8-i CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS
TABLE OF CONTENTS
8.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.1.1 General Approach for LCC and PBP Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 8.1.2 Overview of LCC and PBP Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-2 8.1.3 Use of Whole-Building Simulations in LCC and PBP Analysis . . . . . . . . . . . . 8-6 8.2 LIFE-CYCLE COST INPUTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-7 8.2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-7 8.2.2 Total Installed Cost Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-7 8.2.2.1 Baseline Manufacturer Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-8 8.2.2.2 Standard-Level Manufacturer Price Increases . . . . . . . . . . . . . . . . . . . 8-10 8.2.2.3 Overall Markup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-10 8.2.2.4 Installation Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-12 8.2.2.5 Weighted-Average Total Installed Cost . . . . . . . . . . . . . . . . . . . . . . . 8-14 8.2.3 Operating Cost Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 8.2.3.1 Electricity Price Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-18 8.2.3.2 Electricity Price Trend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-41 8.2.3.3 Repair Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-43 8.2.3.4 Maintenance Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-44 8.2.3.5 Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-45 8.2.3.6 Discount Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-47 8.2.3.7 Effective Date of Standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-53 8.3 PAYBACK PERIOD INPUTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-53 8.3.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-53 8.3.2 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-54 8.4 RESULTS USING TARIFF-BASED ELECTRICITY PRICES . . . . . . . . . . . . . . . . . 8-54 8.4.1 Life-Cycle Cost Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-54 8.4.1.1 LCC Breakdown Based on Average Input Values . . . . . . . . . . . . . . . 8-55 8.4.1.2 Baseline LCC Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-57 8.4.1.3 Differences in LCC Between Baseline and Standard EER Level Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-58 8.4.2 Payback Period Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-64 8.5 RESULTS USING HOURLY BASED ELECTRICITY PRICES . . . . . . . . . . . . . . . 8-68 8.5.1 Life-Cycle Cost Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-68 8.5.1.1 Life-Cycle Cost Breakdown Based on Average Input Values . . . . . . 8-68 8.5.1.2 Baseline LCC Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-71 8.5.1.3 Differences in LCC Between Baseline and Standard EER Level Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-72 8.5.2 Payback Period Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-768-ii LIST OF TABLES Table 8.1.1 Summary Information of Inputs for the Determination of LCC and PBP . . . . . 8-5 Table 8.2.1 Inputs for Total Installed Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-8 Table 8.2.2 ASHRAE/IESNA Standard 90.1- 1999 Minimum EER Requirements for Commercial Unitary Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9 Table 8.2.3 Baseline Manufacturer Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9 Table 8.2.4 Standard-Level Manufacturer Price Increases . . . . . . . . . . . . . . . . . . . . . . . . . 8-10 Table 8.2.5 Overall Baseline Markups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-11 Table 8.2.6 Overall Incremental Markups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-12 Table 8.2.7 Installation Costs for Baseline-Efficient Air Conditioners . . . . . . . . . . . . . . . 8-13 Table 8.2.8 Installation Cost Indices (National Value = 100.0) . . . . . . . . . . . . . . . . . . . . . 8-13 Table 8.2.9 Costs and Markups for Determination of Weighted-average Total Installed Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-15 Table 8.2.10 Weighted-Average Equipment Price, Installation Cost, and Total Installed Costs (2001$) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-16 Table 8.2.11 Inputs for Operating Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 Table 8.2.12 Percentage of All C&I Customers, by Subdivision, in the Utility Sample . . . 8-21 Table 8.2.13 Energy Consumption, Demand, and Marginal Prices for Five Different Customers on One Tariff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-26 Table 8.2.14 Parameters Used in the Calculation of Customer Energy Cost Savings Based on Hourly Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-39 Table 8.2.15 Marginal Prices Computed fr om Air-conditioning Load Reductions Using the Tariff-based and Hourly Based Electricity Price Models . . . . . . . . . . . . . 8-41 Table 8.2.16 Weighted-average Annualized Repair Costs . . . . . . . . . . . . . . . . . . . . . . . . . . 8-43 Table 8.2.17 Annualized Maintenance Costs for Baseline Efficient Air Conditioners . . . . 8-45 Table 8.2.18 Description of Companies Included in Discount Rate Sample . . . . . . . . . . . . 8-49 Table 8.2.19 Average Values for Variables Used to Estimate Company Discount Rates . . 8-50 Table 8.2.20 Estimated Shares of Commercial Air Conditioner Purchases by Ownership Category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-50 Table 8.2.21 Real Discount Rates by Ownership Category . . . . . . . . . . . . . . . . . . . . . . . . . 8-51 Table 8.2.22 Sensitivity of Real Discount Rates to Equity Risk Premium and Risk Free Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-53 Table 8.4.1 Baseline LCC: Tariff-Based Mean, Median, Minimum, and Maximum Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-58 Table 8.4.2 Summary of Tariff-based LCC Results for 

### $ 65,000 Btu/h to <135,000 Btu/h Commercial Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . 8-62 Table 8.4.3 Summary of Tariff-based LCC Results for 

### $ 135,000 Btu/h to <240,000 Btu/h Commercial Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . 8-63 Table 8.4.4 Summary of Tariff-based PBP Results for 

### $ 65,000 Btu/h to <135,000 Btu/h Commercial Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . 8-66 Table 8.4.5 Summary of Tariff-based PBP Results for 

### $ 135,000 Btu/h to <240,000 Btu/h Commercial Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . 8-678-iii Table 8.5.1 Baseline LCC: Hourly Based Mean, Median, Minimum, and Maximum Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-72 Table 8.5.2 Summary of Hourly Based LCC Results for 

### $ 65,000 Btu/h to <135,000 Btu/h Commercial Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . 8-74 Table 8.5.3 Summary of Hourly Based LCC Results for 

### $ 135,000 Btu/h to <240,000 Btu/h Commercial Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . 8-75 Table 8.5.4 Summary of Hourly Based PBP Results for 

### $ 65,000 Btu/h to <135,000 Btu/h Commercial Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . 8-79 Table 8.5.5 Summary of Hourly Based PBP Results for 

### $ 135,000 Btu/h to <240,000 Btu/h Commercial Air Conditioners . . . . . . . . . . . . . . . . . . . . . . . . 8-80

LIST OF FIGURES Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP . . . . . . . . . . . 8-4 Figure 8.2.1 Air Conditioner Equipment (Operating) Weight as a Function of Efficiency . 8-14 Figure 8.2.2 Map showing the Division of the Continental United States into 17 Subdivisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-19 Figure 8.2.3 Customer-Weighted Average Revenues Divided by Sales . . . . . . . . . . . . . . . 8-22 Figure 8.2.4 Effect of a Change in Customer Demand (Energy Consumption is Held Fixed) for a Block-by-Demand Tariff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-25 Figure 8.2.5 Scatter Plot of the Historical Load versus Temperature Data for the Midwest (Subdivision 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-31 Figure 8.2.6 Scatter Plot of the HistoricalLoad versus Temperature for Florida (Subdivision 5.3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-32 Figure 8.2.7 Scatter Plot of Load versus Temperature for the Midwest (Subdivision 3) . . 8-32 Figure 8.2.8 Scatter Plot of the Historical Price versus Load Data for the Midwest (Subdivision 3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-33 Figure 8.2.9 Scatter Plot of the HistoricalPrice versus Load Data for Florida (Subdivision 5.3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-34 Figure 8.2.10 Price Distribution Functions for the Midwest (Subdivision 3): Historical Data and Price-load Model Data Using Historical Loads. . . . . . . . . . . . . . . . 8-36 Figure 8.2.11 Price Distribution Functions for Texas (Subdivision 7.2): Historical Data and Price-load Model Data Using Historical Loads . . . . . . . . . . . . . . . . . . . . 8-36 Figure 8.2.12 Electricity Price Trends for Commercial Rates . . . . . . . . . . . . . . . . . . . . . . . . 8-42 Figure 8.2.13 Survival Functions for Commercial Air Conditioners and Residential Heat Pumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-47 Figure 8.2.14 Distribution of Discount Rates for All Ownership Categories . . . . . . . . . . . . 8-52 Figure 8.4.1 

### $ 65,000 Btu/h to <135,000 Btu/h: Mean Total Installed Costs . . . . . . . . . . . 8-56 Figure 8.4.2 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff- Based Mean Annual Operating Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-56 Figure 8.4.3 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff- Based Mean Life-Cycle Costs . . . . 8-56 Figure 8.4.4 

### $ 135,000 Btu/h to <240,000 Btu/h: Mean Total Installed Costs . . . . . . . . . . 8-568-iv Figure 8.4.5 

### $ 135,000 Btu/h to <240,000 Btu/h: Tariff- Based Mean Annual Operating Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-56 Figure 8.4.6 

### $ 135,000 Btu/h to <240,000 Btu/h: Tariff-Based Mean Life-Cycle Costs . . . 8-56 Figure 8.4.7 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-Based Baseline (10.1 EER) LCC Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-57 Figure 8.4.8 

### $ 135,000 Btu/h to <240,000 Btu/h: Tariff-Based Baseline (9.5 EER) LCC Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-58 Figure 8.4.9 

### $ 65,000 Btu/h to <135,000 Btu/h: Tari ff-Based Frequency Chart of LCC Differences between 11.0 EER and Baseline (10.1 EER) . . . . . . . . . . . 8-59 Figure 8.4.10 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-Based Cumulative Chart of LCC Differences between 11.0 EER and Baseline (10.1 EER) . . . . . . . . . . . 8-60 Figure 8.4.11 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-Based Mean LCC Savings . . . . . . 8-62 Figure 8.4.12 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-Based Percent of Units with LCC Savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-62 Figure 8.4.13 

### $ 135,000 Btu/h to <240,000 Btu/h: Tariff-Based Mean LCC Savings . . . . . 8-63 Figure 8.4.14 

### $ 135,000 Btu/h to <240,000 Btu/h: Tariff-Base Percent of Units with LCC Savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-63 Figure 8.4.15 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-based Frequency Chart of Payback Periods for 11 EER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-65 Figure 8.4.16 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-based Mean and Median Payback Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-66 Figure 8.4.17 

### $ 135,000 Btu/h to <240,000 Btu/h: Tariff- based Mean and Median Payback Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-67 Figure 8.5.1 

### $ 65,000 Btu/h to <135,000 Btu/h: Mean Total Installed Costs . . . . . . . . . . . 8-70 Figure 8.5.2 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Mean Annual Operating Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-70 Figure 8.5.3 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Ba sed Mean Life-Cycle Costs . . . 8-70 Figure 8.5.4 

### $ 135,000 Btu/h to <240,000 Btu/h: Mean Total Installed Costs . . . . . . . . . . 8-70 Figure 8.5.5 

### $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Mean Annual Operating Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-70 Figure 8.5.6 

### $ 135,000 Btu/h to <240,000 Btu/h: Hourly Ba sed Mean Life-Cycle Costs . . 8-70 Figure 8.5.7 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Baseline (10.1 EER) LCC Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-71 Figure 8.5.8 

### $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Baseline (9.5 EER) LCC Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-72 Figure 8.5.9 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Mean LCC Savings . . . . . 8-74 Figure 8.5.10 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Percent of Units with LCC Savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-74 Figure 8.5.11 

### $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Mean LCC Savings . . . . 8-75 Figure 8.5.12 

### $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Percent of Units with LCC Savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-75 Figure 8.5.13 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Frequency Chart of Payback Periods for 11 EER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-778-v Figure 8.5.14 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Frequency Chart of Annual Energy Expense Differences between 11 EER and the Baseline (10.1 EER) . 8-77 Figure 8.5.15 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Mean and Median Payback Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-79 Figure 8.5.16 

### $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Mean and Median Payback Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-808-1 CHAPTER 8. LIFE-CYCLE COST AND PAYBACK PERIOD ANALYSIS
8.1 INTRODUCTION This chapter describes the methodology for analyzing the economic impacts of possible energy-efficiency standards on individual customers. The effect of standards on individual customers includes a change in operating expense (usually decreased) and a change in purchase price (usually increased). This chapter describes two metrics used in the customer analysis to determine the effect of standards on individual customers: • Life-cycle cost(LCC) is the total customer expense over the life of an appliance, including purchase expense and operating costs (including energy expenditures). Future operating costs are discounted to the time of purchase, and summed over the lifetime of the equipment.

• Payback period(PBP) measures the amount of time it takes customers to recover the assumed higher purchase price of more energy-efficient equipment through lower operating costs. Inputs to the LCC and PBP are discussed in sections 8.2 and 8.3 of this chapter, respectively. Results for each metric are presented in sections 8.4 and 8.5, respectively. Key variables and calculations are presented for each metric. The calculations discussed here were performed with a series of Microsoft Excel ® spreadsheets which are accessible over the Internet (http://www.eere.energy.gov/buildings/appliance_standards/commercial/ac_hp.html). Details and instructions for using the spreadsheets are discussed in Appendix P. A more complete set of results is presented in Appendix Q. 8.1.1 General Approach for LCC and PBP Analysis Recognizing that each commercial building is unique, the Department analyzed variability and uncertainty by performing the LCC and PBP calculations detailed here for a representative sample of individual commercial buildings. The sample of buildings used in the LCC and PBP analysis was defined earlier in Chapter 6, Building Energy Use and End-Use Load Characterization Analysis. Within a given building, one or more unitary air-conditioning units can serve the building’s space-conditioning needs, depending on the cooling load requirements of the building. As a result, the Department expressed the LCC and PBP results as the number of unitary air-conditioning units experiencing economic impacts of different magnitudes. The LCC and PBP model was developed using Microsoft Excel ® spreadsheets combined with Crystal Ball ® (a commercially available add-in program). The LCC and PBP analysis explicitly modeled both the uncertainty and the variability in the model’s inputs using Monte Carlo simulation and probability distributions. A detailed explanation of Monte Carlo simulation and the use of probability distributions is contained in Appendix L.8-2 The LCC and PBP results are displayed as distributions of impacts compared to the baseline conditions. Results are presented at the end of this chapter and are based on 10,000 samples per Monte Carlo simulation run. A variety of graphic displays can be created to illustrate the implications of the analysis. Examples of graphic displays are: (1) a cumulative probability distribution showing the percentage of air-conditioning units in U.S. commercial buildings that would experience a net savings, and (2) a frequency chart depicting variation in life-cycle cost for each efficiency level considered. 8.1.2 Overview of LCC and PBP Inputs The LCC is the total customer expense over the life of the equipment, including purchase expense and operating expense (including energy expenditures). Future operating expenses are discounted to the time of purchase and summed over the lifetime of the equipment. The PBP is the change in purchase expense due to an increased efficiency standard divided by the change in annual operating expense that results from the standard. It represents the number of years it will take the customer to recover the increased purchase expense through decreased operating expenses. Inputs to the LCC and PBP analysis are categorized as follows: (1) inputs for establishing the purchase expense, otherwise known as the total installed cost, and (2) inputs for calculating the operating expense. The primary inputs for establishing the total installed cost are:

• Baseline manufacturer price : The price charged by the manufacturer to either a wholesaler or customer for equipment meeting existing minimum efficiency standards. The manufacturer price includes a markup that converts the cost to manufacture to a manufacturer price.

• Standard-level manufacturer price increases : The change in manufacturer price associated with producing equipment at each standard level.

• Markups and sales tax : The markups and sales tax associated with converting the manufacturer price to a customer price. The markups and sale tax are described in detail in Chapter 7, Markups for Equipment Price Determination.

• Installation price : The cost to the customer of installing the equipment. The installation price represents all costs required to install the equipment other than the marked-up customer equipment price. The installation price includes labor, overhead, and any miscellaneous materials and parts. Thus, the total installed cost equals the customer equipment price plus the installation price. The primary inputs for calculating the operating cost are:8-3 • Equipment energy consumption and power demand : The equipment energy consumption is the site energy use associated with providing space-conditioning to the building. The power demand is the maximum power requirement of the equipment (more commonly known as the peak demand) for a specific period of time. Typically, electric utilities measure the peak demand for each month. Both the energy consumption and peak demand are calculated based on hourly whole-building simulations. Chapter 6, Building Energy Use and End-Use Load Characterization Analysis, provides complete details on the whole-building simulations.

• Equipment efficiency : The energy efficiency ratio (EER) is the efficiency descriptor for commercial unitary air conditioners. For commercial unitary heat pumps, the cooling efficiency is represented with the EER while the heating efficiency is represented with the coefficient of performance (COP). The whole-building simulations assign specific baseline and standard level efficiencies to the unitary air-conditioning equipment to determine its corresponding energy consumption and peak demand.

• Electricity prices : Electricity prices are the price per kilowatt-hour (kWh) paid by each customer for electricity. Electricity prices are determined using two approaches: (1) a monthly approach based on the use of tariffs from a representative sample of electric utilities, and ( 2) an hourly approach based on the use of (a) hourly wholesale electricity prices for those regions of the U.S. that are in deregulated electricity markets and (b) hourly system load and generation cost data for those regions of the U.S. that are in regulated electricity markets. The monthly approach calculates energy expenses based on actual electricity prices that customers are currently paying. The hourly approach attempts to calculate energy expenses based upon electricity prices that customers may pay if electricity markets in the U.S. become deregulated.

• Electricity price trends : The Energy Information Administration (EIA)’s Annual Energy Outlook 2003 ( AEO2003 ) is used to forecast electricity prices into the future. For the results presented in this chapter, the Department used the AEO2003 reference case to forecast future electricity prices.

• Maintenance costs : The cost associated with maintaining the operation of the equipment (e.g., cleaning heat exchanger coils, checking refrigerant charge levels).

• Repair costs : The cost associated with repairing or replacing components that have failed.

