Generating Unit Annual Capital 

and Life Extension Costs 

Analysis

December 2019

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585
U.S. Energy Information Administration | Improving the Quality and Scope of EIA Data i

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and 

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are 

independent of approval by any other officer or employee of the United States Government. The views 

in this report therefore should not be construed as representing those of the Department of Energy or 

other Federal agencies.
U.S. Energy Information Administration | Improving the Quality and Scope of EIA Data 2

Generating Unit Annual Capital and Life Extension Costs 

Analysis

In a period of accelerating retirements of electric power generators, EIA sought to revisit its assumptions 

of age-related generation costs. EIA commissioned Sargent & Lundy (S&L) to evaluate capital 

expenditures (CAPEX) and operations and maintenance (O&M) costs for non-nuclear generating units, 

with a particular emphasis on how costs of coal and other fossil-fueled plants change over time. The 

following report represents S&L’s findings. A separate EIA report, Updates to Cost Assumptions in the 

Electricity Market Module (EMM) of the National Energy Modeling System (NEMS),

1 details subsequent 

updates to the EMM module.

The following report was accepted by EIA in fulfillment of contract number DE-EI0003250. All views 

expressed in this report are solely those of the contractor and acceptance of the report in fulfillment of 

contractual obligations does not imply agreement with nor endorsement of the findings contained 

herein. Responsibility for accuracy of the information contained in this report lies with the 

contractor. Although intended to be used to inform the updating of EIA’s EMM module of NEMS, EIA is 

not obligated to modify any of its models or data in accordance with the findings of this report.

 1 https://www.eia.gov/analysis/studies/powerplants/generationcost/pdf/addendum.pdf
Project 13651-001

Generating Unit Annual Capital

and Life Extension Costs Analysis

Final Report on Modeling Aging-Related

Capital and O&M Costs

Prepared for

U.S. Energy Information Administration

SL-014201

May 2018
Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

LEGAL NOTICE 

This report (“Deliverable”) was prepared by Sargent & Lundy, L.L.C. (“Sargent & Lundy”), 

expressly for the sole use of U.S. Energy Information Administration (“Client”) in accordance 

with the agreement between Sargent & Lundy and Client. This Deliverable was prepared using 

the degree of skill and care ordinarily exercised by engineers practicing under similar 

circumstances. Client acknowledges: (1) Sargent & Lundy prepared this Deliverable subject to 

the particular scope limitations, budgetary and time constraints, and business objectives of the 

Client; (2) information and data provided by others may not have been independently verified by 

Sargent & Lundy; and (3) the information and data contained in this Deliverable are time 

sensitive and changes in the data, applicable codes, standards, and acceptable engineering 

practices may invalidate the findings of this Deliverable. Any use or reliance upon this 

Deliverable by third parties shall be at their sole risk. 

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Sargent & Lundy is a full-service architect-engineering firm that has been dedicated exclusively to the 

electric power industry for over 125 years. Sargent & Lundy has provided comprehensive planning, 

development, permitting, technical and financial consulting, engineering, design, construction management, 

and commissioning services for electric power generation and power delivery projects—1,557 clients in 88 

countries worldwide—since its founding in 1891. Having designed 958 power plants, totaling 140,667 MW of 

electric generation capacity, Sargent & Lundy is regarded as one of the oldest, largest, and most 

experienced power generation engineering companies in the U.S. 

Sargent & Lundy’s roles on electric power generation projects include full-design architect-engineer, owner’s 

engineer, lender’s independent engineer/technical advisor, and consultant. Our services include specialized 

technical advisory and consulting services to complete engineering and program management, 

encompassing procurement, construction management, technology transfer, and assistance with 

construction. Sargent & Lundy provides professional consulting, engineering, and design services 

throughout the lifecycle of power generation projects, from project concept and development, through 

detailed design and procurement, to construction and operation. 

55 East Monroe Street • Chicago, IL 60603-5780 USA • 312-269-2000

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EXECUTIVE SUMMARY ................................................................................................................................... 1

Identifying Impacts of Aging on Generation Cost and Operation .......................................................................... 1

Modeling Impacts of Aging in EIA Projections ...................................................................................................... 3

Existing Treatment of Aging in EIA’s Electricity Market Module................................................................. 3

Need for Update to EIA’s Treatment of Aging ............................................................................................... 3

Analysis of Aging Impacts in Publicly-Reported Cost Information ....................................................................... 4

Cost Breakdowns in Reported Data ................................................................................................................ 4

Data Compilation ............................................................................................................................................ 5

Identifying Changes in Spending Patterns over Plant Life ..................................................................................... 6

Differences in Spending Approach by Plant Type .......................................................................................... 6

Potential Benefits of CAPEX and O&M Spending on Future Spending ........................................................ 6

Potential Impacts of Plant Age on Future Spending ....................................................................................... 7

Effect of Plant Capacity (MW) ....................................................................................................................... 7

Effect of Plant Capacity Factor ....................................................................................................................... 8

Effect of External Market Conditions ............................................................................................................. 9

Effect of Regulatory Environment ................................................................................................................ 10

Effect of Fuel Characteristics ........................................................................................................................ 10

Effect of Flue Gas Desulfurization ................................................................................................................ 10

Proposed Updates to EMM Methodology ............................................................................................................. 11

Coal Steam .................................................................................................................................................... 11

Gas/Oil Steam ............................................................................................................................................... 13

Gas/Oil Combined Cycle and Gas/Oil Combustion Turbine ........................................................................ 15

Conventional Hydroelectric .......................................................................................................................... 16

Pumped Storage ............................................................................................................................................. 17

Solar Photovoltaic ......................................................................................................................................... 17

Solar Thermal ................................................................................................................................................ 18

Geothermal .................................................................................................................................................... 18

Wind 19

Recommended Areas for Further Study ................................................................................................................ 20

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1. INTRODUCTION ...................................................................................................................................... 1-1

2. ASSESSMENT METHODOLOGY ......................................................................................................... 2-1

2.1 Background .............................................................................................................................................. 2-1

2.2 Sources of Cost Information .................................................................................................................... 2-2

2.2.1 FERC Form 1 Data ........................................................................................................................... 2-2

2.2.2 Sargent & Lundy Internal Data ........................................................................................................ 2-5

2.2.3 Other Data Sources........................................................................................................................... 2-6

2.3 Data Validation ........................................................................................................................................ 2-6

2.4 Data Normalization .................................................................................................................................. 2-9

2.5 Statistical Tests ....................................................................................................................................... 2-11

2.5.1 Consistency of FERC Form 1 and Sargent & Lundy Internal Data ............................................... 2-11

2.5.2 Significance of Plant Age on Annual Capital and O&M Expenditures ......................................... 2-13

2.5.3 Autocorrelation of Time Series Data.............................................................................................. 2-14

3. COAL STEAM ........................................................................................................................................... 3-1

3.1 Data Description ....................................................................................................................................... 3-1

3.2 Summary of Results ................................................................................................................................. 3-2

3.2.1 Recommended CAPEX Values ........................................................................................................ 3-2

3.2.2 Recommended O&M Values ........................................................................................................... 3-4

3.2.3 Effect of Plant Capacity Factor ........................................................................................................ 3-5

3.2.4 Effect of External Market Conditions .............................................................................................. 3-6

3.2.5 Effect of Regulatory Environment ................................................................................................... 3-7

3.2.6 Effect of Fuel Characteristics ........................................................................................................... 3-8

4. GAS/OIL STEAM ...................................................................................................................................... 4-1

4.1 Data Description ....................................................................................................................................... 4-1

4.2 Summary of Results ................................................................................................................................. 4-1

4.2.1 Recommended CAPEX Values ........................................................................................................ 4-1

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4.2.2 Recommended O&M Values ........................................................................................................... 4-3

4.2.3 Effect of Plant Capacity Factor ........................................................................................................ 4-4

4.2.4 Effect of External Market Conditions .............................................................................................. 4-4

5. GAS/OIL COMBINED CYCLE............................................................................................................... 5-1

5.1 Data Description ....................................................................................................................................... 5-1

5.2 Summary of Results ................................................................................................................................. 5-2

6. GAS/OIL COMBUSTION TURBINE ..................................................................................................... 6-1

6.1 Data Description ....................................................................................................................................... 6-1

6.2 Summary of Results ................................................................................................................................. 6-2

7. CONVENTIONAL HYDROELECTRIC ................................................................................................ 7-1

7.1 Data Description ....................................................................................................................................... 7-1

7.2 Summary of Results ................................................................................................................................. 7-2

8. PUMPED HYDROELECTRIC STORAGE ............................................................................................ 8-1

8.1 Data Description ....................................................................................................................................... 8-1

8.2 Summary of Results ................................................................................................................................. 8-1

9. SOLAR PHOTOVOLTAIC ...................................................................................................................... 9-1

9.1 Data Description ....................................................................................................................................... 9-1

9.2 Summary of Results ................................................................................................................................. 9-1

10. SOLAR THERMAL................................................................................................................................. 10-1

10.1 Data Description .....................................................................................................................................10-1

10.2 Summary of Results ...............................................................................................................................10-1

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11. GEOTHERMAL ......................................................................................................................................11-1

11.1 Data Description .....................................................................................................................................11-1

11.2 Summary of Results ...............................................................................................................................11-1

12. WIND .........................................................................................................................................................12-1

12.1 Data Description .....................................................................................................................................12-1

12.2 Summary of Results ...............................................................................................................................12-2

APPENDIX A. REGRESSION ANALYSIS – COAL STEAM ............................................................... A-1

Capital Expenditures – All Plant Sizes ............................................................................................................... A-2

Operations & Maintenance Expenditures – All Plant Sizes ................................................................................ A-3

Capital Expenditures – Less than 500 MW ......................................................................................................... A-4

Operations & Maintenance Expenditures – Less than 500 MW ......................................................................... A-5

Capital Expenditures – Between 500 MW and 1,000 MW ................................................................................. A-7

Operations & Maintenance Expenditures – Between 500 MW and 1,000 MW ................................................. A-8

Capital Expenditures – Between 1,000 MW and 2,000 MW .............................................................................. A-9

Operations & Maintenance Expenditures – Between 1,000 MW and 2,000 MW ............................................ A-10

Capital Expenditures – Greater than 2,000 MW ............................................................................................... A-12

Operations & Maintenance Expenditures – Greater than 2,000 MW ............................................................... A-13

Capital Expenditures – Capacity Factor Less than 50% ................................................................................... A-14

Operations & Maintenance Expenditures – Capacity Factor less than 50% ..................................................... A-15

Capital Expenditures – Capacity Factor Greater than 50% ............................................................................... A-17

Operations & Maintenance Expenditures – Capacity Factor Greater than 50% ............................................... A-18

Capital Expenditures – Regulated vs. Deregulated ........................................................................................... A-19

Operations & Maintenance Expenditures – Regulated vs. Deregulated ........................................................... A-20

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Capital Expenditures – FGD vs. No FGD ......................................................................................................... A-20

Operations & Maintenance Expenditures – FGD vs. No FGD ......................................................................... A-21

Capital Expenditures – Bituminous vs. Subbituminous .................................................................................... A-22

Operations & Maintenance Expenditures – Bituminous vs. Subbituminous .................................................... A-23

Effect of Plant Capacity Factor ......................................................................................................................... A-24

APPENDIX B. REGRESSION ANALYSIS – GAS/OIL STEAM .......................................................... B-1

Capital Expenditures – All Plant Sizes ............................................................................................................... B-2

Operations & Maintenance Expenditures – All Plant Sizes ................................................................................ B-2

Capital Expenditures – Less than 500 MW ......................................................................................................... B-4

Operations & Maintenance Expenditures – Less than 500 MW ......................................................................... B-4

Capital Expenditures – Between 500 MW and 1,000 MW ................................................................................. B-6

Operations & Maintenance Expenditures – Between 500 MW and 1,000 MW ................................................. B-7

Capital Expenditures – Greater than 1,000 MW ................................................................................................. B-8

Operations & Maintenance Expenditures – Greater than 1,000 MW ................................................................. B-9

APPENDIX C. REGRESSION ANALYSIS – GAS/OIL COMBINED CYCLE................................... C-1

Capital Expenditures – All Plant Sizes ............................................................................................................... C-2

Operations & Maintenance Expenditures – All Plant Sizes ................................................................................ C-2

Capital Expenditures – Less than 500 MW ......................................................................................................... C-4

Operations & Maintenance Expenditures – Less than 500 MW ......................................................................... C-5

Capital Expenditures – Between 500 MW and 1,000 MW ................................................................................. C-6

Operations & Maintenance Expenditures – Between 500 MW and 1,000 MW ................................................. C-7

Capital Expenditures – Greater than 1,000 MW ................................................................................................. C-9

Operations & Maintenance Expenditures – Greater than 1,000 MW ................................................................. C-9

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Capital Expenditures – Capacity Factor Less than 50% ................................................................................... C-11

Operations & Maintenance Expenditures – Capacity Factor Less than 50% .................................................... C-12

Capital Expenditures – Capacity Factor Greater than 50% ............................................................................... C-13

Operations & Maintenance Expenditures – Capacity Factor Greater than 50% ............................................... C-14

APPENDIX D. REGRESSION ANALYSIS – GAS/OIL COMBUSTION TURBINE ......................... D-1

Capital Expenditures – All Plant Sizes ............................................................................................................... D-2

Operations & Maintenance Expenditures – All Plant Sizes ................................................................................ D-2

Capital Expenditures – Less than 100 MW ......................................................................................................... D-4

Operations & Maintenance Expenditures – Less than 100 MW ......................................................................... D-5

Capital Expenditures – Between 100 MW and 300 MW .................................................................................... D-6

Operations & Maintenance Expenditures – Between 100 MW and 300 MW .................................................... D-7

Capital Expenditures – Greater than 300 MW .................................................................................................... D-9

Operations & Maintenance Expenditures – Greater than 300 MW .................................................................. D-10

APPENDIX E. REGRESSION ANALYSIS – CONVENTIONAL HYDROELECTRIC .................... E-1

Capital Expenditures – All Plant Sizes ............................................................................................................... E-2

Operations & Maintenance Expenditures – All Plant Sizes ................................................................................ E-3

APPENDIX F. REGRESSION ANALYSIS – PUMPED HYDROELECTRIC STORAGE ............... F-1

Capital Expenditures – All Plant Sizes ................................................................................................................ F-2

Operations & Maintenance Expenditures – All Plant Sizes ................................................................................. F-3

APPENDIX G. REGRESSION ANALYSIS – SOLAR PHOTOVOLTAIC .......................................... G-1

Capital Expenditures ........................................................................................................................................... G-2

Operations & Maintenance Expenditures ........................................................................................................... G-4

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APPENDIX H. REGRESSION ANALYSIS – SOLAR THERMAL ...................................................... H-1

APPENDIX I. REGRESSION ANALYSIS – GEOTHERMAL ............................................................. I-1

Capital Expenditures ............................................................................................................................................. I-2

Operations & Maintenance Expenditures ............................................................................................................. I-3

APPENDIX J. REGRESSION ANALYSIS – WIND ............................................................................... J-1

Capital Expenditures ............................................................................................................................................ J-2

Operations & Maintenance Expenditures ............................................................................................................ J-8

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Table or Figure Page 

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Table ES-1 — Variables Affecting Annual Changes in Real Spending per kW .................................................... 2

Table ES-2 — High Capacity Factor Coal Plants – Spending Comparison .......................................................... 10

Table ES-3 — Coal Steam CAPEX Results – All MW, All Capacity Factors ..................................................... 11

Table ES-4 — Coal Steam O&M Comparison with Existing EMM .................................................................... 12

Table ES-5 — Gas/Oil Steam CAPEX Results .................................................................................................... 13

Table ES-6 — Gas/Oil Steam O&M Comparison with Existing EMM ............................................................... 14

Table ES-7 — Gas/Oil CC and CT CAPEX and O&M Comparison with Existing EMM .................................. 16

Table ES-8 — Hydroelectric CAPEX and O&M Comparison with Existing EMM ............................................ 17

Table ES-9 — Pumped Storage CAPEX and O&M Comparison with Existing EMM ........................................ 17

Table ES-10 — Geothermal CAPEX and O&M Comparison with Existing EMM ............................................. 19

Table ES-11 — Wind CAPEX and O&M Comparison with Existing EMM ....................................................... 19

Table 2-1 — Summary of Valid Data Points ....................................................................................................... 2-8

Table 3-1 — Coal Steam Cost Data Distribution ................................................................................................. 3-1

Table 3-2 — Effect of Data Validation Filters on Coal Data Points .................................................................... 3-2

Table 3-3 — Coal Steam CAPEX Results – All MW, All Capacity Factors ....................................................... 3-3

Table 3-4 — Coal Plant Indicative Typical CAPEX Projects and Intervals ........................................................ 3-4

Table 3-5 — Coal Steam O&M Comparison with Existing EMM ...................................................................... 3-5

Table 3-6 — High Capacity Factor Coal Plants – Spending Comparison ........................................................... 3-7

Table 4-1 — Gas/Oil Steam Cost Data Distribution ............................................................................................ 4-1

Table 4-2 — Gas/Oil Steam CAPEX Results ...................................................................................................... 4-2

Table 4-3 — Gas/Oil Steam O&M Comparison with Existing EMM ................................................................. 4-3

Table 5-1 — Gas/Oil CC Cost Data Distribution ................................................................................................ 5-1

Table 5-2 — Gas/Oil CC CAPEX and O&M Comparison with Existing EMM ................................................. 5-3

Table 6-1 — Gas/Oil Combustion Turbine Cost Data Distribution ..................................................................... 6-1

Table 6-2 — Gas/Oil Combustion Turbine CAPEX and O&M Comparison with Existing EMM ..................... 6-3

Table 7-1 — Conventional Hydroelectric Cost Data Distribution ....................................................................... 7-1

Table 7-2 — Hydroelectric CAPEX and O&M Comparison with Existing EMM .............................................. 7-2

Table 8-1 — Pumped Storage Cost Data Distribution ......................................................................................... 8-1

Table 8-2 — Pumped Storage CAPEX and O&M Comparison with Existing EMM ......................................... 8-2

Table 9-1 — Solar Photovoltaic Cost Data Distribution ...................................................................................... 9-1

Table 11-1 — Geothermal Cost Data Distribution ............................................................................................11-1

Table 11-2 — Geothermal CAPEX and O&M Comparison with Existing EMM .............................................11-1

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Table 12-1 — Wind Cost Data Distribution ......................................................................................................12-1

Table 12-2 — Wind CAPEX and O&M Comparison with Existing EMM .......................................................12-2

Table A-1 — Regression Statistics – Coal CAPEX for All MW ........................................................................ A-2

Table A-2 — Regression Statistics – Coal O&M for All MW ........................................................................... A-3

Table A-3 — Regression Statistics – Coal CAPEX < 500 MW ......................................................................... A-4

Table A-4 — Regression Statistics – Coal O&M < 500 MW ............................................................................. A-5

Table A-5 — Regression Statistics – Coal CAPEX 500 MW to 1,000 MW ...................................................... A-7

Table A-6 — Regression Statistics – Coal O&M 500 MW to 1,000 MW .......................................................... A-8

Table A-7 — Regression Statistics – Coal CAPEX 1,000 MW to 2,000 MW ................................................... A-9

Table A-8 — Regression Statistics – Coal O&M 1,000 MW to 2,000 MW ..................................................... A-10

Table A-9 — Regression Statistics – Coal CAPEX > 2,000 MW .................................................................... A-12

Table A-10 — Regression Statistics – Coal O&M > 2,000 MW ...................................................................... A-13

Table A-11 — Regression Statistics – Coal CAPEX for Capacity Factor < 50% ............................................ A-14

Table A-12 — Regression Statistics – Coal O&M for Capacity Factor < 50% ................................................ A-15

Table A-13 — Regression Statistics – Coal CAPEX for Capacity Factor > 50% ............................................ A-17

Table A-14 — Regression Statistics – Coal O&M for Capacity Factor > 50% ................................................ A-18

Table A-15 — Regression Statistics – Coal CAPEX for Regulated/Deregulated ............................................ A-19

Table A-16 — Regression Statistics – Coal CAPEX for FGD/No FGD .......................................................... A-21

Table A-17 — Regression Statistics – Coal CAPEX for Bituminous/Subbituminous ..................................... A-22

Table B-1 — Regression Statistics – Gas/Oil Steam CAPEX for All MW ........................................................ B-2

Table B-2 — Regression Statistics – Gas/Oil Steam O&M for All MW ............................................................ B-3

Table B-3 — Regression Statistics – Gas/Oil Steam CAPEX < 500 MW .......................................................... B-4

Table B-4 — Regression Statistics – Gas/Oil Steam O&M < 500 MW ............................................................. B-5

Table B-5 — Regression Statistics – Gas/Oil Steam CAPEX 500 MW to 1,000 MW ...................................... B-6

Table B-6 — Regression Statistics – Gas/Oil Steam O&M 500 MW to 1,000 MW .......................................... B-7

Table B-7 — Regression Statistics – Gas/Oil Steam CAPEX > 1,000 MW ....................................................... B-8

Table B-8 — Regression Statistics – Gas/Oil Steam O&M > 1,000 MW .......................................................... B-9

Table C-1 — Regression Statistics – CC CAPEX for All MW .......................................................................... C-2

Table C-2 — Regression Statistics – CC O&M for All MW .............................................................................. C-3

Table C-3 — Regression Statistics – CC CAPEX < 500 MW ............................................................................ C-4

Table C-4 — Regression Statistics – CC O&M < 500 MW ............................................................................... C-5

Table C-5 — Regression Statistics – CC CAPEX 500 MW to 1,000 MW......................................................... C-6

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Table C-6 — Regression Statistics – CC O&M 500 MW to 1,000 MW ............................................................ C-7

Table C-7 — Regression Statistics – CC CAPEX > 1,000 MW ......................................................................... C-9

Table C-8 — Regression Statistics – CC O&M > 1,000 MW .......................................................................... C-10

Table C-9 — Regression Statistics – CC CAPEX for Capacity Factor < 50% ................................................. C-11

Table C-10 — Regression Statistics – CC O&M for Capacity Factor < 50% .................................................. C-12

Table C-11 — Regression Statistics – CC CAPEX for Capacity Factor > 50% ............................................... C-13

Table C-12 — Regression Statistics – CC O&M for Capacity Factor > 50% .................................................. C-14

Table D-1 — Regression Statistics – CT CAPEX for All MW .......................................................................... D-2

Table D-2 — Regression Statistics – CT O&M for All MW .............................................................................. D-3

Table D-3 — Regression Statistics – CT CAPEX < 100 MW ............................................................................ D-4

Table D-4 — Regression Statistics – CT O&M < 100 MW ............................................................................... D-5

Table D-5 — Regression Statistics – CT CAPEX 100 MW to 300 MW............................................................ D-6

Table D-6 — Regression Statistics – CT O&M 100 MW to 300 MW ............................................................... D-7

Table D-7 — Regression Statistics – CT CAPEX > 300 MW ............................................................................ D-9

Table D-8 — Regression Statistics – CT O&M > 300 MW ............................................................................. D-10

Table E-1 — Regression Statistics – Hydroelectric CAPEX for All MW .......................................................... E-2

Table E-2 — Regression Statistics – Hydroelectric O&M for All MW ............................................................. E-3

Table F-1 — Regression Statistics – Pumped Hydroelectric CAPEX for All MW ............................................. F-2

Table F-2 — Regression Statistics – Pumped Hydroelectric O&M for All MW ................................................ F-3

Table G-1 — Regression Statistics – Solar PV CAPEX for All MW ................................................................. G-3

Table G-2 — Example of Calculation Method Differences ................................................................................ G-4

Table G-3 — Summary of Industry O&M Cost Data for Solar PV .................................................................... G-8

Table I-1 — Regression Statistics – Geothermal CAPEX for All MW ................................................................ I-2

Table I-2 — Regression Statistics – Geothermal O&M for All MW ................................................................... I-3

Table I-3 — Geothermal All MW Summary of Results ....................................................................................... I-4

Table J-1 — Regression Statistics – Wind CAPEX for All MW ......................................................................... J-2

Table J-2 — Wind All MW Summary of Results ................................................................................................ J-3

Table J-3 — Regression Statistics – Wind CAPEX for 0-100 MW..................................................................... J-3

Table J-4 — Wind < 100-MW Summary of Results ........................................................................................... J-4

Table J-5 — Regression Statistics – Wind CAPEX for 100-200 MW................................................................. J-5

Table J-6 — Wind 100-200-MW Summary of Results ....................................................................................... J-6

Table J-7 — Regression Statistics – Wind CAPEX for Greater than 200 MW ................................................... J-6

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Table J-8 — Wind Greater than 200-MW Summary of Results .......................................................................... J-7

Table J-9 — Regression Statistics – Wind O&M for All MW ............................................................................ J-8

Table J-10 — Regression Statistics – Wind O&M for 0-100 MW ...................................................................... J-9

Table J-11 — Regression Statistics – Wind O&M for 100-200 MW ................................................................ J-10

Table J-12 — Regression Statistics – Wind O&M Greater than 200 MW ........................................................ J-11

Figure ES-1 — Capacity Factor vs. Age for All Coal Plants .................................................................................. 8

Figure ES-2 — Capacity Factor vs. Age for All Gas/Oil Steam Plants .................................................................. 8

Figure 2-1 — CAPEX vs. Age for 500-MW Coal Plants – FERC and Sargent & Lundy Data ........................ 2-12

Figure 2-2 — U.S. Power Plant Fleet Capacity by Age and Fuel Type ............................................................. 2-13

Figure 3-1 — Capacity Factor vs. Age for All Coal Plants ................................................................................. 3-6

Figure 4-1 — Capacity Factor vs. Age for All Gas/Oil Steam Plants .................................................................. 4-4

Figure A-1 — Coal Steam Dataset – CAPEX for All MW Plant Sizes .............................................................. A-2

Figure A-2 — Coal Steam Dataset – O&M for All MW Plant Sizes.................................................................. A-3

Figure A-3 — Coal Steam Dataset – CAPEX for Less than 500-MW Plant Size .............................................. A-5

Figure A-4 — Coal Steam Dataset – O&M for Less than 500-MW Plant Size .................................................. A-6

Figure A-5 — Coal Steam Dataset – CAPEX for 500-MW to 1,000-MW Plant Size ........................................ A-7

Figure A-6 — Coal Steam Dataset – O&M for 500-MW to 1,000-MW Plant Size ........................................... A-8

Figure A-7 — Coal Steam Dataset – CAPEX for 1,000-MW to 2,000-MW Plant Size ................................... A-10

Figure A-8 — Coal Steam Dataset – O&M for 1,000-MW to 2,000-MW Plant Size ...................................... A-11

Figure A-9 — Coal Steam Dataset – CAPEX for Greater than 2,000-MW Plant Size ..................................... A-12

Figure A-10 — Coal Steam Dataset – O&M for Greater than 2,000-MW Plant Size ...................................... A-13

Figure A-11 — Coal Steam Dataset – CAPEX for All Plants with Avg. Net Capacity Factor < 50% ............. A-15

Figure A-12 — Coal Steam Dataset – O&M for All Plants with Avg. Net Capacity Factor < 50% ................ A-16

