assumptions and a forecast of future changes in electricity price. Spreadsheet users can select different load growth and future electricity price scenarios. The model applies the selected options for load growth and price trends to the initial cost of losses that were calculated in Step 4. Step 5 results in a projection of those losses and costs into the future. DOE presents its specific load growth and electricity price trends in the LCC inputs section (section 8.3) of this chapter.

8-78.2.2.6 Step 6: Select Discount Rate Step 6: To discount the future stream of costs into a present value, the spreadsheet model selects a discount rate from the discount rate distribution. The LCC spreadsheet selects a discount rate from a weighted sample of discount rate inputs derived from financing costs of transformer purchasers. DOE presents its specific discount rates in the LCC inputs section (section 8.3) of this chapter.
8.2.2.7 Step 7: Calculate Present Value of Future Cost of Losses Step 7: Thespreadsheet model calculates the present value of future operating costs and losses and the present worth per watt of no-load and load losses. This step applies the discount rate selected in Step 6 to the future costs of losses from Step 5 to produce a single, present-valued number. In addition to the costs from Step 5 above, the calculation uses as inputs the effective date of the standard, the transformer lifetime, maintenance costs, and a power factor.
8.2.2.8 Step 8: Results Reported Step 8: The spreadsh eet model records the LCC, LCC savings, payback period, and other results for inclusion in the distribution of results.
8.2.2.9 Step 9: Results of Sample of Transformers Step 9: The spreadsh eet model repeats the calculation until reaching the specified maximumnumber of iterations. When applying a Monte Carlo simulation process, the model performs a userdefined number of iterations and reports the results as distributions. Based on DOE’s experience with expressing results of prior rulemakings as distributions, 10,000 iterations in a Monte Carlo simulation capture sufficient variability.
8.2.2.10 Step 10: Results for “Average” Scenario Step 10: The spreadsh eet model reports the results of the calculation for the “Average” scenario on the “Summary” worksheet of the LCC spreadsheet. The “Average” scenario allowsusers to produce provisional answers without performing a Monte Carlo simulation. For liquidimmersed transformers, DOE extracted the marginal demand cost and marginal energy cost used in the average scenario calculation from a representative LCC Monte Carlo simulation. Similarly, for dry-type transformers, DOE extracted an average marginal demand and energy rate from a sample dry-type Monte Carlo simulation.

8-88.2.2.11 Repeat Process The specific number of iterations required for the Monte Carlo simulation is implemented through the <Repeat Process> decision box. When the desired number of iterations has been reached, the model ends the simulation process and generates result reports.
8.3 LIFE-CYCLE COST INPUTS This section presents the specific LCC inputs used in the spreadsheet model and provides defi nitions and data sources for each component. This section also elaborates on how the LCC spreadsheets apply certain user-chosen inputs to the LCC model. The specific inputs to the model, in the order in which they appear on the left-hand side of the LCC flowchart (Figure 8.2.1), are:

C A an d B transformer selection parameters and usage rates

C Database of transformer designs

C Markup and installation costs

C Transformer loading

C Electricity costs and prices

C Load growth trends

C Electricity cost and price trends

C Discount rate

C Effective date of standard

C Transformer service life

C Maintenance costs

C Power factor 8.3.1 A and B Transformer Selection Parameters and Usage Rates The A and B transformer selection parameters that DOE used in the formal TOC calculation ca n also be used more generally to characterize the value that transformer purchasers place on reducing no-load and load losses in transformers. This is because the A and B parameters express a measure of the economic value of loss reduction in terms of dollars per watt of reduced losses. The ability to measure the economic value of loss reduction in units of dollars per watt is independent of the actual method for estimating that value. The main assumption implicit in using A and B values to represent customer transformer choice decisions is that the value of loss reduction is proportional to the amount by which losses are reduced. Given this wider applicability of the TOC formulation to the expression of loss valuations, DOE used A and B parameters to formulate a transformer customer choice model. To represent the potential range of purchaser valuations given to transformer no-load and load losses, DOE developed three A distributions to represent three customer choice scenarios for each LCC calculation. The key difference among the three scenarios is the fraction of purchasers who are estimated to place a value on reducing transformer losses. Those who place a value on

8-9reducing such losses are described as evaluators, while those who do not consider transformer losses during a purchase are termed non-evaluators. The scenario representing non-evaluation for all purchases has 0 percent evaluators, while the scenario representing evaluation for all purchases has 100 percent evaluators. For liquid-immersed transformers, many purchases are evaluated, so DOE’s default scenario is an evaluation rate of 75 percent, which is approximately consistent with data provided by NEMA3 regarding the fraction of transformers that are TP 1-compliant. As the voluntary industry standard for transformer efficiency, TP 1 has its compliance tracked by NEMA. NEMA expresses the data that are available regarding current market efficiencies in terms of TP1 compliance, rather than as an efficiency distribution. DOE extrapolated these TP 1 compliance data to an estimated distribution of market efficiencies. Table 8.3.1 provides the evaluation usage rates, for the three evaluation scenarios just described, for a range of different A values for liquid-immersed transformers. For medium-voltage, dry-type transformers, DOE assumed 50 percent evaluators for small-capacity transformers—design line (DL) 9 and DL 11 (Table 8.3.2)—and 80 percent for large-capacity transformers—DLs 10, 12, and 13 (Table 8.3.3). These default scenarios produced results roughly consistent with NEMA data regarding the fraction of transformers compliant with TP 1 in the current market. DOE used the 0 percent and 100 percent evaluation scenarios to test the LCC sensitivity to chan ges in the percentage of transformers purchased using evaluation. It estimated the mean value of A for evaluators from values provided by stakeholders and collected from public transformer purchase bids available on the Internet. 5, 6, 7, 8, 9, 10, 11 Then, recognizing that there is substantial variability in the value that transformer purchasers may place on reducing losses, DOE created adistribution that represented this variability, as illustrated in Tables 8.3.1 through 8.3.3. For liquidimmersed transformers, DOE estimated an average A value of $3.85/watt for evaluated purchases from the available data. DOE used a slightly smaller average A value of $2.50/watt for dry-type transformers, based on stakeholder comments.

8-10Table 8.3.1 Distributions of A Values for Liquid-Immersed Transformers: Three Scenarios with Usage Percentages 0% Non-Evaluating A $ Probability % 0.00 100.00 75% Medium-Evaluating A $ Probability % 0.00 25.00 0.39 0.64 0.77 1.28 1.16 2.24 1.54 3.20 1.93 4.27 2.31 5.34 2.70 6.01 3.08 6.68 3.47 6.68 3.85 6.68 4.24 6.12 4.62 5.56 5.01 4.77 5.39 3.97 5.78 3.23 6.16 2.48 6.55 1.93 6.93 1.38 7.32 1.03 7.70 0.69 8.09 0.50 8.47 0.31 100% High-Evaluating A $ Probability % 0.00 0.00 0.39 0.85 0.77 1.71 1.16 2.99 1.54 4.27 1.93 5.70 2.31 7.12 2.70 8.01 3.08 8.90 3.47 8.90 3.85 8.90 4.24 8.16 4.62 7.42 5.01 6.36 5.39 5.30 5.78 4.30 6.16 3.31 6.55 2.58 6.93 1.84 7.32 1.38 7.70 0.92 8.09 0.67 8.47 0.42

8-11Table 8.3.2 Distributions of A Values for Small-Capacity, Medium-Voltage, Dry-Type Transformers (DL 9 and DL 11): Three Scenarios with Usage Percentages 0% Non-Evaluating A $ Probability % 0.00 100.00 50% Medium-Evaluating A $ Probability % 0.00 50.00 0.25 0.93 0.50 1.86 0.75 2.79 1.00 3.72 1.25 4.34 1.50 4.95 1.75 4.95 2.00 4.95 2.25 4.46 2.50 3.96 2.75 3.30 3.00 2.64 3.25 2.08 3.50 1.51 3.75 1.13 4.00 0.76 4.25 0.55 4.50 0.34 4.75 0.23 5.00 0.13 5.25 0.09 5.50 0.05 100% High-Evaluating A $ Probability % 0.00 0.00 0.25 1.86 0.50 3.72 0.75 5.57 1.00 7.43 1.25 8.67 1.50 9.91 1.75 9.91 2.00 9.91 2.25 8.92 2.50 7.93 2.75 6.61 3.00 5.29 3.25 4.15 3.50 3.02 3.75 2.27 4.00 1.51 4.25 1.09 4.50 0.67 4.75 0.47 5.00 0.27 5.25 0.18 5.50 0.10

8-12Table 8.3.3 Distributions of A Values for Large-Capacity, Medium-Voltage, Dry-Type Transformers (DL 10, DL 12, and DL 13): Three Scenarios with Usage Percentages 0% Non-Evaluating A $ Probability % 0.00 100.00 80% Medium-Evaluating A $ Probability % 0.00 20.00 0.40 1.49 0.80 2.97 1.20 4.46 1.60 5.95 2.00 6.94 2.40 7.93 2.80 7.93 3.20 7.93 3.60 7.13 4.00 6.34 4.40 5.29 4.80 4.23 5.20 3.32 5.60 2.42 6.00 1.81 6.40 1.21 6.80 0.87 7.20 054 7.60 0.38 8.00 0.21 8.40 0.15 8.80 0.08 100% High-Evaluating A $ Probability % 0.00 0.00 0.40 1.86 0.80 3.72 1.20 5.57 1.60 7.43 2.00 8.67 2.40 9.91 2.80 9.91 3.20 9.91 3.60 8.92 4.00 7.93 4.40 6.61 4.80 5.29 5.20 4.15 5.60 3.02 6.00 2.27 6.40 1.51 6.80 1.09 7.20 0.67 7.60 0.47 8.00 0.27 8.40 0.18 8.80 0.10
For each value of A that a transformer purchaser may have, there is a range of possible B values that are consistent with the particular A value. (B values relate to the value associated with the load losses.) In general, the ratio of B to A is a measure of the relative importance of load losses and no-load losses. For a transformer that is constantly loaded at 100 percent of rated capacity, the value of B and A should be the same, since both load and no-load losses will always be at their rated values. Load losses increase with the square of the loading, and transformer mean loadings are almost always below 100 percent. Therefore, in practice, B is always less than A, and is approximately equal to A times the square of the expected loading (when peak loads are not considered).

8-13DOE characterized the relationship between customer selection of B and A values by selecting five possible values for the B:A ratio. For liquid-immersed transformers, DOE selected a B:A ratio of 0.30 (approximately the square of 50 percent) as a reasonable median value for the sm aller capacity design lines and 0.50 for the larger capacity design lines, based on the evaluation parameter data for utilities available to DOE. For dry-type transformers, DOE estimated the median ratio of B:A as the square of the transformer efficiency standard evaluation loading. To model variability in the ratio of B:A, DOE selected four other values for the ratio between 0 and 1: 0.6× MedianBA , 0.8 × MedianBA , 0.15 + 0.85 × MedianBA , and 0.35 + 0.65 × MedianBA , where MedianBAis the median value of the B:A ratio. These four values of the ratio provide a distribution between 0 and 1 that has the estimated median value for the B:A ratio and that matches the properties of the evaluation parameter data available to DOE. As described above, DOE selected slightly different median B:A ratios for liquid-immersed and dry-type transformers. The higher B:A ratio for liquid-immersed transformers reflects the morecareful evaluation of peak load impacts of load losses on the part of utilities. The B:A ratio for drytype customers reflects an assumption that C&I customers will tend to value their load losses with an average electricity price. DOE calculated present worth factors for no-load and load losses so that stakeholders could compare DOE’s LCC results with the A and B values that some purchasers use in their TOC evaluation. DOE defines the present worth factor for transformer losses as the present value of the cost of losses per unit rated loss. Rated losses are measured in watts at the rated load. There are distinct present worth factors for no-load and load losses. The present worth factors are different from the A and B values that a purchaser may use for TOC evaluation because the regulatory context is different. Typically, the A and B values used for TOC evaluation are calculated based on the economic assumptions inherent in electric utility rate regulation, and are specified by the Institute of Electrical and Electronics Engineers' Draft Guide for Distribution Transformer Loss Evaluation. 2In contrast, the methods for calculating LCC values for energy efficiency regulations are specified by the Energy Policy and Conservation Act (EPCA). The economic assumption of the LCC calculation differs slightly from rate regulation in the sense that it is not based on the principal of cost recovery for all associated expenses, but instead it is based on a cost accounting for the use of a specific appliance or piece of equipment. To summarize, DOE characterized transformer purchases with respect to efficiency in terms of two economic v aluation parameters. The parameter A expresses the value that a customer gives to reducing no-load losses in dollars per watt, while the parameter B expresses the value given to reducing load losses at rated load. DOE described purchase behavior in terms of evaluators who place a value on reducing losses, and non-evaluators who place no value on reducing losses. DOE investigated three scenarios as sensitivities. For liquid-immersed transformers, the scenarios are 0 percent evaluators, 75 percent evaluators, and 100 percent evaluators. For small-capacity, medium-voltage, dry-type transformers, the scenarios are 0 percent evaluators, 50 percent evaluators, and 100 percent evaluators. For large-capacity, medium-voltage, dry-type transformers, the scenarios are 0 percent evaluators, 80 percent evaluators, and 100 percent evaluators. DOE

8-14selected evaluation percentages that are roughly consistent with NEMA data regarding the fraction of transformers compliant with TP 1 in the current market. For evaluators, DOE used a distribution of A and B values to characterize their behavior. It determined the mean of the distribution of A values from stakeholder and public data regarding evaluation parameters available to DOE. Once DOE chose the A value, it then chose a value for B of less than A, using a B:A ratio. DOE based the B:A ratio on the statistical characteristics of the evaluation parameter data that are available.
8.3.2 Database of Transformer Designs Establishing a relationship between cost and efficiency is an integral part of the rulemaking process. For transformers, DOE derived this relationship from an engineering analysis database of selling prices, no-load losses, and load losses for a wide range of realistic transformer designs contained in the LCC spreadsheets. DOE used a commercial transformer design software company, Optimized Program Service Inc., and its software to create the database of designs. The database consists of a wide range of efficiencies and manufacturer selling prices (including a predetermined manufacturer markup) to represent the variability of designs in the marketplace. The engineering analysis (see Chapter 5) provides more detail on the database structure and the method used to generate this database of transformer designs.
8.3.3 Markup and Installation Costs Markups, shipping costs, sales tax, and installation costs are the costs associated with bri nging a manufactured transformer into service as an installed piece of electrical equipment. For liquid-immersed transformers, which utilities typically purchase directly from ma nufacturers, DOE considered the transformer purchase price to be the manufacturer selling price plus a distributor markup, the shipping cost, and sales tax. DOE added installation costs separately. For dry-type transformers, the distribution channel includes various intermediaries who add their own costs to the manufacturer selling price. These costs include a manufacturer markup, distribution markup, contractor markup, shipping cost, installation costs, and sales tax. See Chapter 7 for a detailed discussion of the markup calculations.
8.3.4 Transformer Loading To estimate the economic burdens and benefits of efficiency improvements, DOE ch aracterized the energy use and losses of the equipment being analyzed, by estimating the loads on them. Because the application of distribution transformers varies significantly by type of transformer (liquid-immersed or dry-type) and ownership (electric utilities own liquid-immersed 95 percent of the time, commercial/industrial entities use mainly dry-type), DOE performed two separate load analyses to evaluate distribution transformer efficiency: one for liquid-immersed transformers and a second for dry-type transformers. Chapter 6 describes these two separate load analyses.

