# Analysis of Heat Rate Improvement Potential at Coal

Fired Power Plants

May 2015

In dependent Statistics & Analysis

www.eia.gov U.S. Department of Energy

Washington, DC 20585U.S. Energy Information Administration | Analysis of Heat Rate Improvement Potential at Coal

- Fired Power Plants 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, analys es, 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 U.S. Department of Energy

or other f ederal agencies

.

### May 2015

U.S. Energy Information Administration | Analysis of Heat Rate Improvement Potential at Coal

- Fired Power Plants 1 

# Introduction

The thermal efficiency of electricity production is represented by the heat rate, which measures the
amount of energy used to generate one kilowatthour of electricity. 1 

### A generating unit with a lower, or
more efficient, heat rate can generate the same quan tity of electricity while consuming less fuel,

compared with a unit with a higher heat rate. Lower fuel use per unit of electricity generated also
reduces the corresponding emissions of pollutants such as sulfur dioxide (SO 2 

### ), nitrogen oxide (NO X 

### ),

mercury (Hg), and carbon dioxide (CO 2 

### ). Consequently, improving heat rates at power plants can lower
fuel costs and help achieve compliance with environmental regulations.

During the development of the Annual Energy Outlook 2015 (AEO2015), the U.S. Energy Informa tion
Administration (EIA) updat ed its modeling capability to include the ability to evaluate the potential for
making heat rate improvement s at existing coal

- fired generators

. The projections in the AEO2015 are
produced by the National Energy Modeling Syst em (NEMS), which is a modular system consisting of

components to represent fuel supply, end

- use consumption and conversion sectors

, as well as modules

for international and macroeconomic activities

.

The Electricity Market Module (EMM) is the electricity su pply component of the NEMS . The EMM
performs three primary functions

– capacity planning, fuel dispatching, and finance and pricing.
Capacity planning decisions include building new plants to satisfy increases in demand and to replace
retiring plants. P lanning decisions also consider retrofits of existing capacity to install pollution control

devices. The f uel dispatching function involves operating the available capacity to meet the demand for
electricity . The finance and pricing function considers th e investment cost s associated with planning
decisions and the operating costs from dispatching activities to develop delivered prices for electricity.

Heat rate improvement is another planning activity

, as it considers the tradeoff between the investment

e xpenditures and the savings in fuel and/or environmental compliance costs. Potential increases in
efficiency can vary depending in part on the type of equipment installed at a generating plant . The

EMM represents 32 configurations of existing coal

- fired plants based on different combinations of
particulate, sulfur dioxide (SO 2 

### ), nitrogen oxide (NO X 

### ), mercury, and carbon emission controls (Table 1)

. These categories form the basis for evaluating the potential for heat rate improvement s

.

EIA entered into a contract with Leido s Corporation (Leidos) to develop a methodology to evaluate the
potential for heat rate improvement at existing coal

- fired generating plants

. Leidos performed a
statistical analysis of the heat rate characteristics of coal

- fired generating units modeled by EIA in the

EMM . Specifically, Leidos developed a predictive model for coal

- fired electric generating unit heat rates

as a function of various unit characteristics. 2 

### Leidos employed statistical modeling techniques to create
the predictive models. 3

1 U.S. Energy Information Administration, Frequently Asked Questions, What is the efficiency of different types of power plants? , accessed January 31, 2015.

2 The characteristics used to predict heat rate included attributes such as nameplate capacity, rank of coal used, NEMS plant
type, flue

- gas desulfurization status, and flue

- gas particulate collector type.

3 This included algorithmic evaluation of potential descriptive variables, and piecewise linear regression analysis. A decision tree

create d 7 sub

- models describing inputs for the heat rate model for different unit categorizations.

### May 2015

U.S. Energy Information Administration | Analysis of Heat Rate Improvement Potential at Coal

- Fired Power Plants 2 

### For the EMM plant types, the coal

- fired generating units were categorized according to quartiles, based

on observed 4 

### versus predicted heat rates. Units in the first quartile (Q1), which perform better than

predicted, were ge nerally associated with the least potential for heat rate improvement. Units in the
fourth quartile (Q4), representing the least efficient units relative to predicted values, were generally
associated with the highest potential for heat rate improvement. Leidos developed a matrix of heat rate
improvement options and associated costs, based on a literature review and the application of

engineering judgment.

Little or no coal

- fired capacity exists for the EMM plant types with mercury and carbon control

confi gurations, therefore estimates were not developed for th ose plant types . These plant types were ultimately assigned the characteristics of the plants with the same combinations of particulate, SO 2 

### , and

NO

x 

### controls. P lant types with relatively few observ ations were combined with other plant types having
similar improvement profiles . As a result, 9 unique plant type combinations were developed for the
purposes of the quartile analysis

, and for each of these combinations Leidos created a minimum and a

maxi mum potential for heat rate improvement along with the associated costs to achieve those
improved efficiencies

. 5



### Leidos used the minimum and maximum characteristics as a basis for developing estimate s of mid

- range cost and heat rate improvement potential. The mid

- range estimates were used as the default values for

the Annual Energy Outlook 2015 ( AEO2015 ) (Table 2). Table 3 contains the minimum and maximum heat

rate improvement s and costs.

Additional details regarding the background and the analy tical methodology are included in the
consultant report prepared by Leidos Corporation ( Appendix ).

4 In this report, observed hea t rates refer to the heat rates contained in EIA’s EMM plant file.

5 Leidos selected the plant type and quartile groupings such that each grouping contained at least 10 generating units, with th e exception of the integrated gasification combined

- cycle (IG) type, which has essentially no heat rate improvement potential.
Some plant types and quartiles also had associated variable operation and maintenance (O&M) costs. The variable O&M costs
were not incorporated into the NEMS EMM model at the time of this ana lysis. However, the impact of omitting variable O&M
cost is expected to be small due to the relative magnitude of the capital and fixed O&M cost components.

### May 2015

U.S. Energy Information Administration | Analysis of Heat Rate Improvement Potential at Coal

- Fired Power Plants 3 

### Table 1 : Existing pulverized c oal p lant t ypes in the NEMS Electricity Market Module

Plant

type Particulate

c ontrols SO 2

c ontrols NO X

c ontrols Mercury

c ontrols Carbon

c ontrols

B1 BH None Any None None

B2 BH None Any None CCS

B3 BH Wet None None None

B4 BH Wet None None CCS

B5 BH Wet SCR None None

B6 BH Wet SCR None CCS

B7 BH Dry Any None None

B8 BH Dry Any None CCS

C1 CSE None Any None None

C2 CSE None Any FF None

C3 CSE None Any FF CCS

C4 CSE Wet None None None

C5 CSE Wet None FF None

C6 CSE Wet None FF CCS

C7 CSE Wet SCR None None

C8 CSE Wet SCR FF None

C9 CSE Wet SCR FF CCS

CX CSE Dry Any None None

CY CSE Dry Any FF None

CZ CSE Dry SCR FF CCS

H1 HSE/Oth None Any None None

H2 HSE/Oth None Any FF None

H3 HSE/Oth None Any FF CCS

H4 HSE/Oth Wet None None None

H5 HSE/Oth Wet None FF None

H6 HSE/Oth Wet None FF CCS

H7 HSE/Oth Wet SCR None None

H8 HSE/Oth Wet SCR FF None

H9 HSE/Oth Wet SCR FF CCS

HA HSE/Oth Dry Any None None

HB HSE/Oth Dry Any FF None

HC HSE/Oth Dry Any FF CCS

Notes: Particulate Controls, BH

– baghouse

, CSE = cold side electrostatic precipitator,
HSE/Oth = hot side electrostatic precipitator/ o ther/ n one;

SO2 Controls

- wet = wet s crubber

, Dry = dry s crubber;
NOx Controls, SCR = selective catalytic reduction;
Mercury Controls

- FF = fabric filter;

Carbon Controls

- CCS = carbon capture and st orage

. Source: U.S. Energy Information Administration/Leidos Corporation.

### May 2015

U.S. Energy Information Administration | Analysis of Heat Rate Improvement Potential at Coal

- Fired Power Plants 4 

### Table 2: Heat rate improvement (HRI) potential and cost (capital, fixed O&M) by Plant Type and quartile as used for input to NEMS

Plant type and quartile combination Count of total units Percentage HRI potential Capital cost (million 2014 $/MW) Average fixed O&M cost (2014 $/MW

- yr)

B1

- Q1 32 (s) 0.01 200

B1

- Q2 15 0.8% 0.10 2,000

B1

- Q3 18 4% 0.20 4,000

B1

- Q4 20 6% 0.90 20,000

B3

- Q1 13 (s) 0.01 300

B3

- Q2 24 0.7% 0.05 1,000

B3

- Q3 16 6% 0.20 3,000

B3

- Q4 15 9% 0.60 10,000

B5C7

- Q1 16 (s) (s) 80

B5C7

- Q2 42 0.8% 0.03 700

B5C7H7

- Q3 84 7% 0.10 2,000

B5C7H7

- Q4 59 10% 0.20 4,000

B7

- Q1 27 (s) (s) 70

B7

- Q2 25 0.8% 0.04 800

B7

- Q3Q4 30 7% 0.30 5,000

C1H1

- Q1 148 (s) 0.01 200

C1H1

- Q2 117 0.8% 0.10 2,000

C1H1

- Q3 72 4% 0.40 8,000

C1H1

- Q4 110 7% 1 .00 30,000

C4

- Q1 15 (s) (s) 80

C4

- Q2 27 0.8% 0.04 900

C4

- Q3 32 6% 0.20 2,000

C4

- Q4 39 10% 0.30 5,000

CX

- Q1Q2Q3Q4 15 7% 0.20 4,000

H4

- Q1Q2Q3 13 3% 0.20 3,000

IG

- Q1 3 (s) (s) 60

Total set 1,027 4% 0.30 6,000

(s) = less than 0.05% for HRI potential or less than 0 .005 million $ / MW for capital cost.

Source: U.S. Energy Information Administration/Leidos Corporation

.

### May 2015

U.S. Energy Information Administration | Analysis of Heat Rate Improvement Potential at Coal

- Fired Power Plants 5 

### Table 3 : Minimum and maximum heat rate improvement (HRI) parameters

Plant type and quartile

combination Count of total

units Percentage HRI potential Capital cost (million 2014 $/MW)

B1

- Q1 32 (s) 0.007

B1

- Q2 15 0.3%

- 1.2% 0.096

- 0.11

B1

- Q3 18 2.1%

- 6.4% 0.20

- 0.26

B1

- Q4 20 3.5%

- 9.4% 0.76

- 0.99

B3

- Q1 13 (s) 0.01

B3

- Q2 24 0.3%

- 1.2% 0.047

- 0.056

B3

- Q3 16 3.1%

- 8.2% 0.19

- 0.30

B3

- Q4 15 5.1%

- 13% 0.50

- 0.72

B5C7

- Q1 16 (s) 0.003

B5C7

- Q2 42 0.3%

- 1.2% 0.031

- 0.036

B5C7H7

- Q3 84 3.6%

- 9.5% 0.11

- 0.16

B5C7H7

- Q4 59 6.0%

- 15% 0.18

- 0.25

B7

- Q1 27 (s) 0.002

B7

- Q2 25 0.3%

- 1.2% 0.035

- 0.042

B7

- Q3Q4 30 3.8%

- 9.8% 0.27

- 0.40

C1H1

- Q1 148 (s) 0.006

C1H1

- Q2 117 0.3%

- 1.2% 0.12

- 0.13

C1H1

- Q3 72 2.0%

- 6.0% 0.36

- 0.49

C1H1

- Q4 110 3.6%

- 9.6% 1.1

- 1.5

C4

- Q1 15 (s) 0.002

C4

- Q2 27 0.3%

- 1.2% 0.041

- 0.048

C4

- Q3 32 3.5%

- 9.1% 0.13

- 0.20

C4

- Q4 39 5.7%

- 14% 0.21

- 0.30

CX

- Q1Q2Q3Q4 15 3.7%

- 9.7% 0.19

- 0.28

H4

- Q1Q2Q3 13 1.9%

- 5.1% 0.14

- 0.21

IG

- Q1 3 (s) 0.002

Total set 1,027 2.0%

- 5.3% 0.24

- 0.32

(s) = less than 0.05% for HRI potential.

Source: U.S. Energy Information Administration /Leidos Corporation.

### May 2015

U.S. Energy Information Administration | Analysis of Heat Rate Improvement Potential at Coal

- Fired Power Plants 6 

# Appendix

– Full Report

# Evaluation of the Potential for Heat **Rate Improvements at Coal**

**- Fired** 

**Power Plants**

**EIA Task 7965, Subtask 14**

Prepared for:
Energy Information Administration

Office of Energy Analysis

Office of Electricity, Coal, Nuclear and Renewables Analysis

Prepared by:
Leidos Corporation

Ken Walsh, Dave Lesneski, Carl Thomason, Jay Ratafia

- Brown



# FINAL REPORT

January 2015

# Table of Contents

Table of Contents

................................ ................................ ................................ .............................. i

List of Tables

................................ ................................ ................................ ................................ .. iii

List of Figures

................................ ................................ ................................ ................................ . iv

List of Abbreviations and Acronyms

................................ ................................ .............................. vi

List of Energy Information Administration ECP Types

................................ ................................ vii

Executive Summary

................................ ................................ ................................ ...................... viii

1 Introduction

................................ ................................ ................................ ............................. 1

2 Literature Review

................................ ................................ ................................ .................... 3

2.1 Heat Rate Improvement Technologies

................................ ................................ ............. 6

2.1.1 Fuel Supply

................................ ................................ ................................ ............... 6

2.1.2 Boiler Island

................................ ................................ ................................ .............. 6

2.1.3 Turbine Island

................................ ................................ ................................ ........... 8

2.1.4 Other Improvements

................................ ................................ ................................ . 9

2.2 Total HRI Studies

................................ ................................ ................................ ........... 10

2.3 Associated Costs

................................ ................................ ................................ ............ 11

3 Statistical Assessment of Heat Rates in the Plant Database

................................ ................. 12

3.1 Data Processing

................................ ................................ ................................ .............. 12

3.2 General Data Population Characteristics

................................ ................................ ........ 12

3.3 Influence of Nameplate Capacity on Heat Rate

................................ ............................. 17

3.4 Slope Function Analysis

................................ ................................ ................................ . 19

3.5 Model Predictions of Heat Rate

................................ ................................ ..................... 23

3.6 Generator Units that Most Likely Can Improve Heat Rates

................................ .......... 24

4 Interpretation of Quartile Data

................................ ................................ .............................. 26

4.1 Subcritical Versus Supercritical Boilers

................................ ................................ ........ 28

4.2 States

................................ ................................ ................................ .............................. 28

4.3 ECP Plant Types

................................ ................................ ................................ ............. 29

4.4 Heat Rates

................................ ................................ ................................ ...................... 31

4.5 Online Year

................................ ................................ ................................ .................... 33

4.6 Planned Retirement Year

................................ ................................ ............................... 33

4.7 Variable O&M Costs

................................ ................................ ................................ ...... 34

4.8 Fuel Choice

................................ ................................ ................................ .................... 35

4.9 Total FGD Cost

................................ ................................ ................................ .............. 37

4.10 FGP C ollector Classification and Hours in Service

................................ ....................... 37

4.11 Cooling System Type and In

- Service Year

................................ ................................ .... 39

4.12 Number of Federal Air Market Programs Affecting Emissions

................................ .... 41

5 Evaluation of Potential HRIs and Associated Costs

................................ ............................. 44

5.1 Heat Rate Improvement Options and Valuation

................................ ............................ 44

i

### 5.2 Heat Rate Improvement Cost Estimation

................................ ................................ ....... 46

6 Applying Potential Heat Rate Improvements to the Entire Database

................................ ... 59

6.1 HRI Potentials as They Relate to Select Input Parameters

................................ ............ 59

6.1.1 Online Year

................................ ................................ ................................ ............. 59

6.1.2 Nameplate Capacity

................................ ................................ ................................ 59

6.1.3 ECP Type

................................ ................................ ................................ ................ 61

6.2 HRI for th e Total Data Set

................................ ................................ ............................. 65

6.3 Associated Costs

................................ ................................ ................................ ............ 67

6.4 Other Considerations

................................ ................................ ................................ ...... 70

7 Recommendations

................................ ................................ ................................ ................. 72

Appendix A . Heat Rate Improvements Assigned to ECP Types

................................ .................. 74

Appendix B. Heat Rate Improvements Assigned to Modified ECP Types

................................ .. 75

Appendix C. Modified Heat Rate Improvements Assigned to ECP Types

................................ .. 77

Appendix D. Modified Heat Rate Improvements Assigned to Modified ECP Types

.................. 78

Appendix E. Midpoints between Minima and Maxima in Appendix A*

................................ ..... 80

Appendix F. Midpoints between Minima and Maxima in Appendix B*

................................ ..... 81

ii

# List of Tables

Table 2

- 1. Existing Coal

- Fired EGU Efficiency Improvements Reported for Actual Efficiency Improvement Projects

................................ ................................ ................... 3

Table 2

- 2. Maximum HRIs and Associated Costs Listed in the 2009 S&L Report

........................ 4

Table 2

- 3. 2010 NETL Assessment of Co al

- Fired Power Plant Efficiencies

............................... 11

Table 3

- 1. Distribution of Numeric Parameters Associated with Coal

- F ired

Generators

................................ ................................ ................................ .................... 13

Table 3

- 2. Numeric Parameter Pairs with Correlation Coefficients with Absolute

Values Greater than 0.60

................................ ................................ .............................. 15

Table 3

- 3. Nominal and Logical Characteristics Considered in this Study

................................ ... 16

Table 3

- 4. Results from the AttributeEvaluator Showing Those Variables that Most Influence the Slope Function Variable and Are Independent of Other

Variables

................................ ................................ ................................ ...................... 21

Table 4

- 1. Distributions of Numeric Parameters among the Quartiles

................................ ......... 26

Table 4

- 2. States Whose Generators Mostly Fall into a Single Quartile.

................................ ..... 28

Table 4

- 3. Distribution of ECP Plant Types among Quartiles

................................ ...................... 30

Table 4

- 4. Number of Generator Units in Quartiles Based on Primary Fuel Usage

..................... 36

Table 4

- 5. Number of Air Market Programs Affecting Generators by State

................................ 41

Table 5

- 1. Maximum Estimates For Heat Rate Improvements and Capital Costs

........................ 47

Table 5

- 2. Minimum Estimates for Heat Rate Improvements and Capital Costs

......................... 52

Table 5

- 3. Fixed and Variable O&M Cost Estimates for Heat Rate Improvements

..................... 56

Table 6

- 1. Calculated HRI Potentials by ECP Type

................................ ................................ ..... 63

Table 6

- 2. Cumulative Percentiles for the HRI Data Sets

................................ ............................. 67

iii

# List of Figures

Figure ES

- 1. Comparison of Reported and Predicted Inverse Heat Rates for All Coal

F ired Generators

................................ ................................ ................................ ......... ix

Figure ES

- 2. Average New Heat Rates by NEMS ECP Type

................................ ......................... x

Figure 1

- 1. Average Heat Rate from Coal

- Fired Units (Weighted to Generators Based on 2012 Electricity Production)

................................ ................................ ....... 1

Figure 3

- 1. Preliminary Decision Tree for Describing Inverse Heat Rates

................................ . 18

Figure 3

- 2. Preliminary Relationship between Nameplate Capacity and

................................ .... 19

Figure 3

- 3. Hi stogram Showing the Distribution for the Slope Function

Transformation

................................ ................................ ................................ .......... 20

Figure 3

- 4. Decision Tree to Describe the Slope Functio n Using the

................................ ......... 23

Figure 3

- 5. Comparison of Reported and Predicted Inverse

................................ ....................... 24

Figure 3

- 6. Quartiles Comparing Reported Heat Rates to Predicted Heat Rates

........................ 25

Figure 4

- 1. Distribution of Units across the Quartiles for Reported

................................ ........... 26

Figure 4

- 2

. Comparisons of Heat Rates for Select Data Subsets by State and Quartile

.............. 29

Figure 4

- 3. Distribution of FGD Type among the Quartiles

................................ ....................... 30

Figure 4

- 4. Distribution of Particulate Control Types among the Quartiles

............................... 31

Figure 4

- 5. Distribution among the Quartiles Based on

................................ .............................. 31

Figure 4

- 6. Distribution of CAMD 2012 Heat Rates among Quartiles

................................ ....... 32

Figure 4

- 7. Distribution of EIA

- Reported Heat Rates among the Quartiles

............................... 32

Figure 4

- 8. Distribution of Quartiles Based on Online Year

................................ ....................... 33

Figure 4

- 9. Distribution of Quartiles for Units Listing Planned Retirement Years

..................... 34

Figure 4

- 10. Distribution of Quartiles among Online Years for Units Listing

.............................. 34

Figure 4

- 11. Distribution of Quartiles Based on Variable O&M Costs

................................ ........ 35

Figure 4

- 12. Distribution of Quartiles Based on Variable O& M Costs (Including Fuel Costs) for Units with Nameplate Capacities Over 100 MW

................................ ..... 35

Figure 4

- 13. Distribution of Total FGD Cost (Th ousands of Dollars) among Quartiles

.............. 37

Figure 4

- 14. Distribution of Quartiles among FGP Collector Types

................................ ............ 38

Figure 4

- 15. Distribution of FGP Collector Types with Online Year

................................ ........... 38

Figure 4

- 16. Fractional Distribution of FGP Service Hours among Quartiles

.............................. 39

Figure 4

- 17. Fractional Distribution of the Quartiles within Cooling System Types

................... 40

Figure 4

- 18. Distribution of Cooling System Types among In

- Service Years

.............................. 40

iv

### Figure 4

- 19. Distribution of Quartiles Based on Number of Federal Air Market Programs

................................ ................................ ................................ ................... 43

