NBS

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Reference

NBSIR 81-2379

A1110S

Comparative Analysis of

Economic Models in Selected

Solar Energy Computer Programs

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Building Technology

Building Economics and Regulatory

Technology Division

Washington, DC 20234

January 1 982

Sponsored by:

Field Applications Branch

Division of Active Heating and Cooling — Office of Solar Applications for Buildings

S. Department of Energy

ashington, DC 20585

• Lou

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NBSIR 81-2379

/ *< COMPARATIVE ANALYSIS OF

ECONOMIC MODELS IN SELECTED

SOLAR ENERGY COMPUTER PROGRAMS

MAR 16 1982

Klo € (x cc &CIOO

ho.

Jeanne W. Powell

Kimberly A. Barnes

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Building Technology

Building Economics and Regulatory

Technology Division

Washington, DC 20234

January 1 982

Sponsored by:

Field Applications Branch

Division of Active Heating and Cooling

Office of Solar Applications for Buildings

U.S. Department of Energy

Washington, DC 20585

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director
',Uc . >J(i * in
ABSTRACT

A variety of computer simulation programs exist for the design and study of

thermal performance and economic feasibility of solar domestic hot water and

space heating systems. Several studies have indicated that the thermal performance algorithms contained in the different programs produce similar results.

However, little comparative analysis has been done of the economic algorithms

in these programs.

This report compares the economic evaluation models in five computer programs

widely used for analyzing solar energy systems: F-CHART 3.0, F-CHART 4.0,

SOLCOST, BLAST, and DOE-2. Differences in analysis techniques and assumptions

among the programs are assessed for their consistency with the federal requirements for life-cycle costing (10 CFR Part 436), effect on predicted economic

performance and optimal system size, ease of use, and general applicability to

diverse system types and building types. The FEDSOL program developed by the

National Bureau of Standards specifically to meet the federal life-cycle cost

requirements serves as a basis for the comparison. Results of the study are illustrated in test cases of two different types of federally owned buildings:

a single-family residence and a low-rise office building.

The study indicated that none of the programs except FEDSOL fully conformed

with the federal requirements for life-cycle cost analysis of renewable energy

projects. However, with considerable manipulation of data inputs and simplification of assumptions, they could provide similar predictions for one measure

of economic performance, net present value savings.

iii
PREFACE

This report was prepared by the Applied Economics Group, Building Economics and

Regulatory Technology Division, Center for Building Technology, National Engineering Laboratory, National Bureau of Standards (NBS), for the Department of

Energy, Office of Solar Applications for Buildings, under Interagency Agreement

E( 49-1 )-3800 , EA-77-A-01-6010.

The work was sponsored by the Solar Federal Buildings Program. The broad

objective of this program is to stimulate the growth and improve the efficiency

of the solar industry by providing funds to federal agencies for the design,

acquisition, construction, and installation of commercially applicable solar

hot water, heating, cooling, and process systems in new and existing federal

buildings

.

Federal agencies considering investments in energy conservation or renewable

energy projects are required to perform a life-cycle cost (LCC) analysis of the

proposed projects in accordance with a uniform methodology and procedures (10

CFR Part 436). This report analyzes the economic models in a number of widely

used solar energy computer programs to determine if they comply with the Federal

LCC Rule; it describes the similarities and differences in the models; and it

illustrates the effects of these differences as they apply to federal buildings

projects.

iv
ACKNOWLEDGMENTS

Acknowledgment and appreciation are extended to those persons in the Center for

Building Technology, National Bureau of Standards, who contributed to the preparation of this report. Rosalie T. Ruegg, Applied Economics Group, Building

Economics and Regulatory Technology Division, provided the initial direction to

the project. Walter Parken and Thomas E. Pierce, Thermal Analysis, Building

Thermal Performance Division provided invaluable assistance in developing the

test cases analyzed in the project, and Dr. Parken reviewed the draft report.

Thanks are also due to Dr. Harold E. Marshall, Group Leader of the Applied

Economics Group, Dr. Stanley Liu, Passive Solar Systems, and Maureen Breitenberg,

Office of Engineering Standards, for the time they spent in reviewing and discussing the draft report. The authors wish to acknowledge William Lemeshewsky,

Program Coordinator of the Solar Federal Buildings Program, and Robert Lorand,

of JRB Associates, Contract Manager, for providing financial support and general

guidance to the project.

v
TABLE OF CONTENTS

Page

ABSTRACT . „ iii

PREFACE . . iv

ACKNOWLEDGMENTS ...» v

1 . INTRODUCTION 1

1 . 1 BACKGROUND 1

1 . 2 PURPOSE AND SCOPE 2

1.3

SELECTION OF MODELS FOR STUDY 3

1 .4 APPROACH AND ORGANIZATION 6

2. THE LIFE-CYCLE COST EVALUATION OF SOLAR FEDERAL BUILDINGS PROJECTS .. 8

2.1 GENERAL OVERVIEW 8

2.2 SPECIFIC REQUIREMENTS FOR SOLAR FEDERAL BUILDINGS PROJECTS ..... 8

2.2.1 Cost Data and Assumptions 8

2.2.2 Inflation and Discounting 10

2.2.3 Study Period 10

2.2.4 Measures of Economic Performance 10

3. COMPARISON OF ECONOMIC MODELS 12

3.1 ELEMENTS OF THE MODELS (INPUT VARIABLES) 12

3.1.1 Investment Costs 12

3.1.2 Annually Recurring O&M Costs (Nonfuel) 17

3.1.3 Nonannual Repair and Replacement Costs and Salvage

Values 18

3.1.4 Energy Costs 19

3.1.5 Financial Variables 22

3.1.6 Treatment of Inflation and Discounting 23

3.2 MEASURES OF ECONOMIC PERFORMANCE 25

3.3 OPTIMIZATION ANALYSES 26

3.3.1 Overview 26

3.3.2 Optimization Features in Programs Selected for Study .... 29

3.4 SUMMARY 33

4. TEST CASES 35

4 . 1 ANALYTICAL FRAMEWORK 35

4.2 CASE 1. SOLAR SPACE HEATING FOR A SINGLE-FAMILY RESIDENCE 36

4.2.1 Analysis of a Specified System Size 39

4.2.2 Determination of Optimal System Size 41

4.3 CASE 2. SOLAR SPACE AND SERVICE WATER HEATING FOR A FEDERAL

OFFICE BUILDING 46

4.3.1 DOE-2 vs. BLAST 47

4.3.2 BLAST and DOE-2 vs. F-CHART (3.0 and 4.0), SOLCOST, and

FEDSOL 54

4.3.3 Comparative Costs for Operating Computer Programs 65

4.3.4 Summary 65

5. CONCLUSIONS 68

REFERENCES 71

vi
LIST OF TABLES AND FIGURES

Tables Page

Table Id Summary of Existing Solar Energy Computer Programs

with Economic Analysis Capability 4

Table 3.1 Comparison of Economic Evaluation Models in Selected

Solar Energy Analysis Programs: Input Variables 13

Table 3.2 Comparison of Economic Evaluation Models in Selected

Solar Energy Analysis Programs: Methods of Analysis 14

Table 4.1 Data and Assumptions for Residential Test Case 38

Table 4.2 Comparison of Combined Predictions of Economic and

Thermal Performance from FEDSOL, F-CHART 3.0,

F-CHART 4.0, and SOLCOST for a 400 ft 2 Residential

Space Heating System in Two Cities 39

Table 4.3 Comparison of Predictions of Economic Performance

Only from F-CHART 3.0, F-CHART 4.0, and SOLCOST

vs. FEDSOL for a 400 ft 2 Residential Space Heating

System in Washington, D.C. 40

Table 4.4 Comparison of Economic Optimization Analyses from

FEDSOL, F-CHART 3.0, F-CHART 4.0, and SOLCOST for a Residential Space Heating System in Two Cities 42

Table 4.5 Building and System Specifications for Office Building

Test Case 48

Table 4.6 Economic Data and Assumptions for Office Building Test

Case 50

Table 4.7 Predictions of Thermal and Economic Performance from

BLAST and DOE-2 for a Space and Service Water Heating

System for an Office Building in Washington, D.C 52

Table 4.8 Comparison of Predictions of Economic and Thermal

Performance from BLAST and DOE-2 vs. FEDSOL, F-CHART

3.0, F-CHART 4.0, and SOLCOST for a 600 ft 2 Space and

Service Water Heating System for a Federal Office

Building in Washington, D.C 56

Table 4.9 Comparison of Predictions of Economic Performance

Only from F-CHART 3.0, F-CHART 4.0, SOLCOST, BLAST,

and DOE-2 vs. FEDSOL for a 600 ft 2 Space and Service

Water Heating System for a Federal Office Building in

Washington, D.C 57

vii
Tables Page

Table 4.10 Comparison of Economic Optimization Analyses from

BLAST and DOE-2 vs. SOLCOST , F-CHART 3.0, F-CHART

4.0, and FEDSOL for a Space and Service Water

Heating System for a Federal Office Building in

Washington, D.C. 60

Table 5.1 Advantages and Disadvantages of Different Models 69

Figures Page

Figure 3.1 Determining the Economically Optimal System Size

Through Minimizing Costs 28

Figure 3.2 Determining the Economically Optimal System Size

Through Maximizing Savings 28

Figure 4.1 Economic and Thermal Performance Curves Derived from

FEDSOL, F-CHART 3.0, F-CHART 4.0, and SOLCOST for a Residential Space Heating System in Bismarck, N.D 43

Figure 4.2 Economic and Thermal Performance Curves Derived from

FEDSOL, F-CHART 3.0, F-CHART 4.0, and SOLCOST for a Residential Space Heating System in Washington, D.C 44

Figure 4.3 Economic and Thermal Performance Curves Derived from

BLAST and DOE-2 for a Space and Service Water Heating

System for a Federal Office Building in Washington, D.C 53

Figure 4.4 Economic and Thermal Performance Curves Derived from

BLAST, FEDSOL, F-CHART 3.0, F-CHART 4.0, and SOLCOST

for a Space and Service Water Heating System for a Federal Office Building in Washington, D.C 61

Figure 4.5 Economic and Thermal Performance Curves Derived from

DOE-2, FEDSOL, F-CHART 3.0, F-CHART 4.0, and SOLCOST for

a Space and Service Water Heating System for a Federal

Office Building in Washington, D.C 62

Figure 4.6 Sensitivity of Optimal System Size to Changes in

Auxiliary Boiler Efficiency: Constant Efficiency vs. Increasing Efficiency 66

viii
1 . INTRODUCTION

1.1 BACKGROUND

A variety of computer simulation programs exist for analyzing the thermal

performance of entire building energy systems or of solar energy systems alone.

Many of these simulation programs include subroutines for conducting an economic analysis of a proposed building system. They range from comprehensive, hourby-hour transient load analysis programs that must be executed on a large mainframe computer to monthly or annual analysis methods designed for evaluating

solar energy systems alone that have been adapted for hand-held or desk-top

calculators. Virtually all the methods available today were developed using

hour-by-hour simulation models. The monthly and annual analysis methods typically are based on correlation studies conducted with large numbers of test

results from the hourly simulation models.

A number of studies funded by the Department of Energy have investigated the

differences in predictions of thermal performance of solar energy systems that

might be expected from the more widely used computer programs, particularly

as applied to active systems in residential buildings. These studies generally

show widespread agreement among the results even for programs of widely varying

levels of analytical detail when applied to standard active system designs.*

Much less attention has been given to comparing the results of the computer

programs as applied to nonresidential buildings or to passive solar buildings.

* In "A Test Problem and Solutions for Solar Heating and Cooling Simulation

Programs," Maybaum reports agreement of "percent solar" figures to within 2 percent solar on an annual basis and 5 percent on a monthly basis in the

analysis of a typical active system for space and domestic water heating in a residential building with four programs, CAL-ERDA, HISPER, LASL (SLR method),

and TRNSYS. (DOE-1 and DOE-2 are later versions of the CAL-ERDA program.)

Differences in the results obtained for space heating only or domestic water

heating only systems were somewhat larger, ranging up to 20 percent solar in

certain months [1].

Solar Environmental Engineering, Inc. (SEEI) has conducted validation and

comparison tests for a number of programs as applied to standard active systems in residential buildings. SEEI found very good agreement in comparing

results from TRNSYS with results from its derivative, F-CHART. The mean

error in predicted monthly fractions of the load supplied by solar was approximately 2 percent and the standard deviation about 7 percent. A

further comparison of results from F-CHART with its derivatives, the GFL and

Relative Areas methods, also showed agreement to within 5 percent. The GFL

and relative areas methods are based on annual calculations; F-CHART on monthly calculations; and TRNSYS on hourly calculations. In extensive com- parisons of results from F-CHART and SOLCOST for a number of cities, SEEI

reported agreement to within +10 percent, 90 percent of the time [2]. In

this test, results were very close for solar fractions ranging between 0.3

and 0.8.

1
Moreover, differences in the economic analysis models contained in the various

programs remain almost entirely unexplored.

Federal agencies proposing solar energy systems for funding are required by law

to provide economic evaluations of those systems according to a specific methodology and set of procedures. The National Energy Conservation and Policy Act

(NECPA) of 1978 directed the Department of Energy to develop uniform methods

and procedures for life-cycle costing (LCC) to be followed by all federal agencies, unless specifically exempted, in evaluating the cost effectiveness of

potential energy conservation and renewable energy investments in federally

owned and leased buildings. The Federal LCC Rule was published in the Federal

Register , January 23, 1980 [3]. It comprises subpart A, Part 436 of 10 CFR as amended by the Energy Security Act (ESA) of 1980 and the Federal Register of

October 27, 1980 [4]. An additional amendment is anticipated in late 1981 [5].

Further revisions of the Federal LCC Rule, primarily to update energy prices,

will be made periodically.

Private investors have different economic concerns and investment criteria from

public investors. For one thing, tax legislation at the federal, state, and

local levels has a very large impact on the outcome of an investment in solar

energy or energy conservation and, therefore, should be accounted for in the

economic analysis of projects to be undertaken in the private sector. Furthermore, tax laws vary considerably for corporate businesses, individual businesses,

and residential investors who occupy their own buildings.

Without knowledge of what is in the economic evaluation routines of the various

energy analysis and solar energy programs, it is impossible to know which economic models conform with the federal requirements and which might be more suitable for analyzing other public or private investments. It is also impossible

to know what biases in results can be expected depending on which economic

evaluation model is used.

1.2 PURPOSE AND SCOPE

The purpose of this report is to compare the economic analysis models contained

in the most widely used solar energy computer programs and to determine if they

comply with the Federal LCC requirements. The report is intended as a reference

tool for assisting engineers, builders, architects, and facilities managers in

carrying out the required life-cycle cost methodology and procedures for solar

federal buildings projects. The focus is on: 1) identifying areas of potential

confusion and incompatibility between the Federal LCC Rule and individual programs; 2) describing how to adjust for differences in the data and input variables in these programs to achieve maximum possible conformity with the Federal

LCC Rule; and 3) illustrating the effects of the remaining differences in the

models. Brief attention is also given to the similarities and differences in

the programs as applied to private investments.

For further instructions and information about carrying out the federal rule for

life-cycle cost evaluation of solar energy projects, the reader is referred to

the Life-Cycle Cost Manual for the Federal Energy Management Programs and to

FEDSOL, Program User’s Manual and Economic Optimization Guide for Solar Federal

Buildings Projects [6,7].

2
1.3 SELECTION OF MODELS FOR STUDY

The Solar Energy Research Institute (SERI) has conducted a survey of existing

analysis methods for the design and study of solar heating and cooling systems

[8]

.

The analysis methods included in the SERI survey range from computer simulations (a mathematical analogy of systems and predictions of system performance) to hand-held programmable calculator applications. Table 1.1, extracted

from the SERI report, is a summary of the most frequently used solar analysis

computer methods that contain economic as well as thermal analysis routines.

This survey provided a basis for selecting models for study.

In order to perform case studies illustrating the differences in actual

computer analyses conducted with these models, it was necessary to restrict

the number of programs examined. The following criteria were applied to narrow

down the list to a manageable group consisting of the most representative and

widely used programs:

1) Each program must be applicable to a range of solar energy system types

and building types.

2) The program must be available on major nationwide time-sharing systems,

and it must be available for purchase at a reasonable cost, in tape or cassette form, for installation on individual main-frame computers.

3) The program code listing must be available for examination by research

analysts

.

4) The program must serve a broad cross-section of the solar engineering

and design community.

Of the fifteen programs in table 1.1, F-CHART (Version 3.0), SOLCOST, DOE-2,

and BLAST stood out as the most frequently cited in the solar energy and energy

conservation literature. At the same time, they served the diverse categories

of users, building types, and systems in the federal building community. Of

these four programs, BLAST and DOE-2 are most suitable for research engineers

and architectural engineers who require a detailed analysis of energy use for

a wide range of buildings and HVAC systems. Given the high level of engineering

sophistication and relatively high cost involved in learning and in executing

these programs, they are most likely to be applied to large-scale commercial

building projects with complex HVAC designs. Because they are highly interactive and much less expensive to learn and use, F-CHART and SOLCOST are more

suitable for builders and other practicing professionals with limited computer

experience and a limited understanding of solar energy fundamentals and heat

transfer principles.

3
Table 1.1. Summary of Existing Solar Energy Computer Programs with

Economic Analysis Capability

Availability Application Intended Users Computation

Interval CO CO

Program

Name

Latest

Version

Purchase

(S)

Time

Share

Special

Arrangements

Comments

User

Manual

Service

Hot

Water

Space

Heating

Space

Cooling

Process

Heat

Active

System

Passive

System

Research

Engineers

Architect/Engineers

Builders Hour

Month

Computer

Versior

Available Economic

Analysi

Sponsor

BLAST* 1980 Nom. •

Training

available 9 CDC • USAF, USA, GSA DOE-2* 1980 400 • © CDC • LASL, DOE EMPSS 1978 500 ©

Consulting

with ADL ® © ® © © A © • IBM 9 EPRI F-CHART ^ 1978 100 •

Training

available 9 © 9 9 © • o CDC, IBM UNIVAC t © DOE FREHEAT 1979 150 Limited documentation 9 © © © CDC 9 DOE HUD-RSVP/2 1979 175 •

Based on F-CHART 9 © © 9 © © • CDC

UNIVAC

9 HUD PACE 1980 100 ©

Based on

F CHART, SLR 9 • © 9 ® © • CDCUNIVAC 9 DOE, SERI SHASP 1978

Avail, on

request 9 9 • 9 © • © UNIVAC 9 DOE

SOLCOST 1979 300 © 9 9 © • 9 © 9 © 9 CDC, IBM UNIVAC 9 DOE

| SOLFIN 2 1980 Nom. Documentation

Cost $6. Ann ual IBM, CDC 9

California Energy Commission SOLOPT 1978 20 9 9 © © 9 9 AMDAHL 9 Texas A&M Univ. SUNCAT 1979 Nom. | Limited

! documentation © 9 © O © ©

Data

General

Eclipse O NCAT

SUNSYM- 1979 • 9

Ottered as

service only 9 © @ © 9 © IBM © Sunworks Comp.

Systems SYRSOL 1978 Nom.

Avail, but not actively marketed

9 © © © 9 9 © © IBM ©

ERDA, NSF, DOE

TRACE SOLAR*

| 1980 9 9

Ottered as

service only 9 9 9 9 • © 9 9 9 9 IBM 9 The Trane Co. TRNSYS 1979 200 9

Training required © © 9 @ CDC, IBM UNIVAC t A DOE

TWO ZONE 1977 No Charge • 9 9 © © O © CDC * LBL WATSUN II, III 1980 Contact Author 9 9 L®

© © 9 IBM *

Nat l Research

| Center of Can ‘Programs are primarily developed for large-scale, multi-zone applications

A Being added

t ANSI 1966 Std. Fortran

Source: Analysis Methods for Solar Heating and Cooling Applications , Solar

Energy Research Institute, 3rd Edition, August 1980, SERI/SP-35-232R.

a The table refers to Version 3.0. Version 4.0 (not available at the time of

the SERI survey) is applicable to process heat systems, to heat pump systems,

and to concentrating (CPC) collectors in addition to flat plate systems for

service hot water or space heating.