8-158.3.5 Electricity Price Analysis This section describes the electricity price analyses DOE used to determine the energy portion of the annual operating expenses for distribution transformers. DOE performed two types of load analysis to evaluate distribution transformer efficiency. The first type of analysis investigated the nature of hourly transformer loads, their correlation with the overall utility system load, and their correlation with hourly electricity costs and prices. The second type of analysis estimated the impacts of transformer loads and the resultant transformer losses on the monthly electricity usage, demand, and electricity bills of C&I customers. DOE refers to the two analyses as hourly and monthly analyses, respectively. DOE used the hourly analysis for the economic analysis of liquid-immersed transformers, which are predominantly owned by utilities whose costs vary by the hour. DOE used the monthly analysis to evaluate dry-type transformers, which are typically owned by C&I establishments that receive monthly electricity bills.
8.3.5.1 Hourly Marginal Electricity Price Model for Liquid-Immersed Transformers For liquid-immersed transformers, DOE used marginal electricity prices. Marginal el

ectricity prices are the prices experienced by utilities for the last kilowatt-hour (kWh) of electricity produced. A utility’s marginal price can be higher or lower than its average price, depending on the relationships between capacity, generation, transmission, and distribution costs. The general structure of the hourly marginal cost equation divides the costs of electricity into capacity components and energy cost components. The capacity components include generation capacity, transmission capacity, and distribution capacity. Capacity components also include a reserve margin needed to ensure system reliability. Energy cost components include a marginal cost of supply that varies by the hour, factors that account for losses, and cost recovery of associated marginal expenses. DOE applied Equation 8.3 to calculate the marginal cost of supply of electricity to cover transformer losses. MEC = (1 + CM) × (GC × IGC + TC × ITC + DC × IDC) + (LAF × EC(hour) + RF) × IEU(hour) Eq. 8.3 where: MEC = marginal electricity cost (2006$), CM = system capacity margin required for reliability, GC = unit cost of generation capacity ($/kW), IGC = incremental system capacity required by the load (kW), TC = unit cost of transmission capacity ($/kW), ITC = incremental transmission capacity required by the load (kW), DC = unit cost of (non-transformer) distribution capacity ($/kW), IDC = incremental distribution capacity required by the load (kW), LAF = load adjustment factor, which is one plus the estimated system losses,

8-16EC(hour) = hourly cost of electrical energy, either from a market or from fuel and operating cost data ($/kWh), RF = additional cost recovery factor ($/kWh), and IEU(hour) = incremental energy use (kWh). DOE calculated the various inputs of this equation as follows: Capacity Margin (CM).This is the fraction of extra or reserve capacity needed to ensure system reliability per unit of additional capacity requirement. DOE used the industry standard of 15 percent. Unit Generation Capacity Cost (GC). This is the annualized cost of unit generating ca pacity for the particular load being served. It includes the cost of capital during the construction period, and the loss adjustment factor to account for losses between the generator and end-use load. DOE’s Energy Information Administration (EIA)'s Annual Energy Outlook 2003 (AEO2003) provides forecasts of such costs for different generation technologies. 12The capacity cost depends on the type of load being served and the source of the electricity. For base load, DOE used the capacity cost for a pulverized coal plant, since this is currently the least-cost, base-load technology. For peak loads, such as those associated with transformer peak-load losses, DOE used conventional combustion turbine capacity costs as the relevant marginal capacity cost. DOE obtained its estimates for generation capacity costs from data and estimates contained in the AEO2003forecast and adjusted the values to 2006$ using the gross domestic product (GDP) price deflator from 4 AEO2007

. These costs are shown in Table 8.3.4, as reported in the AEO2003forecast in 2000$.
Table 8.3.4 Generation Capacity Costs

Technology Year Cost 2000$/kW 2001 1,119 Conventional Pulverized Coal 2010 1,083 2020 1,056 2001 456 Conventional Gas Combined Cycle 2010 448 2020 438 2001 339 Conventional Gas Combustion Turbine 2010 333 2020 326 Incremental Generation Capacity Requirement ( IGC )

. This is the amount of generation capacity required by a load. For the core-loss component, this is equal to the core losses times the loss adjustment factor (see LAF below). For the load-loss component, DOE estimated this by multiplying the peak responsibility factor (PRF) by the transformer peak load and feeding the result

8-17to the load-loss equation. PRF is the fraction of the transformer peak that is coincident with the system peak. DOE calculated a first-year PRF. Note that there is a multi-year delay between the time when new capacity is contracted and when it becomes available. AEO2003capacity cost forecasts are expressed in terms of when the capacity is contracted. DOE translated this cost into the cost at first year of service by adding capital costs as determined by the discount rate. DOE annualized costs by applying the capital recovery factor to capacity costs, assuming that such capacity has a 30-year lifetime. Unit Transmission Capacity Cost (TC). This is the annualized cost per unit for an increment of new transmission capacity. DOE obtained transmission capacity costs from the estimates made by EIA for each of 13 transmission regions in the AEO2003forecast. It used the value of $166/kW, which is the average marginal transmission capacity cost for all 13 transmission regions in 2000$, and adjusted it to 2006$ using the GDP price deflator from AEO2007

. Incremental Transmission Capacity Requirement (ITC). This is the amount of transmission capacity required by an incremental load. DOE assumed that the transmission capacity requirement is the same as the generation capacity requirement. Unit Distribution Capacity Cost (DC). This is the cost per unit of distribution capacity. DOE used distribution capacity costs from the U.S. Federal Energy Regulatory Commission (FERC) Form 1 data on the investment in transformers, substations, lines, and feeders per unit of system peak, 13as analyzed and published by the Regulatory Assistance Project. 14DOE used the average cost of distribution capacity per kilowatt of system peak, and assumed that the real price of distribution capacity is constant (i.e., has no trend up or down). The estimated distribution capacity cost for the analysis is $276/kW in 2000$, which DOE adjusted to 2006$ using the GDP price deflator from AEO2007

. Incremental Distribution Capacity Requirement (IDC). This is the amount of distribution capacity required by an incremental load. DOE assumed this to be the same as the peak incremental energy use. Loss Adjustment Factor (LAF).The loss adjustment factor is the factor that one must multiply with an end-use load to estimate the amount of system electricity needed to supply that load. It is one plus the fractional losses in the system. DOE assumed it to have a constant value of 1.08. Hourly Energy Cost (EC). This is the hourly marginal energy cost that DOE obtained from utility system lambda data or market data. Since DOE obtained this cost from market data, it assumed it to include generation capacity effects and assumed the generation capacity cost to be zero. Additional Cost Recovery Factor (RF). This is a factor that DOE added to the hourly energy cost to account for costs besides energy losses that are associated with supplying that

8-18energy. DOE obtained the per-kWh additional cost recovery factors in Table 8.3.5 using real-time pricing formulas from the utilities listed in the table. Such factors are necessary for marking up the cost of generation to account for other costs that scale with generation, but that are not included in such costs. Such costs may include accounts payable, accounts receivable, and operating capital costs, which will have a component that scales with the volume of electricity sales.
Table 8.3.5 Rep resentative Cost Recovery Factors Utility Recovery Factor Illinois Power Company 0.5 cents/kWh (five-year contract) Pacific Gas and Electric Co. 0.346 cents/kWh PSI Energy Inc. 10% to 25% ofEC(hour)× LAF Carolina Power and Light 20% ofEC(hour) Union Light, Heat, and Power Company 10% ofEC(hour) × LAF Based on this range of observed cost-recovery factors, DOE assumed that the cost recovery f actor is approximately 0.15 (i.e., 15 percent) of the cost of energy. Incremental Energy Use (IEU). This is the incremental hourly energy use calculated from the transformer loading that is obtained from the formula for the transformer losses.
8.3.5.2 Monthly Tariff-Based Analysis for Dry-Type TransformersThis section gives an overview of DOE’s tariff-based analysis of electricity prices for drytype transf ormers. DOE updated the utility tariffs used in the monthly analysis to 2004. Although the electricity prices were updated, DOE used the same distribution of utilities as it used in the advance notice of proposed rulemaking (ANOPR) analysis, which was derived from year 2000 data. Selection of the Sample Utilities

. DOE used three main criteria in developing the utility sample for the utility tariff formulas: 1) The sample of utilities should reflect the distribution of population across the country, with more utilities drawn from more populated areas; 2) the sample should reflect the proportion of customers served by privately owned entities (i.e., investor-owned utilities (IOUs) and power marketers) versus publicly owned utilities (i.e., municipals, cooperatives, State, and Federal); and 3) the sample should cover as many customers as possible. Data on utilities are available from the EIA through their Form 861 filings. 15This form is filed annually by every utility that sells power to final consumers, and includes information on the total sales in megawatt-hours (MWh), total revenues, and the numbers of customers. Utilities supply this information separately for the residential, commercial, and industrial sectors. DOE used data from the year 2000 in this analysis.

8-19DOE first screened the set of utilities in the EIA database to consider only those with customers in all three sectors (residential, commercial, industrial). It then computed, for each subdivision, the percentage of customers served by public versus private utilities. DOE chose thesample utilities to reflect the relative population of the subdivision and the proportion of public-toprivate customers. In most areas, DOE included the largest utilities in the sample to maximize the number of customers represented, but it also included smaller private and public utilities of all sizes. The final set of sampled utilities included 49 privately owned and 41 publicly owned companies. The EIA data for 2000 show that power marketers and other providers of unbundled retail servi ces serve approximately two percent of the commercial customers in the country. Power marketers have the largest market share in New England, so DOE included one such company in its sample for this region. Appendix 8C contains a list of the sample utilities. Subdivision of the Country

. Because of the wide variation in electricity usage patterns, wholesale costs, and retail rates across the country, it is important to consider regional differences in models of electricity prices. For this reason, DOE divided the continental United States into 17 regions or subdivisions as illustrated in Figure 8.3.1. To make maximum use of the location information in DOE’s Commercial Building Energy Consumption Survey (CBECS), 16the breakdown started with the nine U.S. Census divisions. DOE further subdivided these to take into account significant climate variation and the existence of different electricity market or grid structures. DOE based climate divisions on the nine climate regions defined for the continental U.S. by the National Climatic Data Center. 17In addition, it separated out Texas, Florida, New York, and California because their electric grids are operated independently. Figure 8.3.1 illustrates the results. In the figure, the subdivision numbers use the CBECS U.S. Census Division code as the first digit. For example, U.S. Census Division 8 (Mountain) has been separated into two subdivisions, 8.1 and 8.2.

8-20Figure 8.3.1 Map Showing the Division of the Continental United States into 17 Subdivisions
Representativeness of the Sample

. For this rulemaking, DOE defined the representativeness of the sample by the percentage of the total number of C&I customers that were covered. It is relatively easy to get good representation for IOUs, because in most regions there are a few large companies serving many customers. It is more difficult to represent the publicly owned companies, as these tend to be much smaller; to obtain the same level of customer representation, DOE would need to include many more utilities. The sampled utilities serve 60 percent of the C&I customers of private utilities, and 14.4 percent of C&I customers of public utilities. The combined total for the U.S. is 48.5 percent of all C&I customers. Table 8.3.6 gives the percentage of customers by subdivision, broken down by ownership type.

8-21Table 8.3.6 Percentage of All Commercial and Industrial Customers, by Subdivision, in the Utility Sample

Subdivision U.S. Census Division Region Public Customers in Sample % Private Customers in Sample % Percentage of Total Customers in Sample % Number of Customers in Sample

1 New England New England 3.7 43.9 40.3 310,505 2.1 Middle Atlantic New York 88.2 72.4 74.7 671,407 2.2 Middle Atlantic PA, NJ 7.9 50.6 49.4 504,466

3 East North Central WI,IL,IN,OH,MI 8.5 43.5 39.1 831,124 4.1 West North Central MN, IA, MO 5.4 27.0 12.4 44,823

4.2 West North Central ND,SD,NE,KS 9.8 59.0 46.5 387,603

5.1 South Atlantic DE,MD,VA,WV 13.4 72.9 67.5 552,058 5.2 South Atlantic NC,SC,GA 11.8 88.9 64.3 778,500 5.3 South Atlantic Florida 15.1 72.0 58.4 530,513 6.1 East South Central KY,TN 11.5 47.2 20.6 128,694 6.2 East South Central MS,AL 9.9 68.5 42.9 217,970 7.1 West South Central OK,AR,LA 3.5 60.4 44.1 265,412

7.2 West South Central Texas 18.2 23.8 22.2 272,077

8.1 Mountain MT,ID,WY 10.5 52.2 39.6 70,323 8.2 Mountain NV,UT,CO,AZ,NM 5.9 71.8 46.2 310,765 9.1 Pacific WA,OR 16.5 47.9 38.1 243,271 9.2 Pacific California 20.3 97.4 75.8 1,050,862 National Sample USA 14.4 60.0 48.5 7,170,373 The ratio of total revenues to total energy sales—which reflects, on average, the amount of money collected for each kWh sold—also varies significantly among utilities and across different regions. Figure 8.3.2 illustrates the degree of variation. The figure plots the customer-weighted average of utility revenues divided by sales (in units of $/kWh) within each subdivision. The vertical bars show the average value, plus or minus one standard deviation, for all the utilities in the EIA data. The points show the customer-weighted average revenues divided by sales for the

8-22sampled utilities only. There is a large spread in the EIA data, and sample averages are within this range.