Figure 4

- 20. Distribution of Heat Rates Based on Number of

................................ ...................... 43

Figure 6

- 1. Distribution of Units with Particular HRI Potentials by Online Year (Top Representing Maxima an d Bottom Representing Minima)

................................ ...... 60

Figure 6

- 2. Distribution of Units with Particular HRI Potentials by Nameplate Capacity (Top Rep resenting Maxima and Bottom Representing Minima)

.............. 61

Figure 6

- 3. Distribution of Units with Particular HRI Potentials by ECP Type (Top Representing Maxima and Bottom Representing Minima)

................................ ...... 62

Figure 6

- 4. Average HRI Potentials for NEMS ECP Types (Left Side of B ar

........................... 64

Figure 6

- 5. Average New Heat Rates by NEMS ECP Type

................................ ....................... 65

Figure 6

- 6. Fractions of Units with Lower Total HRI Potentials and Lower Percent

HRIs

................................ ................................ ................................ .......................... 66

Figure 6

- 7. Cumulative Percentile Graphs Depicting HRI Costs at Different Units

................... 68

Figure 6

- 8. C umulative Percentile Graphs Depicting HRI Unit Costs at Different

Units

................................ ................................ ................................ .......................... 69

Figure 6

- 9. Relationship between Costs and HRI Potent ial for ECP Types

............................... 70

v

# List of **Abbreviations and Acron**  **yms**

AEO Annual Energy Outlook

AMPD Air Markets Program Data

ASME American Society of Mechanical Engineers

Btu British thermal units

CAMD Clean Air Markets Division

CO Carbon monoxide

CO 2 

### Carbon dioxide

DCS Distributed control system

ECP Electricity Capacity Planning

EFD Electricity Fuel Dispatch

EGU Electricity Generating Unit

EIA Energy Information Administration

EMM Electricity Market Module

EPA Environmental Protection Agency

EPRI Electric Power Research Institute

ESP Electrostatic precipitator

FDS Fuel delivery system

FGD F lue gas desulfurization

FGP F lue gas particulate

GMOD GHG Mitigation Options Database

GWh G igawatt

- hours

HRI H eat rate improvement $ K T housand dollars

kWh K ilowatt

- hours

LP Low pressure

M5P A numeric classifier algorithm within Weka data mining software $M Million dollars

MWh M egawatt

- hours

NEMS National Energy Modeling System

NETL National Energy Technology Laboratory

NO X 

### O xides of nitrogen

NPC Nameplate capacity

NSR New Source Review

O&M Operating and maintenance

R&D Research and development

S&L Sargent & Lundy

SCR S elective catalytic reduction

TC Total costs

TPFS Total potential fuel savings

U.S. United States

vi

# List of **Energy Information Administration ECP Types**

For the database within this study, EIA assign ed twelve codes to coal

- fired electricity generating
units for use in the Electricity Capacity Planning (ECP) submodule within the Electricity Market
Module (EMM) of the National Energy Modeling System (NEMS) . These codes represent the

fuel /technology and em ission control devices. Because the codes are frequently reference d in this
report, the code definitions are included below for coal

- fired units (none were listed with carbon
capture devices) :

Unit
Code Technology Particulate
Control Flue Gas

Desulfurizati on Other Controls

B1

Conventional Baghouse None Any NO
x 

### control

B3 Wet No selective catalytic reduction

B5 Wet Selective catalytic reduction

B7 Dry Any NO
x 

### control

C1

Cold

- side

electrostatic precipitator None Any NO
x 

### control

C4 Wet None

C7 Wet Selective catalytic reduction

CX Dry None

H1

Other or none None Any NO
x 

### control

H4 Wet None

H7 Wet Selective catalytic reduction

IG Integrated gasification

combined cycle -- -- --

vii

## Executive Summary

The Energy Information Administration’s (EIA’s) National Energy Modeling System (NEMS) projects annual loads on electric generating units and the years that different types of coal

- fired

units will retire or implement efficiency improvements based on economic factors. This study

evaluate d how NEMS might assign heat rate improvements (HRIs) to different coal

- fired electric generating units and compute the associate d costs within NEMS. Various unit

- level HRIs we re considered in an effort to match NEMS unit types to particular total improvement levels.

The net heat rate s at existing generators w ere compared to individual numeric parameters from EIA and U.S. Environmental Protection Agency (EPA) records, but the on ly factor that had a linear correlation coefficient better than 0.60 was the variable operating and maintenance costs as expressed in dollars per megawatt

- hour ($/MWh). Despite the influence of many different plant design aspects, the inverse net heat rat es (a plant efficiency me tric ) were clearly dependent (though not linearly) on the nameplate capacit ies (MW) of the boilers associated with each

generator.

Data mining algorithms produce d a piecewise linear regression model to describe the behavior at

the coal

- fired units. Previous studies had yielded Pearson’s correlation coefficients (r 2 

### ) of 0.18, but the piecewise linear modeling approach in this study improved the r 2 

### to 0.62 (for predicting inverse heat rates). Figure ES

- 1 compares the reported and predicted inverse heat rates, and the red dashed lines show slopes corresponding to a 50 % confidence interval. The deviations of reported heat rates in 2012 from the model ed v alues provide benchmark s to differentiate units

with HRI potential s from those that are likely already operating very efficiently. The units lying

to the left of all three lines in Figure ES

- 1 were considered to be those that had the most room for improvement relative to similar units. Those to the right of the lines likely would have less HRI potential because they already operate better than similar units (as described by the model).

Fifty

- six HRI activities were initially under consideration, but the list was reduced to 29

measures because available HRIs should be considered non

- routine activities, should not overlap, and should be beyond the pilot phase of development (in order to allow costing estimates within NEMS). No

- cost and low

- cost improvements could be made at the plants operating at the lowest efficiencies (Quartile 4) while assuming that other plants had already impleme nted these. The study also associated moderate

- and high

- cost HRIs as potential for units that were operating near the modeled heat rate values (Quartiles 2 and 3) but not near the best reported values for similar units (Quartile 1)

.
Units that went online after 1990 generally offered the smallest HRI potentials; 70 % of those units fell into Quartiles 1 and 2, so fewer HRI measures were attached to them. Units going online in the 1970s and 1980s received the earliest air pollution control technologies but h ave not necessarily been upgraded in their lifetimes. The 1970s/1980s units also include the supercritical units, so their heat rates were competitive with other upgraded units. Because NEMS ECP types 1


### categorize the coal

- fired units based on emission cont rol technologies, the analyses show that the emission controls at the units with the highest HRI potential are likely to be characterized by a

combination of these three factors: wet scrubbers, n o use of baghouses or cold

- side ESP

, and

SCR.

1 ECP types defined on page vii

.



### viii

### Figure ES

- 1 . Comparison of R eported and P redicted I nverse H eat R ates for

A ll Coal

- F ired G enerators

Figure ES

- 2 presents the average new heat rate s that might be associated with the average unit s in each ECP type. The left sides of the bars represent the average heat rates if the maximum HRI potentials are realized with every improvement, and the right sides of the bars represent the

resultant heat r ates when only the minimum HRIs are achieved. The plot shows that B5, C7, H7 and some C4 units might have heat rates under 10,000 Btu/kWh if the improvements are made.

In Figure ES

- 2

, the H1 units would continue to average high heat rates (near 16,000 Btu/kWh) even if the improvements described were all undertaken because EIA’s base heat rate s for the se 21 units are high

. The capital costs can be approximated (r 2 

### =0.90) by multiplying the HRI potential by $100,000

- kWh/Btu. However, the total operating and maintenance ( O&M ) costs are not well estimated by

a linear correlation (r 2 

### =0.41). The poor approximations of total O&M c osts are tied to the non

linearity associated with one particular HRI measure with high O&M costs

. I f this one HRI

measure is removed from consideration , v ariable O&M costs drop to just 2 % of the total O&M

costs and the linearity improves (r 2 

### =0.95 and slop e=$1600

- kWh/Btu

- yr)

.

ix

### Figure ES

- 2 . Average N ew H eat R ates by NEMS ECP T ype.

x

## 1 **Introduction**

The NEMS Electricity Market Module (EMM) dispatches generating units in economic merit order, subject to numerous constraints. In order for coal

- fired generating units to be dispatched in
NEMS EMM, all else equal, the associated variable costs must be lowe r than the variable costs

of other generators . A competitive coal

- fired plant may need to implement net heat rate improvements in order to remain an economical choice in the model.

The net heat rate (Btu/kWh) represents the total heat content of the fuel consumed (based on the higher heating value, HHV) divided by the net electricity generation. The net electricity generation is represented by the gross generation minus the electricity consumed inte rnally by the plant (e.g., fuel feed systems, boiler feed pumps, pollution control devices, heat recovery equipment, and other auxiliary loads); whereas gross generation for a unit represents the total amount of electric energy as measured at the generato r terminal

. This study was based on the reference data in the NEMS EMM plant database that EIA provided for this study , as well as information collected from the 2012 EIA

- 860 survey

. An EIA data set of generators that are primarily powered by coal

- fired bo ilers had 2012 net heat rates ranging

from 8800 Btu/kWh to 25,000 Btu/kWh. Weighting this information based on the net MWh electricity produced (using EPA CAMD files), the 2012 state averages ranged from 9700 Btu/kWh (North Carolina) to 11, 500 Btu/kWh (Del aware), as illustrated in Figure 1

- 1

. These

numbers indicate that the North Carolina coal

- fired plants are on average 16 % more efficient

than the Delaware plants. Coa l quality and moisture affect the potential heat rates , but this significant range is also influenced by plant design and operations.

Figure 1

- 1 . Average Heat Rate f rom Coal

- Fired Units (Weighted t o Generators Based o n 2012 Electricity Production )

1

### EIA and EPA regularly collect information about fuel usage, operations, and emissions at power plants, but modifications that are made to improve the net heat rate are not tracked on a national

basis. Impr ovements for the boiler island, turbine island, flue gas system, air pollution controls, and water treatment system may all affect the net heat rate.

This report discusses one NEMS approach that would allow heat rate improvements (and the associated costs) to be incorporated expeditiously for present and future activities

. Chapter 2 lists

some resources that published observed heat rate improvements. Chapter 3 describes the processing of an EIA survey data set to determine which coal

- fired units are likely already operating as efficiently as possible and which units have more potential for improvem ent. Chapter 4 shares analyses that indicate how the potential for improvement is related to other plant characteristics. Chapter 5 discusses the possible heat rate improvement measures, their effects, and their costs. Chapter 6 descr ibes the resulting total heat rate improvements associated

with the various ECP plant types within NEMS and the associated capital, operating, and

maintenance costs. Chapter 7 presents some recommendations for future improvements to the estimates, primarily based on expected new information sources.

2

## 2 **Literature Review**

Some differences in heat rates may be attributed to coal type, moisture content, and boiler sizes, but best practices and technological improvements have also been made at some plants with the goals of decreasing heat rates and subsequently fuel costs. Many heat rate improvements (HRIs)

have been made over the last several decades, and the National Energy Technology Laboratory (NETL) summarized the efficiency improvements for existing coal

- fired power plants in 2008.

In 2014

, the U.S. EPA 2 

### reported these HRIs and the associated efficienc ies (reproduced in Table

2

- 1 ).

Table 2

- 1 . Existing Coal

- Fired EGU Efficiency Improvements Reported f or Actual

Efficienc y Improvement Projects 3

Efficiency Improvement Technology Description Reported Efficiency

Increase a

Combustion

Control Optimization Combustion controls adjust coal and air flow to optimize steam production for the steam turbine/generator set. However, combustion control for a coal

- fired EGU is complex and impacts a number of important operating parameters including combustion efficiency, steam temperature, furnace slagging and fouling, and NO

X formation. The technologies include instruments that measure carbon levels in ash, coal flow rates, air flow rates, CO levels, oxygen levels, slag deposits, and burner metrics as well as advanced coal nozzles and plasma assisted coal combustion. 0.15 to

0.84%

Cooling System Heat Loss Recovery Recover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulation th rough a cooling tower or discharge to a water body. The identified technologies include replacing the cooling tower fill (heat
transfer su rface) and tuning the cooling tower and condenser. 0.2 to 1%

Flue Gas Heat Recovery Flue gas exit temperature from the air preheater can range from 250 to 350°F depending on the acid dew point temperature of the flue gas, which is dependent on

the concent ration of vapor phase sulfuric acid and moisture. For power plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it is sprayed with the FGD reagent slurry. However, it may be possible to

recover some of this lost e nergy in the flue gas to preheat boiler feedwater via use of a condensing heat exchanger. 0.3 to 1.5%

Low

- rank Coal Drying Subbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bituminous coal (less than 10%) . A significant amount of the heat released during combustion of low

- rank coals is used to evaporate this moisture, rather than generate steam for the turbine. As a result, boiler efficiency is typically lower for plants burning low

- rank coal. The technolo gies include using waste heat from the flue gas and/or cooling water systems to dry low

- rank coal prior to combustion. 0.1 to 1.7%

2 U.S. Environmental Protection Agency, GHG Abatement Measures , Technical Support Document for Carbon

Pollution Guidelines for Existing Power Plants: Emission Guidelines for Greenhouse Gas E missions from Existing Stationary Sources: Electric Utility Generating Units

, Docket ID No. EPA

- HQ

- OAR

- 2013

- 0602, June 2014.

D ownloaded from http://www.eenews.net/assets/2014/06/02/ document_gw_04.pdf on December 1, 2014.

3 Ibid.



### 3Efficiency Improvement Technology Description Reported Efficiency

Increase a

Sootblower Optimization Sootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tube surfaces in order to maintain adequate heat transfer. Proper control of the timing and intensity of individual sootblowers is important to maintain steam temperature and boiler efficiency. The identified technologies include intelligent or neu ral network sootblowing (i.e., sootblowing in response to

real

- time conditions in the boiler) and detonation sootblowing. 0.1 to

0.65%

Steam

Turbine Design There are recoverable energy losses that result from the mechanical design or physical condition of the steam turbine. For example, steam turbine manufacturers have improved the design of turbine blades and steam seals which can increase both efficiency and output (i.e., steam turbine dense pack technology). 0.84 to

2.6%

Source: U.S. Department of Energy, National Energy Technology Laboratory, Reducing CO2 Emissions by Improving the Efficiency of the Existing Coal

- fired Power Plant Fleet , DOE/NETL

- 2008/1329 , Pittsburgh, PA. July 23, 2008. Available at:

< http://www.netl.doe.gov/energy

- analyses/pubs/CFPP%20Efficiency

- FINAL.pdf >.

a Reported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual

component efficiencies for a reference plant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the reference power plant has an overall efficiency of 32% and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency impr ovement method was reported to achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a reported 1 00 Btu/kWh decrease in net heat rate would be converted to a 0.30% point increase in overall efficiency.



### However, th is report note d the following :
“The EPA does not have sufficient site specific information to accurately estimate

what percentage of the fleet has adopted various HRI methods, nor how effectively, and is not aware of any other investigator having sufficient information.”

Despite this lack of detailed information, EPA’s method included binning plants into 168 categories and forecasting possible HRIs based on the best 10 % of the units.

T

he 2009 Sargent and Lundy report (S&L Report) quantifies the major retrofits that are not

considered cost

- prohibitive at existing coal

- fired power plants. Activities that were considered

cost

- prohibitive included coal handling, coal ash ha ndling, and feedwater preheating. 4 

### This

literature review presents findings from the S&L Report ( Table 2

- 2 )

, but focuses on work that has been published since issuance of that report.

The sections below outline additional technologies that can be used to reduce heat rates, as well

as studies that describe total possible HRIs. For example, EPRI’s Production Cost Optimization project evaluated five units to find 3

- 5% HRIs through various means. 5 

### In addition, an EPA technical support document associated with the greenhouse gas emission control measure s for power plants discusses possible efficiency gains. For studies that estimate costs associated with these technologies and groups of technologies, the costs are also presented in the final section.

4 Hansel, Peter, Heat Rate Reductions and Carbon Emissions: A Policy Mechanism for Regulating Coal Plants

under 111(d) of the Clean Air Act , April 22, 2014. Downloaded on November 25, 2014 , from http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/8508/Heat%20Rate %20Reductions%20and%20Carb

on%20Emissions%20

- %20Peter%20Hansel%20MP%20Final.pdf?sequence=1

. 5 Korellis, S., Range and Applicability of Heat Rate Improvements , Technical Update, Electric Power Research

Institute Report Number 3002003457, April 2014.



### 4

### Table 2

- 2 . Maximum HRIs and A ssociated C osts L isted in the 2009 S&L Report

Heat Rate Improvement

(Btu/kWh) Capital Cost (2008 $M) Fixed O&M (2008 $K/yr) Variable O&M (2008 $K/yr)

≤200

MW ≤500

MW >500

MW ≤200

MW ≤500

MW >500

MW ≤200

MW ≤500

MW >500

MW ≤200

MW ≤500

MW >500

MW Boiler Island Improvements
Installing
economizer 100 100 100 3 5 8 50 100 150 0 0 0

Neural network 150 100 50 0.5 0.8 0.8 50 50 50 0 0 0
Intelligent

sootblower 150 90 90 0.3 0.5 0.5 50 50 50 0 0 0

Limit air heater leakage 40 40 40 0.5 0.7 1.2 50 75 100 0 0 0

Lower air heater

outlet temperature by controlling acid dew point 120 120 120 3.5 10 18 50 75 100 350 850 1500

Turbine Island Improvements

Turbine

overhaul 300 300 300 12 20 25 0 0 0 0 0 0

Condenser cleaning 70 70 70 0 0 0 30 60 80 0 0 0

Boiler feed

pumps 50 50 50 0.35 0.6 0.8 0 0 0 0 0 0

Flue Gas System Improvements

ID axial fan (and motor) upgrades 50 50 50 6.5 11 16 50 85 130 0 0 0

Variable

frequency drives 100 100 100 2 4 6 20 30 50 0 0 0

Variable

frequency drives

and new centrifugal fans 150 150 150 6.5 11 16 25 38 60 0 0 0

Air Pollution Control Improvements

Removal of

Venturi throat 13 13 13 2.5 2.5 2.5 0 0 0 0 0 0

Turning vanes and perforated gas distribution plates at the inlet 2 2 2 0.25 0.25 0.25 0 0 0 0 0 0

Shutoff spray

level 16 16 16

-- -- -- 0 0 0 0 0 0

Variable

frequency drives 50 50 50 1 3 5 50 100 150 0 0 0

ESP modification 5 5 5 0.2 0.5 0.8 25 25 25 0 0 0

SCR

modification 10 10 10 0.5 1 2 25 50 100 25 60 100

Water Treatment Improvements Cooling tower

advanced packing upgrade 70 70 70 1.5 3 5 75 125 175 0 0 0



### 5

## 2.1 **Heat Rate Improvement Technologies**

The focus of this literature review is literature published after the S&L Report. Within this

section

, the literature review divides the technologies among the fuel supply, boiler island, turbine island, and other systems at the plant. One difficulty assessing the efficiency of various HRIs is that multiple improvement measures are undertaken during outage s, so individual efficiencies cannot be determined from overall plant measurements.

**2.1.1 Fuel Supply** 



### The National Coal Council reports three alternatives f or improving thermal efficiency: 6



### 1) Coal switching (switch from subbituminous to bituminous fuel could increase thermal efficiency by 1.6%) 2) Coal drying (Great River Energy increased plant net generating thermal efficiency by 4% through lignite drying) 3) Coal processing (adding ammonium hydroxide increased the coal heating value from 7,859 to 11,363 Btu/lb whe n 24% moisture was removed)

. An example comes from the Great River Energy’s Coal Creek Station that combusts lignite in a

mine

- mouth operation. Since 2009, the 1180

- MW plant has employed a new technology to compensate for off

- design fuel quality. 7 

### The DryF ining process dried and segregated lignite coal with waste heat energy in a low

- temperature system, reducing the moisture content by 10 % and removing up to 40 % of the sulfur and mercury from the fuel. The flue gas was subsequently reduced by 6 % , and the pl ant efficiency increased by 4 % . The investigators recently reported that the average annual improvement in net heat rate for Unit 1 is 3.4% (since 2009) and for Unit 2

the net HRI is 5.8% ( Unit 2 also benefitted from a steam turbine upgrade). 8



### 2.1.2 **Boiler Islan d** 



### The National Coal Council 9 

### reported that the unit generating efficiency may be improved by

0.160.33 % (lowering heat rate by 50

- 100 Btu/kWh) if failed boiler surfaces are replaced for a cost of $4

- 5 M in a 500 MW plant. The report also mentions that upgrad ed economizers might lower the plant heat rate by 0.5

- 1.0 %

. The report also mentions that advanced boiler materials (including those using “nano

- coatings”) were in the experimental stage and could significantly increase the heat transfer because they minim ize deposit accumulation.

6 Natio nal Coal Council, Reliable & Resilient: The Value of Our Existing Coal Fleet , May 2014. Downloaded from

http://www.nationalcoalcouncil.org/NEWS/NCCValueExistingCoalFleet.pdf . The National Coal Council is a Federal Advisory Committee to the U.S. Secretary o f Energy.

7 Great River Energy, DryFining Fuel Enhancement Process . Online fact sheet downloaded from

http://www.greatriverenergy.com/makingelectricity/ne wprojects/dryfining_factsheet.pdf on November 25, 2014. Also Bullinger, C.W., M.A. Ness, N. Sarunac, E.K Levy, R.S. Weinstein, and D.R. James, Method of enhancing the quality of high

- moisture materials using system heat sources , U.S. Patent 8,579,999 B2, November 12, 2013.

Downloaded from http://www.google.com/patents/US8579999 on November 25, 2014.