4
These latter two programs apply only to solar energy systems. These programs

were developed primarily for systems in residential buildings.*

All of these programs are accessible through a number of nationwide time-sharing

systems, and all are "public domain” programs developed with federal funds.

Thus, the program codes are available for purchase in tape form at relatively

low cost. Unlike the proprietary programs, the code listings can be obtained in

printed form from public sources at a nominal or no charge for examination by

research analysts outside the sponsoring firm.** This feature was considered

essential to the current study because it was necessary to examine portions of

program codes in order to verify what differences in modeling techniques among

the programs were causing differences in results. Other widely used energy

analysis programs frequently cited in the literature, TRACE, for example, were omitted from the study because of their proprietary nature. Although similar

in function and applicability to BLAST and DOE-2, these programs are available

only by special arrangement and at high cost. Generally, these and other protected programs are executed only by the sponsoring firm under contractual

agreement with other firms. They are not available on time-sharing systems,

and their codes are not available for examination by users.

During the course of this study, F-CHART 4.0 was released for public use. Because this program is significantly different from F-CHART 3.0, and appears

to serve a somewhat different section of the building community, it was decided

to include both F-CHART 3.0 and 4.0 in the study.***

The RSVP/2 program was also considered and subjected to preliminary test

analyses. This program is based on the thermal analysis model in F-CHART 3.0,

but its economic analysis model is entirely different [12]. After sample test

runs, it was decided that the differences between RSVP/2 and F-CHART 3.0, as

The user’s manuals for F-CHART and SOLCOST suggest these programs can be

applied to solar energy systems in commercial buildings as well as residential systems providing appropriate assumptions are made about the energy

requirements of the building [9,10,11], However, the applicability of

these programs to commercial buildings is not nearly as well documented and

validated as it is for residential buildings.

Private, commercial time-sharing systems protect the codes for programs

offered through their system libraries, but code listings can be obtained

from the original sponsoring agencies or public model libraries such as the

Solar Energy Information Data Bank (SEIDB). Small differences may be

expected for the same versions of the program on different computers and

time-sharing systems. Commercial time-sharing firms can establish proprietary control by making small changes in the program codes.

F-CHART 3.0 appears to be the most widely used of all the solar energy

models at the current time. F-CHART 4.0 is applicable to a wider variety

of types of active systems and system designs and solar heating/cooling

functions than F-CHART 3.0 (see table 1.1) and is likely to be adopted by

many F-CHART 3.0 users.

5
applied to a federal buildings project, were not sufficient to justify including

a detailed analysis of both in current study.*

The PACE program is a recent modification of the RSVP/2 code that includes a simplified passive system performance calculation based on the Solar Load Ratio

(SLR) method for passive solar buildings. PACE was not available in time to be

included in the current study.

In support of the Solar Federal Buildings Program, the National Bureau of

Standards has developed a solar energy analysis program, called FEDSOL, to

perform economic evaluations according to the Federal LCC Rule. The FEDSOL

program incorporates the SLR method of predicting the performance of standard

active systems in commercial buildings and residential buildings [7]. It can be

used either to perform a combined economic and thermal analysis, according to

the SLR method of predicting the performance of active solar energy systems, or

to perform only an economic analysis.

FEDSOL was included in the study to illustrate the federal requirements for

life-cycle cost analysis. FEDSOL also served as a standard for quantifying the

biases in actual results that might be expected from applying each of the other

models to a federal project.

Computer analyses were performed by contractual agreement with two nationwide

time-sharing systems. The Solar Energy Information Data Bank (SEIDB) maintained

by the Solar Energy Research Institute was used for the analysis of F-CHART

(Versions 3.0 and 4.0) and SOLCOST (Version 3.0). This computer models library

is available to federal agencies and DoE contractors through the TYMNET communications network. FEDSOL also is available to federal agencies and contractors

through the SEIDB program library.** BLAST (Version 3.0) and DOE-2 (Version 2.1) were accessed on CYBERNET, a commercial time-sharing system owned and operated

by Control Data Corporation. A tape can be purchased from the National Technical

Information Service.

1.4 APPROACH AND ORGANIZATION

This report first reviews the requirements for life-cycle cost evaluation of

solar and other renewable energy projects for federal buildings, then examines

and compares each of the models selected for study against the general standard

imposed by the Federal LCC Rule, and finally illustrates the effects of differences in the models as applied to federal building projects in representative

test cases.

RSVP/2 contains a very comprehensive residential income property analysis,

a capability not relevant to the current study but very useful in evaluating a commercial building under individual or corporate ownership or a residential income property.

Contact: Mr. Ralph Ubico , SEIDB Network Coordinator

Solar Energy Research Institute

1536 Cole Boulevard

Golden, Colorado 80401

6
Section 2 provides a general review of the life-cycle cost method and the

Federal LCC Rule. This section identifies the required assumptions about costs,

inflation and discounting, and the time horizon of the project; and it reviews

a number of life-cycle cost evaluation techniques.

Section 3 examines in detail the economic variables and measures of economic

performance provided in each of the models selected for study. This section

compares the assumptions upon which each model is based and suggests procedures

for adjusting for differences in the models to achieve maximum possible con- formity with the Federal LCC Rule. The last subsection describes the lifecycle cost method of designing and sizing solar energy projects for maximum

cost effectiveness and compares the optimization features in the six programs

under study.

Section 4 describes test cases in which the six programs are applied to two

different types of federally owned buildings and energy systems. The test cases illustrate the combined effects of differences in the thermal and economic analysis portions of the programs and the effects of differences in the

economic analysis models alone.

A final section summarizes the findings of the study, describes the advantages

and disadvantages of the different models, and suggests areas for future

research.

7
2. THE LIFE-CYCLE COST EVALUATION OF SOLAR FEDERAL BUILDING PROJECTS

2.1 GENERAL OVERVIEW

Life-cycle costing is a method of economic evaluation which accounts for all of

the relevant costs over the life of a project. It is used to determine the

economic feasibility of projects such as energy conservation or solar energy

that realize their benefits primarily through reducing fuel costs. The basic

steps of a life-cycle cost evaluation of a solar energy project are as follows:

1) Expected cash flows, including all costs, residual values, and special

subsidies or incentives, are estimated based on a comparison of the

proposed investment with its alternative;

2) All cash flows are converted to a common time basis using discounting

procedures; and

3) The elements of cost for the proposed project, including costs for

solar energy and auxiliary equipment, and for the alternative investment which would be made in lieu of the proposed solar energy project,

in time equivalent form, are used to compute various measures of

economic feasibility.

2.2 SPECIFIC REQUIREMENTS FOR SOLAR FEDERAL BUILDINGS PROJECTS

The Federal LCC Rule describes a general economic evaluation model that is

applicable to most investments in solar energy or energy conservation whose

outcomes are not influenced by public tax and incentive policies at the state,

federal, or local level. It also prescribes specific data and assumptions

which must be used in evaluating projects undertaken in the federal sector.

2.2.1 Cost Data and Assumptions

The life-cycle cost evaluation of an investment in solar energy for a federal

building requires an assessment of the following kinds of a solar-related costs over the time horizon of the investment: 1) investment costs, 2) annual nonfuel operation and maintenance costs, 3) nonannual replacement costs, 4) energy

costs, and 5) salvage or resale value net of removal and disposal costs. Since

solar systems will generally be used in conjunction with an auxiliary energy

system (e.g., electricity, natural gas, or oil), it is necessary to consider

the costs of a combined solar energy/auxiliary energy system.

To establish a basis for comparison, these costs must also be assessed for a totally nonsolar energy system which would be used in lieu of a solar energy

system (hereafter called the reference system or nonsolar energy system).

Costs which are expected to be approximately the same for the nonsolar energy

system and the combined solar/auxiliary system, or which are sunk costs (i.e.,

costs incurred regardless of the investment decision), need not be included

because they are unaffected by the investment decision.

8
Investment costs are costs incurred for the design, purchase, and installation

of the solar energy system. These costs are assumed to occur in a lump sum at

the beginning of the base year.* To encourage energy conservation and to adjust

for social benefits of reduced consumption of nonrenewable energy that are not

reflected in average market fuel prices, the Federal LCC Rule further specifies

that solar energy investment costs be reduced to 90 percent of their actual

value

.

Annual nonfuel operation and maintenance costs are costs for repair, operation,

and maintenance that recur annually. These costs are assumed to begin to accrue at the beginning of the base year and are evaluated as lump sum payments

at the end of each year of the study period.

Energy costs are based on an estimate of the quantity of energy delivered

annually to the building boundary with and without use of solar energy, as derived with energy analysis procedures appropriate to the building and system

under study. Energy costs should be calculated using the base-year energy

prices obtained from the local supplier and the projected annual real rates

(excluding inflation) of fuel price escalation published by the Department of

Energy in the Federal LCC Rule, which is revised periodically [5].** When the

actual unit price to the agency is not known, the base-year prices published

in the LCC Rule should be used. Base-year energy prices in the DoE tables are assumed to represent prices per million Btu and per sales unit of purchased

energy at the beginning of the base year. These prices are consistent with the

economic model in the LCC Manual and FEDSOL program but will require adjustments

when used with other economic models.***

The energy price tables contained in the Federal LCC Rule include different

rates of energy price escalation for each 5-year period from 1980-1995. (The

rate for the 1990-95 period is assumed to hold for the remaining years of the

study period after 1995.) To be fully consistent with the Federal Rule, the

economic model used to evaluate a federal solar energy project must provide

for multiple escalation rates.

Nonannual repair and replacement costs and salvage value are costs (or savings)

that occur on an irregular basis. According to the Federal Rule, each is

assumed to be a lump sum payment at the end of the year in which it is expected

to occur.

The beginning of the base year is the time the life-cycle cost analysis is

undertaken.

Average unit prices and projected real rates of energy escalation are provided by DoE for each of 10 DoE regions, for each use sector—residential,

commercial, and industrial—and for a number of fuel types. They appear in

tables B-l through B-ll and C-l through C-ll of the LCC Rule and are revised

periodically.

See section 3.1.3.

9
2.2.2 Inflation and Discounting

Life-cycle costing requires that dollar costs occurring at different calendar

times be adjusted to a common time basis, taking into account the cost of money.

If inflation is included in estimates of future costs and savings, it should

also be included in the discount rate. Alternatively, if all costs and savings

are expressed in constant dollars, i.e., in terms of the purchasing power of a dollar at the beginning of the base year, then inflation should not be included

in the discount rate.*

The Federal LCC Rule requires that the latter approach be followed and further

specifies the discount rate to be used. Under the Federal Rule, all costs and

savings must be estimated in constant dollars and discounted to their present

values using a 7 percent real discount rate.** In estimating energy costs,

which are expected to increase somewhat faster than the rate of general price

inflation, a differential price escalation rate is used to find the constant

dollar equivalents. The projected real rates of energy price escalation contained in the LCC Rule and Manual serve this purpose.

2.2.3 Study Period

The study period, or time horizon, covered by the life-cycle cost analysis of a Federal project should coincide as nearly as possible with the expected service

life of the major portion of the project, not to exceed the life of the building

(or lease). According to the Federal LCC Rule, this study period must not

exceed 25 years. A study period of 20 years is recommended for solar energy

projects unless a different period (not to exceed 25 years) can be supported.***

2.2.4 Measures of Economic Performance

A number of life-cycle cost evaluation techniques exist for determining the

economic performance of solar energy projects. Each technique has its advantages that make it appropriate for some purposes and not appropriate for others.

The measures of economic performance required under the Federal Life-Cycle Cost

Rule are 1) total life-cycle costs (TLCC) , calculated as the sum of the discounted value of all the time equivalent costs over the investor's time horizon;

2) net savings (NS), the difference between the TLCC's of a proposed project and

its alternative and thus a dollar measure of the project's net profitability,

and 3) savings-to-investment ratio (SIR), a numerical ratio describing the size

of savings relative to costs. A supplementary technique is the time to payback

For a more detailed description of the discounting procedure, see references

[6,13,14].

The estimated cost is to be based on the purchasing power of a dollar at the

beginning of the base year.

Some passive solar projects may be expected to last considerably longer than

20 years.

10
(PB). Not truly a life-cycle cost measure, the PB indicates the elapsed time

until cumulative savings (or receipts) are sufficient to cover cumulative costs.

Although this measure may take into account either or both the cost of money

and fuel price escalation over the payback period, the simple payback (SPB)

measure as defined under the Federal LCC Rule takes neither of these factors

into account. Moreover, none of the payback approaches takes into account savings and costs beyond the time that the investment is paid off. The TLCC and

NS measures are the appropriate techniques to use in designing and sizing

projects. The project with lowest total life-cycle costs or maximum savings

is the most cost effective choice, given the conditions assumed and accounted

for in the life-cycle cost analysis.

The SIR technique is most appropriate for assigning priorities to a number of

independent projects competing for funds under a limited budget. Selecting

projects in order of decreasing SIRs until the budget is exhausted will result

in maximum net savings from the entire budget expenditure.*

The internal rate of return measure, although not a required measure under

the Federal LCC Rule, is equally suited to this task of assigning budget

priorities

.

11
3. COMPARISON OF ECONOMIC MODELS

This section compares the economic models in the six computer programs selected

for study using the following analytical approach: 1) The economic variables

and discounting assumptions contained in each model are identified and compared

with the federal requirements; 2) The measures of economic performance calculated by each model are described and analyzed to determine whether the measures

and methods of calculation are consistent with the federal requirements; and 3)

The capabilities of each program for determining the optimal solar energy system size are examined and compared. Also noted are special features of individual programs which may be useful in selecting the economic analysis tool most

appropriate to a particular project and phase of project development.

Tables 3.1 and 3.2 summarize the major differences in the models as applied to

federal solar energy projects. Each X mark in the tabular grid represents a requirement of the Federal LCC Rule that i^s met by the corresponding model in

the first column; each box, a requirement of the Federal LCC Rule that is not

met by the corresponding program in the first column.

By no means exhaustive or comprehensive, these tables provide a minimum standard

for assessing the degree of consistency with the Federal LCC Rule and the analytical scope of the models under study. Each item in the tables is examined in

greater detail later in this section.

3.1 ELEMENTS OF THE MODELS (INPUT VARIABLES)

3.1.1 Investment Costs

As shown in table 3.1, all the models meet the minimum requirements for

assessing solar energy investment costs. Differences in the breakdown of

investment costs into different input variables are not reflected in the table

but are summarized below.

FEDSOL, SOLCOST, and F-CHART 3.0, for example, contain separate variables for

solar energy investment costs that are independent of system size (the fixed

cost) and for investment costs that depend directly on system size (variable

costs). The variable cost is expressed as the average cost per incremental

unit of solar energy system size.* Investment costs are defined to include

costs of installation.

* SOLCOST contains a separate variable for storage cost, per gallon (liquid) or per ton (rock) of storage capability. Using the value for storage volume per

square foot of collector specified by the user, SOLCOST calculates the cost of

storage per square foot of collector and adds this value to collector area

unit costs. An equivalent procedure is to include the cost of storage per

square foot of collector area in the variable unit cost of the system and to

assign a zero value to the storage cost variable. A portion of the total

estimated cost for storage might realistically be included among fixed investment costs.

12
Table

3.1

Comparison

of

Economic

Evaluation

Models

in

Selected

Solar

Energy

Analysis

Programs:

Input

Variables

TO

a)

a

boC 3 3

boH

to coX'H

0) 4-1

X X) > C (J (1) a) H m 0 ^ U

<u c, 0 a) co O Cl, 3 o

Cl

H (It

H <4-1 e

CJ 3

TO 4-1 •H 3 Q Pi 3

TO T—1

3- • TO

TO •H CJ 3 O 4-4 CO 4-) CJ i—1 W 3 3 Pi X 2

bO

Ci3 3 3 3 Cl 4-1 CJ W TO 3 •rH •H 3 3 3 Cl CP X 3J PL, 3

to TO 3 3 > 3

r—1

rH 3 3

CP >

4-4

X 3 t—I 3 3 3 3 B TO 3 3 4-4 3 Ci CJ TO 3 •H 3 O

TO 3 3 rH o

4-1 3 a a.

TO

O 3 3 o 52 Pi Pi CJB TO 3 rH 4-1

4-4 i—1 3 TO CO 3 3 o X 3 <4-1 CJ

C/J 3 3 3 O £

<3 IS <3O

CO

4-1 COoo

XI

x

pi

XXX

XXX

Sf* Sr4 s> s-i KS rS rN rS ks

PiX S-4 ts> K-t

K-4 K-4 rS rS rS rS rM

Pi

Pi

XXX & X

X

s> Cr-/* K^A K^A rS kS rS PS PS X X

X

XX

Pi

Pi

X K<P S> S^4 S*?4 ''N K'N kN P"m KS X

c/j pp

X

X

XXX

C/J 52

CJ

W

—4 &q X

H

H

Pi CP C/J

X C/J

M

H <JX o o o O

H CM 3 3 H w C/J pc • • CJ to 1 O' PC Pi Q CJ o CO X < W w z <3 o w 1 o X o 2 H > 2 X C/J CP Q 3

xloa3

•HX

<03

•H3 • X 3 3

rH

O 3 CJ Pi X

CJ o

CJ 3 X 3

rH • X 3 rH

Ci 3 3 3 X •H X o Ci 3 B 3 X 3 > X •rH CJ

CJ

X

X

X 3 3 3 ,

1 Cl •rH 3

•rH 3 Ci 3 4-4 3

cr 3 X 3 O 3 3 3 X 3 3 X i—1 rH 3

Xl

X Ci 3 3 •H

•rH •rH 3

Ci Ci cr 3 3 3 > > Pi

II II II

PiX

•HM

CL) axX34-) co

a) X<4-1 o

Cl

<0

0)xX0333

•H

013

•HUaX

0) 4-) COB

•H4-) CO33X4-> 3o

CO

0)3

rH3><4-1 O

0)3333

cr

0)

co

to3

a-a3

COX3O3O

<4-1 X

0)

4-4 33Ou

CJ33X33

CJ 3>

rH3

co *333

coOo2

<-3 o33333

X)33

CMI WooX33H

C/J 3

CP33B4-) CO3>33O<4-1 X)3aa3

co

TO33

rH3>

to3

v-l

4-1 TO3

•<-> X)3XX3o<4-1 X34-) 33O3

CJ33 TO X 33 3 rH 3 3

CJ >

CJ3

T-l o3ax

b03333

Cl3

!—I o

co3

Cl3X3

X)IO4-) 33B

4-4 TO3

•<-) X)3

TOO

CJ

4-4 33B

4-4 TO3>33

CJ

13

(F-CHART

3.0

and

S0LC0ST).
Table

3.2

Comparison

of

Economic

Evaluation

Models

in

Selected

Solar

Energy

Analysis

Programs:

Methods

of

Analysis 33 CO > *H 3 CO Pi X 3 i—( 3 3 3 3 M <3 1 co

03 U-i 3 N O 3 rH b0 3D 0 3 *H 3

•H

O CO *H

•H 3 U

CX 4-1

Q 3 o > 1X

-3 3 t—1 3

CO o 3 *H 3 5 1 3 CO O EM <J 3 H fc* 3 3 3 3 -H 3 4-1 > 3 PQ CO CO o 4J

4-» 3 1 3

CO 0 o

tkO 4J •H 3 4-J •H 3 3 > > Pi

03 3

C/3

W 3 S—1

(-i 03 3 C 3 4-1

4-J 3

0) 3 Pi u Pi c m M o

Pi

0)U <—1 3

(X PQ 0 X

•H 3

CO P-,

33

CU

4-> 3 Pi 3 U O 3 O JQ

CO

X

•rH 3 Q P-.3

bO

4-J 3 3 •H 2 >3

CO 1 3 3 4-J CM 3 H O

hJO i—1 3 3 i—1

4->

O

OX

H o

Pi

pi

Pi

Q co W W Pi 2 M 33

3d co O' <

gg

XXX

X

X

X

K^A rN k"n h'S rN X X

X * X

XXX 3 3 3 |—| |—i x x x [|[! X X

X

X

XX I [ X

X

CO

PQs X

XX

H

1-4 pcJ CO o <G o o o H CN

CO gc • • o CO 1 Q D CO 'xT rJ <C W W I O o

fa Pm CO PQ o o 3

rH 3 3 3

33 3 O 0 0

Pi

X o

PQ 3

PQ 33 X 3 3

33

a

•H 0 > 3 3 O rH

i—1 Ma 3 a 0 ai H

4-) 3

C_> o

CJ c i—1 3 3 H

rH S-4 3 3 • 3 33 3 rH 3 3 3 3 3 CM

33 33 3 3 O 0 0

Pi 0 On X X C-3 CM

PQ PQ kQ

X

33 33 i—1 1—1 3 3 3 4-1 3 33 H 3

°H •H 3 3 3 > 33 S-4 CD* O 3 3 3 1-4 Cm CM 3 a 4-1 33 •H 3 3 3 33 3 t-i H 3 3 •H 33 3 3 3 3 3 3 o* 4-J 3 3 3 3 2 2 Pi rH3O

II II ll rH3

Pi

X

CP3

14
F-CHART 4.0 also provides for separate estimates of fixed and variable solar

energy investment costs; however, the analytical format is somewhat different.

