EPRI Power Plant Optimization Guidelines

Power Plant Optimization Guidelines TR-110718 Final Report, December 1998

Effective December 6, 2006, this report has been made publicly available in accordance with Section 734.3(b)(3) and published in accordance with Section 734.7 of the U.S. Export Administration Regulations. As a result of this publication, this report is subject to only copyright protection and does not require any license agreement from EPRI. This notice supersedes the export control restrictions and any proprietary licensed material notices embedded in the document prior to publication

EPRI Project Manager J. Stallings

EPRI • 3412 Hillview Avenue, Palo Alto, California 94304 • PO Box 10412, Palo Alto, California 94303 • USA 800.313.3774 • 650.855.2121 • [email protected] • www.epri.com

DISCLAIMER OF WARRANTIES AND LIMITATION OF LIABILITIES THIS REPORT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS AN ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCH INSTITUTE, INC. (EPRI). NEITHER EPRI, ANY MEMBER OF EPRI, ANY COSPONSOR, THE ORGANIZATION(S) NAMED BELOW, NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM: (A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR IMPLIED, (I) WITH RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS REPORT, INCLUDING MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, OR (II) THAT SUCH USE DOES NOT INFRINGE ON OR INTERFERE WITH PRIVATELY OWNED RIGHTS, INCLUDING ANY PARTY'S INTELLECTUAL PROPERTY, OR (III) THAT THIS REPORT IS SUITABLE TO ANY PARTICULAR USER'S CIRCUMSTANCE; OR (B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY WHATSOEVER (INCLUDING ANY CONSEQUENTIAL DAMAGES, EVEN IF EPRI OR ANY EPRI REPRESENTATIVE HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES) RESULTING FROM YOUR SELECTION OR USE OF THIS REPORT OR ANY INFORMATION, APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS REPORT. ORGANIZATION(S) THAT PREPARED THIS REPORT Energy Technologies Enterprises Corporation

ORDERING INFORMATION Requests for copies of this report should be directed to the EPRI Distribution Center, 207 Coggins Drive, P.O. Box 23205, Pleasant Hill, CA 94523, (925) 934-4212. Electric Power Research Institute and EPRI are registered service marks of the Electric Power Research Institute, Inc. EPRI. POWERING PROGRESS is a service mark of the Electric Power Research Institute, Inc. Copyright © 1998 Electric Power Research Institute, Inc. All rights reserved.

CITATIONS This report was prepared by Energy Technologies Enterprises Corp. 7722 Desdemona Court McLean, Virginia 22102 Principal Investigator S. Tavoulareas This report describes research sponsored by EPRI. The report is a corporate document that should be cited in the literature in the following manner: Power Plant Optimization Guidelines, EPRI, Palo Alto, CA: 1998. TR-110718.

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REPORT SUMMARY During the last five years, new software products have become available that use statistical analysis or neural network techniques to optimize power plant performance based on multiple objectives. The growing impetus to reduce costs of NOx compliance and electricity production has accelerated deployment of these applications in the utility industry. Background Optimization software was being implemented in approximately 130 boilers as of September 1998 (of these applications, more than half were one-time efforts; after an optimized list of set points had been given to the plant, the optimization software was removed). Reported NOx emission reduction has ranged from 5 to 40% and heat rate reduction from 0.5 to 3%. In the combustion area, key objectives are reduction of NOx emissions, heat rate, and unburned carbon. While some U.S. utilities have gained experience with software tools, the majority of utilities need more information on how to select the most appropriate software for their plant optimization programs. Objectives • To determine the potential improvement in plant performance and emission reduction through use of power plant optimization software. • To select the most appropriate optimization type among stand-alone, online/advisory, and closed-loop. • To evaluate various options by carrying out a cost-benefit analysis. Approach The Power Plant Optimization (PPO) Guidelines were developed with the expertise of the contractor and EPRI, as well as advice from the Power Plant Optimization Interest Group (consisting of EPRI members interested and involved in plant optimization). The project team’s approach was to lead utility planners through a five-step process, which methodically would set appropriate optimization objectives, identify projected performance improvements, and estimate costs and benefits of various optimization types. To facilitate this analysis, the team included two Excel™ spreadsheets with the program. A beta version of these Guidelines was distributed to 40 utility engineers, many of whom provided feedback on how to improve them further. Version 1.0, which accompanies this report, incorporates the comments of these utility users. v

Results Forty utility engineers have already used the beta version of these Guidelines. Many of these engineers have used them to make decisions on how to structure optimization programs and how to improve efficiency and cost-effectiveness of plant operations. Use of optimization software is expected to grow significantly, and these Guidelines will play a critical role in this growth. Two case studies carried out by Allegheny Power and Northern State Power are included in this document and provide a good example of how the Guidelines can be used. EPRI Perspective Power plant optimization software has become more widespread as a result of the industry’s attempt to find less expensive ways to comply with NOx emission regulations. Optimization products are of strategic importance to the utility industry, which soon will operate in a deregulated market in which key economic parameters and operating objectives change continuously. Optimization tools will allow utilities to adjust to such dynamic environments. EPRI has played and will continue to play a leading role in this technical area by supporting utility demonstration of all optimization tools, disseminating relevant information, and developing software evaluation guidelines. AP-110718 Interest Categories Fossil steam plant performance optimization Air emissions control Emissions monitoring Fossil steam plant O&M cost reduction Keywords Heat rate Nitrogen oxides Emission control Performance testing Computer applications Computer applications Cost Reduction

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EPRI Licensed Material

INTRODUCTION/ HOW TO USE THIS DOCUMENT As of September 1998, approximately 50 utilities in the U.S. have utilized optimization software in 92 units, mainly to reduce NOx emissions and heat rate. Approximately 40 more optimization projects are planned to be completed by the end of 1998. Industry experience to date suggests that moderate NOx reduction (5-40%) and heat rate improvement (0.5-1.5 percentage points) can be achieved with optimization software. Detailed description of the various software available and the industry experience are provided in EPRI’s Power Plant Optimization Web Site (http://www.epriweb.com/ gg/98funders/ppo/index.html) and the proceedings from EPRI’s Workshops on Power Plant Optimization in 1997 and 1998 (TR-108687 and TR-111316, respectively). The web site is updated monthly and is a good source of information on ongoing projects and key industry developments. Key definitions associated with power plant optimization are provided in Box I. The purpose of this document is to build upon these sources of information and provide utilities with a step-by-step guide on how to: x

determine the potential power plant performance improvement if an optimization package were used,

x

select the most cost effective optimization type among: stand-alone1, on-line/ advisory, and closed-loop, and

x

evaluate alternative optimization software packages.

The approach followed in this document emphasizes: x

low-cost options; it starts from a simple (“quick”) tuning and moves to more expensive optimization and potential hardware changes (e.g. NOx control retrofit options) only as needed or supported by sound economic justification;

1

“Stand-alone” type optimization projects were referred to as “Off-line/One-time” type projects during the EPRI/ESEERCO Conference and in the conference proceedings (EPRI TR-108687).

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x

setting baseline performance after quick tuning to assess the real potential of optimization; tuning and baseline performance are described in detail in EPRI’s “NOx Emissions Testing and Optimization for Coal-fired Utility Boilers” (TR105109) to which the reader is referred frequently;

x

establishing clear objectives for the optimization project; and

x

identifying the type of optimization and the specific software package which is most appropriate and cost-effective. Box I Definitions

Power plant optimization is a process which involves changes in operating variables and equipment settings to achieve a set of objectives. Optimization software may be used in the following three modes: x

Stand-alone: while the optimization system may obtain data electronically from the DCS or other data sources, one or more variables are often provided manually for each iteration; in most cases, this optimization is performed once and then the system is maintained at the same operating conditions. Optimization may be repeated periodically

x

On-line/advisory: all the data required for optimization (variables which change with operation) are obtained electronically; the system provides advice to the plant operator who makes the final decision whether or not the proposed optimum should be implemented

x

Closed-loop: fully integrated into the power plant controls; the optimization system feeds control biases directly into the control system causing the field equipment to change without human intervention.

More information on typical optimization systems, as well as specific software available in the market, is provided in EPRI publications TR-108687 and TR-111316. While this document uses the terms “power plant optimization”, most experience so far is with “boiler optimization.” However, the same software may be used for power plant optimization.