8 Sarunac, N. M. Ness, and C. Bullinger, “Improve Plant Efficiency and Reduce CO 2 Emissions When Firin g High

Moisture Coals.” Power Magazine , November 1, 2014. Downloaded from http://www.powermag.com/improve

plant

- efficienc y

- and

- reduce

- co2

- emissions

- when

- firing

- high

- moisture

- coals/?printmode=1 on November 26, 2014.
9 National Coal Council, Reliable & Resilient: The Value of Our Existing Coal Fleet , May 2014. Downloaded from

http://www.nationalcoalcouncil.org/NEWS/NCCValueExistingCoalFleet.pdf . The National Coal Council is a Federal Advisory Committee to the U.S. Secretary of Energy.



### 6

### In 2010 Breeding, Tandra, and Shah 10 

### presented case studies demonstrating how on

- line cleaning systems could be operated automatically in order to optimize the removal of ash and slag deposits in boilers. One sootblowing system includes fifty retractable sootblowers, eight water cannons, and air heater cleaners. A second installation improved overall plant efficiency up to

1 %

, resulting in a high return on the investment.

Predictive models are used in a U.S. pa tent for a sootblowing control system in order to determine optimal sequences for sootblower and boiler performance. 11 

### The optimization algorithm chooses the boiler zone to be treated and the sootblower type that will achieve the best boiler performance bas ed on current operating conditions.

A 2011 white paper by Labbe and Gordon 12 

### describes four power plant upgrades that contribute to higher efficiencies, lower emissions, greater fuel flexibility, higher availability, lower operating costs, and faster dispat ch rates. Three of the four optimizations directly affect boiler operations: control systems, overfire air, and sootblowers. The sootblower optimization mixed a smart logic system that evaluates performance metrics, fouling conditions, and equipment statu s and programmable sequence blocks that implement the cleaning actions.

The National Coal Council report cites intelligent sootblowers as reducing gross heat rates in older boilers by 30

- 90 Btu/kWh. In cases where slagging and fouling had reduced efficien cy, heat rate reductions were as high as 150 Btu/kWh where lignite or Powder River Basin coals were used, and improvements might be typically made up to 60 Btu/kWh. The cost for an intelligent sootblowing system is cited as $0.5M for a 500

- MW plant.

Labbe and Gordon also described overfire air optimization as a process involving new damper drives to regulate secondary air introduction near each burner, coupled with a new distributed control system (DCS) for modulation. The system reduced NO

x 

### emissions by 10 % and also decreased the heat rate by improving the balance of oxygen distribution. Labbe and Gordon also described how upgrading the DCS controls on two boiler units resulted in heat rate improvements approaching 1 %

.
EPRI’s R ange and A pplicability R eport (2014) list ed several additional measures for the boiler

island not found in the S&L Report but d id not report potential cost. 13 

### Those that would not overlap directly with the S&L Report findings include:

1) Automating boiler drains (to route to deaerators ins tead of condensers at reduced loads)

2) On

- site fuel drying ( resulting in significant decrease s in required air flow)

10 Breeding, C., D. Tandra, and S. Shah, “Boiler Cleaning Using ISB (Intelligent Soot Blowing) System Integration:

Recent Developments and Case Study,” Proceedings of the A merican S ociety of M echanical E ngineers 2010 Power Conference , Chicago, Illinois, 2010.

11 James, J.R., J. McDermott, S. Piche, F. Pickard, and N.J. Parikh, Sootblowing optimizati on for improved boiler

performance , U.S. Patent 8,498,746 B2, July 30, 2013. Downloaded on November 25, 2014 , from http://www.google.com/patents/US8498746

. 12 Labbe, D. and L. Gordon, Continuous Perfor mance Improvement in Power Generation: Optimization in Action

, Invensys Operations Management, March 2011. Downloaded on November 25, 2014 , from http://iom.invensys.com/EN/pdfLibrary/WhitePaper_Invensys_ContinuousPerformanceImprovementInPowerGen eration_03

- 11.PDF

. 13 Korellis, S., Range and Applicability of Heat Rate Improvements , Technical Update, Report Number 3002003457,

Electric Power Research Institute, April 2014.



### 7

### 3) Blowdown recovery tank

4) Air heater baskets

**2.1.3 Turbine Island** 



### An EPRI technical update report 14 

### examined how the heat rates changed at a coal

- fired power plant when it was reduced from base

- load performance to a load

- following mode. The three

- year study examined a plant that had previously operated at 650 MW (rarely below 400 MW) and later operated with a normal minimum operating load near 150 MW. Wh en the heat rate cu rve from a near

- continuous load

- day was compar ed to that for a transient load

- day (four different steady loads), the shape and magnitude matched each other . Another study 15 

### found that adding

one startup

- shutdown cycle caused the effective weekly heat rate to rise 0.62% for small

subcritical coal

- fired boilers, 0.44% for large subcritical coal

- fired boilers and for supercritical boilers, and 0.20% in combined cycle plants.

Korellis 16 

### listed ten different cost

- effective upgrades that might be introduced at coal

- fired units to improve heat rate during cycling operation s :

1) Sliding

- pressure operation (2% decrease in heat rate during part load)

2) Variable

- speed drives for main cycle and auxiliary equipment

3) Boiler draft system control schemes and operating philosophy

4) Automated pulverizer supervisory controls and variations with mill design

5) Optimum partial

- load operation of air quality control systems

6) Feedwater heater drain system modifications for cycling

7) Cooling system optimization

8) Performance mon itoring

9) Reducing warm

- up flow for idle boiler feed pumps

10) Minimizing flow, pressure, and temperature oscillations during cycling operation

The National Coal Council report shared typical heat rate improvements and capital costs for

steam turbine improvements:

1) 10 Btu/kWh for hydrogen purity ($0.25M)

2) 50 Btu/kWh for partial arc admission ($1M)

3) 4 Btu/kWh for control valves

4) 50 Btu/kWh for HP steam seal upgrade ($1M)

5) 95

- 135 Btu/kWh for HP steam path upgrade ($6M)

14 Electric Power Research Institute, Product Abstract for Cycling and Load

- Following Effects on Heat Rate

,

Product ID 1022061, July 18, 2011. Downloaded on Novemb er 25, 2014 , from http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000000001022061

. 15 Kumar, N., P. Besuner, S. Lefton, D. Agan, and D. Hilleman, Power Plant Cycling Costs , Report Number

NREL/SR

- 5500

- 55433, National Renewable Energy Laboratory, April 2012.

16 Korellis, S., “Coal

- Fired Power Plant Heat Rate Improvement Options, Part 1.” Powe r Magazine

, November 1,

2014. Downloaded on November 26, 2014 , from http://www.powermag.com/coal

- fired

- power

- plant

- heat

- rate

improvement

- option s

- part

- 1/?printmode=1

.



### 8

### 6) 20 Btu/kWh for IP steam seal upgrade ($1 M)

7) 50

- 100 Btu/kWh for IP steam path upgrade ($5M)

8) 120 Btu/kWh for LP steam seal upgrade ($0.75M)

9) 65

- 225 Btu/kWh for LP steam path upgrade ($5M)

EPRI’s Range and Applicability Report (2014) listed several additional measures for the turbine

island not found in the S&L Report but did not report potential cost. Those that would not overlap directly with the S&L Report findings include:

1) Running with a single circulation pump under favorable temperatures

2) Condenser ball cleaning system

3) Re

- tubing condensers

4) Water box vacuum priming system

5) Circulating water strainers

6) Circulating water turbine

7) Steam seal upgrade

8) Steam path upgrade

9) LP turbine last

- stage buckets

10) Exhaust hood steam guide modification

11) Rewind generator

12) Increasing hydrogen purity

13) Partial

- arc admission

14) Sliding pressure (a 2% HRI was realized at part load)

**2.1.4 Other Improvements** 



### Labbe and Gordon 17 

### described how a cooling tower advisory system was integrated into the NO

x 

### and heat rate optimization system at a plant in such a manner that the pumps and fans would optimize the net electricity generation. An hourly savings of 1205 kW was reported for a single

month.

EPRI’s Range and Applicability Report (2014) listed several additional measures outside the boiler and turbine islands not found in the S&L Report but d id not report potential cost. Those that would not overlap directly with the S&L Report findings include:

1) Replacement of first point heater (150 Btu/kWh)

2) Supplemental cooling towers

3) Deep lake water intake

4) Power supply upgrade for air pollution control equi pment

17 Labbe, D., and L. Gordon, Continuous Performance Improvement in Power Generation: Optimization in Action

,

Invensys Operations Management, March 2011. Downloaded on November 25, 2014 , from: http://iom.invensys.com/EN/pdfLibrary/WhitePaper_Invensys_ContinuousPerformanceImprovementInPowerGen
eration_03

- 11.PDF

.



### 9

### 5) Upgrade air compressors

6) Plant lighting upgrade

The National Coal Council report 18 

### also cites some additional heat rate improvements:

1) Up to 150 Btu/kWh for advanced process instrumentation and controls ($0.5

- 0.75M)

2) Low temperature heat recovery (air he ater performance, feedwater preheating, and supplemental low temperature gas

- side heat recovery)

3) 15

- 150 Btu/kWh for variable frequency drives ($9

- 11M for a 500

- MW plant)

4) Up to 70 Btu/kWh in summers for replacing or augmenting the cooling tower pack ($1.5

- 5M for a 500

- MW plant)

5) 600

- 1200 Btu/kWh through a topping cycle addition (could be developed within 10 years)

6) 300 Btu/kWh through a bottoming cycle with an organic solvent would supplant use of water for cooling (currently only used in small industri al processes)

Some common recommendations among the five plants in EPRI’s Range and Applicability Report also held potential for HRIs. These included:

1) Heat Rate Awareness training for operations staff (50 to 100 Btu/kWh)

2) Making heat rate information readil y available to more plant personnel (50 to 150 Btu/kWh)

3) Improving utilization of Controllable Losses information by operations staff (75 to 100 Btu/kWh)

4) Initiating routine testing programs (75 to 200 Btu/kWh)

Hansel 19 

### wrote that most plants already have the most advanced water treatment systems, so no opportunities were available there for HRIs.



## 2.2 **Total HRI**  **Studies**

In contrast to studies that examined individual measures, some studies also compared overall plant heat rates and efficiencies. Selected informatio n from a 2010 NETL report 20 

### compares the average fleet efficiencies for different boiler segments against the 90 th 

### percentiles based on a weighting by generation ( Table 2

- 3 ). Those difference s (ranging from 1.8 to 4.2 % ) might
represent a present

- day level of possible HRIs that moves existing boilers toward higher performance levels

.

18 National Coal Council, Reliable & Resilient : The Value of Our Existing Coal Fleet , May 2014. Downloaded from

http://www.nationalcoalcouncil.org/NEWS/NCCValueExistingCoalFleet.pdf . The National Coal Council is a Federal Advisory Committee to the U.S. Secretary of Energy.

19 Hansel, Peter, Heat Rate Reductions and Carbon Emissions: A Policy Mechanism for Regulating Coal Plants

under 111(d) of the Clean Air Act , April 22, 2014. Downloaded from http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/8508/Heat%20Rate%20Reductions%20and%20Car

bon%20Emissions%20

- %20Peter%20Hansel%20MP%20Final.pdf?sequence=1 on November 25, 20 14.

20 DiPietro, P. and K. Krulla , Improving the Efficiency of Coal

- Fired Power Plants for Near Term Greenhouse Gas

Emissions Reductions , DOE/NETL

- 2010/1411, National Energy Technology Laboratory

, 2010. Downloaded on December 1, 2014 , from http://netl.doe.gov/File%20Library/Research/Energy%20Analysis/Publications/DOE

NETL

- 2010

- 1411

- ImpEfficCFPPGHGRdctns

- 0 410.pdf

.



### 10

### Table 2

- 3 . 2010 NETL Assessment of Coal

- Fired Power Plant Efficiencies

Segment Criteria Sub

- population Characteristics Efficiency

Unit Type Coal Type Size (MW) Capacity (GW) # Units Generation (BkWh) Average 90 th

Percentile Difference

Low

Pressure

Subcritical (600

- 1600 psig) Bit

0

- 200 10.3 127 44 29.6% 33.0% 3.4%

Sub

- bit 4.6 59 26 27.5% 29.8% 2.3%

Other 0.6 7 2 27.4% 30.5% 3.1%

High

Pressure

Subcritical (1800

- 2600 psig) Bit 0

- 200 21.6 134 112 32.1% 34.8% 2.7%

200

- 500 33.4 103 189 32.8% 35.6% 2.8%

500+ 29.7 48 176 32.7% 35.0% 2.3%

Sub

- bit 0

- 200 7.2 47 42 30.7% 32.5% 1.8%

200

- 500 31.2 97 191 31.4% 35.6% 4.2%

500+ 64.4 98 401 31.6% 33.8% 2.2%

Other 11.1 28 72 31.7% 35.1% 3.4%

Supercritical (3334+ psig) Bit 60.7 79 372 35.1% 37.3% 2.2%

Sub

- bit 15.0 20 90 35.2% 37.2% 2.0%

Other 8.1 13 55 31.8% 34.9% 3.1%



## 2.3 **Associated Costs**

Although most NETL publications since 2009 deal with newer technologies that affect plant

wide heat rates (e.g., gasification and carbon capture), one subcontract report from NETL lists some costing estimates. 21 

### Cases 9 and 11 list a breakdown of many costs associated with subcritical and supercritical coal

- fired plants without carbon capture.

21 National Energy Technology Laboratory

, Cost and Performance Baseline for Fossil Energy Plants Volume 1:

Bituminous Coal and Natural Gas to Electricity , DOE/NETL

- 2010/1397, last updated in 2013. Downloaded on Nov ember 17, 2014 from http://www.netl.doe.gov/File%20Library/Research/Energy%20Analysis/OE/BitBase_FinRep_Rev2a

3_20130919_1.pdf

.



### 11

## 3 **Statistical Assessment of**  **Heat Rates in**  **the Plant Database** 

The EIA

- 860 and EIA

- 923 surveys reveal a great deal about plant operations, as well as some specific information about generators and boilers. These surveys may also be supplemented by annual information collected by the U.S. Environmental P rotection Agency (EPA). EIA uses the net generation data (gross generation minus parasitic load) and heat input information for fossil

fuel boilers to compute net annual heat rates for each generator over a five

- year period . For the

coal

- fired units, th e 2 012 heat rates varied from 8759 to 25,000 Btu/kWh at 1027 generators for which a single boiler could be matched. 22



### This chapter describes an analysis of the heat rates for coal

- fired boilers as part of an effort to understand how the EIA data might be used to identify units that could implement improvements in future years. This work may support EIA modeling efforts that aim to choose between installing unit improvements and retiring existing units. Other chapters consider the costs, but this chapter focuses on projecting the deviation of existing heat rates from heat rates at units of similar design.



## 3.1 **Data Processing**

The basis for this work is the AEO2014 Plant File Inputs ( AEO 2014 plant listing inputs.xls) that share data records from plants online in 2012 ( 15,993 records). Of these data, 1084 records applied to the coal

- fired EFD plant types (CAV, CSC, and CSU) 23 

### , and each record is associated with a particular generator. According to Assumptions and Updates to the AEO 2014 Plant File

, the heat rates represent the net generation divided by the heat input to the associated boiler (over

a five

- year period) . EIA uses a heat rate ceiling of 25,000 Btu/kWh in its calculations.

An EIA

- 860 file (EnviroAssocY2012.xlsx) provides crosswalks to match the generator ID s to
individual boiler ID s . The file also provides crosswalks to tie boiler IDs to the cooling system

IDs , FGD system IDs, and FGP collection system IDs.

The data were provided from multiple sources and matched based on plant and boiler ID codes:

• EIA

- 923 schedules (2012 data)

• EIA

- 860 surveys

• EPA eGRID 9 th 

### edition (2010 data)

• EPA Clean Air Markets Database (2012 data from ampd.epa.gov/ampd/)

Data were generally provided as plant level, boiler level, or generator level records, but some

information was l isted in multiple records for the plants (e.g., monthly mine fuel receipts). The composite database held more than 200 fields, but many of the parameters were not expected to

correlate at all with heat rates (e.g., cooling water outlet distance from the sh ore) and were

excluded from further consideration.



## 3.2 **General Data Population Characteristics**

Table 3

- 1 describe s the numeric variables that were include d in the study.

22 As per EIA direction, 57 combined h eat and power units were excluded from the analysis.

23 These plant types represent three existing coal

- fired technologies: CAV = New Advanced Coal; CSC = Coal Steam with Scrubber; and CSU = Coal Steam post

- 1965.



### 12

### Table 3

- 1 . Distribution of N umeric P arameters A ssociated with C oal

- F ired G enerators

Variable Number of data points Percentage of missing

points Average Standard

Deviation Minimum 25th percentile 50th percentile 75th percentile Maximum

Heatrate (Btu/kWh) 1027 0% 11 ,300 2 ,400 8

, 759 10

, 247 10

, 644 11

, 287 25

, 000

CAMD2012 heatrate (Btu/kWh) 858 16% 10 ,200 1

, 10 0 7

, 311 9

, 473 10

, 008 10

, 660 18

, 474

Summer Capacity (MW as listed in EPA CAMD2012) 1027 0% 294 250 1 100 200 476 1

, 300

Online Year 1027 0% 1970 14 1944 1958 1969 1979 2012
Variable O&M ($/MWh) 1027 0% 2.60 1.50 0.58 1.46 2.23 3.33 11

Annual Fixed O&M ($/KW) 1027 0% 47 18 7 33 44 60 98

Annual Capital Additions ($/KW) 1027 0% 18 31 0 4 11 21 330

Heat Input (MMBtu) 869 15% 19 ,000,000 18,000,000 1,251 4,525,293 12,519,851 32,227,304 95,309,144

Gross Load (MWh) 860 16% 2,000,000 1,900,000 0 444,604 1,229,583 3,357,001 10,039,827

Nameplate Capacity (MW) 1027 0% 350 290 4 114 253 578 1

, 426

Generator Summer Capacity (MW) 1027 0% 330 270 0 103 232 531 1

, 300

Generator Winter Capacity (MW) 1027 0% 330 270 0 103 235 543 1

, 300

Planned Retirement Year 203 80% 2015 2 2012 2014 2015 2015 2021

FGD In

- service Year 491 52% 1999 13 1971 1985 2007 2010 2019

Pond Landfill Requirements (Acre

- Foot per Year) 339 67% 160 270 0 19 77 200 2

, 714

Specs of Coal Ash 463 55% 12% 5% 0% 10% 10% 10% 40%

Specs of Coal Sulfur 463 55% 0% 1% 0% 0% 0% 0% 10%

FGD Trains Total 467 55% 2 2 1 1 1 3 12

FGD Trains 100% 454 56% 2 1 1 1 1 3 10

FGD Sulfur Removal Efficiency 475 54% 93% 10% 50% 90% 100% 100% 100%
Sulfur Emission Rate (lbs per hr) 473 54% 1500 1800 0 352 743 1

, 858 9

, 999

FGD Cost Structure ($K) 410 60% 130,000 200,000 21 27,982 62,224 177,084 1,445,626
FGD Disposal Cost ($K) 276 73% 14,000 26,000 1 1,776 5,663 14,475 223,438
FGD Other Cost ($K) 196 81% 45,000 152,000 10 470 13,700 34,500 1,938,000

FGD Cost Total ($) 460 55% 159,000 203,000 21 37,738 92,481 236,861 1,503,972

Quantity of FGD Sorbent 390 62% 64 68 0 13 39 98 353



### 13Variable Number of data points Percentage of missing

points Average Standard

Deviation Minimum 25th percentile 50th percentile 75th percentile Maximum

FGD Electrical Consumption 389 62% 56,000 64,000 0 12,667 37,120 70,680 408,706

FGD Efficiency at Annual Operating Factor 409 60% 90 13 0 89 95 97 100

FGD Efficiency at 100% 337 67% 92 11 0 90 95 98 100

FGD Chem Cost 368 64% 2,400 3,600 0 448 1

, 303 2

, 842 27

, 633

FGD Labor Cost 368 64% 1,100 1,400 0 261 652 1

, 298 10

, 370

FGD Waste Cost 344 67% 680 1,100 0 23 289 786 8

, 327

FGD Maintenance Material
Other 362 65% 1,200 1,600 0 350 751 1

, 594 17

, 880

Total O and M 375 63% 5,200 5,700 0 1

, 748 3

, 760 6

, 835 36

, 644

No FGD Control 73 93% 0 0 0 0 0 0 1

FGP Inservice Year 883 14% 1981 11 1949 1975 1979 1985 2012

FGP Installed Cost ($K) 873 15% 16.000 26,000 1 2

, 630 8

, 390 19

, 900 267

, 000

FGP Collection Efficiency 883 14% 99% 1% 80% 99% 100% 100% 100%

Emission Rate (lbs per hr) 883 14% 190 230 0 45 108 250 2

, 120

FGP Hours In Service 864 16% 5,700 2,600 0 3

, 998 6

, 793 7

, 757 8

, 760

Cooling System In

- service Year 848 17% 1970 14 1924 1958 1970 1979 2012

Intake Rate at 100% (Cubic Feet per Second) 844 18% 450 670 1 27 212 535 4

, 711

Number of Federal Air Pollution Programs 24 886 14% 6 2 1 5 6 7 8

24 This variable is a straight count of the number of programs identified by the nominal parameter programs listed in Table 3

- 3

.



### 14

### Some additional numeric fields describing cooling systems were not included in the computations in order to reduce the analysis burden : Chlorine Inservice Year

, Pond Inservice

Year

, Pond Volume (Acre Feet), Tower Inservice Year, Tower Water Rate (Cubic Feet per Second), Cooling System Power Requirement (MW), Cost Ponds (Thousand Dollars), Cost Towers (Thousand Dollars), and Cost Chlorine Equipment (Thousand Dollars)

. The correlations among the numeric values were compared, and those with correlation coefficients greater than 0.6 are pr esented in Table 3

- 2

. Table 3

- 2 clearly point s to redundancies when the heat inputs, gross loads, and capacities are used.