The investment cost variable entitled "base system costs" is defined to include

the total solar energy investment cost; but in an optimization analysis, only

fixed costs are to be included in "base system costs." In the OPTIMIZE command

sequence, the program user must supply additional values for the total variable

costs corresponding to two collector areas designated as the maximum and minimum bounds of the range of sizes to be considered.* (F-CHART 4.0 then calculates the average incremental cost per unit for this range of sizes.) Investment costs can further be broken down into those eligible for tax credits and

those not eligible.

An accurate breakdown of fixed and variable components of investment costs is

essential in using all four of these programs to optimize system size because

the outcome of optimization analyses is strongly dependent on assumptions about

the cost for incremental increases in collector area, storage, and other system

components. Overestimating fixed costs and underestimating variable unit costs

will cause the program to overestimate the optimal system size. Underestimating

fixed costs and overestimating variable costs will have the opposite result.**

In analyzing a system of a pre-specif ied size, it is not necessary to separate

solar energy investment costs into their fixed and variable components. The

total estimated investment cost of the system can be assigned to fixed costs,

and a zero value assigned to variable costs.

BLAST and DOE-2 have no optimization routines requiring a breakdown between

fixed and incremental costs. In these programs, investment cost inputs are expressed as the average cost per unit of solar energy system size, costs for

materials separately from costs for labor. The solar energy system unit can be

defined as the user desires, for example, as the entire system, as one collector

panel, or as one square foot of collector area. This means that if the unit is

defined as the entire system, the cost per unit is the total system cost. Or,

if the unit is defined as one square foot of collector area, the cost per unit

is the average cost of the system per square foot of collector (the total system

* Additional points may be specified if the relationship between total variable

costs and collector area is nonlinear in the range specified. The program

then calculates the average incremental cost for each consecutive pair of

points. There is no provision for specifying nonlinear relationships between

collector area and costs in the other programs.

Different program users are likely to apply different criteria in separating

the estimated total cost of a specific system into its fixed and variable

components. Furthermore, the fixed cost and variable unit cost are likely to

be somewhat different for different ranges of system sizes. To simplify the

calculation of the two types of costs, and at the same time to avoid a common tendency to overestimate the variable unit cost, the fixed cost may be defined

as the cost for the smallest system that is realistic for a particular application of solar energy, including materials, labor, and design and engineering

services; the variable cost as the cost for each additional unit of system

size

.

15
cost, excluding installation costs, divided by the total collector area). The

average cost per collector panel is the total system cost (excluding installation) divided by the number of panels.*

In BLAST, the installation cost Is expressed as a factor that is multiplied by

the average cost per unit to yield the total installed cost per unit. In DOE-2,

costs for installation of solar energy components are expressed in terms of the

average installation cost per unit.

In using BLAST and DOE-2 to analyze a federal project, it may simplify the

preparation of input data to combine materials and labor costs by including the

cost for labor in the equipment cost variable and by using a value of 1.0 for

the installation cost factor (in BLAST) or zero for the installation cost per

unit (in DOE-2).**

Investment costs for auxiliary heating and cooling equipment should be included

in the economic analysis of the solar energy system if these costs are significantly different than for the reference nonsolar equipment that would have

served in lieu of the solar energy system. F-CHART 3.0 contains no explicit

provision for investment costs of nonsolar heating equipment used as an auxiliary system to a solar energy system or in lieu of a solar energy system. To

account for such costs, it is necessary to estimate the difference in the cost

of the auxiliary and reference systems and to subtract the differential of the

reference system cost over the auxiliary system cost from the "fixed" solar

energy investment cost.*** BLAST, DOE-2 and FEDSOL contain separate variables

for investment costs for auxiliary and reference equipment. F-CHART 4.0 and

SOLCOST contain a separate variable for the reference system cost but not the

auxiliary equipment cost. With these two programs, the value attributed to the

reference nonsolar equipment cost should represent the differential of reference system cost over auxiliary system cost. Alternatively, with F-CHART 4.0,

the auxiliary system cost could be added to "base" investment costs for the

solar energy system not eligible for tax credits.

Any of the three methods is equally appropriate providing the values and

units selected accurately describe the system being analyzed. Keep in mind

that, unless the unit is defined as the entire system, both the average cost

per unit and the number of units should change if the system size is changed

in order to account for both fixed and variable system costs.

Separate estimates of labor and materials costs are required only if the user of the program assumes different inflation rates for labor costs and

materials costs, for example, in estimating the future costs of repair and

replacement parts. This should not be the case in evaluating a federal

project since the Federal LCC Rule requires that all future cash flows be

expressed in constant dollars of the base year, i.e., inflation must not be

included except in the case of energy costs, where a differential rate of

price escalation, above general inflation, is used.

*** This should be done after adjusting for any investment credits or tax

credits (see Investment Credit below).

16
When the capital and other nonfuel costs are approximately the same for the

auxiliary equipment and the reference nonsolar heating equipment, the costs

of this equipment do not affect the outcome of the life-cycle cost comparison of the two alternatives and need not be included in the analysis.

These costs can either be given zero values or, in the case of BLAST and

DOE-2, be allowed to assume their default values. x

Investment Credit (Externality Adjustment) . FEDSOL and F-CHART 4.0 contain

investment (or tax) credit variables that can be used to account for the 10

percent investment credit required as an externality adjustment under the

Federal LCC Rule. In using the other programs, all solar energy investment

costs must be adjusted to 90 percent of their actual values before being

input into the program. Other solar energy costs and savings are not

adjusted

.

With BLAST and DOE-2, this approach is equivalent to the 10 percent

investment credit, and no further adjustment need be made. With SOLCOST and

F-CHART 3.0, the values supplied for annual maintenance and insurance costs value* and salvage ** also should be adjusted by dividing the actual estimated

annual cost or salvage value by 0.9, because these programs assume a constant

relationship between solar energy investment costs and these variables.

Reducing investment costs to 90 percent of their actual value will cause the

programs to reduce maintenance and salvage to 90 percent of their actual

value unless the counteradjustments are made. For example, for a solar

energy system costing $10,000 and estimated to have an annual maintenance

cost equal to two percent of the investment cost, reducing investment costs

to 90 percent of their actual value will cause the program to underestimate

life-cycle maintenance costs by $211.90. Applying a factor of 1/0.9 to the

estimate of annual maintenance costs removes this error.

3.1.2 Annually Recurring O&M Costs (Nonfuel)

All the programs allow for assessment of annually recurring costs for

operating and maintaining the solar energy system, usually defined to

include insurance as well as routine nonfuel O&M costs. In FEDSOL, SOLCOST,

and FCHART (3.0 and 4.0), these costs are expressed as a percentage of initial

The default values for investment costs for nonsolar plant equipment are non-zero in DOE-2 and BLAST. However, the costs will be the same for

the auxiliary and reference equipment providing the equipment types and

sizes are the same. Thus, investment costs for the auxiliary equipment

will cancel out the costs of the reference equipment in the life-cycle

cost comparison of the combined solar/auxiliary system versus the

reference nonsolar system. An exception is the calculation of SIR with

D0E-2

.

** In F-CHART 3.0, both data inputs must be adjusted; in SOLCOST, just

the maintenance cost variable. SOLCOST does not include salvage value

in its calculation of life-cycle costs and savings.

17
investment costs. In BLAST, the annual cost for maintenance is specified in

terms of labor hours per year and consumables (cost of repair parts) per operating hour.** A separate value for the cost of labor per hour must also be

specified. In DOE-2, the annual cost for maintenance (labor) and consumables

for the solar energy system is expressed as dollars per year per unit.***

Annually recurring operating and maintenance (O&M) costs for nonsolar equipment

also are explicitly allowed for in all programs except F-CHART 3.0. In SOLCOST

and F-CHART 4.0, these costs are expressed as O&M costs for the reference nonsolar system. (There is no separate annual O&M cost variable for the auxiliary

system.) With these two programs, the value assumed for O&M costs for the

reference system should represent the difference in O&M costs for the auxiliary

and reference systems.

3.1.3 Nonannual Repair and Replacement Costs and Salvage Values

Other solar energy costs may be expected to occur periodically over the life of

the solar energy project, for example, for replacement of worn out or damaged

components and for major overhauls. BLAST, DOE-2, and FEDSOL include provision

for these kinds of costs; the other programs do not. For example, BLAST and

DOE-2 contain separate variables for major and minor overhauls and for the

expected equipment life of each component of each heating and cooling plant

under consideration. The program user may specify the number of operating

hours in the overhaul intervals for each plant component.****

With FEDSOL, the year and dollar amount of the repair or replacement (net of

salvage value) cost must be specified for each occurrence. Up to three

occurrences are allowed for each system (solar, auxiliary, and reference).

SOLCOST contains a separate variable for annual insurance costs, defined

as a percentage of initial investment costs. The user may provide separate estimates of insurance and annual maintenance or combine the two sets

of costs under the maintenance category and place a zero value on the

other. The values supplied for these variables should be adjusted as described under Investment Credit .

By manipulating the data, costs for maintenance hours and consumables can

be combined (see section 3.1.1).

xx* The unit must, of course, be defined as for investment costs (see section

3.1.1).

Vt jS? ^ BLAST and DOE-2 calculate the number of operating hours per year for each

plant component (boiler, chiller, solar energy system, etc.). They use

the resulting data along with default or user-supplied cost estimates to

determine in what year an overhaul or replacement is to occur and to calculate its present value cost. Since the number of operating hours per

year is likely to be lower for auxiliary equipment than for the reference

equipment, overhauls will occur at different times and result in different

present value costs.

18
A salvage value for the solar energy system at the end of the period covered

by the life-cycle cost analysis is allowed in F-CHART (3.0 and 4.0) and FEDSOL

but not in any of the other programs. Although not an official requirement

under the Federal LCC Rule, the salvage value or resale value of certain solar

energy components should be considered when it is expected to have a significant

effect on the outcome of the investment in solar energy. The omission of this

variable is likely to be an important oversight in the evaluation of passive

systems, because these systems may reasonably be expected to serve longer than

the maximum study period of 25 years allowed under the Federal LCC Rule.* On

the other hand, a period of 20-25 years may be the maximum service life that can be expected for an active system. Assuming a salvage value for passive

solar energy systems is one way of accounting for the greater durability and

increased contribution to market value of the building of passive solar components relative to active solar components.

3.1.4 Energy Costs

Energy Prices . As shown in table 3.1, the six programs differ in their

abilities to handle the somewhat specialized energy price data in the Federal

LCC Rule. Due to the importance of energy costs to the economic evaluation of

a solar energy project, procedures for adjusting the required data and assumptions to correspond with the assumptions and limitations of individual models

are discussed in some detail.

F-CHART (3.0 and 4.0) offer the user two methods of supplying energy price

data:

1) Specifying the present cost of fuel for an auxiliary heating system and

a reference nonsolar system and specifying a single uniform annual escalation

rate for the price of fuel used in each system, which is assumed to be constant

throughout the period covered by the analysis; or

2) Specifying the cost of fuel in each year covered by the analysis, for

the auxiliary system and reference system, as a sequence of fuel prices.

With F-Chart 3.0, upon selection of either method, the prices of fuels used in

the auxiliary and reference equipment must be adjusted for their annual

operating efficiencies by dividing the base year prices (per 10^ Btu) by the

operating efficiencies (expressed as a decimal fraction). *

For a more extensive discussion of this issue, see reference [15]. The RSVP/2

and PACE programs include comprehensive analyses of the effects of solar

energy systems on the market value of commercial properties [12].

F-CHART 4.0, FEDSOL, and SOLCOST contain separate variables for the

efficiencies of the nonsolar heating systems. BLAST and DOE-2 calculate

operating efficiencies of all plant components as part of the total simulation of building energy use and automatically assume these values in

calculating building energy costs.

19
The first method is_ not consistent with the federal requirements because it

does not permit use of the different escalation rates for different periods of

time as is required in order to use the DoE tables. The second approach _is

consistent with the federal data requirements providing the following procedures are applied to derive the sequence of values for each year of the study

period: First, derive the annual price escalation adjustment factor for each

escalation period by converting the percentage rates shown in the DoE tables

for the appropriate region, fuel type, and sector to their decimal form and

adding 1.0, i.e.,

e

* escalatibn factor = 1.0 +

100

Next, multiply the base year price (with F-CHART 3.0, the price divided by the

average annual furnace efficiency) by the escalation factor for the first esca- lation period. The resulting value is the end-of-year price for the first year

of the study period. This is the first data entry of the sequence of fuel price

data to be entered into F-CHART. Values for the remaining years are derived by

reapplying the appropriate escalation factor to the result for the preceding

year. Using the most recent DoE tables,** the escalation factor derived from

the escalation rate for the period 1981-85 provides end-of-year prices for years

1-4 of the study period. A new escalation factor is derived for years 5-9 using

the escalation rate for the 1985-90 period. A third factor, covering the

remaining years of the study period, is derived using the rate for the 1990-95

period

.

With SOLCOST, the user may specify either a single rate for fuel price escalation

to serve throughout the study period or multiple rates. Up to four rates and

periods are allowed. The multiple rate approach, with three periods and three

rates, should be specified in performing a Federal LCC analysis. In addition,

base-year prices obtained from the energy supplier or from the DoE tables should

be converted to end-of-year prices before being entered into the program data

files.*** The end-of-base-year price is derived using the procedure described

above for calculating the energy price for the first year for F-CHART 4.0.

SOLCOST, DOE-2, and BLAST require that the unit price be the price per sales

unit ($/gallon for oil; $/therm for natural gas; and $/kWh for electricity);

F-CHART (3.0 and 4.0) and FEDSOL require the price per 10^ Btu.**x *

See section 2.2.

See reference [5]. *** SOLCOST does not allow for energy price escalation in the base year, but

it does count the base year as year one of the first escalation period.

The number of years specified for the first escalation period should

include the base year.

The DoE tables provide base-year prices per sales unit and per 10° Btu.

20
FEDSOL contains the base-year energy prices and projected escalation rates to

be published in the Federal Register in late 1981 as default values. If local

prices at the time the life-cycle cost analysis is performed are available,

they can be used instead of the DoE base-year prices.

With BLAST and DOE-2, the base-year prices obtained from the local supplier or from the DoE energy price tables can be entered directly as input data.

However, BLAST and DOE-2 provide only a single average annual rate of price

escalation. There is no satisfactory way of handling different escalation

rates for different time periods with BLAST and DOE-2.*

Component charges should be used in estimating electricity costs, where

applicable.** DOE-2, BLAST, SOLCOST, and F-CHART 4.0 explicitly provide for

block rate charge schedules for electricity; DOE-2 and BLAST also calculate

demand charges, based on the user-specified unit demand charges and the peak

load calculations performed by the program. FEDSOL and F-CHART 3.0 do not

allow block rate schedules, time-of-day charges, or other component charges.

Energy Requirements . To estimate the dollar value of energy savings, considerable information is required about the building energy loads to which solar

energy is being applied and about the contribution of the solar energy system

towards meeting those energy requirements.

BLAST and DOE-2 are capable of performing a detailed energy analysis of all

building and system components for cooling as well as heating. In fact, it is

not possible to analyze the solar energy system in isolation from the rest of

the building and HVAC components. These computer capabilities are invaluable,

but they require the user to have detailed engineering knowledge of the building and its systems and considerable facility and experience with the programs.

SOLCOST and F-CHART (3.0 and 4.0) can perform a simplified building energy

analysis based on the ASHRAE method, in addition to estimating the thermal performance of a solar energy system. With F-CHART, the user need supply only the

building energy loss (UA) factor (Btu/degree day) to obtain this analysis.

From monthly degree day data for the user-specified location, the program calculates monthly energy requirements for space heating. With SOLCOST, both the

average energy loss coefficient (Btu/degree dayft^) and total floor area must

be specified. Alternatively, with either program the user may specify monthly

energy requirements for space heating.***

All the programs will calculate hot water loads from user-supplied values for

daily usage schedules and water supply temperatures, if desired. (F-CHART and

In the test case, an average of the three rates (weighted by the number of

years in each period) shown in the DoE tables was used.

The DoE energy price tables are based on average prices for each fuel type

throughout each DoE region and use sector (residential, commercial, or industrial )

.

This is a requirement of FEDSOL.

21
SOLCOST assume a 7-day per week operating schedule; FEDSOL allows the number of

operating days per week to be specified.) Alternatively, in evaluating combined space and water heating systems, the energy requirements for water heating (10^ Btu per month) can be combined with space heating requirements and

input into the model.

The electricity required by the solar energy system to operate pumps, controls,

and fans can be accounted for in BLAST,

5

' DOE-2, and FEDSOL, but not the other

programs. In FEDSOL, electrical consumption of the solar energy system is

described in the input data as a fraction of the useful solar energy collected.

The program calculates the annual electricity operating cost using the results

of the performance analysis of the solar energy system and the electricity

prices in the data input files.

BLAST and DOE-2 estimate electrical consumption of the solar energy system from

user-supplied values for pump sizes and from the results of the performance

simulation. The quantity of electricity required by the solar energy system is

then added to the total electrical energy requirements for the solar building

and included in life-cycle energy costs.

3.1.5 Financial Variables

Models limited to the economic variables described above can be used to analyze

a solar energy project undertaken in the public sector or by a non-profit insti

tution. BLAST, DOE-2, and FEDSOL are limited to this type of analysis. None

of these programs includes the effects of income tax laws or of borrowing to

finance the proposed investment.

As shown in table 3.1, a number of additional types of financial analyses can

be provided by the other programs covered in this study. SOLCOST and F- CHART

(3.0 and 4.0) can provide a tax and mortgage analysis for a solar energy system installed in a private residential building or income-producing commercial

building. Where applicable, these capabilities are extremely important to the

outcome of the investment analysis and should be utilized.

As noted in section 3.1.1, the tax credit variables in F-CHART 4.0 can be used

to model the investment credit (externality adjustment) of 10 percent that must

be applied to investment costs for federal solar energy projects.** In using

F-CHART 4.0, the investment credit should be modeled in the following way:

This capability has been recently added to the version of BLAST (version 2.0

level 90) available on the CYBERNET system. It is not included in other

versions of the BLAST program.

See section 3.1.1, Investment Credit . In performing an optimization

analysis, base costs are to include only "fixed costs." Variable costs are specified for at least two system sizes in the optimization command sequence

For each type of cost, the user may specify what portion of the costs is

eligible for tax credits.