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These guidelines consist of 5 basic steps (see the figure on the following page) which are described in the first five sections of the report (one section devoted to each step). x

Step 1: Tune unit and establish baseline performance,

x

Step 2: Establish clear needs and objectives,

x

Step 3: Determine optimization potential/Can optimization alone meet established objectives?

x

Step 4: Identify the most cost-effective type of optimization, and

x

Step 5: Select the best optimization product for your application.

Quick tuning and establishment of baseline performance is recommended as the first step, before establishing objectives for the power plant optimization project. Alternatively, objectives may be set first (especially when the boiler is tuned recently), but they should be re-assessed after the quick tuning is done and baseline performance is established. There are three appendices: x

Appendix A: Software User’s Guide provides guidance on how to use the spreadsheets which accompany this report on a floppy disk. Guidance is also included for the applicable sections (Steps 3, 4, and the case studies in Appendix B).

x

Appendix B: Case Studies provides two examples which illustrate how the guidelines may be applied to specific power plants.

x

Appendix C: Formulas provides all the formulas used for estimating the costs and benefits of power plant optimization in the guidelines and spreadsheets.

The material provided in this report includes both general guidance and step-by-step instructions to evaluate the various options for a specific power plant under consideration. In steps 3 and 4, detailed instructions (highlighted in shaded boxes) provide for using these spreadsheet tools (Tables 3-1 and 4-1).

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Guidelines Process to Evaluate Power Plant Optimization Options

Step 1 Tune unit & Establish baseline performance

Step 2 Establish clear needs & objectives Consider optimization in combination with other options such as:

Step 3 Determine optimization potential/ Can optimization alone meet established objectives?

YES

Step 4 Identify the most cost-effective type of optimization

Step 5 Select the best optimization product for your application x

NO

z NOx controls (Low NOx burners, SNCR, reburning, and SCR) z Hardware modifications for heat rate improvements


ABBREVIATIONS AND ACRONYMNS ABB/CE BOOS CAAA CO2 CO DAS DCS EPA ESP I/O I&C kWh Lbs/MBtu LNCFS-III LOI MOOS MW NOx NPV NSP O&M O2 OEM OFA PA PEPCO ppm PPO PV SNCR SCR SO2 TVA UBC

Asea Brown Boveri/Combustion Engineering Burner out of service Clean Air Act Amendment of 1990 Carbon dioxide Carbon monoxide Data acquisition system Digital control system U.S. Environmental Protection Agency Electrostatic precipitator Input/output Instrumentation and controls Kilowatts per hour Pounds per million British thermal units ABB/CE Low NOx Concentric Firing Systems-III Loss on ignition Mills-out-of-service Megawatt Nitrogen oxides Net present value Northern States Power Operating and maintenance Oxygen Original equipment manufacturer Overfire air Primary air Potomac Electric Power Company Parts per million Power plant optimization Present value Selective non-catalytic reduction Selective catalytic reduction Sulfur dioxide Tennessee Valley Authority Unburned carbon xi


CONTENTS

1 STEP ONE: TUNE UNIT AND ESTABLISH BASELINE PERFORMANCE ........................ 1-1 A. Perform Diagnostic Testing ............................................................................................ 1-2 B. Perform Quick Tuning..................................................................................................... 1-5 What is Quick Tuning? .................................................................................................... 1-5 C. Establish Baseline Performance..................................................................................... 1-7

2 STEP TWO: ESTABLISH CLEAR NEEDS AND OBJECTIVES ......................................... 2-1 B. Differentiate between “firm requirements” and “desirable outcomes” ............................. 2-3 C. Articulate project objectives............................................................................................ 2-4

3 STEP THREE: DETERMINE OPTIMIZATION POTENTIAL/CAN OPTIMIZATION ALONE MEET ESTABLISHED OBJECTIVES?..................................................................... 3-1 Can Optimization Alone Meet Established Objectives? ...................................................... 3-2 Background and Instructions for Completing Table 3-1 ...................................................... 3-4 Information on the Last Unit Tune Up ............................................................................. 3-4 Unit Operating Flexibility (at full load).............................................................................. 3-5 Fuel Flow Biasing ........................................................................................................ 3-5 Air Flow Biasing........................................................................................................... 3-7 Excess O2 (baseline vs. minimum O2 level) ................................................................. 3-8 Operating Flexibility of Burner Tilts (only in the case of T-fired boilers)..................... 3-10 Operating Range of Air and Gas Dampers................................................................ 3-12 Primary Air to Fuel Ratio (PA/Fuel) ........................................................................... 3-12 Other Control Variables............................................................................................. 3-13 Ability to Change Equipment Settings ........................................................................... 3-14 Burner Settings ......................................................................................................... 3-14 Pulverizer Settings (in case of coal-fired power plants)............................................. 3-16 Other Equipment Settings ......................................................................................... 3-16

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Air Distribution Modifications ..................................................................................... 3-17 Coal Pipe Orificing..................................................................................................... 3-18 Mill Modifications ....................................................................................................... 3-21 Expected Performance Improvements .......................................................................... 3-23 Project Classification.............................................................................................. 3-24

4 STEP FOUR: IDENTIFY THE MOST COST-EFFECTIVE TYPE OF OPTIMIZATION ........ 4-1 Types of Optimizations: ...................................................................................................... 4-2 A. Availability of DCS and DAS ....................................................................................... 4-2 B. Continuous vs. One-time or Periodic Optimization...................................................... 4-2 C. Performance Improvement Objectives........................................................................ 4-3 D. Cost-Benefit of Alternative Optimization Types .......................................................... 4-3 Background and Instructions for Completing Table 4-1 ...................................................... 4-7 COSTS ............................................................................................................................... 4-7 UP FRONT COSTS......................................................................................................... 4-7 Up Front License Fees ................................................................................................ 4-7 Additional Computer Software and Hardware ............................................................. 4-8 Installation and Calibration .......................................................................................... 4-8 Training ..................................................................................................................... 4-11 Power Plant Hardware Modifications......................................................................... 4-11 ANNUAL COSTS (O&M Including Recalibration).......................................................... 4-11 Annual Software License and Maintenance Fees ..................................................... 4-12 Technical Support ..................................................................................................... 4-12 OPTIMIZATION BENEFITS .............................................................................................. 4-14 UP FRONT (ONE TIME) BENEFITS ............................................................................. 4-14 NPV of Deferred Costs.............................................................................................. 4-14 NPV of Avoided Costs............................................................................................... 4-14 ANNUAL BENEFITS ..................................................................................................... 4-15 Annual Avoided Costs............................................................................................... 4-15 Fuel Cost Savings ..................................................................................................... 4-16 O&M Impacts ............................................................................................................ 4-17 Value of Change in Unit Availability .......................................................................... 4-18 Value of Change in Unit Output ................................................................................ 4-18 Benefits Due to Emission Reductions ....................................................................... 4-19

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Cost-Benefit Analysis Results ........................................................................................... 4-22

5 STEP FIVE: SELECT THE BEST OPTIMIZATION PRODUCT FOR YOUR APPLICATION........................................................................................................................ 5-1 Selecting an Optimization Package .................................................................................... 5-2 Key Factors......................................................................................................................... 5-2 Comments on the Key Factors for Evaluating Optimization Software................................. 5-3 A. Demonstrated track record of the optimization product in similar applications............ 5-3 B. Experience of the software vendor in supporting product deployment ....................... 5-6 C. Total cost of applying the optimization product........................................................... 5-7 D. Benefits from the utilization of the optimization product.............................................. 5-9

A SOFTWARE USER’S GUIDE .............................................................................................A-1 System Requirements......................................................................................................... A-1 Hardware Requirements ................................................................................................. A-1 Software Requirements................................................................................................... A-1 Installation........................................................................................................................... A-1 Using the Software .............................................................................................................A-2 Table 3-1......................................................................................................................... A-3 Table 4-1......................................................................................................................... A-3 Tables B-3, B-5, B-7, B-8, B-9, and B-11 ........................................................................ A-4 User Tutorial ....................................................................................................................... A-4

B CASE STUDIES..................................................................................................................B-1 Case Study 1: Allegheny Power’s Armstrong 1 Unit ........................................................... B-1 Background.....................................................................................................................B-1 Armstrong Unit 1 ............................................................................................................. B-2 Evaluation of Alternative Power Plant Optimization Options ........................................... B-7 Case Study 2: Northern States Power Riverside 7 Unit .................................................... B-17 Background................................................................................................................... B-17 NSP’s NOx Compliance Plans and Future Needs..................................................... B-17 Description of Riverside 7 ............................................................................................. B-18 Evaluation of Power Plant Optimization Software ......................................................... B-18