Figure A-13 — Coal Steam Dataset – CAPEX for All Plants with Avg. Net Capacity Factor > 50% ............. A-17

Figure A-14 — Coal Steam Dataset – O&M for All Plants with Avg. Net Capacity Factor > 50% ................ A-18

Figure A-15 — Coal Steam Dataset – CAPEX for Regulated/Deregulated ..................................................... A-19

Figure A-16 — Coal Steam Dataset – O&M for Regulated vs. Deregulated ................................................... A-20

Figure A-17 — Coal Steam Dataset – CAPEX for FGD/No FGD ................................................................... A-21

Figure A-18 — Coal Steam Dataset – O&M for FGD vs. No FGD ................................................................. A-22

Figure A-19 — Coal Steam Dataset – CAPEX for Bituminous/Subbituminous .............................................. A-23

Figure A-20 — Coal Steam Dataset – O&M for Bituminous vs. Subbituminous ............................................ A-24

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TABLES AND FIGURES

Table or Figure Page 

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-21 — CAPEX vs. Age for All MW Coal Plants (2017 $/MWh)....................................................... A-24

Figure A-22 — O&M vs. Age for All Coal Plants (2017 $/MWh) .................................................................. A-25

Figure A-23 — Capacity Factor vs. Age for All Coal Plants ............................................................................ A-25

Figure A-24 — Capacity Factor vs. Age for All Gas/Oil Steam Plants ............................................................ A-26

Figure B-1 — Gas/Oil Steam Dataset – CAPEX for All Plant MW Sizes ......................................................... B-2

Figure B-2 — Gas/Oil Steam Dataset – O&M for All Plant MW Sizes ............................................................. B-3

Figure B-3 — Gas/Oil Steam Dataset – CAPEX for Less than 500-MW Plant Size ......................................... B-4

Figure B-4 — Gas/Oil Steam Dataset – O&M for Less than 500-MW Plant Size ............................................. B-5

Figure B-5 — Gas/Oil Steam Dataset – CAPEX for 500-MW to 1,000-MW Plant Size ................................... B-6

Figure B-6 — Gas/Oil Steam Dataset – O&M for 500-MW to 1,000-MW Plant Size ...................................... B-7

Figure B-7 — Gas/Oil Steam Dataset – CAPEX for Greater than 1,000-MW Plant Size .................................. B-9

Figure B-8 — Gas/Oil Steam Dataset – O&M for Greater than 1,000-MW Plant Size ................................... B-10

Figure C-1 — Gas/Oil CC Dataset – CAPEX for All Plant MW Sizes .............................................................. C-2

Figure C-2 — Gas/Oil CC Dataset – O&M for All Plant MW Sizes ................................................................. C-3

Figure C-3 — Gas/Oil CC Dataset – CAPEX for Less than 500-MW Plant Size .............................................. C-4

Figure C-4 — Gas/Oil CC Dataset – O&M for Less than 500-MW Plant Size .................................................. C-5

Figure C-5 — Gas/Oil CC Dataset – CAPEX for 500-MW to 1,000-MW Plant Size ........................................ C-7

Figure C-6 — Gas/Oil CC Dataset – O&M for 500-MW to 1,000-MW Plant Size ........................................... C-8

Figure C-7 — Gas/Oil CC Dataset – CAPEX for Greater than 1,000-MW Plant Size ...................................... C-9

Figure C-8 — Gas/Oil CC Dataset – O&M for Greater than 1,000 MW Plant Size ........................................ C-10

Figure C-9 — CC Dataset – CAPEX for All Plant Sizes and Avg. Net Capacity Factor < 50% ..................... C-11

Figure C-10 — CC Dataset – O&M for All Plant Sizes and Avg. Net Capacity Factor < 50% ....................... C-12

Figure C-11 — CC Dataset – CAPEX for All Plant Sizes and Avg. Net Capacity Factor > 50% ................... C-14

Figure C-12 — CC Dataset – O&M for All Plant Sizes and Avg. Net Capacity Factor > 50% ....................... C-15

Figure D-1 — Gas/Oil CT Dataset – CAPEX for All Plant MW Sizes .............................................................. D-2

Figure D-2 — Gas/Oil CT Dataset – O&M for All Plant MW Sizes.................................................................. D-3

Figure D-3 — Gas/Oil CT Dataset – CAPEX for Less than 100-MW Plant Size .............................................. D-4

Figure D-4 — Gas/Oil CT Dataset – O&M for Less than 100-MW Plant Size .................................................. D-5

Figure D-5 — Gas/Oil CT Dataset – CAPEX for Between 100-MW and 300-MW Plant Size ......................... D-7

Figure D-6 — Gas/Oil CT Dataset – O&M for Between 100-MW and 300-MW Plant Size ............................. D-8

Figure D-7 — Gas/Oil CT Dataset – CAPEX for Greater than 300-MW Plant Size ......................................... D-9

Figure D-8 — Gas/Oil CT Dataset – O&M for Greater than 300-MW Plant Size ........................................... D-10

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TABLES AND FIGURES

Table or Figure Page 

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure E-1 — Conventional Hydroelectric Dataset – CAPEX for All MW Plant Sizes..................................... E-2

Figure E-2 — Conventional Hydroelectric – O&M for All MW Plant Sizes ..................................................... E-3

Figure F-1 — Pumped Hydroelectric Dataset – CAPEX for All MW Plant Sizes .............................................. F-2

Figure F-2 — Pumped Hydroelectric – O&M for All Plant MW Sizes ............................................................... F-3

Figure G-1 — Solar PV Dataset – CAPEX for All MW Plant Sizes .................................................................. G-3

Figure G-2 — Average Site O&M Cost per MWh Generated vs. Project Nameplate Capacity (< 5 MW) ....... G-5

Figure G-3 — Average Site O&M Cost per MWh Generated vs. Project Nameplate Capacity (> 5 MW) ....... G-6

Figure G-4 — Average Site O&M Cost per kW-Year Capacity vs. Project Nameplate Capacity (< 5 MW) .... G-6

Figure G-5 — Average Site O&M Cost per kW-Year Capacity vs. Project Nameplate Capacity (> 5 MW) .... G-7

Figure G-6 — Annual Site O&M Cost per MWh vs. Age of Project ................................................................. G-7

Figure G-7 — Annual Site O&M Cost per kW-Year Capacity vs. Age of Project ............................................ G-8

Figure I-1 — Geothermal Dataset – CAPEX for All MW Plant Sizes ................................................................. I-2

Figure I-2 — Geothermal Dataset – O&M for All MW Plant Sizes ..................................................................... I-3

Figure J-1 — Wind Dataset – CAPEX for All MW Plant Sizes .......................................................................... J-2

Figure J-2 — Wind Dataset – CAPEX for 0-100-MW Plant Sizes ..................................................................... J-4

Figure J-3 — Wind Dataset – CAPEX for 100-200-MW Plant Sizes ................................................................. J-5

Figure J-4 — Wind Dataset – CAPEX for Greater than 200-MW Plant Sizes .................................................... J-7

Figure J-5 — Wind Dataset – O&M for All MW Plant Sizes ............................................................................. J-8

Figure J-6 — Wind Dataset – O&M for 0-100-MW Plant Sizes ......................................................................... J-9

Figure J-7 — Wind Dataset – O&M for 100-200-MW Plant Sizes ................................................................... J-10

Figure J-8 — Wind Dataset – O&M for Plant Sizes Greater than 200 MW ...................................................... J-11

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ACRONYMS AND ABBREVIATIONS

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Term Definition or Clarification

2017$ 2017 dollars

A&G Administrative and general

AEO Annual Energy Outlook

ARIMA Autoregressive integrated moving average

ATB Annual Technology Baseline

CAPEX Capital expenditures

CC Combined cycle

CF Capacity factor

COD Commercial operation date

CT Combustion turbine

DOE Department of Energy

EIA Energy Information Administration

EMM Electricity Market Module

ESP Electrostatic precipitator

FERC Federal Energy Regulatory Commission

FERC Form 1 FERC Form No. 1

FGD Flue gas desulfurization

Hg Mercury

HP High pressure

ID Identifier or induced draft 

IP Intermediate pressure

IPP Independent power producer

IRENA International Renewable Energy Agency

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Term Definition or Clarification

kW Kilowatts

kW-yr Kilowatt-years

LCOE Levelized cost of electricity

LP Low pressure

MMRA Major maintenance reserve account

MW Megawatts

MWh Megawatt-hours

NOX Nitrogen oxide

NREL National Renewable Energy Laboratory

OEA Office of Energy Analysis

O&M Operations and maintenance

PM Particulate matter

PV Photovoltaic

R

2 R-squared

Sargent & Lundy Sargent & Lundy LLC

SO2 Sulfur dioxide

TCP Total Cost of Plant

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EXECUTIVE SUMMARY

IDENTIFYING IMPACTS OF AGING ON GENERATION COST AND OPERATION 

Sargent & Lundy LLC (Sargent & Lundy) was engaged by the Office of Energy Analysis (OEA) of the U.S. 

Energy Information Administration (EIA), an agency within the U.S. Department of Energy (DOE), to conduct 

a study to improve the ability of the Electricity Market Module (EMM) to represent the changing landscape of 

electricity generation and to more accurately represent costs, which will improve projections for generating 

capacity, generator dispatch, and electricity prices. The EMM is a submodule within the EIA’s National Energy 

Modeling System (NEMS), a computer-based energy supply modeling system that is used for the EIA’s Annual 

Energy Outlook (AEO) and other analyses. 

In particular, the purpose of this study was to provide information that may enable the EIA to more accurately 

represent costs associated with operation of the existing fleet of U.S. generators as they age. This includes 

capital expenditures (CAPEX) related to ongoing operations as well as potential increases in operations and 

maintenance (O&M) costs attributable to declining performance due to aging. 

The primary focus of our analysis was existing fossil fuel generators. The study also included existing wind, 

solar, hydro, and other renewable generators. The work scope did not include analysis of nuclear units. 

The generating capacity types represented in the EMM that were included in our analysis comprised: 

 Coal steam plants 

 Gas/oil steam plants 

 Gas/oil combined-cycle (CC) plants 

 Gas/oil combustion turbines (CTs) 

 Conventional hydropower 

 Pumped storage – hydraulic turbine reversible 

 Solar thermal – central tower 

 Solar photovoltaic (PV) – single-axis tracking 

 Geothermal 

 Wind 

For most types of generators evaluated, we did not find a statistically significant relationship between plant age 

and costs (both CAPEX and O&M). CAPEX spending over the life of each plant represents a series of capital 

projects—rather than a single life extension project—that includes both discretionary spending and 

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vendor-specified spending. For discretionary spending, different plants might incur the same type of expense at 

different points in time due to differences in plant-specific economic, locational, or operational circumstances. 

Vendor-specified spending is primarily for major maintenance, typically based on cumulative hours of operation 

and/or cumulative starts, and more commonly applied to gas/oil CC and CT plants. We did, however, find a 

statistically significant relationship between age and CAPEX spending for fossil steam coal generators with flue 

gas desulfurization (FGD) equipment, and between age and O&M spending for conventional hydroelectric 

plants and wind turbines. We also found age and CAPEX spending to be significantly correlated for CC and CT 

plants, although measured in terms of operating hours or starts, rather than years. Table ES-1 summarizes the 

variables found to have a significant effect on annual changes in real spending per kilowatt (kW) for each 

generator type. We recommend the EIA incorporate these variables in the EMM representation of CAPEX and 

O&M. 

Table ES-1 — Variables Affecting Annual Changes in Real Spending per kW 

Generating Capacity CAPEX Spending O&M Spending

Coal Steam Plants Age and FGD (see Table ES-3) -

Gas/Oil Steam Plants Capacity (see Table ES-5) -

Gas/Oil Combined-Cycle Plants Operating Hours (see Table ES-7) -

Gas/Oil Combustion Turbines Starts (see Table ES-7) -

Conventional Hydropower - Age (Regression Equation)

Pumped Storage – Hydraulic Turbine Reversible - -

Solar Thermal – Central Tower - -

Solar Photovoltaic – Single-Axis Tracking - -

Geothermal - -

Wind Capacity (see Table ES-11) Age (Regression Equation)

While we did not find a consistent relationship between aging and CAPEX and O&M costs, changes in 

performance-related factors and external market conditions are also related to changes in these costs over time. 

Examples of these factors and conditions include the following: 

 Plant efficiency (heat rate) 

 Capacity degradation 

 Outage rates 

 Market prices (electricity, fuel) 

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These factors and conditions were not part of the scope of our study. We recommend the EIA consider studying 

these in the future. 

MODELING IMPACTS OF AGING IN EIA PROJECTIONS 

Existing Treatment of Aging in EIA’s Electricity Market Module 

The EMM currently accounts for power plant aging through a one-time step increase in annual CAPEX that is 

intended to extend the life or preserve the performance of an existing generator. In the EMM, costs for plant 

O&M do not vary with plant age. 

As modeled in the EMM, a generating unit is assumed to retire if the expected revenues from the generator are 

not sufficient to cover the annual going-forward costs and if the overall cost of producing electricity can be 

lowered by building new replacement capacity. The going-forward costs include fuel, O&M costs, and annual 

CAPEX. The average annual CAPEX in the EMM is $0.18 per kilowatt-year (/kW-year) for existing CC plants, 

$9/kW-year for existing gas/oil steam plants, and $18/kW-year for existing coal plants (in constant 2017 

dollars). These amounts are increased to $7.25/kW-year, $16/kW-year, and $25/kW-year, respectively, after a 

plant reaches 30 years of age.1

 The average annual CAPEX in the EMM for existing CT plants is $1.52/kW-year 

with no life extension costs. The other generating technologies in the EMM are not currently modeled with 

either CAPEX or life extension costs. 

Need for Update to EIA’s Treatment of Aging 

The existing CAPEX values in the EMM were derived from yearly changes in plant in service accounts reported 

on the Federal Energy Regulatory Commission (FERC) Form No. 1 (“FERC Form 1”).2 The O&M costs in the 

EMM are also derived from FERC Form 1. However, FERC Form 1 does not cover merchant power plants or 

independent power producers (IPPs), leaving a large gap in the data. For example, out of approximately 35,000 

generating units in the U.S., roughly 21,000 (60%) are IPPs. The EIA currently extrapolates data from FERC 

Form 1 to represent all plants covered in the EMM. 

Sargent & Lundy’s update to the EMM treatment of aging examined the potential adaptation of the EMM to 

represent changes in age-related spending patterns by various methods. This examination required the following 

steps: 

 

1

 Internal communication with EIA, February 2018. 

2

 FERC Form 1 is an annual regulatory requirement for major electric utilities, licensees, and others designed to collect 

non-confidential financial and operational information. 

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1. Gathering of in-house data from independent power projects and other plants, in addition to 

FERC Form 1 data. 

2. Incorporation of O&M and capital spending forecasts by plant owners and operators with 

firsthand knowledge of plant operating history and future needs, thereby extending the range of 

plant operating years over which to characterize spending, compared with FERC Form 1 data that 

is limited to historical data. 

3. Removal of capital spending for major modifications relating to environmental compliance, 

which would be modeled on a case-specific basis. 

4. Identification of the most significant variables affecting age-related spending from commonly 

reported plant data—such as plant capacity (kW), annual generation (megawatt-hours [MWh]), 

age, fuel type, emission controls, and regulatory environment—using regression analysis. 

5. Representation of age-related costs as either fixed ($/kW-year) or variable ($/MWh) according to 

generating technology and typical maintenance practices. 

6. Application of capital spending and/or age-related costs to the EMM representations of long-term 

fixed O&M, variable O&M, and ongoing capital spending for each generating technology. 

The assessment methodology used by Sargent & Lundy for the EMM update included an in-depth process of 

data validation, data normalization, and statistical testing, which is described in detail in Section 2. 

ANALYSIS OF AGING IMPACTS IN PUBLICLY-REPORTED COST INFORMATION 

Cost Breakdowns in Reported Data 

Our analysis required an understanding of the cost breakdowns in the reported data between 1) capitalized 

(CAPEX) and expensed (O&M) cost components and 2) fixed O&M and variable O&M cost components. From 

a system modeling perspective, CAPEX and fixed O&M costs are typically expressed in $/kW-year, while 

variable O&M is typically expressed in $/MWh. Normalized cost breakdowns in these units are necessary for 

compatibility with the EMM. 

The reporting formats of our in-house data and the FERC Form 1 data have a clear delineation between CAPEX 

and O&M. However, while the in-house data often contains an explicit breakdown between fixed and variable 

O&M, the FERC Form 1 accounts for O&M are not categorized as such. Rather, the reported O&M costs in a 

given account are the combined fixed and variable costs at the reported generating output. Thus, the variable 

O&M component cannot be clearly delineated from the total reported O&M in the FERC Form 1 data. 

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O&M costs for the following technologies are essentially all fixed: solar thermal (central tower), solar PV 

(single-axis tracking), geothermal, and wind. By definition, fixed O&M costs are independent of plant 

generation, so they are expressed in $/kW-year. 

O&M costs for the following technologies include a significant variable component: coal steam, gas/oil steam, 

gas/oil CC, gas/oil CTs, conventional hydropower, and pumped storage (hydraulic turbine reversible). By 

definition, variable O&M costs are proportional to plant generation and are typically expressed in $/MWh. 

As mentioned, the variable O&M components cannot be clearly delineated from the total reported O&M costs. 

For this assessment, the variable components were combined with the fixed components and expressed in 

$/kW-year. The combined total O&M was found to correspond to the combined total O&M representation in the 

EMM, which includes a $/MWh variable O&M breakout, as presented in the subsections below. 

CAPEX spending values, expressed in $/kW-year, were derived from the new dataset as an additive to the EMM 

O&M costs and as replacements for the existing EMM CAPEX representation for all technologies, except for 

gas/oil CC and gas/oil CTs. CAPEX spending for gas/oil CC and gas/oil CTs was found to be primarily for 

major maintenance events, which are already represented as a $/MWh variable O&M cost in the EMM.

Data Compilation 

The data compilation for this analysis consisted of the following annual plant data (any available data from 1980 

to 2060, historical or forecasted by plant owner): 

 Plant megawatts (MW) (summer) 

 Annual MWh 

 Annual O&M (from FERC Form 1) 

 Annual O&M (from other sources) 

 Annual CAPEX (from FERC Form 1) 

 Annual CAPEX (from other sources) 

 Annual environmental compliance costs 

All available and validated cost data over the plant operating life, historical or forecasted, was normalized as 

follows for each plant: 

 Annual O&M in 2017 $/kW-year versus age (years from commercial operation date [COD]) 

 Annual CAPEX in 2017 $/kW-year versus age (years from COD) 

 Annual O&M + CAPEX in 2017 $/kW-year versus age (years from COD) 

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In all cases, the yearly values are expressed in constant 2017 price levels and would increase annually with the 

inflation rate. 

IDENTIFYING CHANGES IN SPENDING PATTERNS OVER PLANT LIFE 

Differences in Spending Approach by Plant Type 

CAPEX spending over the life of each plant represents a series of capital projects throughout the plant life, 

rather than a single life extension project. This consists of both discretionary spending and vendor-specified 

spending, examples of which are as follows: 

 Discretionary spending is notable for most coal steam and gas/oil steam plants. Different plants 

might incur the same type of expense at different points in time due to differences in plant-specific 

economic, locational, or operational circumstances. Typical industry-standard frequencies for 

repairs and replacements of major equipment within a coal plant are not absolute, but rather 

indicative of when a coal plant may be required to perform the work, based on manufacturer 

experience. An owner may choose to perform the work early, if they have an available outage, or 

defer if, after inspection, the equipment appears to be capable of continued operation without 

repair. 

 Vendor-specified major maintenance spending, such as commonly applied to gas/oil CC and gas/oil 

CTs, is based on cumulative hours of operation and/or cumulative starts. Implicitly, CAPEX 

spending for CC and CT plants is age-related and vendor-specified, and may be expressed as an 

equivalent $/MWh value, which covers: 

 Major maintenance costs for periodic combustion inspections, hot gas path inspections, and 

major overhauls account for nearly all of the CAPEX expenditures. Many plant owners 

choose to capitalize major maintenance expenditures. As these expenditures normally 

follow the equipment vendor’s recommendations, they maintain plant performance and 

extend the plant life. 

 Major one-time costs include rotor replacement, typically at about 150,000 equivalent 

operating hours, 7,000 equivalent starts, or within the first 30 years of plant operation. 

These costs are captured within the dataset. As gas turbines age, major maintenance parts 

often become available from third-party suppliers at a discounted price. 

Potential Benefits of CAPEX and O&M Spending on Future Spending 

CAPEX and O&M spending have a relatively minor effect on future non-fuel O&M spending, on average, 

compared with plant performance-related economic benefits not captured in this analysis, such as: 

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 Reduced fuel expenditures due to improved heat rates 

 Reduced capacity degradation and higher capacity sales 

 Reduced outage costs due to reduced replacement power expenses or higher power sales 

 Increased power sales due to increased net capacity or reduced forced outages 

Potential Impacts of Plant Age on Future Spending 

The spending characteristics described in the previous subsections are evident in the datasets, which reveal 

significant variability in plant spending as a function of age. Sargent & Lundy’s evaluation therefore examined 

additional variables that might explain some of the variability in age-related spending: plant capacity (MW), 

capacity factor, external market conditions, regulatory environment, fuel characteristics, and FGD. These 

additional variables and their effects are described in the following subsections. 

Effect of Plant Capacity (MW) 

The effect of plant MW capacity on age-related spending, expressed in $/kW-year, was examined by breaking 

the dataset into separate plant size categories, summarized as follows: 

 Coal Steam

 All MW

 < 500 MW

 500 MW – 1,000 MW

 1,000 MW – 2,000 MW

 > 2,000 MW

 Gas/Oil Steam

 < 500 MW

 500 MW – 1,000 MW

 > 1,000 MW

 Gas/Oil CC

 All MW

 < 500 MW

 500 MW – 1,000 MW

 > 1,000 MW

 Gas/Oil CT

 All MW

 < 100 MW

 100 MW – 300 MW

 Conventional Hydroelectric

 All MW

 < 100 MW

 100 MW – 500 MW

 > 500 MW

 Pumped Hydroelectric Storage

 All MW

 < 100 MW

 100 MW – 500 MW

 > 500 MW

 Solar Photovoltaic

 < 5 MW

 > 5 MW

 Wind Turbine

 All MW

 < 100 MW

 100 MW – 200 MW

 > 200 MW

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For some of the MW breakdowns above, the age coefficient in the regression analysis of CAPEX or O&M was 

found to be statistically significant. For the other MW breakdowns, an average value by age group was found to 

be more appropriate (see Table ES-1).

Effect of Plant Capacity Factor 

CAPEX and O&M spending for the coal steam plants increased significantly with age when expressed on a 

$/MWh basis. This was primarily a result of significant declines in plant capacity factors over time, as shown in 

Figure ES-1. A similar decline also occurred with the gas/oil steam plants, as shown in Figure ES-2. 

Figure ES-1 — Capacity Factor vs. Age for All Coal Plants 

Figure ES-2 — Capacity Factor vs. Age for All Gas/Oil Steam Plants 

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Effect of External Market Conditions 

The declining capacity factors with age, shown above, may have been a result of external market conditions 

and/or declining plant performance. These are areas for further exploration. 

External market conditions over the same time period that may have contributed to lower capacity factors for 

coal steam and gas/oil steam plants include: 

 Competition with lower gas prices and more efficient gas turbines 

 Competition with renewable energy having lower dispatch costs 

 Lower load growth due to increased amounts of energy efficiency and distributed resources 

For some coal steam and gas/oil steam plants, the decline in capacity factor was also a result of less efficient 

heat rates, increased component failures, and increased outage rates over time. A major contributor to this 

decline in performance is often a result of increased cycling operation. Increased cycling leads to higher O&M 

and CAPEX spending over time.3

External market conditions may have also reduced the number of data points with higher age-related spending, 

due to plant retirements. The least efficient coal steam and gas/oil steam plants would likely retire under the 

following circumstances: 

 Lower efficiency may contribute to less frequent dispatch and more cycling, leading to more 

component failures and higher spending 

 Less frequent dispatch reduces hours of operation and power sales 

 Lower power sales income may not adequately cover plant fixed costs 

Some of the older coal steam plants (23 in this data sample) maintained consistently high capacity factors 

throughout their plant lives, with no real increase in spending. These high capacity factor plants had an installed 

capacity ranging from 70 MW to 2,400 MW, with an average of 850 MW and an average COD of 1961. These 

plants are slightly larger and older, on average, than the entire dataset of coal steam plants, which have an 

average installed capacity of 720 MW and an average COD of 1964. Table ES-2 shows the average capacity 

factors and O&M and CAPEX spending for the entire dataset of coal steam plants compared with the older 

consistently high capacity factor plants. 

 

3

 Kumar, N., Besuner, P., Lefton, S., and Agan, D., Power Plant Cycling Costs, National Renewable Energy Laboratory, 

April 2012. 

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Table ES-2 — High Capacity Factor Coal Plants – Spending Comparison 

Average – All Years Years 1-20 Years 20-40 Years 40-80

Capacity Factor – All Plants 59.1% 66.8% 64.5% 52.9%

Capacity Factor – High CF Plants 74.0% - 72.8% 74.4%

O&M – All Plants (2017 $/kW-yr) 46.01 53.90 40.06 48.77

CAPEX – All Plants (2017 $/kW-yr) 22.78 17.92 26.20 21.25

Total – All Plants (2017 $/kW-yr) 68.67 71.86 66.25 69.82

O&M – High CF Plants (2017 $/kW-yr) 36.65 - 31.07 38.78

CAPEX – High CF Plants (2017 $/kW-yr) 20.26 - 23.13 19.16

Total – High CF Plants (2017 $/kW-yr) 57.02 - 54.20 58.10

Market conditions at the older, high capacity factor plants may have led to fewer competing resources, which 

would support higher levels of dispatch and higher capacity factors. In addition, lower cycling requirements at 

those plants would have reduced spending requirements. 

Effect of Regulatory Environment 

Owners of coal steam plants in deregulated states were found to have no aversion to capital spending compared 

to plant owners in regulated states. Some of the difference may be due to higher labor costs in many of the 

deregulated states. This is the opposite of what would be expected, whereby plant owners in a deregulated 

environment would have a greater incentive to reduce O&M costs that cannot be passed through to ratepayers. 

The higher O&M spending is likely a result of other factors, such as higher average labor costs in deregulated 

states, which tend to have a higher percentage of union labor compared with regulated states. Therefore, the net 

effect of regulatory status on average O&M spending was not apparent at this level of detail. 

Effect of Fuel Characteristics

Sargent & Lundy’s regression analysis compared CAPEX spending for coal steam plants with bituminous and 

subbituminous coal types. The results indicate that average CAPEX spending is not likely affected by coal type 

at a high-level designation (i.e., bituminous/subbituminous) without more detailed coal specifications. 

Effect of Flue Gas Desulfurization 

The regression analysis indicated a significant difference in CAPEX spending for coal plants with FGD. The 

corrosive environment of chemicals and reagents significantly reduces the life of equipment such as pumps, 

mills, nozzles, valves, etc. These components must be replaced more frequently than at plants without FGD. 