Figure 8.3.2 Customer-Weighted Average of Utility Revenue Divided by Sales by Subdivision ($/kWh) Utility Weights

. Theway in which DOE assigned weights to the utilities depended on the application. As will be discussed in more detail below, the marginal rate seen by a particular customer depends both on the tariff and on that customer’s energy use characteristics. The appropriate weight for a utility is the number of customers it has divided by the number of customers i n the subdivision. The weights for all the utilities in a given subdivision thus add up to one. Because the level of customer representation for public utilities is much less than for private ones, DOE included an additional factor to account for the difference, so that the total weight of the sample’s public utilities equals the fraction of public customers in that subdivision. The weight for utility k is: Weight(k,own_type) = ( n(k,own_type)/n(own_type) ) × ( N(own_type)/N), Eq. 8.4 where ow n_type= pub(public) or priv (private). By definition, 3(ow n_type)3(k) Weight(k,own_type) = 1. Eq. 8.5 where:

8-23n(k,pub ) = number of customers served by publicly owned sample utility k, n(k,priv) = number of customers served by privately owned sample utility k, n(pub) = sum(k) n(k,pub)= total customers served by publicly owned sample utilities, n(priv) = sum(k) n(k,priv)= total customers served by privately owned sample utilities, N(pub) = total customers served by all publicly owned utilities in the subdivision, N(priv) = total customers served by all privately owned utilities in the subdivision, and N = N(pub) + N(priv)= total customers in the subdivision.
8.3.5.3 Data Collection and Modeling This section describes the data collection method, the selection of tariffs for a given utility, and how DOE modeled the tariffs. Web Search

. DOE collected the vast majority of the tariff documents directly from utility web sites. Almost every privately owned utility in the sample makes its whole tariff structure available on the web, although not always in easily readable form. In many cases, the full tariff, as approved by the relevant State public utility commission, is published and the actual customer rates have to be tracked down within this document. Many public utilities also put their rate information on the web. In cases where web documents were not available, DOE contacted the utilities directly. The list of utilities in Appendix 8C also includes a uniform resource locator (URL) for each company. Selection of Tariffs for Each Utility

. Utility companies have many tariffs, separated into residential, non-residential, and special-use—such as public street-lighting or agricultural. For the non-residential category, some utilities use the commercial-industrial distinction, but many do not. Therefore, in its tariff database, DOE combined these into one customer category. The goal in collecting the tariffs was to cover the full range of C&I customer types for each utility. In most cases, DOE assigned customers to a specific tariff based on their peak demand over the previous 12 months. In a few cases, it based the assignment on the maximum monthly electricity consumption. Customers are not generally moved from one tariff to another. Most utilities offer only one tariff for each customer size. Some of the larger utilities offer optional time-of-use (TOU) or real-time-pricing (RTP) tariffs. Occasionally, utilities will offer different tariffs for different business types. In all of the cases DOE checked, although the tariffs had different names, the rates were in fact the same. Some utilities do not specify rates for very large customers whose peak demand is on the order of thousands of kWs; instead they negotiate them on a case-by-case basis. For the LCC calculation, DOE used only tariffs that depend on usage and demand data (not TOU nor RTP tariffs), because the energy consumption data it used in its analysis included only monthly demand and usage. DOE’s sampling strategy was to take the default tariff for each customer class. DOE excluded “closed” tariffs; these are tariffs that are being phased out by the utility and so are not

8-24available to new customers. This suggests that such tariffs do not reflect rates that will be seen by the majority of customers in the future. Utilities do not generally make information available on how many customers are on each tar iff; however, this information is not actually needed for the LCC. Instead, what is important to know is the relative numbers of customers who use the distribution transformer equipment covered by the standard who are on different tariffs. The CBECS data on the building weights define the relative proportion of customers of different sizes (here size refers to the customer’s peak load), and the building monthly load data provide the annual peak load—which DOE used to assign a customer to a tariff. The Tariff Model

. To calculate a customer’s electricity bill requires two sets of inputs: the rates charged as defined by the tariff, and information on the customer’s energy use. The customer data consist of the billing demand (i.e., the customer’s peak demand over the billing month) and total energy consumption for the current billing period. In its analysis, DOE assumed the billing period to be one calendar month. While the formulae determining the actual bill can be quite complex, they are based on three types of charges: fixed, energy, and demand. Fixed charges are those paid each month regardless of the level of energy use. They do not contribute to customer marginal rates and so do not have any impact on the operating cost savings used in the LCC. Energy charges are specified in units of cents (¢)/kWh and depend on the customer’s total energy consumption. Demand charges are specified in units of $/kW and depend on the customer’s monthly billing demand. For most of the utilities in the sample, charges also vary seasonally. Energy and demand charges are typically applied in blocks. This means the customer pays one rate for energy use or demand up to a certain level, a second rate for usage up to the next level, and so on. For example, in a tariff with three blocks, the energy charges may be 10¢/kWh for the first 200 kWh, then 8¢/kWh for the next 1,000 kWh, and 6¢/kWh for all remaining energy use. This is an example of declining block rates, where the energy charge decreases as the energy used increases. One may also see inclining block rates. As a variation on this scheme, the block limits may depend on the customer's energy consumption and demand. As an example of one such tariff, Figure 8.3.3 shows a block-by-demand tariff, which is representative of about 20–30 percent of the utilities sampled. This figure is illustrative and does not reflect any particular utility. The first block has a rate of 10¢/kWh for the first 200 kWh of energy used. The second block rate is 6¢/kWh for energy consumption up to 100 times the billing demand. The multiplier 100 is a tariff component with units of kWh/kW, which defines the width of the second block. The third block rate is 3¢/kWh for all subsequent energy use. Besides increasing the complexity of the calculation of the customer bill, this type of variable block size introduces a dependence of the energy charges on the billing demand. Two cases in Figure 8.3.3 illustrate this: in each case, the overall energy use is 3,200 kWh, but the billing demand is different (20 kW versus 21 kW).

8-25In Case 1, the customer energy use is 3200 kWh, and the monthly peak demand is 20 kW. The width of the second block is 100×20 = 2000 kWh. In dollars, the customer will pay: (0.10)×200 + (0.06)×2000 + (0.03)×1000 = 20 + 120 + 30 = $170. In Case 2, the customer energy consumption is also 3200 kWh, but the demand is 21 kW. Now the len gth of the second block is 2100 kWh, so the bill is: (0.10)×200 + (0.06)×2100 + (0.03)×900 = 20 + 126 + 27 = $173. Even though the tariff specifies only energy charges, a change in the customer demand of one kW h as increased the bill by $3. This is because 100 kWh of consumption have been shifted from the third, less expensive, block to the second, more expensive, block. Note that increasing the demand while holding the energy consumption fixed lowers the customer load factor, and in this case increases the effective marginal rate. This is a generic observation of the sampled tariffs.

Figure 8.3.3 Effect of a Change in Customer Demand (Energy Consumption is Held Fixed) for a Block-By-Demand Tariff The important point to take from this is that both demand and energy use contribute to the marginal rates seen by a customer, and often in ways that are not immediately obvious from reading the tariff. For the LCC, the energy cost savings due to the standard depend on the decrement to the total energy consumption, the decrement to the peak demand, and the ratio of the two. The effective marginal rate can vary substantially even for a set of customers on the same tariff. Approximations Used in Modeling the Tariffs

. In cases where the available information on the full set of charges seen by the customer is incomplete, DOE made the following approximations: Riders and adders: Some tariffs, particularly in deregulated areas, include additionalcharges as riders which may not be explicitly defined. The most significant are the fuelcost-recovery adders. These are additional charges passed on to the customer when the utility must spend more than anticipated for fuel. DOE included these costs in its analysis when they were given explicitly; however, these costs are sometimes represented as a

8-26 Cformula based on the utility's expenditures and in these cases could not be modeled within DOE’s framework. This resulted in a possible underestimation of costs for some customers.

C Customer classes: In a few cases, the utility specifies tariffs for “small” and “large” customers, without giving an explicit definition of these terms. In its review of the tariffs in the sample, DOE found that small customers typically have a peak demand on the order of 10 kW, medium customers have a demand on the order of 100 kW, and large customers have peak demands on the order of 1000 kW. DOE used a median value of 50 kW to separate customers into the “small” and “large” classes.

C Ratchets: Ratchets refer to situations where the billing demand for the current month is computed from a formula incorporating the monthly demand over the previous 12 months. For example, the billing demand may be set equal to either the current month’s demand, or 80 percent of the maximum demand over the previous 12 months. A ratchet may also be seasonal. For example, the billing demand may be the greater of the current monthly demand or the peak demand in the most recent summer months. DOE did not include this type of rate formula in its tariff model. Instead, it assumed that the billing demand is the current monthly demand. The rationale behind ratchets is that demand charges are meant to cover capacity costs, and the actual capacity needed to serve a customer is better represented by their peak demand over a year rather than a month. New capacity is needed to serve a customer only when that customer contributes to the total system peak load, so one would expect that summer-peaking utilities would tend to implement ratchets that depend on summer demand. In these cases, the tariff model will underestimate the savings due to standards, since the billing demand decrement in each month will be somewhat less than the decrement for the summer months. The data collected on system loads show that all areas except the Northwest are summer-peaking in an average year. The southern states may have high loads during an especially cold winter, so these areas may implement ratchets that depend on the annual demand. In these cases, buildings with electric heat may have their peak demand occurring in the winter and, if the tariff includes a ratchet, DOE’s model may over-estimate the savings due to standards. Monthly Bill Calculations

. DOE used the calendar month as the billing period. For each building, DOE used the peak demand and total energy consumption data from CBECS 1995 for 12 calendar months. 16DOE kept the customer on the same tariff for both the base and the standards cases. It calculated the monthly bills, adding the distribution transformer losses for both the base and the standards cases. The difference between the annual bills for each standard level gave the associated operating cost savings. DOE calculated the customer marginal demand and energy prices as the net change in the total bill, divided by the net change in demand or energy, respectively. To represent the full range of tariffs in the LCC spreadsheet, DOE created a Tariff Data Model (TDM). The TDM is a data structure used to store all of the tariff information in a format that is consistent across utilities. Briefly, the TDM divides each tariff into a collection of

8-27components. A component is defined by a set of parameters that include a rate, a range of values under which the rate applies, and indicator variables that specify if and when the rate should be applied. Using this component format, one can reconstruct the structure of any tariff from the set of components, and then apply the customer data used to determine whether conditions are met for a particular rate. DOE used the TDM to store all of the tariff information. Its objective was to create a database structure that captured the complexity of a specific rate schedule (e.g., seasonal rates, variable block rates, mixtures of blocks, time-of-use tariffs) with sufficient accuracy to model any utility tariff. In principle, each tariff is simply a list of rates. However, a complete tariff is, in practice, often very complex. Typically, certain rules and conditions must be met for a particular rate to be applied. To capture this complexity, the TDM divides each tariff into a collection of components. A component is defined by a set of parameters that include a rate, a range of values under which the rate applies, and indicator variables that specify if and when the rate should be applied. Using this component format, the structure of any tariff can be reconstructed from the set of components.
TDM Rates

. Each tariff component has a rate parameter. The rate stored in the TDM is a dimensionless, real, numbered value. The “charge type” indicator specifies the units for a particular component. TDM Indicators

. Charge Type.Thecharge-type indicator specifies the units for each component. The indicator is also used to keep track of charges as bills are calculated. There are three basic types of charges included in this sample: fixed charges, energy charges, and demand charges.

C Fi xed charges ($/month) – These are typically monthly customer charges. Fixed rates are also used as minimum customer charges.

C Energy charges (¢/kWh) – These are the basic per-kWh charges associated with a tariff.

C Dem and charges ($/kW) – These are the charges driven by peak consumption. Larger customers are typically subject to tariffs that incorporate demand charges. Block Type

. A block is the range in which a particular rate is applied. In general, these blocks are based on user consumption and demand. To determine these ranges, utilities use a variety of functions. In addition, a particular tariff may use different block functions for each block. However, at the component level, there is a small set of functions that can be used to set the limits of a range. In general, these limits are functions of kW of demand and kWh consumed ( Limit = f {kW, kWh}). The block type indicates which limit function should be applied to each component. DOE modeled several functions.

8-28C Fixed Block – This is the most common block type. DOE based the minimum and maximum limits on a predetermined kWhlevel.

C Block-By-Demand – This is also a common type, though less so then the fixed block. The maximum limit equals a Demand Multiplier × User Demand . In this case, the limit is not known until run time, when the user demand is known. (This type of block structure essentially incorporates a demand charge into an energy charge.) The minimum limit is set to the previous maximum.

C Mix B lock Type 1 – The maximum limit is set by the maximum (kWh, demand multiplier×kW). The minimum limit is set to the previous maximum.

C Mix B lock Type 2 – The maximum limit is set by the minimum (kWh, demand multiplier×kW). The minimum limit is set to the previous maximum.

C Mix B lock Type 3 – The maximum limit is set by the sum (kWh, demand multiplier×kW). The minimum limit is set to the previous maximum. TDM Ranges. The quanti ties that are used to determine limits are stored as ranges. DOE currently has ranges for seasonal limits and block limits.

C kWh Ranges – Minimum and maximum ranges for blocks are stored in units of kWh.

C kW Ranges – Minimum and maximum ranges for blocks are stored in dimensionless units. The block type indicator determines the unit for this range. Fixed blocks are in units of kW, other block types are the demand multiplier units, sometimes referenced by utilities as “Hours” or “kWh/kW.”

C Month Range – Each utility defines the winter and summer months differently. The winter and summer start and end months are stored here.

C Season Type – The season type indicates in which season the component charge can be applied. The Bill Calculator. The bi ll calculator is a set of accounting programs, implemented as functions, which can be accessed from within an Excel spreadsheet. These functions produce monthly bills for a user given the consumption, demand, and month. There are two functions of interest: GetTariffs, and GetCharges

.

• GetTariffs. For a given util ity, a customer is typically assigned to one of several tariffs. GetTariffs determines the tariff to which the user is assigned, given the peak consumption and peak demand levels.

8-29• GetCharges. GetCh arges returns a monthly bill, given the tariff, consumption, and demand. This is the core function that reconstructs a tariff from the TDM. The function reads data from the TDM and evaluates which charges should be applied. It tallies these charges and returns the monthly bill in dollars.
8.3.6 Load Growth Trends The LCC analysis looks at a cross-section of transformers. DOE applied a load growth trend toeach new transformer as part of its sensitivity analysis. Spreadsheet users have the choice of three scenarios using the Transformer Load Growth/Year drop-box on the “Summary” worksheet. The three scenarios for load growth are: no growth, one-half-percent-per-year growth,and one-percent-per-year growth. DOE used as the default scenario a no load growth for liquidimmersed transformers in response to comments on the NOPR analysis. The one-percent-per-year high load growth scenario was identified in the Determination Analysis of Energy Conservation Standards for Distribution Transformers, Report ORNL-6847. 18For the sensitivity analyses, DOE applied a one-percent-per-year load-growth trend to each new liquid-immersed transformer beginning in 2010, the expected effective date of the standard (see section 8.3.9). DOE also expects load growth for dry-type transformer to be no-growth.
8.3.7 Electricity Cost and Price Trends For the relative change in electricity cost and prices for future years, DOE used the price trends from three AEO2007 forecast scenarios from EIA. 4LCC spreadsheet users have the choice of these four scenarios: 1. AEO2007 Low Growth scenario 2. AEO2007 Reference scenario 3. AEO2007 High Growth scenario 4. Constant Real Prices Figure 8.3.4 shows the trends for the three AEO2007 price projections. Because AEO2007 does not forecast beyond 2025, DOE extrapolated the values in later years (i.e., after 2025) from their relative sources. To arrive at values for these later years, DOE used the price trend of the forecast from 2015 to 2025 to establish prices in the years 2026 to 2035. This method of extrapolation is in line with methods currently used by the EIA to forecast fuel prices for the Federal Energy Management Program (FEMP). The default electricity price trend scenario that DOE used in the LCC analysis is the trend from the AEO2005Reference case. Spreadsheets used in calculating the LCC have the capability to analyze the other electricity price trend scenarios, namely the AEO2007 High and Low Growth price trends.