Table 3

- 2 . Numeric Parameter Pairs w ith Correlation Coefficients with Absolute Values

Greater t han 0.60

Parameter 1 Parameter 2 Correlation
Coefficient

Heat R ate (Btu/kWh) Variable O&M ($/MWh) 0.63

Summer Capacity (MW from the EPA CAMD2012 database) Variable O&M ($/MWh)

- 0.61 Heat Input (MMBtu) 0.77
Gross Load (MWh) 0.77 Nameplate Capacity (MW) 0.89

Generator Summer Capacity (MW) 0.90
Generator Winter Capacity (MW) 0.90

Online Year Heat Input (MMBtu) 0.62 Gross Load (MWh) 0.61
Cooling System Inservice Year 0.95

Variable O&M ($/MWh) Annual Fixed O&M ($/kW) 0.67 Heat Input (MMBtu)

- 0.66
Gross Load (MWh)

- 0.66 Nameplate Capacity (MW)

- 0.68 Generator Summer Capacity (MW)

- 0.67
Generator Winter Capacity (MW)

- 0.67

Heat Input (MMBtu) Gross Load (MWh) Nameplate Capacity (MW) Generator Summer Capacity (MW) Generator Winter Capacity (MW) 1.00

0.89

0.90

0.90

Gross Load (MWh) Nameplate Capacity (MW) 0.90
Generator Summer Capacity (MW) 0.90 Generator Winter Capacity (MW) 0.90

Nameplate Capacity (MW) Generator Summer Capacity (MW) 1.00
Generator Winter Capacity (MW) 1.00 Generator Summer Capacity (MW) Generator Winter Capacity (MW) 1.00

Planned Retirement Year FGD Cost Structure ($K)

- 0.66 FGD Disposal Cost ($K)

- 0.64

FGD Trains Total FGD Trains at 100% 0.97 FGD Cost Structure ($K) FGD Cost Total ($) 0.96
FGD Efficiency at Annual Operating

Factor FGD Efficiency at 100% 0.68

FGD Chemical Cost FGD Total O&M 0.87

FGD Waste Cost FGD Total O&M 0.61

FGD Maintenance Material Other FGD Total O&M 0.63



### 15

### In addition to the numeric parameters, Table 3

- 3 lists the nominal and logical parameters that were considered as generator/boiler/plant characteristics that might affect heat rate. Table 3

- 3

also shows some consolidated variables that were evaluated in cases where entries could be grouped.

Table 3

- 3

. Nomi n al and Logical Characteristi cs Considered i n t his Study

Variable Number of

Blanks Populated Entries

NEMS EFD Plant Type 0 CAV, CSC, CSU NEMS ECP Plant Type 25 0 B1, B3, B5, B7, C1, C4, C7, CX, H1, H4, H7, IG

Programs 141 Combination of the entries CAIRNOX, CAIROS, CAIRSO2,
RGGI, TRNOX, TRNOXOS, TRSO2G1, and ARP Fuel Type (Primary) 141 Coal, Coal, Pipeline Natural Gas Operating Status 141 Operating, Operating (Started 01/24/2012)

NOx Control Status 66 CN, CO, NA, OP, OS, OZ, RE, SB, SC, TS

Low NOx Process 1 66 AA, CF, FU, H20, LA, LN, NA, OT, OV, SN, SR

Low NOx Process 2 390 AA, BF, CF, FU, LA, LN, OT, OV, SN, SR

Low NOx Process 3 785 AA, FU, LA, LN, NH3, OT, OV, SN, SR Mercury Emission Control 66 Y, N

Prime Mover 0 CA, CT, ST

GenStatus 0 OA, OP, OS, RE, SB Multiple Fuels 0 Y, N

Turbines 1

, 016 1 Cogenerator 0 Y, N

Sector Name 0 Electric Utility, IPP Non

- CHP Topping or Bottoming 991 B, T

Duct Burners 304 N

Planned Modifications 33 Y, N Planned Uprate Year 1

, 019 2013, 2014, 2016, 2017

Planned Derates Year 1

, 014 2013, 2014, 2015, 2016, 2017

Planned New Prime Mover 1

, 026 ST Planned Repower Year 1

, 018 2013, 2014, 2015, 2016, 2017

Other Modifications Year 1

, 004 2013, 2014, 2015, 2018 Solid Fuel Gasification System 89 Y, N Pulverized Coal Technology 112 Y, N

Fluidized Bed Technology 974 Y, N Subcritical Technology 348 Y, N Supercritical Technology 876 Y, N Ultrasupercritical Technology 1

, 023 Y, N Carbon Capture Technology 1

, 024 Y, N

FGD Status 536 CN, CO, OP, OS, PL, RE, SB, SC FGD Type 1 541 BR, CD, DP, OT, PA, SD, SP, TR, VE FGD Type 2 986 PA, SP, TR, VE FGD Type 3 1

, 026 VE FGD Type 4 1

, 027 none Sorbent Type 1 554 AF, DB, DL, LA, LF, LI, LS, OT, SA, SB, SC, SL Sorbent Type 2 987 DB, DL, LI, MO, SC, SF Sorbent Type 3 1

, 026 DB

25 ECP types defined on page vii

.



### 16

### Variable Number of

Blanks Populated Entries

Sorbent Type 4 1

, 027 none FGDByproductRecovery 554 Y, N Sludge Pond Lined 555 Y, N, NA Flue Gas Bypass FGD 554 Y, N

FGP Status 144 OP, OS, SB, SC FGPCollectorType1 144 BP, BR, BS, EC, EH, EK, EW, MC, OT, WS FGPCollectorType2 889 BP, BR, EC, EK, EW, MC, OT, WS FGPCollectorType3 1

, 011 EK, MC, SC, WS

No FGP Control 972 ., 0, 1 Cooling Status 179 OP, OS, SC Cooling Type 1 179 DC, HRF, OC, ON, OT, RC, RF, RI, RN Cooling Type 2 972 HT, OC, ON, OT, RC, RF, RI, RN Cooling Type 3 1

, 022 RI Cooling Type 4 1

, 027 none Tower Type 1 658 MD, MW, NW, WD Percent Dry Cooling 998 0%, 100%

Unit Type 141 Bubbling fluidized bed boiler, Cell burner boiler, Circulating fluidized bed boiler, Cyclone boiler, Dry bottom turbo

- fired boiler, Dry bottom vertically

- fired boiler, Dry bottom wall

fired boiler, Dry bottom wall

- fired boiler (Started Jan 24, 2012), Tangentially

- fired, Wet bottom turbo

- fired boiler,

Wet bottom wall

- fired boiler

Firing Type 136 CELL, CYCLONE, DRY FRONT/WALL, DRY OPPOS/WALL, DRY STOKER, DRY TANGENTIAL, DRY TURBO, DRY VERTICAL, DRY WALL,
FLUIDIZED, TANGENTIAL, WET TURBO, WET WALL Fuel Type 103 BIT, LIG, SUB, WC Firing Class (consolidation of Firing Type variable) 136 Bottom

- fired, Dry Wall

- fired, Tangential

- fired, Wet

- bottom

Low NOx Summary 1 and Low NOx Summary2 (consolidation of Low NOx Process variables) 66

390 Combustor, Injection, Recirc ulation , Reduction, Other

FGP Collector Summary1 (consolidation of FGPCollectorType1) 144 Baghouse, Cyclone, ESP, WetScrubber, Other



## 3.3 **Influence of Nameplate Capacity on Heat**  **R ate** 

Leidos conducted a preliminary investigation of plotted heat rates versus some of the parameters

listed in Table 3

- 1 and Table 3

- 3 ; the selected parameters were chosen primarily with regard to

the distr ibutions found in the data set and the fractions of missing data. Relationships were easier to identify and more linear when the variables were compared to the inverse heat rate, a measure of unit efficiency. However, the influences of most individual var iables still appeared small against the inverse heat rates that varied from 40 to 114 kWh/ MM Btu.

17

### Therefore, the preliminary investigation included application of the M5P algorithm 26 

### to develop a piecewise linear regression model. A piecewise approach would allow subsets of the inverse

heat rates to be evaluated separately against the various parameters, and the algorithm would identify those parameters with the most influence on the inverse heat rates . Using a leave

- one

out approach for evaluating the model performance, the Weka Explorer software executed the M5P algorithm to predict inverse heat rate with a correlation coefficient of 0.83 and a relative absolute error of just 54%. Roughly thirty parameters were used in the four piecewise linear

models that w ere divided as shown in Figure 3

- 1 . The numbers in parentheses represent the number of records followed by the relative absolute errors for the data s ubset. Those in the largest group (627 records) had nameplate capacities greater than 181 MW and a relative absolute error of only 32%; the predictive ability was much lower for those with nameplate capacit ies less than 106 MW.

Figure 3

- 1 . Preliminary Decision Tree for Describing Inverse Heat Rates

of Coal

- Fired Generators

The decision tree suggested that different linear dependences would be evident if the predicted inverse heat rates were plotted relativ e to the nameplate capacity ( Figure 3

- 2 ). Figure 3

- 2 illustrates that the inverse heat rates were highly dependent on nameplate capacity at low nameplate capacities and less so as the nameplate capacity increased. 27 

### Figure 3

- 2 also illustrates

the best linear fits for the data.

More efficient plants are found for the generators with higher nameplate capacities, but this finding is not very useful for studies into finding ways to increase plant efficiencies. A transformation to mask the dependence of inverse heat rates on nameplate capacity might reveal the influences of various equipment types on the plant efficiencies.

26 The M5P (or M5’) algorithm represents a 1997 improvement of Quinlan’s original M5 algorithm (1992). It builds

piecewise linear regression models of numeric data, but may use nominal, ordinal, and logical parameters within the model tree and within the reg ression equations.

27 Such behavior may have indicated a logarithmic dependence on nameplate capacity, but logarithmic evaluations

did not fit the data well.



### 18

### Figure 3

- 2 . Preliminary Re lationship b etween Nameplate Capacity and

Predicted Inverse Heat Rates



## 3.4 **Slope Function Analysis** 

The three linear fits shown in Figure 3

- 2 suggest that the inverse heat rate data may be transformed to a variable referred to as the dimensionless slope function:

Slope function =

⎩ ⎪ ⎪ ⎨ ⎪ ⎪ ⎧ inverse heat rate

0

. 262 × NPC + 61

. 4 where NPC ≤ 106 MW

inverse heat rate

0

. 045 × NPC + 90

. 5 where NPC ≥ 106 MW

inverse heat rate

0

. 0071 × NPC + 93

. 1 where NPC > 181 MW and NPC < 181 MW

W here

, NPC represents the nameplate capacity. The numerators are surrogates for actual

efficiencies , and the denominators represent the average efficiency for generators of similar

sizes. The Slope F unction is a dimensionl ess parameter. R ecords with slope functions greater than one represent generators that perform better than units of similar size, and those with slope

19

### functions less than one underperform. Figure 3

- 3 illustrates the distribution of the Slope Function
parameter.

Figure 3

- 3 . Histogram Showing t he Distribution f or t he Slope Function Transformation

The M5P algorithm could generate models for Slope Function that had correlation coefficients as high as 0.5, but no single parameter strongly influenced the Slope Function parameter. Some further data cleaning was deemed necessary to be sure that variables were being treated prop erly.

An attribute evaluation determines the parameters that the target parameter is most heavily dependent on but are independent of one another. Four runs were made using the Weka

AttributeEvaluator on the preliminary data sets to determine which variabl es most influence the Slope Functio n variable and are independent of other variables. The Cfs SubsetEval algorithm selectively chooses the parameter sets that are highly correlated with the various classifications but have low intercorrelation, but the Wrap per[M5Rules] algorithm also uses cross validation to estimate the accuracy of the M5Rules algorithm with each subset of parameters. 28 

### The results yielded the following list of variables with at least an occurrence in one run ( Table 3

- 4 ) :

28 The CfsSubsetEval and Wrapper[M5Rules] evaluators were paired with two different methods to searc h for a

good subset of the data attributes: BestFirst and GreedyStepwise . The BestFirst method allows backtracking in the search when the subsetting stops improving, but the GreedyStepwise method stops the search when inclusion of

the next attribute would decrease the evaluation matrix. 

## 0 50 100 150 200 250 300 350Number of Records



## Slope Function

20

### Table 3

- 4 . Results from the AttributeEvalu a tor Showing T hose Variables T hat Most
Influence t he Slope Function Variable a nd A re Independent o f Other Variables

Parameter CfSSubsetEval Wrapper[M5Rules]

NEMS EFD Plant Type ● ● Primary fuel classification (Bituminous, sub

bituminous, lignite, or waste coal) ●

Annual Capital Additions ($/kW) ● Variable O&M ($/MWh) ● Nameplate Capacity (MW) ●
Low NO

x Process 2 ● Mercury Emission Control (Y or N) ● ● Generator status (operating, out of service, retired, etc.) ●

Planned Retirement Year (2012 through 2021) ● ● Solid Fuel Gasification System (Y or N) ●
Subcritical Technology (Y or N) ● FGD Type 1 ●
FGP Status ● FGPCollectorType1 ● Cooling Status ● Number of Federal Air Pollution Programs (0 to 8 and may act as a geographic surrogate) ●



### Some of these parameters that were deemed significant by the AttributeEvaluator did not seem to have enough records to be considered influential in the model development. For example, the Planned Retirement Year parameter is missing in 80% of the records, but the model had been replacing that missing data by the mod e value of 2015. Therefore, the missing data were recoded for Planned Retirement Year to reflect a condition that no retirement date was yet planned.

The At tribute Evaluator was again run to determine the dependences of Slope Function on the

revised variables with three trials ( BestFirst+CfsSubsetEval

, GreedyStepwise+CfsSubsetEval

, and GreedyStepwise+ W rapper [M5Rules]); the fourth case did not converge. The results yielded the following list with at least an occurrence in one run:

• NEMS EFD Plant Type

• Primary fuel classification (Bituminous, sub

- bituminous, lignite, or waste coal)

• Annual Capital Additions ($/KW)

• Variable O&M ($/MWh)

• Nameplate Capacity (MW)

• Low NOx Process Summary 2

• Mercury Emission Control (Y or N)

• Generator status (oper ating, out of service, retired, etc.)

• Solid Fuel Gasification System (Y or N)

• Pulverized Coal Technology (Y or N)

• Subcritical Technology (Y or N)

21

### • FGD Status

• FGD Type 1

• FGP Status

• FGPCollectorType Summary 1

• Cooling Status

• Num ber of Federal Air Pollution Pro grams (0 to 8 and may act as a geographic surrogate)

Using only these variables, a “ leave

- one

- out

- approach ” was again used with the M5P algorithm to predict Slope Function, and the minimum number of records for a decision tree leaf was varied from 4 to 40. The error summaries were similar with correlation coefficients ranging from

0.50 to 0.53.

However, there was still a question about reported data that may be influenced by neighboring equipment. For example, two generators at the PPL Montour plant use Boiler 1 with nameplate capacities of 17.2 and 805.5 MW. The S lope F unction value for the smaller generator i s 1.6 (not

shown in Figure 3

- 3 ) because it operates 60% more effectively than similarly sized generators. All data with slope functions more than 3 st andard deviations from the average (less than 0.70 or greater than 1.26) were filtered and the model rerun. The data fits did not improve, illustrating that removal of outliers did not improve the model performance.

In the Attribute Selector, the one sche me that operated with the M5P subsetting approach ( GreedyStepwise+ W rapper [M5Rules] ) had yielded the following selection of variables to describe the Slope Function:

• NEMS EFD Plant Type

• Primary fuel classification (Bituminous, sub

- bituminous, lignite, or w aste coal)

• Nameplate Capacity (MW)

• Pulverized Coal Technology (Y or N)

• Subcritical Technology (Y or N)

• FGD Status ( Cancelled, New under construction, Operating, Out of service, Planned, Retired, Standby, or Cold Standby )

• FGPCollectorType Summary 1 ( Baghouse, Cyclone, ESP, WetScrubber, or

Other )

These seven variables were used with the M5P classifier algorithm to yield a seven

- rule model that is dependent on this smaller number of parameters and yields a correlation coefficient of 0.49. This is only a mild improvement to the model that had been based only on nameplate capacity, but the model points to physical plant data that might influence heat rates.

Figure 3

- 4 illustrates the decision tree and the seven linear regression models that can be used to fit the Slope Function. For example, the two rules in the lower left of the figure show that the slope function increases by 0.07 or 0.09 if bituminous fuel is used ( instead of lignite). The full models included additional parameters, but only those shown in the figure altered the tenths or hundredths place on the calculated Slope Function.

22

### Figure 3

- 4

. Decision Tree to Describe the Slope Function Using the

M5P Piecewise Linear Regression Model



## 3.5 **Model Predictions of Heat R**  **ate**

In the previous sections, the Slope Function was defined as a ratio of the inverse heat rate (kWh/mmBtu) to a linear function of the nameplate capa city. Therefore, the predicted heat rates may be computed by the expression :

Heat rate � Btu

kWh �

=

⎩ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎧ 10 6

( 0

. 262 × NPC + 61

. 4 ) ( Slope function ) where NPC ≤ 106 MW

10 6

( 0

. 045 × NPC + 90

. 5 ) ( Slope function ) where NPC ≥ 106 MW

10 6

( 0

. 0071 × NPC + 93

. 1 ) ( Slope function ) where NPC > 181 MW and NPC < 181 MW



### W here

, the S lope F unction is found in Figure 3

- 4 on each of the seven leaves of the decision tree.

To ensure that the spectrum of data points is still e asily visible, Figure 3

- 5 presents the inverse of the reported heat rates versus the predicted data poin ts. The best linear fit has an r 2 

### correlation

coefficient of 0. 624 with a slope of 0.987, and the red dashed lines show slopes corresponding to

a 50 % confidence interval. Generators to the left of the lines have lower efficiencies than similar units and represent the generators with the greatest likelihood that the op erations can be improved.

23

### Figure 3

- 5 . Comparison of Reported and Predicted Inverse
Heat Rates for All Generators



## 3.6 **Generator Units that**  **Most Likely Can**  **Improve Heat Rates** 

The generator units in Figure 3

- 5 can be categorized as follows: Q 4 ) fall to the left of the three lines (least efficient compared to predicted values) ; Q 3 ) fall to the right of the first r ed line and to

the left of the blue line ( thir d quartile) ; Q 2 ) fall to the right of the blue line and to the left of the second red line ( secon d quartile) ; and Q 1 ) fall to the right of all three lines (most efficient compared to the predicted values). The predicted values are already dependent on the unit size s, fuel types, and installed control devices.

Figure 3

- 6 presents the fractions of occurrences in each of these quartiles for the different leaves

in the decision tree. The figure illustrates that Quartile 1 (those generators with the low est reported heat rates ( high est efficienc ies) compared to the predicted heat rates ) represents the majority of the generators with nameplate capacities under 181 MW. Generators with nameplate capacities under 181 MW also represent 86 % of the Quartile 1 generators, suggesting that the smallest gen erators have the lea st room for improvements.

24

### Figure 3

- 6

. Quartiles Comparing Reported Heat Rates t o Predicted Heat Rates

The wor st data (Quartile 4) shows reported heat rates that are high er than the predicted heat rates. The majority of the Quartile 4 data is represented by the decision tree leaf in Figure 3

- 6 for generators with nameplate capacities between 181 and 607 MW that are associated with bituminous coal or lignite firing. This statisti cal observation suggests that finding further heat rate improvements at those plants (with nameplate capacities between 181 and 607 MW that are associated with bituminous coal or lignite firing) would be easiest

.

In this statistical manner
, with the assump tion that the least costly measures have already been implemented

, Figure 3

- 6 suggests that the mo st costly HRIs (on a per

- MWh basis) in future years would be expected at the plants with nameplate capacities less than 181 MW. The lea st costly heat rate improvements tha t could be made in future years would be at generators with nameplate capacities between 181 and 607 MW that are associated with bituminous coal or lignite firing. The two leaves in the lower right corner (sub

- bituminous coal with nameplate capacities over 181 MW) would offer interim costs in future years because their data lie primarily in Quartiles 2 and 3.

25

# 4 **Interpretation of Quartile Data**

The previous chapter describes how the quartile information was generated, and this chapter compares the measurement s and configurations associated with the quartiles. The quartile designations were based on how well the reported heat rates aligned with the predicted heat rates from similar units. When the reported heat rate/predicted heat rate was less than 0.94, the d ata appear in Quartile 1 (best performance relative to similar units). When the reported heat rate/predicted heat rate was greater than 1.04, the data appear in Quartile 4 (worst performance relative to similar units). Figure 4

- 1 shows the counts across the quartiles for the different ratios.

The full data set can be fit by a normal distribution curve, but the data from the individual quartiles can not.

Figure 4

- 1 . Distribution of Units across the Quartiles for Reported

Heat Rate/Predicted Heat Rate Ratio

The means and standard deviations for the numeric parameters are shown in Table 4

- 1 for the four quartiles. Note that the standard deviations only give a sense of the data variability

, but do not imply a normal distribution of the data within any of these quartile subsets. In particular, Quartiles 1 and 4 often represent a single tail of a data set.