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PROPOSED UPDATES TO EMM METHODOLOGY 

The EMM captures changes in age-related spending patterns through multiple cost categories: CAPEX, O&M, 

fuel, energy sales, and capacity sales. The updates below relate only to the CAPEX and O&M. The focus of the 

work scope was to more accurately represent power plant aging impacts on CAPEX and O&M. Detailed 

derivations of fixed and variable O&M costs for the EMM were not part of the work scope. 

Sargent & Lundy’s recommended updates to the fixed and variable O&M costs and CAPEX in the EMM for 

each generating technology are summarized in the tables below. Values are in constant 2017 price levels and are 

incurred in every year of plant operation, starting from commercial operation through plant retirement. In all 

cases, the yearly values would increase annually with the inflation rate. 

Coal Steam 

Sargent & Lundy’s analysis of the coal steam dataset (Appendix A) identified two significant variables affecting 

annual changes in real CAPEX spending (on a constant $/kW-year basis): age and FGD. Variables not having a 

significant effect on annual changes in real CAPEX spending (on a constant $/kW-year basis) were: plant 

capacity (kW), fuel type, and regulatory environment. When CAPEX spending was expressed on a constant 

$/MWh basis, it was significantly related to age, primarily as a result of declining MWh generation with age. 

Table ES-3 compares the new CAPEX values derived from the coal steam dataset with the CAPEX values 

currently used in the EMM. The new CAPEX values are similar in magnitude with the current EMM values 

over the long term, except that the new values follow a continuous pattern rather than a step pattern. As 

discussed below, the new values include life extension projects that occur throughout the plant life, including the 

first 30 years of operation. 

Table ES-3 — Coal Steam CAPEX Results – All MW, All Capacity Factors 

Net Total CAPEX

(2017 $/kW-year)

$/kW-yr 

(Years

1-10)

$/kW-yr 

(Years 

10-20)

$/kW-yr 

(Years

20-30)

$/kW-yr 

(Years 

30-40)

$/kW-yr 

(Years

40-50)

$/kW-yr 

(Years

50-60)

$/kW-yr 

(Years 

60-70)

$/kW-yr 

(Years 

70-80)

New Value – No FGD* 17.16 18.42 19.68 20.94 22.20 23.46 24.72 25.98

New Value – with FGD* 22.84 24.10 25.36 26.62 27.88 29.14 30.40 31.66

Existing EMM Value 17.55 17.55 17.55 24.62 24.62 24.62 24.62 24.62

*Calculated to the midpoint of the given age band. 

“Life extension costs” in the existing CAPEX values are covered by the step increase after year 30. Life 

extension costs in the new CAPEX values are distributed throughout the plant life. This is a result of 

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discretionary spending, which is a common practice for most coal steam plants. Different plants might incur the 

same type of expense at different points in time due to differences in plant-specific economic, locational, or 

operational circumstances. 

Typical industry-standard frequencies for repairs and replacement of major equipment within a coal plant are 

not absolute, but rather indicative of when a coal plant may be required to perform the work, based on 

manufacturer experience. An owner may choose to perform the work early, if they have an available outage, or 

defer if, after inspection, the equipment appears to be capable of continued operation without repair. 

The new values also account for CAPEX relating to FGD. An FGD system tends to be capital-intensive to own 

and operate. The corrosive environment of chemicals and reagents significantly reduces the life of equipment 

such as pumps, mills, nozzles, valves, etc. These components must be replaced more frequently than at plants 

without FGD. 

O&M costs for the coal steam plants include a significant variable component. By definition, variable O&M 

costs are proportional to plant generation and are typically expressed in $/MWh. As previously mentioned, the 

variable O&M component cannot be clearly delineated from the total reported O&M in the FERC Form 1 data. 

For this assessment, the variable component was combined with the fixed component and expressed in 

$/kW-year. The combined total O&M in the coal steam plant dataset for this analysis was found to be nearly 

equivalent to the existing combined total O&M representation in the EMM, which already includes the 

necessary $/MWh variable O&M breakout (see Table ES-4). 

Table ES-4 — Coal Steam O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)*

Variable O&M 

(2017 $/kW-yr)**

Total O&M

(2017 $/kW-yr)**

Coal Steam Dataset Results – All Plants 36.81 1.78 9.20 46.01

< 500 MW 44.21 1.78 9.20 53.41

500 MW – 1,000 MW 34.02 1.78 9.20 43.22

1,000 MW – 2,000 MW 28.52 1.78 9.20 37.72

> 2,000 MW 33.27 1.78 9.20 42.47

Existing EMM Value*** 40.63 1.78 9.20 49.83

*Fixed and variable split is estimated using the existing EMM variable O&M cost of $1.78/MWh. 

**Calculated at the coal steam dataset average capacity factor of 59%. 

***Source: Internal communication with EIA, February 2018. 

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Gas/Oil Steam 

The analysis of the gas/oil steam dataset (Appendix B) identified only one significant variable affecting annual 

changes in real CAPEX spending (on a constant $/kW-year basis): plant capacity (kW). That is, CAPEX was 

lower on a $/kW-year basis for larger plant sizes due to economies of scale. When CAPEX spending was 

expressed on a constant $/MWh basis, it was significantly related to age, primarily as a result of declining MWh 

generation with age. 

Table ES-5 compares the new CAPEX values derived from the gas/oil steam dataset with the CAPEX values 

currently used in the EMM. The new CAPEX values are similar in magnitude with the current EMM values 

over the long term, except that the new values follow a continuous pattern rather than a step pattern. As 

discussed below, the new values include life extension projects that occur throughout the plant life, including the 

first 30 years of operation. 

Table ES-5 — Gas/Oil Steam CAPEX Results 

Plant Size

Net Total CAPEX

(2017 $/kW-year)

Years 1-30 Years 30-80

Gas/Oil Steam Dataset Results – All Plants 15.96 15.96

New Value: < 500 MW 18.86 18.86

New Value: 500 MW – 1,000 MW 11.57 11.57

New Value: > 1,000 MW 10.82 10.82

Existing EMM Value 9.14 16.21

“Life extension costs” in the existing CAPEX values are covered by the step increase after year 30. Life 

extension costs in the new CAPEX values are distributed throughout the plant life. This is a result of 

discretionary spending, which is a common practice for most gas/oil steam plants. Different plants might incur 

the same type of expense at different points in time due to differences in plant-specific economic, locational, or 

operational circumstances. 

Typical industry-standard frequencies for repairs and replacement of major equipment within a gas/oil steam 

plant are not absolute, but rather indicative of when a gas/oil steam plant may be required to perform the work, 

based on manufacturer experience. An owner may choose to perform the work early, if they have an available 

outage, or defer if, after inspection, the equipment appears to be capable of continued operation without repair. 

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Typical industry-standard frequencies for repairs and replacement of major equipment are similar to those of 

coal units, as presented in the previous section. 

The use of a constant annual value on the modeling of annual CAPEX would be similar to representing a major 

maintenance reserve account (MMRA), which is commonly used for non-recourse financing of power projects. 

MMRAs are usually required by power project lenders over the tenor of debt as protection against maintenance 

spending uncertainty. An MMRA is typically funded by annual contributions drawn from a project’s cash flow, 

sometimes as a uniform annual amount. Annual contribution levels are based on estimated long-term 

maintenance expenditure patterns. Over the long term, annual contributions represent a smoothed version of 

irregular actual annual values. 

The use of a long-term average value also recognizes the inherent variability in long-term spending patterns for 

any given plant. Since the EMM is a large-scale model, it is conceptually designed to represent plant types as 

averages rather than as individual plants. When summed across a large number of plants in a utility system, 

some of the variability in annual expenditure patterns would tend to even out. The level of accuracy between 

average values and year-specific values for a given plant type is nearly equivalent in large-scale models. 

O&M costs for the gas/oil steam plants include a significant variable component, although typically smaller than 

coal units. The combined total O&M in the gas/oil steam plant dataset for this analysis was found to be 

somewhat lower than the existing combined total O&M representation in the EMM, which already includes the 

necessary $/MWh variable O&M breakout (see Table ES-6). However, the variable O&M of $8.23/MWh in the 

EMM is much higher than values Sargent & Lundy has observed in actual gas/oil steam plants and should not be 

higher than the variable O&M of $1.78/MWh in the EMM used for the coal units. 

Table ES-6 — Gas/Oil Steam O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)*

Variable O&M 

(2017 $/kW-yr)**

Total O&M

(2017 $/kW-yr)**

Gas/Oil Steam Dataset Results – All Plants 24.68 1.00 1.84 26.52

< 500 MW 29.73 1.00 1.84 31.57

500 MW – 1,000 MW 17.98 1.00 1.84 19.82

> 1,000 MW 14.51 1.00 1.84 16.35

Existing EMM Value*** 19.68 8.23 15.14 34.82

*Fixed and variable split is estimated using an approximate value for variable O&M of $1.00/MWh based on confidential projects. 

**Calculated at the gas/oil steam dataset average capacity factor of 21%. 

***Source: Internal communication with EIA, February 2018. 

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Gas/Oil Combined Cycle and Gas/Oil Combustion Turbine 

As with coal steam and gas/oil steam plants, CAPEX spending for gas/oil CC and gas/oil CT plants represents a 

series of capital projects throughout the plant life, which include projects for “life extension.” Most CAPEX 

spending for gas/oil CC and gas/oil CT plants is for vendor-specified major maintenance events. Other CAPEX 

spending, other than for emission control retrofits, is relatively minor. 

Vendor-specified major maintenance spending is based on cumulative hours of operation and/or cumulative 

starts. Implicitly, CAPEX spending for CC and CT plants is age-related and vendor-specified, and may be 

expressed as an equivalent $/MWh value, which covers: 

 Major maintenance costs for periodic combustion inspections, hot gas path inspections, and major 

overhauls account for nearly all of the CAPEX expenditures. Many plant owners choose to 

capitalize major maintenance expenditures. As these expenditures normally follow the equipment 

vendor’s recommendations, they maintain plant performance and extend the plant life. 

 Major one-time costs include rotor replacement, typically at about 150,000 equivalent operating 

hours, 7,000 equivalent starts, or within the first 30 years of plant operation. These costs are 

captured within the dataset. As gas turbines age, major maintenance parts often become available 

from third-party suppliers at a discounted price. 

As with MMRAs described in the previous subsection, major maintenance contracts are priced according to 

smoothed versions of irregular long-term expenditure patterns. Apart from adjustments for operating conditions, 

major maintenance (and nearly all of the CAPEX) is effectively priced as an equal annual value, expressed in 

constant $/MWh with annual escalation. 

Table ES-7 compares the new CAPEX and O&M values derived from the gas/oil CC and CT datasets with the 

values currently used in the EMM. As indicated above, the combined CAPEX and O&M values in the datasets 

would be expected to correspond to the combined CAPEX and O&M in the EMM, with most of the CAPEX in 

the EMM represented as variable O&M. However, some of the EMM values are higher than values Sargent & 

Lundy has observed in actual CC and CT plants, as detailed below: 

 The EMM fixed and variable O&M costs for CC plants are reasonable for smaller CC installations 

(< 500 MW) but high for larger plants. 

 The EMM CAPEX addition of $7/kW-year after 30 years of operation should not be represented as 

a fixed cost. As previously mentioned, age-related costs would be built into the $/MWh variable 

O&M and would be a function of cumulative operating hours rather than operating years. 

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 The EMM fixed and variable O&M costs for CT plants are high for all plant sizes. Since most CT 

plants operate as peaking plants with low capacity factors, the variable O&M component is likely to 

be based on equivalent starts rather than equivalent operating hours. 

Table ES-7 — Gas/Oil CC and CT CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)*

Variable O&M 

(2017 $/kW-yr)*

Total O&M

(2017 $/kW-yr)*

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)**

CC Dataset Results 

(All Plants)

13.08 3.91 (included in 

CAPEX) 13.08 15.76 28.84

< 500 MW 15.62 4.31 (included in 

CAPEX) 15.62 17.38 33.00

500 MW –

1,000 MW 9.27 3.42 (included in CAPEX) 9.27 13.78 23.05

> 1,000 MW 11.68 3.37 (included in 

CAPEX) 11.68 13.57 25.25

Existing EMM 

Value** 27.52 2.64 10.64 38.16 0.18; 7.25(after year 30)

38.34; 45.41 

(after year 30)

CT Dataset Results 

(All Plants)

5.33 (starts based) (included in 

CAPEX) 5.33 6.90 12.23

< 100 MW 5.96 (starts based) (included in 

CAPEX) 5.96 9.00 14.96

100 MW – 300 MW 6.43 (starts based) (included in 

CAPEX) 6.43 6.18 12.61

> 300 MW 3.99 (starts based) (included in 

CAPEX) 3.99 6.95 10.94

Existing EMM 

Value***

12.60 14.63 5.13 17.73 1.52 19.25

*Fixed and variable split is estimated, assuming all CAPEX costs are represented as variable O&M, either hours-based ($/MWh) or starts-based ($/start). 

**Calculated at the dataset average capacity factor of 46% for CC and 4% for CT. 

***Source: Internal communication with EIA, February 2018. 

Conventional Hydroelectric 

Overall, the conventional hydroelectric dataset does not support any age-related CAPEX spending trend across 

the full data and on any of the subsets by plant size. The average CAPEX value over all operating years is 

$22.56/kW-year. The dataset does support age as a statistically significant predictor of O&M spending (on a 

linear trend across all plant ages). Therefore, O&M spending for this dataset may be estimated by the regression 

equation: 

Annual O&M spending in 2017 $/kW-year = 22.360 + (0.073 × age) 

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The CAPEX and O&M values derived from the conventional hydroelectric dataset are significantly higher than 

the existing values used in the EMM (Table ES-8) and outside the range of values published in the AEO4

 and by 

the International Renewable Energy Agency (IRENA).5

 The reasons for this discrepancy are not known without 

having the data sample used for the EMM values. It appears that the EMM does not currently account for 

CAPEX or life extension expenditures for conventional hydroelectric. 

Table ES-8 — Hydroelectric CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)

Conventional Hydroelectric Dataset Results – All Plants 22.00 - 22.56 44.56

Existing EMM Value* 14.58 0.00 0.00 14.58

*Source: Internal communication with EIA, February 2018. 

Pumped Storage 

Overall, the pumped storage dataset does not support any age-related CAPEX or O&M spending trend across 

the full data and on any of the subsets by plant size. The average value over all operating years is 

$14.83/kW-year for CAPEX and $23.63/kW-year for O&M (Table ES-9). The existing values used in the EMM 

are not available. 

Table ES-9 — Pumped Storage CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)

Pumped Storage Dataset Results – All Plants 23.63 - 14.83 38.46

Existing EMM Value N/A N/A N/A N/A

Solar Photovoltaic 

The solar PV dataset does not support any age-related CAPEX spending trend across the full data and on any of 

the subsets by plant size. Sargent & Lundy notes that the average change in the “Total Cost of Plant” (TCP) 

reported in the FERC data for the limited usable dataset (15 sites not filtered out) is approximately $26/kW-year. 

However, due to the limited dataset, lack of clarity on what qualifies as a change to the TCP, and general lack of 

 

4

 Energy Information Administration, Annual Energy Outlook 2018, Cost and Performance Characteristics (Table 8.2), 

February 2018. 

5

 International Renewable Energy Agency, Renewable Energy Technologies: Cost Analysis Series, Hydropower, June 2012. 

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consistency in the FERC capital cost data provided, Sargent & Lundy advises that caution be taken when trying 

to establish any definitive solar PV capital cost trends from the FERC data. 

The solar PV dataset appears to support age as a statistically significant predictor of O&M spending (on a linear 

trend across all plant ages). However, based upon closer inspection of the data, a more appropriate predictor of 

O&M spending for this dataset would be a simple average across all years. This determination is based on the 

lack of data points for plants over 10 years old. 

When considering the average O&M costs per plant as a single data point and then averaging those values, 

Sargent & Lundy calculated an average O&M cost of $75/kW-year from the FERC data for sites under 5 MW. 

Using the same method, an average O&M cost of $15/kW-year was calculated from the FERC data for sites 

over 5 MW. 

By comparison, the EMM uses an average O&M value of $28.47/kW-year for all solar PV plants and an average 

CAPEX value of zero. Neither dataset captures the most recent trends in solar PV technology due to rapid 

changes in cost, size, and efficiency. 

Solar Thermal 

There are no solar thermal power plants that report operating data in FERC Form 1. Industry-wide, there are a 

limited number of solar thermal projects; a majority of which have been constructed within the last 10 years—

the exception being small test facilities and the Solar Energy Generating Systems (SEGS) plants built in the 

1980s. 

Geothermal 

Overall, the geothermal dataset does not support any age-related CAPEX spending trend across the full data and 

on any of the subsets by plant size. Instead, we recommend a simple average be used across the full age range. 

Sargent & Lundy recommends using the indicated $/kW-year average in Table ES-10 for O&M and CAPEX 

spending. As shown in the table, it appears the EMM does not currently account for CAPEX or life extension 

expenditures for geothermal plants. 

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Table ES-10 — Geothermal CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)

Geothermal Dataset Results – All Plants 157.10 - 40.94 198.04

Existing EMM Value** 91.66 0.00 0.00 91.66

**Source: Internal communication with EIA, February 2018. 

Wind

The dataset supports age as a statistically significant predictor of O&M spending (on a linear trend across all 

plant ages). Therefore, O&M spending for this dataset may be estimated by the regression equations shown in 

Table ES-11. Age was not a significant predictor of CAPEX spending, although CAPEX was found to vary 

significantly as a function of capacity (kW). That is, CAPEX was lower on a $/kW-year basis for larger plant 

sizes due to economies of scale. 

The CAPEX and O&M values derived from the wind dataset are significantly higher than the existing values 

used in the EMM. The reasons for this discrepancy are not known without having the data sample used for the 

EMM values. Neither data sample is stratified by wind technology or turbine size. Neither dataset captures the 

most recent trends in wind turbine technology due to rapid changes in cost, size, and efficiency. 

Table ES-11 — Wind CAPEX and O&M Comparison with Existing EMM 

Fixed O&M

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

CAPEX

(2017 $/kW-yr)

Wind Dataset Results – All Plants 31.66 + (1.22 × age) 0.00 18.29

< 100 MW 39.08 + (1.12 × age) 0.00 20.48

100 MW – 200 MW 23.80 + (1.17 × age) 0.00 16.93

> 200 MW 26.78 + (0.92 × age) 0.00 13.48

Existing EMM Value* 29.31 0.00 0.00

*Source: Internal communication with EIA, February 2018. 

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RECOMMENDED AREAS FOR FURTHER STUDY 

Based on our analyses performed for the update to the EMM treatment of age-related spending, Sargent & 

Lundy identified several areas that warrant further study, including: 

 Impact of regional labor cost differences versus the effects of a regulated/deregulated environment; 

 Compatibility of EMM plant technology and size breakdowns and fixed/variable O&M cost 

breakdowns with proposed EMM updates; 

 Identification of the factors supporting consistently high capacity factors over the plant lives at 

particular coal units; and 

 Impact of aging on plant performance (heat rates, capacity derates, etc.). If capacity factors decline, 

regardless of the causes, this includes examining the impact of the lower capacity factors on plant 

costs and performance. 

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SARGENT & LUNDY, L.L.C.

Prepared by:

Terrence P. Coyne

Senior Consultant

Patrick S. Daou

Consultant

Eric R. DeCristofaro

Senior Consultant

Marc E. Lemmons

Project Associate

Sean P. Noonan

Senior Consultant

Ryan M. Swanson

Senior Consultant

John R. Wroble

Principal Consultant

Reviewed by:

Patrick M. Geenen

Project Manager

Approved by: May 7, 2018

Matthew R. Thibodeau

Vice President

Date

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1. INTRODUCTION

Sargent & Lundy LLC (Sargent & Lundy) was engaged by the Office of Energy Analysis (OEA) of the U.S. 

Energy Information Administration (EIA), an agency within the U.S. Department of Energy (DOE), to conduct 

a study to improve the ability of the Electricity Market Module (EMM) to represent the changing landscape of 

electricity generation and to more accurately represent costs, which will improve projections for generating 

capacity, generator dispatch, and electricity prices. The EMM is a submodule within the EIA’s National Energy 

Modeling System (NEMS), a computer-based energy supply modeling system that is used for the EIA’s Annual 

Energy Outlook (AEO) and other analyses. 

In particular, the purpose of this study was to provide information that may enable the EIA to more accurately 

represent costs associated with operation of the existing fleet of U.S. generators as they age. This includes 

capital expenditures (CAPEX) related to ongoing operations as well as potential increases in operations and 

maintenance (O&M) costs attributable to declining performance due to aging. 

The primary focus of our analysis was existing fossil fuel generators. The study also included existing wind, 

solar, hydro, and other renewable generators. The work scope did not include analysis of nuclear units. 

The generating capacity types represented in the EMM that were included in our analysis comprised: 

 Coal steam plants 

 Gas/oil steam plants 

 Gas/oil combined-cycle (CC) plants 

 Gas/oil combustion turbines (CTs) 

 Conventional hydropower 

 Pumped storage – hydraulic turbine reversible 

 Solar thermal – central tower 

 Solar photovoltaic (PV) – single-axis tracking 

 Geothermal 

 Wind 

This final report is the fourth milestone task of the EMM update project, which is organized as follows: 

 Task 1 – Analysis of publicly available information for use in estimating capital costs related to 

ongoing operations for specified plant types. 

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 Task 2 – Analysis of publicly available information for use in estimating changes in O&M 

expenditures due to aging for specified plant types. 

 Task 3 – Interim report on assembled aging-related capital and O&M costs. 

 Task 4 – Final report on modeling aging-related capital and O&M costs. 

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2. ASSESSMENT METHODOLOGY

2.1 BACKGROUND 

The EMM currently accounts for power plant aging through a one-time step increase in annual CAPEX. These 

added expenditures are intended to extend the life or preserve the performance of an existing generator, 

including repowering, major repairs or retrofits, and/or covering increases in maintenance required to mitigate 

the adverse effects of aging, including any decreases in plant performance. The portion of the annual CAPEX 

associated with the step increase is referred as “life extension costs.”

As modeled in the EMM, a generating unit is assumed to retire if the expected revenues from the generator are 

not sufficient to cover the annual going-forward costs and if the overall cost of producing electricity can be 

lowered by building new replacement capacity. The going-forward costs include fuel, O&M costs, and annual 

CAPEX. The average annual CAPEX in the EMM is $0.18 per kilowatt-year (/kW-year) for existing CC plants, 

$9/kW-year for existing gas/oil steam plants, and $18/kW-year for existing coal plants (in constant 2017 

dollars). These amounts are increased to $7.25/kW-year, $16/kW-year, and $25/kW-year, respectively, after a 

plant reaches 30 years of age.6

 The average annual CAPEX in the EMM for existing CT plants is $1.52/kW-year 

with no life extension costs. The other generating technologies in the EMM are not currently modeled with 

either CAPEX or life extension costs. 

The existing CAPEX values in the EMM were derived from yearly changes in plant in service accounts reported 

on the Federal Energy Regulatory Commission (FERC) Form No. 1 (“FERC Form 1”).7

 The O&M costs in the 

EMM are also derived from FERC Form 1. However, FERC Form 1 does not cover merchant power plants or 

independent power producers (IPPs), leaving a large gap in the data. For example, out of approximately 35,000 

generating units in the U.S., roughly 21,000 (60%) are IPPs. The EIA currently extrapolates data from FERC 

Form 1 to represent all plants covered in the EMM. 

Sargent & Lundy’s update to the EMM treatment of aging examined the potential adaptation of the EMM to 

represent changes in age-related spending patterns by various methods. This examination required the following 

steps: 

1. Gathering of in-house data from independent power projects and other plants, in addition to 

FERC Form 1 data. 

 

6

 Internal communication with EIA, February 2018. 

7

 FERC Form 1 is an annual regulatory requirement for major electric utilities, licensees, and others designed to collect 

non-confidential financial and operational information. 

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2. Incorporation of O&M and capital spending forecasts by plant owners and operators with 

firsthand knowledge of plant operating history and future needs, thereby extending the range of 

plant operating years over which to characterize spending, compared with FERC Form 1 data that 

is limited to historical data. 

3. Removal of capital spending for major modifications relating to environmental compliance, 

which would be modeled on a case-specific basis. 

4. Identification of the most significant variables affecting age-related spending from commonly 

reported plant data—such as plant capacity (kW), annual generation (megawatt-hours [MWh]), 

age, fuel type, emission controls, and regulatory environment—using regression analysis. 

5. Representation of age-related costs as either fixed ($/kW-year) or variable ($/MWh) according to 

generating technology and typical maintenance practices. 

6. Application of capital spending and/or age-related costs to the EMM representations of long-term 

fixed O&M, variable O&M, and ongoing capital spending for each generating technology. 

The assessment methodology used by Sargent & Lundy for the EMM update included an in-depth process of 

data validation, data normalization, and statistical testing, which is described in detail in the following 

subsections. 

2.2 SOURCES OF COST INFORMATION 

2.2.1 FERC Form 1 Data 

Sargent & Lundy reviewed the FERC Form 1 data through 2016, financial information available from other 

publicly available sources, and detailed in-house project information with which we are familiar. We assembled 

a sufficient volume of source material for each technology in order to characterize the distribution of capital and 

O&M expenditures over the life of a plant. 

We obtained the FERC Form 1 data via ABB’s Velocity Suite EV Power database. Using the available FERC 

Form 1 data, we assessed and summarized the “Cost of Plant” components of the data by major plant type 

category. The “Cost of Plant” components include the following categories of “Electric Plant in Service” 

accounts in FERC Form 1 data, which have been reported annually since the database’s inception: 

 Steam Power Generation – Cost of Plant 

 310 Land and land rights. 

 311 Structures and improvements. 

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 312 Boiler plant equipment. 

 313 Engines and engine-driven generators. 

 314 Turbo generator units. 

 315 Accessory electric equipment. 

 316 Miscellaneous power plant equipment 

 317 Asset retirement costs for steam production plant. 

 Hydraulic Power Generation – Cost of Plant 

 330 Land and land rights. 

 331 Structures and improvements. 

 332 Reservoirs, dams, and waterways. 

 333 Water wheels, turbines, and generators. 

 334 Accessory electric equipment. 

 335 Miscellaneous power plant equipment. 

 336 Roads, railroads, and bridges. 

 337 Asset retirement costs for hydraulic production plant. 

 Other Power Generation – Cost of Plant 

 340 Land and land rights. 

 341 Structures and improvements. 

 342 Fuel holders, producers, and accessories. 

 343 Prime movers. 

 344 Generators. 

 345 Accessory electric equipment. 

 346 Miscellaneous power plant equipment. 

 347 Asset retirement costs for other production plant. 

The sum of these components includes the original construction cost and all ongoing CAPEX. Therefore, each 

annual FERC Form 1 submittal includes the cumulative additions to the “Total Cost of Plant” (TCP). Annual 

changes in the TCP between each submittal year give an indication of the amount of CAPEX for the given year. 

Sargent & Lundy assessed and summarized these annual changes to derive age-related CAPEX, as discussed in 

the following subsections. 