22
1) Select the scenario for "non-income producing buildings;"

2) Include all solar energy costs under base costs "eligible for tax

credits;" and

3) Specify a value of 10 percent for the state tax credit variable.

(Selecting the federal tax credit option results in a credit of 40

percent for investment costs up to $10,000. The user cannot change

these values.)

SOLCOST allows investment tax credits in both the "residential" and "business"

scenarios. However, there is a difference between the investment credit as specified in the Federal LCC Rule and the tax credits in SOLCOST. Under the

Federal LCC Rule this credit is to occur at the time the initial investment

is undertaken; but in SOLCOST it occurs one year later, presumably at the end

of the tax year in which the initial investment occurs, or at the time that

most tax credits are actually realized. The SOLCOST assumption is realistic

for a private analysis but will cause a small error in a federal building

analysis. For example, an investment credit for federal projects of $1,000

is reduced to a present value of $935 by being discounted one year at a rate

of 7 percent, resulting in an error (excess cost) of $65. To avoid this error,

it is suggested in section 3.1.1 that investment costs be adjusted to 90 percent of their actual value before being entered in SOLCOST' s data files and

that a zero value be assigned to the investment credit variable.

Income tax rates, property tax rates, and other financial data not relevant to

a public investor account for a large portion of the economic variables in the

F-CHART and SOLCOST programs and in other private sector models such as RSVP/2

and PACE. In using these models to evaluate a federal project, the user must

specify a down payment of 100 percent and zero values for all tax rate variables. (The default values for these variables are generally nonzero.)

3.1.6 Treatment of Inflation and Discounting

All the economic models under study use standard discounting procedures that are compatible with the Federal LCC Rule providing the modeling assumptions are understood and the data describing the project under study handled accordingly.

If no mortgage or loan financing is specified, investment costs are assumed to occur in a lump sum at the beginning of the study period; payments for energy

and maintenance are assumed to occur on an annual basis, with the first payments one year after the initial capital investment.* All the economic models

include standard formulas for discounting future costs to their present values.

BLAST allows the user to specify the time of year payments are made. A

value of one designates that payments are made at the end of the year; 0.5

designates that payments are made at mid-year, i.e., the first payment

occurs one-half year after the initial capital investment, the second one

1.5 years after the initial investment, and so on.

23
The nomenclature and definitions of variables in the different models are likely to cause some confusion in performing an analysis of a federal project.

The federal model and FEDSOL refer to year 1 of the study period as the base

year.* Using the most recent federal data (forthcoming) for energy prices,

mid-1981 is the beginning of the base year. Mid-1982 is the end of the base

year and the beginning of year 2. In F-CHART and SOLCOST, the investment

occurs in year 0. Mid-year 0 to mid-year 1 is the first year of the study

period and corresponds to the base year (year 1) in the Federal LCC Rule and

in FEDSOL.

F-CHART (3.0 and 4.0) and SOLCOST are different from all the other models in

that they do not allow for energy price escalation (or for inflation in nonfuel

operation and maintenance costs) during the first (base) year, even though they assume investment costs occur one year before the first payments for operation,

maintenance, and fuel and they discount first year costs one year in calculating

their present value equivalents.** The Federal LCC Rule, FEDSOL, BLAST, and

DOE-2 all include both energy price escalation and the opportunity cost of

money in the first year as well as in the remaining years of the study period.

As applied to federal solar energy projects, the critical factors in adjusting

for the time value of money are:

1) To express all future costs, except for energy, in constant dollars,

i.e., in terms of the buying power of the dollar at the time the

investment is made;

2) To specify an inflation rate of zero for all future costs except

energy;

3) To specify a discount rate of seven percent;

4) To adjust base-year energy prices as described in section 3.1.4; and

5) To use the real rates of energy price escalation contained in the DoE

tables . ***

Note that these real rates of energy price escalation are consistent with the use of a real discount rate, i.e., a rate that adjusts for the time value of

money apart from the effects of inflation. The discount rate of seven percent

required under the Energy Security Act of 1980 and the Federal LCC Rule is

assumed to be a real rate.

See the LCC Manual [6] and FEDSOL User's Manual [7].

In BLAST, the discount rate is referred to as the interest rate.

The user's manuals for F-CHART and SOLCOST provide no documentation of the

economic models in the programs, just lists of variables and general definitions [9,10,11]. The modeling assumptions were identified by analyzing the

program codes and the results of numerous test cases. The manuals for BLAST

and DOE-2 show the general mathematical life-cycle cost model upon which the

economic subroutines are based [16,17].

24
There is a major difference in the discounting approach taken in FEDSOL and that

taken in the other models under study. The model described in the Federal LCC

Rule and the FEDSOL model first perform a life-cycle cost evaluation of each

element of cost and savings included in the analysis, and then calculate the

different measures of economic performance by combining the different elements

of life-cycle costs and savings according to the definitions of each measure.

The other models examined in this study first calculate the net cash flow (net

value of all costs and savings) for each year of the study period, then discount the net cash flow for each year to its present value, and finally sum

the discounted values obtained for each year of the study period.

The two methods of calculating net savings provide equivalent net savings (net

present value) results providing they are applied using consistent data and

assumptions. However, the method used in FEDSOL and in the Federal LCC Rule is

somewhat advantageous for federal projects because it enables direct calculation of the savings-to-investment ratio (SIR) from the intermediate results of

the discounting process, while the other method does not. Except for DOE-2 and

FEDSOL, none of the programs calculates the SIR. Nor can it be calculated from

the information in the program outputs (see section 3.2).

3.2 MEASURES OF ECONOMIC PERFORMANCE

Apart from FEDSOL, none of the programs under study provides all of the measures

of economic performance required under the Federal LCC Rule.

BLAST does not include an economic measure that describes the performance of one plant design relative to another. BLAST calculates the total life-cycle

costs for each combination of heating/cooling equipment (nonsolar or combined

solar and auxiliary) specified in the input code but leaves the calculations

required to compare different project alternatives to the user. Moreover, the

total life-cycle cost routine in some versions of the BLAST program contains a major programming error.* All energy costs are omitted in the final tabulation!

The other programs calculate the net present value savings from the solar energy

investment, a measure used in designing and sizing projects for maximum cost

effectiveness. Apart from the limitations described in section 3.1, they comply

with the Federal Rule as regards this measure.

With DOE-2 and F-CHART 4.0, the calculation of net savings (as well as other

performance measures) is somewhat more complicated than in SOLCOST, FEDSOL, and

F-CHART 3.0 because separate computer runs are required for a baseline, reference case containing no solar energy system and for a combined solar/auxiliary

system case. The user specifies input data for a building with no solar energy

system and executes the program using this data. In subsequent computer runs,

the input data must be revised to reflect the solar energy system and the program re-executed with the new data. With DOE-2 this involves entering the

results from the "baseline" case as data inputs to the analysis of the combined

* This problem was corrected in BLAST 2, Level 90.45 (CYBERNET System), the

version used in the test cases described in this report.

25
solar/auxiliary system. With F-CHART 4.0, the input data must state whether the

current analysis is "reference" or "comparison." The life-cycle cost results for

a case designated "reference" are saved temporarily in the computer memory. In

a subsequent computer run designated "comparison," the results from the preceding "reference" case are called from memory and compared with the results for

the current case.*

Of the programs under study, only FEDSOL and DOE-2 calculate the savings-toinvestment ratio (SIR), the primary measure used by the Solar Federal Buildings

Program and federal agencies in assigning priorities and in allocating budgets

among competing projects.**

Except for FEDSOL, none of the programs calculates a payback measure that is

fully consistent with the Federal LCC Rule since all include either or both

escalation in fuel costs and discounting of future costs and savings. As noted

in section 2.2, the Federal LCC Rule requires that the payback measure be based

on costs which are neither escalated nor discounted.

Either as part of the standard output or as an optional output, all the programs

include a cash flow analysis of the project for each year of the study period.

This type of analysis is useful in obtaining a quick year-by-year overview of

the status of an investment. F-CHART 3.0 and 4.0 include the undiscounted and

discounted values of the net cash flow for each year and cumulative discounted

totals for each year. SOLCOST shows the undiscounted cash flow for each year

and undiscounted cumulative totals; BLAST, the discounted net cash flow for

each year. DOE-2 includes only energy and operating costs in its cash flow

analysis.

3.3 OPTIMIZATION ANALYSIS

3.3.1 Overview

Invariably, there are a large number of choices to be made in selecting the type

of collectors, the number of panels, and the design and engineering scheme most

suited to a particular building and location. To design solar energy projects

for maximum cost effectiveness, it is important to use life-cycle cost economics

during the schematic design and sizing phases of the project. The project with

the highest net savings or, equivalently, lowest total life-cycle costs relative

to the reference nonsolar case is the most cost-effective choice, other things

being the same.

In the calculation of net savings and other economic performance measures

for proposed solar energy systems, the appropriate reference case is a 100

percent nonsolar system. Alternative solar/auxiliary system designs and

sizes should be compared on the basis of their economic performance relative

to a 100 percent nonsolar energy system.

The internal rate of return, provided by F-CHART and SOLCOST, can serve the same purpose as the SIR; however, the Federal rule requires the SIR.

26
The following simple example illustrates how life-cycle cost analysis is used

to to determine the economically optimal size of a solar energy system.

Figure 3.1 shows the cost trade-offs as the solar energy system of a given

design increases in size and cost and as auxiliary energy costs are reduced.

Collector area, as an indicator of overall system size, is shown along the

horizontal axis. Present value costs are shown along the vertical axis.

As collector area (A) increases, the amount of energy supplied by the solar

energy system increases. Thus, auxiliary energy usage and life-cycle costs

for auxiliary energy (LCCa ) decrease as life-cycle costs for solar energy

(LCCS

) increase. The LCCa line is curved toward the origin because the

reduction in auxiliary energy costs associated with an additional unit of

A declines as total A increases. LCCS , in contrast, often increases

linearly with A.

Given this relationship between the increased life-cycle cost of solar energy

and the decreased life-cycle cost of auxiliary energy, there is a solar energy

system size which just balances the increase in the former with the decrease

in the latter. At this point, A*, total life-cycle costs for a combined solar/

auxiliary system (TLCCs , a ) reaches a minimum value. This collector area is the

economically optimal size, given the cost and performance conditions reflected

in the graph.

The collector area that minimizes total life-cycle costs for a combined solar/

auxiliary system also maximizes net savings (NS) for a combined solar/auxiliary

system relative to a reference nonsolar energy system. Figure 3.2 shows the curve for a reference nonsolar system (TLCCiqq%w) in addition to the TLCCS a curve. It also shows the net savings curve, which is the vertical distance

(cost difference) between the TLCCS a curve and TLCC^00%w curve. Note that^NS

reaches a maximum value at A* coinciding with the minimum value of TLCC^, _

.

The same trade-off procedure can be applied to alternative system types, for

example, flat-plate collectors versus concentrating collectors and air systems versus water systems. Other things being equal, the system design and size

with the highest net savings, or lowest excess cost, is the economically

preferred solar energy system.

NS will not necessarily be positive. Nor will the TLCC

g a and NS curves necessarily be U-shaped. Other shapes are possible depending on the economic

performance trade-offs of a particular project. For a discussion of these

conditions, see FEDSOL: Program User's Manual and Economic Optimization

Guide for Solar Federal Buildings Projects [ 7 ]

.

27
Figure 3.1 Determining the economically optimal system size

through minimizing costs

Figure 3.2 Determining the economically optimal system size

through maximizing savings

28
3.3.2 Optimization Features in Programs Selected for Study

F-CHART (3.0 and 4.0), S0LC0ST, and FEDSOL contain algorithms for optimizing

solar energy system size according to the principles described above. In the

optimization mode, these programs determine the collector area that is optimal

under the conditions and assumptions specified in the input data and provide a thermal and economic analysis of the optimally sized system.

In these and similar solar energy analysis programs, the economic optimization

algorithm operates in combination with the thermal performance and economic

analysis models. The thermal performance model predicts the useful quantity

of thermal energy that can be delivered to a building by a solar system of a specified design and size, that is, for a design based on designated values of

collector area, storage volume, component performance characteristics, and

environmental conditions. The economic analysis model determines the economic

performance of this same system. The economic optimization routine evaluates

incremental changes in the thermal performance and life-cycle costs associated

with incremental changes in collector area (or other specified independent variables) and determines the collector area that results in the lowest life-cycle

costs or maximum net savings relative to a 100 percent nonsolar system. In

situations where the system size that minimizes the cost for a combined solar/

auxiliary system results in negative net savings (i.e., it is less cost effective than a reference nonsolar system), the system size resulting in lowest net

losses is designated as the optimal size.

F-CHART (3.0 and 4.0), SOLCOST, and FEDSOL all apply the economic criteria

described above in optimizing system size. However, there are notable differences in the optimization analyses provided by these programs. Differences in

optimization strategies, additional program features of use in optimizing system size, and capabilities for optimizing design variables other than collector

area will be considered in this section.

Optimization Strategies . The optimization algorithms in FEDSOL and F-CHART

(3.0 and 4.0) incorporate the Golden Section Search optimization technique, a mathematical search strategy that enables the system size resulting in lowest

life-cycle cost, or maximum net savings, for a combined solar and auxiliary

system to be determined with a relatively small number of iterative steps.

The "Golden Section" is a portion of the interval between the lower and upper

bounds of the search range, calculated by taking ( SQR( 5)-l )/2 , or approximately

0.618, times the distance between these bounds. This distance is added to the

BLAST and DOE-2 contain no explicit provision for optimizing solar energy

system design or size. However, it is possible to evaluate a number of

heating/cooling plant configurations and sizes in one BLAST or DOE-2 computer

run. These alternatives may include either different combinations and sizes

of solar and nonsolar equipment or different combinations of nonsolar equipment used alone.

For a further explanation of this technique, see references [18,19].

29
lower bound and subtracted from the upper bound to determine the first two

points at which to evaluate the net present value savings (in the case of

FEDSOL) or net present value costs (in the case of F-CHART) of the solar energy

investment. The economic results at the two points are compared to determine

which is better; and consequently, which end of the interval should move to form

a new search interval. The procedure is repeated until the bounds converge on

the optimal collector area. In FEDSOL and F-CHART 3.0, the lower bound of this

interval initially is set at zero, and the upper bound is set at the area which

produces a solar fraction of approximately 99 percent during the month with the

largest heating load. In F-CHART 4.0 the search range must be specified in the

input data. The collector area optimization provided by these three programs

is accurate to within one square foot of the true or constrained economic

optimum under the conditions assumed.

SOLCOST employs a much more limited computer search technique. It evaluates

total life-cycle savings for nine candidate collector areas by looking at

points equal to 10 through 90 percent (at increments of 10 percent) of a maximum area and adjusting for module size. The collector area which results in

largest net life-cycle savings (or lowest losses) of the nine sizes examined is

declared to be optimal and a thermal analysis and cash flow analysis are provided for this system. The maximum area is defined by the program to be the

collector area (or nearest integer multiple of collector module size) required

to provide 100 percent solar heating for the month with the largest heating

load and lowest solar radiation (usually January). Alternatively, the user may

specify an upper limit of sizes to be considered, and the program will treat

that size as the maximum area.

Since the SOLCOST optimization analysis is limited to 10 percent increments in

collector area, the prediction of optimal collector area provided by this program can be assumed to be accurate only to within 10 percent of the range of

sizes considered. The prediction error for the optimal solar fraction may be

considerably larger than 10 percent.

Reducing the range of system sizes considered in the SOLCOST optimization

analysis should tend to minimize the likelihood of large inaccuracies if the

true economic optimum is known to lie in this designated range. However, the

tests conducted with the SOLCOST program for this study demonstrated that this

approach does not guarantee a more accurate prediction of optimal system size

(see section 3.1.2). It merely increases the likelihood of greater accuracy

and reduces the potential for inaccuracy.

Supplementary Features . In many situations, the system size that is optimal

based on life-cycle cost considerations alone is smaller or larger than is

technically or architecturally feasible for the building under study. The

optimization portions of a number of programs contain additional features that are useful in sizing projects under these circumstances.

As noted above, SOLCOST allows the user to specify the maximum collector area

to be considered in the optimization analysis; FEDSOL, on the other hand,

allows the user to specify the minimum acceptable solar heating fraction. The

SOLCOST feature is helpful in situations where the unconstrained optimization

30
analysis would result in a system too large to be feasible for the building

under study. It also can be used to improve the expected accuracy of the SOLCOST optimization analysis (see Optimization Strategies , above). The FEDSOL

method is useful for sizing systems in situations where the economically

optimal size is extremely small, or even zero.

In F-CHART 4.0, the OPTIMIZE command requires the user to specify both the

lower and upper bounds of the search range. The program then provides thermal

and economic analysis results for the economically optimal system within that

range. As an alternative to the OPTIMIZE command, the LOOP command can be used

to obtain a thermal and economic analysis of a number of different sizes in a single computer run. The LOOP command may take any one of three forms: 1)

analyze sizes from a to b in increments of c; 2) analyze size a, multiply by c,

and repeat until size b is reached; and 3) analyze sizes a, b, c, d, e (up to

five values).

F-CHART 3.0 does not allow the program user to specify either a maximum or minimum size constraint for the optimization analysis. Furthermore, it provides

economic and thermal performance results only for the single system size that

is economically optimal given the conditions specified. To develop additional

information, one may run the program in its nonoptimization mode for a number

of different system sizes, but this process is tedious.

SOLCOST and FEDSOL provide tables summarizing the thermal and economic

performance of nine (in the case of SOLCOST) or ten (in the case of FEDSOL)

different system sizes. The tables of results for a number of system sizes can

be used to construct net savings or total life-cycle cost curves similar to

those shown in figure 3.2. The economic penalties from oversizing or undersizing systems can then be identified clearly and weighed against other factors

not included in the economic analysis. It is frequently necessary to consider

a range of sizes to account for all the factors that affect the solar energy

investment decision.

FEDSOL contains a major optimization feature not found in any of the other

programs. In situations where the optimally sized system is less cost effective than a reference nonsolar system, i.e., it results in negative net savings,

FEDSOL provides a breakeven analysis. This analysis pinpoints the reduction in

both fixed and incremental investment costs, or alternatively, the increase in

fuel price escalation, for which the project would be cost effective, i.e.,

yield zero net savings. The breakeven values are determined by adjusting either

the investment cost parameters or the fuel price or escalation rate, one at a time, while holding the other two equal to the values specified in the input

data. Any one of the three conditions of breakeven cost described in the breakeven analysis is sufficient for the project to be cost effective, given the

conditions assumed in the base case.

Reducing incremental investment costs, or alternatively, increasing fuel price, causes the optimal size of the solar energy investment to increase. Thus, the

optimal solar fraction and collector area are higher under breakeven conditions

than under conditions of negative net savings. The breakeven analysis is useful in showing the magnitude of the change required for cost effectiveness and

the effect of this change on optimal system size.

31
Multivariate Optimization. All the economic optimization routines optimize

values for only one solar energy design variable at a time, typically collector

area. The optimal values of other design variables and the associated costs

for increments in system size are assumed to be proportional to collector area

and to collector unit costs under typical conditions. None of the programs is

capable of analyzing economic trade-offs that may exist in sizing such variables as collector area, collector efficiency, storage volume, and heat

exchangers. It is usually necessary to rerun the optimization analysis of

collector area for a number of different types of systems and for different

engineering design assumptions to develop a sufficient body of data to fully

optimize the type of system, the design characteristics, and the system size

for the conditions assumed.