Of particular note in Table 4

- 1 is that the average EIA

- reported heat rates are higher than those reported in the EPA CAMD data set for all four quartiles. The EIA

- reported heat rates are a composite from five years of data, 29 

### but the EPA CAMD data set represents just 2012. Nationally the year 2012 had more cooling degree days than any other year from 2006 through

29 Email correspondence from Eric Krall of EIA on November 7, 2014.



### 26

### Table 4

- 1 . Distributions of N umeric P arameters among the Q uartiles

Quartile 1 (best) Quartile 2 Quartile 3 Quartile 4

Measured Parameters Heat Rate (Btu/kWh) 11, 200 ±1, 4 00 10, 700 ±1, 1 00 10, 8 00 ±1, 400 12, 6 00 ±3, 8 00

CAMD 2012 Heat Rate (Btu/kWh) 10, 4 00 ±1, 200 9, 9 00 ± 9 00 10, 0 00 ±1, 100 10, 30 0 ±1, 200

Online Year 1964 ±16 1972 ±15 1974 ±12 1970 ±12 Variable O&M Cost ($/MWh) 3.5 ±1.5 2.0 ±1.0 2.0 ±1.2 2.8 ±1.6

Annual Fixed O&M Cost ($/kW) 55 ±19 42 ±16 43 ±17 48 ±18

Annual Capital Additions ($/kW) 16 ±31 19 ±37 17 ±20 20 ±34

Heat Input (billion Btu) 5, 500 ±7, 4 00 24,000 ± 18, 000 28,000 ± 19,000 17, 0 00 ±14, 000 Gross Load (GW

- h) 5 50 ± 79 0 2, 5 00 ± 2,000 2, 900 ± 2,000 1, 7 00 ±1, 500
Nameplate Capacity (MW) 15 0 ±170 43 0 ± 2 90 5 10 ± 29 0 3 30 ± 25 0

FGD In Service Year 1981 ±12 1983 ±12 1982 ±11 1980 ±10 FGD Cost Total ($K) 130,000 ±14 0,000 160,000 ±1 30,000 170,000 ±2 50,000 1 60,000 ±22 0,000 FGD Total O&M Cost ($K) 4, 1 0 0 ±3, 7 0 0 4, 2 00 ±3, 3 00 5, 70 0 ±7, 40 0 5, 800 ±5, 400

FGP In Service Year 1981 ±12 1983 ±12 1982 ±10 1980 ±10 FGP Installed Cost ($K) 8, 200 ±15, 6 00 18, 000 ± 21,000 26,000 ±39, 000 13, 000 ±15, 000
FGP Emission Rate (pound s per h ou r) 99 ± 1 80 18 0 ± 20 0 2 50 ± 24 0 2 10 ± 26 0

FGP Hours In Service 4, 1 00 ± 3,000 6, 600 ±2, 10 0 6, 4 00 ±2, 300 5, 400 ±2, 5 00
Cooling System In

ServiceYear 1963 ±16 1973 ±15 1974 ±12 1970 ±11

Intake Rate at 100% (cubic feet per second) 52 0 ± 72 0 4 80 ±610 37 0 ± 700 440 ± 66 0

Number of Federal Air Quality Programs 4.5 ±3.0 4.9 ±2.6 4.8 ±2.6 4.9 ±2.8

Computational Parameters NPC term in “Slope Function Analysis” 88 ±12 95 ±6 95 ±8 91 ±11

Slope Function (reported) 1.04 ±0.10 0.99 ±0.05 0.98 ±0.04 0.92 ±0.11 Slope Function (predicted) 0.93 ±0.07 0.97 ±0.05 1.00 ±0.05 1.01 ±0.07

Predicted Heat Rate (Btu/kWh) 12, 600 ±1, 800 11,000 ±1,200 10, 700 ±1, 400 11, 000 ±2, 5 00

Number of Missing Data Reports Used for Predictions 1. 2 ±0. 8 0. 7 ±0.7 0.5 ±0.7 0.7 ±0.8

Reported Heat Rate/Predicted

Heat Rate 0.89 ±0.04 0.97 ±0.01 1.01 ±0.01 1.11 ±0.11

Inverse Heat Rate Reported (kWh/MMBtu) 91 ±10 95 ±8 94 ±9 84 ±16

Inverse Heat Rate Predicted (kWh/MMBtu) 81 ±10 92 ±8 95 ±9 92 ±14



### 2013, 30 

### so the EPA CAMD rates may reflect that units were operated closer to capacity in 2012 than in some other years.

30 National Weather Service Climate Prediction Center, Degree Days Statistics . Downloaded on November 11

,

2014 , from http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/cdus/degree_days/

.



### 27

### Among the measured numeric parameters in Table 4

- 1 , th e four quartile means only show a consistent trend for a single parameter, FGD [flue gas desulfurization] Total O&M Cost. The means show a significant step (from $4.2M to $5.7M) when moving from Quartile 2 to 3, but the

standard deviations are on the same order of magnitude as the means (indicating significant uncertainty). When the FGD Total O&M Cost was normalized for nameplate capacity or for heat input, the same trend toward rising costs with increasing quartiles was not observed. Only 367

records were populated and are reflected in this comparison (36 from Quartile 1, 92 from Quartile 2, 135 from Quartile 3, and 104 from Quartile 4), so more information about the other plants could help investigators understand the importance that this parameter might h ave on plant efficiency.

The subsections below discuss some observations about the distribution of parameters among the quartiles, including cases where the single parameters did not place the data in alternate quartiles (i.e., affect relative performance). A previous study 31 

### found that relationships between single parameters (e.g., subcritical versus supercritical boilers) were often confounded by other competing factors. For example, almost all the highly efficient supercritical boilers were bu ilt between 1967 and 1980, affecting the heat rates for units built around that time but with high operational costs.



## 4.1 **Subcritical Versus Supercritical Boilers** 

Only 17 records were labeled as “N” for subcritical, and 560 others were labeled as “Y.” This

wid e disparity in record numbers gives at least one explanation why the models did not use this parameter in developing their descriptions.

Staudt and Macedonia cite subcritical boilers as having higher heat rates than supercritical ones,

but the examination of reported net heat rates showed no difference between supercritical and subcritical boilers. Staudt and Macedonia speculate that the older age of operating supercritical boilers might mean that more up

- to

- date technologies could replace the older ones a t these plants. However, they did not investigate which equipment had been updated. This speculated opportunity was not evident in the Leidos data analysis: eleven of the seventeen supercritical units fell into Quartiles 1 and 2.



## 4.2 **States**

Of the states with at least ten generator records in the database, Table 4

- 2 shows which had a majority of records in any given quartile. These state rankings are deceptive in that one m ight think that the heat rates for New York are better than those in Maryland. However, Figure 4

- 2 shows that the mean of the New York heat rates for the seven Quarti le 1 generators are actually higher than those for the means for Quartile 4 generators in Maryland.

Table 4

- 2 . States W hose G enerators M ostly F all into a S ingle Q uartile

Quartile 1 (best) Quartile 2 Quartile 3 Quartile 4 (worst)

New York (7/13) Wyoming (13/24) Arkansas (8/13) Oklahoma (5/10) West Virginia (14/28) Maryland (10/13)

31 Staudt, J.E. and J. Macedonia, “Evaluation of Heat Rates of Coal Fired Electric Power Boilers,” Power Plant

Pollutant Control “Mega” Symposium, Baltimore, Maryland, August 2014.



### 28

### Figure 4

- 2 . Comparisons o f Heat Rates f or Select Data Subsets b y State a nd Quartile

Maryland’s generators may have had higher 2012 heat rates calculated by EIA during the implementation of Maryland’s Healthy Air Act when new control devices began operation at these particular plants (2009

- 2013). 32 

### The CAMD rates, based on just 20 12, align more closely with the predicted rates in Maryland

.



## 4.3 **E CP**  **Plant Types**

With regard to plant types, 33 

### Table 4

- 3 describes the disposition of the different classi fications among the quartiles. Figure 4

- 3

, Figure 4

- 4 , and Figure 4

- 5 show how the various controls appear in the four quartiles. More than 70 % of the units in Quartile 1 did not have FGD controls ( Figure 4

- 3 ), more than 60 % of the units in Quartile 1 did not have SCR controls ( Figure 4

- 5 ), but particulate controls were included on almos t every Quartile 1 unit ( Figure 4

- 4 ).

32 The Maryland Healthy Air Act, Maryland Department of the Environment. Downl oaded from

http://www.mde.md.gov/programs/Air/ProgramsHome/Pages/air/md_haa.aspx on November 11, 2014.

33 ECP types defined on page vii

.



### 29

### Table 4

- 3 . Distribution of ECP Plant Types a mong Quartiles

Particulate

Control Baghouse Cold

- S ide ESP Other/ N one Integrated

Gasification Other

Controls No

FGD Wet FGD, no

SCR Wet FGD,

SCR Dry

FGD No

FGD Wet FGD, no

SCR Wet FGD,

SCR Dry

FGD No

FGD Wet FGD, no

SCR Wet FGD,

SCR

Quartile 1 (best efficiency

relative to prediction ) 32 13 5 27 148 15 11 1 1 1

-- 3

Quartile 2 15 24 6 25 110 27 36 1 6 6

-- -- Quartile 3 18 16 11 21 69 32 71 6 4 6 3

-- Quartile 4

(worst efficiency

relative to prediction ) 20 15 6 9 99 39 51 7 10

-- 1

--

Grand Total 85 68 28 82 426 113 169 15 21 13 4 3



### Figure 4

- 3 . Distribution of FGD Type among the Q uartiles

30

### Figure 4

- 4 . Distribution of P articulate C ontrol Types among the Q uartiles

Figure 4

- 5 . Distribution among the Quartiles Based on Selective Catalytic Reduction ( SCR ) Usage



## 4.4 **Heat Rates**

Staudt and Macedonia reported that low

- capacity boilers had much higher heat rates. They explained that this would be partly due to the fact that low pressure steam cycles are likely more

common in smaller boilers. Figure 4

- 6 shows the distribution of CAMD 2012 heat rates among the generator units, but no obvious differences are noted in the distribution for the four quartiles.

31

### Figure 4

- 6 . Distribution of CAMD 2012 Heat Rate s a mong Q uartiles

The distribution of EIA

- reported heat rates are shown in Figure 4

- 7 , but they show somewhat different trends. Only Quartiles 1 and 2 have heat rates le ss than 10,000 Btu/kWh. Quartile 4 generally is highly represented at the higher heat rates. Because the quartile distinctions are based on heat rates, th ese trends were not unexpected.

Figure 4

- 7

. Distribution of EIA

- R eported Heat Rates a mong t he Quartiles

32

## 4.5 **Online Year**

Figure 4

- 8 shows the number of units in each quartile based on the online year. The most efficient units (Quartile 1 in blue) generally fall into the older units built before 1970, but this finding may not be intuitive. However, the older units are generally the smaller units and will be compared to other smaller units with the ones that are still operating representing only those with economical designs. The older units may also have fallen into environmental programs that grandfathered their high pollutant emission rates until after more efficient control technologies were developed.

Uni ts going online between 1970 and 1985 do not generally fall into the most efficient quartile, indicating that they may currently be the units best suited to HRIs . The history associated with these units (e.g., replacements and upgrades) might provide reaso ns why units of this vintage do not perform as well as units going online in other years. Staudt and Macedonia reported little effect from the online year.

Figure 4

- 8 . Dist ribution of Quartiles Based on Online Y ear



## 4.6 **Planned Retirement Year**

Only 203 records listed planned retirement years, and the years ranged from 2012 to 2021. Figure 4

- 9 displays the number of units from each quartile against the planned retirement years. A considerable number of the planned retirements fell into Quartile 1, and this is likely because they represent the earliest online years. Figure 4

- 10 supports this supposition with its display of the online years for only the units with planned retirement years. Note in Figure 4

- 10 that the majority of retiring units going online after 1965 fall into Quartile 4 (the worst performing units).

33

### Figure 4

- 9 . Distribution of Quartiles f or Units Listing Planned Retirement Years

Figure 4

- 10 . Distribution of Quartiles among Online Years for Units Listing

Planned Retirement Years



## 4.7 **Variable O&M Costs** 

The AEO 2014 includes the Variable O& M costs that range from $0.57/MWh to $6.54/MWh for

coal

- fired units. Variable O&M costs are a key input for calculation of levelized costs, and the reported AEO 2014 values include fuel costs

. Figure 4

- 11 shows the distribution of Variable O&M costs among the quartiles, and Figure 4

- 12 shows the distribution only for the larger un its (over 100 MW). The differences between Figure 4

- 11 and Figure 4

- 12 appear in the bars to the right of $2.5/MWh because smaller units do not have the low er operational unit costs. The Variable O&M costs tend to be highest for the best performing units (Quartile 1), and this likely reflects the fact that the best performing units will be economical even when the fuel costs are high.

34

### Figure 4

- 11 . Distribution of Quartiles Based on Variable O & M Costs ( Including Fuel Costs )

Figure 4

- 12 . Distribution o f Quartiles Based o n Variable O & M Costs (Including Fuel Costs) f or Units w ith Nameplate Capacities O ver 100 MW

The Variable O&M cost distributions for Quartile 4 (worst performing units) are higher than those for Quartiles 2 and 3. This tendency toward higher Variable O&M costs may reflec t that more fuel (and thus more fuel cost) is incurred in Quartile 4 units to generate the same amount of

energy.



## 4.8 **Fuel Choice**

Ninety percent of the records had primary fuels listed as bituminous, sub

- bituminous, lignite, or

waste coal. Table 4

- 4 presents the numbers of units as they were distributed among the quartiles based on the primary fuel usage. Upon examination of the first five rows of this table, the

35

### bituminous coal units appear more frequently in Quartiles 1 and 4, but the sub

- bituminous units dominate Quartiles 2 and 3.

Table 4

- 4 . Number of Generator Units i n Quartiles Based o n Primary Fuel Usage

Quartile 1 (best ) Quartile 2 Quartile 3 Quartile 4

All Units

Bituminous 146 70 124 167

Sub

- bituminous 55 160 108 60 Lignite 1 8 11 8

Waste Coal 4

-- 1 1 Not reported 51 18 13 21 

### Units with Nameplate Capacities Less than or Equal to 100 MW

Bituminous 54 7 11 23

Sub

- bituminous 24 8 12 20 Lignite 1 2

-- -- Waste Coal 4

-- 1 1 Not reported 23 6 6 19 

### Units with Nameplate Capacities Greater than 100 MW but Less than 181 MW

Bituminous 81 20

-- 26

Sub

- bituminous 26 26

-- 4 Lignite

-- 2

-- -- Waste Coal

-- -- -- -- Not reported 8 3 2 2 

### Units with Nameplate Capacities Greater than 181 MW

Bituminous 11 43 111 142

Sub

- bituminous 5 126 96 36 Lignite

-- 4 11 8

Waste Coal

-- -- -- -- Not reported 20 9 5

--



### Data are also presented in Table 4

- 4 us ed different cut points based on nameplate capacities. These cut points represent the decision nodes in the piecewise linear regression models described in the last chapter. In the group of units with nameplate capacities less than or equal to 100 MW, all fuel types are more likely to appear in Quartiles 1 and 4 than in the center quartiles. This finding is a reflection of the high variability in EIA heat rates for the smaller units.

The pie cewise model for the units with n ameplate c apacities g reater than 100 MW but l ess than

181 MW shows a bias toward Quartiles 1 and 2, but all fuels are biased toward these quartiles. This result was anticipated because the model for this node has the same c orrection for both

bituminous and sub

- bituminous fuel.

For units with n ameplate c apacities g reater than 181 MW , two rules apply for bituminous and lignite fuels and two other rules for sub

- bituminous fuel. The rules for sub

- bituminous coal again favor Quar tiles 2 and 3, but the rules for bituminous and lignite fuels tend to put more units in Quartiles 3 and 4. The computed S lope F unction is greater than 1.00 at 81 % of the bituminous/lignite units in the largest size category, so the bias toward low performa nce might be explained by the model bias rather than actual plant performance.

36

## 4.9 **Total FGD Cost**

Staudt and Macedonia reported that the FGD

- controlled units generally were more efficient than

unscrubbed units

, but not by much. When a dry scrubber was added to the unit (comparing against historical data), the heat rates did increase. However, Staudt and Macedonia did not

determine a directional correlation for the addition of wet scrubbers.

The data in this study represent only single snapshots in time of a uni t’s heat rates and not the effect of adding new units. The parameter called FGD Cost Total ranged from $21,000 to $1 .5

billion in the EIA

- 923 forms for 2012. Fifty

- six records showed a total FGD cost exceeding $325M (81% to structure, 4% to disposal, and 1 5% to other costs).

Figure 4

- 13 depicts how the Total FGD Cost parameter is distributed among the quartiles for the

460 units. The total FGD cost for the majority of the units is less than $100M, and all four quartiles have their largest counts in the first two bins. Therefore, the total FGD cost did not appear correlated to the unit performance.

Figure 4

- 13 . Distribution of Total FGD Cost (Thousands o f Dollars) a mong Quartiles



## 4.10 **FGP Collector Classification and Hours in Service** 

FGP collectors were reported for 883 units in ten different categories (3 baghouse types, 4 ESP

types , 2 cyclones, wet scrubber, and other type), but the FGP categories were compiled into five types in Figure 4

- 14

. Figure 4

- 14 shows that most FGP collectors fall into the ESP categories and that ESP systems are more often associated with Quartile 4 than the other quartiles. Eighty

eight percent of the Quartile 4 rec ords with FGP collectors use ESPs, but the percentages are only near 70 % (± 3 % ) for the other three quartiles.

37

### Figure 4

- 14 . Distribution of Q uartiles among FGP C ollector T ypes

This finding indicates that t he FGP collector type might be associated with the unit performance (this parameter does appear in five of the seven slope function models) or at least correlate with another parameter that shows similar patterns. Figure 4

- 15 shows the distribution of FGP collector types based on the online year for the generator unit. The figure clearly indicates that the ESP technology was installed on a majority of the units that went online before 1990, but baghouse technologies are more c ommon in newer units. Figure 4

- 8 showed few Quartile 4 units that went online after 1990, so the lack of ESPs in newer units may indicate why Fi gure 4

- 14 indicated a tendency of ESPs toward Quartile 4.

Figure 4

- 15 . Distribution of FGP Collector Types w ith Online Year

Table 4

- 1 showed that the average numbers of FGP service hours in 2012 were lower for Quartile 1 and Quartile 4 than the other two quartiles, indicating that these uni ts are less likely to produce base load electricity. Figure 4

- 16 shows that the distribution of FGP service hours for

38

### the four quartiles as fractional counts. This di splay clearly shows that Quartile 1 units are more likely to operate FGP controls less than 4000 hours a year than greater that, but the shorter operational hours appear less frequently in the other three quartiles.

Figure 4

- 16 . Fractional Distribution o f FGP Service Hours a mong Quartiles



## 4.11 **Cooling System Type and In**

**- Service Year** 

The primary cooling system types were grouped for this analysis:

• Once through systems without pond or canal (427 units w ith code=ON)

• Cooling ponds or canals (81 units with code=OC or RC) 34



### • Cooling towers (329 units with code=HRF, RF, RI, or RN) 35



### • Other/unknown (3 DC units, 2 OT units, and 185 blank) 36



### Figure 4

- 17 shows the fractional distributions of the quartiles within each cooling system type. The unknown units are more often represented by Quartiles 1 and 4 because reported rates vary more signifi cantly from predicted rates when information is missing. Quartile 1 represents the largest fraction for the “ Once Through w ithout Pond ” type, and Quartile 3 is the largest fraction for the units with cooling towers. The reason for these observations is likely connected to the in

service years.

34 OC = Once through with cooling pond(s) or canal(s); RC = Recirculating with cooling pond(s) or canal(s).

35 HRF = Hybrid: recirculating with forced draft cooling tower(s) with dry cooling; RF = R ecirculating with forced draft cooling tower(s); RI = Recirculating with induced draft cooling tower(s); RF = Recirculating with natural draft cooling tower(s).

36 DC = Dry (air) cooling system; OT = Other.



### 39

### Figure 4

- 17 . Fractional Distribution o f t he Qu artiles w ithin Cooling System Types

Figure 4

- 18 shows the distribution of the three major cooling system types based on the in

service year. It clearly indicates that the oldest units were predominantly once through systems without ponds, and the newest units are predominantly those with cooling towers . As mentioned in previous subsections, Quartile 1 units are generally more common in older systems, and the largest fraction of Quartile 3 units appear around 1980 in Figure 4

- 18 . These observations tend to explain the frequencies observed in Figure 4

- 17

.

Figure 4

- 18 . Distribution of Cooling System Types among In

- Service Years

f or Cooling Systems

40

## 4.12 **Number of Federal Air Market**  **Programs Affecting Emissions**

The EPA CAMD data set also covers s tatutory or regulatory based options for tracking and reducing air pollution emissions

. Annual information for environmental controls at the generator level is available from the EPA Air Markets Program Data (AMPD) database for facility attributes. The AMPD data contain also information on annual and ozone (May

- September)
season programs at t he generating unit level. The market

- based regulatory programs aim to improve air quality through coordinated emission reductions.

The annual programs include :

• Acid Rain Program (ARP),

• Clean Air Interstate Rule (CAIR) NO

x 

### P rogram,

• Cross

- State Air Pollut ion Rule (CSAPR) annual NO

x 

### (TRNOx), Phase I (TRSO2G1) and phase II (TRSO2G2) SO 2 

### P rograms, and

• Regional Greenh ouse Gas Initiative (RGGI)

The ozone season programs include :

• CSAPR ozone season NO
x 

### P rogram (TRNOxOS),

• CAIR ozone season NO
x 

### P rogram (CAIROS),

• NO
x 

### SIP Call P rogram (SIP NOx), and

• NO
x 

### Budget Program (NBP).

Finally, the database indicates when a unit is subject to New Hampshire's NO

x 

### program (NHNOx). None of these eleven program phases began implementation before 1995, and more

than 90 % of the generator units in the database pre

- date the announcements of these programs. Therefore, comparing the heat rate predictions against each of these programs would not be expected to yield significant results. However, some of these programs are implemented at the statewide level (based on unit size), so an accounting of the programs may serve as a surrogate for describing regional behavior. Table 4

- 5 sho ws the state names based on the number of market programs coal

- fired units there are subject to. In rows with multiple bullets, t he table shows that 24 states have some units that are subject to no air market programs and other units subject to 1 to 8 programs.