• Lifetime : The age at which the air-conditioning equipment is retired from service.

• Discount rate : The rate at which future expenditures are discounted to establish their present value.8-4 Baseline Manufacturer Price Std-Level Manufacturer Price
Wholesaler

Markup
General Contractor

Markup

Sales Tax Installation Cost Total Installed Cost

Electricity

Prices Energy Consumption Equipment Price

Lifetime Operating Expense Annual Energy Expense

Lifetime Repair Cost

Discount Rate Maintenance Cost Electricity Price Trend

Annual Operating Expense Payback Period Life-Cycle Cost Mechanical Contractor

Markup

Power Demand

Figure 8.1.1 Flow Diagram of Inputs for the Determination of LCC and PBP Figure 8.1.1 graphically depicts the relationships between the installed cost and operating cost inputs for the calculation of the LCC and PBP.

Table 8.1.1 summarizes the values for the various inputs to the calculation of the LCC and PBP. As noted earlier, most of the inputs are characterized by probability distributions that capture the input’s uncertainty and/or variability. The values provided in Table 8.1.1 are the weighted-average values from the distribution that is being used to characterize the input. Also listed in Table 8.1.1 is the section of the technical support document (TSD) where more detailed information on the input can be found.8-5 Table 8.1.1 Summary Information of Inputs for the Determination of LCC and PBP
Input Equipment Class Weighted-Average Value Characterization TSD Section Reference Total Installed Cost Inputs Baseline Manufacturer Price $ 65 to <135 kBtu/h $2,098 (at 10.1 EER) Single-Point Value 5.7; 8.2.2.1 $ 135 to <240 kBtu/h $3,957 (at 9.5 EER) Single-Point Value

Standard-Level Manufacturer Price Increase $ 65 to <135 kBtu/h 10.5 EER=$47 11.0 EER = $139 11.5 EER = $292 12.0 EER = $543 Normal Probability Distribution

5.7

$ 135 to <240 kBtu/h 10.0 EER = $62 10.5 EER = $165 11.0 EER = $334 11.5 EER = $613 12.0 EER = $1072 Normal Probabilty Distribution

Wholesaler Markup* Both Baseline = 1.36 Incremental = 1.11 Normal Probability Distribution 7.3
Mechanical Contractor Markup a Both Baseline = 1.48-1.70 Incremental = 1.18-1.37 Normal Probability Distribution 7.4
General Contractor Markup* Both Baseline = 1.24 Incremental = 1.13 Normal Probability Distribution 7.5 Sales Tax Both 1.067 Custom Probability Distribution 7.6

Installation Cost $ 65 to <135 kBtu/h $1585 b Single-Point Value with Regional Variability 8.2.2.4 $ 135 to <240 kBtu/h $2142 b Operating Cost Inputs Annual Energy Consumption Both 1997 kWh/ton/yr † Unique value for each building; Wide variability across building set due to building type and climate; Electricity prices have additional variability due to utility service area 6.3.2
Power Demand Both - † 6.3.2

Electricity Price Both Tariff-Based Marginal = 10.0 ¢/kWh 8.2.3.1 Hourly Based Marginal = 9.9 ¢/kWh

Annual Repair Cost $ 65 to <135 kBtu/h $151 † Single-Point Value at baseline; Uncertainty at higher EER levels tied to uncertainty in increased manufacturer prices 8.2.3.3 $ 135 to <240 kBtu/h $279 †
Annual Maintenance Cost Both $200 Single-Point Value 8.2.3.4 Lifetime Both 15.4 Weibull Probability Distribution 8.2.3.5 Discount Rate Both 6.1% Custom Probability Distribution 8.2.3.6 Electricity Price Trend Both AEO 2003 Ref. Case Two sensitivities – High and Low Growth Cases 8.2.3.2 * Markups also characterized with a National A ccount. Refer to section 7.7 for more details. † Value corresponds to baseline EER level. Value changes as a function of EER.8-6 All of the inputs depicted in Figure 8.1.1 and summarized in Table 8.1.1 are discussed further in sections 8.2 and 8.3. 8.1.3 Use of Whole-Building Simulations in LCC and PBP Analysis As detailed in Chapter 6, the Department conducted whole-building simulations on a representative sample of commercial buildings using commercial unitary air-conditioning equipment. The representative sample of buildings was drawn from the 1995 Commercial Building Energy Consumption Survey (CBECS). 1 The 1995 CBECS includes 5,766 building records. As discussed in Chapter 6, Building Energy Use and End-Use Load Characterization, for the whole-building simulation analysis, the Department selected buildings that have at least 70 percent of their floor space conditioned by packaged air-conditioning equipment. Based on this criterion, the sample used in the whole-building simulation analysis consisted of 1,033 building records. The whole-building simulation analysis generates building energy consumption data for each hour of a typical meteorological year. For each of the 1,033 records in the building sample, the hourly building energy consumption is separated into the air-conditioning energy consumption (i.e., the consumption due to the compressor and condenser fan), the supply orventilation fan energy consumption, and the energy consumption due to all other electric enduses in the building. Because the supply fan is integral to the air-conditioning equipment, energy consumption for ventilation, even during periods where mechanical cooling is not required, is included as part of the total air-conditioning energy consumption. The whole-building simulation data are coupled with electricity price data to generate an annual energy expense for air-conditioning for each of the 1,033 building records. The Department used two approaches to generate annual energy expense data: (1) a monthly approach, and (2) an hourly approach. The monthly approach establishes an annual energy expense using electricity prices determined from electric utility tariffs collected in the year 2002. Under the monthly approach, the Department aggregated the hourly simulated energy consumption data into monthly energy consumption and peak demand values. It then coupled the monthly energy consumption and peak demand values with actual electric utility tariffs to calculate a monthly energy expense. It determined an annual energy expense by summing the monthly energy expenses. The hourly approach establishes an annual energy expense using electricity prices that may exist, assuming all electricity markets are deregulated. For electricity markets that are already deregulated, the Department collected actual wholesale hourly electricity prices. For markets that are still regulated, it collected hourly system load and generation cost data and then used these as a proxy for wholesale prices that may exist if the market were deregulated. Under the hourly approach, the Department coupled the hourly simulated energy consumption data directly with hourly electricity price data to calculate an annual energy expense.8-7 

### LCC IC OC
r t t t N 

### = + +
= 

# ∑ ( ) 1 1



### IC EQP INST = + Eq. 8.1 Complete details on how the Department coupled the whole-building simulation data with both tariff-based and hourly based electricity prices are detailed later in this chapter.
8.2 LIFE-CYCLE COST INPUTS
8.2.1 Definition Life-cycle cost is the total customer expense over the life of an appliance, including purchase expense and operating costs (including energy expenditures). Future operating costsare discounted to the time of purchase, and summed over the lifetime of the equipment. Lifecycle cost is defined by the following equation:

where: LCC = life-cycle cost, IC = total installed cost ($), 

### 3 = sum over the lifetime, from year 1 to year N, where N = lifetime of appliance (years), OC = operating cost ($),
r

= discount rate, and t = year for which operating cost is being determined. Because the Department gathered most of its data for the LCC analysis in 2001, the Department expresses dollar values in 2001 dollars. Total installed cost, operating cost, lifetime, and discount rate are discussed in the following sections. 8.2.2 Total Installed Cost Inputs The total installed cost to the customer is defined by the following equation:

where: EQP= equipment price (i.e., customer price for the equipment only), expressed in dollars, and INST = installation cost or the customer price to install equipment (i.e., the cost for labor and materials), also in dollars. The equipment price is based on how the customer purchases the equipment. As discussed in Chapter 7, Markups for Equipment Price Determination, the Department defined8-8 two types of distribution channels to describe how the equipment passes from the manufacturer to the customer: (1) in the first distribution channel, the manufacturer sells the equipment to a wholesaler, who in turn sells it to a general contractor, who in turn sells it to a mechanical contractor, who in turns sells it to the customer; (2) in the second distribution channel, the manufacturer sells the equipment directly to the customer through a national account. The first distribution channel is sub-divided into two types based on the size of the mechanical contractor (measured in annual revenues): (1) small mechanical contractors (those earning annual revenues of $2 million and less) and (2) large mechanical contractors (those earning annual revenues over $2 million). The remainder of this section provides information about the variables the Department used to calculate the total installed cost for commercial unitary air conditioners. Inputs for the determination of total installed cost are shown in Table 8.2.1. Table 8.2.1 Inputs for Total Installed Costs

Baseline manufacturer price ($) Standard-level manufacturer price increases Wholesaler markup General contractor markup Mechanical contractor markup National accounts markup Sales tax Installation cost ($)
8.2.2.1 Baseline Manufacturer Price The baseline manufacturer price is the price charged by manufacturers to either a wholesaler or customer for equipment meeti ng existing minimum efficiency standards. The manufacturer price includes a markup that converts the cost to manufacture (i.e., the manufacturing cost) to a manufacturer price. The Department developed the baseline manufacturer prices through an efficiency level analysis approach. Refer to Chapter 5, Engineering Analysis, for details. Manufacturer prices were developed for two cooling capacity sizes of commercial air conditioners: (1) a 7.5-ton (90,000 Btu/h) single-package air conditioner representative of the 

### $ 65,000 Btu/h to <135,000 Btu/h single-package and split-system unitary air conditioner equipment class, and (2) a 15-ton (180,000 Btu/h) single-package air conditioner representative of the 

### $ 135,000 Btu/h to <240,000 Btu/h single-package and split-system unitary air conditioner equipment class. The Department did not develop manufacturer prices for e ither of the heat pump equipment classes. Section 6313(a)(6)(A) of 42 U.S.C. requires DOE to establish an amended uniform national standard for commercial unitary air conditioners and heat pumps at the minimum level specified in American Society of Heati ng, Refrigeration, and Air-Conditioning Engineers8-9 (ASHRAE)/Illuminating Engineering Society of North America (IESNA) Standard 90.1-1999, unless DOE determines, through a rulemaking supported by clear and convincing evidence, that a more stringent standard is technologically feasible and economically justified and would result in significant additional energy conservation. Because the Department is not able to consider levels lower than that of the most recent ASHRAE/IESNA Standard 90.1, the Department considers the baseline efficiency to be the minimum level specified in ASHRAE/IESNA Standard 90.1-1999. Therefore, DOE selected the ASHRAE/IESNA Standard 90.1-1999 levels as the baseline efficiency levels for the present rulemaking. Table 8.2.2 presents the ASHRAE/IESNA Standard 90.1-1999 minimum space-cooling efficiency levels for the two commercial unitary air conditioner equipment classes being analyzed. Table 8.2.2 ASHRAE/IESNA Standard 90.1-1999 Minimum EER Requirements for Commercial Unitary Air Conditioners
Size Category Heating Section Type Sub-Category Minimum Efficiency

$ 65,000 Btu/h to <135,000 Btu/h Electric Resistance (or None) Split System and Single Package 10.3 EER All Other Split System and Single Package 10.1 EER

$ 135,000 Btu/h to

<240,000 Btu/h Electric Resistance (or None) Split System and Single Package 9.7 EER All Other Split System and Single Package 9.5 EERBecause the Department estimates that a significant portion of the single package airconditioning market has gas heating as compared to either air-conditioning only or electric resistance heating, the baseline efficiency levels are based on equipment with a gas heating section (i.e., 10.1 EER for the 

### $ 65,000 to 135,000 Btu/h equipment class and 9.5 EER for the 

### $ 135,000 to 240,000 Btu/h class). The efficiency level analysis established a baseline manufacturer price for each of the two air conditioner equipment classes. Table 8.2.3 summarizes the manufacturer prices for baseline-efficient commercial air conditioners.
Table 8.2.3 Baseline Manufacturer Prices Baseline Efficiency Baseline Manufacturer Price (2001$) System Type
$ 65,000 Btu/h to <135,000 Btu/h Single Package and Split System Unitary Air Conditioners 10.1 EER $2,098

$ 135,000 Btu/h to <240,000 Btu/h Single Package and Split System Unitary Air Conditioners 9.5 EER $3,9578-10 8.2.2.2 Standard-Level Manufacturer Price Increases The standard-level manufacturer price increase is the change in manufacturer price associated with producing equipment at higher standard levels. The Department developed manufacturer price increases associated with increases in equipment standard levels through an efficiency level analysis approach. Refer to Chapter 5, Engineering Analysis, for details. Manufacturer price increases as a function of e quipment efficiency (i.e., EER) were developed for each of the two cooling capacity sizes of commercial air conditioners: (1) a 7.5-ton (90,000 Btu/h) single-package air conditioner representative of the 

### $ 65,000 Btu/h to <135,000 Btu/h single-package and split-system unitary air conditioner equipment class, and (2) a 15-ton (180,000 Btu/h) single-package air conditioner representative of the 

### $ 135,000 Btu/h to <240,000 Btu/h single-package and split-system unitary air conditioner equipment class. Although the Department expressed the manufacturer price increases as a continuous function of efficiency, it only carried forward and analyzed specific efficiency levels in the LCC and PBP analysis. Table 8.2.4 lists the efficiency levels considered for the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class characterized witha baseline efficiency of 10.1 EER, and for the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class ch aracterized with a baseline efficiency of 9.5 EER. The efficiency level analysis established a distribution of manufacturer price increases for each standard level. Upper and lower bounds were established for each standard level at 95 percent confidence intervals assuming a normal distribution. Table 8.2.4 summarizes the minimum, average, and maximum manufacturer price increases for the commercial air conditioner standard levels considered in the LCC and PBP analysis.
Table 8.2.4 Standard-Level Manufacturer Price Increases Standard-Level Manufacture Price Increase (2001$)
$ 65,000 Btu/h to <135,000 Btu/h Unitary Air Conditioners $ 135,000 Btu/h to <240,000 Btu/h Unitary Air Conditioners EER Min * Mean Max * Min * Mean Max * 10.0

- - - $27 $62 $98 10.5 $33 $47 $61 $70 $165 $259 11.0 $98 $139 $180 $142 $334 $525 11.5 $206 $292 $377 $261 $613 $964 12.0 $383 $543 $702 $457 $1,072 $1,687 * Minimum and maximum values are actua lly the upper and lower bounds at the 95 percent confidence interval of a normal distribution

. 8.2.2.3 Overall Markup For a given distribution channel, the overall markup is the value determined from multiplying all the associated markups and the sales tax together to arrive at a single markup value. The overall markup in turn is multiplied by the baseline or standard-level manufacturer8-11 price to arrive at the price paid by the customer. Because there are baseline and incremental markups associated with the wholesaler, general contractor, and mechanical contractor, the overall markup is also divided into a baseline markup (i.e., a markup used to convert the baseline manufacturer price into a customer price) and an incremental markup (i.e., a markup used to convert an incremental manufacturer price due to an efficiency increase into an incremental customer price). As discussed in Chapter 7, Markups for Equipment Price Determination, overall markups are based on one of three assumed distribution channels as well as whether the equipment is being purchased for the new construction or replacement market. The distribution channel is based on whether equipment is purchased through: (1) small mechanical contractors, (2) large mechanical contractors, or (3) national accounts. Based on input from equipment manufacturers, DOE determined that the new construction and replacement markets represent 30 and 70 percent of the market, respectively. With regard to the distribution channels, based on input from equipment manufacturers, it appears that 50 percent of equipment purchased by end-use customers is from small mechanical contractors, 32.5 percent is from large mechanical contractors, and the remaining 17.5 percent is through national accounts. With two different markets and three different distribution channels, there are a total of six overall baseline and incremental markups possible. All six sets of overall markups and their associated components are presented in Tables 8.2.5 and 8.2.6 for the baseline and incremental markups, respectively. Based on the percentages of the market attributed to new construction and replacements and the percentages attributed to each of the three distribution channels, weighted-average overall markups are also presented in Tables 8.2.5 and 8.2.6. The weighted-average overall baseline markup equals 2.31 while the weighted-average overall incremental markup equals 1.56. Table 8.2.5 Overall Baseline Markups

Market Sector New Construction Replacement WeightedAverage Small Mech. Large Mech. National Account Small Mech. Large Mech. National Account Wholesale 1.36 1.36 1.69 1.36 1.36 1.60 Mechanical Contractor 1.48 1.35 1.70 1.55 General Contractor 1.24 1.24 NA NA Sales Tax 1.07 1.07 1.07 1.07 1.07 1.07 Overall 2.66 2.42 1.80 2.47 2.24 1.71 2.318-12 Table 8.2.6 Overall Incremental Markups

Market Sector New Construction Replacement WeightedAverage Small Mech. Large Mech. National Account Small Mech. Large Mech. National Account Wholesale 1.11 1.11 1.27 1.11 1.11 1.24 Mechanical Contractor 1.26 1.18 1.37 1.29 General Contractor 1.13 1.13 NA NA Sales Tax 1.07 1.07 1.07 1.07 1.07 1.07 Overall 1.69 1.59 1.35 1.63 1.53 1.32 1.56

8.2.2.4 Installation Cost The installation cost is the price to the customer of labor and materials (other than the actual equipment) needed to install air-conditioning equipment. The Department derived installation costs for commercial air conditioners from data in the RS Means Mechanical Cost Data, 2002

. 2This book provides estimates on the person-hours required to install commercial air-conditioning equipment and the labor rates associated with the type of crew required toinstall the equipment. The installation cost was calculated by multiplying the number of personhours by the corresponding labor rate. RS Means provides specific person-hour and labor rate data for the installation of 7.5-ton and 15-ton roof top air conditioners. The Department decided that 7.5-ton and 15-ton roof top air conditioner data are representative of installation costs for the 