C FORMULAS ........................................................................................................................C-1

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LIST OF FIGURES Figure 1-1 Impact of Auxiliary Air Register Settings on NOx Emissions and Heat Rate at PEPCO’s Potomac River Power Plant ........................................................................ 1-6 Figure 3-1 Effect of air biasing on NOx emissions--400 MW tangential-fired Boiler............... 3-5 Figure 3-2 BOOS results for a 365 MW single-wall-fired boiler.............................................. 3-6 Figure 3-3 Effect of Coal Quality on NOx Emissions Typical Uncontrolled ............................ 3-9 Figure 3-4 Hypothetical NOx Reduction with Balanced Combustion .................................... 3-9 Figure 3-5 Effect of Burner Tilt Position on NOx Emissions for Baseline (Uncontrolled) and Low-NOx (Controlled) Operation--400 MW Tangential-Fired Boiler ....................... 3-11 Figure 3-6 Effect of Burner Tilt Position on NOx--105 MNW Tangential-Fired Boiler ........... 3-11 Figure 3-7 Effect of Varying the Ratio of Primary Air to Coal on NOx Emissions--105 MW Tangential-Fired Boiler........................................................................................... 3-13 Figure 3-8 Union Electric/Meramec Unit 4: Unburned Carbon vs. Coal Fineness, Coal Flow Imbalance and Coal Blend.................................................................................... 3-20 Figure 3-9 PEPCo’s Potomac River 4/Effect of Mill Maintenance on LOI ........................... 3-21 Figure 3-10 Smith 2/Relationship Between LOI, NOx and Coal Fineness ........................... 3-22 Figure 3-11 Typical Particle Size Distribution with Static and Dynamic Classifiers .............. 3-23 Figure 3-12 Utility Experience with Combustion Tuning & Optimization NOx Reduction Achieved ....................................................................................................................... 3-24 Figure 4-1 Optimization Cost Effectiveness ........................................................................... 4-3 Figure A-1 A Portion of Table 3-1 Before and After a Change is entered.............................. A-6 Figure A-2 A Portion of Table 3-1 Before and After a Second Input is Entered..................... A-7 Figure A-3 A Portion of Table 4-1 Applied to Boiler XYZ; Before Use and After a Change is Entered .......................................................................................................... A-9 Figure A-4 A Portion of Table 4-1 Applied to Boiler XYZ; After a Second Group of Changes is Entered....................................................................................................... A-11 Figure B-1 Armstrong Units 1 & 2 Boiler Arrangement .......................................................... B-4 Figure B-2 Burner and Mill Arrangement ............................................................................... B-5 Figure B-3 Armstrong Unit 1 NOx Emissions After IFS Burner Installation ............................ B-6 Figure B-4 Armstrong Unit 1 Boiler Efficiency After IFS Burner Installation ........................... B-6 Figure B-5 Alternative Coal Pipe Arrangements for Improved NOx Control......................... B-20

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LIST OF TABLES Table 1-1 References and Guidance Documents .................................................................. 1-2 Table 1-2 Comparison of Three Measurement Approaches .................................................. 1-3 Table 1-3 Suitability of Measurement Approaches for NOx Test Elements (Phases) ............ 1-4 Table 3-1 Project Classification to Determine Potential Performance Improvements Due to Optimization......................................................................................................... 3-3 Table 3-2 Expected Performance Improvements................................................................. 3-23 Table 3-3 Previous Tuning Objective – Safe Operation....................................................... 3-24 Table 3-4 Previous Tuning Objective – Heat Rate Improvement ......................................... 3-24 Table 3-5 Previous Tuning Objective – NOx Reduction...................................................... 3-25 Table 3-6 Previous Tuning Objective – NOx Reduction & Heat Rate Improvement ............ 3-25 Table 4-1 Power Plant Optimization Cost-Benefit Analysis.................................................... 4-5 Table B-1 Allegheny's Experience with Power Plant Optimization........................................ B-2 Table B-2 Characteristics of Eastern Bituminous Coal Burned in Armstrong 1...................... B-3 Table B-3 Project Classification to Determine Potential Performance Improvements Due to Optimization Armstrong 1/Advanced Control for Coal Flow Distribution .............. B-9 Table B-4 Expected Performance Improvements with Optimization (Case 1)...................... B-10 Table B-5 Project Classification to Determine Potential Performance Improvements due to Optimization Armstrong 1/ No Coal Flow Distribution Control ............................ B-11 Table B-6 Expected Performance Improvements with Optimization (Case 2)...................... B-12 Table B-7 Power Plant Optimization Cost-Benefit Analysis--Armstrong 1 Advanced Control for Coal Flow Distribution.................................................................................. B-13 Table B-8 Power Plant Optimization Cost-Benefit Analysis--Armstrong 1 Without Coal Distribution Control........................................................................................................ B-15 Table B-9 Project Classification to Determine Potential Performance Improvements Due to Optimization—Riverside 7 ................................................................................. B-23 Table B-10 Expected Performance Improvements with Optimization (Case 1).................... B-24 Table B-11 Power Plant Optimization Cost-Benefit Analysis—Riverside 7 .......................... B-25

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1 STEP ONE: TUNE UNIT AND ESTABLISH BASELINE PERFORMANCE


Step 2 Establish clear needs & objectives Consider optimization in combination with other options such as:


YES

z NOx controls (Low NOx NO

burners, SNCR, reburning, and SCR)

z Hardware modifications

for heat rate improvements


Step 5 Select the best optimization product for your application 1-1

EPRI Licensed Material Step One: Tune Unit and Establish Baseline Performance

Unit performance deteriorates with time and requires periodic retuning and optimization. Before optimization is carried out, tuning is recommended because: x

it may be adequate to satisfy low-to-moderate performance improvement requirements (e.g., NOx emission reduction less than 15%),

x

it is important even if other performance improvement options are implemented, because it reduces the magnitude of improvement needed and costs associated with it. For example, if a 15% NOx reduction improvement is achieved through tuning, low NOx burners may be adequate to meet NOx regulations instead of low NOx burners with overfire air, potentially saving $5-10 per kW.

Before tuning is performed, diagnostic testing is recommended. After tuning, baseline performance should be established to serve as the reference point for evaluating performance improvement alternatives. The following table provides references to guidance documents which will prove helpful in planning and implementing such programs. Table 1-1 References and Guidance Documents Key Actions

Sources/Support Material

Perform diagnostic testing to identify problems and areas of potential performance improvement

Section 5 of EPRI’s “NOx Emissions Testing and Optimization for Coal-fired Utility Boilers” (TR-105109)

Perform Quick Tuning; in most cases, quick tuning improves performance without any outof-pocket costs


Establish Baseline Performance


A. Perform Diagnostic Testing Diagnostic testing is beneficial because it helps to: x

verify proper functioning of plant components,

x

identify off-design operating conditions and problem areas,

x

identify site-specific constraints and practices which may preclude the unit from achieving better performance, and

1-2

EPRI Licensed Material Step One: Tune Unit and Establish Baseline Performance x

identify potential improvements which can be achieved with minimum effort and budget.

Detailed guidance on how to carry out a diagnostic program is provided in Section 5, Table 3-1 of EPRI’s “NOx Emissions Testing and Optimization for Coal-fired Utility Boilers” (TR-105109). In most cases, Type 1 testing (types of testing are shown in Tables 1-2 and 1-3) is sufficient. Type 2 or 3 testing should be performed only in case one or more of the following conditions exist: x

relatively high performance improvement is being sought; for example, NOx emission reduction in the 20-35% range with simultaneous improvement in one or more of the following: heat rate, unburned carbon, outlet steam temperatures, and/or opacity,

x

there are indications of non-uniform air and coal flow distribution in the coal pipes and flue gas in backpass ducts, which may require multiple gas sampling and coal pipe measurements, and/or

x

past operating experience and maintenance records suggest high corrosion rate and/or malfunctioning equipment.