Table 4

- 5 . Number o f Air Market Programs Affecting Generators b y State

State Number of Programs

0 1 3 5 6 7 8

Alabama ●
Arkansas ● ●
Arizona ● ●
Colorado ● ●
Connecticut ●
Delaware ● ●
Florida ● ● Georgia ● ●
Iowa ● ●



### 41

### State Number of Programs

0 1 3 5 6 7 8

Illinois ● ●
Indiana ● ●
Kansas ● Kentucky ●
Louisiana ●
Massachusetts ● Maryland ● Michigan ● ●
Minnesota ● ●
Missouri ● ● Mississippi ● ●
Montana ● ●
North Carolina ● ●

North Dakota ● ●
Nebraska ● ● New Hampshire ● New Jersey ●
New Mexico ●
Nevada ● ●

New York ● ●
Ohio ● ●
Oklahoma ● Oregon ● Pennsylvania ● ●
South Carolina ● ●
South Dakota ● ●
Tennessee ●
Texas ● ●

Utah ● ● Virginia ● ● Washington ●
Wisconsin ● ● West Virginia ● ● Wyoming ● ●



### Figure 4

- 19 shows the quartile distributions based on the number of Federal air market programs. It does not indicate that fewer or more Federal air market programs lean toward particular quartiles. However, Figure 4

- 20 does show that the generators associated with the highest heat rates are generally affected by no progr ams, and those with the lowest heat rates (less than 10,500 Btu/kWh) are more likely to be affected by 6 or 7 programs.

42

### Figure 4

- 19 . Distribution o f Quartiles Based on Number of F ederal Air Market Programs

Figure 4

- 20 . Distribution of Heat Rates Based on Number of

Federal Air Market Programs

43

## 5 **Evaluation of Potential HRIs**  **and Associated Cost s** 

**5.1 Heat Rate Improvement Options**  **and Valuation**

Chapter 2 described a variety of the available published reports discussing heat rate improvements at coal

- fired power plants and their associated costs. It illustrated that a wide range of improvements was possible at a wide range of capital and operating costs. This chapter presents the Leidos analysis of what fraction of the units could have the measures applied, the anticipated HRIs , and the anticipated c osts (in 2014 dollars).

The improvements are considered in five different plant areas: boiler island, turbine island, flue gas system, air pollution controls, and water treatment system . Some previous reports considered regular outage maintenance activiti es (e.g., replacement of first point heater) as HRIs , but this chapter focuses on non

- routine retrofit measures that reflect a change in design or operations.

Considerable overlap exists between the different HRI activities, and many activities cited in th e literature might be considered subsets of other activities. To avoid double

- counting future potential HRIs in the coal

- fired fleet, some subcategories were eliminated. Also eliminated were those measures typically performed as maintenance activities duri ng scheduled outages ; these activities restore heat rate but are not considered to actually improve plant performance.

The existing literature also stresses that the HRIs should not be considered ad ditive. The potential efficiency gained by one boiler isla nd HRI should not necessarily be added to another boiler island HRI or HRIs elsewhere at the plant without a full heat rate evaluation. Such an evaluation should also include consideration of the expected unit capacity factor and the expected load changes.

In addition, some activities require significant capital investment and are likely to trigger the New Source Review (NSR) provisions of the Clean Air Act. Installation of additional control systems under the NSR would often negate the HRIs, so special con ditions were assigned such that previously uncontrolled systems could not gain HRIs from certain measures that involved high capital cost investments.

Some technologies were also identified as existing only in the R&D or pilot phase at this point.

Literatu re works and patents indicate that some degree of success is being made in these areas, but often readers would find it difficult to classify the HRIs in these pilot studies as maxima or minima. The public literature on these studies generally does not cit e costs, so full cost evaluations would be necessary for EIA to establish ranges on the capital, operating, and maintenance costs across all potential plants. The recommendation s for these cost evaluations are listed in Chapter 7

.

As discussed in Chapter 3 , many HRI activities are already unde rway at coal

- fired plants because improved heat rates are associated with fuel cost savings . Therefore, it is assumed that units falling into Quartile 1 had most likely already made improvements and that Quartile 4 units were the least likely to have made improvements. Expert Leidos

- based experience was used to judge how commonly practiced the various HRI activities had been deployed through 2014:

• Novel

– still available for Quartiles 1, 2, 3, and 4

• Uncommon today

– available for Quartiles 2, 3, and 4

• 50/50 today

– available for Quartiles 3 and 4

44

### • In most units today

– available for Quartile 4

• Maybe in 10 years

– available for Quartiles 1, 2, and 3 (assuming that Quartile 4 units would make other improvements before investing in the newer technologies)

I nitially 56 HRI activities were under consideration, but the criteria listed in the preceding paragraphs reduced the list to just the 29 measures shown in Table 5

- 1

. Table 5

- 1 presents the highest HRIs and maximum capital costs, and Table 5

- 2 presents the minima. Both of these

tables include special conditions and notes column s that identify the source and/or assumptions

associated with the HRI valuations and capital costs

.
The approach used to generally assess the range of HRI potential for the large numbers of coal

fired units of varied types and existing condition is as follows:

• Detailed assessment of individual units or sample cases was not possible within the scope of this project , requiring a much more general approach based on literature data and engineering experience and judgment associated with decades of coal

- fired plant upgrade projects. Note that s ite

- specific equipment review and related cost appraisals are the most appropriate methods for assessing all required equipment, material, and installation and operating labor for unique projects.

• The 2009 S&L Report results were used as the reference valu es for those HRI options

covered in th is report . Leidos engineering staff considered the max imum and minimum HRI values established in the report and used engineering experience and judgment and

other available internal and literature data (as discussed in Section 2 of this report) to
determine if updates were deemed appropriate . For all HRIs, Le idos staff either decided

on more conservative HRI values or accepted the S&L Report values.

• Other HRI options not included in the S&L Report were assessed based on Leidos experience, engineering judgment, and internal and literature data sources as available.

• L iterature sources reviewed are presented in Section 2. Key sources used were:

o Coal

- Fired Power Plant Heat Rate Reductions, Prepared by Sargent & Lundy for Perrin Quarles Associates, Inc., under U.S.EPA Contract No. EP

- W

- 07

- 064, January
22, 2009

.

 Study of various methods to reduce the heat rate of existing U.S. coal

- fired power plants in a range of sizes − 200 MW, 500 MW, and 900 MW (see Section 2.1)

 Cost basis

– Vendor quotes

 Cost Year basis

– 2008 (mid

- year assumed)

o Reliable & Resilient / The V alue of Our Existing Coal Fleet

- An Assessment of Measures to Improve Reliability & Efficiency While Reducing Emissions, National Coal Council study at the request of the U.S. Department of Energy, May 2014.

 Study identifies various methods to reduce the heat rate of existing U.S. coal

- fired power plants for plants of various sizes (see Section 2.1)

 Cost basis

– Various literature and regulatory sources

 Cost Year basis

– various

45

### o ASME Energy Forum, Power Plant Efficiency: Saving Fuel, Officiated by Licata

E

nergy and Environmental Consulting , September 25, 2014.

 Presenting organizations were: Anthony Licata ( Cycling Ops. /Air Heaters

/ Emissions Controls ); Burns & McDonnell (Impact of New Source Review) ; Siemens Energy (Turbine Island) ; Robert Som m

erlad ( Combus tion/ Boiler Island

/ Neural Networks & Intelligent Sootblowing )

o EPRI Range and Applicability of Heat Rate Improvements Technical Update. April 2014. Electric Power Research Institute Report Number 3002003457

.

 S ummarizes methodologies and tools for assessing and implementing measures for improving heat rate in coal

- fired power plants. In addition, the report attempts to better bracket the range of achievable improvements possible for an existing

coal

- fired power plant.

 No cost data included



## 5.2 **Heat Rate Improvement Cost Estimation** 

Tables 5

- 1 and 5

- 2 , respectively, present the maximum and minimum estimates for the total capital investment (order of magnitude) associated with HRI equipment modifications

. Table

5

- 3 presents a comparable list of the associated fixed and variable O&M costs associated with

each HRI.

Costs for the HRI options included in the S&L report (vendor quotes) were accepted for use in this study, but were escalated to November 2014 dollars (from mid

- year 2008) using the RS

Means Construction Facilities Cost Data

- 28th annual edition Historical Cost Indexes

.

• Historical Cost Index for 2008 = 180.4

• Historical Cost Index Estimated for November 2014 = 198.2

• Calculated escalation from 2008 to November 2014 = 10%

T

he same composite construction cost escalation index has been used for both capital costs and

fixed and variable O&M because the S&L R eport did not break out the cost split betw een

m

aterials , l abor , and e quipment

. Readers should think of these numbers and the upgrade choice in NEMS as the heat rate evaluation findings that are conducted at a plant prior to a planned outage. A utility may conduct/contract a heat rate evaluation p rior to an outage, and the results will tell them the expected HRI and potential cost. Sometimes no cost

- prohibitive solutions will be identified, so

the HRI and cost would both be zero.

If many of these evaluations are done across a broad selection within one ECP type, the average HRI times the total number of generators will represent the total potential fuel savings (TPFS), and the average cost times the total number of generators will represent the total costs (TC). The

same TPFS and TC will result if t he average non

- zero HRI is multiplied by the total number of generators (with non

- zero HRIs) and the average non

- zero cost is multiplied by the total number of generators (with non

- zero HRIs). The ratio of TC to TPFS will also be the same and
represents a national average improvement. Therefore, no distortion is observed in the data as long as the TPFS and TC are both divided by the same number of generators (equal denominators for TPFS/units and TC/units).

46

### Table 5

- 1

. Maximum Estimates For Heat Rate Improvements a nd Capital Costs

Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 millions

of dollars ) Notes Q1 Q2 Q3 Q4 <=200

MW < =500

MW >500

MW <=200

MW <=500

MW >500

MW

Boiler Island

Redesign and replace
economizer ● limited applicability, very
site

- specific 60 40 20 3.3 5.5 8.8 HRI Reference : ASME Energy Forum Sept 2014. Larger opportunity for economizer based efficiency improvement on smaller,

older units which demonstrated higher heat rate. HRI based on engineering judgment for reasonable gains based on 24°F reduction in exit

gas temperatu re for <200 MW; 16°F <500 MW; 10°F >500 MW.

Cost Reference: S&L Report

Fuel Delivery System (FDS) Upgrades ● ● 84 84 84 7 12 15 HRI Reference : ASME Energy Forum Sept 2014. HRI from boiler efficiency gain from reduced excess air, plus auxiliary power reduction.

Cost Reference: Leidos Estimate

Neural

network ● ● ● 50 30 30 0.55 0.83 0.83 HRI Reference: Engineering judgment based on project experience , information in S&L

R eport
Cost Reference: S&L Report

Intelligent

sootblower ● ● 70 70 70 0.33 0.55 0.55 HRI Reference : EPRI Report April

2014

Cost Reference: S&L Report

Digital

controls ● 0 0 0 2.2 3.80 4.40 F or FDS, neural network, intelligent sootblower, performance monitoring (including feedwater heater monitoring), routine testing

Cost Reference: S&L Report

Limit air

heater leakage ● 8 5 5 0.55 0.77 1.32 HRI Reference: Engineering judgment based on fan power usage reduction.
Cost Reference: S&L Report

Nano

coatings in
boilers future only



### 47Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 millions

of dollars ) Notes Q1 Q2 Q3 Q4 <=200

MW < =500

MW >500

MW <=200

MW <=500

MW >500

MW

Blowdown recovery tank ● 1 1 1 0.1 0.1 0.1 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Coal switching ● future only; switching from

low sulfur to higher sulfur coal unlikely/problem

atic

Coal drying ● ● ● ● future only; currently limited

to mine mouth application

Coal processing (additives) future only

Air heater

baskets ● ● 5 5 5 0.23 0.39 0.45 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Lower air

heater outlet
temperature by controlling

acid dew point ● ● ● No FGD. This is a very site

specific issue,

difficult to

estimate across a wide range. 5 5 5 3.85 11 19.8 HRI Reference: Engineering judgment based on project experience ; see special condition

note.
Cost Reference: S&L Report

Turbine Island

Blade path upgrade ● ● No NSR triggered 200 300 300 13.2 22 27.5 HRI Reference: values based on S&L report. Blade path upgrade for

< 200MW steam turbine units

discounted because these units are in a population class with lower pressure cycles and have smaller opportunity for heat rate improvement.

Cost Reference: S&L Report



### 48Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 millions

of dollars ) Notes Q1 Q2 Q3 Q4 <=200

MW < =500

MW >500

MW <=200

MW <=500

MW >500

MW

Turbine overhaul (not including blade path upgrade) ● part of normal

maintenance 0 0 0 0 0 0 Steam turbine performance degrades over time, increasing heat rate. Maintenance is done on 7 to 10 year cycles to regain lost heat rate, but does not provide a net gain.

Condenser cleaning ● P art of normal

maintenance 0 0 0 0 0 0 Part of normal maintenance

– does

not result in net HRI

Boiler feed

pumps ● 50 50 50 0.39 0.66 0.88 HRI Reference: S&L R eport
Cost Reference: S&L Report

Variable

speed drives

for main cycle and auxiliary equipment ● ● 50 50 50 4 6 8 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Cooling
system optimization ● ● 50 50 50 0.2 0.35 0.5 HRI Reference: Engineering judgment based on review of various sources (S&L, EPRI)
Cost Reference: Leidos Estimate

Exhaust hood steam guide
modification ● ● ● ● 2 2 2 0.3 0.3 0.3 HRI Reference : Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Rewind generator ● 30 40 40 4 5.5 7 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate Topping cycle
addition ● ● ● future only

Bottoming cycle with organic

solvent ● ● ● future only

Flue Gas System

ID Axial Fan (and motor) upgrades ● ● ● 80 80 80 7 .7 1 2.1 17.6 HRI Reference: Engineering judgment based on project experience and information in S&L

report Cost Reference: S&L Report



### 49Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 millions

of dollars ) Notes Q1 Q2 Q3 Q4 <=200

MW < =500

MW >500

MW <=200

MW <=500

MW >500

MW

Variable

Frequency

Drives ● ● 70 70 70 2.2 4.4 6.6 HRI Reference: Engineering judgment based on project experience and information in S&L

report
Cost Reference: S&L Report

Variable

Frequency

Drives and

new centrifugal

fans ● ● 150 150 150 7.15 12.1 17.6 HRI Reference: S&L Report
Cost Reference: S&L Report

Air Pollution Control

Removal of
Venturi

throat ● 13 13 13 2.75 2.75 2.75 HRI Reference: S&L Report

Cost Reference: S&L Report

Turning

vanes and perforated

gas distribution plates at the
inlet ● 2 2 2 0.275 0.275 0.275 HRI Reference: S&L Report

Cost Reference: S&L Report

Shutoff spray

level ● 16 16 16 0 0 0 HRI Reference: S&L Report

Cost Reference: S&L Report

Variable

Frequency

Drives ● 50 50 50 1.1 3.3 5.5 HRI Reference: S&L Report

Cost Reference: S&L Report

ESP

Modification ● ● 5 5 5 0.22 0.55 0.88 HRI Reference: S&L Report

Cost Reference: S&L Report

SCR

Modification ● 10 10 10 0.55 1.1 2.2 HRI Reference: S&L Report

Cost Reference: S&L Report

Water Treatment Cooling

Tower

Advanced Packing Upgrade ● ● 70 70 70 1.65 3.3 5.5 HRI Reference: S&L Report

Cost Reference: S& L Report



### 50Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 millions

of dollars ) Notes Q1 Q2 Q3 Q4 <=200

MW < =500

MW >500

MW <=200

MW <=500

MW >500

MW

Supplemental cooling tower ● ● ● This is a very

site

- specific issue, difficult to

estimate across a wide range. 10 10 10 2 3 4 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Other

HR

awareness training,

make HR

information
available ● R equires upgraded control

system 150 150 150 HRI Reference : EPRI Report April

2014

Cost Reference: Leidos Estimate

Initiating

routine testing

programs ● 200 200 200 0.1 0.1 0.1 HRI Reference : EPRI Report April

2014
Cost Reference: Leidos Estimate

Feedwater

Heater Monitoring ● R equires upgraded control
system 60 60 60 HRI Reference : EPRI Report April

2014
Cost Reference: Leidos Estimate



### 51

### Table 5

- 2 . Minimum Estimates f or Heat Rate Improvements a nd Capital Costs

Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 $M)

Notes Q1 Q2 Q3 Q4 <=200

MW <=500

MW >500

MW <=200

MW <=500

MW > 500

MW

Boiler Island

Redesign and replace economizer ● limited applicability, very site

specific 20 20 10 2.5 3.5 5 HRI Reference : ASME Energy Forum Sept 2014. Larger opportunity for economizer based efficiency improvement on smaller, older units which demonstrated higher heat rate. HRI based on engineering judgment for reasonable gains based on 24°F reduction in exit gas temperature for <200 MW; 16°F <500 MW; 10°F >500

MW.

Cost Reference: Leidos Estimate

Fuel Delivery System (FDS) Upgrades ● ● 30 30 30 3.5 6 7.5 HRI Reference : ASME Energy Forum Sept 2014. HRI from boiler efficiency gain from reduced excess air, plus auxiliary power reduction.

Cost Reference: Leidos Estimate

Neural network ● ● ● 10 10 10 0.55 0.83 0.83 HRI Reference: Engineering judgment based on project experience, information in S&L Report

Cost Reference: S&L Report

Intelligent

sootblower ● ● 20 20 20 0.33 0.55 0.55 HRI Reference: EPRI Report April
2014

Cost Reference: S&L Report

Digital controls ● 0 0 0 2.2 3.80 4.40 For FDS, neural network, intelligent sootblower, performance monitoring (including feedwater heater monitoring), routine testing

Cost Reference: Leidos Estimate

Limit air heater leakage ● 8 5 5 0.55 0.77 1.32 HRI Reference: Engineering judgment based on fan power usage reduction.

Cost Reference: S&L Report

Nano

- coatings in

boilers future only

Blowdown recovery tank ● 1 1 1 0.1 0.1 0.1 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate



### 52Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 $M)

Notes Q1 Q2 Q3 Q4 <=200

MW <=500

MW >500

MW <=200

MW <=500

MW > 500

MW

Coal switching ● future only; switching

from low

sulfur to higher sulfur
coal unlikely/probl

ematic

Coal drying ● ● ● ● future only; currently

limited to

mine mouth application Coal processing (additives) future only

Air heater baskets ● ● 5 5 5 0.23 0.39 0.45 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Lower air heater outlet temperature by controlling acid dew point ● ● ● No FGD. This is a very site specific issue, difficult

to estimate
across a wide

range. 5 5 5 3.85 11 19.8 HRI Reference: Engineering judgment based on project experience; see special

condition note.
Cost Reference: S&L Report

Turbine Island

Blade path upgrade ● ● No NSR triggered 100 150 150 6 11 14 HRI Reference: values based on S&L report. Blade path upgrade for

< 200MW steam turbine units discounted because these units are in a population class with lower pressure cycles and have smaller opportunity for heat rate improvement.

Cost Reference: S&L Report

Turbine overhaul (not including blade path upgrade) ● part of normal

maintenance 0 0 0 0 0 0 Steam turbine performance degrades over time, increasing heat rate. Maintenance is done on 7 to 10 year cycles to regain lost heat rate, but does not provide a net gain.



### 53Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 $M)

Notes Q1 Q2 Q3 Q4 <=200

MW <=500

MW >500

MW <=200

MW <=500

MW > 500

MW

Condenser cleaning ● 0 0 0 0 0 0 Part of normal maintenance

– does not

result in net HRI

Boiler feed pumps ● 20 20 20 0.3 0.5 0.8 HRI Reference: Engineering judgment, S&L Report

Cost Reference: S&L Report

Variable

- speed
drives for main cycle and auxiliary equipment ● ● 20 20 20 2 3 4 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Cooling system optimization ● ● 25 25 25 0.15 0.3 0.4 HRI Reference: Engineering judgment
based on review of various sources (S&L, EPRI)

Cost Reference: Leidos Estimate

Exhaust hood steam guide

modification ● ● ● ● 2 2 2 0.3 0.3 0.3 HRI Reference : Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Rewind generator ● 30 40 40 4 5.5 7 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate Topping cycle
addition ● ● ● future only

Bottoming cycle with organic

solvent ● ● ● future only

Flue Gas System ID Axial Fan (and motor) upgrades ● ● ● 10 10 10 5 10 12 HRI Reference: S&L R eport
Cost Reference: S&L Report

Variable

Frequency Drives ● ● 30 30 30 2 4 6 HRI Reference: Engineering judgment based on project experience and information in S&L report

Cost Reference: S&L Report

Variable

Frequency Drives

and new centrifugal fans ● ● 60 60 60 5 8 14 HRI Reference: Engineering judgment based on project experience and information in S&L report

Cost Reference: Leidos Estimate

Air Pollution Control

Removal of

Venturi throat ● 7 7 7 1.4 1.4 1.4 HRI Reference: S&L Report

Cost Reference: S&L Report



### 54Affected Quartiles Special

Condition Heat Rate Improvement (Btu/kWh) Capital Cost (2014 $M)

Notes Q1 Q2 Q3 Q4 <=200

MW <=500

MW >500

MW <=200

MW <=500

MW > 500

MW Turning vanes and perforated gas distribution plates

at the inlet ● 1 1 1 0.275 0.275 0.275 HRI Reference: S&L Report

Cost Reference: S&L Report

Shutoff spray level ● 0 0 0 0 0 0 HRI Reference: S&L Report

Cost Reference: S&L Report

Variable

Frequency Drives ● 20 20 20 1.1 3.3 5.5 HRI Reference: S&L Report

Cost Reference: S&L Report

ESP Modification ● ● 2 2 2 0.22 0.55 0.88 HRI Reference: S&L Report

Cost Reference: S&L Report

SCR Modification ● 5 5 5 0.55 1.1 2.2 HRI Reference: S&L Report

Cost Reference: S&L Report

Water Treatment Cooling Tower Advanced Packing Upgrade ● ● 0 0 0 1.65 3.3 5.5 HRI Reference: S&L Report
Cost Reference: S&L Report

Supplemental cooling tower ● ● ● This is a very site specific issue, difficult

to estimate

across a wide

range. 10 10 10 2 3 4 HRI Reference: Engineering judgment based on project experience

Cost Reference: Leidos Estimate

Other

HR awareness training, make HR
information

available ● requires upgraded
control

system 50 50 50 HRI Reference: EPRI Report April
2014

Cost Reference: Leidos Estimate

Initiating routine testing programs ● 75 75 75 0.1 0.1 0.1 HRI Reference: EPRI Report April
2014

Cost Reference: Leidos Estimate

Feedwater Heater Monitoring ● requires upgraded

control

system 30 30 30 HRI Reference: EPRI Report April
2014

Cost Reference: Leidos Estimate



### 55

### Table 5

- 3

. Fixed and Variable O & M Cost Estimates f or Heat Rate Improvements

Affected Quartiles Special

Condition Fixed O&M Costs (2014 $/yr) Variable O&M Costs (2014 $M)

Notes Q1 Q2 Q3 Q4 <=200

MW <=500

MW >500

MW <=200

MW <=500

MW >500

MW

Boiler Island

Redesign and replace

economizer ● limited applicability, very site

specific 55,000 110,000 165,000 0 0 0 Cost Reference: S&L Report

Fuel Delivery System (FDS) Upgrades ● ● Cost Reference : ASME Energy Forum Sept 2014.