8-30Figure 8.3.4 AEO 2007 Electricity Price Forecasts

8.3.8 Discount Rate The discount rate is the rate at which future expenditures are discounted to estimate their present value. DOE derived the discount rates selected for the transformer LCC analysis from estimates of the cost of capital for companies that purchase transformers. Following financial theory, the cost of capital can be interpreted in three ways: 1) It is the discount rate that should be used to reduce the future value of cash flows to be derived from a typical company project or investment; 2) it is the economic cost to the firm of attracting and retaining capital in a competitive environment; and 3) it is the return that investors require from their investment in a firm’s debt or equity. 19DOE primarily used the first interpretation. Most companies use both debt and equity capital to fund investments; for most companies, therefore, the cost of capital is the weighted average of the cost to the firm of equity and debt financing. 20 DOE estimated the cost of equity financing using the Capital Asset Pricing Model (CAPM). The CAPM, among the most widely used models to estimate the cost of equity financing, assumes that the cost of equity is proportional to the amount of systematic risk associated with a firm. For example, the cost of equity financing tends to be high when a firm faces a large degree of systematic risk, and the cost tends to be low when the firm faces a small degree of systematic risk.

8-31The degree of systematic risk facing a firm and the subsequent cost of equity financing are determined by several variables, including the risk coefficient of a firm (beta, or B), the expected return on risk-free assets (R f ), and the additional return expected on assets facing average market risk (which is known as the equity risk premium, or ERP). The beta indicates the degree of risk associated with a given firm, relative to the level of risk (or price variability) in the overall stock market. Betas usually vary between 0.5 and 2.0. A firm with a beta of 0.5 faces half the risk of other stocks in the market; a firm with a beta of 2.0 faces twice the overall stock market risk. Following this approach, the cost of equity financing for a particular company is given by the equa tion: k e= R f + (B × ERP) Eq. 8.6 where:

R k

e = the cost of equity for a company, f = the expected return of the risk-free asset, B = the beta of the company stock, and ERP = the expected equity risk premium, or the amount by which investors expect the future return on equities to exceed that on the riskless asset. The cost of debt financing (k d) is the yield or interest rate paid on money borrowed by a company (raised, for example, by selling bonds). As defined here, the cost of debt includes compensation for default risk and excludes deductions for taxes. DOE estimated the cost of debt for companies by adding a risk adjustment factor to the cu rrent yield on long-term corporate bonds (the risk-free rate). This procedure is used to estimate current (and future) company costs to obtain debt financing. The adjustment factor is based on indicators of company risk, such as credit rating or variability of stock returns. The discount rate of companies is the weighted average cost of debt and equity financing, less expected inflation. DOE estimated the discount rate using the equation: k = (k e× w e ) + (k d × w d ) Eq. 8.7 where: k = the (nominal) cost of capital, k

e and k

d = the expected rates of return on equity and debt, respectively, and w

e and w d = the proportion of equity and debt financing, respectively. The real discount rate deducts expected inflation from the nominal rate.

8-32The expected return on risk-free assets, or the risk-free rate, is defined by the current yield on long-term government bonds. 21The ERP represents the difference between the expected (average) stock market return and the risk-free rate. As shown in Table 8.3.7, DOE used an ERP estimate of 5.5 percent, which it took from the Damodaran Online site (a private website, associated with New York University’s Stern School of Business, that aggregates information on corporate finance, investment, and valuation). 22 DOE calculated an expected inflation of 2.3 percent from the average of the last five quarters’ change in GDP prices. 23DOE obtained the cost of debt, percent debt financing, and systematic firm risk from information provided at the Damodaran Online website. aTable 8.3.7 shows average values across all private companies. However, the cost of debt, percent debt financing, and systematic firm risk vary by sector. For example, average systematic firm risk of all private firms in the industrial sector is close to 1.0, while systematic firm risk in the utility sector averages close to 0.6.
Table 8.3.7 Variables Used to Estimate Company Discount Rates Variable Symbol Average Value Source

Risk-free asset return R f 5.5% Bloomberg Professional 24 Equity risk premium ERP 5.5% Damodaran Online22 Expected inflation r 2.3% U.S. Bureau of Economic Analysis 23 Cost of debt (after tax) k d 9.0% Damodaran Online; FERC Form 1 13 Percent debt financing w d 27% Damodaran Online Systematic firm risk B 0.99 Damodaran Online Transformers are purchased and owned by electric utilities (publicly and investor-owned), C&I companies, the owners of commercial buildings (property owners), and the government. Table 8.3.8shows the typical owners of transformers, grouped by transformer type owned (represented by the design lines DOE used in the engineering analysis). DOE used a sample of 4,294 companies drawn from these owner categories to represent transformer purchasers. It took the sample from the list of companies included in the Value Line investment survey 25and listed on the Damodaran Online website. DOE obtained the cost of debt, the firm beta, the percent of debt and equity financing, the risk-free return, and the equity risk premium from Damodaran Online.

a DOE estimated percent debt for firms in the property-owning category using data from Bloomberg Professional. 24 DOE took the cost of debt for publicly owned utilities from FERC Form 1 filings. 13

8-33DOE estimated the cost of debt financing for these companies from the long-term government bond rate and the standard deviation of the stock price. 22Publicly owned utilities, including municipals and cooperatives, do not issue stock and tend to be financed with debt. DOE obtained the cost of debt for these companies from information provided in FERC Form 1 filings. Finally, for government-office-type owners, the discount rate represents an average of the Federal rate and the State and local bond rate. DOE drew the Federal rate directly from the U.S. Office of Management and Budget discount rate for investments in government building energy efficiency. 26 DOE estimated the State and local discount rate from the interest rate on State and local bonds between 1977 and 2001. 27DOE used this information to estimate the weighted-average cost of capital for the sample of companies included in the commercial property owner, commercial and industrial company, and utility database.

Table 8.3.8 Typical Owners of Different Types of Transformers Design Line Typical Ownership Categories 1, 2, 3, 4 Electric utilities, both publicly owned and investor-owned
5 Electric utilities, commercial property owners, commercial and industrial compan ies, government offices 9, 10, 11, 12, 13 Commercial and industrial companies As previously mentioned, the cost of capital may be viewed as the discount rate that should be used to reduce the future value of typical company project cash flows. It is a nominal discount rate, since anticipated future inflation is included in both stock and bond expected returns. Deducting expected inflation from the cost of capital provides estimates of the real discount rate by ownership category (see Table 8.3.9). The mean real discount rate for these companies varies between 3.3 percent (government offices) and 7.5 percent (industrial companies).

8-34Table 8.3.9 Real Discount Rates by Transformer Ownership Category
Ownership Category SIC Codes* Mean Real Discount Rate % Standard Deviation % Number of Obser vations Industrial Companies 1 - 4 7.5 3.2 2409 Commercial Companies 5 - 8 7.3 4.7 1773 Commercial Property Owners 6720 4.5 0.9 8 Utilities, Investor-Owned 49 4.2 1.5 63 Utilities, Publicly Owned n/a 4.3 1.1 16 Government Offices n/a 3.3 2.1 25

* SIC Codes refer to the U.S. Standard Industrial Classification system. Source: Lawrence Berkeley National Laboratory (LBNL) calculations based on firms sampled from the Damodaran Online website. Because IOUs purchase the bulk of many transformer design lines evaluated here, the discount rates calculated for that sector (see Table 8.3.9) are particularly important. DOEestimated that the average IOU real discount rate is 4.2 percent. The 4.2 percent figure is an aftertax discount rate, representing the return required by such utilities to attract financing. Private financial data companies, including Ibbotson Associates and Bloomberg Professional, offer similar estimates. The Bloomberg Professional online service estimates the cost of IOU capital to be 4.4 percent. aIbbotson Associates estimates the cost of capital in this sector—Standard Industrial Classification (SIC) 49—after deducting 2.3 percent inflation, to be 5.0 percent. 28 DOE’s approach for estimating the cost of capital provides a measure of the discount rate spread as well as the average discount rate. DOE inferred the discount rate spread by ownership category from the standard deviation, which ranges between 0.9 percent and 4.7 percent (Table 8.3.9). Publicly owned utility and property owner discount rates are narrowly concentrated around their mean value. By contrast, C&I company discount rates are dispersed across a broader range. Different combinations of commercial property owners and commercial, industrial, and utility buyers purchase the different transformer design lines included in the engineering analysis (Chapter 5). Accordingly, DOE constructed the discount rates associated with any given design line from different combinations of commercial property owner, commercial, industrial, and utility discount rates. For example, transformer design line 9 is purchased by commercial property

a This is the average cost of capital, after deducting 2.3 percent inflation, for the set of investor-owned utilities sampled in this analysis. DOE obtained this estimate from the Bloomberg Professional service, during December, 2001. 24

8-35owners, C&I companies, and the government. Roughly 19 percent of customers are commercial property owners, 19 percent are industrial companies, 54 percent are commercial companies, and 7.9percent are government offices (Table 8.3.10). 29 DOE estimated discount rate distributions for the different design lines as a weighted average of the distributions for the different ownership types.
Table 8.3.10 Transformer Ownership by Design Line Transformer Design Line Property Own ers % Industrial Companies % Commercial Companies % InvestorOwned Utilities % Publicly Owned Utilities % Government Offices % 1 0.4 0.5 0.9 72.0 26.0 0.2 2 0.4 0.5 0.9 72.0 26.0 0.2 3 2.1 2.4 4.5 80.0 10.0 1.0 4 0.4 0.5 0.9 72.0 26.0 0.2 5 9.5 9.5 27.0 35.0 15.0 4.0 9 19.0 19.0 54.0 0 0 7.9 10 19.0 19.0 54.0 0 0 7.9 11 19.0 19.0 54.0 0 0 7.9 12 19.0 19.0 54.0 0 0 7.9 13 19.0 19.0 54.0 0 0 7.9 Source: DOE contractors. 8.3.9 Effective Date of Standard The effective date of the new energy efficiency standard for distribution transformers is three years after DOE issues the fi nal rule. DOE assumed that it will issue the final rule in 2007, so the new standard will take effect in 2010. DOE calculated the LCC for all users as if each new distribution transformer purchase occurs in the year the standard takes effect. It based the cost of the equipment on that year; however, as stated above, DOE expresses all dollar values in 2006$.
8.3.10 Transformer Service Life DOE defined distribution transformer service life as the age at which the transformer retires f rom service. DOE assumed, based on ORNL-6847, Determination Analysis of Energy Conservation Standards for Distribution Transformers, 18that the average life of distribution transformers is 32 years. This lifetime assumption includes a constant failure rate of 0.5

8-36percent/year due to lightning and other random failures unrelated to transformer age, and an additional corrosive failure rate of 0.5 percent/year starting at year 15. DOE adjusted the retirement distribution to maintain an average life of 32 years.
8.3.11 Maintenance Costs The maintenance cost is the cost to the consumer of maintaining equipment operation. The maintenance cost is not the cost associated with the replacement or repair of components that have failed. Rather, it is associated with general maintenance. DOE assumed that the cost for general maintenance will not change with increased efficiency. In practice, there is little scheduled maintenance for transformers. Maintenance consists of brief annual checks for dust buildup, vermin infestation, and accident or lightning damage.
8.3.12 Power Factor The power factor is the real power divided by the apparent power. Real power is the time average of the instantaneous product of voltage and current. Apparent power is the product of the root mean square (RMS) voltage times the RMS current. Transformer efficiency specifications, such as NEMA's TP 1-2002, assume a power factor of 1.0. 1Therefore, DOE used a power factor of 1.0, both in calculating the efficiency levels in the engineering analysis and when preparing candidate standard levels for the NOPR. However, in real-world installations, the loads experienced by distribution transformers are li

kely to have power factors of less than 1.0. Because the LCC analysis models transformers that are installed and operating in the field, DOE created the LCC spreadsheet with an adjustable power factor, enabling the LCC to run at lower power factor values. In the absence of any specific data or guidance on the appropriate power factor, DOE used 1.0 for this LCC analysis.
8.3.13 Default Scenario DOE developed distinct scenarios for several key input parameters. It describes these dis tinct scenarios as low, medium, and high. For each of the key inputs, DOE chose the medium designation as the default scenario. The overall default scenario used in the LCC analysis has the following values:

C transformer load growth/year: medium (one percent) for liquid-immersed, low (zero percent) for dry-type

C transformer loading (relative to current estimate): medium (zero percent)

C electricity prices (relative to current estimate): medium (zero percent)

C transformer customer As and Bs: medium

C future energy price trend: AEO2005reference

8-37Other scenarios can readily be used to explore sensitivities to variations of these key variables.
8.4 LIFE-CYCLE COST RESULTS This section presents LCC results for the efficiency improvement levels evaluated for all 13 design lines. Table 8.4.1 provides an overview of the six candidate standard levels DOE evaluated for each of the 10 design lines examined for the NOPR. The lowest efficiency candidate standard level is NEMA’s TP 1-2002 and the highest is the most efficient design identified in the engineering analysis. The remaining four efficiency levels are spread between these two bounds. DOE expresses all the candidate standard levels in terms of efficiency, with no explicit or implicit technology assumed. The column labeled “TP 1+” shows by how much the given standard level exceeds TP 1-2002. DOE based the results presented in this section on the inputs described in section 8.3. Table 8.4.2 provides the overview for the four recombined standard levels that DOE exam ined for liquid-immersed transformers for the final rule. The recombined levels have much greater consistency in the level of stringency between single-phase and three-phase transformers and between small and large transformers.
Table 8.4.1 Trial Standard Levels Evaluated For Each Design Line (TSL 1 through TSL 6)
Design Level 1 TP 1 Level 2 Level 3 Level 4 Level 5 Level 6
Line TP 1+ % Effic'y % TP 1+ % Effic'y % TP 1+ % Effic'y % TP 1+ % Effic'y % TP 1+ % Effic'y % TP 1+ % Effic'y % 1 0.00 98.9 0.00 98.90 0.00 98.90 0.14 99.04 0.29 99.19 0.69 99.59 2 0.00 98.7 0.03 98.73 0.06 98.76 0.09 98.79 0.26 98.96 0.76 99.46 3 0.00 99.3 0.08 99.38 0.16 99.46 0.24 99.54 0.44 99.74 0.45 99.75 4 0.00 98.9 0.18 99.08 0.36 99.26 0.36 99.26 0.68 99.58 0.71 99.61 5 0.00 99.3 0.06 99.36 0.12 99.42 0.17 99.47 0.41 99.71 0.41 99.71 9 0.00 98.6 0.22 98.82 0.44 99.04 0.66 99.26 0.81 99.41 0.81 99.41 10 0.00 99.1 0.12 99.22 0.23 99.33 0.35 99.45 0.41 99.51 0.41 99.51 11 0.00 98.5 0.17 98.67 0.34 98.84 0.51 99.01 0.59 99.09 0.59 99.09 12 0.00 99.0 0.12 99.12 0.23 99.23 0.35 99.35 0.40 99.40 0.40 99.40 13 0.00 99.0 0.15 99.15 0.30 99.30 0.45 99.45 0.55 99.55 0.55 99.55

8-38Table 8.4.2 Trial Standard Levels Evaluated For Each Design Line (TSL A through TSL D) Level A Level B Level C Level D Design

Line TP 1+ % Effic'y % TP 1+ % Effic'y % TP 1+ % Effic'y % TP 1+ % Effic'y % 1 0.29 99.19 0.18 99.08 0.18 99.08 0.14 99.04 2 0.34 99.04 0.21 98.91 0.21 98.91 0.09 99.79 3 0.24 99.54 0.17 99.47 0.12 99.42 0.08 99.38 4 0.29 99.19 0.18 99.08 0.18 99.08 0.18 99.08 5 0.24 99.54 0.17 99.47 0.12 99.42 0.12 99.42
One of the primary impacts of an energy efficiency standard is the change in the set of transform er designs available for purchase and their corresponding loss characteristics, i.e., load losses (LL) and no-load losses (NL). This impact is illustrated in the LL versus NL graph (fourth from right worksheet in the LCC spreadsheet). Figure 8.4.1 provides an illustration of the LL versus NL graph taken from the LCC spreadsheet for design line 1, using the TP 1-2002 standard level. Since each design line has a unique set of engineering constraints, the LL-versus-NL graph for each design will be different. This graph plots an example of a Crystal Ball LCC run (limited to 200 iterations for legibility in this graphic format). It shows different sets of designs by their load losses at rated load, and their no-load losses. Potential designs are shown as both small dots and small squares. The standard level is illustrated by a thick line (those designs that satisfy the standard are to the left of this line). The selected designs not subject to standard constraints are plotted as triangles. The designs subject to standards constraints are plotted as dots. As the standard level increases, the thick line moves parallel to the left (to the area of the graph with lower losses), and so does the set of constrained designs. The standard level is selected with the corresponding pull-down menu on the “Summary” sheet. Some of the selected designs meeting the standard (especially those with higher load losses) are plotted to the right of the line. This is because the efficiency rating and design assumptions use different percentage loading definitions. The efficiency level rating is defined at 50 percent (liquid-immersed and medium-voltage, dry-type), while the load and no-load losses for the design assumptions are defined by nameplate loading (100 percent). For those designs with higher load losses, the heating from losses causes the actual efficiency to drop, shifting the design to the right.