Table 1-2 Comparison of Three Measurement Approaches Measurement Approach Element

Type 1

Type 2

Type 3

Scope

Quick NOx Emissions assessment with minimal performance data

Emissions characterization with appropriate combustion diagnostics

Emissions characterization with comprehensive combustion diagnostic and performance testing

Instrumentation and sampling and analytical procedures

See Table 1-2 of TR-105109

See Table 1-2 of TR-105109

See Table 2 of TR-105109

Applicability of Results

Provides quick, inexpensive assessment of NOx emissions; potentially inadequate for NOx retrofit assessment

Provides sufficient data to identify emissions range and dominant parameters and to assess NOx retrofit options

Provides instrumentation and measurements suitable for combustion tuning and optimization of combustion system for reduced NOx emissions

Emissions measurement precision

r25%

r10-15%

r5-10%

Relative Cost

Low

Moderate

High

Source: TR-105109

1-3

EPRI Licensed Material Step One: Tune Unit and Establish Baseline Performance Table 1-3 Suitability of Measurement Approaches for NOx Test Elements (Phases) Measurement Approach Element Baseline Testing Combustion Equipment diagnostic testing

Type 1

Type 2

Type 3

X

X X

X X

X

X

Parametric Testing (preliminary and detailed) Combustion Tuning

X

Source: TR-105109

Diagnostic testing may include the following inspections and measurements: x

visual inspection of combustion equipment,

x

measurement of gas composition uniformity at economizer exit,

x

measurement of excess O2 before and after the air heater to estimate air in-leakage through the boiler casing and the air heater,

x

measurement of unburned carbon in the flyash and combustion efficiency,

x

measurement of coal fineness,

x

determination of uniformity of air and fuel distribution to the burners,

x

inspection of air heaters for potential plugging, and

x

inspection of plant instrumentation to ensure proper calibration.

All components which may affect measurements used for performance assessment should be checked. Examples of components to check include: x

Instrumentation and controls: Make sure that the existing instrumentation is calibrated and in good working condition. In particular, accurate measurement of excess O2, NOx and CO are essential. Also, the controls should be able to achieve repeatable operating conditions (same control settings should result in approximately the same plant performance).

x

Air registers: Make sure that they are in good operating condition and set properly (based on operating guidelines).

1-4


Dampers: Air and gas dampers (the latter in case of split backpass) and associated drives may not be in good operating condition, adversely affecting combustion, NOx generation, and general unit performance. Tempering air and overfire air dampers may have similar effects.

x

Burner and overfire air adjustment mechanisms: Burner tilts and yaws in T-fired boilers may not be in good operating condition or may require tuning. Similarly, overfire air adjustment mechanisms in wall-fired boilers may not be operating properly.

Certain measurements are inherently variable (e.g., feeder speed of pulverizers) and the appropriate average should be used. However, there may be cases in which key measurements are not repeatable and plant optimization may not be feasible or practical. Such case has been observed in a couple of power plants which did not have well-calibrated instrumentation.

B. Perform Quick Tuning A power plant may not have been tuned recently or may have been tuned for an operating objective different from present operating requirements. For example, the objectives of the previous tuning could have been safe operation and heat rate improvement, but future operation may need to focus on balancing NOx emissions requirements and heat rate improvement. Knowledge of the operating condition and limitations of the specific plant equipment, NOx formation mechanisms, and power plant engineering principles can be applied in a systematic but quick way (Quick Tuning) to improve plant performance.

What is Quick Tuning? Tuning, as defined in TR-105109, involves Type 3 testing (see Tables 1-2 and 1-3) in which operational modifications are made to the combustion process to achieve specific operating objectives such as NOx emissions reduction or heat rate improvement. A key component of this type of tuning is parametric testing which involves the use of specialized instrumentation such as multiple-point sampling system to analyze gas and unburned carbon. Quick tuning is a simplified tuning (Type 1 instead of Type 3 testing) which can be carried out by a plant performance engineer (either from the utility staff or an outside consultant) in a period of 3-5 working days. The general approach followed is to check the operating condition of key components based on visual observation, evaluate whatever data are already available at the plant, and attempt to improve performance through adjustments of control variables and changes in equipment set-points. The available instrumentation is usually adequate, provided that there are O2, NOx, and CO monitors available. 1-5


Examples of components which may be tuned include: x

Adjust air registers: Very often the registers are set-up to maximize combustion efficiency (reduce unburned carbon). Sometimes they are in a sub-optimum setting either because there is a problem with their controls (such as: broken drives and not properly operating position sensor) or because they have not been tuned recently. Resetting of the air registers is simple and can improve both combustion efficiency and NOx emissions. The latter can be achieved if the air register settings contribute to “air staging” (creating air-lean flame by distributing more air to the outside of the flame and towards the top burner elevations). Tuning of Potomac Electric Power Co.’s (PEPCO) Potomac River power plant (see Figure 1-1) is a good example of how the auxiliary air register settings affect NOx emissions and heat rate. If NOx reduction is a key operating objective, setting the air registers as shown in Figure 11 could be a good starting point.

Burner Elevation

5

1

1

Original Setting (Baseline)

Aux Air Fuel Air Air Flow Burner Elevation

0 O2 (%) 5

0

NOx (lb/MBtu)

1

0

' Heat Rate (%)

1

Tuned for NOx Minimization

Air Flow Source: PEPCo

0

O2 (%)

0

NOx (lb/MBtu)

0

' Heat Rate (%)

Figure 1-1 Impact of Auxiliary Air Register Settings on NOx Emissions and Heat Rate at PEPCO’s Potomac River Power Plant

x

1-6

Adjust damper set-points: Changes in damper settings may improve air or gas nonuniformities and enhance combustion.


Assess low excess air operation: Lowering excess air is desirable because it reduces NOx emissions and improves boiler efficiency. However, below a certain excess air level, unburned carbon and CO emissions start increasing and counter-balance the benefits of low excess air operation. It is therefore desirable to identify the optimum excess air level and operate as close to this level as possible. The optimum excess air level can be identified by reducing the excess air and monitoring CO emissions and unburned carbon.

x

Assess combustion staging: Creating fuel-rich and fuel-lean combustion zones in the boiler has been proven to reduce NOx emissions. Within certain limits, such changes do not adversely affect combustion efficiency. Techniques which have been well documented in the literature (EPRI’s TR-105109 provides good guidance on combustion staging) include:

x

¡

fuel staging (biasing fuel distribution among the burner elevations),

¡

air staging (see above example from PEPCo’s Potomac River plant),

¡

burners out of service (BOOS), sometimes called mills out of service (MOOS), and

¡

simulated overfire air (OFA).

Improve pulverizer performance: Air-to-fuel ratio and quick tuning of the pulverizer (e.g., adjustment of spring tension and outlet temperature) can improve coal fineness, combustion efficiency, and general plant performance. Balancing of air and coal flows is very important, especially for wall-fired boilers, but it may not be part of quick tuning because it requires more extensive effort to assess the level of balancing needed and to implement the necessary modifications (e.g., coal pipe orificing).

Changes made under quick tuning may have adverse impacts on unburned carbon, CO emissions, slagging, fouling corrosion, unit generating capacity, rate of load change and unit heat rate. More information regarding assessment of adverse impacts of tuning (combustion tuning trade-offs) is provided in Section 7 of TR-105109.

C. Establish Baseline Performance It is essential to establish baseline performance which reflect what can be achieved by a well-tuned unit with reasonable effort of the plant staff. Baseline performance then can be used as a reference point to decide what improvements need to be made and how well the performance improvements will perform.

1-7


Depending on the dispatch profile (cycling duty) of the unit, baseline tests may involve testing only at full load or at various loads. Detailed guidance on how to plan and implement baseline testing is provided in Section 4 of EPRI’s “NOx Emissions Testing and Optimization for Coal-fired Utility Boilers” (TR-105109)..

1-8


2 STEP TWO: ESTABLISH CLEAR NEEDS AND OBJECTIVES


Step 2 Establish clear needs & objectives

Consider optimization in combination with other options such as:


YES






Step 5 Select the best optimization product for your application

2-1

EPRI Licensed Material Step Two: Establish Clear Needs and Objectives

A. Identify desired performance improvements relative to baseline performance Performance improvements are usually dictated by regulatory requirements (e.g., reduction of NOx emissions or opacity) and/or driven by economics (e.g., need to reduce heat rate or unburned carbon to make it easier to sell the flyash). In determining the unit’s performance improvement needs, the following options should be investigated: x

Reduce NOx emissions: ¡

What are the unit, plant, and system NOx reduction requirements?

¡

Are these requirements clear or might future regulations demand additional NOx reductions? Is there adequate information to identify how NOx requirements may change over time (covering the life of the unit under consideration)?

¡

Is there flexibility in terms of NOx averaging?

¡

Is NOx emission banking an option in case the unit overcomplies?