Sargent & Lundy also assessed and summarized the annual O&M expenditures for each technology as reported 

under the “Electric Operation and Maintenance Expenses” accounts in FERC Form 1: 

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 Steam Power Generation – O&M 

 500 Operation supervision and engineering. 

 502 Steam expenses. 

 505 Electric expenses. 

 506 Miscellaneous steam power expenses. 

 507 Rents. 

 509 Allowances. 

 510 Maintenance supervision and engineering. 

 511 Maintenance of structures. 

 512 Maintenance of boiler plant. 

 513 Maintenance of electric plant. 

 514 Maintenance of miscellaneous steam plant. 

 Hydraulic Power Generation – O&M 

 535 Operation supervision and engineering. 

 536 Water for power. 

 537 Hydraulic expenses. 

 538 Electric expenses. 

 539 Miscellaneous hydraulic power generation expenses. 

 540 Rents. 

 541 Maintenance supervision and engineering. 

 542 Maintenance of structures. 

 543 Maintenance of reservoirs, dams, and waterways. 

 544 Maintenance of electric plant. 

 545 Maintenance of miscellaneous hydraulic plant. 

 Other Power Generation – O&M 

 546 Operation supervision and engineering. 

 548 Generation expenses. 

 549 Miscellaneous other power generation expenses. 

 550 Rents. 

 551 Maintenance supervision and engineering. 

 552 Maintenance of structures. 

 553 Maintenance of generating and electric plant. 

 554 Maintenance of miscellaneous other power generation plant. 

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The above O&M expenditures are reported for individual power plants. Administrative and general (A&G) 

expenses in FERC accounts 920 through 935 are reported for the entire utility company. A&G expenses in these 

accounts were not included in this evaluation because of the significant differences in company sizes, mix of 

resources, and methods of allocating costs to individual power plants. In a similar manner, corporate-level A&G 

costs were also excluded from Sargent & Lundy’s internal data. 

The above FERC accounts 500 to 554 correspond to the following fixed and variable O&M components: 

 Fixed O&M 

 Labor 

 Maintenance materials 

 Supplies and miscellaneous expenses 

 Variable O&M 

 Consumables (chemicals, water, waste disposal, etc.) 

 Other costs proportional to generating output 

The FERC accounts do not explicitly break out labor costs, as most of the accounts include both labor and 

non-labor expenditures. Likewise, the FERC accounts are not categorized according to fixed and variable cost 

components. The O&M costs in a given account are combined fixed and variable costs at the reported 

generating output. 

2.2.2 Sargent & Lundy Internal Data 

In addition, Sargent & Lundy compared publicly available, non-fuel-related financial and cost data with a 

characterization of proprietary information with which we are familiar, to the extent permissible by applicable 

confidentiality agreements (information about plant location, equipment type, or plant configuration was never 

disclosed from the proprietary data). We utilized our knowledge of actual projects to assemble a characterization 

of life extension/repowering costs from our in-house data. 

A large portion of the in-house data used in this report was developed from business plan forecasts that capture 

actual budgeted costs for scheduled projects as well as longer-term projections. Historical spending data for 

standalone projects was not usable for this analysis, unless Sargent & Lundy had access to the complete O&M 

or CAPEX spending totals at a given plant for a given year. For consistent comparisons with other plants over 

time, each O&M or CAPEX data point needed to represent a comprehensive total of all spending projects. 

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2.2.3 Other Data Sources 

Other publicly available data sources were searched, including regulated utility filings with public utility 

commissions, routine financial reports for publicly traded companies, utility integrated resource plans, data 

reported by various municipalities and electric cooperatives, and requests for proposals (RFPs) for plant 

improvements at public power entities. Cost data from each of these sources was found to be unsuitable for this 

study for one or more of the following reasons: 

 Cost data was for initial capital investment costs only, with no O&M or ongoing CAPEX spending 

reported; 

 Annual O&M or annual CAPEX amounts were for limited purposes and not representative of a 

complete year; and/or 

 Annual O&M and annual CAPEX amounts were aggregated across business units and not assigned 

to specific plants. 

 Several publications or studies of power plant aging and life extension costs were used, which are cited herein. 

2.3 DATA VALIDATION 

Sargent & Lundy’s approach to validating the FERC Form 1 data involved the following steps (note that 

capitalized words are proper FERC Form 1 terms): 

1. For each Plant/Prime Mover combination (e.g., steam turbine, CC, simple-cycle CT), determine 

the difference between the prior and current year TCP reported in the FERC data. Note that a 

plant can have multiple prime movers on site (e.g., CT units and steam turbine units). Fortunately, 

that data is reported separately. 

2. Flag and invalidate any years where the difference is negative (i.e., a decreasing value of the 

TCP). 

3. Identify if the TCP difference is significantly due to asset retirement costs. If so, flag this plant 

reporting year consider it invalid, as capital would have been spent on non-aging items. 

4. Identify if there has been any year-to-year change in nameplate capacity. If so, flag this plant 

reporting year and consider it invalid, because the TCP would be assumed to be spent on an 

expansion or addition. 

5. Identify if any sulfur dioxide (SO2), nitrogen oxide (NOX), particulate matter (PM), or mercury 

(Hg) control equipment was installed for the plant reporting year. If so, flag that plant reporting 

year and consider it invalid, because capital would have been spent on non-aging items. The year 

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prior to or after the actual emissions control installation date is sometimes flagged as well, 

because of when the spending occurred (this is usually a judgement call). 

6. Identify if any unit at the plant has been retired in a plant reporting year. If so, flag that plant 

reporting year and consider invalid, because capital would have been spent on non-aging items. 

Also, if the plant’s TCP dropped significantly the last few years before retirement, flag those 

plant reporting years and consider them invalid. 

7. Cross-check if any additional units at the plant site (using the same technology) show too great of 

time duration between installed dates of the units. If the first unit and the last unit installed is 

greater than 10 years apart, then flag the data and consider it invalid, because the TCP difference 

would not reflect the actual age of the plant (considered to be the age of the first unit). This was 

flagged as “Removed due to non-equal units at site.”

8. If any TCP is reported to be zero for most of all of the reporting years of the plant, consider the 

data invalid. 

9. If the TCP difference is highly volatile, flag and invalidate at discretion. For example, if one year 

TCP drops from $2,000/kW-year to $1,000/kW-year and then back to $2,000/kW-year in the year 

after, this would be considered highly volatile for those two reporting years. 

10. If a reporting plant has only one or two years of reported TCP data, flag the plant and do not use 

its data. 

11. If any plant reports negative Total O&M Costs, flag that year and do not use it. 

12. Use only data that is valid for both CAPEX spending and O&M spending in the analysis of 

combined CAPEX and O&M spending. Otherwise, analyze CAPEX spending and O&M 

spending separately. Sargent & Lundy found that a large portion of the data points determined to 

be valid for CAPEX spending were also valid for O&M spending. 

The resulting data points from this validation process are summarized in Table 2-1. 

For each year of plant data, we also compiled the associated nameplate capacity (MW) and annual generation 

(MWh). EIA Form 860 was used to confirm the plant technology, environmental equipment, year in service, and 

other attributes. 

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Table 2-1 — Summary of Valid Data Points 

Technology / (Dataset 

Identifier) Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy 

Internal Data

O&M 

Data 

Points

CAPEX 

Data 

Points

O&M 

Data 

Points

CAPEX 

Data 

Points

Coal (10)

All MW All 3,713 3,098 3,109 655 615

< 500 MW All 1,592 1,274 1,284 318 318

500 MW – 1,000 MW All 986 689 689 337 297

1000 MW – 2,000 MW All 813 813 814 0 0

> 2,000 MW All 322 322 322 0 0

All MW < 50% 965 889 896 76 76

All MW > 50% 2,748 2,209 2,213 579 539

Gas/Oil Steam (20)

All MW All 2,220 2,204 2,226 20 16

< 500 MW All 1,377 1,361 1,366 20 16

500 MW – 1,000 MW All 488 488 489 0 0

> 1,000 MW All 355 355 355 0 0

Gas/Oil Combined Cycle 

(30)

All MW All 1,367 980 981 408 387

< 500 MW All 764 462 463 304 302

500 MW – 1,000 MW All 547 462 463 104 85

> 1,000 MW All 177 177 177 0 0

All MW < 50% 843 661 662 203 182

All MW > 50% 524 319 319 205 205

Gas/Oil Combustion 

Turbine (40)

All MW All 5,041 4,905 4,949 437 136

< 100 MW All 2,873 2,873 2,911 189 0

100 MW – 300 MW All 1,341 1,239 1,248 177 102

> 300 MW All 901 867 875 71 34

Conventional Hydroelectric 

(50)

All MW All 2,179 2,179 2,180 0 0

< 100 MW All 1,272 1,272 1,272 0 0

100 MW – 500 MW All 924 924 925 0 0

> 500 MW All 41 41 41 0 0

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Technology / (Dataset 

Identifier) Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy 

Internal Data

O&M 

Data 

Points

CAPEX 

Data 

Points

O&M 

Data 

Points

CAPEX 

Data 

Points

Pumped Storage 

Hydroelectric (55)

All MW All 226 226 227 0 0

< 100 MW All 12 12 12 0 0

100 MW – 500 MW All 88 88 88 0 0

> 500 MW All 126 126 126 0 0

Solar Thermal (60) 0

Solar Photovoltaic (65) All MW All 57 410 57 0 0

Geothermal (70)

Wind Turbine (80)

All MW All 310 310 310 270 0

< 100 MW All 174 174 174 165 0

100 MW – 200 MW All 91 91 91 56 0

> 200 MW All 51 51 51 73 0

Note: A data point is one reported value for one year by one plant, i.e., a plant that reports values for 25 years will have 25 data points. 

2.4 DATA NORMALIZATION

Sargent & Lundy developed a Microsoft Excel model template for compiling and normalizing all of the CAPEX 

and O&M data, subsequent to the initial review and validation steps outlined in the previous sections. The data 

normalization consisted of the following steps: 

Step 1: Assign data “identifiers” for each plant: 

Technology ID: 

 10 = Coal Steam Plants 

 20 = Gas/Oil Steam Plants 

 30 = Gas/Oil CC Plants 

 40 = Gas/Oil CTs 

 50 = Conventional Hydropower; Pumped Storage – Hydraulic Turbine Reversible 

 60 = Solar Thermal – Central Tower; 

 65 = Solar PV – Single-Axis Tracking 

 70 = Geothermal 

 80 = Wind 

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Data source: 

 1 = FERC Form 1 

 2 = Sargent & Lundy Internal Data 

 3 = Other Public Source 

Step 2: Enter basic information for each plant: 

 Year of commercial operation date (COD) 

 End year of project life or forecast period 

 Nameplate capacity (MW) 

 Summer net capacity (MW) 

Step 3: Adjust pricing basis for raw data: 

 If provided in current dollars, adjust to 2017 dollars 

 If provided in 2017 dollars, do not adjust 

 If provided in constant dollars of another reference year, adjust to 2017 dollars 

Step 4: Enter annual data for each plant (any available data from 1980 to 2060, historical or forecasted by plant 

owner): 

 Plant MW (summer) 

 Annual MWh 

 Annual O&M (from FERC Form 1) 

 Annual O&M (from other sources) 

 Annual CAPEX (from FERC Form 1) 

 Annual CAPEX (from other sources) 

 Annual environmental compliance costs 

Using the inputs from Steps 1-4 above, the “Normalizer” worksheet derives the following for each plant: 

 Annual O&M in 2017 $/kW-year versus age (years from COD) 

 Annual CAPEX in 2017 $/kW-year versus age (years from COD) 

 Annual O&M + CAPEX in 2017 $/kW-year versus age (years from COD) 

The output worksheets (“O&M,” “CAPEX,” and “O&M + CAPEX”) each have the following user-selected 

filters: 

 Technology ID (10, 20, 30, etc.) 

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 Data source (1,2, or 3) 

 MW range (low, high) 

 Outlier maximum $/kW 

 Annual O&M + CAPEX in 2017 $/kW-year versus age (years from COD) 

Each output worksheet (“O&M,” “CAPEX,” and “O&M + CAPEX”) calculates the following for a given 

user-defined set of filters: 

 $/kW-year (2017 dollars) versus age 

 Statistical tests of linear curve fit: annual spending in 2017 $/kW-year = $/kW-year (y-intercept) + 

[constant × age (years from COD)] 

 Average $/kW-year (2017 dollars) for age bands (10-year bands, 30-year bands, and all-years band) 

In all cases, the yearly values are expressed in constant 2017 price levels and increase annually with the inflation 

rate. 

2.5 STATISTICAL TESTS 

2.5.1 Consistency of FERC Form 1 and Sargent & Lundy Internal Data

FERC Form 1 data only covers historical data for utilities that are required to file and does not include the 

owners’ projected expenditures or any data for merchant plants and independent power plants. Most of Sargent 

& Lundy’s proprietary data, on the other hand, covers the owners’ projected expenditures for utility plants and 

includes both historical and projected expenditures for merchant plants and independent power plants. The data 

points from both data sources were judged to be complementary and combined as a single dataset. 

The compatibility of the FERC data and Sargent & Lundy internal data is illustrated by the CAPEX spending for 

a sample of 500-MW coal plants (Figure 2-1). This example is based on a sample of 11 plants from the Sargent 

& Lundy data and 12 plants from the FERC data, each sample having an average plant capacity of 

approximately 500 MW and an average age of approximately 30 years. Each data point in the figure is the 

average value for all the plants that have a valid data point at the given plant age. There are a total of 175 valid 

data points for the FERC plants and 200 valid data points for the Sargent & Lundy plants. In this particular 

sample, all of the FERC data is historical and all of the Sargent & Lundy data is owners’ projected expenditures.

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Figure 2-1 — CAPEX vs. Age for 500-MW Coal Plants – FERC and Sargent & Lundy Data 

As discussed in Section 0, CAPEX spending for coal plants does not follow a uniform pattern for all plants. For 

example, different plants might incur the same type of expense at different points in time due to differences in 

plant-specific economic, locational, or operational circumstances. 

For some utility plants, data was available from both FERC Form 1 and proprietary data. The historical O&M 

and CAPEX spending for these plants were examined in each year to verify their consistency. 

The distribution of valid data points for each technology versus age (years from COD in which the spending 

occurs) was examined to verify consistency with typical plant ages nationwide. Figure 2-2 shows a recent 

distribution of the U.S. power plant fleet by unit age and fuel type as reported by FERC8

. This distribution 

indicates a large portion of coal-fired capacity with ages of 30-50 years, and a large portion of gas-fired capacity 

(mostly CT or CC) with ages under 20 years. The valid data points assembled in this report were found to be 

representative of these major age and technology cohorts. 

 

8

 North American Electric Reliability Corporation, State of Reliability 2017, June 2017 (p.116) 

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Figure 2-2 — U.S. Power Plant Fleet Capacity by Age and Fuel Type 

A recent study found that the average age of the U.S. generator fleet has increased significantly over time, due in 

part to regulatory uncertainty in a deregulated market environment. At the same time, the average expected 

physical life of the fleet has been decreasing as a result of new investments in smaller, shorter-lived capacity. 

This has been a means of mitigating the regulatory risk of more limited stranded cost recovery mechanisms.9

 In 

another recent study, this one on the causes of power plant retirements, the strongest predictors of retirements 

were found to be SO2 emission rates, planning reserve margins, variations in load growth or contraction, the age 

of older thermal plants, the ratio of coal to gas prices, and delivered natural gas prices. The impacts of annual 

CAPEX and O&M spending on retirement decisions were not specifically identified.10

2.5.2 Significance of Plant Age on Annual Capital and O&M Expenditures 

For each technology group, Sargent & Lundy performed a regression analysis on the O&M spending, CAPEX 

spending, and combined O&M plus CAPEX spending using the following linear equation: 

 Annual spending in 2017 $/kW-year = $/kW-year (y-intercept) + (constant × age) 

 

9 Rode, D., Fischbeck, P., and Paez, A., “Power Plant Lives and their Policy Implications,” Energy Policy, 106 (2017) 222-

232, April 1, 2017. 

10 Mills, A., Wiser, R., and Seel, J, “Power Plant Retirements: Trends and Possible Drivers,” Energy Analysis and 

Environmental Impacts Division, Lawrence Berkeley National Laboratory, November 2017. 

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The purpose of the regression analysis was to determine whether plant age is a statistically significant predictor 

of annual spending. The regression coefficient for age measures the change (+ or -) in annual spending as a 

function of plant age, measured as the number of years from the COD. Its statistical significance is measured by 

the p-value, which tests the null hypothesis that the coefficient is equal to zero (i.e., has no effect on spending). 

The R-squared (R2) statistic (“coefficient of determination”) is an indication of the goodness of fit of the 

regression equation to the real data points. A low R2 indicates that the regression equation explains a relatively 

small amount of the variability of the data around its mean. A low p-value (< 0.05) indicates that the age 

coefficient is statistically significant, regardless of the R2 statistic. A low p-value corresponds approximately to 

a t-value that is greater than 2 or less than -2. For higher p-values, the simple average $/kW-year per year may 

be a more appropriate estimation for a given age band (e.g., 20-year bands and all-years band). Depending on 

the characteristics of the dataset, especially the number of data points, Sargent & Lundy applied engineering 

judgement (as further described in each section that follows) in our recommendations. 

2.5.3 Autocorrelation of Time Series Data 

In addition to the correlation between annual spending and plant age, an autocorrelation may also exist between 

spending in a given year and spending in previous years. Autocorrelation commonly occurs with time series 

data. If statistical tests verify the presence of autocorrelation, a lagged (autoregressive) variable may be added to 

improve the goodness of fit (R2) of the regression model. Models with this functional form are referred to as 

“autoregressive integrated moving average” (ARIMA) models. 

ARIMA models are typically constructed for the purpose of predicting the future from a given point in time, 

based on correlations with historical values and other exogenous variables. The functional form of an ARIMA 

model may better capture curvilinear or cyclical data trends and therefore improve the goodness of fit. For the 

purposes of this study, an ARIMA model was not necessary or appropriate. The datasets in this analysis already 

capture plant O&M and CAPEX spending patterns throughout a typical plant lifespan. The purpose of this study 

was to represent costs for generators as they age, and not to predict future spending from a given point in time. 

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3. COAL STEAM

3.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for coal steam plants were compiled using the assessment methodology 

described in Section 2. The valid data points derived from this process were distributed as follows: 

 O&M expenditures: 

 456 plants in FERC data and 32 plants from Sargent & Lundy internal data 

 3,098 valid data points in FERC data, 655 valid data points in Sargent & Lundy internal 

data 

 CAPEX: 

 457 plants in FERC data and 29 plants from Sargent & Lundy internal data 

 3,109 valid data points in FERC data, 615 valid data points in Sargent & Lundy internal 

data 

The coal steam data was broken down by plant MW capacity and average capacity factor—as summarized in 

Table 3-1—for the regression analysis shown in Appendix A. 

Table 3-1 — Coal Steam Cost Data Distribution 

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 3,713 3,098 3,109 655 615

< 500 MW All 1,592 1,274 1,284 318 318

500 MW – 1,000 MW All 986 689 689 337 297

1,000 MW – 2,000 MW All 813 813 814 0 0

> 2,000 MW All 322 322 322 0 0

All MW < 50% 965 889 896 76 76

All MW > 50% 2,748 2,209 2,213 579 539

Table 3-2 below identifies the relative effects in the data validation process of the top three data filters on the 

number of valid data points. These filters are described as follows: 

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 Change in Capacity: A change in nameplate capacity of 20% or more during the reported time of 

the unit. Data points prior to the change in capacity are no longer comparable to the data points 

after the change in capacity, so the entire unit was filtered out. 

 Negative Change in Total Cost: Any year with a decrease in the cumulative historical capital cost 

reported in the FERC data was not included. 

 Environmental Retrofit: Data points in years where SO2, NOX, PM, or Hg removal equipment was 

installed were filtered out. 

Table 3-2 — Effect of Data Validation Filters on Coal Data Points 

Coal Steam – FERC Dataset Data Points

Total Data Points, Unfiltered 6,699

Total Data Points, Filtered Out 3,774

Top Three Filters

Change in Capacity 1,659

Negative Change in Total Cost 889

Environmental Retrofit 599

Total Data Points, Valid (FERC Only) 2,925

3.2 SUMMARY OF RESULTS 

3.2.1 Recommended CAPEX Values 

The analysis of the coal steam dataset (Appendix A) identified two significant variables affecting annual 

changes in real CAPEX spending (on a constant $/kW-year basis): age and flue gas desulfurization (FGD). 

Variables not having a significant effect on annual changes in real CAPEX spending (on a constant $/kW-year 

basis) were: plant capacity (kW), fuel type, and regulatory environment. When CAPEX spending was expressed 

on a constant $/MWh basis, it was significantly related to age, primarily as a result of declining MWh

generation with age. 

Table 3-3 below compares the new CAPEX values derived from the coal steam dataset with the CAPEX values 

currently used in the EMM. The new CAPEX values are similar in magnitude with the current EMM values 

over the long term, except the new values follow a continuous pattern rather than a step pattern. As discussed 

below, the new values include life extension projects that occur throughout the plant life, including the first 30 

years of operation. 

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Table 3-3 — Coal Steam CAPEX Results – All MW, All Capacity Factors 

Net Total CAPEX

(2017 $/kW-year)

$/kW-yr 

(Years 

1-10)

$/kW-yr 

(Years 

10-20)

$/kW-yr 

(Years

20-30)

$/kW-yr 

(Years 

30-40)

$/kW-yr 

(Years 

40-50)

$/kW-yr 

(Years

50-60)

$/kW-yr 

(Years

60-70)

$/kW-yr 

(Years 

70-80)

New Value – No FGD* 17.16 18.42 19.68 20.94 22.20 23.46 24.72 25.98

New Value – with FGD* 22.84 24.10 25.36 26.62 27.88 29.14 30.40 31.66

Existing EMM Value 17.55 17.55 17.55 24.62 24.62 24.62 24.62 24.62

*Calculated from the following regression equation to the midpoint of the given age band: 

Annual CAPEX spending in 2017 $/kW-year = 16.53 + (0.126 × age) + (5.68 × FGD) 

Where FGD = 1 if a plant has FGD; zero otherwise 

“Life extension costs” in the existing CAPEX values are covered by the step increase after year 30. Life 

extension costs in the new CAPEX values are distributed throughout the plant life. This is a result of 

discretionary spending, which is a common practice for most coal steam plants. Different plants might incur the 

same type of expense at different points in time due to differences in plant-specific economic, locational, or 

operational circumstances. 

Typical industry-standard frequencies for repairs and replacement of major equipment within a coal plant are 

not absolute, but rather indicative of when a coal plant may be required to perform the work, based on 

manufacturer experience. An owner may choose to perform the work early, if they have an available outage, or 

defer if, after inspection, the equipment appears to be capable of continued operation without repair. 

The new values also account for CAPEX relating to FGD. An FGD system tends to be capital-intensive to own 

and operate. The corrosive environment of chemicals and reagents significantly reduces the life of equipment 

such as pumps, mills, nozzles, valves, etc. These components must be replaced more frequently compared with 

plants without FGD. 

Table 3-4 below provides indicative typical industry-standard frequencies for repairs and replacement of major 

equipment within a coal plant. 

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Table 3-4 — Coal Plant Indicative Typical CAPEX Projects and Intervals 

Project Description

Typical Frequency of 

Repairs/Replacement 

from COD

(Years)

Boiler

Coal mills and exhausters, burner tips and ignitors 5

Lower nose tube, burner panels, economizer banks, air heater tubes, and baskets 15

Lower and upper waterwalls, superheater and reheater horizontal sections and pendants, 

economizer header, coal feeders, mill motors 20

Superheater and reheater header, feedwater supply piping 25

Mud and steam drums 30

Turbine and Generator

Control valves, nozzle block 12

Electro-hydraulic control system (EHC), governor, turbine controls, generator rotor, turbine 

lubrication pumps 15

Stop valves, low-pressure (LP) turbine and blades, LP casing/diaphragms, 20

Steam chest, high-pressure/intermediate-pressure (HP/IP) turbine with blades, HP/IP 

casing/diaphragm, generator stator, exciter 25

HP/IP rotor, LP rotor, isophase 30

Balance of Plant

Condensate pumps, cooling tower fill, cooling tower fan drives and blades, conveyor belts, conveyer 

idlers/pulleys/motors, coal crushing equipment 10

Slag conveyors and tanks 12

Induced draft (ID) fans, electrostatic precipitator (ESP) casing, ESP plates/wires, deaerator, 

circulating water pumps, boiler feed pumps, distributed control system (DCS)/unit controls, boiler 

master/combustion controls, coal handling dust control system

15

Forced draft (FD) fans, primary air (PA) fans, fan motors, windbox and ductwork, ESP 

transformer/rectifier (TR) sets and rappers, condenser valves and cleaner system, LP feedwater 

heaters, HP feedwater heaters, gland coolers, conveyor structures, coal unloading equipment, fuel 

oil heaters, and delivery pumps

20

Condenser retube, deaerator storage tank, vacuum pumps/steam air ejectors, pump motors 25

Main power transformer, auxiliary transformer 30

3.2.2 Recommended O&M Values 

The analysis required an understanding of the cost breakdowns in the reported data between 1) capitalized 

(CAPEX) and expensed (O&M) cost components and 2) fixed O&M and variable O&M cost components. From 

a system modeling perspective, CAPEX and fixed O&M costs are typically expressed in $/kW-year, while 

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variable O&M is typically expressed in $/MWh. Normalized cost breakdowns in these units are necessary for 

compatibility with the EMM. 

O&M costs for the coal steam plants include a significant variable component. By definition, variable O&M 

costs are proportional to plant generation and are typically expressed in $/MWh. As previously mentioned, the 

variable O&M component cannot be clearly delineated from the total reported O&M in the FERC Form 1 data. 

For this assessment, the variable component was combined with the fixed component and expressed in 

$/kW-year. The combined total O&M in the coal steam plant dataset for this analysis was found to be nearly 

equivalent to the existing combined total O&M representation in the EMM, which already includes the 

necessary $/MWh variable O&M breakout (Table 3-5).

Table 3-5 — Coal Steam O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)*

Variable O&M 

(2017 $/kW-yr)**

Total O&M

(2017 $/kW-yr)**

Coal Steam Dataset Results – All Plants 36.81 1.78 9.20 46.01

< 500 MW 44.21 1.78 9.20 53.41

500 MW – 1,000 MW 34.02 1.78 9.20 43.22

1,000 MW – 2,000 MW 28.52 1.78 9.20 37.72

> 2,000 MW 33.27 1.78 9.20 42.47

Existing EMM Value*** 40.63 1.78 9.20 49.83

*Fixed and variable split is estimated using the existing EMM variable O&M cost of $1.78/MWh. 

**Calculated at the coal steam dataset average capacity factor of 59%. 

***Source: Internal communication with EIA, February 2018. 

CAPEX and O&M spending have a relatively minor effect on future non-fuel O&M spending, on average, 

compared with plant performance-related economic benefits not captured in this analysis, such as: 

 Reduced fuel expenditures due to improved heat rates 

 Reduced capacity degradation and higher capacity sales 

 Reduced outage costs due to reduced replacement power expenses or higher power sales 

 Increased power sales due to increased net capacity or reduced forced outages 

3.2.3 Effect of Plant Capacity Factor 

CAPEX and O&M spending for the coal steam plants increased significantly with age when expressed on a 

$/MWh basis. This was primarily a result of significant declines in plant capacity factors over time, as shown in 

Figure 3-1. 