8-39Figure 8.4.1 Design Load Losses (LL) versus No-Load Losses (NL) for TP-1-2002, Design Line 1

The following 10 tables present the summary results from DOE’s LCC analysis. For each evaluated design line and each candidate standard level, DOE presents the percent efficiency, the percent of evaluated transformer purchases that would experience negative, zero, and positive LCC savings when subject to the candidate standard level, and the mean LCC savings. DOE presents these summary results for consideration; it has not selected any specific candidate standard level for any design line. Graphical representations of these results, which provide a clearer indication of the full distributions, are included in Appendix 8A.
8.4.1 Design Line 1 Results Table 8.4.3 presents the summary of the LCC analysis for the representative unit from design line 1, a 50 kVA, liquid-immersed, single-phase, pad-mounted transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.94 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $1,512, while the installation cost was $1,442.

8-40Table 8.4.3 Summary Life-Cycle Cost Results for Design Line 1 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D Efficiency (%) 98.90 98.90 98.90 99.04 99.19 99.59 99.19 99.08 99.08 99.04 Transformers with Net LCC Cost (%) 2.0 2.0 2.0 16.9 63.3 96.7 63.3 24.8 24.8 16.9 Transformers with Zero LCC Change (%) 66.1 66.1 66.1 50.0 7.0 0.0 7.0 38.8 38.8 50.0 Transformers with Net LCC Benefit (%) 31.9 31.9 31.9 33.2 29.7 3.3 29.7 36.5 36.5 33.2 Mean LCC Savings ($) 124 124 124 98 -62 -1074 -62 90 90 98 Mean LCC Savings for those effected 366 366 366 195 -67 -1074 -67 147 147 195 8.4.2 Design Line 2 Results Table 8.4.4 presents the summary of the LCC analysis for the representative unit from design line 2, a 25 kVA, liquid-immersed, single-phase, pole-mounted transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.66 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $865, while the installation cost was $1075.
Table 8.4.4 Summary Life-Cycle Cost Results for Design Line 2 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D Efficiency (%) 98.70 98.73 98.76 98.79 98.96 99.46 99.04 98.91 98.91 98.79 Transformers with Net LCC Cost (%) 12.1 10.5 9.6 12.4 57.7 99.5 79.6 42.5 42.5 12.4 Transformers with No Change in LCC (%) 42.0 38.4 36.3 34.1 10.0 0.0 0.1 16.5 16.5 34.1 Transformers with Net LCC Benefit (%) 45.9 51.1 54.2 53.5 32.3 0.5 20.3 41.0 41.0 53.5 Mean LCC Savings ($) 59 65 76 76 -24 -1094 -113 22 22 76 Mean LCC Savings for those effected 101 105 120 115 -26 -1094 -113 27 27 115

8.4.3 Design Line 3 Results Table 8.4.5 presents the summary of the LCC analysis for the representative unit from design line 3, a 500 kVA, liquid-immersed, single-phase distribution transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.35 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $6,530, while the installation cost was $2,535.

8-41Table 8.4.5 Summary Life-Cycle Cost Results for Design Line 3 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D Efficiency (%) 99.30 99.38 99.46 99.54 99.74 99.75 99.54 99.47 99.42 99.38 Transformers with Net LCC Cost (%) 1.4 1.4 7.7 44 .3 83 .7 87 .3 44 .3 8.1 2.5 1.4 Transformers with No Change in LCC (%) 66 .6 59 .0 49 .1 2.1 0.2 0.0 2.1 47 .1 56 .5 59 .0 Transformers with Net LCC Benefit (%) 32 .0 39 .6 43 .2 53 .6 16 .2 12 .7 53 .6 44 .8 41 .0 39 .6 Mean LCC Savings ($) 1132 1464 1560 1308 -2341 -3460 1308 1597 1555 1464 Mea n LC C Sa vings for those effected 3391 3566 3064 1336 -2345 -3460 1336 3020 3576 3566 8.4.4 Design Line 4 Results Table 8.4.6 presents the summary of the LCC analysis for the representative unit from design line 4, a 150 kVA, liquid-immersed, three-phase distribution transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.85 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $3,914, while the installation cost was $2,260.
Table 8.4.6 Summary Life-Cycle Cost Results for Design Line 4 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D Efficiency (%) 98.90 99.08 99.26 99.26 99.58 99.61 99.19 99.08 99.08 99.08 Transformers with Net LCC Cost (%) 9.6 20.7 18.9 18.9 78.0 86.9 32.4 20.7 20.7 20.7 Transformers with No Change in LCC (%) 54.4 20.6 13.0 13.0 0.1 0.0 13.0 20.6 20.6 20.6 Transformers with Net LCC Benefit (%) 36.0 58.7 68.2 68.2 21.9 13.1 54.6 58.7 58.7 58.7 Mean LCC Savings ($) 368 503 737 737 -780 -1586 397 503 503 503 Mean LCC Savings for those effected 808 634 847 847 -781 -1586 457 634 634 634 8.4.5 Design Line 5 Results Table 8.4.7 presents the summary of the LCC analysis for the representative unit from design line 5, a 1500 kVA, liquid-immersed, three-phase distribution transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.37 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $17,403, while the installation cost was $4,232.

8-42Table 8.4.7 Summary Life-Cycle Cost Results for Design Line 5 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D Efficiency (%) 99.30 99.36 99.42 99.47 99.71 99.71 99.54 99.47 99.42 99.42 Transformers with Net LCC Cost (%) 5.1 4.8 12 .6 21 .4 84 .8 84 .8 52 .3 21 .4 12 .6 12 .6 Transformers with No Change in LCC (%) 66 .7 61 .7 45 .5 33 .0 0.0 0.0 4.7 33 .0 45 .5 45 .5 Transformers with Net LCC Benefit (%) 28 .2 33 .5 41 .9 45 .6 15 .2 15 .2 43 .1 45 .6 41 .9 41 .9 Mean LCC Savings ($) 1597 2168 2480 2626 -5905 -5905 1193 2626 2480 2480 Mea n LC C Sa vings for those effected 4788 5657 4547 3921 -5907 -5907 1251 3921 4547 4547 8.4.6 Design Line 9 Results Table 8.4.8 presents the summary of the LCC analysis for the representative unit from design line 9, a 300 kVA, medium-voltage, dry-type, three-phase transformer with a 45 kilovolt (kV) basic impulse insulation level (BIL). For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.75. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $8 ,251, while the installation cost was $2,111.

Table 8.4.8 Summary Life-Cycle Cost Results for Design Line 9 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Efficiency (%) 98.60 98.82 99.04 99.26 99.41 99.41 Transformers with Net LCC Cost (%) 0.3 2.3 8.6 31.9 62.1 62.1 Transformers with No Change in LCC (%) 61.0 41.4 22.0 0.0 0.0 0.0 Transformers with Net LCC Benefit (%) 38.7 56.3 69.4 68.1 37.9 37.9 Mean LCC Savings ($) 1032 1863 3114 3223 186 186 Mean LCC Savings for those effected 2645 3179 3992 3224 186 186 8.4.7 Design Line 10 Results Table 8.4.9 presents the summary of the LCC analysis for the representative unit from design line 10, a 1500 kVA, medium-voltage, dry-type, three-phase transformer with a 45kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.09 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $31,311, while the installation cost was $4,196.

8-43Table 8.4.9 Summary Life-Cycle Cost Results for Design Line 10 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Efficiency (%) 99.10 99.20 99.30 99.39 99.51 99.51 Transformers with Net LCC Cost (%) 14.3 16.6 18.5 31.1 69.4 69.4 Transformers with No Change in LCC (%) 44.8 31.6 24.1 9.5 0.0 0.0 Transformers with Net LCC Benefit (%) 41.0 51.7 57.4 59.5 30.7 30.7 Mean LCC Savings ($) 4370 5719 7408 7774 -2116 -2116 Mean LCC Savings for those effected 7913 8364 9756 8585 -2116 -2116 8.4.8 Design Line 11 Results Table 8.4.10 presents the summary of the LCC analysis for the representative unit from design line 11, a 300 kVA, medium-voltage, dry-type, three-phase transformer with a 95kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.34 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $12,202, while the installation cost was $2,500.
Table 8.4.10 Summary Life-Cycle Cost Results for Design Line 11 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Efficiency (%) 98.50 98.67 98.84 99.01 99.09 99.09 Transformers with Net LCC Cost (%) 3.4 5.1 13.1 24.9 36.5 36.5 Transformers with No Change in LCC (%) 36.4 27.7 10.8 0.7 0.0 0.0 Transformers with Net LCC Benefit (%) 60.3 67.2 76.1 74.4 63.5 63.5 Mean LCC Savings ($) 3110 4280 5057 5365 4472 4472 Mean LCC Savings for those effected 4888 5921 5670 5400 4472 4472 8.4.9 Design Line 12 Results Table 8.4.11 presents the summary of the LCC analysis for the representative unit from design line 12, a 1500 kVA, medium-voltage, dry-type, three-phase transformer with a 95kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.12 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $35,773, while the installation cost was $4,300.

8-44Table 8.4.11 Summary Life-Cycle Cost Results for Design Line 12 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Efficiency (%) 99.00 99.12 99.23 99.35 99.51 99.51 Transformers with Net LCC Cost (%) 5.0 4.0 8.6 24.2 71.9 71.9 Transformers with No Change in LCC (%) 66.8 56.5 43.8 16.7 0.0 0.0 Transformers with Net LCC Benefit (%) 28.2 39.5 47.6 59.1 28.1 28.1 Mean LCC Savings ($) 2790 4863 6471 7904 -3417 -3417 Mean LCC Savings for those effected 8408 11174 11517 9492 -3417 -3417 8.4.10 Design Line 13 Results Table 8.4.12 presents the summary of the LCC analysis for the representative unit from design line 13, a 2000 kVA, medium-voltage, dry-type, three-phase transformer with a 125kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.20 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $44,843, while the installation cost was $4,739.
Table 8.4.12 Summary Life-Cycle Cost Results for Design Line 13 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Efficiency (%) 99.00 99.15 99.30 99.45 99.55 99.55 Transformers with Net LCC Cost (%) 5.6 7.2 7.4 46.0 78.1 78.1 Transformers with No Change in LCC (%) 71.4 55.2 45.4 1.5 0.0 0.0 Transformers with Net LCC Benefit (%) 23.1 37.6 47.2 52.6 21.9 21.9 Mean LCC Savings ($) 827 3658 6950 6832 -9886 -9886 Mean LCC Savings for those effected 2887 8163 12731 6933 -9886 -9886

8.5 LIFE-CYCLE COST SENSITIVITY ANALYSIS DOE recognizes that there is always some uncertainty associated with engineering and economic analyses. To minimize that uncertainty, DOE strives to use the best techniques and the best data at its disposal. To cover the widest possible set of scenarios in this analysis, DOE used distributions of values for key inputs. For some variables, DOE went one step further by including in the analysis tool, i.e., the LCC spreadsheet, the ability to repeat the very same LCC analysis using values different from the default set used to produce DOE’s results. Detailed descriptions of all of the LCC input variables are included in the discussion of inputs in section 8.3, with additional information in Chapters 6 and 7. This section focuses on five key variables and the impact on the LCC results of assigning them a range of different values. The five variables and the location of their descriptive materials are as follows: 1. percentage of transformers purchased using A & B evaluation (see section 8.3.1);

8-452. transformer loading (see Chapter 6); 3. electricity price trends (see section 8.3.7); 4. load growth trends (see section 8.3.6); and 5. equipment price scenarios (see Appendix 5E). This sensitivity analysis examines how sensitive the LCC results are to changes in key DOE assumptions. For the NOPR, DOE conducted the sensitivity analysis on design lines 1 and 12. This analysis treats each variable independently, i.e., default values remain in effect for all variables except the one being examined. Sensitivity results should always be compared to the default results. Each of the five variables has three values — low, medium, and high — that are described in more detail in the individual input variable sections. The variable that characterizes the percentage of transformers purchased using A and B eval uation uses the same set of low and high values for both liquid-immersed and dry-type transformers, but different medium values for the different types of transformers. The low value represents a scenario where no transformer purchases are evaluated, i.e., a non-evaluating scenario. The high value represents a scenario where all transformer purchases are evaluated. The medium value for liquid-immersed transformers represents a scenario where 75 percent of the purchases are evaluated. The medium value for small-capacity, medium-voltage, dry-type transformers represents a scenario where 50 percent of purchases are evaluated. The medium value for large-capacity, medium-voltage, dry-type transformers represents a scenario where 80 percent of purchases are evaluated. For transformer loading, the medium scenario represents the output of the load simulation described in Chapter 6. The low scenario decreases the medium scenario load by 15 percent and the high scenario increases the medium scenario load by 15 percent. For load growth, the low, medium, and high annual scenarios are zero percent, one percent, and two percent, respectively. Electricity price trends use the AEO2007 low, reference, and high growth scenarios. For equipment price scenarios, the medium scenario represents the five-year average material prices described in Appendix 5E. The high and low price scenarios are also described in Appendix 5E.
8.5.1 Design Line 1 Summary Sensitivity ResultsThe representative unit from design line 1 is a 50 kVA, liquid-immersed, single-phase, padmounted transf ormer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.94 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $1,512, while the installation cost was $1,442.Table 8.5.1 and Figure 8.5.1 illustrate the LCC sensitivity to changes in the five userselectabl e variables. Each of the sensitivity runs for all the variables causes some change in the LCC results. However, for design line 1, it is the change in percentage of transformers purchased using A & B evaluation that results in the most significant changes in in LCC savings for all candidate standard levels examined. DOE found that the variable labeled A & B Distributionstands