### $ 65,000 Btu/h to <135,000 Btu/h and the 

### $ 135,000 Btu/h to <240,000 Btu/h air conditioner equipment classes, respectively. Labor rates vary significantly from region to region of the country and the RS Means data provide the necessary information to capture this regional variability. RS Means provides cost indices that reflect the labor rates for 295 cities in the United States. Several cities in all 50 states of the United States and the District of Columbia are identified in the RS Means data. These cost indices were incorporated into the analysis to vary the installation cost depending on the location of the commercial air-conditioning customer. Since data were not available to indicate how installation costs vary with equipment efficiency, the Department considered two scenarios: (1) it varied installation costs in direct proportion with the weight of the equipment and (2) it kept installation costs flat. For the former case, the engineering analysis developed linear re lationships of operating weight as a function of equipment efficiency for 7.5-ton and 15-ton commercial air conditioners. Thus, under this scenario, installation costs varied linearly with equipment efficiency. Table 8.2.7 summarizes the nationally representative person-hours and labor rates associated with the installation of 7.5-ton and 15- ton roof top air conditioners as presented in RS Means Mechanical Cost Data . In Table 8.2.7, both bare installation costs (i.e., costs before overhead and profit (O&P)) and installation costs including O&P are provided. The Department8-13 decided that the 7.5-ton and 15-ton installation costs that include O&P represent the installation costs for baseline efficient systems (i.e., 10.1 EER for 7.5-ton systems and 9.5 EER for 15-ton systems).
Table 8.2.7 Installation Costs for Baseline-Efficient Air Conditioners 2002 Base Costs (2001$) Labor w/ O&P (2001$)
System Type * Personhours Cost per Person-hour Total Labor Cost Cost per Person-hour Total Labor Cost 7.5-ton cooling, 170 kBtu/h heating 32.26 $32.58 $1,051 $49.13 $1,585 15-ton cooling, 270 kBtu/h heating 42.03 $33.78 $1,420 $50.95 $2,142 * Description as in RS Meansfor roof top air conditioners with standard c ontrols, curb, economizer, and a single-zone, electric cooling, gas heating unit.
Table 8.2.8 summarizes the cost indices for installations in each of the 50 States of the U.S., plus the District of Columbia, used to vary the nationally representative installation costs in Table 8.2.7. To arrive at an average index for each state, the city indices in each state were weighted by their population. Population estimates for the year 1999 from the U.S. Census Bureau were used to calculate a weighted-average index for each state. Table 8.2.8 Installation Cost Indices (National Value = 100.0)

State Index State Index State Index Alabama 57.7 Kentucky 70.4 North Dakota 66.2 Alaska 109.0 Louisiana 58.2 Ohio 100.1 Arizona 79.0 Maine 80.2 Oklahoma 64.7 Arkansas 53.2 Maryland 83.4 Oregon 111.4 California 120.7 Massachuse tts 111.0 Pennsylvania 114.5 Colorado 80.7 Michigan 104.7 Rhode Island 102.6 Connecticut 104.5 Minnesota 109.8 South Carolina 44.7 D.C. 89.8 Mississippi 44.6 South Dakota 39.3 Delaware 108.0 Missouri 96.9 Tennessee 65.9 Florida 59.1 Montana 78.9 Texas 65.5 Georgia 59.2 Nebraska 84.5 Utah 72.0 Hawaii 124.8 Nevada 105.5 Vermont 72.0 Idaho 78.5 New Hampshire 90.9 Virginia 68.5 Illinois 118.3 New Jersey 120.8 Washington 104.0 Indiana 89.7 New Mexico 74.9 West Virginia 88.2 Iowa 82.7 New York 155.3 Wisconsin 96.3 Kansas 74.4 North Carolina 48.2 Wyoming 54.5 Figure 8.2.1 shows the relationship between equipment weight (otherwise known as operating weight) and efficiency for 7.5-ton and 15- ton air conditioners. Each point in the figure represents an actual model available. A least-squares linear fit was developed to relate equipment8-14 Weight = 42.134(EER) + 498.07 Weight = 171.8(EER) + 194.61

600 800 1000 1200 1400 1600 1800 2000 2200 2400

8.5 9 9.5 10 10.5 11 11.5 12 EER (Btu/hr/W)Operating Weight (lbs) 15 tons 7.5 tons

Figure 8.2.1 Air Conditioner Equipment (Operating) Weight as a Function of Efficiency weight to efficiency (the equations relating equipment weight to efficiency are shown in the figure). Because of the increased weight of moreefficient units, the Department decided to vary installation costs in direct proportion to operating weight.

The default installation cost scenario in the LCC and PBP analyses uses installation costs that vary with equipment weight. Spreadsheets used in calculating the LCC and PBP are also the basis for calculating the LCC and PBP, based on the constant installation cost scenario. 8.2.2.5 Weighted-Average Total Installed Cost As presented in Eq. 8.1, the total installed cost is the sum of the equipment price and the installation cost. The Department derived the customer equipment price for any given standard level by multiplying the baseline manufacturer price by the baseline markup and adding to it the product of the incremental manufacturer price and the incremental markup. Because manufacturer prices, markups, and the sales tax are represented by probability distributions, the resulting total installed cost for a particular standard level will not be a single-point value, but rather a distribution of values. With this said, the weighted-average total installed costs for the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class and the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class are presented for the baseline level and each standard level to provide an indication of the increase in the total installed cost due to an efficiency increase.8-15 

### IC EQP INST MFG MU INST BASE BASE BASE

BASE BASE OVERALL BASE 65 135 65 135 65 135

65 135 65 135 

### 2 31 −−−

− − 

### = + =⋅+ = ⋅+ = +

= $2098 . $1585 $4855 $1585 $6440 The derivation of the total installed cost is relatively straightforward. The baseline manufacturer price and the standard-level manufacturer price increases are the starting points for determining the total installed cost (values are taken directly from Tables 8.2.3 and 8.2.4). The baseline and incremental markups, the sales tax, and installation costs are used to convert themanufacturer prices into total installed costs. Table 8.2.9 summarizes all of the weightedaverage or mean costs and markups necessary for determining the weighted-average baseline and standard-level total installed costs. Table 8.2.9 Costs and Markups for Determinatio n of Weighted-average Total Installed Costs Variable Weighted-Average or Mean Value Baseline Manufacturer Price $ 65,000 Btu/h to <135,000 Btu/h Unitary Air Conditioners = $2098; $ 135,000 Btu/h to <240,000 Btu/h Unitary Air Conditioners = $3957 Std-Level Manuf. Price Increase Refer to Mean Values in Table 8.2.4 for each equipment class Overall Markup - Baseline 2.32 Overall Markup - Incremental 1.56 Installation Cost - Baseline $ 65,000 Btu/h to <135,000 Btu/h Unitary Air Conditioners = $1585; $ 135,000 Btu/h to <240,000 Btu/h Unitary Air Conditioners = $2142 Installation Cost - Incremental Increases in direct proportion to equipment weight - Refer to Fig. 8.2.1 To illustrate the derivation of the weighted-average total installed cost, the Department presents the calculation below for a baseline (i.e., 10.1 EER) and 11 EER air conditioner for the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class. For baseline 

### $ 65,000 Btu/h to <135,000 Btu/h unitary air conditioners, the calculation of the total installed cost ( IC Base 65-135 ) is as follows:

where: EQP= equipment price, expressed in dollars, and INST = installation cost or the customer price to install equipment, expressed in dollars, MFG = manufacturer price, expressed in dollars, and MU = markup. In this specific example, MFG is the baseline manufacturer price for the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class and MU is the overall baseline markup.
The calculation of the 11 EER air-conditioner total installed cost includes the use of a manufacturer price adder. In addition, since the Department derived an incremental markup8-16 

### IC EQP EQP INST Weight Weight MFG MU MFG MU

INST EER EER lb lb EER BASE EER BASE EER

BASE

BASE BASE OVERALL EER INCR OVERALL

BASE 11 65 135 65 135 11 65 135 65 135 11 65 135

65 135

65 135 11 65 135

65 135 

### 42134 11 498 07 42134 101 498 07
2 31 156 962 924 −− −− −

−

− −

− 

### =+ +⋅        

=⋅+ ⋅

+ ⋅ ⋅ + ⋅ +      

= ⋅+ ⋅ + ⋅      

= ++

= ∆
∆

. .

.. . $2098 . ($139 . ) $1586 .

. $4855 $217 $1650 $6722 based on incremental equipment price changes, the derivation of the 11 EER total installed cost is based on determining the change in equipment price over the baseline equipment price. Also note that, since the installation cost scales with the equipment weight, the ratio of the 11 EER to the baseline (10.1 EER) equipment is used to increase the installation cost for the 11 EER unit. (Refer back to Figure 8.2.1 for the equation used to calculate equipment weight as a function of EER.) The Department calculated the 11 EER total installed cost ( IC11 EER 65-135 ) as follows:

Table 8.2.10 presents the weighted-average equipment price, installation costs, and total installed costs for the two air conditioner equipment classes at the baseline level and each standard level. Table 8.2.10 Weighted-Average Equipment Price, Installation Cost, and Total Installed Costs (2001$)
$ 65,000 and <135,000 Btu/h

Unitary Air Conditioners $ 135,000 and <240,000 Btu/h Unitary Air Conditioners

EER Equipment Price Installation Cost Total Installed Cost Equipment Price Installation Cost Total Installed Cost 9.5 a

- - - $9,157 $2,142 $11,299 10.0 -

- - $9,254 $2,243 $11,497 10.1 b $4,855 $1,585 $6,440

- - - 10.5 $4,928 $1,614 $6,542 $9,414 $2,343 $11,757 11.0 $5,072 $1,650 $6,722 $9,677 $2,444 $12,121 11.5 $5,309 $1,686 $6,995 $10,111 $2,545 $12,656 12.0 $5,700 $1,722 $7,422 $10,826 $2,646 $13,472

a9.5 EER is baseline efficiency for the $ 135,000 Btu/h to <240,000 Btu/h equipment class.
b10.1 EER is baseline efficiency for the $ 65,000 Btu/h to <135,000 Btu/h equipment class.8-17 

### OC EC RC MC = + + 8.2.3 Operating Cost Inputs The Department based the operating cost for the LCC analysis on energy consumption data developed from whole-building simulations on a sample of buildings from the 1995 CBECS. After the LCC analysis was performe d, the Department generated a distribution of LCC differences (i.e., the LCC difference between the baseline equipment and equipment with a higher efficiency level) to determine the mean LCC difference, as well as the percentage of buildings analyzed that had LCC savings associated with more-efficient equipment. The Department defined the operating cost by the following equation:

where: EC = energy expenditure associated with operating the equipment, RC = repair cost associated with component failure, and MC = service cost for maintaining equipment operation. The remainder of this section provides information about the variables the Department used to calculate the operating cost for commercial air conditioners. Table 8.2.11 shows the inputs for the determination of operating costs. Note that although the lifetime, discount rate, and effective date of the standard are not needed for determining the operating cost, they are required for establishing the operating cost’s present value. The base case and standard case designs define the efficiency levels of the design of interest (standard case design) and what design (base case design) it is being judged against.

Table 8.2.11 Inputs for Operating Costs

Electricity price ($) Electricity price trend Repair cost ($) Maintenance cost ($) Lifetime Discount rate Effective date of standard Base case design Standard case design8-18 8.2.3.1 Electricity Price Analysis Introduction to the analysis of electricity prices . This section describes the electricity price analysis used to develop the energy portion of the annual operating expenses for central air-conditioning in the commercial sector. The electric power industry is currently in a state of transition between two different business models, from regulated monopoly utilities providing bundled service to all customers in their service area, to a system of deregulated independent suppliers who compete for customers. While it is unclear when this transition will be completed, it is possible that, in the near future, customers will see a very different pricing structure for electricity. To account for the impacts of this change on the LCC, the Department used two different electricity price models in this analysis. The first uses information on utility tariffs for commercial customers collected in 2001 from a sample of 90 utilities across the country. The second analysis is based on electricity production prices that vary on an hourly basis, and is used to model a scenario in which customers are charged directly for the costs incurred to the electricity provider in supplying the air-conditioning end-use. The Department refers to the two analyses as “tariff-based” and “hourly based,” respectively. Both analyses use the same building simulation data, discussed in Chapter 6, to compute energy savings under different standard levels. 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 both models of electricity prices. For this reason, the Department divided the continental U.S. into 17 regions or subdivisions. To make maximum use of the location information in CBECS, the breakdown started with the nine census divisions. These were further subdivided to take into account significant climate variation and the existence of different electricity market or grid structures. Th e Department based climate divisions on the nine climate regions defined for the continental U.S. by the National Climactic Data Center. 3 In addition, it separated out Texas, Florida, New York and California because their electric grids are operated independently. Figure 8.2.2 illustrates the results. Every building in the simulation sample was assigned to a subdivision. In Figure 8.2.2, the subdivision numbers use the CBECS census code as the first digit. For example, census division 8 (Mountain) has been separated into two subdivisions 8.1 and 8.1. With this model, electricity costs and savings can be computed on a per-subdivision basis. Results in the LCC were aggregated up to the national level. For the purposes of this analysis, the Department assumed that each building in the simulation data set corresponds to one customer of a utility.8-19 Figure 8.2.2 Map showing the Division of the Co ntinental United States into 17 Subdivisions
Tariff-based Analysis. This section gives an overview of the tariff-based analysis of electricity prices. Selection of the Sample Utilities The Department used three main criteria in developing the utility sample: (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 (investor-owned utilities (IOUs) and power marketers) versus publicly owned utilities (municipals, cooperatives, state, and federal); and (3) the sample should cover as many customers as possible. Data on utilities are available from DOE’s EIA through the Form 861 filings. 4This form is filed annually by every utility that retails 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. The Department used data from the year 2000 in this analysis as it was the most complete data set at the time the analysis was conducted. The Department 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,8-20 for each subdivision, the percentage of customers served by public versus private utilities. The sample utilities were chosen to reflect the relative population of the subdivision and the proportion of public to private customers. In most areas, the Department included the largest utilities in the sample to maximize the number of customers represented, but it also included smaller private utilities and public utilities of all sizes. The final set of sampled utilities includes 49 privately owned and 41 publicly owned companies. The EIA data for 2000 show that power marketers and other providers of unbundled retail services serve only two percent of the commercial customers in the Nation. Power marketers have the largest market share in New England, so the Department included one such company in the sample for this region. Appendix M contains a list of the sample utilities. Representativeness of the Sample For this rulemaking, the Department defined the representativeness of the sample by the percentage of the total number of commercial and industrial (C&I) customers who 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 because these tend to be much smaller. Consequently, to obtain the same level of customer representation, many more utilities would need to be included. The sampled utilities serve 60 percent of the C&I customers of private utilities, and 14.4 percent of C&I customers for public utilities. The combined total for the U.S. is 48.5 percent of all C&I customers. Table 8.2.12 gives the percentage of customers by subdivision, broken down by ownership type.8-21 Table 8.2.12 Percentage of All C&I Customers, by Subdivision, in the Utility Sample

Subdivision Census Division Region Public Customers in Sample (%) Private Customers in Sample (%) Fraction 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 USA 14.4 60.0 48.5 7,170,373

The ratio of total revenues to total energy sales, which reflects, on an average basis, the amount of money collected for each kWh sold, also varies significantly among utilities and across different regions. Figure 8.2.3 illustrates th e degree of variation. The figure plots the customer-weighted average of utility revenues divided by sales (in units of $ per 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 sampled utilities only. There is a large spread in the EIA data, and sample averages are within this range.8-22 Customer-weighted Average Revenue/Sales

0 2 4 6 8 10 12 14 16

1 2.1 2.2 3 4.1 4.2 5.1 5.2 5.3 6.1 6.2 7.1 7.2 8.1 8.2 9.1 9.2 Subdivisioncents/kWh

Figure 8.2.3 Customer-Weighted Average Revenues Divided by Sales

Utility weights The way in which weights are assigned to the utilities will depend 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. Because utility rates within a given subdivision may vary substantially, and because there are a relatively small number of customers (buildings) in the sample, to avoid any bias the Department assigned each customer to each utility in its subdivision. Once a customer is assigned to a utility, it is automatically assigned to one of the utility tariffs based on the customer’s annual peak load. The appropriate weight for the customer-utility combination is the product of the CBECS building weight and a utility weight. For this case, the appropriate weight for the utility is the number of customers it has, divided by the number of customers in the subdivision. The weights for all the utilities in a given subdivision will then add up to one. Because the level of customer representation for public utilities is much less than for private ones, the Department included an additional factor to account for the difference, so that the total weight of the sample public utilities equals the fraction of public customers in that subdivision. In equation form, definitions for a given subdivision are the following: n(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 ,8-23 n(pub) = sum(k) n(k,pub) = total customers served by publicly owned sample utilities n(priv) = sum(k) n(k,pub) = 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. The weight for utility k is: Weight(k,own_type) = ( n(k,own_type)/n(own_type) ) * ( N(own_type)/N) , where own_type = public or private. By definition, 