F-CHART 4.0 provides the greatest flexibility in the selection of independent

design variables that can be optimized and in modeling the relationships among

different design parameters. With F-CHART 4.0, the user may select the variable to be optimized from an extensive list of system design parameters. This

parameter becomes the independent variable in an objective function that minimizes total life-cycle costs. In addition, dependent relationships between the

independent variable and five additional variables can be specified. These

relationships may be linear, quadratic, exponential, or otherwise.*

SOLCOST is unique in providing a capability for combined optimization of solar

collector area and building load reduction variables such as double-glazed

windows and higher levels of insulation. Both the calculated building load

coefficient (Btu/DD« ft^) } with and without each conservation alternative, and

the cost of each alternative must be supplied as input data. The program:

1) Determines the optimal collector area, optimal solar heating fraction,

and net life-cycle savings for the base case and the optimally sized

solar energy system;

2) Evaluates each conservation option; and

3) Compares the life-cycle cost results for that option with the results

for the base case.

From this set of analyses, the user must:

1) Select the best conservation options;

2) Calculate the new average building load coefficient (Btu/DD-ft^) for

this combined set of options;

To define the relationships, the user specifies a minimum of two (and maximum

of five) data points, each of which includes a value for the independent

variable, the corresponding value for each dependent variable (up to five),

and the cost associated with the value of the independent variable for that

data point [above system base (fixed) cost]. The different data points

define the range of values to be considered in the optimization analysis.

32
3) Scale down the collector area in proportion to the reduction in

building load; and

4) Rerun the program to verify that the optimal solar heating fraction

for the reduced heating load is approximately the same as the optimal

solar heating fraction for the base case load and to obtain the lifecycle cost analysis for the combined optimization of solar collector

area and building load reduction.*

While this joint optimization procedure requires extensive hand calculations

and possibly a number of iterative computer runs, it requires many fewer calculations and runs than would be required to analyze each possible combination

of energy conservation alternatives in a separate computer run.**

3.4 SUMMARY

An analysis of the economic models contained in the six programs selected for

study showed that none of the programs except FEDSOL met all the Federal LCC

requirements. F-CHART 4.0 nearly conformed with the federal requirements.

With a certain amount of data manipulation, it can account for all the federal

data and assumptions and should provide net savings results nearly identical

to results obtained with FEDSOL providing other economic and noneconomic data

and assumptions are the same. F-CHART 4.0 also provides the most comprehensive

optimization analysis. The major limitation of F-CHART 4.0 in performing an analysis of a federal building project is that it does not provide two of the

required economic measures of performance, the savings-to-investment ratio and

simple (undiscounted and unescalated) payback.

F-CHART 3.0 and SOLCOST have the same limitation. Additional differences in

the input variables and discounting assumptions in these programs increase the

degree of data manipulation required to evaluate a federal project above that

required for F-CHART 4.0. The optimization procedure used in FEDSOL and F-CHART

(3.0 and 4.0) is more analytically detailed and accurate than that used in SOLCOST, although SOLCOST contains some useful features not found in the other

programs

.

In analyzing a federal solar energy project, it is necessary to supplement all

of the programs except FEDSOL and DOE-2 with hand calculations of the savingsDeveloped by Dennis Barley, the optimization procedure in SOLCOST is

similar to those reported in separate studies by Noll, Balcomb, and Sav.

These studies have demonstrated that the optimal solar fraction tends to

be independent of the building energy load, i.e., constant with changes

in the load, and that the optimal collector area changes approximately

in proportion with changes in the building energy load [20,21,22,23].

If the fuel type or furnace efficiency is subject to change, it is necessary

to repeat the entire combined optimization analysis iteratively until the

combination of energy conservation, solar, and fuel options that maximizes

net savings is determined.

33
to-investment ratio, and all except FEDSOL with hand calculations of simple

payback. Alternatively, FEDSOL can be used to perform an economic analysis of

the final design, using thermal performance and sizing data obtained from

another program.

With BLAST and DOE-2, it is not possible to take into account certain data and

assumptions required for federal projects. In addition, BLAST does not calculate the required measures of economic performance, and neither program performs an optimization analysis. Furthermore, the economics portions of these

programs are substantially more cumbersome to use than those of the solar

energy programs. BLAST and DOE-2 do have the advantage of greater flexibility

in the types and combinations of heating and cooling equipment that can be

included in the life-cycle cost analysis of a solar energy system.

In analyzing a solar energy project for a privately owned building, the economic

models in F-CHART (3.0 and 4.0) or SOLCOST are likely to be more appropriate

than those in FEDSOL, BLAST, or DOE-2. The F-CHART (3.0 and 4.0) and SOLCOST

models include the effects of tax laws and of financing a solar energy project

with borrowed funds. Although each contains a somewhat different set of variables, all three are generally suitable for evaluating an investment in solar

energy for a public building, a private residential building, or an incomeproducing property. The economic models in FEDSOL, BLAST, and DOE-2, on the

other hand, are more appropriate for analyzing solar energy projects in public

buildings or other non-profit institutions, where tax and mortgage analyses are

not required.

34
4. TEST CASES

In this section, the six computer programs under study are illustrated in actual

analyses of two different types of buildings and systems. The objective of

running test cases is to illustrate the differences in predictions of economic

performance and optimal system size that result from applying the six different

programs to the same federal solar energy projects.*

The buildings and systems selected as test cases do not actually exist, nor have

they been proposed for actual construction. However, they are intended to be

representative of two major classes of buildings owned by the federal

government—a single-family detached residence and a low-rise office building

under current economic conditions.

Although the study focused on comparing and analyzing the economic results

obtained with the different programs, it was necessary to understand and use

the thermal as well as the economic analysis capabilities of each program in

order to conduct a meaningful comparison of the economic results. None of the

programs except FEDSOL enables an economic analysis to be performed independently of the thermal analysis, and this FEDSOL capability is limited to the

analysis of a specified system size. The size optimization process is dependent on an integrated thermal and economic analysis of incremental changes in

the thermal output relative to the incremental changes in system size and cost.

4.1 ANALYTICAL FRAMEWORK

In order to isolate the effects of specific differences in the economic models,

and to illustrate the underlying interdependence among the thermal and economic

features of each program, the following three-fold analysis approach was followed:

Step 1. For each program, a detailed set of data inputs for a complete

thermal and economic analysis was prepared and the computer analyses performed for a specified system size. These analyses showed

the differences in the predictions of thermal and economic performance for a specified system size resulting from application of the

total integrated programs.

Step 2. In a second set of analyses, the thermal analysis results obtained

in step 1 from all the programs except FEDSOL were supplied as thermal data inputs to FEDSOL to perform a series of independent

economic analyses. Using FEDSOL as a standard of conformity to

the Federal LCC Rule, it was then possible to quantify precisely

the effects of differences and inconsistencies between each of

The test cases are not intended to describe the economic feasibility of actual

solar energy projects. Nor should they be used to judge the relative capabilities of different computer programs as design tools for estimating the

thermal performance of alternative buildings and systems.

35
the non-federal economic models and FEDSOL as applied to the

analysis of a specified system and size.

Step 3. Keeping all data and assumptions except system size the same

as in step 1, the programs with optimization capability were rerun in their collector area optimization modes to obtain the

optimized size and optimized economic results. Those programs

with no built-in optimization subroutines were rerun for a number of different system sizes. These analyses illustrated

the differences in predictions of optimal system size and economic performance.

In order to be able to compare the results obtained with the different programs,

it was essential to be consistent in the selection of the thermal and economic

data inputs used in implementing each of the models. Since each program contains somewhat different sets of engineering and economic variables and each

represents a different level of analytical detail, this was an extremely com- plex task. It was necessary first to establish a complete set of economic and

engineering design assumptions and then to determine for each program what com- bination of variables and values was most consistent with these assumptions.

Finally, the adjustment procedures described in section 3 were followed to make

each economic analysis conform as nearly as possible with the Federal LCC

requirements

.

The only intentional difference in the data and assumptions which were used

with different models was to allow selected economic variables included in one

or more, but not all, of the economic models to assume positive values. The

inability of a particular economic model to take into account certain costs and

savings that are important to the outcome of an actual solar energy investment

causes a bias in the results. Assuming positive, realistic values for certain

variables included in some, but not all, of the programs served to illustrate

the potential bias caused by differences in the analytical flexibility of

different models, given the specific conditions assumed in the test cases.

A major portion of the total research effort was devoted to developing the input

data required to simulate the thermal performance of the solar energy system

and, in the case of BLAST and DOE-2, the building, occupants, equipment, and all

energy-using systems. A large number of computer runs and extensive consultation with specialists in HVAC engineering were required to identify the design

and performance assumptions underlying each model and to establish input data

that could simulate these same buildings and systems.

4.2 CASE 1. SOLAR SPACE HEATING FOR A SINGLE-FAMILY RESIDENCE

The residential case selected for study was a moderately well insulated,

medium-sized, single-family detached residence with 1,500 square feet of living

area. It was assumed to be owned by the federal government. The solar energy

system specified for this residence was an active system for space heating only.

The hypothetical solar energy project was evaluated for two cities —Bismarck,

North Dakota and Washington, D.C. —using the FEDSOL, SOLCOST, F-CHART 3.0, and

F-CHART 4.0 programs.

36
DOE-2 and BLAST were not included in the residential case. For reasons of time

and cost, it was necessary to limit their use in this study to a single case. Since BLAST and DOE-2 are more likely to be used to analyze a large building

than a single-family residence, the office building case was selected.*

Table 4.1 provides a general description of the building and of the solar and

reference/auxiliary heating systems which served as the residential test case. Also shown are the estimated monthly energy requirements for space heating and

the economic data and assumptions used in this case. Collector efficiencies

assumed for the solar energy system were those cited in a recent study by Los

Alamos National Laboratory as typical of single-glazed collectors with nonselective absorber surfaces [24].**

Solar energy system costs were derived from general cost functions prepared by

Honeywell, Inc, in 1978, under contract to the National Bureau of Standards

[25]. The costs were not based on actual estimates received from manufacturers

but were intended to be representative of costs for residential space heating

systems. Energy price data were obtained from the tables of energy costs and

projected rates of price escalation published in the Federal LCC Rule, as amended in the Federal Register on October 27, 1980 [4]. All economic analyses

were conducted in constant 1980 dollars. The period covered by the life-cycle

cost analyses was mid-1980 to mid-2000.

It was possible to use essentially identical radiation data in all the programs

included in the residential analyses. The program data files for the four

programs included in this case contain the same 12 monthly values for average

daily radiation on a horizontal surface.*** Furthermore, SOLCOST, F-CHART 3.0,

and FEDSOL use very similar methods of calculating the beam, diffuse, and

reflected components of radiation on a tilted collector surface.**** The radiation models in these programs resulted in only minor differences in calculated

values for incident solar energy. F-CHART 4.0 contains a different radiation

model from that used in the other programs. To account for the effects of

Due to the high level of training and engineering sophistication required

to use BLAST and DOE-2, they are relatively impractical and costly for

residential applications, except perhaps on a laboratory research basis.

The collector and efficiency characteristics used in the study were those

recommended for single-glazed, non-selective collectors in the DOE-2 user’s

manual and in the recent paper by Schnurr, et al .

,

describing the revised

SLR design method for active systems in commercial buildings [24]. Other

design parameters were consistent with those recommended for liquid and

air systems in references [24,26].

Geographical data for all the computer analyses in these test cases were

obtained from Input Data for Solar Systems [27],

The radiation models in F-CHART 3.0, SOLCOST, and FEDSOL are based on the

procedures described by Klein, as derived from the earlier work by Liu and

Jordan and Page [28].

37
Table 4.1 Data and Assumptions for Residential Test Case

Building Description

Locations

:

Floor Area: Building UA: Conventional Fuel Type: Auxiliary Fuel Type: Operating Efficiency of Reference System: Operating Efficiency of Auxiliary System: Monthly Space Heating Loads:

Bismarck, North Dakota and Washington, D.C. 1,500 ft? 18,000 Btu/°F • Day Electricity

Electricity

100 percent

100 percent

(10® Btu/Month)

Bismarck, N.D, Jan 31

,

.70 Jul 0..32 Feb 25..96 Aug 0..63 Mar 22..27 Sep 4..54 Apr 11..88 Oct 10..15 May 6..10 Nov 19..49 Jun 2..20 Dec 27..56

Washington, D.C. Jan 18..36 Jul 0 ..0 Feb 15..73 Aug 0 ..0 Mar 12..94 Sep 0,.77 Apr 6..42 Oct 5..23 May 2..35 Nov 10..96 Jun 0,.09 Dec 17..29

Annual 162.80 Annual 90.14

Solar Energy System Description

System Type: Liquid-based, Indirect System for Space Heating Only

Collector

Type: Single-glazed, flat-plate, non-selective absorber surface Performance Parameters: FR (Tot) = 0.780 FR(UL) = -1.320

Area: 400 ft^ Tilt: 48.57

Azimuth: South Flow Rate: 0.046 gpm/ft^

Heat Exchanger Effectiveness: 0.70

Storage Tank

Capacity: 15 lb of water/ft^ of collector area Height to Diameter Ratio: 2.0 Heat Loss Coefficient: 0.05 Btu/h°F-ft2

Economic Data and Assumptions

Investment Costs Solar System - Fixed Cost: $10,270.0

Solar System - Variable Cost: $17.78/ft2

Investment Credit: 10 percent Auxiliary System (Electric Resistance) Costs: 0 (same as reference system) Reference System (Electric Resistance) Costs: 0

Other Nonenergy Costs - Solar Annual Operating and Maintenance Costs:

Solar Replacement Costs: Salvage Value at End of Study Period

:

1

0

15

percent of total solar investment cost*3

percent of total solar investment cost 0

Other Nonenergy Costs - Auxiliary and Reference System: 0

Energy Costs Electricity Price - Base year (1980): Electricity Price Escalation Rates (real rates)

first 5 years: next 5 years

:

after 10 years: Operating Cost for Solar Energy System:

Discount Rate (real rate):

$18.85/10® Btu

-0.02 percent

0.87 percent

-0.36 percent

2 percent of useful solar energy collected^

7 percent

Study Period : 20 years

a With FEDSOL, S0LC0ST, and F-CHART 4.0, the investment credit was modeled by

applying a value of 10 percent for the investment credit input variable; with F-CHART 3.0, the investment credit was modeled by applying an adjustment factor of 0.9 to all solar energy investment cost inputs before running the program (see sections 3.1.1 and 3.1.5).

® With F-CHART 3.0, a value of 1.11 percent (of energy investment costs) was used for maintenance costs, as recommended in section 3.1.1 for situations

when the investment cost inputs have been adjusted for investment credits.

c S0LC0ST does not include a residual value for salvage or resale of the system

in its calculation of measures of economic performance (see section 3.1.5),

i.e., with SOLCOST, the salvage value was assumed to be zero.

d Only FEDSOL allowed this variable; with the other programs, operating costs

for electricity were assumed to be zero.

38
this difference in the radiation models, all F-CHART 4.0 analyses were conducted

using monthly values of incident solar energy calculated by FEDSOL.

4.2.1 Analysis of a Specified System Size

Table 4.2 shows the results for both Washington, D.C. and Bismarck, N.D.

obtained in step 1: the combined thermal and economic analysis of a solar

energy system specified to contain 400 square feet of collector area. The

buildings and heating systems were assumed to be identical for the two locations. Differences in results for the two cities reflect the differences in

energy requirements for space heating, differences in solar radiation for the

two cities, and differences in base-year energy prices and projected future

energy prices for the two locations.

With one exception—the F-CHART 3.0 analysis for Washington, D.C. — the

predictions of annual solar fraction for each city agreed to within two percent, generally supporting the findings of earlier comparative studies of

these thermal models (see section 1.1). The predictions of net present value

savings varied over a range of about $1,000 for each city. This variation in

net savings may not be terribly large compared with an initial investment cost

$17,400, but it is significant because the ranking of different programs (shown

in parentheses) in terms of annual solar fraction did not correspond with the

ranking by net savings. If the economic models were fully consistent and

these differences negligible, the ranking of programs by net savings should

correspond with the ranking by solar fraction. However, under the conditions

assumed in this test case, differences in the economic models were sufficient

to cause the ranking by net savings to be different from the ranking by system

thermal performance (i.e., solar fraction).

Table 4.2 Comparison of Combined Predictions of Economic and Thermal

Performance from FEDSOL, F-CHART 3.0, F-CHART 4.0, and SOLCOST

for a 400 ft^ Residential Space Heating System in Two Cities 3

City Program Annual Solar

(%)

Fraction Net Savings

($)

Bismarck, N.D.

FEDSOL 31.7 (1) -7,713 (2)

F-CHART 3 31.5 (2) -7,582 (1)

F-CHART 4 29.6 (4) -8,122 (3)

SOLCOST 30.8 (3) -8,488 (4)

Washington, D.C.

FEDSOL 39.4 (3) -9,758 (2)

F-CHART 3 36.9 (4) -10,078 (3)

F-CHART 4 41.5 (1) -9,236 (1)

SOLCOST 40.1 (2) -10,271 (4)

a Data and assumptions are described in table 4.1.

39
Step 2 of the residential test case served to pinpoint the differences in

predicted economic performance caused by differences in the economic models.

In this second series of computer analyses, the thermal analysis results for

Washington, D.C. from SOLCOST, F-CHART 3.0, and F-CHART 4.0 were supplied as input data to FEDSOL for economic analyses only, and the results compared

with those for the first set of analyses for Washington, D.C. The results

are shown in table 4.3.

For this set of analyses in which the effects of differences in the thermal performance predictions had been removed, the differences in net savings results

between FEDSOL and each of the other programs (+$128 for F-CHART 3.0, +$146 for

F-CHART 4.0, and -$638 for SOLCOST) are accounted for almost entirely by two

economic variables that exist and were utilized in FEDSOL but not in the other

programs and by the treatment of the investment credit variable in SOLCOST.

In SOLCOST, the omission of the salvage or resale value of the project accounted

for -$674. (As noted in section 3.1.5, SOLCOST does not include the salvage

value of the system in its estimate of costs and savings for the last year

of the study period.) The life-cycle cost of electrical energy used to

operate the solar energy system (not included in any of the programs except

FEDSOL) accounted for all but $7 of the difference in results for FEDSOL and

Table 4.3 Comparison of Predictions of Economic Performance Only

from F-CHART 3.0, F-CHART 4.0, and SOLCOST vs. FEDSOL for a 400 ft^ Residential Space Heating System in Washington, D.C. a

Difference

Annual After AdjustSolar Net Savings Difference ment for

Fraction Omitted

Program (%) ($) ($) Variables

F-CHART 3

36.9

-10,078

+128 -7

FEDSOL -10,206

F-CHART 4

41.5

-9,236

+146 -6

FEDSOL -9,382

SOLCOST

40.1

-10,271

-638 +1 1 l b

FEDSOL -9,633

a Solar fraction and net savings results from F-CHART 3.0, F-CHART 4.0, and SOLCOST were taken directly from table 4.2 (Washington, D.C.). FEDSOL results were obtained by conducting a set of independent economic analyses with FEDSOL where

all input data were as described in table 4.1 except that the annual solar fraction values derived with each of the other programs were also supplied as data

inputs to perform an economic analysis only.

b Adjusting for the difference in treatment of the investment credit variable

reduces this value to -$3.

40
F-CHART 3.0 and all but $6 of the difference for FEDSOL and F-CHART 4.0, and

it reduced the difference for FEDSOL and SOLCOST (after accounting for salvage

value) to +$111. The remainder of the difference for FEDSOL and F-CHART 3.0

and F-CHART 4.0 is due to small rounding errors. Of the remaining difference

in results for SOLCOST and FEDSOL, all but -$3 can be attributed to discounting

the investment credit one year.*

Steps 1 and 2 verified that the F-CHART (3.0 and 4.0), SOLCOST, and FEDSOL

programs can produce similar estimates of net present value savings for a specified residential system size providing 1) all data have been treated

consistently, in accordance with the procedures described in section 3, and 2)

costs and savings accounted for in one or more of the programs, but not all, are sufficiently small when evaluated over the life cycle of the project that

their effect on the outcome of the particular project is negligible.