¡

Is buying NOx emission allowances an option? If yes, at what cost?

x

Lower heat rate: This objective is usually driven by economics; there are no specific requirements, but targets may be set based on the historical records and the assessment of potential heat rate improvement. Depending on how well the unit is tuned, heat rate improvements due to optimization may range from 0.5 to 1.5 percentage points at full load or up to 5 percentage points at low loads.

x

Reduce unburned carbon (UBC): ¡

Are there any unburned carbon requirements, such as maximum UBC (or LOI), to sell the flyash?

¡

Are there any other adverse impacts from high UBC such as reduced collection efficiency of the ESP?

If there are no specific requirements to reduce or keep UBC to below a certain level, economics should dictate the optimum level. x

Lower opacity

x

Lower CO emissions:

2-2

EPRI Licensed Material Step Two: Establish Clear Needs and Objectives

¡

What is the maximum level of CO emissions set by the manufacturer or the plant operator?

¡

Is the unit exceeding it on the average? occasionally?

x

Requirements and constraints associated with outlet steam temperatures: Are boiler outlet steam temperatures below design levels? If yes, is it required to increase these temperature for safe operation? If operating safety is not jeopardized, determine the relationship between steam temperature (both superheat and reheat) and improved heat rate (% improvement in heat rate per degree steam temperature increase).

x

Other requirements: While not easy to quantify, other unit performance improvements are not uncommon, for example: ¡

improve operating safety; this may suggest increasing the excess O2 (keeping it above a lower limit) or reducing UBC,

¡

reduce potential for waterwall corrosion by increasing excess O2 above a certain level, or

¡

reduce maintenance requirements and improve equipment reliability.

B. Differentiate between “firm requirements” and “desirable outcomes” As already indicated in the above questions, it is very important to differentiate between “firm requirements” and “desirable outcomes.” Firm requirements may include: x

Annual average NOx of the unit to comply with CAAA requirements; for example: 340 ppm (0.45 lbs/MBtu) for T-fired and 375 ppm (0.50 lbs/MBtu) for wall-fired boilers burning coal,

x

NOx cap at plant and/or system level; for example: specified numbers of tons per year from a multi-unit power plant or a power system,

x

UBC (or LOI) below a certain level for selling the flyash, if flyash disposal is an available option, keeping UBC below a certain level may not be a firm requirement,

x

Maximum opacity as specified by local or federal regulations, for example, 15% opacity may be set as a maximum,

x

Maximum CO emissions; maximum daily average and/or instantaneous values may be specified, and 2-3

EPRI Licensed Material Step Two: Establish Clear Needs and Objectives x

Also, minimum steam temperature level or maximum superheat-reheat temperature differential may be specified.

“Desirable outcomes” may include: x

Lower NOx emissions than required by regulations to allow more expensive units in the system to operate at higher NOx levels (NOx averaging or trading); therefore, reducing system compliance costs,

x

Lower heat rate, and

x

Higher steam temperatures.

To the extent possible, firm requirements should be minimized, so that optimization may determine the best operating conditions. The fewer firm requirements, the greater the operating efficiency that can be achieved through optimization.

C. Articulate project objectives It is important that clear objectives are articulated which reflect: x

firm requirements,

x

desirable outcomes, and

x

references to important factors and key assumptions.

Examples of project objectives: x

“Reduce annual average NOx emissions from the present baseline level of 390 ppm (0.52 lbs/MBtu) to 320 ppm (0.43 lbs/MBtu) without adverse operating impacts on heat rate, LOI, CO, and opacity. NOx reductions beyond this target are not of any value to the utility at this point in time.”

x

“Reduce annual average NOx emissions from the present baseline level of 390 ppm (0.52 lbs/MBtu) to 320 ppm (0.43 lbs/MBtu). Further NOx reduction and heat rate improvement are desirable to the extent that these improvements are economic. Also, adverse O&M impacts are acceptable provided that they result in lower overall production cost and do not jeopardized plant safety.” (Notes: value of additional NOx reduction to the system is worth 400-500 $/ton of NOx removed; marginal cost of power is 8 cents/kWh).

2-4

EPRI Licensed Material Step Two: Establish Clear Needs and Objectives x

“Reduce heat rate while keeping annual average NOx emissions below 360 ppm (0.48 lbs/MBtu). Additional NOx reductions are not of any value to the utility at this point in time.”

2-5


3 STEP THREE: DETERMINE OPTIMIZATION POTENTIAL/CAN OPTIMIZATION ALONE MEET ESTABLISHED OBJECTIVES?




z NOx controls (Low NOx


YES

NO





Step 5 Select the best optimization product for your application 3-1

EPRI Licensed Material Step Three: Determine Optimization Potential/Can Optimization Alone Meet Established Objectives?

Can Optimization Alone Meet Established Objectives? Deciding whether optimization alone can meet the established objectives involves: x

estimating the level of performance improvement to be expected through optimization, and

x

comparing this level to the desirable level of performance improvement established in Step 2; optimization is adequate if it meets the “firm requirements.

If optimization is not adequate to meet the established objectives, it is still a very costeffective option and should be evaluated in combination with other options such as low NOx burners, SNCR, or SCR. Estimating the potential performance improvement requires consideration of sitespecific factors and requirements, as well as the performance improvement potential of the various types of optimization software. Table 3-1 provides a structure through which the user may determine the potential performance improvement achieved through optimization by considering site-specific factors (see printout of Table 3-1 on the next page and the text Box 3-1 which describes the software version of Table 3-1. Box 3-1 Software Guidance on Table 3-1

For system requirements, installation and loading of the Excel files included on the floppy disk, see Appendix A. The Excel file “Table 3-1.xls” provided on the floppy disk should be used to carry out Step 3 of the guidelines. A print out of the table appears in these guidelines on page 3-3. The user should answer the questions about the technical circumstances of the boiler in question by entering the appropriate score from 1 to 3 based on the instructions given in this section. The table then automatically calculates a Total Score between 100 and 300 which indicates the degree to which optimization can improve boiler performance. Tables 3-2 and 3-2a to 3-2d on pages 37-39 of the guidelines (they are not included on the disk) provide the performance improvements that can be expected based on the score generated in Table 3-1. The main criteria for estimating the level of performance improvement are grouped into the following four categories: 1. time since last tuning and its objective (weighting: 30%),

3-2


Table 3-1 Project Classification to Determine Potential Performance Improvements Due to Optimization

3-3


2. unit operating flexibility (weighting: 20%), 3. ability to change equipment settings (weighting: 20%), and 4. ability to modify hardware (weighting: 30%). As shown in parentheses (above), a weighting factor is applied to each category reflecting its importance relative to potential performance improvement. While some of the weighting factors for these categories may seem arbitrary, they reflect actual industry experience (the authors have participated in 10-12 optimization projects) and provide an effective way to categorize each project. As such, Table 3-1 could be used either as an analytical tool or as a general guideline. In each category, a number of questions are presented. By responding to these questions (in shaded boxes), the spreadsheet automatically provides a Total Score which classifies the project into one of three categories (A, B or C). The expected performance improvements (mainly NOx reduction and heat rate improvement) are shown in Tables 3-2 and 3-2a through 3-2d. Table 3-2 shows performance improvement for each of the three project classifications. Tables 3-2a through 3-2d further narrow down the expected performance improvement based on the objectives of the last tuning.

Background and Instructions for Completing Table 3-1 Information on the Last Unit Tune Up Combustion system tuning here refers to systematic checking and adjustment of control variables and equipment settings to ensure that unit operating objective are met. Such tuning may be done by the boiler manufacturer, a service company (engineering consultant), or plant personnel. The longer the time since the last tuning, the higher the possibility and potential for performance improvement. When was the unit last tuned?

3-4

x

Enter 1 if less than a year,

x

Enter 2 if between 1-2 years, or

x

Enter 3 if more than 2 years,


Tuning Objective? x

Enter 1 if the goal was to ensure safe operation (stable flame and complete combustion),

x

Enter 2 if the goal was to improve heat rate,

x

Enter 3 if the goal was to reduce NOx emissions, or

x

Enter 4 if the goal was to reduce both heat rate and NOx emissions.