Neural network ● ● ● 55,000 55,000 55,000 0 0 0 Cost Reference: S&L Report Intelligent

sootblower ● ● 55,000 55,000 55,000 0 0 0 Cost Reference: S&L Report

Digital controls ● 100,000 100,000 100,000 0 0 0 Cost Reference: Leidos

Estimate

Limit air heater leakage ● 55,000 82,500 110,000 0 0 0 Cost Reference: S&L Report

Nano

- coatings in

boilers future only

Blowdown recovery tank ● 0 0 0 0 0 0 Cost Reference: Leidos

Estimate

Coal switching ● future only; switching from

low sulfur to higher sulfur

coal unlikely/proble
matic

Coal drying ● ● ● ● future only; currently
limited to mine

mouth application Coal processing (additives) future only

Air heater

baskets ● ● 20,000 30,000 40,000 0 0 0 Cost Reference: Leidos
Estimate



### 56Affected Quartiles Special

Condition Fixed O&M Costs (2014 $/yr) Variable O&M Costs (2014 $M)

Notes Q1 Q2 Q3 Q4 <=200

MW <=500

MW >500

MW <=200

MW <=500

MW >500

MW

Lower air heater

outlet temperature by controlling acid dew point ● ● ● No FGD. This is a very site specific issue,

difficult to

estimate across a wide range. 55,000 82,500 110,000 385,000 935,000 1,650,000 Cost Reference: S&L Report

Turbine Island Blade path upgrade ● ● No NSR triggered 0 0 0 0 0 0 Cost Reference: S&L Report

Turbine overhaul (not including blade path upgrade) ● part of normal
maintenance 0 0 0 0 0 0 Cost Reference: S&L Report

Condenser cleaning ● 0 0 0 0 0 0 Part of normal maintenance

– does not result in net HRI

Boiler feed

pumps ● 0 0 0 0 0 0 Cost Reference: S&L Report

Variable

- speed

drives for main cycle and auxiliary equipment ● ● 55,000 110,000 165,000 0 0 0 Cost Reference: Leidos

Estimate

Cooling system optimization ● ● 55,000 110,000 165,000 0 0 0 Cost Reference: Leidos

Estimate

Exhaust hood steam guide

modification ● ● ● ● 10000 10000 10000 0 0 0 Cost Reference: Leidos

Estimate

Rewind generator ● 10000 10000 10000 0 0 0 Cost Reference: Leidos

Estimate Topping cycle

addition ● ● ● future only

Bottoming cycle with organic

solvent ● ● ● future only

Flue Gas System ID Axial Fan (and motor) upgrades ● ● ● 55,000 93,500 143,000 0 0 0 Cost Reference: S&L Report

Variable

Frequency Drives ● ● 22,000 33,000 55,000 0 0 0 Cost Reference: S&L Report



### 57Affected Quartiles Special

Condition Fixed O&M Costs (2014 $/yr) Variable O&M Costs (2014 $M)

Notes Q1 Q2 Q3 Q4 <=200

MW <=500

MW >500

MW <=200

MW <=500

MW >500

MW

Variable

Frequency Drives

and new centrifugal fans ● ● 27,500 41,800 66,000 0 0 0 Cost Reference: S&L Report

Air Pollution Control

Removal of

Venturi throat ● 0 0 0 0 0 0 Cost Reference: S&L Report

Turning vanes and perforated gas distribution plates at the inlet ● 0 0 0 0 0 0 Cost Reference: S&L Report

Shutoff spray

level ● 0 0 0 0 0 0 Cost Reference: S&L Report

Variable

Frequency Drives ● 55,000 110,000 165,000 0 0 0 Cost Reference: S&L Report

ESP Modification ● ● 27,500 27,500 27,500 0 0 0 Cost Reference: S&L Report

SCR Modification ● 27,500 55,000 110,000 27,500 66,000 110,000 Cost Reference: S&L Report

Water Treatment Cooling Tower

Advanced Packing Upgrade ● ● 82,500 137,500 192,500 0 0 0 Cost Reference: S&L Report

Supplemental cooling tower ● ● ● This is a very site specific issue, difficult
to estimate

across a wide

range. 82,500 137,500 192,500 0 0 0 Cost Reference: S&L Report

Other

HR awareness training, make

HR information
available ● requires upgraded control system 100,000 100,000 100,000 Cost Reference: Leidos

Estimate

Initiating routine testing programs ● 100,000 100,000 100,000

- - - Cost Reference: Leidos

Estimate

Feedwater Heater Monitoring ● requires upgraded control system Cost Reference: Leidos

Estimate



### 58

## 6 **Applying Potential Heat Rate Improvements to the Entire Database**

T

his chapter presents the results from applying the HRIs to the population of generators in the data set. The first section shares the HRIs associated with generators of different vintages, nameplate capacity, and ECP type. The second section describes the range of HRIs across the entire data set, and the third section describes the associated range in costs. The final section details some additional considerations that are warranted when working with this data set.



## 6.1 **HRI Potentials as They Relate**  **to Select In put Parameters** 

Although the data set contains many parameters, this analysis focused on differentiating the HRI potentials associated with major design considerations: online year, nameplate capacity, and ECP
type. 37



### 6.1.1 **Online Year**



### Initially the online year parameter was a likely candidate for assessing potential HRIs, but operations that were later upgraded and regulatory concerns significantly affect the potential

HRIs. Figure 6

- 1 shows the HRI potentials assigned to different units based on the online years. Units going online after 1990 generally offered the smallest HRI potentials in Figure 6

- 1 ; 70 % of those units fell into Quartiles 1 and 2, so fewer HRI measures were attached to them.

Those going online in the 1970s and 1980s received earliest air pollution control technologies and have not necessarily been upgraded in their li fetimes. They also include the supercritical units, so their heat rates were competitive with other upgraded units for forty years which could explain why those units have not been retired.

**6.1.2 Nameplate Capacity** 



### A second likely candidate parameter for describ ing HRI potentials was the nameplate capacity. Figure 6

- 2 presents histograms showing how the number of potential HRIs is influenced by the nameplate capacity. The low est bars on each graph show that some units of every size category have limited potential for HRI, but the highest two bars on each graph indicate that every generator size category has some units with high potential for HRI.

If the composition of the firs t bar in the graphs is compared to the joint composition in the last two bars, units with nameplate capacities under 100 MW or over 500 MW are represented similarly. However, a higher fraction of units with nameplate capacities between 200 and 500

MW have significant HRI potentials, and a lower fraction of units with nameplate capacities between 100 and 200 MW have significant HRI potentials.

37 ECP types defined on page vii

.



### 59

### Figure 6

- 1

. Distribution o f Units w ith Particular HRI Potentials b y Online Year (Top Representing Maxima a nd Bottom Representing Minima )

60

### Figure 6

- 2 . Distribution o f Units w ith Particular HRI Potentials b y Nameplate Capacity (Top Representing Maxima a nd Bottom Representi ng Minima)

**6.1.3 ECP Type** 



### Figure 6

- 3 illustrates how the distributions are affected by ECP type, but no trends are directly

obvious from this pair of graphs. Half of the B

- and C

- type units (baghouse and cold

- side ESP)

had the maximum HRI potentials below 15 0 Btu/kWh, but only one third of the H

- type units ( Figure 6

- 3 ). Nearly 60 % of the unscrubbed units (B1, C1, and H1) and dry scrubbed units (B7 and CX) had only maximum HRI potentials below 150 Btu/kWh, but only 36 % of wet scrubbed units (B3, B5, C4, C7, H4, and H7) had maximum HRI potentials below 150 Btu/kWh. Only

29 % of the units with SCR (B5, C7, and H7) had maximum HRI potentials below 150 Btu/kWh. From these analyses, the controls at the units with the most HRI potential are likely to be characterized by a combination of these three factors:

• wet scrubbers

• no baghouses or cold

- side ESP

• SCR

61

### Figure 6

- 3 . Distribution o f Units w ith Particular HRI Potentials b y ECP Type (Top Representing Maxima a nd Bottom Representing Minima )

Although they do not affect the statistics above significantly, the four H7 units meet this description and all had HRI potentials of 976 or 1590 Btu/kWh. The online years for the H7 units ranged from 1965 to 1977. Table 6

- 1 shows the ranges in HRI potentials, the average new heat rates if those were applied, and the percentage that may be gained from the HRI potential. Aside

from the three IG units, the average HRI potentials range from 160

- 460 Btu/kWh for C1 units to

440

- 1130 for H7 units. T he average new heat rates for the H7 units even approach those for the IG units if all of the HRIs are employed.

62

### Table 6

- 1 . Calculated HRI Potentials by ECP T ype

ECP

Code 38 Count

of

Total

Units Average

HRI

Potential (Btu/kWh) Average New

Heat Rate (Btu/kWh) Percent

HRI

Potential Average Capital

Cost ($M) Average

Fixed

O&M

Cost ($K/yr) Average

Variable

O&M Cost

($K/yr)

B1 85 17 0

- 480 11, 4 00

- 11, 7 00 1.4%

- 4.0% 32

– 40 63 0 64 0

B3 68 230

- 610 10, 7 0 0

- 11,100 2.0%

- 5.4% 30

– 42 5 90

-

B5 28 290

- 750 9, 60 0

- 10, 0 0 0 2.8%

- 7.3% 45

- 64 83 0 19

B7 82 17 0

- 4 60 10, 4 00

- 10, 7 00 1.6%

- 4.2% 24

– 34 4 50 5

C1 426 16 0

- 4 60 11, 20 0

- 11, 50 0 1.4%

- 3.9% 29

- 35 5 90 58

C4 113 330

- 85 0 9, 900

- 10, 4 0 0 3.1%

- 7.9% 46

– 66 900

-

C7 169 3 60

- 920 9, 400

- 9, 9 00 3.5%

- 9.0% 56

- 79 1, 100 26

CX 15 4 40

- 11 00 10, 500

- 11, 200 3.7%

- 9.7% 60

– 86 1, 200

-

H1 21 290

- 81 0 15, 900

- 16, 400 1.7%

- 4.9% 35

- 44 800 6 50

H4 13 2 10

- 56 0 10, 5 00

- 10, 90 0 1.9%

- 5.1% 33

– 48 59 0

-

H7 4 440

- 11 0 0 9, 2 00

- 9, 9 00 4.2%

- 1 1 % 77

- 11 0 1, 400 28

IG 3 2

- 2 9, 2 00

- 9, 2 00 0.02%

- 0.02% 0.30

- 0.30 10

-

T otal Set 1027 23 0

- 6 20 10,700

- 11, 100 2.1%

- 5.5% 36

- 49 71 0 3 20



### Table 6

- 1 shows that the minimal percentage in HRI potential is 1.4 % (ignoring the three IG units). Overall the percentage improvements for the units in the data set average 2.1 % using the

minimum HRI values and 5.5 % using the maximum HRIs. The cost data from Table 6

- 1 will be

discussed in Section 6.3

. Figure 6

- 4 also illustrates the range in HRI potentials for the various ECP types within NEMS

. Aside from the IG units, the B1, B7, and C1 units have the least average HRI potential and the smallest range. The CX and H7 units have the highest HRI potentials but have a much greater range (indicating the associated uncertainty )

.

38 ECP types defined on page vii

.



### 63

### Figure 6

- 4 . Average HRI Potentials for NEMS ECP Types (Left Side of Bar Represents Minimum HRIs and Right Side Maximum HRIs)

Figure 6

- 5 presents the average new heat rate that might be associated with the average unit in each ECP type. The left sides of the bars represent the average heat rates if the maximum HRI potentials a re realized with every improvement, and the right sides of the bars represent the resultant heat rates when only the minimum HRIs are achieved. The plot shows that B5, C7, H7 and some C4 units might have heat rates under 10,000 Btu/kWh if the improvements are made.

64

### Figure 6

- 5 . Average New Heat Rates b y NEMS ECP Type

In Figure 6

- 5

, t he H1 units would continue to average high heat rates (near 16,000 Btu/kWh) even if the improvements described in Chapter 5 were all undertaken because EIA’s base heat rate median and average for these 21 units are 16,000 and 16,700 Btu/kWh

.



## 6.2 **HRI for the Total Data Set**

The upper graph in Figure 6

- 6 shows the range of total HRI potentials for all units in the data set. The blue curve represents the fraction of units that have minimum HRI potentials less than the

indicated value and the r ed curve those with maximum HRI potentials. For example, 75 % of the units have minimum HRI potentials less than 490 Btu/kWh and maximum HRI potentials under 1290 Btu/kWh with all measures employed.

65

### Figure 6

- 6 . Fractions o f Units w ith Lower Total HRI Potentials a nd Lower Percent H RI s

Th e lower graph in Figure 6

- 6 present s information in terms of the percen t HRI potentials. T he

percentages above 15 % in this graph represent mostly C7 units that went online between 1970

and 198 2

. The lower graph has a similar shape to the upper one but is smoothed out because each
computed HRI potential is divided by the reported heat rate for individual units.

66

### Table 6

- 2 also presents the HRI potentials in tabular form for selected benchmarks corresponding to the horizontal lines in Figure 6

- 6

.

Table 6

- 2 . Cumulative Percentiles for the HRI Data Sets

HRI Potential

(Btu/kWh) Percent HRI

Potential Capital Costs ($ million) O&M Costs ($ million per year)

25 th percentile 32

- 12 0 0.22

- 0.87% 7.9

- 9.9 0.2

50 th percentile 230

- 680 1.3

- 3.8% 29

- 41 0.5

75 th percentile 490

– 1

, 30 0 3.7

- 9.7% 70

- 95 1.6

Maximum 6 50

– 1

, 6 0 0 6.4

- 16% 10 0

- 1 40 2.1 Average 230

- 620 2.0

- 5.5% 36

- 49 1.0



## 6.3 **Associated Costs**

Table 6

- 1 and Table 6

- 2 also included total cost information about the HRIs, listed by ECP type
and by percenti le, respectively. Aside from the IG units, Table 6

- 1 shows that capital costs were smallest for the 87 B7 units ($24

- 34 million) and highest for the four H7 units ($7 7

- 112 million). The total O&M costs were highest for the 21 H1 units ($1.45 million/year) and lowest for the B7
units ($0.46 million/year). Overall, the average capital costs were estimated between $36 and $49 million, the fixed O&M costs at $0.7 million p er year and the variable O&M costs at $0.3
million per year.

Table 6

- 2 lists the capital and total O&M costs based on percentiles. Those values are displayed graphica lly in Figure 6

- 7

. If the costs were similar between the 25 th 

### and 75 th 

### percentiles, then one might conclude that a reasonable approximation for the costs would be a median value. However, the capital costs vary from $7.9 to $95 million between the 25 th 

### and 75 th 

### percentiles and the O&M costs vary from $0.2 and $1.6 million pe r year. Therefore, the median values ($35 million and $0.5 million per year) are not good representations of the middle 50 % of the data.

67

### Figure 6

- 7 . Cumulative Percentile Graphs Depicting HRI Costs a t Different Units

In contrast, the first cumulative percentile graph shown in Figure 6

- 8 has a much tighter

distribution between the 25 th 

### and 75 th 

### per centiles. The median value of $ 130 ,000

- kWh/Btu is not

far from the 25 th 

### percentile value ($110,000

- kWh/Btu) or the 75 th 

### percentile value ($ 230 ,000

- kWh/Btu). However, the range for the total O&M costs divided by the HRI was still between $3,500

- kWh/Btu

- yr a nd $67,000

- kWh/Btu

- yr for the middle 50 % of the data.

68

### Figure 6

- 8 . Cumulative Percentile Graphs Depicting HRI Unit Costs at Different U nits

Figure 6

- 9 shows how the average HRI potentials for the twelve ECP types relate to the capital and total O&M costs. The capital c osts can be well approximated (r 2 

### =0.90) by multiplying the HRI potential by $100,000

- kWh/Btu, and that agrees well with the median value of $ 130 ,000

- kWh/Btu when all of the units were considered in Figure 6

- 8

.

69

### Figure 6

- 9 . Relationship b etween Costs a nd HRI Potential f or ECP Types

However, the total O&M costs are not well estimated (r 2 

### =0.41). The p oor approx imations of total

O&M costs are tied to the non

- linearity of the variable O&M costs and one HRI measure in particular

. The HRI described as “Lower air heater outlet temperature by controlling acid dew point” may be applied to units without FGD (B1, C1, and H1) but only improves the heat rate by 5 Btu/kWh at a high variable O&M cost ($ 390 ,000 to $1, 700 ,000 per year). Variable O&M costs drop to just 2 % of the total O&M costs if this one HRI measure is removed from consideration (r

2 

### =0.95 and slope= $1600

- kWh/Btu

- yr)

.



## 6.4 **Other Considerations**

No intent to describe these improvements at site level was attempted because every plant is unique in design, load demands, coal type, and water availability, among others

. Such efforts should likely examine more O&M cost variations with plant size and capacity factors.

Some additional measures could have been included in Chapter 5 but could not be assessed on a

fleet

- wide lev el. These include:

• Sliding pressure operation

• Boiler draft system control schemes and operating philosophy

• Automated pulverizer supervisory controls and variations with mill design

• Optimum partial load operation of air quality control systems

• Increasing hy drogen purity

• Power supply upgrade for air pollution controls

Some additional measures were not costed in this effort because they are still in the development phase. It may be appropriate for the NEMS EMM module to apply a generic technology

improvement f actor to the HRIs for the entire fleet after a decade or two. However, the NSR triggering concerns may limit the market penetration.

Figure 6

- 1

, Figure 6

- 2 and Figure 6

- 3 also illustrate that the distribution of HRI potentials among

the units does not form a normal distribution. Half of the units have maximum HRI potential s

70

### less than 150 Btu/kWh, but the remaining half average 1160 Btu/kWh for the maximum HRI potential s. The nearest unit to the average has a maximum HRI potential of 1290 Btu/k Wh. The quartile approach to fleet characterization and application of specific controls is likely responsible for some of the behavior. These data distributions suggest that users should be cautious about applying assumptions about averages to these data types. The cumulative percentile approach (Section 6.2 ) may yield better ranges than the use of averages as

substitutions for individual units.

The S&L report was w orking from a fleet average heat rate of 10,400 Btu/kWh, but the EIA

2012 data had an average heat rate of 11, 3 00 Btu/kWh. A speaker 39 

### at the Annual EPA

A&WMA Information Exchange suggested that the heat rates may have risen between 2008 and

2012 because mo re coal

- fired units were now operating in cycling modes rather than using a

base load. Another study mentioned that the change in heat rates was due to a change in the calculation methodology. The percent HRIs shared in Table 6

- 1 and Table 6

- 2 are based on the 2012 numbers but would have been higher in many cases if 2008 heat rates had been used. For example, if the total HRI at a typical plant was 400 Btu/kWh, this would have repres ented a 3.8% improvement in 2008 but only 3. 5 % in 2012.

39 Licata, Anthony, “ASME Heat Rate Improvement,” 39 th Annual EPA

- A&WMA Information Exchange , Research

Triangle Park, North Carolina, December 3, 2014.



### 71

## 7 **Recommendations**

Several possible directions for future data acquisition or ground truthing are recommended :

1. A utility industry survey could be conducted to assess those heat rate improvement

measures that have already been deployed at each plant in order to populate future

databases.

2. Gurbakhash Bhander at U.S. EPA has been developing the GHG Mitigation Options Database (GMOD) and Analysis Tool to show the costs associate d with various operations (including power sector measures such as leakage repair, boiler condenser cleaning, and intelligent sootblowers). 40 

### Bhander will notify Leidos when EPA management has approved the tool for release, and its numbers would need to be

converted from lbs CO 2 

### /Btu to kWh/Btu before comparing to those values represented in this report.

3. Public comments about EPA’s proposed power plant rules and the Clean Power Plan likely contain a significant amount of technical data from the utility compa nies and the states about the HRI potential at each of their power plants. These data sources represent direct assessments by some rule stakeholders and likely will represent at least minimum

HRIs and maximum estimated costs. When available, r eview of thi s information is

recommended.

4. Review of the literature for this project has identified the subject of increased coal

- fired plant cycling operation as an important impact on future performance. While m ost of the

U.S. coal

- fired plants were generally designe d for base

- load operation

, the increased use of intermittent electricity generation

, and gas

- fired units fed by relatively inexpensive natural gas, has altered actual generation conditions such that many of these units now operate in a continuous load

- foll owing mode to match the generation demand. Therefore

, the se units frequently operate with large load changes throughout the day and week . This

new er mode of cycling operation for the aging inventory of coal

- fired units will result in

increased plant heat r ate (decreased efficiency), even though the actual average capacity factor of these units may not cha nge appreciably

. This was out of scope for this study, but we recommend that EIA further investigate this phenomenon to account for the future

modeled heat rate performance of the coal

- fired plants

.
5. Preliminary costing could be sought for the technologies that are still considered in the R&D or pilot phase (e.g., nano

- coatings on boiler walls that prevent heat degradation). Such options would only be av ailable within NEMS in later years. Literature works and patents indicate that some degree of success is being made in these areas, but often readers would find it difficult to classify the HRIs in these pilot studies as maxima or minima. The public litera ture on these studies generally does not cite costs, so full cost evaluations would be necessary for EIA to establish ranges on the capital, operating, and maintenance costs across all potential plants.