However, these two sets of analyses also showed that relatively minor

differences or limitations in particular models, such as the omission of one or two secondary economic variables, may bias the project or design selection

process.** Depending on the types of costs and savings which are expected to

be significant for a particular project or project design, the project that

appears most cost effective when evaluated with one model may appear less cost

effective than one or more alternative projects when the project is evaluated

with a different model. Moreover, if alternative projects have different types

of cost profiles, this situation may occur whether the same model is used to

evaluate all projects and alternative designs or whether different models are used

.

4.2.2 Determination of Optimal System Size

All four programs included in the residential case study contain subroutines

for determining the solar energy system size which results in lowest life-cycle

costs or maximum net savings for a combined solar/auxiliary system. Step 3 of

the residential test case consisted of a third set of computer analyses in

which all data inputs were as described in step 1 (see table 4.1), except that

an economic optimization analysis was specified. Table 4.4 shows the results

for this set of analyses. For a more comprehensive comparison, net savings

As noted in section 3.1.5, SOLCOST assumes the investment credit accrues one year after the initial investment is made, instead of simultaneously with

the initial investment, the assumption of the Federal LCC Rule.

The variables considered were secondary because they were not official

requirements under the Federal LCC Rule as it applies to solar energy

projects. While the net savings measure is appropriate for determining the

most cost effective design and size of project, the SIR measure, not the NS

measure, should be used to assign priorities to projects competing for

Federal funds. Since none of the programs except FEDSOL and DOE-2 calculates

the SIR measure, only the NS measure was included in the results in the test cases

.

41
Table 4.4 Comparison of Economic Optimization Analyses from

FEDSOL, F-CHART 3.0, F-CHART 4.0, and SOLCOST for

a Residential Space Heating System in Two Cities 3

City Program

Optimal

Annual Solar Fraction

(%)

Optimal

Collector Area

(ft 2

)

Net

Savings

($)

Bismarck, N.D.

FEDSOL 29.6 (1) 365 (1) -7,701 (2)

F-CHART 3 26.6 (3) 311 (3) -7,478 (1)

F-CHART 4 21.3 (4) 244 (4) -7,875 (3)

SOLCOST 27.9 (2) 346 (2) -8,381 (4)

Washington, D.C.

FEDSOL 21.9 (3) 184 (3) -9,177 (3)

F-CHART 3 18.6 (4) 152 (4) -9,146 (2)

F-CHART 4 26.7 (1) 209 (1) -8,638 (1)

SOLCOST 22.7 (2) 194 (2) -9,737 (4)

aData and assumptions are described in table 4.1.

curves and annual solar fraction curves corresponding to collector areas from

0 to 500 square feet were also developed, as shown in figures 4.1 and 4.2.

For Washington, the collector area designated by the programs as optimal ranged

from a low of 152 square feet (F-CHART 3.0) to a high of 209 square feet

(F-CHART 4.0), a difference of 57 square feet. For Bismarck, the range was 244

square feet (F-CHART 4.0) to 365 square feet (FEDSOL), a difference of 121 feet.

Over the range of sizes shown in figures 4.1 and 4.2, the predictions of system

performance (annual solar fraction) for any given collector area agreed to

within 2 percent for Bismarck and 5 percent for Washington, D.C., but the predictions of optimal solar fraction varied by as much as 8 percent for both

cities

.

Consider the results from SOLCOST and FEDSOL. We see that FEDSOL and SOLCOST

consistently gave similar predictions of optimal system size and optimal solar

fraction despite their different optimization strategies and despite the effects

of two additional variables included in the FEDSOL analyses but not in the SOLCOST analyses. In both figures the annual solar fraction curves developed from

thermal performance estimates from these two programs were very close for collector areas up to approximately 350 square feet, a range that included all the

predictions of optimal size. The net savings curves were also similar in shape,

although separated by a considerable vertical distance. As a result, under the

* Note that the FEDSOL, SOLCOST, and F-CHART 4.0 performance curves can be

generated from the output from a single computer run, while a number of com- puter runs are required to generate such curves with F-CHART 3.0. See

section 3.3.

42
NET

SAVINGS

|$KJ

ANNUAL

SOLAR

FRACTION

|%

0 100 200 300 400 500

COLLECTOR AREA |FT 2

)

Figure 4.1 Economic and thermal performance curves derived from FEDSOL,

F-CHART 3.0, F-CHART 4.0, and SOLCOST for a residential space

heating system in Bismarck, N.D.

43
NET

SAVINGS

|$Xj

ANNUAL

SOLAR

FRACTION

(%

0 100 200 300 400 500

COLLECTOR AREA |FT 2

)

Figure 4.2 Economic and thermal performance curves derived from FEDSOL,

F-CHART 3.0, F-CHART 4.0, and SOLCOST for a residential space

heating system in Washington, D.C.

44
conditions assumed in the test cases, the two programs resulted in very similar

economic optimums for the independent variable (collector area) and for the performance function (solar fraction), but significantly different economic opti- mums for the objective function (net savings). This is illustrated by the small

horizontal spread but large vertical spread among the maximum points on the net

savings curves.

The differences in the predictions of maximum net savings ($680 for Bismarck,

$660 for Washington) are accounted for almost entirely by the differences in

input variables examined in step 2. Moreover, these differences are captured

almost entirely by the vertical spread between the net savings curves. Under

the particular cost assumptions used in the test case, the differences in input

variables did not change the trade-offs between auxiliary energy costs and

solar energy costs for increases in system size, i.e., the differences had

little effect on optimal system size.*

The inaccuracies of the limited optimization search scheme used in SOLCOST were

not evident from these results. For both cities, the nine collector areas evaluated in SOLCOST' s optimization routine included one size very near to the

true economic optimum under the conditions assumed. In separate tests for both

cities, an effort was made to improve the accuracy of the SOLCOST optimization

result by setting 600 square feet as the upper bound of the search range.**

However, reducing the range of areas covered by SOLCOST’ s optimization search

resulted in a less accurate prediction of optimal size for Bismarck and only

slightly improved prediction for Washington, D.C., i.e., in the supplementary

analysis for Bismarck, the optimized value of net savings was -$8,400, compared

with -$8,381 in the original case; for Washington, D.C., the supplementary

analysis resulted in an optimized value of net savings of -$9,734, compared with

-$9,737 in the original case.

To explore the precise effects of the variables omitted in the SOLCOST analyses, an additional computer analysis was made with FEDSOL using zero values for the

electricity operating cost variable and salvage value variable. The results

Given the particular cost assumptions used in the test case, the marginal

effects of the omitted variables were small. As described in section 3.3,

the economically optimal size and maximum net savings corresponds to the

point where the cost associated with the last increment in collector area

equals the dollar value of energy savings, evaluated over the life cycle of

the project. This size corresponds to the optimum of the net savings

function, the highest point on the net savings curve (for systems larger

than zero collector area).

JL JL The SOLCOST procedure is described in detail in section 3.3. Unless the user specifies otherwise, SOLCOST designates the system size capable of

meeting 100 percent of the heating load in January as the upper bound of

the search range. Reducing the range of sizes covered by the computer

analysis narrows the gap between the nine sizes considered in SOLCOST'

s

search scheme and, therefore, increases the likelihood that one of the

nine sizes considered will be close to the true economic optimum.

45
of this analysis were then compared with the SOLCOST optimization results shown

in table 4.4. Once the differences in results caused by these two variables had

been removed, the difference in predicted optimal size between the two programs

was reduced to three square feet for Bismarck but increased to 21 square feet

for Washington, D.C. This demonstrated that the SOLCOST optimization technique

in some cases could achieve a comparable level of accuracy to the more analytically precise method used in the FEDSOL and the F-CHART programs, but that this

accuracy was subject to variation. For both cities, over half of the remaining

difference is because the investment credit was discounted one year in SOLCOST

but not in FEDSOL.

Despite the use of the same type of optimization strategy as FEDSOL, F-CHART 3.0

and F-CHART 4.0 provided considerably different predictions of optimal system

size from SOLCOST and FEDSOL. Moreover, the results from the two F-CHARTs were

quite different. The degree of the difference and the ranking of the F-CHART

results relative to each other and to the other programs were different for the

two cities.

For Washington, F-CHART 3.0 gave the lowest prediction of optimal collector

area by more than 30 square feet; but for Bismarck, F-CHART 3.0 gave the second

lowest, by over 60 square feet. Removing the effects of one variable (electricity operating costs) included in the FEDSOL analysis but not in F-CHART (3.0

and 4.0) actually increased the differences in prediction of optimal system size

for F-CHART (3.0 and 4.0) and FEDSOL in three out of four test cases (both programs for Bismarck and F-CHART 3.0 for Washington, D.C.).

Variations in the optimization results obtained with the four programs appear

due in large part to significant differences among the programs in their predictions of system performance for incremental changes in collector area; i.e.,

they reflect differences in the slopes of the solar fraction curves and net

savings curves in the general vicinity of the economic optimums. While the curves derived with SOLCOST and FEDSOL tended to be similar in position and

shape for a large range of collector areas, the F-CHART curves were considerably

different from each other and from the SOLCOST and FEDSOL curves. For Washington, the F-CHART 4.0 curve was considerably steeper than the other curves for

sizes up to about 300 square feet. In this same case, the F-CHART 3.0 curve

was steeper than the SOLCOST and FEDSOL curves for collector areas below 150

square feet and became increasingly flatter than the other two for larger sizes.

The differences in the trade-offs between increased system size and energy costs were sufficient for F-CHART (3.0 and 4.0) and the other programs to result in

considerably different economic optimums. In comparison, differences in the

economic models appeared to be of minor importance. Other results are possible,

of course, under conditions different from those assumed in the test cases.

4.3 CASE 2. SOLAR SPACE AND SERVICE WATER HEATING FOR A FEDERAL OFFICE

BUILDING

The Federal office building serving as a test case was a three-story building,

100 feet on a side, of metal curtain-wall construction. Three hundred people

were assumed to occupy the building ten hours per day, five days per week.

The analysis was conducted for Washington, D.C.

46
This test building included a number of energy conserving features, including

two variable air volume air handling systems and an enthalpy controlled economizer cycle, with system shut off during nonoperating hours. The solar energy

system specified was an active system for service hot water and space heating

employing single-glazed flat plate collectors. The plant equipment which

served as both the reference system and as the auxiliary (back-up) system to

solar was assumed to be an oil-fired steam boiler sized to meet 100 percent of

the peak hourly load for space heating.

4.3.1 DOE-2 vs. BLAST

A complete energy and economic analysis of the building was performed with

both BLAST and DOE-2 for a reference nonsolar building and for a number of

alternative solar heating systems ranging from 100 to 1,000 square feet of

collector area. The design parameters for the solar energy system were selected to correspond with those recommended by Los Alamos National Laboratory for a standard active, liquid system and for single-glazed flat plate

collectors with selective absorber coatings [23].

Table 4.5 summarizes the building and system specifications used to develop

input codes for running BLAST and DOE-2. Economic data and assumptions are shown in table 4.6.

Like in the residential case, all economic analyses were conducted in 1980

dollars; the period covered was mid-1980 to mid-2000. Base-year prices for

electricity and distillate fuel oil were obtained from the Federal Register , October 27, 1980, table C-l (DoE-region 3, commercial sector) [4]. Since

DOE-2 and BLAST do not allow the multiple fuel price escalation rates con- tained in the DOE tables, a single average annual escalation rate of 3.6 percent was used in the analyses for distillate fuel oil and a rate of 0.035

percent for electricity. These values represented the average (weighted by

the number of years in each period) of the annual real rates of fuel price

escalation published in the Federal Register , October 27, 1980 for purchase

of these fuels in DoE Region 3 by the commercial sector.

The costs assumed for nonsolar plant equipment, maintenance, and overhaul

(i.e., for the boiler, chiller, and cooling tower) were the default values

for these variables in the DOE-2 program.

Solar energy system costs were intended to be representative of costs for

a small office building system for space heating and service hot water (200-

600 square feet of collector area).*

Both fixed and variable costs were somewhat higher than those specified

by Honeywell as typical for residential space heating and domestic hot

water systems, and lower than those specified as typical of space and

water heating systems for larger commercial building systems (systems over 600 square feet) [24],

47
Table 4.5 Building and System Specifications for Office Building Test Case

Building Envelope Description

Location:

Dimensions

:

Floor Area:

Net Wall Area:

Window Area:

Roof Area:

10 Thermal Zones:

Washington, D.C.

100 ft wide, 100 ft long, 36 ft high (3-story)

30.000 ft (10,000 slab on grade; 20,000 interior floor

construction)

9,756 ft 2

( curtain wall insulated with 2 in rigid foam,

u = 0.13)

4,644 ft 2 (double-glazed)

10.000 ft 2 (flat roof with 2 in insulation)

2 interior, with no exterior walls

8 exterior, with exterior walls on 2 sides

Operational Assumptions

Occupied Hours: 10 hours/days; 5 days /week

Peak Occupancy: 240 people (activity level 0.45 kBtu/h per person)

Peak Lights: 256.15 kBtu/h

Hot Water Demand: 300 gal/day (for 5 day/week)

Temperature Control: winter 67°F (setback 56°F) summer 77°F (setback 99°F)

Air Handling System Description

2 Variable Air Volume Systems with Reheat Coils:

Total Design Supply Air Volume: System 1 - 8,870

System 2-21 ,880

(The following apply to both systems)

Fan System Schedule: Weekdays - 7:00 am to 6:00

6:00 pm to 7:00

Weekends - 12:00 am to 12:00

Intermittent Operation (i.e„, system is off during

or cooling demand occurs in

Minimum Air Fraction: 0.2 of the maximum cfm

Mixed Air Control: Enthalpy Economy Cycle

Heating Coils Off June through August

Cooling Coils Off During Off Hours

System 1 services interior

zones; System 2 services

exterior zones,

cfm

cfm

pm - ON

am - OFF am - OFF

off hours unless a heating

one or more of the zones)

Cold Deck Fixed Temperature:

Cold Deck Throttling Range:

Heating Coil Energy Supply:

55°F

1°F

Hot Water/Solar (Heat Source)

48
Table 4.5 (continued)

Nonsolar Plant Equipment Description

1 Steam Boiler: 600 kBtu/h

Hours to Minor Overhaul: 4,317

Hours to Major Overhaul: 21,587

1 Hermetic Centrifugal Chiller: 1,000 kBtu

1 Cooling Tower: 1,200 kBtu/h

Average Annual Efficiency of Boiler without Solar: BLAST

(600 ft system) D0E-2

50.5%

47.9%

Average Annual Efficiency of Boiling with Solar:

(600 ft system)

BLAST - 51.7%

DOE-2 - 52.6%

Solar Energy System Description

System Type: Liquid-based, space heating and service hot water system

Collector:

Type: Single-glazed, flat plate, selective surface

Performance Parameters: FR (yet) = 0.705

Fr

(UL) = 0.887

Tilt: 48.57°

Azimuth: South

Flow Rate through Collector: 0.046 gpm/ft^

Heat Exchange Effectiveness: 0.70

Storage Tank:

Capacity: 15.0 lb water/ft^ collector area Height-to-Diameter Ratio: 3.0

Heat Loss Coefficient: 0.5 Btu/h°F • ft2

Pump Energy: 10% of useful solar energy collected - DOE-2

11% of useful solar energy collected - BLAST

49
Table 4.6 Economic Data and Assumptions for Office Building Test Case

Nonsolar Plant Equipment Costs

First Cost:

Annual Operation and Maintenance Cost:

Minor Overhaul Cost:

Major Overhaul Cost:

$69,113

$ 1,384/year

$ 120

$ 1,499

Solar Energy System Costs

First Cost —Fixed: $25,000

Variable: $22.00/ft2

Investment Credit: 10% of total first

Annual Operation and Maintenance Cost:

cost

1% of total investment cost

per year (before credit)

Energy Costs

Electricity price in base year: $18.41/10^ Btu ($. 063/kWh)

Distillate price in base year: $7.05/10^ Btu ($. 978/gal)

Energy Price Real Escalation Rates

1st 5 yrs 2nd 5 yrs >10 yrs Annual Average

Electricity -0.01% 0.89% -0.37% 0.035%

Distillate 3.38% 2.89% 4.07% 3.60%

Real Discount Rate: 7.0%

Study Period: 20 years

50
It was not possible to use precisely the same radiation data with the two

programs. The version of BLAST available on CYBERNET at the time of the

study utilized radiation data constructed from TRY weather tapes. CYBERNET'

s

version of DOE-2 could be used with either TMY or TRY tapes, but the radiation data processor in DOE-2 is different from that in BLAST. For maximum

consistency in the application of the two programs, the TRY data were used

with both. However, the values for monthly incident solar radiation

(Btu/ft^ . mo on a titled surface) calculated by BLAST were consistently

lower than those calculated by any of the other programs.

Table 4.7 summarizes the results of the energy and economic analyses performed

with DOE-2 and BLAST for nine heating/cooling equipment combinations; including a reference nonsolar heating plant and eight solar/auxiliary plants. The

only difference in assumptions among the latter eight plants was the size of

the solar energy system.

The net savings column represents the difference between total life-cycle

costs for the reference (0 collector area) plant and total life-cycle costs

for the solar/auxiliary plant of the size specified in the first column. (As

noted in section 3.2, DOE-2 calculates net savings along with other measures

of economic performance, while BLAST calculates investment costs, recurring

and periodic non-energy costs, energy costs, and total life-cycle costs for

each combination of plant equipment but provides no comparative measures of

economic performance. The net savings results for BLAST shown in table 4.7 were calculated by hand from the results in the preceding columns.)

Predicted energy requirements for heating were similar for the two programs,

agreeing to within 20 x 10^ Btu on an annual basis. Furthermore, the difference in the estimates of total life-cycle costs for the reference system was within $180. Although the predicted energy requirements for space heating

were higher with BLAST, this difference was offset almost entirely by DOE-2 's predictions of slightly lower boiler efficiencies, of higher requirements for

cooling, and of higher periodic costs.

The results obtained from the analyses of the smaller solar/auxiliary systems

were very close. For solar energy systems with collector areas below 300

square feet, net savings from the two programs were within $1,000. However,

BLAST predicted increasingly lower annual solar heating fractions and net

savings (higher dollar losses) than DOE-2 as solar energy system size

increased. This is illustrated in the curves of these data in figure 4.3.*

Note that the system size resulting in lowest dollar losses differed by a factor of two for the two programs! That is, for the DOE-2 analyses, the

size resulting in lowest dollar losses for a combined solar/auxiliary system

appears to be approximately 400 square feet, compared with approximately 200

square feet for BLAST.

The predictions of both useful solar energy and annual solar heating fraction

were consistently lower for BLAST than for DOE-2.

51
Table

4.7

Predictions

of

Thermal

and

Economic

Performance

from

BLAST

and

DOE-2

fora

Space

and

Service

Water

Heating

System

for

an

Office

Building

in

Washington,

D.C.

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52
NET

SAVINGS

($K|

ANNUAL

SOLAR

FRACTION

(%|

Figure 4.3 Economic and thermal performance curves derived from BLAST

and DOE-2 for a space and service water heating system for

a federal office building in Washington, D.C. a

a Data and assumptions are as described in table 4.5.

53
4.3.2 BLAST and DOE-2 vs. F-CHART (3.0 and 4.0), SOLCOST, and FEDSOL

Using the values for energy use predicted in the BLAST and DOE-2 analyses, the

four solar energy programs also were applied to the test office building.

The threefold analysis approach described in section 4.1 was used to compare

results from BLAST with those from F-CHART (3.0 and 4.0), SOLCOST, and FEDSOL;

and, in a separate set of analyses, the results from DOE-2 with those from

F-CHART (3.0 and 4.0), SOLCOST, and FEDSOL. The comparisons with BLAST were

based on energy requirements predictions for space heating and boiler operating

efficiencies obtained in the BLAST simulation. Similarly, comparisons with

D0E-2 were based on energy requirements predictions and boiler operating efficiencies obtained in the DOE-2 simulations.