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Figure 3-1 — Capacity Factor vs. Age for All Coal Plants 

3.2.4 Effect of External Market Conditions 

The declining capacity factors with age may have been a result of external market conditions and/or declining 

plant performance. These are areas for further exploration. 

External market conditions over the same time period that may have contributed to lower capacity factors for 

coal steam plants include: 

 Competition with lower gas prices and more efficient gas turbines 

 Competition with renewable energy having lower dispatch costs 

 Lower load growth due to increased amounts of energy efficiency and distributed resources 

For some coal steam plants, the decline in capacity factor was also a result of less efficient heat rates, increased 

component failures, and increased outage rates over time. A major contributor to this decline in performance is 

often a result of increased cycling operation. Increased cycling leads to higher O&M and CAPEX spending over 

time.11

External market conditions may have also reduced the number of data points with higher age-related spending, 

due to plant retirements. The least efficient coal steam plants would likely retire under the following 

circumstances: 

 

11 Kumar, N., Besuner, P., Lefton, S., and Agan, D., Power Plant Cycling Costs, National Renewable Energy Laboratory, 

April 2012. 

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 Lower efficiency may contribute to less frequent dispatch and more cycling, leading to more 

component failures and higher spending 

 Less frequent dispatch reduces hours of operation and power sales 

 Lower power sales income may not adequately cover plant fixed costs 

Some of the older coal steam plants (23 in this data sample) maintained consistently high capacity factors 

throughout their lives, with no real increase in spending. These high capacity factor plants had an installed 

capacity ranging from 70 MW to 2,400 MW, with an average of 850 MW and an average COD of 1961. These 

plants are slightly larger and older, on average, than the entire dataset of coal steam plants, which have an 

average installed capacity of 720 MW and an average COD of 1964. Table 3-6 shows the average capacity 

factors and O&M and CAPEX spending for the entire dataset of coal steam plants compared with the older 

consistently high capacity factor plants. 

Table 3-6 — High Capacity Factor Coal Plants – Spending Comparison 

Average – All Years Years 1-20 Years 20-40 Years 40-80

Capacity Factor – All Plants 59.1% 66.8% 64.5% 52.9%

Capacity Factor – High CF Plants 74.0% - 72.8% 74.4%

O&M – All Plants (2017 $/kW-yr) 46.01 53.90 40.06 48.77

CAPEX – All Plants (2017 $/kW-yr) 22.78 17.92 26.20 21.25

Total – All Plants (2017 $/kW-yr) 68.67 71.86 66.25 69.82

O&M – High CF Plants (2017 $/kW-yr) 36.65 - 31.07 38.78

CAPEX – High CF Plants (2017 $/kW-yr) 20.26 - 23.13 19.16

Total – High CF Plants (2017 $/kW-yr) 57.02 - 54.20 58.10

Market conditions at the older, high capacity factor plants may have led to fewer competing resources, which 

would support higher levels of dispatch and higher capacity factors. In addition, lower cycling requirements at 

those plants would have reduced spending requirements. 

3.2.5 Effect of Regulatory Environment 

Owners of coal steam plants in deregulated states were found to have no aversion to capital spending compared 

to plant owners in regulated states (see Appendix A). Some of the difference may be due to higher labor costs in 

many of the deregulated states. This is the opposite of what would be expected, whereby plant owners in a 

deregulated environment would have a greater incentive to reduce O&M costs that cannot be passed through to 

ratepayers. The higher O&M spending is likely a result of other factors, such as higher average labor costs in 

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deregulated states, which tend to have a higher percentage of union labor compared with regulated states. 

Therefore, the net effect of regulatory status on average O&M spending was not apparent at this level of detail. 

3.2.6 Effect of Fuel Characteristics 

Sargent & Lundy’s regression analysis compared CAPEX spending for coal steam plants with bituminous and 

subbituminous coal types (Appendix A). The results indicate that average CAPEX spending is not likely 

affected by coal type at a high-level designation (i.e., bituminous/subbituminous) without more detailed coal 

specifications. 

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4. GAS/OIL STEAM

4.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for gas/oil steam plants were compiled using the assessment 

methodology described in Section 2. The valid data points derived from this process were distributed as follows: 

 O&M Expenditures 

 283 plants in FERC data and four plants from Sargent & Lundy internal data 

 2,204 valid data points in FERC data, 20 valid data points in Sargent & Lundy internal data 

 CAPEX 

 283 plants in FERC data and four plants from Sargent & Lundy internal data 

 2,226 valid data points in FERC data, 16 valid data points in Sargent & Lundy internal data 

The gas/oil steam data was broken down by plant MW capacity, as summarized below in Table 4-1, for the 

regression analysis shown in Appendix B. 

Table 4-1 — Gas/Oil Steam Cost Data Distribution 

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 2,220 2,204 2,226 20 16

< 500 MW All 1,377 1,361 1,366 20 16

500 MW – 1,000 MW All 488 488 489 0 0

> 1,000 MW All 355 355 355 0 0

4.2 SUMMARY OF RESULTS 

4.2.1 Recommended CAPEX Values 

Sargent & Lundy’s analysis of the gas/oil steam dataset (Appendix B) identified only one significant variable 

affecting annual changes in real CAPEX spending (on a constant $/kW-year basis): plant capacity (kW). That is, 

CAPEX was lower on a $/kW-year basis for larger plant sizes due to economies of scale. When CAPEX 

spending was expressed on a constant $/MWh basis, it was significantly related to age, primarily as a result of 

declining MWh generation with age. 

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Table 4-2 compares the new CAPEX values derived from the gas/oil steam dataset with the CAPEX values 

currently used in the EMM. The new CAPEX values are similar in magnitude with the current EMM values 

over the long term, except that the new values follow a continuous pattern rather than a step pattern. As 

discussed below, the new values include life extension projects that occur throughout the plant life, including the 

first 30 years of operation. 

Table 4-2 — Gas/Oil Steam CAPEX Results 

Plant Size

Net Total CAPEX

(2017 $/kW-year)

Years 1-30 Years 30-80

Gas/Oil Steam Dataset Results – All Plants 15.96 15.96

New Value: < 500 MW 18.86 18.86

New Value: 500 MW – 1,000 MW 11.57 11.57

New Value: > 1,000 MW 10.82 10.82

Existing EMM Value 9.14 16.21

“Life extension costs” in the existing CAPEX values are covered by the step increase after year 30. Life 

extension costs in the new CAPEX values are distributed throughout the plant life. This is a result of 

discretionary spending, which is a common practice for most gas/oil steam plants. Different plants might incur 

the same type of expense at different points in time due to differences in plant-specific economic, locational, or 

operational circumstances. 

Typical industry-standard frequencies for repairs and replacement of major equipment within a gas/oil steam 

plant are not absolute, but rather indicative of when a gas/oil steam plant may be required to perform the work, 

based on manufacturer experience. An owner may choose to perform the work early, if they have an available 

outage, or defer if, after inspection, the equipment appears to be capable of continued operation without repair. 

Typical industry-standard frequencies for repairs and replacement of major equipment are similar to those of 

coal units, as presented in the previous section. 

The use of a constant annual value on the modeling of annual CAPEX would be similar to representing a major 

maintenance reserve account (MMRA), which is commonly used for non-recourse financing of power projects. 

MMRAs are usually required by power project lenders over the tenor of debt as protection against maintenance 

spending uncertainty. An MMRA is typically funded by annual contributions drawn from a project’s cash flow, 

sometimes as a uniform annual amount. Annual contribution levels are based on estimated long-term 

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maintenance expenditure patterns. Over the long term, annual contributions represent a smoothed version of 

irregular actual annual values. 

The use of a long-term average value also recognizes the inherent variability in long-term spending patterns for 

any given plant. Since the EMM is a large-scale model, it is conceptually designed to represent plant types as 

averages rather than as individual plants. When summed across a large number of plants in a utility system, 

some of the variability in annual expenditure patterns would tend to even out. The level of accuracy between 

average values and year-specific values for a given plant type is nearly equivalent in large-scale models. 

4.2.2 Recommended O&M Values 

The analysis required an understanding of the cost breakdowns in the reported data between 1) capitalized 

(CAPEX) and expensed (O&M) cost components and 2) fixed O&M and variable O&M cost components. From 

a system modeling perspective, CAPEX and fixed O&M costs are typically expressed in $/kW-year, while 

variable O&M is typically expressed in $/MWh. Normalized cost breakdowns in these units are necessary for 

compatibility with the EMM. 

O&M costs for the gas/oil steam plants include a significant variable component, although typically smaller than 

coal units. The combined total O&M in the gas/oil steam plant dataset for this analysis was found to be 

somewhat lower than the existing combined total O&M representation in the EMM, which already includes the 

necessary $/MWh variable O&M breakout (see Table 4-3). However, the variable O&M of $8.23/MWh in the 

EMM is much higher than values Sargent & Lundy has observed in actual gas/oil steam plants and should not be 

higher than the variable O&M of $1.78/MWh in the EMM used for the coal units. 

Table 4-3 — Gas/Oil Steam O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)*

Variable O&M 

(2017 $/kW-yr)**

Total O&M

(2017 $/kW-yr)**

Gas/Oil Steam Dataset Results – All Plants 24.68 1.00 1.84 26.52

< 500 MW 29.73 1.00 1.84 31.57

500 MW – 1,000 MW 17.98 1.00 1.84 19.82

> 1,000 MW 14.51 1.00 1.84 16.35

Existing EMM Value*** 19.68 8.23 15.14 34.82

*Fixed and variable split is estimated using an approximate value for variable O&M of $1.00/MWh based on confidential projects. 

**Calculated at the gas/oil steam dataset average capacity factor of 21%. 

***Source: Internal communication with EIA, February 2018. 

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CAPEX and O&M spending have a relatively minor effect on future non-fuel O&M spending, on average, 

compared with plant performance-related economic benefits not captured in this analysis, such as: 

 Reduced fuel expenditures due to improved heat rates 

 Reduced capacity degradation and higher capacity sales 

 Reduced outage costs due to reduced replacement power expenses or higher power sales 

 Increased power sales due to increased net capacity or reduced forced outages 

4.2.3 Effect of Plant Capacity Factor 

CAPEX and O&M spending for the gas/oil steam plants increased significantly with age when expressed on a 

$/MWh basis. This was primarily a result of significant declines in plant capacity factors over time, as shown in 

Figure 4-1. 

Figure 4-1 — Capacity Factor vs. Age for All Gas/Oil Steam Plants 

4.2.4 Effect of External Market Conditions 

The declining capacity factors with age may have been a result of external market conditions and/or declining 

plant performance. These are areas for further exploration. 

External market conditions over the same time period that may have contributed to lower capacity factors for 

gas/oil steam plants include: 

 Competition with more efficient gas turbines 

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 Competition with renewable energy having lower dispatch costs 

 Lower load growth due to increased amounts of energy efficiency and distributed resources 

For some gas/oil steam plants, the decline in capacity factor was also a result of less efficient heat rates, 

increased component failures, and increased outage rates over time. A major contributor to this decline in 

performance is often a result of increased cycling operation. Increased cycling leads to higher O&M and 

CAPEX spending over time. 

External market conditions may have also reduced the number of data points with higher age-related spending, 

due to plant retirements. The least efficient gas/oil steam plants would likely retire under the following 

circumstances: 

 Lower efficiency may contribute to less frequent dispatch and more cycling, leading to more 

component failures and higher spending 

 Less frequent dispatch reduces hours of operation and power sales 

 Lower power sales income may not adequately cover plant fixed costs 

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5. GAS/OIL COMBINED CYCLE

5.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for gas/oil CC plants were compiled using the assessment methodology 

described in Section 2. The valid data points derived from this process were distributed as follows: 

 O&M Expenditures 

 144 plants in FERC data and 20 plants from Sargent & Lundy internal data 

 980 valid data points in FERC data, 408 valid data points in Sargent & Lundy internal data 

 CAPEX 

 142 plants in FERC data and 17 Sargent & Lundy proprietary plants with valid data 

 981 valid data points in FERC data, 387 valid data points in Sargent & Lundy internal data 

The gas/oil CC data was broken down by plant MW capacity and average capacity factor, as summarized below 

in Table 5-1, for the regression analysis shown in Appendix C. 

Table 5-1 — Gas/Oil CC Cost Data Distribution 

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 1,367 980 981 408 387

< 500 MW All 764 462 463 304 302

500 MW – 1,000 MW All 547 462 463 104 85

> 1,000 MW All 177 177 177 0 0

All MW < 50% 843 661 662 203 182

All MW > 50% 524 319 319 205 205

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5.2 SUMMARY OF RESULTS 

As with coal steam and gas/oil steam plants, CAPEX spending for gas/oil CC plants represents a series of 

capital projects throughout the plant life, which includes projects for “life extension.” Most CAPEX spending 

for gas/oil CC plants is for vendor-specified major maintenance events. Other CAPEX spending, other than for 

emission control retrofits, is relatively minor. 

Vendor-specified major maintenance spending is based on cumulative hours of operation and/or cumulative 

starts. Implicitly, CAPEX spending for CC plants is age-related and vendor-specified, and may be expressed as 

an equivalent $/MWh value, which covers: 

 Major maintenance costs for periodic combustion inspections, hot gas path inspections, and major 

overhauls account for nearly all of the CAPEX expenditures. Many plant owners choose to 

capitalize major maintenance expenditures. As these expenditures normally follow the equipment 

vendor’s recommendations, they maintain plant performance and extend the plant life.

 Major one-time costs include rotor replacement, typically at about 150,000 equivalent operating 

hours, 7,000 equivalent starts, or within the first 30 years of plant operation. These costs are 

captured within the dataset. As gas turbines age, major maintenance parts often become available 

from third-party suppliers at a discounted price. 

As with MMRAs (described in Section 4.2.1), major maintenance contracts are priced according to smoothed 

versions of irregular long-term expenditure patterns. Apart from adjustments for operating conditions, major 

maintenance (and nearly all of the CAPEX) is effectively priced as an equal annual value, expressed in constant 

$/MWh with annual escalation. 

Table 5-2 compares the new CAPEX and O&M values derived from the gas/oil CC dataset with the values 

currently used in the EMM. As previously mentioned, the combined CAPEX and O&M in the dataset would be 

expected to correspond to the combined CAPEX and O&M in the EMM, with most of the CAPEX in the EMM 

represented as variable O&M. However, some of the EMM values are higher than values Sargent & Lundy has 

observed in actual CC plants, as detailed below: 

 The EMM fixed and variable O&M costs for CC plants are reasonable for smaller CC installations 

(< 500 MW) but high for larger plants. 

 The EMM CAPEX addition of $7/kW-year after 30 years of operation should not be represented as 

a fixed cost. As previously mentioned, age-related costs would be built into the $/MWh variable 

O&M and would be a function of cumulative operating hours rather than operating years. 

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Table 5-2 — Gas/Oil CC CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

Variable O&M 

(2017 $/kW-yr)*

Total O&M

(2017 $/kW-yr)*

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)*

CC Dataset 

Results – All 

Plants

13.08 3.91 (included in 

CAPEX) 13.08 15.76 28.84

< 500 MW 15.62 4.31 (included in 

CAPEX)

15.62 17.38 33.00

500 MW –

1,000 MW 9.27 3.42 (included in CAPEX) 9.27 13.78 23.05

> 1,000 MW 11.68 3.37 (included in 

CAPEX)

11.68 13.57 25.25

Existing EMM 

Value** 27.52 2.64 10.64 38.16 0.18; 7.25(after year 30)

38.34; 45.41 

(after year 30)

*Calculated at the gas/oil CC dataset average capacity factor of 46%. Fixed and variable O&M split is estimated. 

**Source: Internal communication with EIA, February 2018. 

CAPEX and O&M spending have a relatively minor effect on future non-fuel O&M spending, on average, 

compared with plant performance-related economic benefits not captured in this analysis, such as: 

 Reduced fuel expenditures due to improved heat rates 

 Reduced capacity degradation and higher capacity sales 

 Reduced outage costs due to reduced replacement power expenses or higher power sales 

 Increased power sales due to increased net capacity or reduced forced outages 

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6. GAS/OIL COMBUSTION TURBINE

6.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for gas/oil CT plants were compiled using the assessment methodology 

described in Section 2. The valid data points derived from this process were distributed as follows: 

 O&M Expenditures 

 625 plants from FERC data and 27 plants from Sargent & Lundy internal data 

 4,905 valid data points in FERC data, 437 valid data points in Sargent & Lundy internal 

data 

 CAPEX 

 579 plants from FERC data and five plants from Sargent & Lundy internal data 

 4,949 valid data points in FERC data, 136 valid data points in Sargent & Lundy internal 

data 

The CT data was broken down by plant MW capacity, as summarized below in Table 6-1, for the regression 

analysis shown in Appendix D. 

Table 6-1 — Gas/Oil Combustion Turbine Cost Data Distribution 

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 5,041 4,905 4,949 437 136

< 100 MW All 2,873 2,873 2,911 189 0

100 MW – 300 MW All 1,341 1,239 1,248 177 102

> 300 MW All 901 867 875 71 34

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6.2 SUMMARY OF RESULTS 

As with coal steam and gas/oil steam plants, CAPEX spending for gas/oil CT plants represents a series of capital 

projects throughout the plant life, which includes projects for “life extension.” Most CAPEX spending for 

gas/oil CT plants is for vendor-specified major maintenance events. Other CAPEX spending, other than for 

emission control retrofits, is relatively minor. 

Vendor-specified major maintenance spending is based on cumulative hours of operation and/or cumulative 

starts. Implicitly, CAPEX spending for CTs is age-related and vendor-specified, and may be expressed as an 

equivalent $/MWh value, which covers: 

 Major maintenance costs for periodic combustion inspections, hot gas path inspections, and major 

overhauls account for nearly all of the CAPEX expenditures. Many plant owners choose to 

capitalize major maintenance expenditures. As these expenditures normally follow the equipment 

vendor’s recommendations, they maintain plant performance and extend the plant life.

 Major one-time costs include rotor replacement, typically at about 150,000 equivalent operating 

hours, 7,000 equivalent starts, or within the first 30 years of plant operation. These costs are 

captured within the dataset. As gas turbines age, major maintenance parts often become available 

from third-party suppliers at a discounted price. 

As with MMRAs (described in Section 4.2.1), major maintenance contracts are priced according to smoothed 

versions of irregular long-term expenditure patterns. Apart from adjustments for operating conditions, major 

maintenance (and nearly all of the CAPEX) is effectively priced as an equal annual value, expressed in constant 

$/MWh with annual escalation. 

Table 6-2 compares the new CAPEX and O&M values derived from the gas/oil CT datasets with the values 

currently used in the EMM. As previously mentioned, the combined CAPEX and O&M in the datasets would be 

expected to correspond to the combined CAPEX and O&M in the EMM, with most of the CAPEX in the EMM 

represented as variable O&M. However, EMM fixed and variable O&M costs across all plant sizes are higher 

than values Sargent & Lundy has observed in actual CT plants. Since most CT plants operate as peaking plants 

with low capacity factors, the variable O&M component is likely to be based on equivalent starts rather than 

equivalent operating hours. 

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Table 6-2 — Gas/Oil Combustion Turbine CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

Variable O&M 

(2017 $/kW-yr)*

Total O&M

(2017 $/kW-yr)*

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)*

CT Dataset Results 

– All Plants 5.33 (starts based) (included in CAPEX) 5.33 6.90 12.23

< 100 MW 5.96 (starts based) (included in 

CAPEX) 5.96 9.00 14.96

100 MW – 300 MW 6.43 (starts based) (included in 

CAPEX) 6.43 6.18 12.61

> 300 MW 3.99 (starts based) (included in 

CAPEX)

3.99 6.95 10.94

Existing EMM 

Value** 12.60 14.63 5.13 17.73 1.52 19.25

*Calculated at the gas/oil CC dataset average capacity factor of 4%. 

**Source: Internal communication with EIA, February 2018. 

CAPEX and O&M spending have a relatively minor effect on future non-fuel O&M spending, on average, 

compared with plant performance-related economic benefits not captured in this analysis, such as: 

 Reduced fuel expenditures due to improved heat rates 

 Reduced capacity degradation and higher capacity sales 

 Reduced outage costs due to reduced replacement power expenses or higher power sales 

 Increased power sales due to increased net capacity or reduced forced outages 

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Conventional Hydroelectric

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

7. CONVENTIONAL HYDROELECTRIC

7.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for conventional hydroelectric plants were compiled using the 

assessment methodology described in Section 2. The valid data points derived from this process were distributed 

as follows: 

 O&M Expenditures 

 348 plants in FERC data 

 2,179 valid data points in FERC data 

 CAPEX 

 348 plants in FERC data 

 2,180 valid data points in FERC data 

The conventional hydroelectric data was broken down by plant MW capacity, as summarized below in Table 

7-1, for the regression analysis shown in Appendix E. 

Table 7-1 — Conventional Hydroelectric Cost Data Distribution 

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 2,179 2,179 2,180 0 0

< 100 MW All 1,272 1,272 1,272 0 0

100 MW – 500 MW All 924 924 925 0 0

> 500 MW All 41 41 41 0 0

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7.2 SUMMARY OF RESULTS 

Sargent & Lundy’s linear regression analysis of the dataset for conventional hydroelectric plants (Appendix E) 

supports age as a statistically significant predictor of CAPEX spending (on a linear trend across all plant ages). 

CAPEX spending for this dataset may be estimated by the regression equation: 

Annual CAPEX spending in 2017 $/kW-year = 7.269 + (0.296 × age) 

The dataset also supports age as a statistically significant predictor of O&M spending (on a linear trend across 

all plant ages). Therefore, O&M spending for this dataset may be estimated by the regression equation: 

Annual O&M spending in 2017 $/kW-year = 22.360 + (0.073 × age) 

The CAPEX and O&M values derived from the conventional hydroelectric dataset are significantly higher than 

the existing values used in the EMM (Table 7-2) and outside the range of values published in the AEO12 and by 

the International Renewable Energy Agency (IRENA).13 The reasons for this discrepancy are not known without 

having the data sample used for the EMM values. It appears that the EMM does not currently account for 

CAPEX or life extension expenditures for conventional hydroelectric. 

Table 7-2 — Hydroelectric CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)

Conventional Hydroelectric Dataset Results – All Plants 22.00 - 22.56 44.56

Existing EMM Value* 14.58 0.00 0.00 14.58

*Source: Internal communication with EIA, February 2018. 

 

12 Energy Information Administration, Annual Energy Outlook 2018, Cost and Performance Characteristics (Table 8.2), 

February 2018. 

13 International Renewable Energy Agency, Renewable Energy Technologies: Cost Analysis Series, Hydropower, June 

2012. 

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Pumped Hydroelectric Storage

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

8. PUMPED HYDROELECTRIC STORAGE

8.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for pumped storage plants were compiled using the assessment 

methodology described in Section 2. The valid data points derived from this process were distributed as follows: 

 O&M Expenditures 

 37 plants in FERC data 

 226 valid data points in FERC data 

 CAPEX 

 37 plants in FERC data 

 227 valid data points in FERC data 

The pumped storage data was broken down by plant MW capacity, as summarized below in Table 8-1, for the 

regression analysis shown in Appendix F. 

Table 8-1 — Pumped Storage Cost Data Distribution 

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 226 226 227 0 0

< 100 MW All 12 12 12 0 0

100 MW – 500 MW All 88 88 88 0 0

> 500 MW All 126 126 126 0 0

8.2 SUMMARY OF RESULTS 

Overall, the pumped storage dataset does not support any age-related CAPEX or O&M spending trend across 

the full data and on any of the subsets by plant size. The average value over all operating years is 

$14.83/kW-year for CAPEX and $23.63/kW-year for O&M (Table 8-2). The existing values used in the EMM 

are not available. 

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Table 8-2 — Pumped Storage CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)

Pumped Storage Dataset Results – All Plants 23.63 - 14.83 38.46

Existing EMM Value N/A N/A N/A N/A

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Solar Photovoltaic

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

9. SOLAR PHOTOVOLTAIC

9.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for solar PV storage plants were compiled using the assessment 

methodology described in Section 2. The FERC data includes 105 solar PV installations ranging in capacity 

from 10 kW to 36 MW. 

The solar PV data, summarized below in Table 9-1, was used for the regression analysis shown in Appendix G. 

Table 9-1 — Solar Photovoltaic Cost Data Distribution

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 57 410 57 0 0

9.2 SUMMARY OF RESULTS 

The solar PV dataset does not support any age-related CAPEX spending trend across the full data and on any of 

the subsets by plant size (see Appendix G). Sargent & Lundy determined that a significant portion of the data 

needed to be filtered out, resulting in a limited dataset of 15 sites. The average annual CAPEX (i.e., change in 

TCP) for these sites was approximately $26/kW-year. However, due to the limitations of the solar PV dataset, 

described in Appendix G, Sargent & Lundy advises that caution be taken when trying to establish any definitive 

solar PV capital cost trends from the FERC data. 

The solar PV dataset appears to support age as a statistically significant predictor of O&M spending (on a linear 

trend across all plant ages). However, based on a closer inspection of the data, a more appropriate predictor of 

O&M spending for this dataset would be a simple average across all years. This determination is based on the 

lack of data points for plants over 10 years old and the fact that nearly all data points for plants over 10 years old 

are reported as having zero O&M expenses. Additionally, many of these plants also reported zero O&M 

expenses for all years of operation. 

Solar PV O&M activities include a variety of work scopes, including administrative work, monitoring, cleaning, 

preventative maintenance, and corrective maintenance. Some specific examples of O&M activities may include 

cleaning modules, monitoring system voltage and current, inspecting and cleaning electrical equipment, 

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inspecting modules for damage, inspecting mounting systems, and checking invertor settings. The cost of O&M 

is dependent on several factors, including the number of components, the type of system (e.g., roof, tracking, 

ground mount, fixed, etc.), warranty coverage, and location. Environmental conditions, such as hail, sand/dust, 

snow, salt in air, high winds, etc., also play a significant role in O&M costs. For these reasons, a higher level of 

variation is expected when compared to traditional generating technologies. 

An average O&M cost of $75/kW-year was calculated from the FERC data for sites under 5 MW, and 

$15/kW-year for sites over 5 MW. Sargent & Lundy notes that, compared to other industry metrics shown in 

Appendix G, the FERC data averages are similar for the sites over 5 MW but much higher for the sites under 5 

MW.

If the results of the regression analysis are used, the average O&M costs are reduced to $41/kW-year for sites 

under 5 MW and $10/kW-year for sites over 5 MW. The regression analysis uses each year of plant data as a 

unique data point, which captures the years in which zero O&M costs were reported. 

By comparison, the EMM uses an average O&M value of $28.47/kW-year for all solar PV plants and an average 

CAPEX value of zero.14 Neither dataset captures the most recent trends in solar PV technology due to rapid 

changes in cost, size, and efficiency. 