8-46significantly apart from the other variables for most candidate standard levels. The reason that the A & B distribution is so important is that it describes whether or not the transformer purchasers place an economic value on losses even without a standard. If transformer purchasers already place an economic value on losses, then standards will have a small impact on purchase behavior, and the impact of a candidate standard level will be small. If no purchasers place an economic value on losses without a standard, then a standard can have a large beneficial impact from decreased energy costs. The A & B distribution options describe the full range of possible purchase behavior scenarios. At the higher efficiency levels represented by candidate standard levels 5 and 6, equipment price has a significant impact. At level 6, equipment price shows the largest difference from thedefault assumptions, because of the limited material options available to produce such highefficiency transformers.
Table 8.5.1 Savings ($), Summary Table for Design Line 1 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D Efficiency (%) 98.9 98.9 98.9 99.04 99.19 99.59 99.19 99.08 99.08 99.04 Baseline 124 124 124 98 -62 -1074 -62 90 90 98 High Load Growth 126 126 126 131 1 -943 1 125 125 131 Non Evaluating A & B Distribution 444 444 444 391 183 -864 183 385 385 391 High Evaluating A & B Distribution 19 19 19 2 -144 -1140 -144 -7 -7 2 Low Loading 122 122 122 76 -103 -1160 -103 67 67 76 High Loading 126 126 126 123 -14 -974 -14 117 117 123 Low Electricity Price 108 108 108 73 -102 -1176 -102 62 62 73 High Electricity Price 140 140 140 122 -22 -972 -22 118 118 122 Low Equipment Price Scenario 108 108 108 73 -102 -1176 -102 62 62 73 High Equipment Price Scenario 68 68 68 -35 -60 -1463 -60 -63 -63 -35 Low Electricity Price Trend Scenario 121 121 121 93 -70 -1095 -70 84 84 93 High Electricity Price Trend Scenario 127 127 127 102 -54 -1054 -54 95 95 102

8-47Figure 8.5.1 Design Line 1 Sensitivity Results by Efficiency Level

8.5.2 Design Line 12 Summary Sensitivity ResultsThe representative unit from design line 12 is a 1500 kVA, medium-voltage, dry-type, threephase transformer with a 95kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.12 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $35,773, while the installation cost was $4,300.Table 8.5.2 and Figure 8.5.2 illustrate the LCC sensitivity to changes in the five userselectable variables for design line 12. Each of the sensitivity runs for all the variables causes some change in the LCC results. The changes in results tend to increase with the efficiency of the candidate standard level. For design line 12, the change in percentage of transformers purchased using A & B evaluation results in significantly larger changes in the LCC savings than do the other variables for all but the highest two candidate standard levels examined. As with design line 1, this outcome occurs because the purchase behavior described by A & B evaluation strongly affects how much energy is used when there is no standard, and thus affects the potential savings. The high savings from the low A & B distribution case result from the fact that DOE assumes that few purchasers place an economic value on losses in that scenario without standards. For most candidate standard levels, the variable labeled A & B Distribution stands significantly apart from the other variables. LCC savings tend to increase as efficiency level increases up to candidate standard level 4, and drop off for levels 5 and 6. At the higher efficiency levels of 5 and 6, low loading, high

8-48material prices, low A & B evaluation, and high electricity prices all result in significant drops in LCC savings.
Table 8.5.2 LCC Savings ($), Summary Table for Design Line 12 Trial Standard Level 1 (TP 1) 2 3 4 5 6 Efficiency (%) 99.00 99.12 99.23 99.35 99.51 99.51 Baseline 2790 4863 6471 7904 -3417 -3417 Non Evaluating A & B Distribution 10824 15326 18924 21077 9640 9640 High Evaluating A & B Distribution 717 2119 3238 4516 -6763 -6763 Low Loading 1672 4333 5121 5293 -8489 -8489 High Loading 3421 5146 7248 9449 -433 -433 Low Electricity Price 2228 3839 4979 5647 -7090 -7090 High Electricity Price 3325 5849 7946 10184 287 287 Low Equipment Price Scenario 2642 4004 5491 7299 2397 2397 High Equipment Price Scenario 2853 2764 5434 5404 -16292 -16292 Low Electricity Price Trend Scenario 2670 4649 6175 7476 -4116 -4116 High Electricity Price Trend Scenario 8713 11611 11965 10035 -2708 -2708

Figure 8.5.2 Design Line 12 Sensitivity Results by Efficiency Level

8-498.6 PAYBACK PERIOD The PBP analysis is a common technique used to evaluate investment decisions. A more energy -efficient device will usually cost more to purchase than a device of standard energy efficiency. However, the more efficient device will usually cost less to operate, due to the reduction in energy use. The payback period is the time (usually expressed in years) it takes to recover the additional first cost of the efficient device, through its energy cost savings. Because the LCC analysis uses distributions of inputs, DOE gives results such as payback periods in the form of distributions.
8.6.1 Payback Period The PBP is the ratio of the increase in purchase expense (i.e., from a less efficient design to a more efficient design) to the decrease in annual operating expenditures. This type of calculation is known as a simple payback period because it does not take into account changes in operating expense over time. PBP is found using the equation: Eq. 8.8 where: )FC = i nstalled purchase price (first cost) of a transformer satisfying the candidate standard level minus the installed purchase price (first cost) of a transformer in the absence of the standard (assumes the transformer meeting the standard is more expensive than the transformer not subject to the standard) (2006$), and )OC = operating cost of the transformer not subject to the standard minus the operating cost of the transformer subject to the standard (assumes the transformer meeting the candidate standard level has lower energy consumption, and hence lower operating cost, than the transformer not subject to the standard). Because )OC is expressed in annual terms, PBP is expressed in years. DOE calculates the PBP both for a distribution of transformers and for an average transformer. The PBP for the national average transformer uses the average values of the first-cost increase and operating-cost savings to calculate the PBP.

8-508.6.2 Inputs The inputs to PBP are: 1) the purchase expense, otherwise known as the total installed consumer cost, or first cost, for each selected design; and 2) the annual (first year) operating expenditures for each selected design. The inputs to the purchase expense are the equipment price and the installation cost with appropriate markups. The inputs to the operating costs are the annual (first year) energy consumption and the electricity price. The distributional PBP uses the same inputs as the LCC analysis described in section 8.3, except that, since this is a simple payback, the electricity price DOE used is only for the year the standard takes effect, assumed here to be 2010.
8.6.3 Baseline Scenario Complications Since distribution transformers are not currently subject to energy efficiency standards, DOE developed the baseline scenario to estimate transformer purchase behavior in the current market. DOE’s default assumption for the baseline scenario was that some portion of transformer purchase decisions are based on TOC-type evaluations. Specifically, the default assumptions for the use ofTOC-type evaluation are 75 percent for liquid-immersed transformers and 50 percent for mediumvoltage, dry-type transformers, all using distributions of A and B evaluation factors. Especially at the lower candidate standard levels that DOE evaluated, the transformer purchases based on TOC may not satisfy the basic PBP assumptions of higher purchase price and lower operating costs for the transformers subject to the candidate standard. When these basic assumptions are not satisfied, the traditional PBP calculation loses its validity. For example, a current (i.e., not subject to standards) transformer purchase decision based on TOC may have identical first cost ()FC = 0) to a transformer just meeting TP 1, i.e., candidate standard level 1. In addition, the transformer meeting the standard may have a different operating cost from the transformer purchased without the standard. In such a situation, PBP = 0. In another example, a current transformer purchase decision based on TOC may result in a transformer costing more to purchase and install and consuming less electricity than a transformer that just meets TP 1. In this case, the PBP calculation for the standards case is nonsensical, since it would imply a negative payback period. DOE’s method of calculating PBP is shown below. )FC = FC(Standard) - FC(Baseline) ()FC is normally positive) )OC = OC(Standard) - OC(Baseline) ()OC is normally negative)
PBP = - )FC / )OC (For the normal ly positive )FC and the normally negative )OC, PBP is positive)

8-51Because )FC can also be zero or negative, and because )OC can also be zero or negative, there are nine possible cases that can be grouped into five computational cases. Table 8.6.1 shows these nine possible relationships between )FC and )OC and the resultant effect on PBP.
Table 8.6.1 Possible )FC and )OC Combinations, for Payback Period Analysis Case # Possible Cases Effect on PBP Calculation 1 )FC > 0 and )OC < 0 Well-defined PBP 2 )FC = 0 and )OC = 0 Unaffected by the standard 3 )FC < 0 and )OC = 0 )FC > 0 and )OC = 0 Division by zero: PBP is undefined
4 )FC = 0 and )OC > 0 )FC = 0 and )OC < 0 PBP = 0
5 )FC < 0 and )OC < 0, or )FC > 0 and )OC > 0, or )FC < 0 and )OC > 0 Not valid: negative or double negative PBP

8.6.4 Payback Period Results Tables 8.6.2 through 8.6.11 illustrate, for each of the 10 design lines and their 6 candidate standard levels, the mean PBP and the percentage of the 10,000 Monte Carlo simulations where the PBP calculation applies. For each candidate standard level for each design line, the sum of the categories “Transformers Unaffected by the Standard,” “Transformers having Well-Defined Payback Period,” and “Transformers having Undefined Payback Period” should equal 100 percent. a As the efficiency of the candidate standard levels increases, the percentage of purchase decisions where the PBPs are well-defined increases. A complete set of PBP histograms for all cases presented in these tables is included in Appendix 8A. DOE calculates the PBP both for a distribution of transformers and for an average transformer. The PBP for the national average transformer uses the average values of the first cost increase and operating cost savings to calculate the PBP for the average transformer.

a For simplicity of presentation, Cases 3, 4, and 5 in Table 8.6.1 have been grouped together into the “Transformers having Undefined Payback Period” category in Tables 8.6.2 through 8.6.11.

8-528.6.5 Design Line 1 Results Table 8.6.2 presents the summary of the PBP analysis for the representative unit from design line 1, a50 kVA, liquid-immersed, single-phase, pad-mounted transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.94 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $1,512, while the installation cost was $1,442.

Table 8.6.2 Summary of Payback Period Results for Design Line 1 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D Mean Payback (Years) 5.9 5.9 5.9 18.2 35.2 45.9 35.2 21.3 21.3 18.2 Transformers Unaffected by the TSL (%) 66.1 66.1 66.1 50.0 7.0 0.0 7.0 38.8 38.8 50.0 Transformers having Well Defined Payback (%) 25.0 25.0 25.0 48.0 88.8 99.7 88.8 58.3 58.3 48.0 Transformers having Undefined Payback (%) 8.9 8.9 8.9 2.0 4.2 0.3 4.2 2.9 2.9 2.0 Mean Incremental First Cost ($) 59 59 59 262 481 2087 481 286 286 262 Mean Operating Cost Savings ($) 25 25 25 27 23 55 23 25 25 27 Payback of Average Transformer 2.4 2.4 2.4 9.7 20.9 37.9 20.9 11.4 11.4 9.7 8.6.6 Design Line 2 Results Table 8.6.3 presents the summary of the PBP analysis for the representative unit from design line 2, a25 kVA, liquid-immersed, single-phase, pole-mounted transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.66 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $865, while the installation cost was $1075.
Table 8.6.3 Summary of Payback Period Results for Design Line 2 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D Mean Payback (Years) 12.7 11.7 10.6 12.2 27.3 59.4 34.2 22.5 22.5 12.2 Transformers Unaffected by the TSL (%) 42.0 38.4 36.3 34.1 10.0 0.0 0.1 16.4 16.4 34.1 Transformers having Well Defined Payback (%) 53.9 57.0 58.4 60.9 88.3 100.0 97.7 81.7 81.7 60.9 Transformers having Undefined Payback (%) 4.1 4.6 5.3 5.0 1.7 0.0 2.2 1.8 1.8 5.0 Mean Incremental First Cost ($) 84 94 85 96 295 1721 409 234 234 96 Mean Operating Cost Savings ($) 11 12 12 12 15 33 17 15 15 12 Payback of Average Transformer 7.6 7.8 7.1 8.0 19.7 52.1 24.0 15.6 15.6 8.0

8-538.6.7 Design Line 3 Results Table 8.6.4 presents the summary of the PBP analysis for the representative unit from design li

ne 3, a 500 kVA, liquid-immersed, single-phase distribution transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.35 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $6,530, while the installation cost was $2,535.
Table 8.6.4 Summary of Payback Period Results for Design Line 3 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D M ean Pa yba ck (Y ear s) 4.0 5.1 10 .5 19 .4 30 .4 34 .1 19 .4 10 .6 6.4 5.1 Transformers Unaffected by the TSL (%) 66 .6 59 .0 49 .1 2.1 0.2 0.0 2.1 47 .1 56 .5 58 .9 Transformers having Well Defined Payback (%) 31 .4 40 .4 50 .3 93 .6 99 .8 10 0.0 93 .6 52 .0 43 .2 40 .4 Transformers having Undefined Payback (%) 2.0 0.7 0.6 4.3 0.0 0.0 4.3 0.9 0.3 0.7 Mean Incremental First Cost ($) 532 991 1784 2226 9294 10629 2226 1722 1237 991 Mean Operating Cost Savings ($) 235 272 288 209 396 406 209 282 288 272 Payba ck of Avera ge T ransformer 2.3 3.6 6.2 10 .6 23 .5 26 .2 10 .6 6.1 4.3 3.6 8.6.8 Design Line 4 Results Table 8.6.5 presents the summary of the PBP analysis for the representative unit from design li

ne 4, a 150 kVA, liquid-immersed, three-phase distribution transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.85 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $3,914, while the installation cost was $2,260.
Table 8.6.5 Summary of Payback Period Results for Design Line 4 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D M ean Pa yba ck (Y ear s) 12 .7 14 .4 13 .1 13 .1 30 .7 35 .7 16 .7 14 .4 14 .4 14 .4 Transformers Unaffected by the TSL (%) 54 .4 20 .6 13 .0 13 .0 0.1 0.0 12.97 20 .6 20 .6 20 .6 Transformers having Well Defined Payback (%) 43 .5 77 .2 87 .0 87 .0 99 .4 99 .9 86 .9 77 .2 77 .2 77 .2 Transformers having Undefined Payback (%) 2.1 2.2 0.0 0.0 0.4 0.1 0.1 2.2 2.2 2.2 Mean Incremental First Cost ($) 669 947 1622 1622 4086 5054 1650 947 947 947 Mean Operating Cost Savings ($) 86 91 143 143 186 194 121 91 91 91 Payba ck of Avera ge T ransformer 7.8 10 .4 11 .3 11 .3 22 .0 26 .0 13 .6 10 .4 10 .4 10 .4

8.6.9 Design Line 5 Results Table 8.6.6 presents the summary of the PBP analysis for the representative unit from design line 5, a1500 kVA, liquid-immersed, three-phase distribution transformer. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.37 percent.