Energy requirements for service water heating were calculated by the individual

programs. The estimated hot water requirement was based on usage of 30 gallons

per hour for a 10 hour day, 5 days per week, with the water temperature set at

130°F and average water supply temperature of 56°F. In FEDSOL and F-CHART (3.0

and 4.0), hot water usage was modeled as gallons per day; in SOLCOST, as the

daily energy requirement for water heating. Of these four programs, only FEDSOL

allows the number of days of operation to be specified. The daily values in the

other programs were adjusted so that the number of gallons per week corresponded

with estimated usage for a five-day week. The radiation data used with FEDSOL,

F-CHART (3.0 and 4.0), and SOLCOST were the same as the data used with the residential test case for Washington, D.C.

Solar energy design parameters and economic data were selected to conform as nearly as possible with those used in the BLAST and DOE-2 analyses, with the

following exceptions: a salvage value of 15 percent of the initial cost of

the solar energy system was assumed in the F-CHART (3.0 and 4.0) and FEDSOL

analyses (the other programs did not allow for salvage or resale value); the

multiple rates of fuel price escalation contained in the DoE tables were used

with the solar energy programs; and the F-CHART (3.0 and 4.0) and the SOLCOST

analyses did not include periodic overhaul costs or electricity costs for

operating the solar energy system, since there was no reasonably satisfactory

way of modeling them with these programs. In the analyses with FEDSOL, major

overhaul costs were modeled as repair/replacement costs. Minor overhaul costs

for the reference and auxiliary systems predicted by the BLAST and DOE-2 programs were omitted from the FEDSOL analyses because the limited number of

occurrences of nonannual costs permitted in FEDSOL were fully utilized in

modeling costs for major overhauls.

From the plant analyses conducted by DOE-2 and BLAST, it was determined that

the electricity required by the solar energy system for operation of pumps

amounted to 10 percent (with DOE-2) or 11 percent (with BLAST) of useful solar

energy collected. Thus, these values were assumed for the electricity operating cost variable in FEDSOL (10 percent in the comparisons with DOE-2, 11 percent in the comparisons with BLAST). Since this type of energy cost was not

allowed in F-CHART (3.0 and 4.0) and SOLCOST, it was omitted from the analyses

conducted by those programs.

54
The investment credit of 10 percent allowed for renewable energy systems

undertaken by the federal government was modeled as suggested in section 3.1.1,

i.e., with F-CHART 4.0 and FEDSOL, the investment tax credit variable was

used; but with the other programs, investment costs for solar energy equipment

were adjusted to 90 percent of their actual value. In SOLCOST and F-CHART 3.0,

annual operating and maintenance costs and salvage value also were adjusted as suggested in section 3.1.1.

Thermal and Economic Analysis of a Specified System Size . Table 4.8 shows the

results for step 1, the thermal and economic analyses of a 600 square foot system. Part A is based on energy use data predicted by BLAST; Part B on energyuse data predicted by DOE-2.

The dominant feature of these results is that the programs developed primarily

for analysis of residential systems—SOLCOST and F-CHART (3.0 and 4.0) —consistently provided higher predictions of annual solar fraction and net savings

(i.e., lower dollar losses) than those provided by the programs developed for

multi-zone buildings—DOE-2, BLAST, and FEDSOL. The predictions of annual

solar fraction and net savings obtained with FEDSOL and DOE-2 were very close,

agreeing to within 0.6 percent and $330.* BLAST gave the lowest predictions

for both annual solar fraction and net savings; SOLCOST consistently gave the

highest predictions for annual solar fraction, but its predictions of net

savings were lower than those of F-CHART (3.0 and 4.0).

Overall, the results for the office building test case showed considerably

greater spread than was exhibited in the residential analysis of a specified

system size (section 4.2.5). Although the system size and costs assumed in the

office building case were less than twice those assumed in the residential

case, the differences in the annual solar fractions were within 14 percent, as compared to 3 percent in the residential analysis for Washington, D.C.; and

the differences in the predictions of net life-cycle savings were within

$6,000, compared with $1,000 in the residential analysis for Washington, D.C.

However, this spread is reduced to about nine percent and $2,000 if one does

not include either the BLAST or SOLCOST results.

Economic Analysis Only of a Specified System Size . Table 4.9 summarizes the

results for the economic analyses performed in step 2. In Part A, the results

from F-CHART (3.0 and 4.0), SOLCOST, and BLAST obtained in step 1 (Part A) are compared with the results of economic analyses performed with FEDSOL using the

predictions of thermal performance from each of the other programs. In Part B,

the results obtained in step 1 (Part B) from F-CHART (3.0 and 4.0), SOLCOST,

and DOE-2 are compared with the results of economic analyses performed with

FEDSOL using predictions of thermal performance from the other programs. All

economic data were the same as for step 1.

By eliminating the effects of differences in the thermal performance and

radiation models, the difference in net savings results for FEDSOL and each

JU Since the SLR method used in FEDSOL is based on correlation analyses conducted

with DOE-2 for a system design similar to that used in the test case, one would hope for this agreement.

55
Table 4.8 Comparison of Predictions of Economic and Thermal Performance

from BLAST and DOE-2 vs. FEDSOL, F-CHART 3.0, F-CHART 4.0, and

SOLCOST for a 600 ft^ Space and Service Water Heating System

for a Federal Office Building in Washington, D.C.

A. Analyses Based on Building Energy Use for Space Heating Predicted by

BLAST (133.54 x 10 6 Btu) a

Program

Annual Solar Fraction

(%)

Net Savings

($)

BLAST 30.3

FEDSOL 36.5

F-CHART 3 41.0

F-CHART 4 43.0

SOLCOST 45.1

(5) -26,071 (5)

(4) -23,253 (4)

(3) -21,632 (2)

(2) -20,935 (1)

(1) -21,704 (3)

B. Analyses Based on Building Energy Use for Space Heating Predicted by

DOE-2 (115.26 x 10 6 Btu) a

Program

Annual Solar Fraction

(%)

Net Savings

($)

DOE-2 38.3

FEDSOL 37.7

F-CHART 3 41.5

F-CHART 4 43.6

SOLCOST 44.9

(4) -23,130 (5)

(5) -22,491 (4)

(3) -20,940 (2)

(2) -20,298 (1)

(1) -21,393 (3)

a Energy requirements for water heating were calculated by the individual

programs

.

56
Table 4.9 Comparison of Predictions of Economic Performance Only from

F-CHART 3.0, F-CHART 4.0, S0LC0ST, BLAST, and DOE-2 vs. FEDSOL

for a 600 ft^ Space and Service Water Heating System for a Federal Office Building in Washington, D.C.

A. Analyses Based on Results in Table oo Part A

a

Difference after

Adjustment for

Program Annual Net Difference Omitted Variables

Solar Savings and Treatment of

Fraction Periodic Costs

(%) ($) ($) ($)

BLAST

30.3

-26,071

-905 -2 FEDSOL -25,166

F-CHART 3 41 .0 -21,632

+179 +11

FEDSOL -21,811

F-CHART 4 43.0-20,935

+253 +6

FEDSOL -21,188

SOLCOST

45.1

-21 ,704

-1,150 +4

FEDSOL -20,554

B. Analyses Based on Results in Table 4.8, Part Ba

Difference after

Adjustment for

Program Annual Net Difference Omitted Variables

Solar Savings and Treatment of

Fraction Periodic Costs

(%) ($) ($) ($)

DOE-2

38.3

-23,130

-687 + 10 FEDSOL -22,443

F-CHART 3 41.5

-20,940

+474 +1 FEDSOL -21,414

F-CHART 4 43.6

-20,298

+533 - 8 FEDSOL -20,831

SOLCOST

44.9

-21,393

-899 + 2 FEDSOL -20,494

a FEDSOL analyses were conducted using predictions of thermal performance

from each of the other programs.

57
of the other programs was reduced from a maximum of approximately

$3,000 (see table 4.8) to a maximum of approximately $1,000 (see

table 4.9). Through a series of supplementary computer analyses

and hand calculations, it was determined that the remaining differences except for approximately $11* result from variables con- tained in some of the programs but not others, and in the case of

BLAST and DOE-2, by the method used to calculate periodic costs

for major and minor overhauls of plant equipment.

The differences in the results from FEDSOL and F-CHART (3.0 and

4.0) reflect the cost of electricity for operating the solar

energy system and the savings in periodic overhaul costs for the

nonsolar heating plant equipment due to the operation of the solar

energy system, two elements of cost not allowed for in the F-CHART

analyses. Although each of these costs and cost savings was fairly substantial under the test case assumptions (up to $1,700),

the two elements of cost tended to offset one another. Their

omission had a relatively small net effect amounting, on a lifecycle basis, to only $179 and $474 in the F-CHART 3.0 cases and

$253 and $533 in the F-CHART 4.0 cases.

The S0LC0ST results include this same effect. Under the assumptions

of the test case, the omission of the salvage value of the solar

energy system caused S0LC0ST to overestimate solar energy investment

costs by $1,481.

In BLAST and DOE-2, the omission of salvage value caused the same overestimate of solar energy investment costs as in SOLCOST. Offsetting differences were caused by the methods used to treat periodic costs by the BLAST, FEDSOL, and D0E-2 programs and by the use

of an average annual projected rate of fuel price escalation with

BLAST and DOE-2.

The plant portions of the BLAST and DOE-2 programs predicted that

a minor overhaul would occur almost every year for the reference

boiler, but only every other year (in the case of BLAST) or every

third year (in the case of DOE-2) for the boiler serving as backup to the 600 ft^ solar energy system; and it calculated the lifecycle costs associated with these minor overhauls. This difference

in costs for the auxiliary and reference boiler reflected a cost

savings for the combined solar/auxiliary energy system (not

accounted for by FEDSOL) of $544 in the BLAST case and $407 in the

D0E-2 case.

Small differences are to be expected due to rounding errors, for

example, from hand calculating energy prices for each year for

the F-CHART (3.0 and 4.0) analyses, from units conversions, and

from rounding thermal performance data to three decimal places

before performing economic analyses with FEDSOL.

58
All periodic costs for major overhauls predicted by BLAST and DOE-2 were included in the FEDSOL analyses,* but differences in the way these costs were modeled resulted in different present value equivalents. FEDSOL assumes that

each overhaul cost, like other costs evaluated by the program, reflects a lump sum payment at the end of the year in which the payment is expected to occur

and that this cost is the same whether or not the time remaining until the next

overhaul is less than the time remaining in the study period. Presumably, this

cost reflects wear and tear during the operation of the equipment over the

period since the previous overhaul, not operation of this equipment in the

future.

Like FEDSOL, BLAST assumes that payments for the cost of overhaul are actually

made at the end of the year of occurrence, and BLAST discounts the cost to its

present value accordingly. However, BLAST prorates the cost of the final overhaul (major and minor) for each plant component according to the number of years

remaining in the study period. In the test case, for example, the cost of

$1,499 for major overhaul of the reference boiler in the 16th year was prorated

by an adjustment factor of 0.786 (calculated as the remaining number of years

in the study period, multiplied by the number of annual operating hours, divided

by the number of hours to major overhaul). In discounting, the prorated cost

of $1,178 was then treated as if it occurred at the end of the 16th year.

Like FEDSOL, DOE-2 did not prorate the final major overhaul costs according to

the number of years remaining in the study period. It assumed that the cost of

the final major overhaul was the same whether or not the study period ended

before the time predicted for the next overhaul of the particular piece of

plant equipment. Unlike FEDSOL and BLAST, DOE-2 discounted each overhaul cost

(minor and major) according to the precise time of year in which the cost was predicted to occur. For example, in the test case, the final major overhaul

for the reference boiler was assumed to occur at 1,999 operating hours out of

2,745 hours into the 16th year, and thus was discounted 15.728 (15 + 1,999

2,745 = 15.728) years to a present value equivalent of $517, while FEDSOL

predicted $508 and BLAST, $399.

The effect of these differences in the treatment of major overhaul costs was

that FEDSOL predicted higher net life-cycle savings of the solar energy investment by approximately $1,200 compared with BLAST, and by $900 compared with

DOE-2.

Finally, the use of a single average annual projected rate of fuel price

escalation, instead of the multiple rates in the DoE tables, caused BLAST to

overestimate life-cycle savings in boiler fuel by approximately $190, and

DOE-2 by approximately $351.

BLAST predicted major overhauls for the reference boiler in the 6th, 11th,

and 16th years and for the auxiliary back-up boiler in the 10th year; DOE-2

predicted major overhauls for the reference boiler in the 8th and 16th years;

for the auxiliary boiler, in the 16th year. No periodic overhaul costs were assumed for the solar energy system. Nonfuel costs for operation and maintenance of solar equipment were assumed to be covered under annually recurring

maintenance costs (see table 4.5).

59
Table 4.10 Comparison of Economic Optimization Analyses from BLAST and

DOE-2 vs. SOLCOST, F-CHART 3.0, F-CHART 4.0, and FEDSOL for

a Space and Service Water Heating System for a Federal Office

Building in Washington, D.C.

A. Analyses Based on Building Energy Use for Space Heating Predicted by BLAST

(133.54 x 10 6 Btu) a

Program

Annual Solar Fraction

(%)

Optimal Area

(ft 2

) Net Savings

($)

BLAST

FEDSOL

F-CHART 3

F-CHART 4

SOLCOST

17.1/19.6 (5)

18.5 (4)

28.8 (3)

31.2 (1)

30.0 (2)

200/250 (5)

241 (4)

326 (2)

337 (1)

305 (3)

-22,185 (4)

-22,256 (5)

-20,457 (2)

-19,837 (1)

-20,821 (3)

B. Analyses

(115.26

Based on Building Energy Use

x 10^ Btu) a for Space Heating Predicted by DOE-2

Program

Annual Solar Fraction

(%)

Optimal Area

(ft 2

) Net Savings($)

DOE-2

FEDSOL

F-CHART 3

F-CHART 4

SOLCOST

27.8/30.5 (1)

18.3 (5)

27.1 (3)

28.0 (2)

25.1 (4)

350/400 (1)

213 (4)

276 (2)

271 (3)

205 (5)

-21,580 (5)

-20,587 (4)

-19,245 (2)

-18,859 (1)

-19,547 (3)

a Energy requirements for water heating were calculated by the individual

programs

.

For all the programs, given the particular assumptions used in the test case,

the effects of individual differences in the economic models tended to offset

one another so that the combined effect of all differences on the predicted

economic performance of the project was smaller than the sum of the absolute

values of these differences.

Determination of Optimal System Size . Table 4.10 and figures 4.4 and 4.5 show

the results for step 3, in which optimization analyses performed with FEDSOL,

SOLCOST, and F-CHART (3.0 and 4.0) are compared with the results from BLAST and

D0E-2 for eight different system sizes. As before, Part A is based on the

energy use data from BLAST; Part B, on the energy use data from DOE-2. The

thermal and economic performance results shown for BLAST in Part A and for DOE-2

in Part B are taken directly from table 4.7 and figure 4.3.

60
NET

SAVINGS

|$K|

ANNUAL

SOLAR

FRACTION

(%1

0 100 200 300 400 500 600 700 800 900 1000

COLLECTOR AREA (FT2

]

COLLECTOR AREA (FT 2

]

Figure 4.4 Economic and thermal performance curves derived from BLAST, FEDSOL,

F-CHART 3.0, F-CHART 4.0, and SOLCOST for a space and service water

heating system for a federal office building in Washington, D.C.

61
NET

SAVINGS

($K)

ANNUAL

SOLAR

FRACTION

|%]

Figure 4.5 Economic and thermal performance curves derived from DOE-2, FEDSOL

,

F-CHART 3.0, F-CHART 4.0, and SOLCOST for a space and service water

heating system for a federal office building in Washington, D.C.

62
The data and assumptions used in the optimization analyses were those described

in step 1 of the federal office building test case, with the following

exceptions

:

1) An optimization analysis was specified with F-CHART (3.0 and 4.0),

FEDSOL, and SOLCOST.

2) Where possible (i.e., in F-CHART 4.0 and SOLCOST) the upper bound of

the search range was set at 1,000 square feet, the largest system size

included in the BLAST and D0E-2 analyses; and where possible (i.e.,

in all but FEDSOL) the minimum bound was zero square feet. For

FEDSOL, the minimum acceptable solar fraction was specified to be one percent (the minimum allowable).

3) An average annual efficiency of 51 percent was assumed for the

auxiliary boiler in all the optimization analyses performed with

F-CHART (3.0 and 4.0), SOLCOST, and FEDSOL. This value represented

the average of the predictions from the BLAST and DOE-2 programs for

a boiler serving as backup to a solar energy system sized at 1,000

square feet of collector area or less.

For the most part, the optimization analyses illustrated in table 4.10 and

figures 4.4 and 4.5 tended to support the findings in the residential test case

and the findings in steps 1 and 2 of the office building test case. For any

given collector area, the three programs developed primarily for residential

application consistently provided higher predictions of annual solar fraction

and net savings than the programs developed for commercial application. Furthermore, if one examines the F-CHART (3.0 and 4.0), SOLCOST, and FEDSOL results

alone, they appear to be generally consistent with the results obtained in the

residential test case. Differences in the economic optimums and in the economic

performance curves can be explained by the differences in the thermal performance curves, by the differences in the variables included in the different

economic models, and by the different optimization strategies. For example,

the omission of the salvage value variable in SOLCOST can make the test case system look more cost effective when analyzed with F-CHART 4.0 than with SOLCOST, even though the predicted annual energy savings is higher with SOLCOST.

Finally, because of the search method used, the SOLCOST optimization analyses

were less accurate than those of F-CHART (3.0 and 4.0) and FEDSOL.

The optimization analyses performed in the federal office building case did

highlight differences in the models which were not apparent in the previous

analyses. The limitations of the SOLCOST optimization, for example, were more evident in the office building test case than in the residential test case.

In a series of optimization analyses with the SOLCOST program (not shown) using

different upper bounds for the optimization search (600 square feet, 1,000

square feet, and default) the predicted optimal system size varied over a range

of 209 square feet when using the DOE-2 energy use data and over a range of 132

square feet when using the BLAST energy use data. When an upper bound of 1,000

square feet was used for the optimization search (as in the test case), SOLCOST

underestimated optimal system size given the conditions assumed in the SOLCOST

analyses. With no user-specified upper bound, the program overestimated optimal system size by an even larger amount.

63
In addition, there was an incongruity in the DOE-2 and BLAST optimization

results that could not be explained by the differences in the models examined

earlier.* Note that DOE-2 provided the highest prediction of optimal system

size of all the optimization analyses, including those based on the BLAST energy

use data.** Moreover, the system size which minimized dollar losses was almost

twice as large for DOE-2 as for FEDSOL even though the FEDSOL predictions of

thermal performance (annual solar fraction) tended to correspond closely with*

the DOE-2 predictions of thermal performance for any given collector area and

the differences in the economic variables analyzed in step 2 suggested a higher

prediction of optimal system size with FEDSOL than with DOE-2.*

The analyses by DOE-2 and BLAST of just eight system sizes were able to capture

trade-offs in auxiliary energy costs and solar energy costs resulting from

increased solar energy system size which were missed with the optimization

routines in the other programs. All programs considered adjust investment

costs and auxiliary energy requirements for increases in system size. FEDSOL

also adjusts for differences in solar pump power requirements. In addition to

calculating these types of costs, BLAST and DOE-2 recalculate the overhaul

costs for each piece of plant equipment and the operating efficiency for each

plant component each time the size of one plant component (for example, the

solar energy system) changes. In particular, DOE-2 predicted an increase in

the efficiency of the auxiliary boiler from 47.9 percent with the totally

nonsolar system to 53.4 percent with a 1,000 square foot system. This change

could not be accounted for in the optimization analyses with FEDSOL, SOLCOST,

and F-CHART (3.0 and 4.0).****

Because of the similarity in the thermal performance curves from DOE-2 and

FEDSOL, the effect of changes in the operating efficiency of the auxiliary

boiler on optimal system size can be examined through a set of sensitivity

analyses. In a supplementary set of analyses, the effects of all differences

Even with the limited number of system sizes examined, it is apparent that

the maximum point of the D0E-2 net savings curve corresponds to a considerably larger system size than is predicted by the other programs.

