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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-76
8-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 from 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 . . . . . . . . . . . . . . . . . . . . . . . . 8-67
8-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 Historical Load 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 Historical Price 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 . . . . . . . . . . 8-56
8-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: Tariff-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 Based 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 Based 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-77
8-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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-80
8-1

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

Markupa 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 $1585b

Single-Point Value with

Regional Variability 8.2.2.4 $135 to <240 kBtu/h $2142b

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 Account. 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 or

ventilation 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 costs

are 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 defined
8-8

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 meeting 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 either 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 Heating, Refrigeration, and Air-Conditioning Engineers
8-9

(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 EER

Because 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 $3,957
8-10

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 equipment 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 with a baseline efficiency of 10.1 EER, and for

the $135,000 Btu/h to <240,000 Btu/h equipment class characterized 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 actually 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 manufacturer
8-11

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

Mechanical Contractor 1.48 1.35 1.70 1.55 1.60

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 2.31
8-12

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

Mechanical Contractor 1.26 1.18 1.37 1.29 1.24

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.

2

 This 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 to

install 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 relationships 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 Department
8-13

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*

PersonhoursCost 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 Means for roof top air conditioners with standard controls, 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 Massachusetts 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 equipment
8-14

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 more efficient 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 the

manufacturer 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 Determination 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 (ICBase 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 markup
8-16

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

9.5a - - - $9,157 $2,142 $11,299

10.0 - - - $9,254 $2,243 $11,497

10.1b $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

a

 9.5 EER is baseline efficiency for the $135,000 Btu/h to <240,000 Btu/h equipment class.

b

 10.1 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 performed, 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 design
8-18

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. The 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 Continental 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.4 This 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 the 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

Subdivision

cents/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 collection 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 Department assigned customers to a specific tariff based

on their peak demand over the previous 12 months. In a few cases, the assignment was based on
8-24

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 all
8-25

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, and 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 rather
8-28

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 tariff
8-29

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. Historical 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. Historical
8-30

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.6 The 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.12 The 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 (LTMY) 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). The 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 Load versus 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 the
8-33

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

occurrence i and 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 data on 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 bins. 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 the 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 high degree of similarity. Figures 8.2.10 and 8.2.11

show the price distributions that resulted from using 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 $/MWh

Frequency

Historica

Run 1

Run 2

Figure 8.2.10 Price Distribution Functions for the 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 $/MWh

Frequency

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 Logarithmic
8-37

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 was

combined-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 forecasts14 for 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 indicates 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. In the 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 the
8-39

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 from Air-conditioning Load Reductions Using

the Tariff-based and Hourly Based Electricity Price Models

Tariff-based Hourly Based

Subdivision Weight Census 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 WI,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 values
8-42

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

Year

Commercial 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 than 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 provides

estimates 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 $65,000
8-45

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 costs 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

Annual

Personhours

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

Mat’l

Cost

per

Personhour

Annual

Labor

Cost

Annual

Total

Cost Mat’l

Cost 

per

Personhour

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 multiplying the 1996 material costs provided in the RS Means Facilities

Maintenance & Repair Cost Data, 1995, by the ratio of the producer price index 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 23rd year. Because the actual survival function ends abruptly in the 24th year, the

Weibull-based function no longer approximates the actual function after the 23rd year. 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 conditioner

analysis 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 typical company project or investment.24 Most 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 (Rf

), 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. Betas
8-48

k R ERP ( ) e f = +⋅ β

k kw kw ee dd = ⋅ + ⋅

usually vary between 0.5 and 2.0. A firm with a beta of 0.5 faces half the risk of other stocks in

the market; a firm with a beta of 2.0 faces twice the overall stock market risk. 

The following equation gives the cost of equity financing for a particular company: 

where:

ke = the cost of equity for a company,

Rf = 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.26 The Department used a 5.5 percent estimate for the ERP based 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, 

kd = the expected rate of return on debt,

we = the proportion of equity financing in total annual financing, and

wd = 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.28
8-49

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)

Discount

Rate 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 information provided at the Damodaran Online site,27

Bloomberg Professional,29 and FERC Form 1 filings (for public utilities).30 It estimated the cost

of debt financing from the long-term government 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 (Rf

) 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) (kd) 5.9% Stern Business School, Damodaran Online

Percent debt financing (wd) 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

Associates32 and 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 ERP is based on expectations about future stock market growth and it is not

surprising that estimates of the ERP in the finance literature differ widely. The Bloomberg

Professional financial service sets upper and lower bounds on the ERP at 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. 33,34 However, 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 ERP and 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 ERP and

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 Rates 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: ERP set equal to 1% and risk free rate set to 4.0%

†

 Mid-Range: ERP set equal to 5.5% and risk free rate set to 5.5%

‡

 Upper bound: ERP set 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 also 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

EER

Total 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

EER

Annual 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

EER

Life-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

EER

Total 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

EER

Annual 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

EER

Life-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

Costs
8-57

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: Tariff-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: Tariff-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. In
8-59

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-Based 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 in LCC (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 level (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-Based 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 (90th percentile), the change in LCC is a reduction of $1,310

or less. The largest reduction in LCC is $7,363 (100th percentile). 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

EER

Mean 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

EER

Percent 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) Shown 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

EER

Mean 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

EER

Percent 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 Distribution of Results

(2001$)

Percent

of Units

 with

LCC

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean Savings

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 also 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 EER

Tables 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 chart 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 (50th percentile) PBP and the mean PBP.
8-66

0

2

4

6

8

10

10.5 11 11.5 12

EER

Payback 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 8.1
8-67

0

1

2

3

4

5

6

10 10.5 11 11.5 12

EER

Payback 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 5.5
8-68

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 a

result, 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

EER

Total 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

EER

Annual 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

EER

Life-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

EER

Total 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

EER

Annual 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

EER

Life-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

Costs
8-71

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: Hourly 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 step

in 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: Hourly 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 differences 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 levels
8-73

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

EER

Mean 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

EER

Percent 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) Shown 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

EER

Mean 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

EER

Percent 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 Distribution of Results

(2001$)

Percent

of Units

with

LCC

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean Savings

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 Q

contains 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 shows 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 distribution 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 (50th percentile) PBP and the mean PBP. 
8-79

0

1

2

3

4

5

6

7

10 10.5 11 11.5 12

EER

Payback 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 6.2
8-80

0

1

2

3

4

5

9.5 10 10.5 11 11.5 12

EER

Payback 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 $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 0.5 0.8 0.9 1.1 1.3 1.4 1.6 1.7 1.9 2.1 5.3 1.4

10.5 0.6 1.0 1.1 1.4 1.6 1.8 1.9 2.1 2.3 2.6 6.8 1.8

11.0 0.8 1.3 1.5 1.8 2.1 2.3 2.5 2.7 3.0 3.3 9.3 2.3

11.5 1.1 1.7 2.0 2.4 2.8 3.1 3.3 3.6 3.9 4.4 14 3.1

12.0 1.6 2.5 2.9 3.4 4.0 4.4 4.7 5.1 5.6 6.4 24 4.5
8-81

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