40 Bhander, G., N. Hutson, J. Rosati, F. Princiotta, K. Pelt, J. Staudt, and J. Petrusa, “GHG Mitigation Options

Database (GMOD) and Analysis Tool,” International Journal of Greenhouse Gas Control , 26, 2014, 1

- 8.



### 72

### 6. Minima and maxima were used to bound the resultant H RI potentials and costs shared in this report. However, those values could be viewed as 95 th 

### percentile limits two standard deviations from an average. A Monte Carlo simulation that allows random selection of individual HRI potentials and costs from among each range would likely result in smoother distribution curves than those shown in Chapter 6 and would result in best guesses for many HRI measures, not just the mi nimum and maximum values.

The NEMS modeling of HRI changes may also look toward new possibilities:

1. Upgrade the ECP type if a plant triggers NSR, and consider adding these costs as a

condition associated with the HRI.

2. Allow an assortment of HRI packages to be applied at a unit instead of just a single total

HRI and associated total cost. These might be scoped out as boiler island, turbine island, flue gas, air pollution control, and water treatment improvement packages. However, this approach might require f ive flags associated with units to indicate where HRIs had already been made. A second approach would be to have HRI packages assembled for different geographic areas based on local climates (e.g., water cooling issues) and/or regulations (e.g., acid rain programs).

73

## Appendix A. Heat Rate Improvements Assigned to ECP Types **41**

ECP

Code Count

of Total

Units Average

HRI

Potential (Btu/kWh) Average New

Heat Rate (Btu/kWh) Percent

HRI

Potential Average Capital Cost ($M) Average

Fixed

O&M Cost

($K/yr) Average

Variable O&M

Cost ($K/yr) Average Capital

Cost ($M/MW

NPC) Average

Fixed

O&M

Cost ($/yr

MW NPC) Average

Variable

O&M

Cost ($/yr

- MW

NPC)

B1 85 170

- 480 11,000

- 12,000 1.4%

- 4.0% 32

- 40 630 330

- 640 0.24

- 0.31 6,000 1,700

- 3,300

B3 68 230

- 610 11,000 2.0%

- 5.4% 30

- 42 590 0 0.17

- 0.25 3,800 0

B5 28 290

- 750 10,000 2.8%

- 7.3% 45

- 64 830 19 0.087

- 0.13 1,600 36

B7 82 170

- 460 10,000

- 11,000 1.6%

- 4.2% 23

- 34 450 5 0.11

- 0.16 2,200 36

C1 426 160

- 460 11,000

- 12,000 1.4%

- 3.9% 29

- 35 590 300

- 590 0.27

- 0.35 6,900 2,000

- 4,000

C4 113 330

- 850 10,000 3.1%

- 7.9% 46

- 66 900 0 0.12

- 0.17 2,500 0

C7 169 360

- 920 9,400

- 10,000 3.5%

- 9.0% 56

- 79 1,100 26 0.11

- 0.15 2,100 55

CX 15 440

- 1,100 10,000

- 11,000 3.7%

- 9.7% 60

- 86 1,200 0 0.19

- 0.28 3,900 0

H1 21 290

- 810 16,000 1.7%

- 4.9% 35

- 44 800 330

- 650 2.3

- 3.0 63,000 14,000

- 26,000

H4 13 210

- 560 11,000 1.9%

- 5.1% 33

- 48 590 0 0.14

- 0.21 2,600 0

H7 4 440

- 1,100 9,200

- 10,000 4.2%

- 11% 77

- 110 1,400 28 0.12

- 0.17 2,100 50

IG 3 2 9,200 0.02% 0.3 10 0 0.002 64 0

Total

Set 1027 230

- 620 11,000 2.1%

- 5.5% 36

- 49 710 160

- 320 0.24

- 0.32 5,800 1,300

- 2,500

41 ECP types defined on page vii

.



### 74

## Appendix B. Heat Rate Improvements Assigned to Modified ECP Types ( **Allowing ECP**  **Types to be Categorized by Quartile and Assuring at Least 10 Records in Each Non** 

**- IG**  **Group )** 

ECP Code Count of

Total

Units Average

HRI

Potential (Btu/kWh) Average New

Heat Rate (Btu/kWh) Percent

HRI

Potential Average Capital Cost ($M) Average

Fixed

O&M

Cost ($K/yr) Average

Variable

O&M

Cost ($K/yr) Average Capital

Cost ($M/MW

NPC) Average

Fixed

O&M

Cost ($/yr

MW NPC) Average

Variable

O&M

Cost ($/yr

MW NPC)

B1

- Q1 32 2 12,000 0.0% 0.3 10 0 0.007 250 0

B1

- Q2 15 40

- 140 11,000 0.3%

- 1.2% 22

- 24 360 430

- 870 0.096

- 0.11 1,800 1,700

- 3,400

B1

- Q3 18 230

- 680 10,000 2.1%

- 6.4% 66

- 84 1,100 690

- 1,400 0.20

- 0.26 3,700 1,800

- 3,500

B1

- Q4 20 500

- 1,300 13,000

- 14,000 3.5%

- 9.4% 60

- 76 1,400 440

- 830 0.76

- 0.99 20,000 4,500

- 8,400

B3

- Q1 13 2 12,000 0.0% 0.3 10 0 0.010 330 0

B3

- Q2 24 30

- 130 11,000 0.3%

- 1.2% 13

- 15 290 0 0.047

- 0.056 1,100 0

B3

- Q3 16 350

- 930 10,000

- 11,000 3.1%

- 8.2% 44

- 68 750 0 0.19

- 0.30 3,300 0

B3

- Q4 15 620

- 1,600 11,000 5.1%

- 13% 65

- 93 1,400 0 0.50

- 0.72 12,000 0

B5C7

- Q1 16 2 10,000 0.0% 0.3 10 0 0.003 84 0

B5C7

- Q2 42 30

- 120 10,000 0.3%

- 1.2% 16

- 20 380 0 0.031

- 0.036 700 0

B5C7H7

- Q3 84 370

- 970 9,200

- 10,000 3.6%

- 9.5% 64

- 94 1,100 0 0.11

- 0.16 1,800 0

B5C7H7

- Q4 59 640

- 1,600 9,100

- 10,000 6.0%

- 15% 84

- 120 1,700 86 0.18

- 0.25 3,800 180

B7

- Q1 27 2 11,000 0.0% 0.3 10 0 0.002 68 0

B7

- Q2 25 30

- 130 10,000 0.3%

- 1.2% 15

- 17 340 0 0.035

- 0.042 820 0

B7

- Q3Q4 30 440

- 1,100 10,000

- 11,000 3.8%

- 9.8% 52

- 77 950 15 0.27

- 0.40 5,400 99

C1H1

- Q1 148 2 11,000 0.0% 0.3 10 0 0.006 190 0

C1H1

- Q2 117 40

- 140 11,000 0.3%

- 1.2% 22

- 25 360 440

- 880 0.12

- 0.13 2,300 2,100

- 4,100

C1H1

- Q3 72 230

- 690 11,000 2.0%

- 6.0% 53

- 68 1,000 520

- 1,000 0.36

- 0.49 7,700 2,900

- 5,800

C1H1

- Q4 110 500

- 1,300 13,000 3.6%

- 9.6% 59

- 75 1,400 420

- 790 1.1

- 1.5 31,000 6,500

- 12,000



### 75ECP Code Count of

Total

Units Average

HRI

Potential (Btu/kWh) Average New

Heat Rate (Btu/kWh) Percent

HRI

Potential Average Capital Cost ($M) Average

Fixed

O&M

Cost ($K/yr) Average

Variable

O&M

Cost ($K/yr) Average Capital

Cost ($M/MW

NPC) Average

Fixed

O&M

Cost ($/yr

MW NPC) Average

Variable

O&M

Cost ($/yr

MW NPC)

C4

- Q1 15 2 11,000 0.0% 0.3 10 0 0.002 80 0

C4

- Q2 27 30

- 120 10,000 0.3%

- 1.2% 15

- 17 330 0 0.041

- 0.048 940 0

C4

- Q3 32 370

- 970 10,000 3.5%

- 9.1% 62

- 92 1,000 0 0.13

- 0.20 2,300 0

C4

- Q4 39 630

- 1,600 9,500

- 10,000 5.7%

- 14% 73

- 100 1,500 0 0.21

- 0.30 4,600 0

CX

- Q1Q2Q3Q4 15 440

- 1,100 10,000

- 11,000 3.7%

- 9.7% 60

- 86 1,200 0 0.19

- 0.28 3,900 0

H4

- Q1Q2Q3 13 210

- 560 11,000 1.9%

- 5.1% 33

- 48 590 0 0.14

- 0.21 2,600 0

IG

- Q1 3 2 9,200 0.0% 0.3 10 0 0.002 64 0

Total Set 1027 230

- 620 11,000 2.0%

- 5.3% 36

- 49 710 160

- 320 0.24

- 0.32 5,800 1,300

- 2,500



### 76

## Appendix C. Modified Heat Rate Improvements Assigned to ECP Types ( **If Lowering Air Heater Outlet Temperature**  **I s**  **N ot**  **C onsidered a Cost** 

**- Effective Measure)** 

ECP

Code Count

of Total

Units Average

HRI

Potential (Btu/kWh) Average New

Heat Rate (Btu/kWh) Percent

HRI

Potential Average Capital Cost ($M) Average

Fixed

O&M Cost

($K/y r) Average

Variable O&M

Cost ($K/yr) Average Capital

Cost ($M/MW

NPC) Average

Fixed

O&M

Cost ($/yr

MW NPC) Average

Variable

O&M

Cost ($/yr

MW NPC)

B1 85 170

- 480 11,000

- 12,000 1.4%

- 4.0% 25

- 33 580 13 0.21

- 0.28 5,600 130

B3 68 230

- 610 11,000 2.0%

- 5.4% 30

- 42 590 0 0.17

- 0.25 3,800 0

B5 28 290

- 750 10,000 2.8%

- 7.3% 45

- 64 830 19 0.087

- 0.13 1,600 36

B7 82 170

- 460 10,000

- 11,000 1.6%

- 4.2% 23

- 34 450 5 0.11

- 0.16 2,200 36

C1 426 160

- 460 11,000

- 12,000 1.4%

- 3.9% 22

- 29 540 13 0.23

- 0.31 6,400 140

C4 113 330

- 850 10,000 3.1%

- 7.9% 46

- 66 900 0 0.12

- 0.17 2,500 0

C7 169 360

- 920 9,400

- 10,000 3.5%

- 9.0% 56

- 79 1,100 26 0.11

- 0.15 2,100 55

CX 15 440

- 1,100 10,000

- 11,000 3.7%

- 9.7% 60

- 86 1,200 0 0.19

- 0.28 3,900 0

H1 21 290

- 810 16,000 1.7%

- 4.9% 28

- 37 730 13 2.1

- 2.8 59,000 1,400

H4 13 210

- 560 11,000 1.9%

- 5.1% 33

- 48 590 0 0.14

- 0.21 2,600 0

H7 4 440

- 1,100 9,200

- 10,000 4.2%

- 11% 77

- 110 1,400 28 0.12

- 0.17 2,100 50

IG 3 2

- 2 9,200 0.02% 0.3 10 0 0.002 64 0

Total

Set 1027 230

- 620 11,000 2.1%

- 5.5% 33

- 45 690 12 0.22

- 0.29 5,500 110



### 77

## Appendix D. Modified Heat Rate Improvements Assigned to Modified ECP Types ( **If Lowering Air Heater Outlet Temperature**  **I s**  **N ot**  **C onsidered a**  **Cost**

**- Effective Measure**  **)**

ECP Code Count

of Total

Units Average

HRI

Potential (Btu/kWh) Average New

Heat Rate (Btu/kWh) Percent

HRI

Potential Average Capital Cost ($M) Average

Fixed

O&M

Cost ($K/yr) Average

Variable

O&M

Cost ($K/yr) Average Capital

Cost ($M/MW

NPC) Average

Fixed

O&M Cost ($/yr

- MW

NPC) Average

Variable

O&M

Cost ($/yr

- MW

NPC)

B1

- Q1 32 2 12,000 0.02% 0.3 10 0 0.007 250 0

B1

- Q2 15 30

- 130 11,000 0.3%

- 1.2% 12

- 14 280 0 0.059

- 0.071 1,400 0

B1

- Q3 18 220

- 670 10,000 2.1%

- 6.3% 49

- 67 1,000 0 0.16

- 0.22 3,400 0

B1

- Q4 20 490

- 1,300 13,000

- 14,000 3.5%

- 9.3% 51

- 68 1,300 54 0.68

- 0.91 19,000 560

B3

- Q1 13 2 12,000 0.02% 0.3 10 0 0.010 330 0

B3

- Q2 24 30

- 130 11,000 0.3%

- 1.2% 13

- 15 290 0 0.047

- 0.056 1,100 0

B3

- Q3 16 350

- 930 10,000

- 11,000 3.1%

- 8.2% 44

- 68 750 0 0.19

- 0.30 3,300 0

B3

- Q4 15 620

- 1,600 11,000 5.1%

- 13% 65

- 93 1,400 0 0.50

- 0.72 12,000 0

B5C7

- Q1 16 2 10,000 0.02% 0.3 10 0 0.003 84 0

B5C7

- Q2 42 32

- 120 10,000 0.3%

- 1.2% 16

- 20 380 0 0.031

- 0.036 700 0

B5C7H7

- Q3 84 370

- 970 9,200

- 10,000 3.6%

- 9.5% 64

- 94 1,100 0 0.11

- 0.16 1,800 0

B5C7H7

- Q4 59 640

- 1,600 9,100

- 10,000 6.0%

- 15% 84

- 120 1,700 86 0.18

- 0.25 3,800 180

B7

- Q1 27 2 11,000 0.02% 0.3 10 0 0.002 68 0

B7

- Q2 25 32

- 130 10,000 0.3%

- 1.2% 15

- 17 340 0 0.035

- 0.042 820 0

B7

- Q3Q4 30 440

- 1,100 10,000

- 11,000 3.8%

- 9.8% 52

- 77 950 15 0.27

- 0.40 5,400 99

C1H1

- Q1 148 2 11,000 0.02% 0.3 10 0 0.006 190 0

C1H1

- Q2 117 32

- 130 11,000 0.3%

- 1.2% 12

- 14 280 0 0.073

- 0.090 1,800 0

C1H1

- Q3 72 220

- 680 11,000 1.9%

- 5.9% 41

- 56 870 0 0.30

- 0.43 7,000 0

C1H1

- Q4 110 490

- 1,300 13,000 3.5%

- 9.6% 50

- 66 1,300 51 1.0

- 1.4 29,000 810

C4

- Q1 15 2 11,000 0.02% 0.3 10 0 0.002 80 0



### 78ECP Code Count

of Total

Units Average

HRI

Potential (Btu/kWh) Average New

Heat Rate (Btu/kWh) Percent

HRI

Potential Average Capital Cost ($M) Average

Fixed

O&M

Cost ($K/yr) Average

Variable

O&M

Cost ($K/yr) Average Capital

Cost ($M/MW

NPC) Average

Fixed

O&M Cost ($/yr

- MW

NPC) Average

Variable

O&M

Cost ($/yr

- MW

NPC)

C4

- Q2 27 32

- 120 10,000 0.3%

- 1.2% 15

- 17 330 0 0.041

- 0.048 940 0

C4

- Q3 32 370

- 970 10,000 3.5%

- 9.1% 62

- 92 1,000 0 0.13

- 0.20 2,300 0

C4

- Q4 39 630

- 1,600 9,500

- 10,000 5.7%

- 14% 73

- 100 1,500 0 0.21

- 0.30 4,600 0

CX

- Q1Q2Q3Q4 15 440

- 1,100 10,000

- 11,000 3.7%

- 9.7% 60

- 86 1,200 0 0.19

- 0.28 3,900 0

H4

- Q1Q2Q3 13 210

- 560 11,000 1.9%

- 5.1% 33

- 48 590 0 0.14

- 0.21 2,600 0

IG

- Q1 3 2 9,200 0.02% 0.3 10 0 0.002 64 0

Total Set 1027 230

- 620 11,000 2.0%

- 5.3% 33

- 45 690 12 0.22

- 0.29 5,500 110



### 79

## Appendix E. Midpoints **between Minima and Maxima in Appendix A** *****

ECP

Code Count

of

Total

Units HRI

Potential (Btu/kWh) New Heat Rate (Btu/kWh) Percent

HRI

Potential Capital Cost ($M) Fixed

O&M Cost

($K/yr) Variable

O&M Cost

($K/yr) Capital

Cost ($M/MW

NPC) Average

Fixed

O&M

Cost ($/yr

MW NPC) Average

Variable

O&M

Cost ($/yr

- MW

NPC)

B1 85 300 12,000 3% 40 600 500 0.30 6000 3000

B3 68 400 11,000 4% 40 600 0 0.20 4000 0

B5 28 500 9,800 5% 50 800 20 0.10 2000 40

B7 82 300 11,000 3% 30 500 5 0.10 2000 40

C1 426 300 11,000 3% 30 600 400 0.30 7000 3000

C4 113 600 10,000 5% 60 900 0 0.10 2000 0

C7 169 600 9,600 6% 70 1,000 30 0.10 2000 60

CX 15 800 11,000 7% 70 1,000 0 0.20 4000 0

H1 21 600 16,000 3% 40 800 500 3.00 60000 20000

H4 13 400 11,000 3% 40 600 0 0.20 3000 0

H7 4 800 9,600 8% 90 1,000 30 0.10 2000 50

IG 3 2 9,200 0.0% 0 10 0 0.00 60 0

TOTAL

SET 1027 400 11,000 4% 40 700 200 0.30 6000 2000



### * This table presents the midpoints between the minima and maxima. Determination of actual averages would require enough full

- scale plant evaluations to

determine the appropriate distributions of data between the minima and maxima.



### 80

## Appendix F. Midpoints between Minima and Maxima in Appendix **B** *****

ECP Code Count of

Total

Units HRI Potential (Btu/kWh) New Heat Rate (Btu/kWh) Pe rcent HRI

Potential Capital Cost ($M) Fixed O&M

Cost ($K/yr) Variable O&M Cost ($K/yr) Capital Cost ($M/MW

NPC) Average

Fixed

O&M Cost ($/yr

- MW

NPC) Average

Variable

O&M Cost ($/yr

- MW

NPC)

B1

- Q1 32 2 12,000 0.0% 0 10 0 0.01 200 0

B1

- Q2 15 90 11,000 0.8% 20 400 700 0.10 2000 3000

B1

- Q3 18 500 10,000 4% 70 1000 1000 0.20 4000 3000

B1

- Q4 20 900 13,000 6% 70 1000 600 0.90 20000 6000

B3

- Q1 13 2 12,000 0.0% 0 10 0 0.01 300 0

B3

- Q2 24 80 11,000 0.7% 10 300 0 0.05 1000 0

B3

- Q3 16 600 11,000 6% 60 700 0 0.20 3000 0

B3

- Q4 15 1000 11,000 9% 80 1000 0 0.60 10000 0

B5C7

- Q1 16 2 10,000 0.0% 0 10 0 0.00 80 0

B5C7

- Q2 42 80 9,900 0.8% 20 400 0 0.03 700 0

B5C7H7

- Q3 84 700 9,500 7% 80 1000 0 0.10 2000 0

B5C7H7

- Q4 59 1000 9,500 10% 100 2000 90 0.20 4000 200

B7

- Q1 27 2 11,000 0.0% 0 10 0 0.00 70 0

B7

- Q2 25 80 10,000 0.8% 20 300 0 0.04 800 0

B7

- Q3Q4 30 800 11,000 7% 60 900 10 0.30 5000 100

C1H1

- Q1 148 2 11,000 0.0% 0 10 0 0.01 200 0

C1H1

- Q2 117 90 11,000 0.8% 20 400 700 0.10 2000 3000

C1H1

- Q3 72 500 11,000 4% 60 1000 800 0.40 8000 4000

C1H1

- Q4 110 900 13,000 7% 70 1000 600 1.00 30000 9000

C4

- Q1 15 2 11,000 0.0% 0 10 0 0.00 80 0

C4

- Q2 27 80 10,000 0.8% 20 300 0 0.04 900 0

C4

- Q3 32 700 10,000 6% 80 1000 0 0.20 2000 0

C4

- Q4 39 1000 10,000 10% 90 2000 0 0.30 5000 0

CX

- Q1Q2Q3Q4 15 800 11,000 7% 70 1000 0 0.20 4000 0



### 81ECP Code Count of

Total

Units HRI Potential (Btu/kWh) New Heat Rate (Btu/kWh) Pe rcent HRI

Potential Capital Cost ($M) Fixed O&M

Cost ($K/yr) Variable O&M Cost ($K/yr) Capital Cost ($M/MW

NPC) Average

Fixed

O&M Cost ($/yr

- MW

NPC) Average

Variable

O&M Cost ($/yr

- MW

NPC)

H4

- Q1Q2Q3 13 400 11,000 3% 40 600 0 0.20 3000 0

IG

- Q1 3 2 9,200 0.0% 0 10 0 0.00 60 0

TOTAL SET 1027 400 11,000 4% 40 700 200 0.30 6000 2000



### * This table presents the midpoints between the minima and maxima. Determination of actual averages would require enough full

- scale plant evaluations to

determine the appropriate distributions of data between the minima and maxima.



### 82