Unit Operating Flexibility (at full load) Fuel Flow Biasing

450

0.6 Baseline

300

0.4

Biased Firing (m inim um fuel air, m axim um auxiliary air to top elevations)

0.2

150

0.0

N O x E m issions (pp m )

N O x E m issions (Lb /M B tu)

The capability to bias fuel flow relative to the baseline setting presents the potential to reduce NOx emissions by up to 20-25%. More fuel is typically directed to the lower burner elevations than the upper elevations with the possibility of no fuel in the top burner. Figure 3-1 shows the NOx reduction achieved at Kansas Power & Light’s Lawrence #5 unit due to biased firing

0 0

1

2

3

4

5

6

7

E xcess O 2 (% ) Source: Kansas P ower & Light Co, Law rence U nit 5; E PR I Report TR -102906

Figure 3-1 Effect of air biasing on NOx emissions--400 MW tangential-fired Boiler

3-5


When there is adequate fuel supply system capacity, burners out of service (BOOS) may be used to reduce NOx emissions. Figure 3-2 shows the impact of BOOS on NOx emissions in a 365 MW front wall-fired boiler.

1.0

750

0.8

600

0.6

450

0.0

0.0

NOx Emissions (ppm)

NOx Emissions (Lb/MBtu)

Range in NOx emissions for each pattern reflects differences in excess O2 levels

Top pulverizer Bottom pulverizer

Burner Firing Pattern Burner in Service

Burner Out Of Service

Source: Energy Technology Consultants, Inc.

Figure 3-2 BOOS results for a 365 MW single-wall-fired boiler

Fuel biasing may have an impact on combustion efficiency, because it affects the fuel distribution along the height of the combustion zone and the residence time of the fuel particles in the furnace.

3-6


Fuel Flow Biasing Specify the level of fuel flow biasing capability at full load as follows: x

Enter 1 for no biasing; i.e. if the fuel feed system (or mills in the case of coal-fired power plants) are operating at or near maximum capacity,

x

Enter 2 if some fuel biasing is available, but not enough to have one mill or row of burners out of service, or

x

Enter 3 if there is enough fuel feed system capacity to have one mill or row of burners out of service at full load.

Air Flow Biasing Air flow biasing is similar to fuel biasing in that the air distribution along the height of the combustion zone can be altered relative to baseline conditions. Introducing more air through the top burner elevations and less through the lower elevations delays combustion and reduces NOx emissions. Examples of air flow biasing are shown in Figure 1-1 and 3-1. Air flow biasing can be used independently from other control changes or may be combined with fuel biasing. The highest level of biasing is achieved when the top burner elevation is out of service and more air flow is biased towards the upper burner elevations. This type of operation very often is referred to as “simulated overfire air” and achieves the highest NOx reduction. Non-uniform air flow distribution is usually indicated by different excess O2 measurements from one side of the boiler backpass (flue gas duct) to the other. 1-3 percentage points difference is not unusual in many boilers. Reducing this difference allows the total excess air to be reduced as well.

3-7


Air Flow Biasing Specify the level of air flow biasing capability at full load as follows: x

Enter 1 for no biasing; this is the case in which the air supply system (fans) are operating at or near maximum capacity,

x

Enter 2 if air biasing is possible and can increase or decrease air flow rate through each air compartment by up to 20% relative to baseline conditions, or

x

Enter 3 if more than 20% air flow biasing can be achieved.

Excess O2 (baseline vs. minimum O2 level) The impact of excess O2 (excess air) on NOx emissions and combustion efficiency has been well documented: Figure 3-3 shows how excess O2 impacts NOx for various types of coals. Lower excess O2 results in lower NOx and higher boiler efficiency. However, below a certain level of excess O2, the combustion efficiency (more specifically, the unburned carbon and CO emissions) is adversely affected. Figure 3-4 illustrates the relationship between excess O2, NOx, and unburned carbon. Maximum O2 is also limited by equipment capacity constraints. 1 2 00 B itu m in o u s 1 0 50

1 .4 1 .2

900 S u bb itu m in o u s 750

1 .0

600

0 .8 L ig nite 0 .6

450

0 .4

300 0

0 .0 0

1

2

3

4

5

6

E xc e s s O 2 (% ) S o urc e: E E R

Figure 3-3 Effect of Coal Quality on NOx Emissions Typical Uncontrolled

3-8

7

N O x E m is s io ns (p p m )

N O x E m is s io ns (L b /M B tu )

1 .6


NOx

Avg. O2-Unbal.

UBC

Avg. O2-Bal.

NOx Reduction with Bal. Comb.

Unbalanced Balanced

Avg. UBC (b oth cases)

Excess O 2 Note: Points on curves represent average operating conditions for each burner mill group (assumes 3 mills total).

Source: EPRI/TR-105109

Figure 3-4 Hypothetical NOx Reduction with Balanced Combustion

The maximum excess O2 and the level of excess O2 at which combustion efficiency starts decreasing define an operating range. The wider this range, the higher the potential for performance improvements. The excess O2 operating range changes with load and operating conditions. If this operating range has not been established, it is recommended that it be established, at least at full load, by reducing the excess O 2 until CO emissions and/or unburned carbon start increasing significantly. For such measurements, a CO monitor and LOI analysis are required.

3-9


Excess O2 (baseline vs. Minimum O2 level) Indicate the excess O2 operating range at full load as follows: x

Enter 1 if the baseline excess O2 is within 0.5 percentage point from the minimum excess O2,

x

Enter 2 if the baseline excess O2 is within 0.5 and 1.5 percentage point from the minimum excess O2, or

x

Enter 3 if the baseline excess O2 is at least 1.5 percentage points higher than the minimum excess O2.

Operating Flexibility of Burner Tilts (only in the case of T-fired boilers) Burner tilt has an impact on NOx emissions. While in most cases increasing tilt increases NOx (see Figure 3-5), under certain circumstances, (see Figure 3-6), the opposite may be true. Also, tilt affects combustion efficiency (up tilt reduces the coal particle residence time) and boiler outlet steam temperatures. The higher the flexibility to change burner tilt the higher the potential for performance impacts.

600

U n co n tro lled 0 .6 0

450

0 .4 0

300 C o n tro lled

0 .0 0 -1 5

0 -1 0

-5

0

+5

+10

+15

B u r n e r T ilt A n g le (d e g ) S o u r c e : U ta h P o w e r & L ig h t C o m p a n y , H u n te r U n it 2

3-10

+20

+25

N O x E m is s io n s (p p m )

N O x E m is s io n s (L b /M B tu )

0 .8 0

EPRI Licensed Material Step Three: Determine Optimization Potential/Can Optimization Alone Meet Established Objectives? Figure 3-5 Effect of Burner Tilt Position on NOx Emissions for Baseline (Uncontrolled) and Low-NOx (Controlled) Operation--400 MW Tangential-Fired Boiler

Operating Flexibility of Burner Tilts (only in the case of T-fired boilers) Indicate the burner tilt operating range at full load as follows:

x

Enter 2 if the burner tilts are operational, but over a limited range (e.g., -15 to +10 degrees), or

x

Enter 3 if the burner tilts are operational over the full operating range, typically -30 to +30 degrees.

0.70

525

0.68

510

0.66

495

0.64

480

0.62

465

0.60 -20

-10

0

+10

NOx Emissions (ppm)

Enter 1 if the burner tilts are not operational (e.g., stuck),


x

455 +20

Burner Tilt Angle (deg) Source: Potom ac Electric Power Company, Potom ac River Station

Figure 3-6 Effect of Burner Tilt Position on NOx--105 MNW Tangential-Fired Boiler

3-11


Operating Range of Air and Gas Dampers The more flexibility to adjust gas and air dampers, the higher the potential for performance improvements, for example: x

Air dampers which control the air flow rate to the four corners of T-fired boilers or to front versus rear wall of opposed wall-fired boilers,

x

Air dampers which control the air flow to individual burners, or

x

Gas dampers in split backpass boilers.

Operating Range of Air and Gas Dampers Indicate the level of flexibility of air and/or gas dampers as follows: x

Enter 1 if adjustable dampers do not exist or the dampers can not be adjusted, either because they are not operational (e.g., stuck) or because they can not be controlled remotely,

x

Enter 2 if the dampers can be adjusted, but to a limited extent (e.g., half operating range), or

x

Enter 3 if the dampers can be adjusted over the full operating range.

Primary Air to Fuel Ratio (PA/Fuel) PA/Fuel ratio has an impact on both NOx emissions and combustion efficiency. The relationship between NOx and primary air velocity (an indicator of PA/Fuel ratio) for an 105 MW T-fired boiler is shown in Figure 3-7 (see next page). The more flexibility the boiler has to adjust PA/Fuel ratio, the higher the probability of performance improvements. However, this flexibility is typically limited by available primary air fan capacity and pulverizer (in the case of coal-fired power plants) operating constraints.