8-54The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $17,403, while the installation cost was $4,232.
Table 8.6.6 Summary of Payback Period Results for Design Line 5 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 A B C D M ean Pa yba ck (Y ear s) 9.0 8.8 12 .4 14 .1 28 .4 28 .4 20 .0 14 .1 12 .4 12 .4 Transformers Unaffected by the TSL (%) 66 .7 61 .7 45 .5 33 .0 0.0 0.0 4.6 33 .0 45 .5 45 .5 Transformers having Well Defined Payback (%) 32 .2 37 .9 53 .8 66 .4 99 .9 99 .9 95 .3 66 .4 53 .8 53 .8 Transformers having Undefined Payback (%) 1.2 0.4 0.7 0.5 0.1 0.1 0.1 0.5 0.7 0.7 Mean Incremental First Cost ($) 2409 3693 4265 5391 22624 22624 9794 5391 4265 4265 Mean Operating Cost Savings ($) 473 613 576 606 1047 1047 710 606 576 576 Payba ck of Avera ge T ransformer 5.1 6.0 7.4 8.9 21 .6 21 .6 13 .8 8.9 7.4 7.4 8.6.10 Design Line 9 Results Table 8.6.7 presents the summary of the PBP analysis for the representative unit from design line 9 , a 300 kVA, medium-voltage, dry-type, three-phase transformer with a 45kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.75. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $8 ,251, while the installation cost was $2,111.
Table 8.6.7 Summary of Payback Period Results for Design Line 9 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Mean Payback (Years) 1.2 3.3 6.6 13.1 22.1 22.1 Transformers Unaffected by the TSL (%) 61.0 41.4 22.0 0.0 0.0 0.0 Transformers having Well Defined Payback (%) 21.3 58.1 77.0 99.6 99.7 99.7 Transformers having Undefined Payback (%) 17.8 0.5 1.0 0.4 0.3 0.3 Mean Incremental First Cost ($) 151 480 1267 3418 8332 8332 Mean Operating Cost Savings ($) 230 261 377 473 606 606 Payback of Average Transformer 0.7 1.8 3.4 7.2 13.8 13.8 8.6.11 Design Line 10 Results Table 8.6.8 presents the summary of the PBP analysis for the representative unit from design line 1 0, a 1500 kVA, medium-voltage, dry-type, three-phase transformer with a 45kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.09 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $31,311, while the installation cost was $4,196.

8-55Table 8.6.8 Summary of Payback Period Results for Design Line 10 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Mean Payback (Years) 12.1 11.9 12.1 14.8 26.7 26.7 Transformers Unaffected by the TSL (%) 44.8 31.6 24.1 9.5 0.0 0.0 Transformers having Well Defined Payback (%) 53.4 67.5 75.5 90.1 99.8 99.8 Transformers having Undefined Payback (%) 1.8 0.8 0.4 0.5 0.2 0.2 Mean Incremental First Cost ($) 4556 6994 9813 12533 28350 28350 Mean Operating Cost Savings ($) 904 1099 1393 1503 1866 1866 Payback of Average Transformer 5.0 6.4 7.0 8.3 15.2 15.2 8.6.12 Design Line 11 Results Table 8.6.9 presents the summary of the PBP analysis for the representative unit from design line 1 1, a 300 kVA, medium-voltage, dry-type, three-phase transformer with a 95kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 98.34 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $12,202, while the installation cost was $2,500.
Table 8.6.9 Summary of Payback Period Results for Design Line 11 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Mean Payback (Years) 4.2 5.2 8.1 11.4 14.5 14.5 Transformers Unaffected by the TSL (%) 36.4 27.7 10.8 0.7 0.0 0.0 Transformers having Well Defined Payback (%) 62.8 71.5 88.3 98.6 99.6 99.6 Transformers having Undefined Payback (%) 0.8 0.8 0.9 0.8 0.4 0.4 Mean Incremental First Cost ($) 1001 1612 2482 3949 5643 5643 Mean Operating Cost Savings ($) 421 538 583 667 720 720 Payback of Average Transformer 2.4 3.0 4.3 5.9 7.8 7.8 8.6.13 Design Line 12 Results Table 8.6.10 presents the summary of the PBP analysis for the representative unit from design line 12, a 1500 kVA, medium-voltage, dry-type, three-phase transformer with a 95kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.12 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $35,773, while the installation cost was $4,300.

8-56Table 8.6.10 Summary of Payback Period Results for Design Line 12 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Mean Payback (Years) 8.1 7.0 9.2 13.8 28.5 28.5 Transformers Unaffected by the TSL (%) 66.8 56.5 43.8 16.7 0.0 0.0 Transformers having Well Defined Payback (%) 32.6 43.1 55.7 81.9 99.7 99.7 Transformers having Undefined Payback (%) 0.5 0.4 0.5 1.4 0.3 0.3 Mean Incremental First Cost ($) 2682 4338 6155 8826 28034 28034 Mean Operating Cost Savings ($) 794 1104 1260 1310 1751 1751 Payback of Average Transformer 3.4 3.9 4.9 6.7 16.0 16.0 8.6.14 Design Line 13 Results Table 8.6.11 presents the summary of the PBP analysis for the representative unit from design line 13, a 2000 kVA, medium-voltage, dry-type, three-phase transformer with a 125kV BIL. For this unit, the average efficiency of the baseline transformers selected during the LCC analysis was 99.20 percent. The consumer equipment cost before installation, which includes the manufacturer selling price, shipping costs, distributor markup, and retail markup, was $44,843, while the installation cost was $4,739.

Table 8.6.11 Summary of Payback Period Results for Design Line 13 Representative Unit Trial Standard Level 1 (TP 1) 2 3 4 5 6 Mean Payback (Years) 9.6 9.9 9.4 19.3 31.7 31.7 Transformers Unaffected by the TSL (%) 71.4 55.2 45.4 1.5 0.0 0.0 Transformers having Well Defined Payback (%) 27.8 44.4 54.3 97.9 99.8 99.8 Transformers having Undefined Payback (%) 0.9 0.4 0.3 0.7 0.2 0.2 Mean Incremental First Cost ($) 1370 5341 8469 14787 39827 39827 Mean Operating Cost Savings ($) 309 961 1505 1544 2127 2127 Payback of Average Transformer 4.4 5.6 5.6 9.6 18.7 18.7

8.7 REBUTTABLE PRESUMPTION The rebuttable presumption is a simplified method of determining the economic justification of a proposed energy efficiency standard. In evaluating the rebuttable presumption, DOE estimates the additional cost of purchasing a more efficient, standard-compliant product, and compares this cost to the value of the energy savings during the first year of operation of the product as determined by the applicable test procedure. If the additional first cost is less than three times the value of the energy savings (i.e., when the rebuttable payback period is less than three years), then the rebuttable presumption that such a standard level is economically justified is satisfied.. The rebuttable payback period differs from payback periods presented in other parts of this chapter in two important ways:

8-57• The rebuttable payback period uses test procedure loading levels to evaluate losses, rather than DOE’s estimate of i n-service loading conditions.

• Other payback periods may consider total operating costs, whereas the rebuttable presumption payback period considers only the value of energy savings. In the case of distribution transformers, however, DOE estimates that the change in operating costs is solely due to energy savings. There are three key inputs into the rebuttable payback calculation: (1) the average efficiency; (2) the average installed cost; and (3) the cost of electricity. Given the average efficiency of the transformer, DOE calculated the losses on the transformer assuming the loading conditions from the test procedure. Multiplying the losses times the cost of electricity provided the operating cost, and then dividing incremental operating costs into incremental installed cost provided the estimate of the rebuttable payback period. Tables 8.7.1 through 8.7.8 show the different inputs into the rebuttable presumption pa yback period calculation. Table 8.7.1 shows the average transformer efficiency as a function of design line and standard level for TSL 1 through TSL 6, while table 8.7.2 shows the average efficiency for TSL D through TSL A. This is the average efficiency as determined by the customer choice model of the LCC calculation. The customer choice model provides a range of transformer design efficiencies that depends on a distribution of customer choices with respect to the value that customers place on reducing transformer design losses. Tables 8.7.3 and 8.7.4 show the average transformer installed cost as a function of both design line and standard level. Tables 8.7.5 and 8.7.6show the average marginal cost of electricity as a function of both design line and standard le

vel. There is a substantial difference between the marginal cost of electricity for liquid-immersed and dry-type transformers because liquid-immersed transformers tend to be owned by utilities, which pay the wholesale (rather than retail) cost of electricity. Tables 8.7.7 and 8.7.8 show the first-year operating cost, which is the annual losses calculated from the test procedure assumptions times the average marginal cost of electricity. Tables 8.7.9 and 8.7.10 show the rebuttable payback period as a function of design line and sta ndard level. The table records how the rebuttable presumption is not satisfied for any candidate level for design lines 1, 2, 4, 5, 10, and 13. For design lines 3 and 12, it is satisfied for candidate standard level 1 only. For design line 11, it is satisfied for candidate standard levels 1 and 2. For design line 9, it is satisfied for levels 1 through 3. Assuming the design line results are representative of the corresponding rated capacity ranges as specified in the engineering chapter (Chapter 5), the rebuttable presumption results by product class are as follows: The rebuttable presumption condition is not satisfied for product class 1 for any trial standard level for the small size categories. For the larger rated capacities for product class 1, the rebuttable presumption is satisfied for level 1. For product class 2 (three-phase, liquid-immersed transformers), the rebuttable presumption is not satisfied for any trial standard level. For product classes 5 and 6, the rebuttable presumption is satisfied for standard levels 1 through 3 for the smaller rated capacities, and not satisfied at all for the larger transformers. For

8-58product classes 7 and 8, the rebuttable presumption is satisfied for levels 1 and 2 for small rated capacities and for level 1 for large rated capacities. And for product classes 9 and 10, the rebuttable presumption is not satisfied for any trial standard level.

Table 8.7.1 Average Tr ansformer Efficiency (TSL 1 through TSL 6) Design

Line Rated Capacity kVA Base % TSL 1 (TP 1) % TSL 2 % TSL 3 % TSL 4 % TSL 5 % TSL 6 % 1 50 98.94 99.03 99.03 99.03 99.10 99.21 99.59 2 25 98.66 98.80 98.81 98.83 98.85 98.98 99.47 3 500 99.35 99.42 99.46 99.49 99.55 99.74 99.75 4 150 98.85 99.02 99.15 99.35 99.35 99.59 99.61 5 1,500 99.37 99.41 99.44 99.46 99.49 99.71 99.71 9 300 98.75 98.84 98.92 99.07 99.26 99.41 99.41 10 1,500 99.09 99.20 99.27 99.34 99.40 99.51 99.51 11 300 98.34 98.63 98.74 98.88 99.03 99.10 99.10 12 1,500 99.12 99.18 99.24 99.28 99.36 99.52 99.52 13 2,000 99.21 99.21 99.28 99.34 99.46 99.55 99.55

8-59Table 8.7.2 Average Transformer Efficiency (TSL D through TSL A) Design

Line Rated Capacity kVA Base % TSL D % TSL C % TSL B % TSL A % 1 50 98.94 99.10 99.12 99.12 99.21 2 25 98.66 98.85 98.94 98.94 99.05 3 500 99.35 99.46 99.47 99.49 99.55 4 150 98.85 99.15 99.15 99.15 99.28 5 1,500 99.37 99.46 99.46 99.49 99.59

Table 8.7.3 Average Transformer Installed Cost (TSL 1 through TSL 6) Design

Line Rated Capacity kVA Base $ TSL 1 (TP 1) $ TSL 2 $ TSL 3 $ TSL 4 $ TSL 5 $ TSL 6 $ 1 50 2,954 2,974 2,974 2,974 3,085 3,401 5,041 2 25 1,939 1,988 1,997 1,994 2,003 2,205 3,660 3 500 9,065 9,243 9,472 9,974 11,244 18,345 19,694 4 150 6,174 6,478 6,926 7,586 7,586 10,254 11,227 5 1,500 21,635 22,438 23,050 23,961 25,245 44,252 44,252 9 300 10,362 10,385 10,642 11,344 13,778 18,695 18,695 10 1,500 35,507 37,960 40,256 42,941 46,821 63,856 63,856 11 300 14,702 15,332 15,859 16,902 18,612 20,344 20,344 12 1,500 40,073 40,956 41,953 43,511 47,324 68,109 56,940 13 2,000 49,582 49,971 51,965 54,182 64,086 89,406 89,406

8-60Table 8.7.4 Average Transformer Installed Cost (TSL D through TSL A) Design

Line Rated Capacity kVA Base $ TSL D $ TSL C $ TSL B $ TSL A $ 1 50 2,954 3,085 3,129 3,129 3,401 2 25 1,939 2,003 2,135 2,135 2,347 3 500 9,065 9,472 9,603 9,976 11,244 4 150 6,174 6,926 6,926 6,926 7,610 5 1,500 21,635 23,961 23,961 25,245 30,973

Table 8.7.5 Average Marginal Electricity Cost (TSL 1 through TSL 6)

Design

Line Rated Capacity kVA Base $ TSL 1 (TP 1) $ TSL 2 $ TSL 3 $ TSL 4 $ TSL 5 $ TSL 6 $ 1 50 0.049 0.050 0.050 0.050 0.049 0.049 0.048 2 25 0.048 0.049 0.049 0.049 0.048 0.048 0.048 3 500 0.049 0.049 0.049 0.049 0.049 0.050 0.050 4 150 0.048 0.048 0.048 0.052 0.052 0.049 0.048 5 1,500 0.050 0.050 0.050 0.050 0.050 0.051 0.051 9 300 0.072 0.072 0.073 0.072 0.072 0.071 0.071 10 1,500 0.070 0.070 0.071 0.071 0.071 0.069 0.069 11 300 0.073 0.073 0.073 0.073 0.072 0.072 0.072 12 1,500 0.070 0.070 0.071 0.071 0.070 0.069 0.069 13 2,000 0.070 0.070 0.071 0.071 0.070 0.069 0.069

8-61Table 8.7.6 Average Marginal Electricity Cost (TSL D through TSL A) Design

Line Rated Capacity kVA Base $ TSL D $ TSL C $ TSL B $ TSL A $ 1 50 0.049 0.049 0.049 0.049 0.049 2 25 0.048 0.048 0.049 0.049 0.048 3 500 0.049 0.049 0.049 0.049 0.049 4 150 0.048 0.048 0.048 0.049 0.049 5 1,500 0.050 0.050 0.050 0.050 0.052

Table 8.7.7 First Year Operating Cost for Rebuttable Presumption, Based on DOE Test Procedure (TSL 1 through TSL 6) Design

Line Rated Capacity kVA Base $ TSL 1 (TP 1) $ TSL 2 $ TSL 3 $ TSL 4 $ TSL 5 $ TSL 6 $ 1 50 115 106 106 106 97 85 43 2 25 71 65 64 63 62 55 28 3 500 703 616 582 544 483 285 272 4 150 367 311 272 224 224 132 122 5 1,500 2,105 1,936 1,857 1,773 1,679 962 962 9 300 1,196 1,119 1,038 891 698 553 553 10 1,500 4,224 3,701 3,437 3,112 2,789 2,230 2,230 11 300 1,614 1,335 1,217 1,081 927 862 862 12 1,500 4,088 3,812 3,586 3,351 2,952 2,205 2,205 13 2,000 4,960 4,870 4,510 4,108 3,370 2,705 2,705

8-62Table 8.7.8 First Year Operating Cost for Rebuttable Presumption, Based on DOE Test Procedure (TSL D through TSL A) Design Line Rated Capacity kVA Base $ TSL D $ TSL C $ TSL B $ TSL A $ 1 50 115 97 95 95 85 2 25 71 62 57 57 51 3 500 703 582 569 542 483 4 150 367 272 272 272 234 5 1,500 2,105 1,773 1,773 1,679 1,388

Table 8.7.9 Rebuttable Payback for Rebuttable Presumption Period (TSL 1 through TSL 6) Design

Line Rated Capacity kVA TSL 1 (TP 1)

yrs TSL 2
yrs TSL 3
yrs TSL 4
yrs TSL 5
yrs TSL 6
yrs 1 50 3.7 3.7 3.7 6.3 11.8 26.1 2 25 7.8 8.4 8.0 8.2 13.0 30.8 3 500 2.3 * 3.8 5.6 9.3 20.6 22.9 4 150 4.7 7.6 10.7 10.7 16.5 19.4 5 1,500 4.5 5.5 6.8 8.3 19.1 19.1 9 300 0.5 * 1.7 * 3.0 * 6.4 12.0 12.0 10 1,500 4.4 5.8 6.5 7.6 13.6 13.6 11 300 2.2 * 2.8 * 4.0 5.4 7.0 7.0 12 1,500 3.0 * 3.7 4.5 6.2 14.3 14.3 13 2,000 4.1 5.2 5.3 8.9 17.1 17.1

*Values less than 3 indicate a rebuttable presumption that the standard level is economically justified.