 

14 Internal communication with EIA, February 2018. 

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Solar Thermal

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10. SOLAR THERMAL

10.1 DATA DESCRIPTION 

There are no solar thermal power plants that report operating data in FERC Form 1. Industry-wide, there are a 

limited number of solar thermal projects; a majority of which have been constructed within the last 10 years—

the exception being small test facilities and the Solar Energy Generating Systems (SEGS) plants built in the 

1980s. 

10.2 SUMMARY OF RESULTS 

The U.S. National Renewable Energy Laboratory (NREL) published an Annual Technology Baseline (ATB) in 

2017 that estimates the capital and O&M cost of a 100-MWnet solar power tower plant with 10 hours of thermal 

storage, based on cost models benchmarked with industry data.15 The estimate includes future projections based 

on possible reductions in costs (high, mid, or low). The 2017 ATB includes a 2015 baseline. An update is 

expected to be made available in 2018. 

 

15 NREL 2017 Annual Technology Baseline (https://atb.nrel.gov/electricity/2017/index.html?t=sc) 

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Geothermal

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

11. GEOTHERMAL

11.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for geothermal plants were compiled using the assessment 

methodology described in Section 2. The FERC data includes five geothermal installations ranging in capacity 

from 23 MW to 1,224 MW. 

The geothermal data summarized in Table 11-1 was used for the regression analysis shown in Appendix I. 

Table 11-1 — Geothermal Cost Data Distribution

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy 

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 36 38 36 0 0

11.2 SUMMARY OF RESULTS 

Overall, the geothermal dataset does not support any age-related CAPEX spending trend across the full data and 

on any of the subsets by plant size. Instead, we recommend a simple average be used across the full age range. 

Sargent & Lundy recommends using the indicated $/kW-year average in Table 11-2 for O&M and CAPEX 

spending. As shown in the table, it appears the EMM does not currently account for CAPEX or life extension 

expenditures for geothermal plants. 

Table 11-2 — Geothermal CAPEX and O&M Comparison with Existing EMM 

Fixed O&M 

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

CAPEX

(2017 $/kW-yr)

Total O&M and 

CAPEX

(2017 $/kW-yr)

Geothermal Dataset Results – All Plants 157.10 - 40.94 198.04

Existing EMM Value* 91.66 0.00 0.00 91.66

*Source: Internal communication with EIA, February 2018. 

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Wind

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

12. WIND

12.1 DATA DESCRIPTION 

Annual O&M and CAPEX expenditures for wind plants were compiled using the assessment methodology 

described in Section 2. The valid data points derived from this process were distributed as follows: 

 O&M Expenditures 

 73 plants in FERC and 24 from Sargent & Lundy proprietary plants with valid data 

 310 valid data points in FERC, 270 valid data points in Sargent & Lundy proprietary plants 

 CAPEX 

 97 plants in FERC with valid data 

 310 valid data points in FERC 

Sargent & Lundy’s dataset includes both actual historical cost reporting from operating wind projects as well as 

forecasted budgetary cost projections prepared by project developers and operators with large project portfolios. 

Operating costs are assumed to include all expenses related to the maintenance of the wind project, such as 

planned and unplanned maintenance of the wind turbines and electrical balance of plant (including labor, parts, 

materials, and consumables) as well as operating expenses (such as facility monitoring and management fees, 

utilities, land lease and royalty payments, professional service fees, taxes, and insurance). 

The wind data was broken down by plant MW capacity, as summarized below in Table 12-1, for the regression 

analysis shown in Appendix J. 

Table 12-1 — Wind Cost Data Distribution 

Plant Size

Average Net 

Capacity 

Factor (%)

Valid Data 

Points

FERC Data Sargent & Lundy

Internal Data

O&M 

Data 

Points

CAPEX

Data 

Points

O&M 

Data 

Points

CAPEX

Data 

Points

All MW All 310 310 310 270 0

< 100 MW All 174 174 174 165 0

100 MW – 200 MW All 91 91 91 56 0

> 200 MW All 51 51 51 73 0

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Wind

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12.2 SUMMARY OF RESULTS 

The dataset supports age as a statistically significant predictor of O&M spending (on a linear trend across all 

plant ages). Therefore, O&M spending for this dataset may be estimated by the regression equations shown in 

Table 12-2. Age was not a significant predictor of CAPEX spending, although CAPEX was found to vary 

significantly as a function of capacity (kW). That is, CAPEX was lower on a $/kW-year basis for larger plant 

sizes due to economies of scale. 

The CAPEX and O&M values derived from the wind dataset are significantly higher than the existing values 

used in the EMM. The reasons for this discrepancy are not known without having the data sample used for the 

EMM values. Neither data sample is stratified by wind technology or turbine size. Neither dataset captures the 

most recent trends in wind turbine technology due to rapid changes in cost, size, and efficiency. 

Table 12-2 — Wind CAPEX and O&M Comparison with Existing EMM 

Fixed O&M

(2017 $/kW-yr)

Variable O&M 

(2017 $/MWh)

CAPEX

(2017 $/kW-yr)

Wind Dataset Results – All Plants 31.66 + (1.22 × age) 0.00 18.29

< 100 MW 39.08 + (1.12 × age) 0.00 20.48

100 MW – 200 MW 23.80 + (1.17 × age) 0.00 16.93

> 200 MW 26.78 + (0.92 × age) 0.00 13.48

Existing EMM Value* 29.31 0.00 0.00

*Source: Internal communication with EIA, February 2018. 

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Appendix A. Regression Analysis – Coal Steam 

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CAPITAL EXPENDITURES – ALL PLANT SIZES 

The results of the linear regression analysis of CAPEX spending for coal steam plants of all MW sizes (full 

dataset) are summarized in the table below and plotted in the figure below. Since the p-value for the age 

coefficient (“slope”) is 0.19, which is greater than 0.05, the dataset does not support age as a statistically 

significant predictor of CAPEX spending (on a linear trend across all plant ages). However, age and FGD are 

significant variables when an FGD variable is added to the regression equation (see below). 

Table A-1 — Regression Statistics – Coal CAPEX for All MW 

t statistic p-value

Observations 3,724

Simple Average ($/kW) 22.782

Intercept 25.499 11.4859 4.95E-30

Slope -0.069 -1.3054 1.92E-01

R

2

0.00046

Figure A-1 — Coal Steam Dataset – CAPEX for All MW Plant Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

y = -0.0694x + 25.499

R2 = 0.0005

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90

Capital Expenditures (2017$/kW)

Plant Age

Data Points Regression Fit

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OPERATIONS & MAINTENANCE EXPENDITURES – ALL PLANT SIZES 

The results of the linear regression analysis of O&M spending for coal steam plants of all MW sizes (full 

dataset) are summarized in the table below and plotted in the figure below. Since the p-value for the age 

coefficient (“slope”) is 0.38, which is greater than 0.05, the dataset does not support age as a statistically 

significant predictor of O&M spending (on a linear trend across all plant ages). 

Table A-2 — Regression Statistics – Coal O&M for All MW 

t statistic p-value

Observations 3,753

Simple Average ($/kW) 46.013

Intercept 44.893 33.2097 3.08E-212

Slope 0.028 0.8843 3.77E-01

R

2

0.00021

Figure A-2 — Coal Steam Dataset – O&M for All MW Plant Sizes 

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

y = 0.0285x + 44.893

R2 = 0.0002

0

20

40

60

80

100

120

140

0 10 20 30 40 50 60 70 80 90

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

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Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing coal steam plants are described in Section 3. 

CAPITAL EXPENDITURES – LESS THAN 500 MW 

The results of the linear regression analysis of CAPEX spending for coal steam plants less than 500 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.28, which is greater than 0.05, the dataset does not support age as a statistically significant 

predictor of CAPEX spending (on a linear trend across all plant ages). 

Table A-3 — Regression Statistics – Coal CAPEX < 500 MW

t statistic p-value

Observations 1,602

Simple Average ($/kW) 21.187

Intercept 25.059 6.5593 7.28E-11

Slope -0.089 -1.0685 2.85E-01

R

2

0.00071

`

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 21 - 

40) =

Data 

Points 

(years 41 - 

80) =

Data 

Points (all 

years) =

All MW, All Capacity Factors

Net Total O&M- 2017 $/kW 53.90 40.06 48.77 46.01 440 1,448 1,865 3,753

Net Total Capex - 2017 $/kW 17.92 26.20 21.25 22.78 441 1,450 1,833 3,724

Net Total O&M and Capex - 2017 $/kW 71.86 66.25 69.82 68.67 440 1,448 1,825 3,713

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Figure A-3 — Coal Steam Dataset – CAPEX for Less than 500-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – LESS THAN 500 MW 

The results of the regression analysis of O&M spending for coal steam plants less than 500 MW are summarized 

in the table below and plotted in the figure below. Since the p-value for the age coefficient (“slope”) is less than 

0.05, age is a statistically significant predictor of O&M spending (on a linear trend across all plant ages). 

Therefore, O&M spending for this dataset may be estimated by the following regression equation: 

Annual spending in 2017 $/kW-year = 63.494 + (-0.232 × age) 

Table A-4 — Regression Statistics – Coal O&M < 500 MW

t statistic p-value

Observations 1,592

Simple Average ($/kW) 53.406

Intercept 63.494 24.4603 2.03E-112

Slope -0.232 -4.0977 4.38E-05

R

2

0.01045

y = -0.0888x + 25.059

R2 = 0.0007

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70 80 90

Capital Expenditures (2017$/kW)

Plant Age

Data Points Regression Fit

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Figure A-4 — Coal Steam Dataset – O&M for Less than 500-MW Plant Size 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing coal steam plants are described in Section 3. 

y = -0.2316x + 63.494

R2 = 0.0104

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70 80 90

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

`

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 21 - 

40) =

Data 

Points 

(years 41 - 

80) =

Data 

Points (all 

years) =

< 500 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 68.13 47.13 53.16 53.41 169 355 1,068 1,592

Net Total Capex - 2017 $/kW 21.01 22.83 20.67 21.19 169 357 1,076 1,602

Net Total O&M and Capex - 2017 $/kW 89.14 69.91 73.93 74.65 169 355 1,068 1,592

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CAPITAL EXPENDITURES – BETWEEN 500 MW AND 1,000 MW 

The results of the linear regression analysis of CAPEX spending for coal steam plants between 500 MW and 

1,000 MW are summarized in the table below and plotted in the figure below. Since the p-value for the age 

coefficient (“slope”) is 0.26, which is greater than 0.05, the dataset does not support age as a statistically 

significant predictor of CAPEX spending (on a linear trend across all plant ages). 

Table A-5 — Regression Statistics – Coal CAPEX 500 MW to 1,000 MW

t statistic p-value

Observations 986

Simple Average ($/kW) 23.021

Intercept 27.129 6.8576 1.24E-11

Slope -0.106 -1.1195 2.63E-01

R

2

0.00127

Figure A-5 — Coal Steam Dataset – CAPEX for 500-MW to 1,000-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

y = -0.1056x + 27.129

R2 = 0.0013

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70 80 90

Capital Expenditures (2017$/kW)

Plant Age

Data Points Regression Fit

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – BETWEEN 500 MW AND 1,000 MW 

The results of the linear regression analysis of O&M spending for coal steam plants between 500 MW and 1,000 

MW are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is less than 0.05, age is a statistically significant predictor of CAPEX spending (on a linear trend 

across all plant ages). Therefore, O&M spending for this dataset may be estimated by the regression equation: 

Annual spending in 2017 $/kW-year = 38.253 + (0.100 × age) 

Table A-6 — Regression Statistics – Coal O&M 500 MW to 1,000 MW

t statistic p-value

Observations 1,026

Simple Average ($/kW) 42.223

Intercept 38.253 22.0915 9.54E-89

Slope 0.100 2.4710 1.36E-02

R

2

0.00593

Figure A-6 — Coal Steam Dataset – O&M for 500-MW to 1,000-MW Plant Size 

 Note: Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

y = 0.0998x + 38.253

R2 = 0.0059

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70 80 90

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing coal steam plants are described in Section 3. 

CAPITAL EXPENDITURES – BETWEEN 1,000 MW AND 2,000 MW 

The results of the regression analysis of CAPEX spending for coal steam plants between 1,000 MW and 2,000 

MW are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.83, which is greater than 0.05, the dataset does not support age as a statistically significant 

predictor of CAPEX spending (on a linear trend across all plant ages). 

Table A-7 — Regression Statistics – Coal CAPEX 1,000 MW to 2,000 MW

t statistic p-value

Observations 814

Simple Average ($/kW) 23.448

Intercept 22.453 4.6325 4.21E-06

Slope 0.030 0.2174 8.28E-01

R

2

0.00006

`

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 21 - 

40) =

Data 

Points 

(years 41 - 

80) =

Data 

Points (all 

years) =

500 MW - 1000 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 38.15 42.09 43.40 42.22 138 369 519 1,026

Net Total Capex - 2017 $/kW 12.27 32.63 18.71 23.02 138 369 479 986

Net Total O&M and Capex - 2017 $/kW 50.41 74.72 60.65 64.49 138 369 479 986

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-7 — Coal Steam Dataset – CAPEX for 1,000-MW to 2,000-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – BETWEEN 1,000 MW AND 2,000

MW

The results of the regression analysis of O&M spending for coal steam plants between 1,000 MW and 2,000 

MW are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is less than 0.05, age is a statistically significant predictor of O&M spending (on a linear trend across 

all plant ages). Therefore, O&M spending for this dataset may be estimated by the regression equation: 

Annual spending in 2017 $/kW-year = 44.494 + (-0.202 × age) 

Table A-8 — Regression Statistics – Coal O&M 1,000 MW to 2,000 MW 

t statistic p-value

Observations 813

Simple Average ($/kW) 37.722

Intercept 44.494 14.7620 7.42E-44

Slope -0.202 -2.3785 1.76E-02

R

2

0.00693

y = 0.0297x + 22.453

R2 = 6E-05

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70

Capital Expenditures (2017$/kW)

Plant Age

Data Points Regression Fit

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Regression Analysis – Coal Steam

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-8 — Coal Steam Dataset – O&M for 1,000-MW to 2,000-MW Plant Size 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing coal steam plants are described in Section 3. 

y = -0.2021x + 44.494

R2 = 0.0069

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

`

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 21 - 

40) =

Data 

Points 

(years 41 - 

80) =

Data 

Points (all 

years) =

1000 MW - 2000 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 53.51 32.80 40.62 37.72 107 478 228 813

Net Total Capex - 2017 $/kW 22.56 23.31 24.16 23.45 108 478 228 814

Net Total O&M and Capex - 2017 $/kW 76.28 56.11 64.78 61.20 107 478 228 813

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES – GREATER THAN 2,000 MW 

The results of the regression analysis of CAPEX spending for coal steam plants greater than 2,000 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is less than 0.05, age is a statistically significant predictor of CAPEX spending. However, the linear 

regression analysis shows the intercept value (i.e., the CAPEX cost during the first year) to be less than zero. 

This is because of the lack of data for plant ages up to 20 years—the limited amount of data causes the 

regression analysis to be distorted and unrealistic. 

Table A-9 — Regression Statistics – Coal CAPEX > 2,000 MW 

t statistic p-value

Observations 322

Simple Average ($/kW) 28.303

Intercept -8.891 -0.8468 3.98E-01

Slope 1.162 3.6556 3.00E-04

R

2

0.04009

Figure A-9 — Coal Steam Dataset – CAPEX for Greater than 2,000-MW Plant Size 

y = 1.1619x - 8.8912

R2 = 0.0401

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60

Capital Expenditures (2017$/kW)

Plant Age

Data Points Regression Fit

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – GREATER THAN 2,000 MW 

The results of the regression analysis of O&M spending for coal steam plants greater than 2,000 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.59, which is greater than 0.05, the dataset does not support age as a statistically significant 

predictor of O&M spending (on a linear trend across all plant ages). 

Table A-10 — Regression Statistics – Coal O&M > 2,000 MW 

t statistic p-value

Observations 322

Simple Average ($/kW) 42.474

Intercept 39.987 8.3303 2.39E-15

Slope 0.078 0.5348 5.93E-01

R

2

0.00089

Figure A-10 — Coal Steam Dataset – O&M for Greater than 2,000-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

y = 0.0777x + 39.987

R2 = 0.0009

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing coal steam plants are described in Section 3. 

CAPITAL EXPENDITURES – CAPACITY FACTOR LESS THAN 50% 

The results of the regression analysis of CAPEX spending for coal steam plants of all MW sizes and with 

capacity factors less than 50% are summarized in the table below and plotted in the figure below. Since the 

p-value for the age coefficient (“slope”) is 0.87, which is greater than 0.05, age is not a statistically significant 

predictor of CAPEX spending. 

Table A-11 — Regression Statistics – Coal CAPEX for Capacity Factor < 50% 

t statistic p-value

Observations 972

Simple Average ($/kW) 21.063

Intercept 20.027 3.1188 1.87E-03

Slope 0.022 0.1663 8.68E-01

R

2

0.00003

`

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 21 - 

40) =

Data 

Points 

(years 41 - 

80) =

Data 

Points (all 

years) =

> 2000 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 46.55 40.91 48.04 42.47 26 246 50 322

Net Total Capex - 2017 $/kW 8.65 27.06 44.64 28.30 26 246 50 322

Net Total O&M and Capex - 2017 $/kW 55.20 67.97 92.67 70.78 26 246 50 322

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-11 — Coal Steam Dataset – CAPEX for All Plants with Avg. Net Capacity Factor < 50% 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – CAPACITY FACTOR LESS THAN 

50% 

The results of the regression analysis of O&M spending for coal steam plants of all MW sizes and with capacity 

factors less than 50% are summarized in the table below and plotted in the figure below. Since the p-value for 

the age coefficient (“slope”) is 0.26, which is greater than 0.05, age is not a statistically significant predictor of 

O&M spending. 

Table A-12 — Regression Statistics – Coal O&M for Capacity Factor < 50% 

t statistic p-value

Observations 965

Simple Average ($/kW) 49.454

Intercept 54.374 12.0380 3.43E-31

Slope -0.105 -1.1234 2.62E-01

R

2

0.00131

y = 0.0222x + 20.027

R2 = 3E-05

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70 80

Capital Expenditures (2017$/kW)

Plant Age

Data Points Regression Fit

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-12 — Coal Steam Dataset – O&M for All Plants with Avg. Net Capacity Factor < 50% 

Note: Age coefficient in above regression equation is not statistically significant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing coal steam plants are described in Section 3. 

y = -0.1053x + 54.374

R2 = 0.0013

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70 80

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

`

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 21 - 

40) =

Data 

Points 

(years 41 - 

80) =

Data 

Points (all 

years) =

All MW, Capacity Factors 0 - 50%

Net Total O&M- 2017 $/kW 76.43 40.01 50.07 49.45 45 177 743 965

Net Total Capex - 2017 $/kW 19.62 23.74 20.51 21.06 45 179 748 972

Net Total O&M and Capex - 2017 $/kW 96.04 63.66 70.63 70.54 45 177 743 965

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES – CAPACITY FACTOR GREATER THAN 50% 

The results of the regression analysis of CAPEX spending for coal steam plants of all MW sizes and with 

capacity factors greater than 50% are summarized in the table below and plotted in the figure below. Since the 

p-value for the age coefficient (“slope”) is 0.25, which is greater than 0.05, age is not a statistically significant 

predictor of CAPEX spending. 

Table A-13 — Regression Statistics – Coal CAPEX for Capacity Factor > 50% 

t statistic p-value

Observations 2752

Simple Average ($/kW) 23.389

Intercept 25.947 10.7905 1.29E-26

Slope -0.070 -1.1446 2.52E-01

R

2

0.00048

Figure A-13 — Coal Steam Dataset – CAPEX for All Plants with Avg. Net Capacity Factor > 50% 

Note: Age coefficient in above regression equation is not statistically significant. 

y = -0.0702x + 25.947

R2 = 0.0005

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70 80 90

Capital Expenditures (2017$/kW)

Plant Age

Data Points Regression Fit

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – CAPACITY FACTOR GREATER 

THAN 50% 

The results of the regression analysis of O&M spending for coal steam plants of all MW sizes and with capacity 

factors greater than 50% are summarized in the table below and plotted in the figure below. Since the p-value 

for the age coefficient (“slope”) is 0.85, which is greater than 0.05, age is not a statistically significant predictor 

of O&M spending. 

Table A-14 — Regression Statistics – Coal O&M for Capacity Factor > 50% 

t statistic p-value

Observations 2788

Simple Average ($/kW) 44.822

Intercept 44.575 32.6995 8.78E-199

Slope 0.007 0.1954 8.45E-01

R

2

0.00001

Figure A-14 — Coal Steam Dataset – O&M for All Plants with Avg. Net Capacity Factor > 50% 

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

y = 0.0067x + 44.575

R2 = 1E-05

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70 80 90

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing coal steam plants are described in Section 3. 

CAPITAL EXPENDITURES – REGULATED VS. DEREGULATED 

The results of the regression analysis of CAPEX spending for coal steam plants of all MW sizes (full dataset) in 

regulated versus deregulated locations are summarized in the table below. Since the p-value for the age 

(“slope”) and regulation/deregulation coefficients are much greater than 0.05, age and regulatory status are not 

statistically significant predictors of CAPEX spending. 

Table A-15 — Regression Statistics – Coal CAPEX for Regulated/Deregulated 

Coefficients Standard Error t Stat P-Value

Intercept 23.22826383 2.9645403 7.835367875 6.36821E-15

Age 0.097334249 0.064355791 1.512439626 0.130523796

Reg./Dereg. (1/0) -2.479225741 2.148990587 -1.153669893 0.248724297

Figure A-15 — Coal Steam Dataset – CAPEX for Regulated/Deregulated 

`

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 21 - 

40) =

Data 

Points 

(years 41 - 

80) =

Data 

Points (all 

years) =

All MW, Capacity Factors 50% - 100%

Net Total O&M- 2017 $/kW 51.33 40.07 47.92 44.82 395 1,271 1,122 2,788

Net Total Capex - 2017 $/kW 17.73 26.55 21.75 23.39 396 1,271 1,085 2,752

Net Total O&M and Capex - 2017 $/kW 69.11 66.62 69.25 68.01 395 1,271 1,082 2,748

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – REGULATED VS. DEREGULATED 

The regression analysis of O&M expenditures indicates that the p-value for the age (“slope”) and 

regulated/deregulated coefficients are much less than 0.05 (i.e., statistically significant). However, the outliers 

before year 20 may tend to distort the regression analysis. After year 20, a visual inspection of the data points 

indicates higher O&M spending in deregulated states compared with regulated states (Figure A-16). This is the 

opposite of what would be expected, whereby plant owners in a deregulated environment would have a greater 

incentive to reduce O&M costs that cannot be passed through to ratepayers. The higher O&M spending is likely 

a result of other factors, such as higher average labor costs in deregulated states, which tend to have a higher 

percentage of union labor compared with regulated states. Therefore, the net effect of regulatory status on 

average O&M spending is not apparent at this level of detail. 

Figure A-16 — Coal Steam Dataset – O&M for Regulated vs. Deregulated 

CAPITAL EXPENDITURES – FGD VS. NO FGD 

The results of the regression analysis of CAPEX spending for coal steam plants of all MW sizes (full dataset) 

with and without FGD are summarized in the table below. The p-value for the age (“slope”) coefficient is 

slightly greater than 0.05 (nearly statistically significant) while the p-value for the FGD/no-FGD coefficient is 

much less than 0.05 (statistically significant). A visual inspection of the difference between the FGD and 

no-FGD data points in Figure A-17 shows a similarity in CAPEX spending amounts across all ages. Therefore, 

average CAPEX spending may be represented by the following regression equation: 

Annual CAPEX spending in 2017 $/kW-year = 16.53 + (0.126 × age) + (5.68 × FGD) 

Where FGD = 1 if plant has FGD; zero otherwise 

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Table A-16 — Regression Statistics – Coal CAPEX for FGD/No FGD 

Coefficients Standard Error t Stat P-Value

Intercept 16.52586075 3.06139723 5.39814323 7.2399E-08

Age 0.126266024 0.065143952 1.93826166 0.05268181

FGD/No FGD (1/0) 5.6788887 1.913609818 2.96763146 0.00302395

Figure A-17 — Coal Steam Dataset – CAPEX for FGD/No FGD 

OPERATIONS & MAINTENANCE EXPENDITURES – FGD VS. NO FGD 

The regression analysis of O&M expenditures indicates that the p-value for the age (“slope”) and FGD/no-FGD 

coefficients are much less than 0.05 (i.e., statistically significant). However, outliers before year 15 may tend to 

distort the regression analysis. A visual inspection of the difference between the FGD and no-FGD data points in 

Figure A-18 shows a similarity in O&M spending amounts across all ages after year 15. The differences in 

annual coal plant spending due to having FGD is more significant in the CAPEX accounts, as shown in the 

previous subsection, rather than the O&M accounts. 

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-18 — Coal Steam Dataset – O&M for FGD vs. No FGD 

CAPITAL EXPENDITURES – BITUMINOUS VS. SUBBITUMINOUS 

The results of the regression analysis of CAPEX spending for coal steam plants of all MW sizes (full dataset) in 

bituminous versus subbituminous coal types are summarized in the table below. The p-value for the age 

(“slope”) coefficient is much greater than 0.05 (not statistically significant), while the p-value for the 

bituminous/subbituminous coefficient is much less than 0.05 (statistically significant). However, the outliers 

before year 20 may tend to distort the regression analysis. Further, a visual inspection of the difference between 

the bituminous and subbituminous data points in Figure A-19 shows a similarity in CAPEX spending amounts 

across all ages. Therefore, average CAPEX spending is not likely affected by coal type at a high-level 

designation (i.e., bituminous/subbituminous) without more detailed coal specifications. 

Table A-17 — Regression Statistics – Coal CAPEX for Bituminous/Subbituminous 

Coefficients Standard Error t Stat P-Value

Intercept 15.39252046 2.257695952 6.817800442 1.08205E-11

Age -0.00350504 0.054578287 -0.064220408 0.948798346

Bit./Sub. (1/0) 10.93481186 1.525466511 7.168175624 9.20398E-13

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-19 — Coal Steam Dataset – CAPEX for Bituminous/Subbituminous 

OPERATIONS & MAINTENANCE EXPENDITURES – BITUMINOUS VS. SUBBITUMINOUS 

The regression analysis of O&M expenditures indicates that the p-value for the age (“slope”) and 

bituminous/subbituminous coefficients are much less than 0.05 (statistically significant). However, as with 

CAPEX spending, the outliers before year 20 may tend to distort the regression analysis. Further, a visual 

inspection of the difference between the bituminous and subbituminous data points in Figure A-20 shows a 

similarity in O&M spending amounts across all ages. Therefore, average O&M spending is not likely affected 

by coal type at a high-level designation (i.e., bituminous/subbituminous) without more detailed coal 

specifications. 