### 3 (own_type) 

### 3 (k) Weight(k,own_type) = 1. The one exception to this rule is New York, because of its large size, the Department broke out the state ownership type separately. The weights are calculated as described above, but using three ownership categories: public (municipal, cooperative, Federal), private, and State. Data Collection and Modeling This section briefly describes the data coll ection method, the selection of tariffs for a given utility, and how the tariffs were modeled.
Web search . The Department collected the vast majority of the tariff documents directly from utility web sites. Almost every privately owned utility in the sample makes its tariff available on the web. 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, the Department contacted the utilities directly. An archive of the documents the Department used in the analysis is available at http://eetd.lbl.gov/ea/ees/tariffs/index.php. The list of utilities is in Appendix M. Selection of tariffs for each utility . Utility companies have many tariffs which are 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, so in its tariff database the Department 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, the Departme nt assigned customers to a specific tariff based on their peak demand over the previous 12 months. In a few cases, the assignment was based on8-24 the maximum monthly electricity consumption. Customers are not generally moved from one tariff to another unless a change in their electricity use characteristics persists for several months. 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. For some utilities, TOU tariffs are mandatory. Occasionally, utilities will offer different tariffs for different business types. In all of the cases checked, although the tariffs have different names, the rates are 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 rates with them on a case-by-case basis. The Department’s sampling strategy was to take the default tariff for each customer class. In cases where TOU is mandatory, it modeled these tariffs, but in cases where TOU rates are optional they were not included in the sample. The Department also excluded “closed” tariffs. These are tariffs which are being phased out by the utility and so are not available 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 tariff; 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 air-conditioning equipment covered by the standard and 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 simulation data provide the annual peak load which was used to assign a customer to a tariff. Modeling the tariffs . 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 and total energy consumption for the current billing period. In its analysis, the Department took the billing period to be one calendar month. The billing demand is the customer’s peak demand over the billing month. While the formulae determining the actual bill can be quite complex, they are essentially 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 ¢/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. The same definitions hold for TOU tariffs. The difference is that the billing demand and energy consumption are computed separately for the peak, off-peak, and shoulder periods defined by the utility. For most of the utilities in the sample, charges also vary seasonally. Energy and demand charges are typically applied in blocks. This means that 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 forth. 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 all8-25 Block 1 10 ¢/kWh Block 2 6 ¢/kWh Block 3 3¢/kWh¢/kWh 10

kWh 200 2000 2100 Case 2: 2100 kWh Case 1: 2000 kWh 200 kWh @ 10¢/kWh 100 kWh/kW @ 6 ¢/kWh All Other @ 3 ¢/kWh

Figure 8.2.4 Effect of a Change in Customer Demand (Energy Consumption is Held Fixed) for a Block-by-Demand Tariff remaining energy use. This is an example of declining block rates, where the energy charge decreases as the amount of energy used increases. One may also see inclining block rates. As a variation on this theme, the block limits may depend on the customer's energy consumption and demand. As an example of one such tariff, Figure 8.2.4 shows a block-by-demand tariff, that occurs in approximately 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 8.2.4 illustrate this. In each case, the overall energy use is 3,200 kWh, but the billing demand is different (20kW versus 21 kW). In 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 3200 kWh, but the demand is 21 kW. Now the length 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 has increased the bill by $3. This is because 100 kWh of consumption have been shifted from the third, cheaper, 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.8-26 The important point to take from this analysis 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 will 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. This is illustrated in Table 8.2.13 for a set of five buildings which are all on the tariff General Service-GS2 of the Boston Edison Company, for the month of July. The energy consumption and demand are shown for the case EER=8.5, and the marginal rate was computed by comparing to EER=10. In the table, the smallest building happens to have the lowest marginal rate because the first 10 kW of billing demand are free. Overall, the pattern of lower marginal rates associated with higher load factors (flatter loads) is clear. Table 8.2.13 Energy Consumption, Demand, a nd Marginal Prices for Five Different Customers on One Tariff Boston Edison Company General Service Tariff G-2 Data for the month of July
Building Type Building Size SqFt Energy Consumption kWh Billing Demand kW Load Factor Marginal Rate ¢/kWh Small Office 3000 3372 12.7 36% 18.0 Small Office 5000 5507 19.9 37% 15.9 Restaurant 9000 21704 53.7 54% 10.3 Small Office 23750 27794 108.2 35% 18.4 Warehouse 55000 32867 134.2 33% 17.3

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 some approximations as follows.



### C Riders and adders : Some tariffs, particularly in deregulated areas, include additional charges as riders which may not be explicitly defined. The most significant are the fuel cost recovery adders. These are additional charges passed on to the customer when the utility must spend more than anticipated for fuel. These costs are included when they are given explicitly; however, they are sometimes represented as a formula based on the utility's expenditures and so cannot be modeled within the Department’s framework. This results in a possible underestimation of costs for some customers.



### C Sales taxes : Sales taxes have not been included in this version of the analysis. Sales taxes may add 5-10 percent to the customer electricity bill.8-27 

### C Industrial loads : The LCC analysis is based on estimates of energy consumption for commercial buildings. Although the tariff sample includes the tariffs appropriate to industrial (particularly light manufacturing) buildings, the Department has not conducted a loading analysis for these types of buildings. If, for an industrial building, the hour in which the building peak load occurs is systematically and significantly offset from the hour in which the air-conditioning peak load occurs, there may be an overestimate of the savings due to peak demand reduction. On the other hand, if loads in industrial buildings are statistically flat, then the marginal prices derived from commercial building loads will be adequate.



### C Industrial buildings : If the size distribution (both by floor-space and by annual peak load) of light industrial buildings that use the equipment covered by this rulemaking is significantly different from the size distribution of buildings in CBECS, it may be necessary to adjust the weights to reflect this. However, as the weights are determined primarily by population density, such an adjustment should not have a large impact on the aggregate LCC results.



### C Delivery voltages : The larger utilities may offer service to customers at a variety of voltages, and there may be different rates associated with different voltages. For this analysis, in cases where more than one service voltage is defined, the secondary voltage tariff has been used, as this represents the largest customer category. Typically the difference in the tariff is primarily in the fixed charge, which does not affect the marginal rate. However, in some cases customers taking service at higher voltages may see a lower effective marginal price. Federal Energy Regulatory Commission (FERC) data indicate that, at the most, on the order of 10 percent of customers may be on higher voltage tariffs, so the overall impact on the LCC results should not be significant.



### 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, the Department 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 correspond to peak demands on the order of 1000 kW. The Department 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. So, for example, the billing demand may be the greater of the current monthly demand or the peak demand in the most recent summer months. The Department did not include this type of rate formula in its tariff models. 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 its peak demand over a year rather8-28 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 attributed to standards, because 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, the Department’s model will over-estimate the savings due to standards. The building simulations use gas as the heating fuel in all cases, so the Department is not able to determine the season of peak load for buildings with electric heat. However, a review of the CBECS data shows that, out of the 1,033 buildings simulated for this analysis, 125 indicate that their peak load occurs in the winter or in no particular season. Of these, 80 are in the southern states and represent only 6.5 percent of the total floor space in the sample. This is unlikely to impact the LCC results significantly. Monthly Bill Calculations The Department used the calendar month as the billing period. For each building, it processed the hourly simulation data for each standard level to compute the peak demand and total energy consumption for the 12 calendar months. The building location (the subdivision) was used to assign each building to a group of utilities, and the annual peak demand for the base case was used to assign each building to a specific tariff. The Department kept the customer on the same tariff for all the standard levels. For buildings assigned to TOU tariffs, it re-processed the hourly data to compute the peak demand and total energy consumption for the 12 calendar months during the peak, off-peak, and shoulder hours as defined by the utility. These numbers were input to a spreadsheet which then calculated the total customer bill in each month. The Department repeated the calculation for each standard level, and then totaled the monthly bills to arrive at an annual electricity bill. The difference between the annual bills for each standard level gave the associated operating cost savings. To compute the base case air-conditioning expense, it took the annual bill and multiplied it by the ratio of the total air-conditioning energy use to the total building electricity use. Customer marginal prices were calculated as the net change in the total bill, divided by the net change in energy consumption between two standard levels. To represent the full range of tariffs in the spreadsheet, the Department 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 components. A component is defined by a set of parameters which 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 tariff8-29 from the set of components, and then apply the customer data used to determine whether conditions are met for a particular rate. The TDM and the individual customer data are input to a Bill Calculator, which is a set of accounting programs implemented as functions in a Microsoft Excel ® spreadsheet. These functions produce monthly bills for a customer. For the LCC analysis, the Department used a batch version of this spreadsheet to process all the data for all buildings and standard EER levels. To present all the information on the collected tariffs in an accessible format, a version of the Bill Calculator has been implemented in a spreadsheet that includes customer data for a set of representative buildings. The spreadsheet allows the user to choose a utility and a particular tariff, and then choose from a set of buildings covered by that tariff. The spreadsheet calculates monthly bills for January and July for the chosen building, along with marginal rates and a breakdown of the bill into fixed, demand, and energy charges. The full information on the tariff is also displayed on the page. The Bill Calculator spreadsheet can be obtained at http://eetd.lbl.gov/ea/ees/tariffs/index.php. Hourly Based Analysis. The goal of the hourly based electricity price analysis was to estimate the real cost of meeting air-conditioning loads for each building in each subdivision, and translate these to cost-savings that result from a given EER level. The Department intended to model a scenario that monitors customer loads on an hourly basis and customers that experience hourly varying energy prices. (This is often referred to as real-time pricing.) Cost savings from a standard include two components: the marginal generation cost reduction which scales with the amount of energy saved, and capacity cost-savings that scale with the coincident peak load reductions. (The coincident peak load reduction is the load reduction during the hour of the system peak.) In this analysis, the Department treated each subdivision as if it were a single electricity system or control area with a single hourly varying marginal generation price. Note that, in this context, price refers to the amount paid by the utility to acquire or generate power, not to the price paid by the final consumer. The dependence of system load on weather and system price on load creates a correlation between the weather-sensitive air-conditioning load in each building and the time-varying generation marginal price. This substantially increases the cost of meeting air-conditioning loads relative to base loads. Since the building simulations used typical meteorological year (TMY) weather data to represent the correlations correctly, the Department had to produce a set of corresponding TMY system loads and prices for each subdivision. To do this, the Department constructed a model for the load-temperature relationship and a model for the price-load relationship, from historical data. Inputs to the Hourly Based Analysis The analysis required the following four hourly data streams: customer loads, local temperatures, system loads, and system prices. The Department took customer loads from the building simulations described in Chapter 6. Hi storical data on hourly loads are available from FERC through Form 714 filings. 5 Every utility in the country with an annual peak load above 200 MW in its planning area must submit this form, in which it reports a time series of its hourly load each year. The public may access these load data through the FERC website. Historical8-30 data on hourly prices come from two sources. For regulated utilities, FERC Form 714 also requires filing the hourly time series of system lambdas each year. The system lambda represents the cost to meet the next kW of load, as computed for the local control area using the utility’s automatic dispatch methodology. 6The DOE analysis used system load and lambda data from the year 2000 for all subdivisions except New England, New York, California, and PJM (Pennsylvania-New Jersey-Maryland). The latter correspond to deregulated wholesale markets, which are administered by independent system operators (ISOs). For these areas, the Department collected load data and day-ahead market clearing prices directly from the ISO websites. The analysis used market data from 1999 for California, 7 and from 2000 for New York, 8 PJM, 9 and New England. 10 The Department used two types of weather data for the analysis: actual historical weather data and TMY data (30-year average of weather data). The Department purchased historical data for the years 1999 and 2000 from the National Climatic Data Center, and used it to construct the models. 11 These data include temperature measurements at several hundred weather stations throughout the country. The TMY weather data are freely available over the web from the National Renewable Energy Laboratory. 12The TMY data include temperatures at about 230 stations in the continental U.S., which are a subset of those included in the historical data. In its analysis, the Department used historical and TMY data from the same set of stations.Inside each subdivision, the Department calculated a single load, temperature, and pricetime series by taking the appropriately weighted combinations of the available data. For the temperature, it first computed the average in each hour over all of the stations in a given state. The Department computed the time series for the subdivision as a population-weighted sum of the temperatures in each state. The Department used this method both for the historical data and the TMY data. For system loads, it used a simple sum over all the grids or portions of grids within a subdivision. For the price, it used a weighted average of the marginal price-time series for each control area inside a given subdivision to construct an average marginal price. The Department used the average annual load in each control area as the weight. Modeling the Load-temperature Relationship The load-temperature model is straightforward conceptually, consisting of three basic steps: bin the data, use a simple fit inside each bin, and then add random fluctuations. The predictable portion of the system load depends on two factors: the daily activity patterns, which can be captured by a calendar and a clock; and the seasonally varying loads (primarily space conditioning and lighting), which depend on temperature and the hours of sunrise and sunset. The Department accounted for the load variations due to time of day, day of week, and natural light in the model by the way it sorts the data into bins. It modeled load variations due to temperature by the fit within a bin. The Department constructed the model from historical data and used it to generate a TMY system load time series ( L TMY ) from the TMY temperature time series, for the calendar year used in the building simulations.8-31 Figure 8.2.5 Scatter Plot of the Historical Load versus Temperature Data for the Midwest (Subdivision 3) To get a sense of the overall relationship the Department tried to model, Figures 8.2.5 and 8.2.6 show scatter plots of the temperature and load in each hour for the Midwest (subdivision 3) and Florida (subdivision 5.3). Th e climate and demographics within a given subdivision determine the shape and location of the cloud of points, and thus do not depend strongly on the year of the data. The details of that year’s weather determines the actual distribution of points within the cloud. Appendix N presents scatter plots for all the subdivisions. While it is clear that the load increases with low and high temperatures, a wide range of loads is possible at any given temperature. This range is greatly reduced if the data are appropriately binned, as illustrated in Figure 8.2.7. The Department sorted the hourly data for temperature and system load according to: (1) hour of day, H, (2) a flag, DN, indicating whether the given hour is day or night, and (3) a flag, DT, indicating whether the day type was peak (weekday) or off-peak (weekend and holiday). For some hours, it is always day or night, while for morning and evening hours the light depends on the time of year. The figure shows historical data for the whole year in light gray, and two bins corresponding to the hours from 1 to 2 a.m. (“open square” marks) and 1 to 2 p.m. (“x” marks), both for a weekday. The difference in the time of day accounts for a large portion of the variability in load values.8-32 Figure 8.2.6 Scatter Plot of the Historical Load versus Temperature for Florida (Subdivision 5.3)

Figure 8.2.7 Scatter Plot of Loadversus Temperature for the Midwest (Subdivision 3)

Within each bin, the Department constructed a fit between the historical load data and temperature data using a least-squares polynomial approximation. The order of the polynomial depends on the number of points in the bin, up to order three. Then, the Department used the8-33 Figure 8.2.8 Scatter Plot of the Historical Price versus Load Data for the Midwest (Subdivision 3) same polynomial to compute the fitted TMY load data. To incorporate the fluctuations around the fitted values in each bin, DOE computed a frequency distribution for the difference between the historical and the fitted loads. The Department used this frequency distribution to generate random increments to the fitted TMY loads. In the final step, the Department reconstructed the time series from the model data for each bin. Appendix O explains each of these steps in detail. Modeling the Price-load Relationship The price-load model is conceptually similar to the load-temperature model, in that the three basic steps are to bin the data, use a simple fit inside each bin, and then add random fluctuations. The main differences come from the fact that the price-load relationship is considerably more random than the load-weather relationship, and that prices tend to spike, meaning that there are infrequent, but very large excursions away from the average price levels. Figures 8.2.8 and 8.2.9 show scatter plots of price versus load for the Midwest and Florida. The figures use a logarithmic scale for the price. The overall shape and location of the cloud of points depends on the mix of generation available in the region, and on the load characteristics as determined by climate and demographics. Again, this shape will be insensitive to the year of the data, while the details for a given year determine the exact placement of points within the cloud. Appendix N includes price-load plots for all the subdivisions.8-34 Figure 8.2.9 Scatter Plot of the Historical Price versus Load Data for Florida (Subdivision 5.3)

The presence of a few points near the top of the graph indicate price spikes. While spikes tend to correlate with very high loads, they may also occur at moderately high loads. The spikes are truly random in the sense that they occur for reasons which cannot be explicitly represented in a model, such as transmission or generation outages. To represent these rare events accurately using a distribution, the Department would need more than one year’s data and need to generate more than one year’s model data. Because this is not possible in the context of the current analysis, the Department developed another method for dealing with price spikes. It first removed the spikes entirely by capping the price at a value representative of the upper limit of the cloud of points in the price-load plot, here chosen to be $150/MWh. It saved the hour of occurrenceiand value P(i) of each price spike. The Department then constructed a price-load model from the capped data, and then generated a capped price-time series for the TMY year. In the final step, the Department added back the price spikes to the TMY price-time series. The price-load relationship is not as regular as the load-temperature relationship, and the Department found empirically that binning the dataon hour of day and day of week did not lead to a reduction in the range of prices seen for a given load. For this reason, the model uses only two binary variables to divide the data into bi ns. The first is a flag HT indicating whether a given hour is peak (weekdays 7 a.m.- 7 p.m.), or off-peak (all other hours). The second is a flag SW indicating whether the season is Summer (May-Oct) or Winter (Nov-Apr). These distinctions are important because high prices and high price volatility occur primarily for summer and during peak hours. This process led to four separate data bins.8-35 Inside each hour-type/season bin, the Department computed a fitted price versus load function, along with a frequency distribution for the difference between the hourly prices and the fitted price. For the TMY data, it sorted the data in the same way into hour-type/season bins, and assigned a fitted price to each hourly load based on the model. The Department computed random deviations from the average, using the modeled distribution. The final step in this process was to restore the spike prices. Two properties characterize spikes: they occur for higher loads, and they come in clusters (that is, spikes will typically occur for a sequence of anywhere from two to 12 consecutive hours). The Department restored price spikes, as they occurred in the historical data, to the TMY price data, preserving their values and the clustering properties. In the model, the beginning hour of a sequence of price spikes is a random variable, subject to the condition that the load in that hour be above 90 percent of its annual peak value. Appendix O discusses all the steps in the price-load model in greater detail. Validation of the Model TMY Prices
Because the TMY weather data differ from th e historical weather, the distribution of prices the Department computed from the model should differ from the historical prices. This makes validation of the model somewhat complicated. The average generation price as a function of load depends primarily on the type of generation available in the region, so it should be roughly the same for the historical and the modeled data. The maximum discrepancy between the average price-load curves for the historical and TMY data was less than five percent. One can also generate modeled prices for the historical load data using the price-load model. Because of the large degree of randomness in the price-load relationship, the output of the model will differ somewhat, but there should be a hi gh degree of similarity. Figures 8.2.10 and 8.2.11 show the price distributions that resulted from usi ng historical load data to generate model prices for two regions of the country. The regions shown in the figures are the Midwest (subdivision 3) and Texas (subdivision 7.2), respectively. Because the model prices are the result of a Monte Carlo simulation, different runs of the model will give different output. The figures include two runs. They clearly show that the price model correctly reproduces the price distributions seen in the original data.8-36 Price distributions for the Midwest (subdivision 3)