The predicted energy reqirements for heating were higher for BLAST than

DOE-2. Other things being the same, the optimal collector area may be

expected to increase with increases in energy load [19,20,21,22].

Incremental solar energy costs were lower for FEDSOL than DOE-2, on a net

basis, because of the differences in the variables examined in step 2.

The single boiler sized to meet the peak space heating load operated at

very low part-load ratios for the major part of operating hours. Under

these conditions, the solar energy system tended to reduce the inefficiencies of operating the boiler for short periods of time to meet small

loads. Other results are possible, of course, given other assumptions

about the design and size of the boiler equipment. For example, two small

boilers might exhibit different trade-offs with increased solar energy

system size than one boiler sized to meet peak loads.

64
in the FEDSOL and DOE-2 economic models, except boiler operating efficiency,

were removed by placing zero values on minor overhaul costs, electricity operating costs, and salvage value in both programs and by using an average annual

rate of fuel price escalation, instead of multiple rates, with FEDSOL. A new

net savings curve based on these revised data inputs was generated with DOE-2,

and a series of curves for three different auxiliary boiler efficiencies was generated with FEDSOL. The results are shown in figure 4.6.

Given the same system size and boiler efficiency, the difference in predicted

net savings for the two programs is due to small differences in predicted thermal performance of the solar energy system. An increase in boiler efficiency

would tend to decrease the optimal size of the solar energy system, providing

this boiler efficiency were constant for all system sizes. (Note that the

extreme points of the FEDSOL curves tend to move slightly to the left, corresponding to a smaller collector area, as the boiler efficiency increases.) However, when boiler efficiency changes simultaneously with system size, as in the

DOE-2 analyses, increases in boiler efficiency cause the optimal system size to

increase substantially.

This effect was much smaller in BLAST because BLAST predicted much smaller

changes in boiler efficiency over the range of system sizes examined. The

difference in the economic performance results from BLAST relative to the other

programs is largely explained by the vastly different predictions of thermal

performance for most of the system sizes considered and by the modeling differences examined in steps 1 and 2.

4.3.3 Comparative Costs for Operating Computer Programs

The cost of a single computer run in the federal office building test case varied from under $1 for an economic analysis only with FEDSOL to over $100 for

a complete load, systems, plant, and economic analysis with BLAST and DOE-2.

For the office building test case, the cost of a combined thermal performance

and economic optimization analysis was $1 to $1.50 with FEDSOL and F-CHART 3.0

and $4 to $5 with SOLCOST and F-CHART 4.O.* The cost of a thermal and economic

analysis of the reference plant and a single combined solar and auxiliary plant

(after government discount and at overnight charge rate) was $14 for BLAST and

$18 for DOE-2.**

4.3.4 Summary

The test cases illustrate the differences in predictions of economic performance

and optimal system size that may be expected in applying the six programs to a federal solar energy project. To summarize these findings:

1) All six economic models will provide essentially the same predictions

for net savings (net present value), an important measure of economic

* GSA System (FEDSOL) and SEIDB System (FEDSOL, F-CHART 3.0, and FEDSOL 4.0,

and SOLCOST).

4* CYBERNET System.

65
NET

SAVINGS

|$K]

ANNUAL

SOLAR

FRACTION

(%)

Figure 4.6 Sensitivity of optimal system size to changes in auxiliary boiler

efficiency: constant efficiency3 vs. increasing efficiency^3

a FEDSOL optimization analysis,

b DOE-2 analysis of specific system sizes.

66
performance, under the following circumstances: a) all data and

assumptions are handled consistently; b) differences in the modeling

assumptions described in section 3 are understood and accounted for;

c) the analyses are limited to a basic set of economic variables

included in all the programs; and d) the models are applied to the same system size and the same set of thermal performance data.

2) Where applicable to the project under study, "secondary” economic

variables are important to the outcome of the economic analyses. Even

if a particular "secondary" cost element does not represent a large

dollar value on a life-cycle basis, it is likely to affect the optimal

sizing of projects, the ranking of alternative projects, and the

assignment of priorities to competing projects.

3) Although the optimization routines in the F-CHART (3.0 and 4.0) and

FEDSOL programs and to a lesser extent the S0LC0ST program are capable

of mathematical precision, their usefulness is tempered by the assumptions underlying the optimization procedures, for example, the assumption that auxiliary system efficiencies and costs remain constant over

the range of collector areas considered in the optimization analysis.

During the design development process, a detailed life-cycle cost

analysis for a number of specific system sizes that includes the

effects of solar energy system size on the operation of auxiliary

equipment is probably necessary to develop accurate estimates of the

total impact of the solar energy system on building costs. The high

cost of BLAST and DOE-2, in terms of both computer charges and input

preparation time, is likely to preclude their use in evaluating large

numbers of system designs and sizes.

The test cases were limited to economic variables relevant to the analysis of

a federal building project. In addition to the variables examined in these

test cases, a private investor would require variables for modeling taxes,

investment incentives, and mortgage arrangements and different measures of

economic performance.

67
5. CONCLUSIONS

This report compared the economic evaluation models in five of the most widely

used computer programs for analyzing solar energy systems: F- CHART 3.0, F- CHART

4.0, SOLCOST, BLAST, and DOE-2. Focusing on the use of these programs for evaluating federal solar energy projects, this report identified the major areas of

similarity and difference among the programs. It provided guidance in adjusting

the data and assumptions used with each program to achieve maximum possible con- formity with the Federal LCC Rule. It also illustrated the effects of the differences in the models when applied to typical active solar energy projects for

two types of buildings: a single-family detached residence and a low-rise

office building. The computer program FEDSOL, developed specifically for the

evaluation of solar energy projects for federal buildings, served as a reference

in quantifying the differences in the models and in measuring the effects of

inconsistencies with the Federal LCC Rule.

The user's manuals for solar energy and building energy analysis computer

programs typically provide little information about the economic analysis

models contained in the programs. For the most part, the discussion of economics in these manuals is limited to a list of economic variables, accompanied

by brief definitions. As a result, these "black box" models have often been

viewed with some suspicion. Without a more detailed knowledge of the underlying assumptions, it is difficult, if not impossible, to select appropriate

data for use with each model or to have much confidence in their results.

Except for FEDSOL, none of the economic models included in the study met all

the Federal LCC requirements. Under a simplified set of assumptions and with a certain amount of data manipulation, all provided similar predictions of net

present value savings from the solar energy investment, a required measure of

economic performance and one suitable for use in selecting the most costeffective project design and size.

There was considerable variation in the flexibility of the programs in

evaluating different types of solar energy costs and savings. Table 5.1

summarizes the advantages and disadvantages of the economics portions of the

different programs. As shown in table 5.1, the F-CHART (3.0 and 4.0) and

SOLCOST programs had the advantages of meeting the minimum federal requirements

for life-cycle cost analysis with regards to the net savings measure and of

providing an easy-to-use, relatively low-cost economic optimization analysis.

BLAST and DOE-2 were more costly to run and difficult to use. However, the

capabilities of the programs for simulating the operation of all systems and

plant equipment, not just the solar energy components, resulted in more a com- prehensive analysis of the total effect of the solar energy system on building

energy use and cost than the results provided by the other programs. FEDSOL

offered the unique advantage of drawing upon the thermal analysis capabilities

and results of each of the other programs (as appropriate to a particular

project) to perform a comprehensive, low-cost economic analysis that included

all the measures of economic performance required under the Federal LCC Rule.

It was necessary to limit the focus of this comparative analysis to factors

affecting the use of the different models for analyzing projects for federal

68
Table 5.1 Advantages and Disadvantages of Different Models

MODEL ADVANTAGES DISADVANTAGES

SOLCOST

1. Moderate cost.

2. Can save data files.

3. Moderately easy to use.

4. Includes tax and mortgage analysis.

5. Prints table of results for nine system sizes.

6. Includes limited optimization of collector area and building load variables

.

7. Performs simplified energy analysis

for space and water heating

(ASHRAE method), if desired.

1. Thermal analysis not validated for multi-zone

buildings

.

2. Does not calculate SIR (savings-to-investment

ratio) (federal requirement).

3. Does not allow for salvage or resale value.

F-CHART 3.0

1. Low cost.

2. Very easy to use.

3. Includes tax and mortgage analysis.

4. Performs simplified energy analysis

for space and water heating (ASHRAE

method), if desired.

5. Optimizes collector area.

1. Cannot save data files.

2. User must input sequence of values for energy

prices in each year of study period to assume different rates of fuel price escalation for different time periods.

3. Does not calculate SIR.

4. Thermal analysis not validated for multi-zone

buildings

.

5. No investment credit variables.

6. No analysis of nonenergy costs of reference

and auxiliary systems.

F-CHART 4.0

1. Moderately low cost.

2. Moderately easy to use.

3. Includes tax and mortgage analysis.

4. Performs simplified energy analysis

for space and water heating (ASHRAE

methods), if desired.

5. Optimizes a number of design variables.

1. User must input sequence of values for energy

prices in each year of study period to assume different rates of fuel price escalation for different time periods.

2. Does not calculate SIR.

3. Thermal analysis not validated for multi-zone

buildings

.

BLAST

1. Performs comprehensive analysis of costs for energy, capital equipment,

and overhaul for all heating and cooling plant components (auxiliary,

solar, and reference).

2. Can save data files.

3. Thermal analysis applicable to multizone buildings.

1. High cost.

2. Difficult to use.

3. Does not calculate net savings or SIR.

4. No provision for different rates of fuel

price escalation for different time periods,

(federal requirement).

5. No mortgage or tax analysis.

6. No investment credit variables.

7. Does not allow for salvage or resale value.

DOE-2

1. Performs comprehensive analysis

of costs for energy, capital

equipment , and overhaul for all heating and cooling plant

components (auxiliary, solar and reference)

.

2. Can save data files.

3. Thermal analysis applicable to multizone buildings.

1. High cost.

2. Difficult to use.

3. No provision for different rates of fuel price

escalation for different time periods.

4. No mortgage or tax analysis.

5. No investment credit variable.

6. No provision for salvage or resale value.

7. Must perform baseline (reference) building

analysis in separate computer run from solar energy analysis.

FEDSOL

1 . Low cost

.

2. Very easy to use.

3. Meets all Federal LCC Requirements.

4. Contains data required under Federal LCC Rule as default values.

5. Can perform an economic analysis

independently of thermal analysis,

i.e., with solar performance data

from another source.

6. Optimizes collector area.

7. Prints table of results for 10 system

sizes

.

8. Performs breakeven analysis (under conditions of negative net savings).

9. Can save data files.

10. Solar energy performance analysis

applicable to multi-zone commercial

buildings and single-zone residential

buildings

.

1. Thermal analysis applicable only to standard

active (flat plate) systems for space heating

or combined space and service water heating.

2. No provision for mortgage or tax analysis.

69
buildings. An effort was made to summarize the capabilities of the six programs

in evaluating a private sector investment in solar energy and to identify which

programs appeared most suited to that task. Further work is needed to compare

the different methods of treating such factors as market value, taxes, incentives, and mortgage financing and to illustrate the effects of these differences

on solar energy decision-making in the private sector.

70
REFERENCES

1. Freeman, T. L.; Maybaum, M. W.; and Chandra, S., "A Comparison of Four

Solar System Simulation Programs in Solving a Solar Heating Problem,”

Conference on Systems Simulation and Economic Analysis for Solar Heating

and Cooling , June 27-29, 1978, San Diego, California (Washington, D.C.:

U.S. Department of Energy, 1978).

2. Solar Federal Buildings Program, Solar Design Workbook , "Active System

Design and Sizing Methods," (Golden, Colorado: Solar Energy Research

Institute and Los Alamos National Laboratory, 1981), SERI/SP-62-308

.

3. "Federal Energy Management Program, Methodology and Procedures for

Life-Cycle Cost Analysis," Federal Register , Rules and Regulations,

Vol. 45, No. 16, January 23, 1980.

4. "Federal Energy Management Program, Methodology and Procedures for

Life-Cycle Cost Analysis, Average Fuel Cost," Federal Register , Rules

and Regulations, Vol. 45, No. 196, 209 amended October 27, 1980.

5. "Federal Energy Management Program, Methodology and Procedures for

Life-Cycle Cost Analysis, Average Fuel Cost," Federal Register , Rules

and Regulations, forthcoming in late 1981.

6. Ruegg, Rosalie T., Life-Cycle Costing Manual for the Federal Energy

Management Programs , NBS Handbook 135 (Washington, D.C.: National

Bureau of Standards, 1980).

7. Powell, Jeanne W. ; and Rodgers, Jr., Richard C. , FEDSOL: Program User's

Manual and Economic Optimization Guide for Solar Federal Buildings

Projects (Washington, D.C.: National Bureau of Standards, 1981).

National Bureau of Standards Interagency Report 81-2342.

8. Solar Energy Research Institute, Analysis Methods for Solar Heating and

Cooling Applications , 3rd Edition (Golden, Colorado: Solar Energy

Research Institute, 1980). SERI/SP-35-232 R.

9. University of Wisconsin-Madison, F- CHART Version 3.0 Users Manual —An

Interactive Program for Designing Solar Heating Systems (Golden,

Colorado: Solar Energy Information Data Bank, December 1978), SERI/

SP-35-124.

10. University of Wisconsin-Madison, F- CHART 4.0 Users Manual—A Design

Program for Solar Heating Systems (Golden, Colorado: Solar Energy

Information Data Bank, September 1980), SERI/SP-35-124 R1

.

11. SOLCOST Service Center, SOLCOST—Solar Energy Design Program for

Non-Thermal Specialists (Golden, Colorado: Solar Energy Information

Data Bank, January 1980) SERl/SP-751-686.

71
12 . Booz, Allen & Hamilton, Inc., RSVP/ 2 : Residential Solar Viability

Program—Users Manual (Golden, Colorado: Solar Energy Information Data

Bank, September 1979), SERl/SP-751-685.

13. Ruegg, Rosalie T. ; McConnaughey , John S.; Sav, Thomas G; and Hockenbery,

Kimberly A., Life-Cycle Costing (Washington, D.C.: U.S. Government Printing Office, 1978), National Bureau of Standards Building Science Series

113.

14. Marshall, Harold E.; and Ruegg, Rosalie T., Simplified Energy Design

Economics (Washington, D.C.: U.S. Government Printing Office, 1980),

National Bureau of Standards Special Publication 544.

15. Powell, Jeanne W. , An Economic Model for Passive Solar Designs in

Commercial Environments (Washington, D.C.: U.S. Government Printing

Office, 1980), National Bureau of Standards Building Science Series 125.

16. CYBERNET Services, BLAST II, Volume I User Information Manual (Minneapolis,

Minnesota: Control Data Corporation, 1980).

17. Lawrence Berkeley Laboratory, DOE-2 Users Guide (Version 2.1) (Berkeley,

California: Lawrence Berkeley Laboratory, May 1980), LBL-8689 Rev. 1.

18. Beightler, Charles S.; Phillips, Don T. ; and Wilde, Douglas J., Foundations

of Optimization, 2nd Edition (Englewood Cliffs, New Jersey: Prentice Hall,

Inc., 1979).

19. Brent, Richard P., Algorithms for Minimizations Without Derivatives

(Englewood Cliffs, New Jersey: Prentice Hall, Inc., 1973).

20. Barley, C. Dennis, "Load Optimization in Solar Space Heating Systems,"

Solar Energy , Vol. 23 (London: The Pergamon Press, 1979).

21. Noll, Scott; and Thayer, Mark, "Passive Solar, Auxiliary Heat, and

Building Conservation: A Graphical Analysis," Proceedings of the

Fourth National Passive Solar Conference , Kansas City, Missouri,

October 3-5, 1979 (Dover, Delaware: American Section of the International Solar Energy Society, 1979).

22. Balcomb, J. Douglas, "Conservation and Solar: Working Together,"

Proceedings of the Fifth National Passive Solar Conference , Amherst,

Massachusetts, 1980.

23. Sav, G. Thomas, "Economic Optimization of Solar Energy and Energy

Conservation in Commercial Buildings," Conference on Systems Simulation

and Economic Analysis for Solar Heating and Cooling , June 27-29, 1978,

San Diego, California (Washington, D.C.: Department of Energy, 1978).

72
24. Schnurr, Norman M. ; Hunn, Bruce D.; and Williamson, III, Kenneth D., "The

Solar Load Ratio Method Applied to Commercial Building Active Solar System

Sizing," Third Annual Systems Simulation, Economic Analysis/Solar Heating

and Cooling Operational Results Conference , April 27-May 1, 1981, Reno,

Nevado

.

25. Ruegg, Rosalie T.; Powell, Jeanne W.; Sav, G. Thomas; and Pierce,

Thomas E., Economic Feasibility of Solar Heating and Hot Water for Commer - cial Buildings (Washington, D.C.: National Bureau of Standard, 1981) (In

press )

.

26. Department of Energy, DOE Facilities Solar Design Handbook (Washington, D.C.

U.S. Government Printing Office, 1978), D0E/AD-0006/ 1

.

27. Cinquemani , V.; Owenby, Jr., J. R. ; and Baldwin, R. G., Input Data for Solar

Systems (Asheville, North Carolina: National Climatic Center, 1978).

28. Klein, S. A., "Calculation of Monthly Average Insolation on Tilted Surfaces,

Solar Energy, Vol. 19 (London: Pergamon, 1977).

73
NBS-H4A (rev. 2-80

U.S. DEPT. OF COMM.

BIBLIOGRAPHIC DATA

1. PUBLICATION OR

REPORT NO.

2. Performing Organ. Report No. 3. Publication Date

SHEET (See instructions) NBSIR 81-2379 January 1982

4, TITLE AND SUBTITLE

COMPARATIVE ANALYSIS OF ECONOMIC MODELS IN SELECTED SOLAR ENERGY COMPUTER PROGRAMS

5. AUTHOR(S)

Jeanne W. Powell and Kimberly A. Barnes

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions) 7. Contract/Grant No. NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE 8. Type of Report & Period Covered

WASHINGTON, D.C. 20234

Final

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City, State, ZIP)

Field Applications Branch

Division of Active Heating and Cooling

Office of Solar Applications for Buildings

U.S. Dept, of Energy Washington, D.C. 20585

10. SUPPLEMENTARY NOTES

] Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bi bl iography or literature survey, mention it here)

A variety of computer simulation models exists for the design and study of thermal

performance and economic feasibility of solar domestic hot water and space heating

systems. Several studies have indicated that the thermal performance algorithms

contained in the different models produce similar results. However, little

comparative analysis has been done of the economic algorithms in these programs.

This report compares the economic evaluation models in five computer programs

widely used for analyzing solar energy systems: F-CHART 3.0, F-CHART 4.0, SOLCOST,

BLAST, and DOE-2. Differences in analysis techniques and assumptions among the

programs are assessed from the point of view of consistency with the Federal

requirements for life-cycle costing (10 CFR Part 436), effect on predicted economic

performance and optimal system size, ease of use, and general applicability to diverse system types and building types. The FEDSOL program developed by the

National Bureau of Standards specifically to meet the Federal life-cycle cost requirements serves as a basis for the comparison. Results of the study are illustrated in test cases of two different types of Federally owned buildings:

a single-family residence and a low-rise office building.

The study indicated that none of the programs except FEDSOL fully conformed with

the Federal requirements for life-cycle cost analysis of renewable energy projects.

However, with considerable manipulation of data inputs and simplification of assumptions, they could provide similar predictions for one measure of economic

performance, net present value savings.

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and .separate key words by semicolons)

computer simulation models; Federal Life-Cycle Cost Rules; life-cycle cost analysis;

net savings; solar energy computer program; solar energy economics; solar energy

systems

.

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USCOMM-DC 6043-P80