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Regression Analysis – Coal Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-20 — Coal Steam Dataset – O&M for Bituminous vs. Subbituminous 

EFFECT OF PLANT CAPACITY FACTOR 

CAPEX and O&M spending for the coal steam plants increased significantly with age when expressed on a 

$/MWh basis. This was primarily a result of significant declines in plant capacity factors over time. Figure A-21

and Figure A-22 indicate real annual increases in CAPEX and O&M spending for the coal steam plants in 

constant 2017 $/MWh versus plant age, with linear regression results as follows: 

 Annual CAPEX in 2017 $/MWh = 3.27 + (0.0426 × age) 

 Annual O&M in 2017 $/MWh = 5.44 + (0.133 × age) 

Figure A-21 — CAPEX vs. Age for All MW Coal Plants (2017 $/MWh) 

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-22 — O&M vs. Age for All Coal Plants (2017 $/MWh) 

In both of the above regression results, the age coefficient was found to be statistically significant. This was 

determined to be a result of the average decline in capacity factors for the coal steam plants, as shown in Figure 

A-23. A similar decline also occurred with the gas/oil steam plants, as shown in Figure A-24. 

Figure A-23 — Capacity Factor vs. Age for All Coal Plants 

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure A-24 — Capacity Factor vs. Age for All Gas/Oil Steam Plants 

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Appendix B. Regression Analysis – Gas/Oil Steam 

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Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES – ALL PLANT SIZES 

The results of the regression analysis of CAPEX spending for gas/oil steam plants of all MW sizes (full dataset) 

are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.29, which is greater than 0.05, age is not a statistically significant predictor of CAPEX spending. 

Table B-1 — Regression Statistics – Gas/Oil Steam CAPEX for All MW 

t statistic p-value

Observations 2,226

Simple Average ($/kW) 15.955

Intercept 10.504 1.9741 4.85E-02

Slope 0.122 1.0551 2.91E-01

R

2

0.00050

Figure B-1 — Gas/Oil Steam Dataset – CAPEX for All Plant MW Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – ALL PLANT SIZES 

The results of the linear regression analysis of O&M spending for gas/oil steam plants of all MW sizes (full 

dataset) are summarized in the table below and plotted in the figure below. Since the p-value for the age 

coefficient (“slope”) is less than 0.05, age is a statistically significant predictor of O&M spending (on a linear 

trend across all plant ages). However, the limited number of data points before year 20 may distort the 

regression analysis. 

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Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Table B-2 — Regression Statistics – Gas/Oil Steam O&M for All MW 

t statistic p-value

Observations 2,224

Simple Average ($/kW) 26.723

Intercept 12.661 3.8863 1.05E-04

Slope 0.315 4.4455 9.20E-06

R

2

0.00882

Figure B-2 — Gas/Oil Steam Dataset – O&M for All Plant MW Sizes 

Note: Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil steam plants are described in Section 4. 

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data 

Points (all 

years) =

All MW, All Capacity Factors

Net Total O&M- 2017 $/kW 39.39 23.48 28.18 26.72 19 733 1,472 2,224

Net Total Capex - 2017 $/kW 8.91 14.18 16.93 15.96 19 733 1,474 2,226

Net Total O&M and Capex - 2017 $/kW 48.30 37.53 45.10 42.63 19 731 1,470 2,220

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Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES – LESS THAN 500 MW 

The results of the regression analysis of CAPEX spending for gas/oil steam plants less than 500 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.32, which is greater than 0.05, age is not a statistically significant predictor of CAPEX spending. 

Table B-3 — Regression Statistics – Gas/Oil Steam CAPEX < 500 MW

t statistic p-value

Observations 1382

Simple Average ($/kW) 18.392

Intercept 27.202 3.1265 1.81E-03

Slope -0.178 -0.9867 3.24E-01

R

2

0.00071

Figure B-3 — Gas/Oil Steam Dataset – CAPEX for Less than 500-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – LESS THAN 500 MW 

The results of the linear regression analysis of O&M spending for gas/oil steam plants less than 500 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.90, which is greater than 0.05, age is not a statistically significant predictor of O&M spending (on 

a linear trend across all plant ages). 

114/179
B-5

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Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Table B-4 — Regression Statistics – Gas/Oil Steam O&M < 500 MW

t statistic p-value

Observations 1,381

Simple Average ($/kW) 31.827

Intercept 31.143 5.7925 8.58E-09

Slope 0.015 0.1305 8.96E-01

R

2

0.00001

Figure B-4 — Gas/Oil Steam Dataset – O&M for Less than 500-MW Plant Size 

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil steam plants are described in Section 4. 

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data 

Points (all 

years) =

< 500 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 88.54 33.36 30.98 31.83 7 324 1,050 1,381

Net Total Capex - 2017 $/kW 17.44 22.13 17.82 18.83 7 324 1,051 1,382

Net Total O&M and Capex - 2017 $/kW 105.98 55.32 48.78 50.60 7 322 1,048 1,377

115/179
B-6

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Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES – BETWEEN 500 MW AND 1,000 MW 

The results of the regression analysis of CAPEX spending for gas/oil steam plants between 500 MW and 1,000 

MW are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is less than 0.05, age is a statistically significant predictor of CAPEX spending. However, the 

regression analysis shows the intercept value (i.e., the CAPEX cost during the first year) to be less than zero. 

This is because of the lack of data for plant ages up to 20 years—the limited amount of data causes the 

regression analysis to be distorted and unrealistic. 

Table B-5 — Regression Statistics – Gas/Oil Steam CAPEX 500 MW to 1,000 MW

t statistic p-value

Observations 489

Simple Average ($/kW) 11.570

Intercept -8.988 -1.4118 1.59E-01

Slope 0.501 3.3322 9.27E-04

R

2

0.02229

Figure B-5 — Gas/Oil Steam Dataset – CAPEX for 500-MW to 1,000-MW Plant Size 

y = 0.5005x - 8.9309

R2 = 0.0222

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

116/179
B-7

SL-014201

Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – BETWEEN 500 MW AND 1,000 MW 

The results of the regression analysis of O&M spending for gas/oil steam plants between 500 MW and 1,000 

MW are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is less than 0.05, age is a statistically significant predictor of O&M spending. However, the regression 

analysis shows the intercept value (i.e., the O&M cost during the first year) to be less than zero. This is because 

of the lack of data for plant ages up to 20 years—the limited data causes the regression analysis to be distorted. 

Table B-6 — Regression Statistics – Gas/Oil Steam O&M 500 MW to 1,000 MW

t statistic p-value

Observations 488

Simple Average ($/kW) 19.823

Intercept -1.776 -0.4606 6.45E-01

Slope 0.527 5.7810 1.33E-08

R

2

0.06434

Figure B-6 — Gas/Oil Steam Dataset – O&M for 500-MW to 1,000-MW Plant Size 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

y = 0.5269x - 1.7761

R2 = 0.0643

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50 60 70

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

117/179
B-8

SL-014201

Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil steam plants are described in Section 4. 

CAPITAL EXPENDITURES – GREATER THAN 1,000 MW 

The results of the regression analysis of CAPEX spending for gas/oil steam plants greater than 1,000 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.24, which is greater than 0.05, age is not a statistically significant predictor of CAPEX spending. 

Table B-7 — Regression Statistics – Gas/Oil Steam CAPEX > 1,000 MW

t statistic p-value

Observations 355

Simple Average ($/kW) 10.815

Intercept 2.743 0.3846 7.01E-01

Slope 0.203 1.1660 2.44E-01

R

2

0.00384

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data 

Points (all 

years) =

500 MW - 1000 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 10.10 15.82 23.61 19.82 7 225 256 488

Net Total Capex - 2017 $/kW 1.94 6.32 16.43 11.57 7 225 257 489

Net Total O&M and Capex - 2017 $/kW 12.04 22.14 40.07 31.40 7 225 256 488

118/179
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Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure B-7 — Gas/Oil Steam Dataset – CAPEX for Greater than 1,000-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – GREATER THAN 1,000 MW 

The results of the regression analysis of O&M spending for gas/oil steam plants greater than 1,000 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is less than 0.05, age is a statistically significant predictor of O&M spending (on a linear trend across 

all plant ages). However, the limited number of data points before year 20 may distort the regression analysis. 

Table B-8 — Regression Statistics – Gas/Oil Steam O&M > 1,000 MW

t statistic p-value

Observations 355

Simple Average ($/kW) 16.353

Intercept 9.374 5.1812 3.71E-07

Slope 0.176 3.9752 8.53E-05

R

2

0.04285

y = 0.2033x + 2.7433

R2 = 0.0038

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

119/179
B-10

SL-014201

Regression Analysis – Gas/Oil Steam

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure B-8 — Gas/Oil Steam Dataset – O&M for Greater than 1,000-MW Plant Size 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil steam plants are described in Section 4. 

y = 0.1758x + 9.3736

R2 = 0.0428

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data 

Points (all 

years) =

> 1000 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 11.59 15.44 17.50 16.35 5 184 166 355

Net Total Capex - 2017 $/kW 6.70 9.78 12.09 10.82 5 184 166 355

Net Total O&M and Capex - 2017 $/kW 18.29 25.22 29.60 27.17 5 184 166 355

120/179
Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Appendix C. Regression Analysis – Gas/Oil Combined Cycle 

121/179
C-2

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Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES – ALL PLANT SIZES 

The results of the regression analysis of CAPEX spending for gas/oil CC plants of all MW sizes (full dataset) 

are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.63, which is greater than 0.05, age is not a statistically significant predictor of CAPEX spending. 

Table C-1 — Regression Statistics – CC CAPEX for All MW 

t statistic p-value

Observations 1,368

Simple Average ($/kW) 15.765

Intercept 15.134 9.2176 1.11E-19

Slope 0.041 0.4853 6.28E-01

R

2

0.00017

Figure C-1 — Gas/Oil CC Dataset – CAPEX for All Plant MW Sizes 

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

OPERATIONS & MAINTENANCE EXPENDITURES – ALL PLANT SIZES 

The results of the linear regression analysis of O&M spending for gas/oil CC plants of all MW sizes (full 

dataset) are summarized in the table below and plotted in the figure below. Since the p-value for the age 

coefficient (“slope”) is much lower than 0.05, the dataset appears to support age as a statistically significant 

predictor of O&M spending (on a linear trend across all plant ages). 

122/179
C-3

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Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Table C-2 — Regression Statistics – CC O&M for All MW 

t statistic p-value

Observations 1,388

Simple Average ($/kW) 13.080

Intercept 15.597 24.8961 2.19E-113

Slope -0.159 -5.0573 4.82E-07

R

2

0.01812

Figure C-2 — Gas/Oil CC Dataset – O&M for All Plant MW Sizes 

Note: Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

All MW, All Capacity Factors

Net Total O&M- 2017 $/kW 14.16 10.56 10.26 13.08 978 344 66 1,388

Net Total Capex - 2017 $/kW 15.45 16.37 17.56 15.76 979 326 63 1,368

Net Total O&M and Capex - 2017 $/kW 29.64 27.24 28.19 29.00 976 326 63 1,365

123/179
C-4

SL-014201

Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CC plants are described in Section 5. 

CAPITAL EXPENDITURES – LESS THAN 500 MW 

The results of the regression analysis of CAPEX spending for gas/oil CC plants under 500 MW are summarized 

in the table below and plotted in the figure below. Since the p-value for the age coefficient (“slope”) is 0.76, 

which is greater than 0.05, age is not a statistically significant predictor of CAPEX spending. 

Table C-3 — Regression Statistics – CC CAPEX < 500 MW

t statistic p-value

Observations 765

Simple Average ($/kW) 17.378

Intercept 16.747 6.4870 1.57E-10

Slope 0.036 0.3007 7.64E-01

R

2

0.00012

Figure C-3 — Gas/Oil CC Dataset – CAPEX for Less than 500-MW Plant Size 

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

124/179
C-5

SL-014201

Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – LESS THAN 500 MW 

The results of the regression analysis of O&M spending for gas/oil CC plants less than 500 MW are summarized 

in the table below and plotted in the figure below. Since the p-value for the age coefficient (“slope”) is less than 

0.05, age is a statistically significant predictor of O&M spending (on a linear trend across all plant ages). 

However, the outliers before year 20 and relatively low number of data points after year 40 may distort the 

regression analysis. 

Table C-4 — Regression Statistics – CC O&M < 500 MW

t statistic p-value

Observations 766

Simple Average ($/kW) 15.619

Intercept 19.163 25.2973 4.82E-103

Slope -0.201 -5.7467 1.31E-08

R

2

0.04143

Figure C-4 — Gas/Oil CC Dataset – O&M for Less than 500-MW Plant Size 

Note: Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

125/179
C-6

SL-014201

Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CC plants are described in Section 5. 

CAPITAL EXPENDITURES – BETWEEN 500 MW AND 1,000 MW 

The results of the regression analysis of CAPEX spending for gas/oil CC plants between 500 MW and 1,000 

MW are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.52, which is greater than 0.05, age is not a statistically significant predictor of CAPEX spending. 

Table C-5 — Regression Statistics – CC CAPEX 500 MW to 1,000 MW

t statistic p-value

Observations 426

Simple Average ($/kW) 13.780

Intercept 14.933 6.3972 4.19E-10

Slope -0.077 -0.6252 5.32E-01

R

2

0.00092

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

< 500 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 17.10 13.01 12.27 15.62 498 216 52 766

Net Total Capex - 2017 $/kW 16.83 17.78 21.01 17.38 499 214 52 765

Net Total O&M and Capex - 2017 $/kW 34.00 30.72 33.28 33.03 497 214 52 763

126/179
C-7

SL-014201

Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure C-5 — Gas/Oil CC Dataset – CAPEX for 500-MW to 1,000-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – BETWEEN 500 MW AND 1,000 MW 

The results of the regression analysis of O&M spending for gas/oil CC plants between 500 MW and 1,000 MW 

are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is less than 0.05, age is a statistically significant predictor of O&M spending (on a linear trend across 

all plant ages). However, the outliers before year 20 and relatively low number of data points after year 40 may 

distort the regression analysis. 

Table C-6 — Regression Statistics – CC O&M 500 MW to 1,000 MW

t statistic p-value

Observations 445

Simple Average ($/kW) 9.269

Intercept 11.915 17.1008 1.04E-50

Slope -0.167 -4.7810 2.38E-06

R

2

0.04907

127/179
C-8

SL-014201

Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Figure C-6 — Gas/Oil CC Dataset – O&M for 500-MW to 1,000-MW Plant Size 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CC plants are described in Section 5. 

y = -0.1674x + 11.915

R2 = 0.0491

0

10

20

30

40

50

60

0 10 20 30 40 50 60

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

500 MW - 1000 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 10.68 6.50 2.78 9.27 307 124 14 445

Net Total Capex - 2017 $/kW 14.38 13.36 1.28 13.78 307 108 11 426

Net Total O&M and Capex - 2017 $/kW 25.06 20.38 4.15 23.33 306 108 11 425

128/179
C-9

SL-014201

Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES – GREATER THAN 1,000 MW 

The results of the regression analysis of CAPEX spending for gas/oil CC plants greater than 1,000 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.30, which is greater than 0.05, age is not a statistically significant predictor of CAPEX spending. 

Table C-7 — Regression Statistics – CC CAPEX > 1,000 MW

t statistic p-value

Observations 177

Simple Average ($/kW) 13.566

Intercept 9.464 2.0308 4.38E-02

Slope 0.507 1.0309 3.04E-01

R

2

0.00604

Figure C-7 — Gas/Oil CC Dataset – CAPEX for Greater than 1,000-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – GREATER THAN 1,000 MW 

The results of the regression analysis of O&M spending for gas/oil CC plants greater than 1,000 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.13, which is greater than 0.05, age is not a statistically significant predictor of O&M spending. 

y = 0.5066x + 9.4642

R2 = 0.006

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25

Capital Expenditures (2017$/kW)

Plant Age

Data Points Regression Fit Linear (Data Points)

129/179
C-10

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Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Table C-8 — Regression Statistics – CC O&M > 1,000 MW

t statistic p-value

Observations 177

Simple Average ($/kW) 11.676

Intercept 16.545 4.4651 1.43E-05

Slope -0.601 -1.5389 1.26E-01

R

2

0.01335

Figure C-8 — Gas/Oil CC Dataset – O&M for Greater than 1,000 MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

y = -0.6013x + 16.545

R2 = 0.0134

0

20

40

60

80

100

120

140

0 5 10 15 20 25

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

> 1000 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 11.85 4.14 - 11.68 173 4 0 177

Net Total Capex - 2017 $/kW 13.37 22.06 - 13.57 173 4 0 177

Net Total O&M and Capex - 2017 $/kW 25.22 26.20 - 25.24 173 4 0 177

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C-11

SL-014201

Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CC plants are described in Section 5. 

CAPITAL EXPENDITURES – CAPACITY FACTOR LESS THAN 50% 

The results of the regression analysis of CAPEX spending for gas/oil CC plants of all MW sizes (full dataset) 

with capacity factors under 50% are summarized in the table below and plotted in the figure below. Since the 

p-value for the age coefficient (“slope”) is 0.71, which is greater than 0.05, age is not a statistically significant 

predictor of CAPEX spending. 

Table C-9 — Regression Statistics – CC CAPEX for Capacity Factor < 50% 

t statistic p-value

Observations 844

Simple Average ($/kW) 15.554

Intercept 14.774 5.7075 1.59E-08

Slope 0.041 0.3659 7.15E-01

R

2

0.00016

Figure C-9 — CC Dataset – CAPEX for All Plant Sizes and Avg. Net Capacity Factor < 50%

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

131/179
C-12

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Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – CAPACITY FACTOR LESS THAN 

50% 

The results of the regression analysis of O&M spending for gas/oil CC plants of all MW sizes (full dataset) with 

capacity factors under 50% are summarized in the table below and plotted in the figure below. Since the p-value 

for the age coefficient (“slope”) is less than 0.05, age is a statistically significant predictor of O&M spending (on 

a linear trend across all plant ages). However, the outliers before year 20 and relatively low number of data 

points after year 40 may distort the regression analysis. 

Table C-10 — Regression Statistics – CC O&M for Capacity Factor < 50% 

t statistic p-value

Observations 864

Simple Average ($/kW) 10.791

Intercept 12.292 13.9850 3.33E-40

Slope -0.077 -2.0625 3.95E-02

R

2

0.00491

Figure C-10 — CC Dataset – O&M for All Plant Sizes and Avg. Net Capacity Factor < 50% 

Note: Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

132/179
C-13

SL-014201

Regression Analysis – Gas/Oil Combined Cycle

Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CC plants are described in Section 5. 

CAPITAL EXPENDITURES – CAPACITY FACTOR GREATER THAN 50% 

The results of the regression analysis of CAPEX spending for gas/oil CC plants of all MW sizes (full dataset) 

with capacity factors greater than 50% are summarized in the table below and plotted in the figure below. Since 

the p-value for the age coefficient (“slope”) is 0.37, which is greater than 0.05, age is not a statistically 

significant predictor of CAPEX spending. 

Table C-11 — Regression Statistics – CC CAPEX for Capacity Factor > 50% 

t statistic p-value

Observations 524

Simple Average ($/kW) 16.104

Intercept 14.630 7.3893 5.90E-13

Slope 0.149 0.9054 3.66E-01

R

2

0.00157

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

All MW, Capacity Factors 0 - 50%

Net Total O&M- 2017 $/kW 11.54 9.65 10.26 10.79 500 298 66 864

Net Total Capex - 2017 $/kW 15.35 15.46 17.56 15.55 501 280 63 844

Net Total O&M and Capex - 2017 $/kW 26.95 25.41 28.19 26.53 499 280 63 842

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Figure C-11 — CC Dataset – CAPEX for All Plant Sizes and Avg. Net Capacity Factor > 50%

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

OPERATIONS & MAINTENANCE EXPENDITURES – CAPACITY FACTOR GREATER 

THAN 50% 

The results of the linear regression analysis of O&M spending for gas/oil CC plants of all MW sizes (full 

dataset) with capacity factors greater than 50% are summarized in the table below. Since the p-value for the age 

coefficient (“slope”) is 0.33, which is greater than 0.05, age is not a statistically significant predictor of CAPEX 

spending (on a linear trend across all plant ages). 

Table C-12 — Regression Statistics – CC O&M for Capacity Factor > 50% 

t statistic p-value

Observations 524

Simple Average ($/kW) 16.855

Intercept 17.665 17.5298 1.93E-54

Slope -0.082 -0.9777 3.29E-01

R

2

0.00183

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Figure C-12 — CC Dataset – O&M for All Plant Sizes and Avg. Net Capacity Factor > 50% 

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CC plants are described in Section 5. 

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

All MW, Capacity Factors 50% - 100%

Net Total O&M- 2017 $/kW 16.90 16.44 - 16.85 478 46 0 524

Net Total Capex - 2017 $/kW 15.55 21.89 - 16.10 478 46 0 524

Net Total O&M and Capex - 2017 $/kW 32.46 38.32 - 32.98 477 46 0 523

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Appendix D. Regression Analysis – Gas/Oil Combustion Turbine 

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CAPITAL EXPENDITURES – ALL PLANT SIZES 

The results of the regression analysis of CAPEX spending for gas/oil CT plants of all MW sizes (full dataset) 

are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.09, which is greater than 0.05, dataset does not support age as a statistically significant predictor 

of CAPEX spending (on a linear trend across all plant ages). 

Table D-1 — Regression Statistics – CT CAPEX for All MW 

t statistic p-value

Observations 5065

Simple Average ($/kW) 6.897

Intercept 8.737 7.3087 3.12E-13

Slope -0.068 -1.6948 9.02E-02

R

2

0.00057

Figure D-1 — Gas/Oil CT Dataset – CAPEX for All Plant MW Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – ALL PLANT SIZES 

The results of the regression analysis of O&M spending for gas/oil CT plants of all MW sizes (full dataset) are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.062, which is greater than 0.05, the dataset does not support age as a statistically significant 

predictor of O&M spending (on a linear trend across all plant ages). 

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Table D-2 — Regression Statistics – CT O&M for All MW 

t statistic p-value

Observations 5283

Simple Average ($/kW) 5.331

Intercept 6.641 8.5764 1.27E-17

Slope -0.048 -1.8683 6.18E-02

R

2

0.00066

Figure D-2 — Gas/Oil CT Dataset – O&M for All Plant MW Sizes 

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

All MW, All Capacity Factors

Net Total O&M- 2017 $/kW 7.86 3.99 6.11 5.33 1,418 3,118 747 5,283

Net Total Capex - 2017 $/kW 9.17 5.78 7.40 6.90 1,360 3,054 651 5,065

Net Total O&M and Capex - 2017 $/kW 16.43 9.43 10.92 11.49 1,341 3,040 640 5,021

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CT plants are described in Section 6. 

CAPITAL EXPENDITURES – LESS THAN 100 MW 

The results of the regression analysis of CAPEX spending for gas/oil CT plants less than 100 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.002, which is less than 0.05, age is a statistically significant predictor of CAPEX spending (on a 

linear trend across all plant ages). Therefore, CAPEX spending for this dataset may be estimated by the 

regression equation: 

Annual CAPEX spending in 2017 $/kW-year = 15.651 + (-0.227 × age) 

Table D-3 — Regression Statistics – CT CAPEX < 100 MW

t statistic p-value

Observations 2,911

Simple Average ($/kW) 9.003

Intercept 15.651 6.6753 2.94E-11

Slope -0.227 -3.0345 2.43E-03

R

2

0.00316

Figure D-3 — Gas/Oil CT Dataset – CAPEX for Less than 100-MW Plant Size 

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OPERATIONS & MAINTENANCE EXPENDITURES – LESS THAN 100 MW 

The results of the regression analysis of O&M spending for gas/oil CT plants less than 100 MW are summarized 

in the table below and plotted in the figure below. Since the p-value for the age coefficient (“slope”) is 0.966, 

which is greater than 0.05, the dataset does not support age as a statistically significant predictor of O&M 

spending (on a linear trend across all plant ages). 

Table D-4 — Regression Statistics – CT O&M < 100 MW

t statistic p-value

Observations 3,062

Simple Average ($/kW) 5.958

Intercept 6.001 5.5008 4.09E-08

Slope -0.001 -0.0423 9.66E-01

R

2

0.00000

Figure D-4 — Gas/Oil CT Dataset – O&M for Less than 100-MW Plant Size 

Notes: Age coefficient in above regression equation is not statistically significant. 

 Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CT plants are described in Section 6. 

CAPITAL EXPENDITURES – BETWEEN 100 MW AND 300 MW 

The results of the regression analysis of CAPEX spending for CT plants between 100 MW and 300 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.939, which is greater than 0.05, age is not a statistically significant predictor of CAPEX spending. 

Table D-5 — Regression Statistics – CT CAPEX 100 MW to 300 MW 

t statistic p-value

Observations 1,350

Simple Average ($/kW) 6.183

Intercept 6.254 6.0376 2.02E-09

Slope -0.003 -0.0768 9.39E-01

R

2

0.00000

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

< 100 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 8.76 4.93 7.40 5.96 489 2,060 513 3,062

Net Total Capex - 2017 $/kW 15.08 7.98 6.64 9.00 497 1,999 415 2,911

Net Total O&M and Capex - 2017 $/kW 24.04 12.31 10.26 14.02 489 1,978 406 2,873

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Figure D-5 — Gas/Oil CT Dataset – CAPEX for Between 100-MW and 300-MW Plant Size 

Note: Age coefficient in above regression equation is not statistically significant. 

OPERATIONS & MAINTENANCE EXPENDITURES – BETWEEN 100 MW AND 300 MW 

The results of the regression analysis of O&M spending for gas/oil CT plants between 100 MW and 300 MW 

are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.023, which is less than 0.05, age is a statistically significant predictor of O&M spending (on a 

linear trend across all plant ages). Therefore, O&M spending for this dataset may be estimated by the regression 

equation: 

Annual O&M spending in 2017 $/kW-year = 10.569 + (-0.162 × age) 

Table D-6 — Regression Statistics – CT O&M 100 MW to 300 MW 

t statistic p-value

Observations 1,416

Simple Average ($/kW) 6.430

Intercept 10.569 5.1759 2.59E-07

Slope -0.162 -2.2723 2.32E-02

R

2

0.00364

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Figure D-6 — Gas/Oil CT Dataset – O&M for Between 100-MW and 300-MW Plant Size 

Note: Sequential data points with identical values are forecasted values for the same plant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CT plants are described in Section 6. 