0 500 1000 1500 2000 2500 3000

8 13 20 32 50 79 126 200 316 501 794 Price $/MWhFrequency Historica Run 1 Run 2

Figure 8.2.10 Price Distribution Functions for th e Midwest (Subdivision 3): Historical Data and Price-load Model Data Using Historical Loads. Note: The Horizontal Price Scale is Logarithmic

Price distributions for Texas (subdivision 7.2)

0 200 400 600 800 1000 1200 1400 1600 1800 2000

8 9 10 10 12 13 14 17 19 22 26 32 Price $/MWhFrequency Historical Run 1 Run 2

Figure 8.2.11 Price Distribution Functions for Texas (Subdivision 7.2): Historical Data and Price-load Model Data Using Historical Loads. Note: The Horizontal Price Scale is Logarithmic8-37 Calculation of Total Avoided Costs In the hourly based method, the Department computed the energy-cost savings due to a given standard level assuming that all savings to the utility were passed on to the customer. It developed a model to estimate the utility’s avoided costs. There are two component costs: avoided generation costs and avoided capacity costs. For this analysis, the avoided capacity costs depend on the customer load reductions during the hour of the system peak. Costs for capacity include generation, transmission, and distribution capacity. Also, the model includes factors that account for losses and additional costs that scale with generation. The Department converted the utility’s avoided costs to customer savings by applying a fixed charge rate (FCR) to the capacity costs. The FCR is a factor that converts a given capacity investment to the annual revenue requirement needed to cover all costs associated with the investment. For regulated areas, the equation is as follows: ECS = (1 + CM)*( IGC*GF*LAF + ITC*TF + IDC*DF)*PS + LAF*(1 + CRF)*GS The Department defined inputs to this equation as follows:



### C Energy Cost Savings (ECS ): This is the total annual dollar savings to the customer resulting from the reduction in the hourly air-conditioning load at a given EER level.



### C Capacity Margin (CM): This is the fraction of reserve capacity needed to assure system reliability per unit of additional capacity required. It is set to 15 percent.



### C Incremental Generation Cost (IGC) : This is the cost in $/kW of new generation capacity. The AEO2003 provides the overnight costs for different types of generation technology. For most areas of the country, DOE assumed that new generation wascombined-cycle gas-oil. For the south (subdivisions 6.2, 7.1, 7.2 and 5.3), airconditioning contributes to the base-load, so DOE assumed that new capacity was a 50- 50 combination of coal and combined-cycle gas-oil plants. The Department added the cost of financing plant construction to the overnight costs, assuming that the plant owner’s cost of capital is five percent.



### C Generation FCR (GF) : This is the fixed charge rate used to convert an investment in generation capacity to an annual revenue requirement. The FCR depends on the cost of capital, tax rates, and insurance costs which vary depending on the type of company. The LCC spreadsheet uses values drawn from a triangular distribution with a mean of 0.15. 13



### C Loss Adjustment Factor (LAF ): This factor accounts for transmission losses. To compute the amount of energy that must be generated to supply a given load, the load must be multiplied by the LAF. The Department set the LAF to a constant value of 1.08.8-38 

### C Incremental Transmission Cost (ITC): This is the cost in $/kW of new transmission capacity. These costs were input to the 2003 version of the National Energy Modeling System (NEMS) used by EIA in its energy forecasts 14for each of 13 regions. Here, the Department converted them to costs for the 17 subdivisions.



### C Transmission FCR (TF) : This is the fixed charge rate used to convert an investment in transmission capacity to an annual revenue requirement. Here, the Department input the value 0.12 to NEMS. 15



### C Incremental Distribution Cost (IDC): This is the cost in $/kW of new distribution capacity. Distribution capacity includes costs for transformers and substations, and costs for lines and feeders. The price model includes only the former, as energy-efficiency standards for commercial unitary air-conditioning equipment will probably not have a large impact on the number of power lines going to buildings. These costs on average were $42/kW. 16



### C Distribution FCR (DF) : This is the fixed charge rate used to convert an investment in distribution capacity to an annual revenue requirement. It is set equal to TF.



### C Peak Load Savings (PS ): This is the customer’s load saving due to standards during the hour of system peak load. Because of the high degree of randomness in the load model, the Department computed the customer’s contribution to system peak load savings by averaging the customer’s load over the top 10 hours of the system load.



### C Cost Recovery Factor (CRF ): This factor accounts for additional costs incurred in supplying electricity which do not appear in the marginal generation cost. It includes costs for ancillary services and may include administrative overhead. Limited information from real-time pricing formulas indi cates that this factor is on the order of 15 percent. This is consistent with data on the average cost of ancillary services in deregulated wholesale electricity markets. Inthe LCC analysis this factor is drawn from a triangular distribution with a mean of 0.15.



### C Generation Cost Savings (GS ): These are avoided generation costs from the reduction in the customer’s hourly load due to standards. The Department computed this factor by taking the sum over all hours of the customer’s hourly energy savings multiplied by the hourly generation marginal price. In deregulated wholesale markets, DOE assumed that hourly prices included a margin to cover generation capacity investments, so the term depending on IGC does not appear. However, load-serving entities in some markets must also purchase installed capacity reservations in amounts determined by their annual or seasonal peak load. While capacity markets presently are not everywhere, they are included in slightly altered form in FERC’s proposed Standard Market Design. 17 The Department expects that capacity markets will be the8-39 model for continuing deregulation of wholesale markets, so it used them in the cost model for each deregulated region. The equation becomes: S = (1 + CM)*( ICAP + ITC*TF + IDC*DF)*DP + LAF*(1 + CRF)*DG All terms are as in the first equation, except for the Installed Capacity Cost (ICAP) , which is the amount in $/kW that the utility must pay annually to reserve capacity. This amount should be slightly below the annual carrying cost of a combustion turbine. Data from PJM and New York indicate that ICAP payments add about 1¢/kWh to the average energy price. Using the market data on total annual consumption and peak loads, this converts to a value of about $45/kW-year. 18 Table 8.2.14 summarizes the values of all the model parameters for each subdivision.
Table 8.2.14 Parameters Used in the Calculation of Customer Energy Cost Savings Based on Hourly Prices

Sub Div Total Cap Cost Generation Cost Parameters

Plant Type* CM IGC $/kW GF † Transmission OverNight $/kW ITC+IDC $/kW TF ICAP $/kW-yr $/KW-yr LF CRF † 1 CCNatGas 1.15 n.a. 0.15 151 208 0.12 45 81 1.08 0.15 2.1 CCNatGas 1.15 n.a. 0.15 151 208 0.12 45 81 1.08 0.15 2.2 CCNatGas 1.15 n.a. 0.15 151 208 0.12 45 81 1.08 0.15 3 CCNatGas 1.15 631 0.15 124 179 0.12 n.a. 142 1.08 0.15 4.1 CCNatGas 1.15 631 0.15 123 178 0.12 n.a. 142 1.08 0.15 4.2 CCNatGas 1.15 631 0.15 123 178 0.12 n.a. 142 1.08 0.15 5.1 CCNatGas 1.15 631 0.15 129 184 0.12 n.a. 143 1.08 0.15 5.2 CCNatGas 1.15 631 0.15 129 184 0.12 n.a. 143 1.08 0.15 5.3 Half&Half 1.15 1030 0.15 129 184 0.12 n.a. 217 1.08 0.15 6.1 CCNatGas 1.15 631 0.15 128 183 0.12 n.a. 143 1.08 0.15 6.2 Half&Half 1.15 1030 0.15 128 183 0.12 n.a. 217 1.08 0.15 7.1 Half&Half 1.15 1030 0.15 128 183 0.12 n.a. 217 1.08 0.15 7.2 Half&Half 1.15 1030 0.15 133 189 0.12 n.a. 217 1.08 0.15 8.1 CCNatGas 1.15 631 0.15 259 328 0.12 n.a. 163 1.08 0.15 8.2 CCNatGas 1.15 631 0.15 226 328 0.12 n.a. 158 1.08 0.15 9.1 CCNatGas 1.15 631 0.15 259 291 0.12 n.a. 163 1.08 0.15 9.2 CCNatGas 1.15 n.a. 0.15 259 328 0.12 45 97 1.08 0.15 * CCNatGas = Combined-cycle oil-gas; Half & Half = 50-50 coal and combined cycle. † These numbers represent the mean of a triangular distribution.8-40 The above equations convert the energy and capacity savings for each customer to a net reduction in the customer’s energy bill, assuming that utilities base charges to customers on their hourly loads and the electricity provider’s hourly varying production costs. Note that the generation and capacity cost estimates are valid only at the margin, so the model can only be used to compute the energy cost savings due to standards. Results for the Tariff-based and Hourly Based Electricity Price Analyses. A later section presents detailed results for the LCC separately for the two types of electricity price models. Table 8.2.15 shows the marginal price associated with air-conditioning loads in each subdivision as computed with the two electricity price analysis methods. The Department calculated marginal prices by taking the ratio, for each building, of the total cost-savings to the total energy-savings between standard levels EER=9.5 and EER=11.0. (The reduction in the hourly air-conditioning energy use is basically linear in 1/EER, so computing the marginal rate does not depend on which two EER levels are used.) The Department then computed the customer-weighted average value for each subdivision. For the hourly based analysis, the customer weights were equal to the CBECS building weights. For the tariff-based analysis, the customer weights were equal to the product of the CBECS building weight and the utility weight. The table also includes the percentage of the marginal price attributable to demand charges for the tariff-based and capacity charges for the hourly based analysis.8-41 Table 8.2.15 Marginal Prices Computed fromAir-conditioning Load Reductions Using the Tariff-based and Hourly Based Electricity Price Models Tariff-based Hourly Based
Subdivision Weight Cens us Division Region Marginal ¢/kWh % Demand Marginal ¢/kWh % Capacity 1 4.7 New England New England 9.5 53% 10.7 43% 2.1 7.4 Middle Atlantic New York 14.6 53% 10.5 35% 2.2 5.6 Middle Atlantic PA, NJ 10.5 27% 8.7 48% 3 13.7 East North Central W I,IL,IN,OH,MI 10.8 46% 11.0 65% 4.1 0.8 West North Central MN, IA, MO 6.2 44% 8.4 60% 4.2 4.7 West North Central ND,SD,NE,KS 7.1 30% 9.8 60% 5.1 5.6 South Atlantic DE,MD,VA,WV 7.9 41% 9.9 63% 5.2 7.9 South Atlantic NC,SC,GA 7.3 22% 7.4 68% 5.3 6.6 South Atlantic Florida 8.0 36% 11.0 66% 6.1 5.1 East South Central KY,TN 6.5 38% 8.0 68% 6.2 5.4 East South Central MS,AL 6.1 39% 12.8 70% 7.1 5.3 West South Central OK,AR,LA 5.8 26% 11.6 76% 7.2 9.5 West South Central Texas 10.0 23% 10.8 75% 8.1 0.6 Mountain MT,ID,WY 6.1 20% 4.5 43% 8.2 4.2 Mountain NV,UT,CO,AZ,NM 8.8 35% 9.5 69% 9.1 1.7 Pacific WA,OR 4.5 33% 5.4 24% 9.2 11.2 Pacific California 18.5 21% 8.5 46% USA 100.0 USA 10.0 35% 9.9 60%

8.2.3.2 Electricity Price Trend The electricity price trend provides the relative change in electricity prices for future years out to the year 2030. Estimating future electricity rates is very difficult, especially considering that there are efforts in many states throughout the country to restructure the electricity supply industry. In a regulated electricity market, each building is assigned to a particular utility company, and the rates offered by that utility can be obtained from surveys. With restructuring, customers in the future will be able to purchase electricity from a large set of suppliers. The Department applies a projected trend in national average electricity prices to each customer’s energy prices. In the LCC analysis, the following four scenarios can be analyzed: • Constant energy prices at 2001 values8-42 6.40 6.60 6.80 7.00 7.20 7.40 7.60 7.80 8.00 8.20 8.40

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 YearCommercial Elec. Price (cents/kWh in 2001$) High Growth AEO 2003 Ref Case Low Growth

Figure 8.2.12 Electricity Price Trends for Commercial Rates • AEO2003 , High Economic Growth

• AEO2003 , Reference Case

• AEO2003 , Low Economic Growth Figure 8.2.12 shows the trends for the three AEO2003 price projections. The Department extrapolated the values in later years (i.e., after 2025) from their relative sources because AEO2003 does not forecast beyond 2025. To arrive at values for these later years, the Department used the price trend from 2015 to 2025 of the forecast to establish prices in the years 2025 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 used in the LCC analysis is the trend from the AEO2003 Reference Case. Spreadsheets used in calculating the LCC have the capability to analyze the other electricity price trend scenarios, namely, the AEO2003 High and Low Growth price trends and constant energy prices.8-43 

### RC EQP LIFE = ⋅ 05

. 8.2.3.3 Repair Cost The repair cost is the cost to the consumer for replacing or repairing components in the air-conditioning equipment which have failed. The Department based the annualized repair cost for baseline efficiency air-conditioning equipment (i.e., the cost the customer pays annually for repairing the equipment) on the following expression:

where: RC = repair cost, EQP= equipment price (customer price for the equipment only), and LIFE = average lifetime of the equipment (15.4 years). Because data were not available to indicate how repair costs vary with equipment efficiency, the Department considered two scenarios: (1) repair costs that varied in direct proportion with the manufacturer price of the equipment, and (2) repair costs that were kept flat (i.e., did not increase with efficiency). For the case where repair costs are a function of equipment price, Table 8.2.16 shows the weighted-average annualized repair costs for the baseline level and each standard level for the two classes of commercial air conditioners. Since equipment prices are a function of variables which are represented by distributions rather th an single point-values (i.e., manufacturer price, markups, and sales tax), repair costs are actually represented by a distribution of values rather than just the weighted-average values shown in Table 8.2.16. Table 8.2.16 Weighted-average Annualized Repair Costs

EER $ 65,000 Btu/h to <135,000 Btu/h Unitary Air Conditioners $ 135,000 Btu/h to <240,000 Btu/h Unitary Air Conditioners 9.5* - $279 10.0 - $284 10.1 † $151

- 10.5 $155 $291 11.0 $162 $303 11.5 $174 $323 12.0 $194 $355 * 9.5 EER is baseline efficiency for the $ 135,000 Btu/h to <240,000 Btu/h equipment class † 10.1 EER is baseline efficiency for the $ 65,000 Btu/h to <135,000 Btu/h equipment class The Department used repair costs that vary with manufacturer price as the default annualized repair cost scenario in the LCC and PBP analysis. Spreadsheets used in calculating the LCC and PBP are able to calculate LCC and PBP based on the constant repair cost scenario.8-44 8.2.3.4 Maintenance Cost 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 (as discussed above). Rather, the maintenance cost is associated with general maintenance (e.g., checking and maintaining refrigerant charge levels and cleaning heat exchanger coils). The Department took annualized maintenance costs for commercial air conditioners from data in RS Means Facilities Maintenance & Repair Cost Data , 1995. 19 This book providesestimates on the person-hours, labor rates, and materials required to maintain commercial airconditioning equipment. RS Means specifies the following 11 actions that constitute required annual maintenance: • Check with operating or area personnel for deficiencies. • Check tensions, condition, and alignment of belts; adjust as necessary. • Lubricate shaft and motor bearings. • Replace air filters. • Clean electrical wiring and connections; tighten loose connections. • Clean coils, evaporator drain pan, blowers, fan motors, and drain piping as required. • Perform operational check of unit; make adjustments on controls and other components as required. • During operation of unit, check refrigerant pressure; add refrigerant as necessary. • Check compressor oil level; add oil as required.
• Clean area around unit. • Fill out maintenance checklist and report deficiencies. The Department calculated the annualized maintenance cost by multiplying the number of person-hours by the corresponding labor rate and adding to it the associated materials costs. RS Means provides specific cost data for the maintenance of roof top air conditioners ranging in cooling capacity from 3 through 24 tons. Since the maintenance cost data in RS Means provide no distinction based on cooling capacity (at least between 3 and 24 tons), the Department took the rooftop air conditioner data to be representative of maintenance costs both for the 

### $ 65,0008-45 Btu/h to <135,000 Btu/h equipment class and the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class. Because data were not available to indicate how maintenance costs vary with equipment efficiency, the Department decided to use cost s that stay constant as equipment efficiency increases. Table 8.2.17 summarizes the nationally representative annualized maintenance costs for 3-ton through 24-ton rooftop air conditioners as presented in RS Means Facilities Maintenance & Repair Cost Data . Table 8.2.17 provides both bare maintenance costs (i.e., costs before O&P) and maintenance costs including O&P. The maintenance costs that include O&P represent the maintenance costs for baseline efficient systems (i.e., 10.1 EER for 7.5-ton systems and 9.5 EER for 15-ton systems).
Table 8.2.17 Annualized Maintenance Costs for Baseline Efficient Air Conditioners

System Type AnnualPersonhours Bare Costs (2001$) Labor and Materials w/ O&P (2001$)

Mat’l Cost perPersonhour Annual Labor Cost Annual Total Cost Mat’l Cost perPersonhour Annual Labor Cost Annual Total Cost Package unit, air-cooled , 3-tons through 24-tons 2.402 $51.71* $36.20 † $86.95 $139 $64.64* $54.60 † $135.33 $200 * The DOE derived 2001 material costs by multip lying the 1996 material costs provided in the RS Means Facilities

Maintenance & Repair Cost Data , 1995, by the ratio of the producer price inde x values for unitary air conditioners for the years 2001 and 1996 (123.7 and 119.6, respectively) from the U.S. Department of Labor, Bureau of Labor Statistics. 20 † Labor rates from RS Means Mechanical Cost Data , 2002 for steamfitter or pipefitter.