3-12


Primary Air to Fuel Ratio (PA/Fuel) Indicate the level of flexibility of PA/Fuel ratio as follows:

x

Enter 2 if some operating flexibility exists, for example, PA/Fuel ratio may change within the range 1.8 to 2.0, or

x

Enter 3 if there is more flexibility with the PA/fuel ratio, for example PA fuel ratio may change within the range of 1.5 to 2.5.

0.45

337.5

0.40

300

0.35

262.5

0.30 70

80

90

100

NOx Emissions (ppm )

Enter 1 if there is no operating flexibility; PA/Fuel ratio has to comply with predetermined settings,


x

225 110

Average Primary Air Velocity (ft/sec) Note: Tests conducted at reduced load of 35 MW

Source: Potomac Electric Power Company, Potomac River Station

Figure 3-7 Effect of Varying the Ratio of Primary Air to Coal on NOx Emissions--105 MW Tangential-Fired Boiler

Other Control Variables Control variables other than those discussed above may be available, depending on the configuration of the boiler. “Control variables” are defined as all the operating parameters which can be changed remotely (from the control room). Such variables may include tempering air dampers and exit temperature control of the pulverizers or the ability to mix different coals.

3-13


Other Control Variables Indicate the level of flexibility provided by these categories of control variables as follows: x

Enter 1 if there is no operating flexibility,

x

Enter 2 if some operating flexibility exists, for example, air registers may change by up to 30% of the original setting, or

x

Enter 3 if the control variables can change throughout their “design” operating range.

Ability to Change Equipment Settings This category includes changes which cannot be made from the control room, but require manual adjustment of equipment. In most cases, these changes can be made while the boiler is operating. For example, adjustments of burner settings can be done while the burner is operating. Some of the pulverizer changes may require the pulverizer to be off-line, but this does not mean that the boiler must be off-line as well; adjustments could be made to one pulverizer at a time, while the boiler is operating at or near full load. Burner Settings Changing the original burner settings may improve boiler performance. Burner settings which should be considered include: x

air registers,

x

air sleeve dampers and coal nozzle axial position (in some wall-fired boilers),

x

yaw of burners and/or overfire air ports (in the case of T-fired boilers), and

x

relationship between burner tilt and overfire air tilt (in the case of T-fired boilers); in most cases, the overfire air tilt angle is set based on the burner tilt angle; the original setting could be changed or the overfire air tilt may be decoupled from the burner tilt.

These parameters are usually set during combustion system tuning which is carried out by the burner vendor. While readjustment of these parameters may yield performance improvements, it is not always easy to do so. Constraints may relate to: 3-14

EPRI Licensed Material Step Three: Determine Optimization Potential/Can Optimization Alone Meet Established Objectives? x

contractual requirements specified by the burner vendor regarding operation and maintenance of the combustion system,

x

physical layout of the combustion system which makes it difficult to adjust burner settings, and

x

limited personnel with experience to make the necessary adjustments.

3-15


Burner Settings Indicate the level of flexibility to adjust the burner settings: x

Enter 1 if there is no or very limited flexibility to adjust the burner settings as part of an optimization program,

x

Enter 2 if there is some flexibility, for example, half of the burner settings can be adjusted, or

x

Enter 3 if most of the burner settings can be adjusted.

Pulverizer Settings (in case of coal-fired power plants) Very often pulverizer performance can be improved through changes such as: x

journal spring pressure,

x

adjustment of classifier blades and clearances,

x

adjustment of flow straighteners, and

x

changes in outlet temperature set-point.

Pulverizer Settings (in case of coal-fired power plants) Depending on the flexibility to adjust pulverizer settings: x

Enter 1 if there is no or very limited flexibility to adjust the pulverizer settings as part of an optimization program,

x

Enter 2 if there is some flexibility, for example, half of the pulverizer settings can be adjusted, or

x

Enter 3 if most of the pulverizer settings can be adjusted.

Other Equipment Settings Changes in the settings of other equipment may help improve overall plant performance, for example, in some wall-fired boilers with low NOx burners, the burner coal nozzle is adjustable. Changing the position of the nozzle affects NOx emissions, LOI, and boiler efficiency.

3-16


Other Equipment Settings Depending on the flexibility to adjust the settings of other equipment: x

Enter 1 if there is no other equipment,

x

Enter 2 if there is one other piece of equipment that can be adjusted, or

x

Enter 3 if there are more than one piece of equipment that can be adjusted.

Hardware Modifications This category includes hardware modifications which are not considered major in terms of amount of investment, but can improve plant performance significantly. Examples of such modifications include: x

modifications of the air distribution system,

x

coal pipe orificing, and

x

installation of dynamic classifiers.

In most cases, hardware modifications require the equipment to be off-line. In some cases, boiler shut-down may be avoided, for example, dynamic classifiers and coal pipe orificing may be done one pulverizer at a time while the boiler is operating. Air Distribution Modifications Better control of air flow distribution along the height of the combustion zone and the individual burners usually results in better performance. In many cases, the air flow distribution can be improved through: x

addition of dampers,

x

addition of turning vanes, and

x

adjustment of existing directional plates/vanes.

An example of a successful application of such modifications is Potomac Electric Power Co.’s (PEPCo) Potomac River #4 optimization which is described in Box 3-2 (see next page).

3-17


Air Distribution Modifications x

Enter 1 if air distribution modifications will not be included in the optimization program, or

x

Enter 3 air distribution modifications will be included in the optimization program.

Note: there is no “Enter 2” option in this and several of the following boxes

Coal Pipe Orificing In case of significant coal pipe-to-pipe flow imbalance (more than 5% from uniform flow distribution), orificing may improve the coal flow distribution, combustion efficiency, and overall plant performance. Figure 3-8 provides an example of the

3-18


Box 3-2 PEPCo Modifies the Air Distribution System for NOx Optimization Installation of splitter dampers, tuning vanes and windbox compartmentalization at PEPCo’s Potomac River 4 balanced the air distribution from the front to the rear corners and resulted in a more uniform air distribution in each burner. While it was not possible to assess the NOx reduction achieved with these modifications (because other modifications were made at the same time followed by optimization), they were a contributing factor to the overall NOx reduction achieved which reached 30-35%.

Hot Air from Air Heater

Before Modifications

To Front Corners

To Rear Corners

PEPCO’s Potomac River 4 Combustion Air System

Furnace Hot Air from Air Heater

After Modifications

Splitter Damper

To Front Corners

To Rear Corners

Turning Vane Windbox Compartmentalization Furnace

Source: PEPCO

3-19


beneficial impacts of improved fuel balancing, as well as higher coal fineness at Union Electric’s Meramec #4 unit (a front wall-fired 360 MW boiler retrofitted with B&W’s XCL burners). Improving the coal balance from ± 10% to ± 5% from the uniform flow distribution reduces unburned carbon in the flyash by 2-3 percentage points.

25 +/- 10% Fuel Balance

99.0% 50 Mesh 70% 200 Mesh

% Carbon in Flysash

20

15 +/- 5% Fuel Balance

10

+/- 10% Fuel Balance 99.9% 50 Mesh 80% 200 Mesh

5 +/- 5% Fuel Balance 0 0

25

50

75

100

% of Coal A (Kerr McGee mine) Remaining is Coal B (Rend Lake mine) Source: Union Electric

Figure 3-8 Union Electric/Meramec Unit 4: Unburned Carbon vs. Coal Fineness, Coal Flow Imbalance and Coal Blend

Coal Pipe Orificing x Enter 1 if coal pipe orificing will not be included in the optimization

program, or x Enter 3 if coal pipe orificing will be included in the optimization

program.

3-20


Mill Modifications Mill modifications, including installation of exhausters, riffle distributors, and dynamic classifiers, may result in better coal fineness and coal size distribution control, which in turn improves combustion efficiency and plant performance. Coal fineness and size distribution have a significant effect on the combustion process, and control of size distribution can be used to reduce LOI. Generally, higher coal fineness results in better coal combustion. Coarse coal particles (usually defined to be larger than 150 microns [remaining on 100 mesh]) have been shown to be the primary source of unburned carbon. The percentage of coal remaining on 50 mesh (300 micron) is increasingly used as the key indicator of adequate coal size distribution.