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

100 MW - 300 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 9.97 5.18 3.24 6.43 442 794 180 1,416

Net Total Capex - 2017 $/kW 6.32 6.07 6.38 6.18 407 762 181 1,350

Net Total O&M and Capex - 2017 $/kW 15.14 9.09 9.66 10.98 402 759 180 1,341

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES – GREATER THAN 300 MW 

The results of the regression analysis of CAPEX spending for gas/oil CT plants greater than 300 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient 

(“slope”) is 0.010, which is less than 0.05, age is a statistically significant predictor of CAPEX spending (on a 

linear trend across all plant ages). Therefore, CAPEX spending for this dataset may be estimated by the 

regression equation: 

Annual CAPEX spending in 2017 $/kW-year = 2.945 + (0.193 × age)

Table D-7 — Regression Statistics – CT CAPEX > 300 MW

t statistic p-value

Observations 909

Simple Average ($/kW) 6.952

Intercept 2.945 1.6382 1.017E-01

Slope 0.193 2.5842 0.010

R

2

0.00731

Figure D-7 — Gas/Oil CT Dataset – CAPEX for Greater than 300-MW Plant Size 

y = 0.1925x + 2.9452

R2 = 0.0073

0

20

40

60

80

100

120

140

0 10 20 30 40 50 60

Capital Expenditures ($/kW)

age

Data Points Regression Fit

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – GREATER THAN 300 MW 

The results of the regression analysis of O&M spending for CT plants greater than 300 MW are summarized in 

the table below and plotted in the figure below. Since the p-value for the age coefficient (“slope”) is 

significantly less than 0.05, age is a statistically significant predictor of O&M spending (on a linear trend across 

all plant ages). Therefore, O&M spending for this dataset may be estimated by the regression equation: 

Annual O&M spending in 2017 $/kW-year = 5.474 + (-0.072 × age) 

Table D-8 — Regression Statistics – CT O&M > 300 MW

t Statistic p-value

Observations 938

Simple Average ($/kW) 3.994

Intercept 5.474 12.8980 3.75E-35

Slope -0.072 -4.0612 5.29E-05

R

2

0.01732

Figure D-8 — Gas/Oil CT Dataset – O&M for Greater than 300-MW Plant Size 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

y = -0.0725x + 5.4736

R2 = 0.0173

0

10

20

30

40

50

60

0 10 20 30 40 50 60

O&M (2017$/kW)

Plant Age

Data Points Regression Fit

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Final v01

Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing gas/oil CT plants are described in Section 6. 

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average 

$/kW (all 

years) =

Data 

Points 

(years 1 - 

20) =

Data 

Points 

(years 

21 - 40) 

=

Data 

Points 

(years 

41 - 80) 

=

Data Points 

(all years) =

> 300 MW, All Capacity Factors

Net Total O&M- 2017 $/kW 5.03 2.78 3.46 3.99 488 396 54 938

Net Total Capex - 2017 $/kW 5.26 7.58 16.50 6.95 457 397 55 909

Net Total O&M and Capex - 2017 $/kW 9.30 10.38 20.11 10.42 451 396 54 901

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Appendix E. Regression Analysis – Conventional Hydroelectric 

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CAPITAL EXPENDITURES – ALL PLANT SIZES 

The results of the linear regression analysis of CAPEX spending for conventional hydroelectric plants of all 

MW sizes (full dataset) are summarized in the table below and plotted in the figure below. Since the p-value for 

the age coefficient (“slope”) is significantly less than 0.05, age is a statistically significant predictor of CAPEX 

spending (on a linear trend across all plant ages). Therefore, CAPEX spending for this dataset may be estimated 

by the regression equation: 

Annual CAPEX spending in 2017 $/kW-year = 7.269 + (0.296 × age) 

Table E-1 — Regression Statistics – Hydroelectric CAPEX for All MW

t statistic p-value

Observations 2180

Simple Average ($/kW) 21.999

Intercept 7.269 1.4681 1.42E-01

Slope 0.296 3.1441 1.69E-03

R

2

0.00452

Figure E-1 — Conventional Hydroelectric Dataset – CAPEX for All MW Plant Sizes 

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – ALL PLANT SIZES 

The results of the linear regression analysis of O&M spending for conventional hydroelectric plants of all MW 

sizes (full dataset) are summarized in the table below and plotted in the figure below. Since the p-value for the 

age coefficient (“slope”) is significantly less than 0.05, age is a statistically significant predictor of O&M 

spending (on a linear trend across all plant ages). Therefore, O&M spending for this dataset may be estimated by 

the regression equation: 

Annual O&M spending in 2017 $/kW-year = 22.360 + (0.073 × age) 

Table E-2 — Regression Statistics – Hydroelectric O&M for All MW 

t statistic p-value

Observations 1,272

Simple Average ($/kW) 24.473

Intercept 22.360 13.7360 3.92E-40

Slope 0.073 2.5053 1.24E-02

R

2

0.00492

Figure E-2 — Conventional Hydroelectric – O&M for All MW Plant Sizes 

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Appendix F. Regression Analysis – Pumped Hydroelectric Storage 

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CAPITAL EXPENDITURES – ALL PLANT SIZES 

The results of the linear regression analysis of CAPEX spending for pumped hydroelectric storage plants of all 

MW sizes (full dataset) are summarized in the table below and plotted in the figure below. Since the p-value for 

the age coefficient (“slope”) is greater than 0.05, the dataset does not support age as a statistically significant 

predictor of CAPEX spending (on a linear trend across all plant ages). The dataset was not divided by unit 

capacity due to the limited number of data points. 

Table F-1 — Regression Statistics – Pumped Hydroelectric CAPEX for All MW 

t statistic p-value

Observations 227

Simple Average ($/kW) 11.398

Intercept -6.907 -0.4501 6.53E-01

Slope 0.743 1.2723 2.06E-01

R

2

0.01278

Figure F-1 — Pumped Hydroelectric Dataset – CAPEX for All MW Plant Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

OPERATIONS & MAINTENANCE EXPENDITURES – ALL PLANT SIZES 

The results of the linear regression analysis of O&M spending for pumped hydroelectric storage plants of all 

MW sizes (full dataset) are summarized in the table below and plotted in the figure below. Since the p-value for 

the age coefficient (“slope”) is greater than 0.05, the dataset does not support age as a statistically significant 

predictor of O&M spending (on a linear trend across all plant ages). The dataset was not divided by unit 

capacity due to the limited number of data points. 

Table F-2 — Regression Statistics – Pumped Hydroelectric O&M for All MW 

t statistic p-value

Observations 226

Simple Average ($/kW) 23.634

Intercept 15.296 2.9021 4.08E-03

Slope 0.288 1.7010 9.03E-02

R

2

0.01275

Figure F-2 — Pumped Hydroelectric – O&M for All Plant MW Sizes 

 

Note: Age coefficient in above regression equation is not statistically significant. 

The simple average O&M and CAPEX values for each 20-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing pumped hydroelectric storage plants are described in Section 8. 

Average 

$/kW 

(years 1 - 

20) =

Average 

$/kW 

(years 21 - 

40) =

Average 

$/kW 

(years 41 - 

80) =

Average $/kW 

(all years) =

Data Points 

(years 1 - 

20) =

Data 

Points 

(years 21 - 

40) =

Data 

Points 

(years 41 - 

80) =

Data Points 

(all years) =

All MW, All Capacity Factors

Net Total O&M- 2017 $/kW 18.97 23.41 31.00 23.63 50 140 36 226

Net Total Capex - 2017 $/kW 22.94 11.93 14.92 14.83 50 141 36 227

Net Total O&M and Capex - 2017 $/kW 41.91 35.34 45.92 38.46 -- -- -- --

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Appendix G. Regression Analysis – Solar Photovoltaic 

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Generating Unit Annual Capital and Life Extension Costs Analysis Project 13651-001

CAPITAL EXPENDITURES 

Annual CAPEX, labeled in FERC Form 1 as TCP, are broken down into subcategories, including: 

 Land & Land Rights 

 Structures & Improvements 

 Reservoirs, Dams & Waterways 

 Water Wheels 

 Turbines & Generators 

 Accessory Electric Equipment 

 Equipment 

 Asset Retirement Costs 

 Roads, and Railroads & Bridges 

These subcategories are based on traditional power generation technologies and have minimal applicability to 

solar PV. Expected CAPEX for solar PV, such as inverter replacement and repair or module replacement, are 

clearly not applicable to any of the categories listed in FERC Form 1. 

In the FERC Form 1 data, only 10 of the solar PV sites had a breakdown of TCP into the above subcategories, 

with even fewer providing such a breakdown for more than one year. As discussed in Section 9, the year-overyear change in TCP is the sole source of annual CAPEX information in FERC Form 1. Of this data, Sargent & 

Lundy determined that a significant portion of it needed to be filtered out due to the following reasons: 

 A negative change in the TCP between two consecutive years 

 A change in the capacity of the plant greater than 20% 

 A significant increase in TCP without a capacity increase 

 Large unexplained fluctuations (e.g., negative to positive) in TCP from year to year 

 Large gaps in annual data 

After filtering out clearly suspect data, about one-third of the remaining data was for plants having only three 

years of data or less. In addition, many of the plants reported no changes in TCP, suggesting that most annual 

expenditures at those sites were being reported as O&M rather than being capitalized. 

Thus, Sargent & Lundy had to rely on a limited dataset for solar PV consisting of 15 sites. The average change 

in TCP for these sites was approximately $26/kW-year. Based on the available FERC Form 1 information, it 

cannot be determined whether this change in TCP was due to typical CAPEX for solar PV, such as inverter or 

module replacement, or other factors. 

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The results of the linear regression analysis of CAPEX spending for solar PV plants of all MW sizes (full 

dataset) are summarized in the table below and plotted in the figure below. Since the p-value for the age 

coefficient (“slope”) is 0.16, which is greater than 0.05, the dataset does not support age as a statistically 

significant predictor of CAPEX spending (on a linear trend across all plant ages). In addition, as indicated in the 

table below, there are a relatively small number of data points for CAPEX (less than 60 points). The average 

CAPEX across all years is approximately $26/kW-year (2017 dollars). 

Table G-1 — Regression Statistics – Solar PV CAPEX for All MW

t statistic p-value

Observations 57

Simple Average ($/kW) 26.026

Intercept 42.978 3.2248 2.12E-03

Slope -5.618 -1.4387 1.56E-01

R

2

0.03627

Figure G-1 — Solar PV Dataset – CAPEX for All MW Plant Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

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OPERATIONS & MAINTENANCE EXPENDITURES 

Solar PV O&M activities include a variety of work scopes, including administrative work, monitoring, cleaning, 

preventative maintenance, and corrective maintenance. Some specific examples of O&M activities may include 

cleaning modules, monitoring system voltage and current, inspecting and cleaning electrical equipment, 

inspecting modules for damage, inspecting mounting systems, and checking invertor settings. The cost of O&M 

is dependent on several factors, including the number of components, the type of system (e.g., roof, tracking, 

ground mount, fixed, etc.), warranty coverage, and location. Environmental conditions, such as hail, sand/dust, 

snow, salt in air, high winds, etc., also play a significant role in O&M costs. For these reasons, a higher level of 

variation is expected when compared to traditional generating technologies. 

The total production cost, which is the sum of the total operating expense and total maintenance expense, was 

reported for slightly over half of the sites. Of the sites reporting, several sites only reported this data in certain 

years, leaving gaps in the data. Subcategories for operating costs and maintenance cost were provided in the 

FERC Form 1 data, but rarely was the reported data broken into subcategories. 

Sargent & Lundy organized the FERC Form 1 data into two presentation formats. In the first format, the annual 

O&M cost was averaged across all years of the reported data to obtain the average annual O&M cost per plant. 

This resulted in approximately 60 data points. In the second format, the annual O&M cost was averaged across 

each year of operation. This resulted in approximately 200 data points. The average O&M cost results are not 

equal between the two presentation formats. Table G-2 provides a simple example of these differing results, 

using FERC Form 1 O&M data from three plants. 

Table G-2 — Example of Calculation Method Differences 

O&M Cost ($/kW-year)

Age 

(Years) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Plant Average

Example 

Plant 1 127.8 0.0 0.1 0.0 - - - - - - - - - - - 32.0

Example 

Plant 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Example 

Plant 3 32.2 15.3 24.8 - - - - - - - - - - - - 24.1

Example Average

(All Data Points)

9.1

Example Average

(of Plant Averages) 18.7

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In the example above, a single plant with more data points is able to sway the average O&M cost across the 

three plants. The values calculated below are based on averaged data points (i.e., a data point is the average 

annual O&M cost across the reported data for a given plant). 

Figure G-2 and Figure G-3 show the average site O&M cost, expressed in $/MWh, for sites with a capacity less 

than 5 MW and greater than 5 MW, respectively. In general, these figures show a high level of variability across 

sites, with smaller sites having a higher O&M cost per MWh produced. Several data points were for sites having 

very low capacity factors (less than 5%), which also results in higher O&M costs per MWh. For the sites greater 

than 5 MW, the average O&M cost was $8.5/MWh. When expressed on the basis of cost per kW of capacity 

(see Figure G-4 and Figure G-5), the average O&M for sites greater than 5 MW was $15/kW-year.

Figure G-2 — Average Site O&M Cost per MWh Generated vs. Project Nameplate Capacity 

(< 5 MW) 

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Figure G-3 — Average Site O&M Cost per MWh Generated vs. Project Nameplate Capacity 

(> 5 MW) 

Figure G-4 — Average Site O&M Cost per kW-Year Capacity vs. Project Nameplate Capacity 

(< 5 MW) 

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Figure G-5 — Average Site O&M Cost per kW-Year Capacity vs. Project Nameplate Capacity 

(> 5 MW) 

The figures below show the annual site O&M cost (in $/MWh and $/kW-year) versus the age of the project. In 

general, little correlation can be seen between age and O&M cost. 

Figure G-6 — Annual Site O&M Cost per MWh vs. Age of Project 

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Figure G-7 — Annual Site O&M Cost per kW-Year Capacity vs. Age of Project 

Sargent & Lundy compiled O&M data from other sources in Table G-3 below for comparison against the FERC 

data. In general, the O&M costs in $/kW-year capacity are in the same range as the FERC data for sites over 

5-MW capacity. 

Table G-3 — Summary of Industry O&M Cost Data for Solar PV 

O&M Cost Sources O&M Cost 

$/kW-yr

Notes Report Source 

Data Year

NREL & Sunshot 15 Fixed 2015

NREL & Sunshot 18 Single-Axis Tracking 2015

Sunshot + NREL 20.5 Good O&M 2016

Sunshot + NREL 25.0 Optimal O&M 2016

IRENA Power to Change 10 Minimum 2015

IRENA Power to Change 18 Maximum 2015

Utility Scale Solar 17 Overall 2014

Utility Scale Solar 2016 7 Minimum 2016

Utility Scale Solar 2016 27 Maximum 2016

Utility Scale Solar 2016 18 Mean 2016

NREL U.S. Solar Photovoltaic System

Cost Benchmark: Q1 2017 15.4 Fixed LCOE Assumption 2017

NREL U.S. Solar Photovoltaic System 

Cost Benchmark: Q1 2018 18.5 SAT LCOE Assumption 2017

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Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing solar PV plants are described in Section 9. 

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Appendix H. Regression Analysis – Solar Thermal 

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There are no solar thermal power plants that report operating data in FERC Form 1. Industry-wide, there are a 

limited number of solar thermal projects; a majority of which have been constructed within the last 10 years—

the exception being small test facilities and the Solar Energy Generating Systems (SEGS) plants built in the 

1980s. 

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Appendix I.Regression Analysis – Geothermal 

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CAPITAL EXPENDITURES 

The results of the linear regression analysis of CAPEX spending for geothermal plants of all MW sizes (full 

dataset) are summarized in the table below and plotted in the figure below. Although the p-value is less than 

0.05, the dataset is inconclusive because the intercept is negative due to no plants reporting data between ages 

and 0 and 10. 

Table I-1 — Regression Statistics – Geothermal CAPEX for All MW 

t statistic p-value

Observations 36

Simple Average ($/kW) 40.948

Intercept -150.830 -1.7907 8.23E-02

Slope 10.006 2.3736 2.34E-02

R

2

0.14215

Figure I-1 — Geothermal Dataset – CAPEX for All MW Plant Sizes 

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OPERATIONS & MAINTENANCE EXPENDITURES 

The results of the linear regression analysis of O&M spending for geothermal plants of all MW sizes (full 

dataset) are summarized in the table below and plotted in the figure below. Since the p-value for the age 

coefficient is 0.071, which is greater than 0.05, the dataset does not support age as a statistically significant 

predictor of O&M spending (on a linear trend across all plant ages). 

Table I-2 — Regression Statistics – Geothermal O&M for All MW 

t Statistic p-value

Observations 36

Simple Average ($/kW) 157.103

Intercept 175.369 2.6984 1.08E-02

Slope -0.953 -0.2930 7.71E-01

R

2

0.00252

Figure I-2 — Geothermal Dataset – O&M for All MW Plant Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

The simple average O&M and CAPEX values for each five-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

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Table I-3 — Geothermal All MW Summary of Results 

Average 

$/kW-yr

(Years 

1-5)

Average 

$/kW-yr 

(Years 

6-10)

Average 

$/kW-yr 

(Years 

11-15)

Average 

$/kW-yr 

(Years 

16-20)

Average 

$/kW-yr 

(Years 

21-25)

Average 

$/kW-yr 

(Years 

26-30)

Average 

$/kW-yr 

(All 

Years)

Data 

Points 

(Years 

1-5)

Data 

Points 

(Years 

6-10)

Data 

Points 

(Years 

11-15)

Data 

Points 

(Years 

16-20)

Data 

Points 

(Years 

21-25)

Data 

Points 

(Years 

26-30)

Data 

Points 

(All 

Years)

All MW, All Capacity Factors

Net Total 

O&M –

2017 

$/kW-yr

-- 300.62 170.44 124.24 149.97 166.77 157.10 -- 1 12 10 6 7 36

Net Total 

CAPEX –

2017 

$/kW-yr

-- -- 72.05 30.16 27.64 114.45 40.94 -- 1 12 10 6 7 36

Starting with the initial analysis of CAPEX and O&M raw data, as presented above, Sargent & Lundy 

developed recommended changes to the existing values used in the EMM. The recommended changes for 

existing geothermal plants are described in Section 11. 

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Appendix J. Regression Analysis – Wind 

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CAPITAL EXPENDITURES 

Full Dataset 

The results of the linear regression analysis of CAPEX spending for wind plants of all MW sizes (full dataset) 

are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient is 

0.224, which is greater than 0.05, the dataset does not support age as a statistically significant predictor of 

CAPEX spending (on a linear trend across all plant ages). 

Table J-1 — Regression Statistics – Wind CAPEX for All MW 

t Statistic p-value

Observations 310

Simple Average ($/kW) 18.285

Intercept 22.241 5.7807 1.82E-08

Slope -0.686 -1.2194 2.24E-01

R

2 0.00480

Figure J-1 — Wind Dataset – CAPEX for All MW Plant Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

The simple average O&M and CAPEX values for each five-year age band, expressed in constant 2017 

$/kW-year, are summarized in the table below. 

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Table J-2 — Wind All MW Summary of Results 

Average 

$/kW-yr

(Years 

1-5)

Average 

$/kW-yr

(Years 

6-10)

Average 

$/kW-yr

(Years 

11-15)

Average 

$/kW-yr

(Years 

16-20)

Average 

$/kW-yr

(All 

Years)

Data 

Points 

(Years 

1-5)

Data 

Points 

(Years 

6-10)

Data 

Points 

(Years 

11-15)

Data 

Points 

(Years 

16-20)

Data 

Points 

(All 

Years)

All MW, All Capacity Factors

Net Total CAPEX – 2017 $/kW-yr 21.06 10.97 32.62 21.60 18.29 168 112 23 7 310

Starting with the initial analysis of CAPEX raw data, as presented above, Sargent & Lundy developed 

recommended changes to the existing values used in the EMM. The recommended changes for existing wind 

plants are described in Section 12. 

0-100 MW 

The results of the linear regression analysis of CAPEX spending for wind plants between 0 MW and 100 MW 

are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient is 

0.706, which is greater than 0.05, the dataset does not support age as a statistically significant predictor of 

CAPEX spending (on a linear trend across all plant ages). Therefore, a more appropriate predictor of CAPEX 

spending for this dataset is a simple average by plant age band, as discussed in Section 12. 

Table J-3 — Regression Statistics – Wind CAPEX for 0-100 MW 

t Statistic p-value

Observations 174

Simple Average ($/kW) 20.483

Intercept 22.342 3.7750 2.20E-04

Slope -0.297 -0.3779 7.06E-01

R

2

0.00083

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Figure J-2 — Wind Dataset – CAPEX for 0-100-MW Plant Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

The simple average CAPEX values for each five-year age band, expressed in constant 2017 $/kW-year, are 

summarized in the table below. 

Table J-4 — Wind < 100-MW Summary of Results 

Average 

$/kW-yr

(Years 

1-5)

Average 

$/kW-yr

(Years

6-10)

Average 

$/kW-yr

(Years

11-15)

Average 

$/kW-yr 

(Years

16-20)

Average 

$/kW-yr

(All 

Years)

Data 

Points 

(Years 

1-5)

Data 

Points 

(Years 

6-10)

Data 

Points 

(Years 

11-15)

Data 

Points 

(Years 

16-20)

Data 

Points 

(All 

Years)

< 100 MW, All Capacity Factors

Net Total CAPEX –

2017 $/kW-yr

22.83 11.62 35.35 21.60 20.48 89 58 20 7 174

Starting with the initial analysis of CAPEX raw data, as presented above, Sargent & Lundy developed 

recommended changes to the existing values used in the EMM. The recommended changes for existing wind 

plants are described in Section 12. 

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100-200 MW 

The results of the linear regression analysis of CAPEX spending for wind plants between 100 MW and 200 MW 

are summarized in the table below. Since the p-value for the age coefficient is 0.224, which is greater than 0.05, 

the dataset does not support age as a statistically significant predictor of CAPEX spending (on a linear trend 

across all plant ages). 

Table J-5 — Regression Statistics – Wind CAPEX for 100-200 MW 

t Statistic p-value

Observations 310

Simple Average ($/kW) 16.935

Intercept 22.241 5.7807 1.82E-08

Slope -0.686 -1.2194 2.24E-01

R

2

0.00480

Figure J-3 — Wind Dataset – CAPEX for 100-200-MW Plant Sizes 

Note: Age coefficient in above regression equation is not statistically significant. 

The simple average CAPEX values for each five-year age band, expressed in constant 2017 $/kW-year, are 

summarized in the table below. 

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Table J-6 — Wind 100-200-MW Summary of Results 

Average 

$/kW-yr

(Years 1-5)

Average 

$/kW-yr 

(Years 

6-10)

Average 

$/kW-yr 

(Years 

11-15)

Average 

$/kW-yr 

(Years

16-20)

Average 

$/kW-yr

(All 

Years)

Data 

Points 

(Years 

1-5)

Data 

Points 

(Years 

6-10)

Data 

Points 

(Years 

11-15)

Data 

Points 

(Years 

16-20)

Data 

Points 

(All 

Years)

100 - 200 MW, All Capacity Factors

Net Total CAPEX – 2017 $/kW-yr 20.36 12.20 14.41 -- 16.93 52 36 3 -- 91

Starting with the initial analysis of CAPEX raw data, as presented above, Sargent & Lundy developed 

recommended changes to the existing values used in the EMM. The recommended changes for existing wind 

plants are described in Section 12. 

Greater than 200 MW 

The results of the linear regression analysis of CAPEX spending for wind plants greater than 200 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient is 0.006, 

which is less than 0.05, the dataset does support age as a statistically significant predictor of CAPEX spending 

(on a linear trend across all plant ages). However, a visual inspection of the data in the graph below shows that 

there are a limited number of data points over 10 years, which may be skewing the regression. 

Table J-7 — Regression Statistics – Wind CAPEX for Greater than 200 MW

t Statistic p-value

Observations 91

Simple Average ($/kW) 16.935

Intercept 29.387 5.6538 1.87E-07

Slope -2.474 -2.7612 6.99E-03

R

2

0.07891

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Figure J-4 — Wind Dataset – CAPEX for Greater than 200-MW Plant Sizes 

The simple average CAPEX values for each five-year age band, expressed in constant 2017 $/kW-year, are 

summarized in the table below. 

Table J-8 — Wind Greater than 200-MW Summary of Results 

Average $/kW-yr

(Years 1-5)

Average $/kW-yr

(Years 6-10)

Average $/kW-yr

(All Years)

Data Points 

(Years 1-5)

Data Points 

(Years 6-10)

Data Points 

(All Years)

> 200 MW, All Capacity Factors

Net Total CAPEX – 2017 $/kW-yr 16.61 8.65 13.48 31 20 51

Starting with the initial analysis of CAPEX raw data, as presented above, Sargent & Lundy developed 

recommended changes to the existing values used in the EMM. The recommended changes for existing wind 

plants are described in Section 12. 

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OPERATIONS & MAINTENANCE EXPENDITURES 

Full Dataset 

The results of the linear regression analysis of O&M spending for wind plants of all MW sizes (full dataset) are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient is 

significantly less than 0.05, age is a statistically significant predictor of O&M spending (on a linear trend across 

all plant ages). Therefore, O&M spending for the dataset may be estimated by the regression equation: 

Annual O&M spending in 2017 $/kW-year = 31.661 + (1.222 × age) 

Table J-9 — Regression Statistics – Wind O&M for All MW 

t statistic p-value

Observations 580

Simple Average ($/kW) 42.680

Intercept 31.661 12.7763 4.24E-33

Slope 1.222 5.3515 1.26E-07

R

2

0.04721

Figure J-5 — Wind Dataset – O&M for All MW Plant Sizes 

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0-100 MW 

The results of the linear regression analysis of O&M spending for wind plants between 0 MW and 100 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient is 0.003, 

which is less than 0.05, the dataset age is a statistically significant predictor of O&M spending (on a linear trend 

across all plant ages). Therefore, O&M spending for the dataset may be estimated by the regression equation: 

Annual O&M spending in 2017 $/kW-year = 39.083 + (1.119 × age) 

Table J-10 — Regression Statistics – Wind O&M for 0-100 MW 

t Statistic p-value

Observations 339

Simple Average ($/kW) 49.888

Intercept 39.083 9.0574 1.10E-17

Slope 1.119 2.9310 3.61E-03

R

2

0.02486

Figure J-6 — Wind Dataset – O&M for 0-100-MW Plant Sizes 

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100-200 MW 

The results of the linear regression analysis of O&M spending for wind plants between 100 MW and 200 MW 

are summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient is 

significantly less than 0.05, age is a statistically significant predictor of O&M spending (on a linear trend across 

all plant ages). Therefore, O&M spending for the dataset may be estimated by the regression equation: 

Annual O&M spending in 2017 $/kW-year = 23.797 + (1.174 × age) 

Table J-11 — Regression Statistics – Wind O&M for 100-200 MW 

t Statistic p-value

Observations 147

Simple Average ($/kW) 35.645

Intercept 23.797 14.1919 3.27E-29

Slope 1.174 6.5971 7.33E-10

R

2

0.23086

Figure J-7 — Wind Dataset – O&M for 100-200-MW Plant Sizes 

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Greater than 200 MW 

The results of the linear regression analysis of O&M spending for wind plants greater than 200 MW are 

summarized in the table below and plotted in the figure below. Since the p-value for the age coefficient is 

significantly less than 0.05, age is a statistically significant predictor of O&M spending (on a linear trend across 

all plant ages). Therefore, O&M spending for the dataset may be estimated by the regression equation: 

Annual O&M spending in 2017 $/kW-year = 26.783 + (0.925 × age) 

Table J-12 — Regression Statistics – Wind O&M Greater than 200 MW 

t statistic p-value

Observations 124

Simple Average ($/kW) 35.645

Intercept 26.783 17.5334 3.90E-35

Slope 0.925 7.0885 9.55E-11

R

2

0.29171

Figure J-8 — Wind Dataset – O&M for Plant Sizes Greater than 200 MW

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