8.2.3.5 Lifetime The Department defines lifetime as the age when an air conditioner retires from service. It based the median lifetime of commercial air conditioners on data from the 1999 ASHRAE HVAC Applications Handbook , 21 which gives a median lifetime of 15 years for rooftop air conditioners. The Department found no other data to indicate a different median or mean lifetime for commercial air-conditioning equipment. Because "survival function" more accurately represents equipment lifetime rather than a single-point value, the Department created a survival function for commercial air conditioners based on data for residential heat pump systems. First, it created a Weibull distribution to approximate the actual survival function for residential heat pumps. Once established, it modified the residential heat pump-based survival function to yield a median lifetime (15 years) equal to that for commercial air conditioners.8-46 ( ) 

### F age e age 

### ( ,,) α β β α 

### = − 1 The Department used the residential heat pump survival function as an estimate of the shape of the survival function for commercial air conditioners on a survey performed for the Electric Power Research Institute of 2,184 heat pump installations in a seven-state region of the United States. 22 The first step in creating the survival function for commercial air conditioners was to approximate the shape of the actual survival function for residential heat pumps. To do this, the Department used a Weibull distribution. The equation representing the Weibull cumulative distribution function is:

where: age = age of the air conditioner, and 

### "

and 

### $ = alpha and beta, respectively, two parameters of the Weibull distribution. The Department varied both alpha and beta until the Weibull cumulative distribution was roughly equal to the actual survival function for residential heat pumps. Through a trial and error process, the Department chose values of 4.00 and 19.76 for alpha and beta, respectively. To create a survival function for commercial air conditioners, it held the value for alpha (4.00) constant and varied beta until the Weibull cumulative distribution yielded a median value of 15. The resulting value for beta was 16.44. The Department held alpha constant because it defined the shape of the function.Figure 8.2.13 shows the actual survival function for residential heat pumps, the Weibullbased survival function for residential heat pumps, and the Weibull-based survival function created for commercial air conditioners used in the LCC analysis. (Note that the survey the Department used to establish the actual survival function for residential heat pumps covered only the first 19 years of the equipment’s life. To complete the entire survival function, the Department used an extrapolation based on estimates performed by others. 23 ) The Weibull-based survival function for residential heat pumps closely approximates the actual survival function until the 23 rdyear. Because the actual survival function ends abruptly in the 24 thyear, the Weibull-based function no longer approximates the actual function after the 23 rdyear. The mean lifetime from the derived Weibull-based commercial air conditioner survival function is 15.4 years.8-47 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

0 5 10 15 20 25 30 35 Age (year)Percent Surviving Residential HP Actual Residential HP Weibull Commercial AC Weibull

Comm AC Median Lifetime = 15 years

Figure 8.2.13 Survival Functions for Commercial Air Conditioners and Residential Heat Pumps

8.2.3.6 Discount Rate The discount rate is the rate at which future expenditures are discounted to establish their present value. The Department derived the discount rates for the commercial air conditioneranalysis by estimating the cost of capital of companies that purchase commercial airconditioning equipment. The cost of capital is commonly used to estimate the present value of cash flows to be derived from a typi cal company project or investment. 24Most companies use both debt and equity capital to fund investments, so their cost of capital is the weighted average of the cost to the firm of equity and debt financing. 25 The Department estimated the cost of equity financing by 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 proportionate to the amount of systematic risk associated with a firm. The cost of equity financing tends to be high when a firm faces a large degree of systematic risk and it tends to be low when the firm faces a small degree of systematic risk. The Department determined the cost of equity financing by using several variables, including the risk coefficient of a firm (beta), the expected return on “risk free” assets (R f ), and the additional return expected on assets facing average market risk, also known as the equity risk premium or ERP . The risk coefficient or “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. Betas8-48 

# ( ) k R ERP

e f



### = +⋅ β



### k kw kw ee dd 

### = ⋅ + ⋅ usually vary between 0.5 and 2.0. A firm with a betaof 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. The following equation gives the cost of equ ity financing for a particular company:

where: ke= the cost of equity for a company, R

f = the expected return of the risk free asset, 

### $

= the beta of the company stock, and ERP= the expected equity risk premium. The Department defined the risk free rate as the yield (December 2001) on long-term government bonds. 26The Department used a 5.5 percent estimate for the ERPbased on data from the Damodaran Online site. 27 The cost of debt financing is the yield or interest rate paid on money borrowed by a company (for example, by selling bonds). As defined here, the cost of debt includes compensation for default risk (the risk that a firm will go bankrupt) and excludes deductions for taxes. The Department estimated the cost of debt for companies by adding a risk adjustment factor to the current yield on long term corporate bonds (the risk free rate). It used this procedure to estimate current (and future) company costs to obtain debt financing. It based the adjustment factor on indicators of company risk, such as credit rating or variability of stock returns. The weighted average cost of capital (WACC) of a company is the weighted average cost of debt and equity financing:

where: k = the (nominal) cost of capital, ke= the expected rate of return on equity, k

d = the expected rate of return on debt, w e= the proportion of equity financing in total annual financing, and w d = the proportion of debt financing in total annual financing. The cost of capital is a nominal rate, because it includes anticipated future inflation in the expected returns from stocks and bonds. The real discount rate or WACC deducts expected inflation ( r) from the nominal rate. The Department calculated expected inflation (2.3 percent) from the average of the last five quarters' change in gross domestic product (GDP) prices. 288-49 To estimate the WACC of commercial air conditioner purchasers, the Department used a sample of companies drawn from a database of 7,319 U.S. companies given on the Damodaran Online site. 27 This database includes most of the publicly-traded companies in the U.S. The Department divided the companies into the nine ownership categories shown in Table 8.2.18 according to their type of activity. The Department sought financial information for only 10 percent of the industrial and office/service sector firms in the full sample to keep the database manageable. The Department chose the 10 percent sub-sample by listing the companies alphabetically and drawing every tenth firm on the list. In cases where one or more of the variables needed to estimate the discount rate was missing or could not be obtained, the Department discarded the firm from the analysis. Overall, it discarded about 80 percent of the firms in the full database for this reason. It most frequently discarded firms from the medical service and property ownership categories. Table 8.2.20 describes the economic sectors represented in each of the ownership categories as well as the number of companies used for determining discount rates. Ultimately, the Department used a sample of 973 companies to represent commercial air conditioner purchasers. Table 8.2.18 Description of Companies Included in Discount Rate Sample

Ownership Category Economic Sectors SIC Codes Full US Firm Sample (1) DiscountRate SubSample Retail stores Retail 53, 54, 56 280 218 Property owners and managers Holding companies, real estate companies 6720 152 11
Medical services Hospitals and medical service companies 8000 207 115 Industrial companies Mining, manufacturing, transportation, communications 1000-4000 3913 253 Lodging Hotels 7000 84 51 Food service companies Restaurants, grocery stores 5400, 5812 121 88 Office/Service sector Banks, financial services, offices 5910-9913 2391 128 Public for profit

- N.A. 41 41 Public not for profit Educational services, entertainment 7950, 8299 130 68 Source: Lawrence Berkeley National Laboratory (LBNL) calculations applied to firms sampled from the Damodaran Online data site For each firm in the sample, the Department derived the cost of debt, percent debt financing, and systematic firm risk from info rmation provided at the Damodaran Online site, 27 Bloomberg Professional, 29 and FERC Form 1 filings (for public utilities). 30It estimated the cost of debt financing from the long-term govern ment bond rate (5.5 percent) and the standard deviation of the stock price. For the government (public) sector, the Department based the cost of capital on average interest rates for Treasury, state, and municipal bonds. Table 8.2.20 shows average values for the cost of debt, percent debt financing, and systematic firm risk for the sample companies.8-50 Table 8.2.19 Average Values for Variables Used to Estimate Company Discount Rates

Variable Average Value Source Risk free asset return (R f ) 5.5% Bloomberg Financial. December 2001 Equity risk premium (ERP ) 5.5% Stern Business School, Damodaran Online Expected inflation ( r) 2.3% Bureau of Economic Analysis Cost of debt (after tax) ( k d) 5.9% Stern Business School, Damodaran Online Percent debt financing ( w d) 44% Stern Business School, Damodaran Online Systematic firm risk ($ ) 0.93 Stern Business School, Damodaran Online To estimate the shares of major categories in total commercial sector purchases of commercial air conditioners (Table 8.2.20), the Department used the share of each category in total commercial building square footage with package air-conditioning, as given by EIA’s 1999 CBECS. 31
Table 8.2.20 Estimated Shares of Commercial Air Conditioner Purchases by Ownership Category Ownership Category Percent * Retail stores 16.5% Property owners and managers 21.2% Medical services 6.7% Industrial firms 4.9% Lodging 4.0% Food service companies 5.3% Office/Service sector 19.4% Public for profit 11.0% Public not for profit 11.0% Source: 1999 CBECS * The share of total square footage occupied by property owners is actually 42%. However, in some of these buildings the tenants purchase commercial air conditioners. The Department assumed that this is the case for half of the square footage occupied by property owners. DOE allocated this quantity among the other categories in proportion to their shares of total square footage. Deducting expected inflation from the cost of capital provides the estimates of the real discount rate by ownership category shown in Table 8.2.21. The average discount rate, weighted by the percentage shares of total air conditioner purchases, is 6.1 percent.a Average cost of capital, after deducting 2.3 percent inflation, for a sample of service and retail companies. The estimate was obtained from the Bloomberg Professional service during December, 2001.
8-51 Table 8.2.21 Real Discount Rates by Ownership Category

Ownership Category SIC Code Ownership Shares Mean Real Discount Rate (WACC) Standard Deviation Number of Observations Retail stores 53, 54, 56 16.5% 7.1% 2.1% 218 Property owners and managers 6720 21.2% 5.2% 0.7% 11 Medical services 8000 6.7% 7.0% 1.7% 115 Industrial companies 1000-4000 4.9% 6.9% 3.2% 253 Hotels 7000 4.0% 5.6% 1.5% 51 Food service companies 5400, 5812 5.3% 6.1% 1.4% 88 Office/Service sector 5910-9913 19.4% 6.9% 2.1% 128 Public for profit N.A. 11.0% 3.0% 0.7% 41 Public not for profit 7950, 8299 11.0% 7.3% 1.8% 68 Weighted Average N.A. 6.1% 1.6% N.A. Source: CBECS, Damodaron Online and LBNL calculations Retail firms, property owners, and medical service companies purchase the bulk of commercial air conditioners, so the discount rates calculated for those sectors are particularly important. The Department estimated retail and property owner discount rates to average 7.1 percent and 5.2 percent, respectively. It estimated medical service discount rates to average 7.0 percent. These figures are after-tax discount rates, representing the return required by such firms to attract financing. They fall between discount rate estimates available from Ibbotson Associates 32and Bloomberg Professional, a two influential financial data companies. The Bloomberg Professional online service estimates tend to be slightly lower (e.g., retail cost of capital equal to 5.2 percent after deducting 2.3 percent for inflation) and Ibbotson Associates estimates tend to be somewhat higher (e.g., retail cost of capital equal to 9.2 percent). The different estimates available from Bloomberg and Ibbotson result from different assumptions about expected bond rates, risk premium, and other variables. The Department's approach for estimating the cost of capital provides a measure of the discount rate spread as well as the average discount rate. The standard deviation infers the discount rate spread by ownership category, which ranges between 0.7 percent and 3.2 percent (Table 8.2.21). The distribution of the weighted average discount rate reflects the discount rate distributions of all nine ownership categories (Figure 8.2.14).8-52 0% 5% 10% 15% 20% 25%0-1%1-2%2-3%3-4%4-5%5-6%6-7%7-8%8-9%9-10%10-11%11-12%12-13%13-14%

Discount Rate (WACC)Probability

Figure 8.2.14 Distribution of Discount Rates for All Ownership Categories
Discount rates are particularly sensitive to the size of the ERP and the risk-free rate. Thus, the Department conducted a sensitivity analysis to evaluate the effects of changes in these variables. The ERPis based on expectations about future stock market growth and it is not surprising that estimates of the ERPin the finance literature differ widely. The Bloomberg Professional financial service sets upper and lower bounds on the ERPat 1 percent and 10 percent, respectively. 26 There is also variability in the expected risk free rate, which is based on the return to long-term government bonds. For example, the return on long-term government bonds averaged just under 5.5 percent between 1926 and 2000. 33,34However, average bond returns ranged between 4.0 percent in the first half and 7.0 percent in the second half of that period, holding expected inflation constant. 27,33 In the sensitivity analysis, the Department obtained a lower-bound estimate of company real discount rates by calculating the cost of capital of the companies in our sample assuming a 1 percent ERPand a 4.0 percent risk-free rate. It obtained an upper bound estimate of company real discount rates by estimating the cost of capital of companies assuming a 10 percent ERPand a 7 percent risk free rate. Following these assumptions, the lower-bound average discount rate was 2.5 percent (with rates ranging from 2.2 percent to 3.0 percent). The upper-bound average was 9.6 percent (with rates ranging from 3.0 percent to 11.5 percent (Table 8.2.22).8-53 Table 8.2.22 Sensitivity of Real Discount Rate s to Equity Risk Premium and Risk Free Rate Lower Bound Average Real Discount Rate * Mid-Range Average Real Discount Rate † Upper Bound Average Real Discount Rate ‡ Retail stores 2.6% 7.1% 11.5% Property owners and managers 2.2% 5.2% 8.2% Medical services 2.7% 7.0% 11.4% Industrial companies 2.5% 6.9% 11.2% Hotels 2.3% 5.6% 9.0% Food service companies 2.6% 6.1% 9.7% Office/Service sector 2.5% 6.9% 11.3% Government § 3.0% 3.0% 3.0% Assembly and religous 2.8% 7.3% 11.8% Weighted-Average 2.5% 6.1% 9.6% * Lower bound: ERPset equal to 1% and risk free rate set to 4.0% † Mid-Range: ERPset equal to 5.5% and risk free rate set to 5.5% ‡ Upper bound: ERPset equal to 10% and risk free rate set to 7.0% § Government discount rates are independent of equity risk premium and risk-free rate. 8.2.3.7 Effective Date of Standard The effective date is the future date when a new standard becomes operative. Pursuant to 42 U.S.C. 6313(a)(6)(c), the effective date of any new energy-efficiency standard for commercial unitary air conditioners and heat pumps will be four years after the final rule is published. The Department calculated the LCC for all customers as if they each would purchase a new commercial air conditioner in the year the standard takes effect. Consistent with its published regulatory agenda, the Department assumed that the final rule would be issued in 2004 and that, therefore, the new standards would take effect in 2008 and used these dates in the ANOPR analyses. It based the cost of the equipment on this year; however, all dollar values are expressed in 2001 dollars. Annual energy prices are considered for the life of the commercial air conditioner.

8.3 PAYBACK PERIOD INPUTS
8.3.1 Definition The payback period is the amount of time it takes the consumer to recover the assumed higher purchase expense of more energy-efficient equipment as a result of lower operating costs. Numerically, 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 is does not take into account changes in operating expense over time or the time value of money, that is, the calculation is done at an effective discount rate of 0 percent.8-54 

### PBP IC OC = ∆ ∆ The equation for PBP is:

where: 

### ) IC = difference in the total installed cost between the more efficient standard level and the base case design, and 

### ) OC = difference in annual operating expenses. Payback periods are expressed in years. Payback periods greater than the life of the product mean that the increased total installed cost is not recovered in reduced operating expenses. 8.3.2 Inputs The data inputs to PBP are the total installed cost of the equipment to the customer for each efficiency level and the annual (first year) operating expenditures for each efficiency level. The inputs to the total installed cost are the equipment price and the installation cost. The inputs to the operating costs are the annual energy cost, the annual repair cost, and the annual maintenance cost. The PBP uses the same inputs as the LCC analysis described in section 8.2, except that electricity price trends and discount rates are not required. Since the PBP is a “simple” payback, the required electricity rate is only for the year in which a new standard is to take effect – in this case, the year 2008. The electricity rate used in the PBP calculation was the price projected for that year. Discount rates are not required for the simple PBP calculation.