8-63Table 8.7.10 Rebuttable Payback for Rebuttable Presumption Period (TSL D through TSL A) Design Line Rated Capacity kVA TSL D yrs TSL C yrs TSL B yrs TSL A yrs 1 50 6.3 7.2 7.2 11.8 2 25 8.2 12.1 12.1 16.1 3 500 3.8 4.3 4.3 9.3 4 150 7.6 7.6 7.6 10.6 5 1,500 6.8 6.8 8.3 12.8

*Values less than 3 indicate a rebuttable presumption that the standard level is economically justified.

8.8 USER INSTRUCTIONS FOR SPREADSHEETS To execute the LCC spreadsheet, it is necessary for the user to have the appropriate hardware and software tools. DOE assumed the user has a reasonably current computer operating under the Windows operating system. The development team uses relatively new systems and has not defined the minimum system requirements. At a minimum, users need Microsoft Excel to execute the spreadsheet. For full functionality in running Monte Carlo simulations, users will need a copy of a spreadsheet add-in called Crystal Ball, in addition to Excel. Without Crystal Ball, one can still use the LCC spreadsheet model, but will not be able to examine inputs and outputs as distributions. Approximate results are provided through a sample calculation that uses average values for the inputs and outputs, as displayed in the “Summary” worksheet.
8.8.1 Startup The LCC spreadsheet is a stored Excel file. It can be found on the DOE website at http://www.eere.energy.gov/buildings/appliance_standards/commercial/distribution_transformers.ht ml. Open the file. (Each computer system will have a unique setup for loading a file. Users should refer to their software manuals if they have problems loading the spreadsheet file.) For users new to Excel and/or Crystal Ball, section 8.8.2 contains basic instructions for operating the LCC spreadsheets.
8.8.1.1 Sheet Overview Each of the LCC spreadsheets for the five liquid-immersed transformer design lines contains the foll owing 17 worksheets:

8-64C Options

C Description

C Summary

C A & B Distribution

C Design Table

C Load & Price Parameters

C Utilities

C Discount Rate

C Hourly Loads

C Hourly Prices

C Annual Energy Price Forecast

C Baseline LCC

C Design Option LCC

C Lifetime

C Load-Price Charts

C Results - LLvNL

C LL vs NL Each of the LCC spreadsheets for the eight dry-type transformer design lines contains the foll owing 17 worksheets:

C Description

C Summary

C A & B Distribution

C Design Table

C Demand & Usage

C Utilities

C Discount Rate

C Annual Energy Price Forecast

C Baseline LCC

C Design Option LCC

C Lifetime

C Load-Price Charts

C Results - LLvNL

C LL vs NL

C Tariffs

C Components

C Options Most of the worksheets consist of the data inputs used in the spreadsheet calculations. To ensure f unctionality in the spreadsheet model while maintaining reasonable size, DOE pre-processed some variables into a representative equation. “Hourly Loads” and “Hourly Prices” are the two best examples of using an equation (Fourier transform) to express a complex set of data in an

8-65equation. The “Load-Price Charts” worksheet provides a graphical expression of the “Hourly Loads” and “Hourly Prices.” The spreadsheet/user interface is centered in the “Summary” worksheet. This worksheet contains a number of user-selectable options. These options, each with its own pull-down menu, are:

• Candidate Standard Level • Transformer Load Growth/Year • Transformer Loading • Electricity Prices • Transformer Customer As & Bs • Future Energy Price Trend • Equipment Price Scenario (liquid-immersed only) • Efficiency Standard Effective Date Changing user-selectable options will produce average results shown on the “Summary,” i.e. , results using the mean inputs. Because of the very nature of Monte Carlo mathematics, the average results will not be identical to the mean of the distribution produced from Monte Carlo Crystal Ball simulations, but will provide quick feedback reflecting nominal results to spreadsheet users. DOE conducted an example Monte Carlo run of 100 iterations to produce “Results-LLvNL” which is shown graphically as “LL vs NL.” Selecting different standard levels in the “Summary” worksheet will produce a different graphical result on the “LL vs NL” sheet, as well as different average results on the “Summary” worksheet.
8.8.2 Basic Instructions for Operating the Life-Cycle Cost Spreadsheets 1. Once you have downloaded the LCC file from the Web, open the file using Excel. Atthe bottom, click on the tab for sheet “Summary.” 2. Use Excel’s View/Zoom commands at the top menu bar to change the size of the display to make it fit your monitor. 3. You can interact with the spreadsheet by clicking choices or entering data using the graphical interface that comes with the spreadsheet. Select choices from the various userselectable options. To produce sensitivity results using Crystal Ball, select Runfrom the Runmenu (on the menu bar). To make basic changes in the Runsequence, including altering the number of trials, select Run Preferences from the Runmenu. After each simulation run, the user needs to select Reset (also from the Runmenu) before Runcan be selected again. Once Crystal Ballhas completed its run sequence, it will produce a series of distributions. Using the menu bars on the distribution results, it is possible to obtain further statistical information. The time taken to complete a run sequence can be reduced by minimizing the Crystal Ballwindow in Excel. A step-by-step summary of the procedure for running a distribution analysis is outlined below:

8-661. Find the Crystal Ball toolbar (at top of screen). 2. Click on Runf rom the menu bar. 3. Select Run Preferences and ch oose either Monte Carlo or Latin Hypercube. aSelect number of Trials (DOE suggests 10,000). 4. To run the simulation, choose the following sequence (on the Crystal Ball toolbar): Run Reset Run 5. Now wait until the program informs you that the simulation is completed. DOE provides the following instructions to view the output generated by Crystal Ball: 1. After the simulation has finished, click on the Windows tab bar labeled Crystal Ball to see the distribution charts. 2. The LCC savings and paybacks are defined as Forecast cells. The frequency charts display the results of the simulations, or trials, performed by Crystal Ball. Click on any chart to bring it into view. The charts show the low and high endpoints of the forecasts. The View selection on the Crystal Ball toolbar can be used to specify whether cumulative or frequency plots are to be shown. 2a. To calcul ate the probability that a particular value of LCC savings will occur, either type 0 in the box by the left arrow, or move the arrow key with the cursor to 0 on the scale. The value in the Certainty box shows the likelihood that the LCC savings will occur. 2b. To ca lculate the certainty of the payback period being below a certain number of years, insert that value in the far-right box. 3. To generate a printed report, select Create Reportfrom the Runmenu. The toolbar choice ofForecast Windowsallows you to select the charts and statistics in which you are interested. For further information on Crystal Ball outputs, refer to Understanding the Forecast Chart in the Crystal Ball manual.

a Because of the nature of the program, there is some variation in results due to random sampling when MonteCarlo or Latin Hypercube sampling is used.

8-67REFERENCES

1. National Electrical Manufacturers Association. NEMA Standards Publication TP 1-2002; Guide for Determining Energy Efficiency for Distribution Transformers. 2002. National Electrical Manufacturers Association. Rosslyn, VA. Report No. TP 1-2002. 2. Institute of Electrical and Electronics Engineers Inc. Draft Guide for Distribution Transformer Loss Evaluation.October, 2001. 345 East 47th Street, New York, NY. Report No. IEEE PC57.12.33/D8. 3. Gray, E. Energy Conservation P rogram for Consumer Products: Energy Conservation Standards for Distribution Transformers, DOE Docket Number EE-RM/ST-00-550, comment No. 26. Octobe r 25, 2002. National Electrical Manufacturers Association. 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. Saint, R. - Principal Engineer Research & Technical Services National Rural Electric Cooperative Association (NRECA). E-mail to Antonio Bouza, DOE, Distribution Transformer Purchases by Co-op. Per sonal communication. March 8, 2005. 6. University of Alaska - Fairbanks. Request for Proposals No. 04P0016DH - High Voltage Transformer. 2005. (Last accessed March 9, 2005.) This material is available in Docket #83. 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.uaf.edu/purch/04_solicitations/04P0016DH.pdf>
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8-6820585-0121, telephone (202) 586-2945 for more information.<http://www.atcoelectric.com/B_Regulatory/1349514_Responses/EX_02-006_AE_REAAAMDC_Responses_2004-09-23.pdf> 9. Greenville Utilities. Adver tisements for Bids for Padmount Transformers. 2005. (Last accessed March 9, 2005.) This material is available in Docket #83. Contact Ms. BrendaEdwards-Jones, U.S. Department of Energy, Building Technologies Program, Mailstop EE2J, 1000 Independence Avenue, SW, Washington, DC, 20585-0121, telephone (202) 586­ 2945 for more information. <http://www.guc.com/doing_business/PDF/05-08.pdf> 10. City of Dover - Delaware. Specifications for Three Phase Pad-Mounted Distribution Tran sformers. (Last accessed March 9, 2005.) This material is available in Docket #83. 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.cityofdover.com/media/bids/12-4%20KV%20Transformer%20Spec.pdf> 11. Brownsville Public Utilities Board. Legal Notice and Invitation to Bid for Polemount Tran sformers: B #033-05. 2005. (Last accessed March 9, 2005.) This material is available in Docket #83. 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.brownsvillepub.com/bids/specs/2003/B03305.doc> 12. U.S. Department of Energy - Energy Information Administration. Annual Energy Outlook 2003: With Projections Through 2025.Ja nuary, 2003. Washington, DC. Report No. DOE/EIA-0383(2003). <http://www.eia.doe.gov/oiaf/aeo> 13. U.S. Department of Energy - Federal Energy Regulatory Commission. Form No. 1 ­ Elect ric 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> 14. Shirley, W. Simplified Distribution System Cost Methodologies for Distributed Gener ation. February 2001. Regulatory Assistance Project. (Last accessed June, 2002.) Discussion Draft. 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. Final, published version of paper is at:. < http://www.raponline.org/Pubs/DRSeries/DistCost.PDF>

8-6915. U.S. Department of Energy - Energy Information Administration. Form EIA-861: Annual Elect ric Utility Data. 2001. (Last accessed July, 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/cneaf/electricity/page/eia861.html> 16. U.S. Department of Energy-Energy Information Administration. 1995 Commercial Building Ener gy Consumption and Expenditures (CBECS). 1995. (Last accessed May 19, 2005.) Public Use Data, Microdata file on EIA website. 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/cbecs/microdat.html> 17. National Oceanic & Atmospheric Administration - National Climactic Data Center. U.S. Stand ard Regions for Temperature & Precipitation. (Last accessed June 8, 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.ncdc.noaa.gov/img/climate/research/usrgns_pg.gif> 18. Barnes, P. R., J. W. Van Dyke, B. W. McConnell, and S. Das. Determination Analysis of Energy Conservation Standards for Distribution Transformers. 1996. Oak Ridge National Laboratory. Oak Ridge, TN. Report No. ORNL-6847. 19. Ibbotson Associates. Stocks, Bonds, Bills, and Inf lation . 1999. Ibbotson Associates: Chicago, IL. 20. Modigliani, F. and M. Miller. The Cost of Capital, Corporation Finance and the Theory of Investment. American Economic Review. 1958. (June): pp. 261-297. 21. Bondsonline Group Inc. Bonds Online. 2003. (Last accessed February 22, 2003.) 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.bondsonline.com> 22. Damodaran, A. Damodaran Online. 2003. Stern School of Business, New York University. (Last accessed February, 2003.) 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

8-70(202) 586-2945 for more information. <http://www.stern.nyu.edu/~adamodar/New_Home_Page/data.html.> 23. U.S. Department of Commerce - Bureau of Economic Analysis. GDP price change: Q3, 2000 -Q3, 2001. 2002. (Last accessed February, 2003.) 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. Note: this quarterly report is no longer accessible from the BEA site. <http://www.bea.doc.gov/bea/glance.htm> 24. Bloomberg, L. P. Bloomberg Professional. 2003. (Last accessed February, 2003.) 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://about.bloomberg.com/professional/profservice.html> 25. Value Line. Value Line Investment Survey. 2003. (Last accessed January, 2003.) 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.valueline.com> 26. U.S. Office of Management and Budget. Cir cular No. A-94. Appendix C. 2002. (Last accessed November, 2002.) This material is available in Docket #86. Contact Ms. BrendaEdwards-Jones, U.S. Department of Energy, Building Technologies Program, Mailstop EE2J, 1000 Independence Avenue, SW, Washington, DC, 20585-0121, telephone (202) 586­2945 for more information. <http://www.whitehouse.gov/omb/circulars/a094/a94_appxc.html> 27. Federal Reserve Board. Statistics: Releases and Historical Data - Selected Interest Rates ­ State and Local Bonds. 2002. (Last accessed February, 2003.) 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.federalreserve.gov/releases/h15/data/m/slbond.txt> 28. Ibbotson Associates. Cost of Capital 2001 Yearbook. 2001. Ibbotson Associates: Chicago, IL. 29. HVOLT Consultants Inc., P. Hopkinson, and J. Puri. Distribution Transformer Market Shipment Estimates for 2001. February 17, 2003. 3704 High Ridge Road, Charlotte, NC, 28270, tel: (704) 846-3290, e-mail: P.Hopkinson@hotmail.com. Prepared for Navigant Consulting, Inc., Washington DC.

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