8.4 RESULTS USING TARIFF-BASED ELECTRICITY PRICES 8.4.1 Life-Cycle Cost Results This section presents LCC results for the efficiency improvement levels specified in the engineering analysis (Chapter 5) and al so presented in section 8.2.2.2, Standard-level Manufacturer Price Increases. The results presented here are based on annual operating costs calculated from tariff-based electricity prices. Section 8.2 presents the tariff-based electricity price inputs as well as all other LCC inputs. Because the values of most inputs are uncertain in this analysis, the Department represents them as a distribution of values rather than a single point-value. Thus, the Department represents the LCC results as a distribution of values. Before proceeding with the presentation of the distributional LCC results, the Department presents average values for total installed costs, annual operating costs, and LCC to show how they vary with efficiency for each of the two commercial air conditioner equipment classes.8-55 8.4.1.1 LCC Breakdown Based on Average Input Values For each equipment class, Figures 8.4.1 through 8.4.6 show how, on an average basis, the total installed costs, annual operating costs, and LCC vary with efficiency. Figures 8.4.1 through 8.4.3 pertain to commercial air conditioners in the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class while Figures 8.4.4 through 8.4.6 pertain to commercial air conditioners in the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class. The Department divided the figures for total installed cost into equipment price and installation cost. It divided the figures for annual operating cost into annual electricity, repair, and maintenance costs. It divided the figures for LCC into total installed cost and lifetime operating cost. Although these figures are based on mean or average values rather than results from the Monte Carlo simulation analysis, they demonstrate how the various inputs impact LCC. In view of the total installed cost results for both equipment classes, the equipment price is the largest contributor to increased total cost since the installation cost remains relatively constant across efficiencies. Annual electricity cost is the largest contributor to the overall operating cost at any efficiency level. As efficiency increases, electricity cost decreases. For example, at an efficiency level of 12 EER, the drop in the annual electricity cost more than offsets the relatively small increase in repair costs. The Department assumes that the maintenance cost remains constant across all efficiency levels, so it does not affect changes in the overall annual operating cost as efficiency increases. The LCC results reveal that as efficiency increases, the lifetime operating cost has more of an impact on the LCC than the total installed cost. In other words, the decrease in lifetime operating costs offsets the increase in total installed cost that occurs with increased equipment efficiency. As a result, the LCC at all efficiency levels is lower than that for the baseline level. For both commercial air conditioner equipment classes, the minimum LCC occurs at an efficiency of 11.5 EER. For efficiencies beyond 11.5 EER, the incremental increase in total installed costs offsets the incremental decrease in lifetime operating costs. Again, the results shown in Figures 8.4.1 through 8.4.6 are based upon average input values rather than input distributions. Thus , although one can observe how the various inputs impact LCC and, in turn, how the resulting LCC changes with efficiency, one should only draw conclusions from the distribution of LCC results presented in Section 8.4.1.2.8-56 $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000

10.1 10.5 11.0 11.5 12.0 EERTotal Installed Cost Equipment Price Installation Cost

Figure 8.4.1 $ 65,000 Btu/h to <135,000 Btu/h: Mean Total Installed Costs

$0 $400 $800 $1,200 $1,600 $2,000

10.1 10.5 11.0 11.5 12.0 EERAnnual Operating Cost Electricity Repair Maint.

Figure 8.4.2 $ 65,000 Btu/h to <135,000 Btu/h: Tariff-Based Mean Annual Operating Costs

$0 $5,000 $10,000 $15,000 $20,000 $25,000

10.1 10.5 11.0 11.5 12.0 EERLife-Cycle Cost Total Installed Cost Lifetime Operating Cost

Figure 8.4.3 $ 65,000 Btu/h to <135,000 Btu/h: Tariff-Based Mean Life-Cycle Costs $0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000

9.5 10.0 10.5 11.0 11.5 12.0 EERTotal Installed Cost Equipment Price Installation Cost

Figure 8.4.4 $ 135,000 Btu/h to <240,000 Btu/h: Mean Total Installed Costs

$0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500

9.5 10.0 10.5 11.0 11.5 12.0 EERAnnual Operating Cost Electricity Repair Maint.

Figure 8.4.5 $ 135,000 Btu/h to <240,000 Btu/h: Tariff-Based Mean Annual Operating Costs

$0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000 $45,000

9.5 10.0 10.5 11.0 11.5 12.0 EERLife-Cycle Cost Total Installed Cost Lifetime Operating Cost

Figure 8.4.6 $ 135,000 Btu/h to <240,000 Btu/h: Tariff-Based Mean Life-Cycle Costs8-57 Frequency Chart

Mean = $20,514 .000 .007 .014 .020 .027

0 68.25 136.5 204.7 273

$0 $12,500 $25,000 $37,500 $50,000 10,000 Trials 123 Outliers Forecast: 10.1 EER Baseline LCC

Figure 8.4.7 

### $ 65,000 Btu/h to <135,000 Btu/h: Tari ff-Based Baseline (10.1 EER) LCC Distribution 8.4.1.2 Baseline LCC Distributions The Department used the Monte Carlo simulation method of analysis, which relies on Crystal Ball ® (i.e., random sampling from distributions), to conduct the LCC analysis. Crystal Ball ® is a commercially available add-in to Microsoft Excel. ® The results presented here are based on 10,000 samples per Monte Carlo simulation run. The Department’s first step in developing LCC results was to establish the baseline LCC for each of the two commercial air conditioner equipment classes. Figures 8.4.7 and 8.4.8 show the frequency charts for the baseline LCC for the two equipment classes. A frequency chart shows the distribution of LCCs with the corresponding probability of occurrence. As discussed earlier, the Department’s baseline efficiency level is 10.1 EER for the 

### $ 65,000 Btu/h to <135,000 Btu/h air conditioner equipment class, and. 9.5 EER for the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class. Table 8.4.1 summarizes the baseline distributions depicted in Figures 8.4.7 and 8.4.8 by showing the mean, median, minimum, and maximum LCCs.8-58 Frequency Chart

.000 .007 .014 .021 .028

0 69.5 139 208.5 278

$0 $25,000 $50,000 $75,000 $100,000 10,000 Trials 128 Outliers Forecast: 9.5 EER Baseline LCC

Figure 8.4.8 

### $ 135,000 Btu/h to <240,000 Btu/h: Tari ff-Based Baseline (9.5 EER) LCC Distribution
Table 8.4.1 Baseline LCC: Tariff-Based Mean, Median, Minimum, and Maximum Values Equipment Class Minimum Median Mean Maximum

$ 65,000 Btu/h to <135,000 Btu/h $6,667 $18,605 $20,514 $93,747

$ 135,000 Btu/h to <240,000 Btu/h $11,395 $34,876 $39,044 $197,535 8.4.1.3 Differences in LCC Between Baseline and Standard EER Level Equipment This section presents the differences in the LCC of standard-level equipment relative to the baseline commercial air conditioner design. The LCC differences are depicted as a distribution of values. The Department presents the results in two types of charts within Crystal Ball ® : (1) a frequency chart showing the distribution of LCC differences with the corresponding probability of occurrence, and (2) a cumulative chart showing the cumulative distribution of LCC differences along with the corresponding probability of occurrence. In each chart, the Department provides the mean LCC difference along with the percent of the population for which the LCC will decrease. In the case of commercial air conditioners, the population is the number of commercial air-conditioning units compared to the number of commercial buildings that utilize commercial air conditioning. Figures 8.4.9 and 8.4.10 depict the frequency and cumulative LCC results, respectively, for the case of an 11 EER efficiency level for the 65,000 to 135,00 Btu/h equipment class. In8-59 Frequency Chart

Certainty is 92.60% from $0 to +Infinity

Mean = $533 .000 .011 .022 .032 .043

0 108 216 324 432

($1,200) $200 $1,600 $3,000 $4,400 10,000 Trials 8 Outliers Forecast: 11.0 EER Delta LCC

Figure 8.4.9 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-B ased Frequency Chart of LCC Differences between 11.0 EER and Baseline (10.1 EER) both figures, a text box next to a vertical line at that value on the x-axis shows the mean change in LCC (a savings of $609 in the examples here). The phrase “Certainty is 94.61% from $0 to +Infinity” means that 94.61 percent of commercial air-conditioning units will have LCC savings due to the increased efficiency level (11 EER) compared to the baseline efficiency level (i.e., 10.1 EER). Figure 8.4.9 is an example of a frequency chart. The y-axes show the number of commercial air-conditioning units (“Frequency” at right y-axis) and percent of all commercial air-conditioning units (“Probability” at left y-axis). In this example, 10,000 units were examined (“10,000 trials”) and almost all the results are displayed (except for “8 outliers”). The x-axis is the difference in LCC between a baseline efficiency level and a higher efficiency level (in this example, 11 EER). The x-axis begins with negative values on the left, which indicate that standards for those units increase costs (increased LCC). Moving toward the right on the axis, values greater than zero indicate reductions inLCC (LCC savings). LCC savings occur when increased total installed costs are more than compensated by reductions in operating costs. In Figure 8.4.9, going from the baseline efficiency le vel (10.1 EER) to the 11 EER efficiency level provides commercial air-conditioning units with average LCC savings of $533, ranging from LCC increases of approximately $325 (at the left) to LCC savings of approximately $4400 (at the right). (The the minimum and maximum values cannot be read with precision from the charts; rather, the Crystal Ball ® output provides them in a statistical summary. In this example, increases in LCC actually extend to $333 while LCC savings reach as high as $6097 but, because they are considered outliers, they are not shown.)8-60 Cumulative Chart

Certainty is 92.60% from $0 to +Infinity

Mean = $533 .000 .250 .500 .750 1.000

0 10000

($1,200) $200 $1,600 $3,000 $4,400 10,000 Trials 8 Outliers Forecast: 11.0 EER Delta LCC

Figure 8.4.10 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-B ased Cumulative Chart of LCC Differences between 11.0 EER and Baseline (10.1 EER) The vertical axis in Figure 8.4.10 is the cumulative probability (left axis) or frequency (right axis) that the LCC difference will be greater than the value on the horizontal axis. Starting at the left, there is a 0 percent probability that a unit will have an increase in LCC larger than approximately $325. At the right, there is almost a 100 percent probability that a unit will either have an increase in LCC or reduction in LCC of less than approximately $4400.

Appendix Q contains the frequency and cumulative charts for all the efficiency levels considered for both commercial air conditioner equipment classes. These charts provide more complete information, but the Department provides a summary of the change in LCC from the baseline by percentile groupings (i.e., of the distribution of results) for each of the equipment classes in Tables 8.4.2 and 8.4.3. The tables also show the mean and the percent of LCCs that are reduced for each standard EER level. As an example of how to interpret the information in Tables 8.4.2 and 8.4.3, here is a review of the 11 EER efficiency level for 

### $ 65,000 Btu/h to <135,000 Btu/h commercial air conditioners. The 11 EER efficiency level in Table 8.4.2 (row 2) shows that the change in LCC (zero percentile column) is a maximum increase of $324. (Negative values are net increases.) For 90 percent of the cases studied (90 thpercentile), the change in LCC is a reduction of $1,310 or less. The largest reduction in LCC is $7,363 (100 thpercentile). The mean change in LCC is a net savings of $609. The last column shows that 95 percent of the sample has LCC savings (i.e., reductions in LCC greater than zero).8-61 Figures 8.4.11 through 8.4.14 graphically depict the LCC results. The first figure for each equipment class shows the mean LCCs, while the second figure shows the percentage of units at each efficiency level with reduced LCCs.8-62 $290 $533 $598

$399

$0 $100 $200 $300 $400 $500 $600 $700

10.5 11.0 11.5 12.0 EERMean LCC Savings

Figure 8.4.11 $ 65,000 Btu/h to <135,000 Btu/h: TariffBased Mean LCC Savings

98% 93% 81%

59%

0% 20% 40% 60% 80% 100%

10.5 11.0 11.5 12.0 EERPercent of Units with LCC Savings

Figure 8.4.12 $ 65,000 Btu/h to <135,000 Btu/h: TariffBased Percent of Units with LCC Savings Table 8.4.2 Summary of Tariff-based LCC Results for 

### $ 65,000 Btu/h to <135,000 Btu/h Commercial Air Conditioners

EER Decrease in LCC from Baseline (10.1 EER) Sho wn by Percentiles of the Distribution of Results (2001$) Percent of Units

with LCC Savings 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean 10.5 ($100) $55 $105 $143 $185 $228 $276 $338 $424 $587 $2,798 $290 98% 11.0 ($333) $31 $132 $222 $311 $398 $505 $635 $823 $1,181 $5,764 $533 93% 11.5 ($745) ($143) $11 $144 $273 $401 $550 $753 $1,032 $1,543 $8,356 $598 81% 12.0 ($1,606) ($567) ($360) ($183) ($27) $153 $349 $601 $968 $1,606 $10,273 $399 59%8-63 $959 $1,704 $2,199 $2,359 $2,027

$0 $500 $1,000 $1,500 $2,000 $2,500

10.0 10.5 11.0 11.5 12.0 EERMean LCC Savings

Figure 8.4.13 $ 135,000 Btu/h to <240,000 Btu/h: TariffBased Mean LCC Savings

100% 99% 97% 91% 77%

0% 20% 40% 60% 80% 100%

10.0 10.5 11.0 11.5 12.0 EERPercent of Units with LCC Savings

Figure 8.4.14 $ 135,000 Btu/h to <240,000 Btu/h: TariffBase Percent of Units with LCC Savings Table 8.4.3 Summary of Tariff-based LCC Results for 

### $ 135,000 Btu/h to <240,000 Btu/h Commercial Air Conditioners

EER Decrease in LCC from Baseline (9.5 EER) Shown by Percentiles of the Di stribution of Results (2001$) Percent of Units

with LCC Savings 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean 10.0 ($180) $283 $481 $422 $537 $655 $915 $1,090 $1,340 $1,817 $8,243 $959 100% 10.5 ($409) $432 $696 $910 $1,128 $1,363 $1,619 $1,958 $2,428 $3,340 $15,472 $1,704 99% 11.0 ($1,058) $376 $759 $1,08 $1,397 $1,712 $2,088 $2,540 $3,223 $4,518 $21,997 $2,199 97% 11.5 ($1,944) $41 $531 $928 $1,331 $1,744 $2,214 $2,821 $3,670 $5,312 $27,235 $2,359 91% 12.0 ($3,647) ($794) ($181) $330 $825 $1,365 $1, 942 $2,634 $3,621 $5,504 $30,431 $2,027 77%8-64 8.4.2 Payback Period Results This section presents PBP results for the efficiency improvement levels specified in the engineering analysis (Chapter 5) and al so presented in section 8.2.2.2, Standard-level Manufacturer Price Increases. The results presented here are based on annual operating costs calculated from tariff-based electricity prices. Section 8.2 describes the tariff-based electricity price inputs as well as all other PBP inputs. Similar to the LCC differences, the Department depicts PBP results as a distribution of values. Thus, it presents the results as a frequency chart showing the distribution of PBPs with the corresponding probability of occurrence. Each chart provides the mean PBP. Figure 8.4.15 is an example of a frequency chart showing the distribution of payback periods for the 11 EER efficiency level for the 

### $ 65,000 Btu/h to <135,000 Btu/h commercial airconditioning equipment class. The chart is the result of 10,000 Monte Carlo simulation runs (i.e., 10,000 samples from each of the distribution inputs. Appendix Q contains the frequency charts for all the efficiency levels considered for both commercial air conditioner equipment classes. In Figure 8.4.15, the y-axes show the number of commercial air-conditioning units (“Frequency” at right y-axis) and percent of all commercial air-conditioning units (“Probability” at left y-axis). In this example, 10,000 units were examined (“10,000 trials”) and almost all the results are displayed (except “15 outliers”). The x-axis is the PBP of a higher efficiency level (in this example, 11 EER) relative to the baseline efficiency level (10.1 EER). In Figure 8.4.15, an 11 EER efficiency level provides commercial air-conditioning units with an average PBP of 3.5 years, and range from just greater than 0 years to approximately 20 years depending upon the unit. (The minimum and maximum values cannot be read with precision from the charts; rather, the Crystal Ball ® output provides them in a statistical summary. In this example, the PBP extends from a minimum of 0.4 years to a maximum of 65 years.)8-65 Frequency Chart

years Mean = 3.5 .000 .010 .020 .030 .041

0 101.5 203 304.5 406

0.0 5.0 10.0 15.0 20.0 10,000 Trials 15 Outliers Forecast: 11.0 EER Payback Period

Figure 8.4.15 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-based Frequency Chart of Payback Periods for 11 EERTables 8.4.4 and 8.4.5 summarize the PBP results for each of the two commercial airconditioning equipment classes. Results are summarized for PBP by percentile groupings (i.e., percentile of the distribution of results). The ch art also shows the PBP for each efficiency level. Figures 8.4.16 and 8.4.17 graphically depict the PBP results. For each equipment class, the figure shows both the median (50 thpercentile) PBP and the mean PBP.8-66 0 2 4 6 8 10

10.5 11 11.5 12 EERPayback Period (years)

Median Mean

Figure 8.4.16 

### $ 65,000 Btu/h to <135,000 Btu/h: Tariff-based Mean and Median Payback Periods Table 8.4.4 Summary of Tariff-based PBP Results for 

### $ 65,000 Btu/h to <135,000 Btu/h Commercial Air Conditioners Payback Period in Years Shown by Percentiles of the Distribution of Results EER 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean 10.5 0.3 1.2 1.6 1.8 2.1 2.3 2.6 2.9 3.3 4.1 25 2.6 11.0 0.4 1.6 2.0 2.4 2.7 3.1 3.5 3.9 4.5 5.7 65 3.5 11.5 0.6 2.2 2.8 3.3 3.8 4.3 4.9 5.6 6.5 8.3 >100 5.1 12.0 0.8 3.1 4.0 4.8 5.6 6.4 7.4 8.5 10 14 >100 8.18-67 0 1 2 3 4 5 6

10 10.5 11 11.5 12 EERPayback Period (years)

Median Mean

Figure 8.4.17 

### $ 135,000 Btu/h to <240,000 Btu/h: Tariff-based Mean and Median Payback Periods Table 8.4.5 Summary of Tariff-based PBP Results for 

### $ 135,000 Btu/h to <240,000 Btu/h Commercial Air Conditioners Payback Period in Years Shown by Percentiles of the Distribution of Results EER 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean 10.0 0.3 0.8 1.0 1.2 1.3 1.5 1.6 1.8 2.1 2.6 15 1.6 10.5 0.3 1.0 1.2 1.4 1.6 1.8 2.1 2.3 2.7 3.3 20 2.0 11.0 0.4 1.2 1.6 1.8 2.1 2.4 2.7 3.0 3.5 4.4 46 2.7 11.5 0.4 1.6 2.0 2.4 2.8 3.2 3.6 4.2 4.9 6.2 86 3.7 12.0 0.5 2.2 2.8 3.4 3.9 4.5 5.2 6.1 7.3 10 >100 5.58-68 8.5 RESULTS USING HOURLY BASED ELECTRICITY PRICES 8.5.1 Life-Cycle Cost Results This section presents LCC results based on annual operating costs calculated from hourly based electricity prices. Section 8.2 describes the hourly based electricity price inputs as well as all other LCC inputs. As with the LCC results based on tariff-based electricity prices, the Department represents LCC results based on hourly electricity prices as a distribution of values. Before proceeding with the presentation of the distributional LCC results, the Department presents average values for total installed costs, annual operating costs, and LCC to show how they vary with efficiency for each of the two commercial air-conditioning equipment classes. 8.5.1.1 Life-Cycle Cost Breakdown Based on Average Input Values For each equipment class, Figures 8.5.1 through 8.5.6 show how, on an average basis, the total installed costs, annual operating costs, and LCC vary with efficiency. Figures 8.5.1 through 8.5.3 pertain to commercial air conditioners in the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class, while Figures 8.5.4 through 8.5.6 pertain to commercial air conditioners in the 

### $ 135,000 Btu/h to <240,000 Btu/hr equipment class. The Department divided figures for total installed cost into equipment price and installation cost. It divided the figures for annual operating cost into annual electricity, repair, and maintenance costs. The figures for LCC are divided into total installed cost and lifetime operating cost. Although the following figures are based on mean or average values rather than results from the Monte Carlo simulation analysis, they serve to demonstrate how the various inputs ultimately impact LCC. Because electricity prices only impact the annual operating costs, which do not contribute to the total installed cost, the figures below for total installed cost (Figures 8.5.1. and 8.5.4) are identical to those presented earlier in Section 8.4 on the tariff-based LCC results (Figures 8.4.1 and 8.4.4). In reviewing the total installed cost results for either equipment class, the largest contributor to increased total installed cost is the equipment price, since the installation cost remains relatively constant across efficiency. With regard to annual operating cost, the hourly based results reveal similar results to the tariff-based anaytic results, namely, that the largest contributor to the overall operating cost at any efficiency level is the annual electricity cost. As efficiency increases, the electricity cost decreases. Even at an efficiency level of 12 EER, the drop in the annual electricity cost more than offsets the relatively small increase in repair costs (see Figures 8.5.2 and 8.5.5). Because the Department assumes that maintenance cost remains constant across all efficiency levels, it does not contribute to the change in the overall annual operating cost as efficiency increases.8-69 The hourly based LCC results reveal the same observations as for the tariff-based results, namely that, as efficiency increases, the lifetime operating cost has greater impact on the LCC than the total installed cost. In other words, the increase in total installed cost that occurs when equipment efficiency is increased is offset by the decrease in lifetime operating costs. As aresult, the LCC at all efficiency levels is lower than that for the baseline level. As with the tariffbased results, the minimum hourly based LCC occurs at an efficiency of 11.5 EER for both commercial air-conditioning equipment classes. For efficiencies beyond 11.5 EER, the incremental decrease in lifetime operating costs is offset by the incremental increase in total installed costs. Again, the results shown in Figures 8.5.1 through 8.5.6 are based upon average input values rather than input distributions. Thus , although one can observe how the various inputs impact life-cycle cost and, in turn, how the resulting LCCs change with efficiency, one should only draw conclusions from the distribution of LCC results that are presented in Section 8.5.1.2.8-70 $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 $8,000

10.1 10.5 11.0 11.5 12.0 EERTotal Installed Cost Equipment Price Installation Cost

Figure 8.5.1 $ 65,000 Btu/h to <135,000 Btu/h: Mean Total Installed Costs

$0 $400 $800 $1,200 $1,600 $2,000

10.1 10.5 11.0 11.5 12.0 EERAnnual Operating Cost Electricity Repair Maint.

Figure 8.5.2 $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Mean Annual Operating Costs

$0 $5,000 $10,000 $15,000 $20,000 $25,000

10.1 10.5 11.0 11.5 12.0 EERLife-Cycle Cost Total Installed Cost Lifetime Operating Cost

Figure 8.5.3 $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Mean Life-Cycle Costs $0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000

9.5 10.0 10.5 11.0 11.5 12.0 EERTotal Installed Cost Equipment Price Installation Cost

Figure 8.5.4 $ 135,000 Btu/h to <240,000 Btu/h: Mean Total Installed Costs

$0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500

9.5 10.0 10.5 11.0 11.5 12.0 EERAnnual Operating Cost Electricity Repair Maint.

Figure 8.5.5 $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Mean Annual Operating Costs

$0 $5,000 $10,000 $15,000 $20,000 $25,000 $30,000 $35,000 $40,000 $45,000

9.5 10.0 10.5 11.0 11.5 12.0 EERLife-Cycle Cost Total Installed Cost Lifetime Operating Cost

Figure 8.5.6 $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Mean Life-Cycle Costs8-71 Frequency Chart

Mean = $20,697 .000 .007 .013 .020 .027

0 66.5 133 199.5 266

$0 $12,500 $25,000 $37,500 $50,000 10,000 Trials 85 Outliers Forecast: 10.1 EER Baseline LCC

Figure 8.5.7 

### $ 65,000 Btu/h to <135,000 Btu/h: Hour ly Based Baseline (10.1 EER) LCC Distribution 8.5.1.2 Baseline LCC Distributions The Department used the Monte Carlo simulation method of analysis, which relies on Crystal Ball ® (i.e., random sampling from distributions), to conduct the LCC analysis. The results presented here are based on 10,000 samples per Monte Carlo simulation run. As was stated in section 8.4 for the tariff-based LCC results, the Department’s first stepin developing LCC results was to establish the baseline LCC for each of the two commercial airconditioning equipment classes. Figures 8.5.7 and 8.5.8 show the frequency charts for the hourly based baseline LCC for the two equipment classes. A frequency chart shows the distribution of LCCs with the corresponding probability of occurrence. As discussed earlier, the Department’s baseline efficiency level is 10.1 EER for the 

### $ 65,000 Btu/h to <135,000 Btu/h airconditioning equipment class, and 9.5 EER for the 

### $ 135,000 Btu/h to <240,000 Btu/h equipment class. Table 8.5.1 summarizes the baseline distributions depicted in Figures 8.5.7 and 8.5.8 by showing the mean, median, minimum, and maximum LCCs. Although the hourly based baseline LCCs have nearly the same mean and median values as those from the tariff-based baseline LCCs, the range of possible baseline LCCs is smaller than the tariff-based values.8-72 Frequency Chart

Mean = $39,401 .000 .007 .014 .021 .029

0 71.25 142.5 213.7 285

$0 $25,000 $50,000 $75,000 $100,000 10,000 Trials 70 Outliers Forecast: 9.5 EER Baseline LCC

Figure 8.5.8 

### $ 135,000 Btu/h to <240,000 Btu/h: Hour ly Based Baseline (9.5 EER) LCC Distribution
Table 8.5.1 Baseline LCC: Hourly Based Mean, Median, Minimum, and Maximum Values Equipment Class Minimum Median Mean Maximum

$ 65,000 Btu/h to <135,000 Btu/h $5,967 $18,850 $20,697 $84,447

$ 135,000 Btu/h to <240,000 Btu/h $12,272 $35,641 $39,401 $139,357

8.5.1.3 Differences in LCC Between Baseline and Standard EER Level Equipment This section presents the differences in the LCC of standard EER level equipment relative to the baseline EER commercial air conditioner design. As described earlier in section 8.4 for the tariff-based LCC results, the LCC differen ces are depicted as a distribution of values. The results are presented in two types of charts within Crystal Ball ® : (1) a frequency chart showing the distribution of LCC differences with its corresponding probability of occurrence, and (2) a cumulative chart showing the cumulative distribution of LCC differences along with the corresponding probability of occurrence. In each chart, the Department provides the mean LCC difference along with the percent of the population for which the LCC will decrease. In the case of commercial air conditioners, the population is the number of commercial air-conditioning units compared to the number of commercial buildings that use commercial air conditioning. Appendix Q contains the frequency and cumulative charts for all the efficiency levels8-73 considered for both commercial air-conditioning equipment classes. Section 8.4.1.3 has a complete description of how to interpret these charts. The charts contained in Appendix Q provide more complete information, but Tables 8.5.2 and 8.5.3 below provide a summary of the change in LCC from the baseline EER by percentile groupings (i.e., of the distribution of results) for each of the equipment classes. The mean and the percent of LCCs that are reduced for each standard EER level are also shown. Figures 8.5.9 through 8.5.12 graphically depict the LCC results. The first figure for each equipment class shows the mean LCCs, while the second figure shows the percentage of units at each efficiency level with reduced LCCs. A comparison between the hourly based LCC results (Tables 8.5.2 and 8.5.3) and those from the tariff-based analysis (Tables 8.4.2 and 8.4.3) shows that the hourly based mean LCC savings at each efficiency level are virtually the same as those from the tariff-based analysis. On the other hand, the percentage of units with LCC savings under the hourly based analysis is slightly greater at each efficiency level than for the tariff-based analysis. These results indicate that the range of electricity prices realized from the hourly based analysis are lower than those from the tariff-based analysis. As a result, using the hourly based prices eliminates those extreme electricity price cases present under the tariff-based analysis that result in units achieving either large LCC increases or large LCC savings. Because units with extreme LCC increases are eliminated from the hourly based analysis, there are fewer units faced with LCC increases and, as a result, there are more units achieving LCC savings at each efficiency level.8-74 $293 $541 $610

$414

$0 $100 $200 $300 $400 $500 $600 $700

10.5 11.0 11.5 12.0 EERMean LCC Savings

Figure 8.5.9 $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Mean LCC Savings

99% 97% 90%

70%

0% 20% 40% 60% 80% 100%

10.5 11.0 11.5 12.0 EERPercent of Units with LCC Savings

Figure 8.5.10 $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Percent of Units with LCC Savings Table 8.5.2 Summary of Hourly Based LCC Results for 

### $ 65,000 Btu/h to <135,000 Btu/h Commercial Air Conditioners

EER Decrease in LCC from Baseline (10.1 EER) Sho wn by Percentiles of the Distribution of Results (2001$) Percent of Units

with LCC Savings 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean 10.5 ($70) $98 $149 $189 $227 $263 $304 $354 $424 $534 $1,373 $293 99% 11.0 ($234) $128 $235 $319 $399 $475 $563 $671 $820 $1,055 $2,806 $541 97% 11.5 ($530) $8 $166 $285 $402 $511 $641 $795 $1,019 $1,360 $3,842 $610 90% 12.0 ($104) ($354) ($154) ($1) $144 $287 $448 $647 $931 $1,372 $4,364 $414 70%8-75 $967 $1,718 $2,222 $2,390 $2,067

$0 $500 $1,000 $1,500 $2,000 $2,500 $3,000

10.0 10.5 11.0 11.5 12.0 EERMean LCC Savings

Figure 8.5.11 $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Mean LCC Savings

100% 100% 99% 97% 89%

0% 20% 40% 60% 80% 100%

10.0 10.5 11.0 11.5 12.0 EERPercent of Units with LCC Savings

Figure 8.5.12 $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Percent of Units with LCC Savings Table 8.5.3 Summary of Hourly Based LCC Results for 

### $ 135,000 Btu/h to <240,000 Btu/h Commercial Air Conditioners

EER Decrease in LCC from Baseline (9.5 EER) Shown by Percentiles of the Di stribution of Results (2001$) Percent of Units

with LCC Savings 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean 10.0 ($60) $403 $552 $665 $773 $882 $999 $1,141 $1,329 $1,655 $4,560 $967 100% 10.5 ($209) $658 $934 $1,147 $1,352 $1,557 $1,781 $2,046 $2,402 $3,014 $8,489 $1,718 100% 11.0 ($490) $728 $1,108 $1,410 $1,699 $1,984 $2,310 $2,684 $3,193 $4,067 $11,816 $2,222 99% 11.5 ($983) $515 $995 $1,364 $1,725 $2,080 $2,494 $2,970 $3,610 $4,716 $14,436 $2,390 97% 12.0 ($1,840) ($121 $426 $861 $1,281 $1,695 $2, 177 $2,741 $3,512 $4,794 $16,156 $2,067 89%8-76 8.5.2 Payback Period Results This section presents PBP results for the efficiency improvement levels specified in the engineering analysis (Chapter 5) and also presented in section 8.2.2.2, Standard-level Manufacturer Price Increases. The Department presents results here based on annual operating costs calculated from hourly based electricity prices. Section 8.2 describes the hourly based electricity price inputs as well as all other inputs to PBP. Similar to the LCC differences, the Department depicts the PBP results as a distribution of values. The results are presented as a frequency chart showing the distribution of PBPs with its corresponding probability of occurrence. Each chart provides the mean PBP. Appendix Qcontains the frequency charts for all the efficiency levels considered for both commercial airconditioning equipment classes. Section 8.4.2 on the tariff-based PBPs describes how to interpret the PBP frequency charts. Figure 8.5.13 below shows the PBP results for the 

### $ 65,000 Btu/h to <135,000 Btu/h equipment class at 11 EER. Note the bi-modal nature of the frequency chart. This is common to all the hourly PBP distributions. The bi-modal nature is due to the annual energy expense portion of the PBP calculation. Figure 8.5.14 s hows the distribution of annual electricity expense differences between 11 EER and the baseline (10.1 EER). As indicated by the figure, the annual energy expense differences are distributed relatively unevenly (i.e., expenses are not smoothly distributed). The uneven nature of the di stribution is due to the hourly electricity price component of the energy expense. Hourly electricity prices vary in a semi-discontinuous manner around the Nation (i.e., there are relatively few hourly prices around the price distribution average which, therefore, results in a bi-modal distribution). Thus, the energy expenses also vary in a bi-modal fashion. Because the energy expense difference is a primary component to the PBP, the PBP distributions are also bi-modal.8-77 Frequency Chart

Mean = 3.0 .000 .012 .023 .035 .047

0 116.2 232.5 348.7 465

0.0 3.8 7.5 11.3 15.0 10,000 Trials 0 Outliers Forecast: 11.0 EER Payback Period

Figure 8.5.13 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Frequency Chart of Payback Periods for 11 EER

Frequency Chart

.000 .006 .012 .018 .023

0 58.5 117 175.5 234

($200) ($163) ($125) ($88) ($50) 10,000 Trials 90 Outliers Forecast: Elec Diff: 11 EER and Base

Figure 8.5.14 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Frequency Chart of Annual Energy Expense Differences between 11 EER and the Baseline (10.1 EER)8-78 Tables 8.5.4 and 8.5.5 summarize the hourly based PBP results for each of the two commercial air-conditioning equipment classes. The Department summarized the results for the PBP by percentile groupings (i.e., percentile of the distribution of results). The table also shows mean PBP for each efficiency level. Figures 8.5.15 and 8.5.16 graphically depict the PBP results. For each equipment class, the figure shows both the median (50 thpercentile) PBP and the mean PBP.8-79 0 1 2 3 4 5 6 7

10 10.5 11 11.5 12 EERPayback Period (years)

Median Mean

Figure 8.5.15 

### $ 65,000 Btu/h to <135,000 Btu/h: Hourly Based Mean and Median Payback Periods Table 8.5.4 Summary of Hourly Based PBP Results for 

### $ 65,000 Btu/h to <135,000 Btu/h Commercial Air Conditioners Payback Period in Years Shown by Percentiles of the Distribution of Results EER 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean 10.5 0.8 1.3 1.5 1.7 2.0 2.2 2.4 2.6 2.8 3.1 7.0 2.2 11.0 1.1 1.7 2.0 2.3 2.7 2.9 3.2 3.4 3.7 4.2 10 3.0 11.5 1.5 2.3 2.7 3.2 3.7 4.1 4.4 4.7 5.2 5.9 17 4.2 12.0 2.1 3.4 4.0 4.7 5.4 6.0 6.5 7.0 7.8 9.0 35 6.28-80 0 1 2 3 4 5

9.5 10 10.5 11 11.5 12 EERPayback Period (years)

Median Mean

Figure 8.5.16 

### $ 135,000 Btu/h to <240,000 Btu/h: Hourly Based Mean and Median Payback Periods Table 8.5.5 Summary of Hourly Based PBP Results for 

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