LOI (%)

Figure 3-9 shows the effect of improved size distribution on LOI at PEPCo’s Potomac River #4, a 100 MW T-fired boiler without combustion NOx controls. This improvement was the result of pulverizer maintenance and tuning. As this figure shows, the reduction of larger particles (percentage remaining on 50 mesh [300 micron] was reduced from 3% to 0.5%, resulting in lower LOI even though the percentage through 200 mesh (75 micron) did not change significantly. Furthermore, improved coal size distribution allows operation at lower excess O2, which reduces NOx emissions.

Before Plus 50 mesh 2 to 3% Minus 200 mesh 74%

20 18 16 14 12 10 8 6 4 2 0

After 0.5% 73%

Before mill maintenance After mill maintenance

0

1

2

3

4

5

6

Economizer O2 (%) Source: PEPCo

Figure 3-9 PEPCo’s Potomac River 4/Effect of Mill Maintenance on LOI

The beneficial effect of higher coal fineness and improved size distribution on both LOI and NOx emissions is also demonstrated by Figure 3-10 which shows the test results 3-21


from Gulf Power’s Smith #2 (a 180 MW T-fired boiler) retrofitted with ABB/CE’s LNCFS III.

increa sing e xce

300

ss oxy

0.3

0.2

gen

225

150 low fineness medium fineness

0.1

75

high fineness

0 0.0

2.0

4.0

6.0

8.0

10.0

12.0

NOx emissions (ppm)

NOx emissions (lbs/Mbtu)

0.4

0 14.0

LOI (%) Source: Southern Company Services

Figure 3-10 Smith 2/Relationship Between LOI, NOx and Coal Fineness

As coal fineness improves, the same NOx emission level can be achieved with lower LOI and excess O2. For example, 225 ppm (0.30 lbs/MBtu) NOx level can be achieved with low fineness2 coal resulting in 10-11% LOI or with high fineness coal resulting in 3% LOI. Such relationships are useful in optimizing unit performance to satisfy its operating objectives (e.g., NOx requirements and LOI constraints). One of the options used to improve and control coal size distribution is dynamic classifiers which achieve a better particle size distribution than static classifiers, (see Figure 3-11). Additional advantages of dynamic classifiers include: x

better control of particle size distribution,

x

improved flexibility which is particularly important when the coal quality varies, and

x

better load response and unit turndown.

2

The low and high fineness represent the minimum and maximum fineness achievable with the pulverizers available at Smith 2; different pulverizers may achieve wider range of finenesses.

3-22


100

% Passing

90

80

Static Dynamic 70

60 60

100

140

180

220

260

300

Sieve (micron) Source: EnTEC

Figure 3-11 Typical Particle Size Distribution with Static and Dynamic Classifiers

Mill Modifications x Enter 1 if mill modifications will not be included in the optimization

program, or x Enter 3 if mill modifications will be included in the optimization

program.

Expected Performance Improvements Based on the Total Score in Table 3-1, the user’s project falls in one of the classifications shown in Table 3-2. Table 3-2 Expected Performance Improvements Project Classification

Total Score

% NOx Reduction

Group A

100-160

5.0 - 15.0

Heat Rate Improvement 0.00 - 0.75

Group B

160-240

15.0 - 30.0

0.50 - 1.25

Group C

240-300

25.0 - 40.0

1.00 - 1.50

The NOx reduction for Groups A, B and C is shown in Figure 3-12.

3-23


45

Group C

40

Group B

% NOx Reduction

35 30 25

Group A

20 15 10 5 0 Increasing Operating Flexibility Worsening Operating Condition of Existing Equipment

Figure 3-12 Utility Experience with Combustion Tuning & Optimization NOx Reduction Achieved

The expected performance improvement should be adjusted further based on the objective of the previous tuning as shown in Tables 3-2a through d. x

If the objective of the previous tuning was “safe operation,” use the upper end of the projected performance for both NOx and heat rate, as shown in Table 3-2a.

Table 3-3 Previous Tuning Objective – Safe Operation Project Classification Group A

NOx 10.0 - 15.0

Heat Rate 0.25 - 0.50

Group B Group C

22.5 - 30.0 32.5 - 40.0

0.75 - 1.25 1.25 - 1.50

x

If the objective of the previous tuning was “heat rate improvement,” use the upper end of the projected NOx reduction, but the lower end of the heat rate improvement, as shown in Table 3-2b.

Table 3-4 Previous Tuning Objective – Heat Rate Improvement Project Classification

NOx

Heat Rate

Group A

10.0 - 15.0

0.0 - 0.40

Group B

22.5 - 30.0

0.50 - 0.75

Group C

32.5 - 40.0

1.00 - 1.25

3-24

EPRI Licensed Material Step Three: Determine Optimization Potential/Can Optimization Alone Meet Established Objectives? x

If the objective of the previous tuning was “NOx reduction,” use the lower end of the projected NOx reduction range, but the upper end of the heat rate improvement, as shown in Table 3-2c, and

Table 3-5 Previous Tuning Objective – NOx Reduction Project Classification Group A Group B Group C x

NOx 5.0 - 10.0 15.0 - 22.5 25.0 - 32.5

Heat Rate 0.25 - 0.50 0.75 - 1.25 1.25 - 1.50

If the objective of the previous tuning was “combined NOx reduction and heat rate improvement,” use the lower end of both NOx reduction and heat rate improvement, as shown in Table 3-2d.

Table 3-6 Previous Tuning Objective – NOx Reduction & Heat Rate Improvement Project Classification Group A Group B

NOx 5.0 - 10.0 15.0 - 22.5

Heat Rate 0.0 - 0.40 0.50 - 0.75

Group C

25.0 - 32.5

1.00 - 1.25

The user is encourage to override the above performance predictions, if he/she has a better understanding of expected performance improvements. For example, prior efforts to improve heat rate may have proven that the maximum heat rate reduction may be limited to a specific level which is outside the ranges provided in Table 3-2. LOI, CO emissions, and opacity can be kept at present levels or improved slightly depending on the level of improvements being sought for NOx and heat rate; the higher the NOx and heat rate improvements sought, the lower the possibility for significant LOI, CO and opacity improvements. Compare potential performance improvements to project objectives and decide if plant optimization is a feasible and suitable option. If optimization seems to meet objectives, proceed to Step 4. If it does not, hardware modifications should be considered. For NOx Control Retrofit Options, see the following EPRI publications: 1) Retrofit NOx Controls for Coal-Fired Utility Boilers 1996 Update Addendum (TR-102906-Addendum), and 2) Retrofit NOx Control Guidelines for Gas- and Oil-Fired Boilers (TR-102413).

3-25


4 STEP FOUR: IDENTIFY THE MOST COST-EFFECTIVE TYPE OF OPTIMIZATION





YES







4-1

EPRI Licensed Material Step Four: Identify the Most Cost-Effective Type of Optimization

Types of Optimizations: x

Stand-alone (previously referred to as Off-line/One-time)

x

On-line/advisory

x

Closed-loop

The selection of the most appropriate and cost-effective optimization type depends on the following: x

Availability of Digital Control Systems (DCS) and Data Acquisition Systems (DAS),

x

The need for continuous vs. one-time optimization,

x

The performance improvement objectives, and

x

The cost-effectiveness of alternatives.

A. Availability of DCS and DAS Availability of DCS and DAS are essential for on-line/advisory and closed-loop optimization. DCS and DAS make stand-alone optimization easier to carry out, but they are not essential. Therefore, if the plant under consideration does not have nor is there a plan to install DAS and DCS, it is not suitable for on-line/advisory and closed-loop optimization.

B. Continuous vs. One-time or Periodic Optimization If continuous performance optimization is desirable, stand-alone systems are not suitable. Onetime or periodically repeated stand-alone optimization may be suitable and more costeffective in cases in which the plant needs to reach a certain performance level below which there is no strong incentive to optimize further. An example is the case where there is a need to limit NOx emissions beyond a certain level (e.g. 0.50 lbs/MBtu), but there is not an incentive to achieve the minimum NOx possible. If continuous optimization is needed and the desirable optimization objective is at the upper end of the projected performance improvement range with optimization software, on-line/advisory or closed-loop systems are required. Some utilities have decided to utilize closed-loop optimization on the basis that it does not require continuous support from plant performance engineers (after it has been installed and calibrated) and the plant operator does not need to be involved continuously to implement optimum settings.

4-2


C. Performance Improvement Objectives If the performance improvement sought is in the upper portion of the projected performance improvement range, on-line/advisory and closed-loop systems are more likely to achieve this objective. Stand-alone optimization is feasible, but its cost-effectiveness needs to be assessed in more detail with consideration given to site-specific requirements such as the required frequency of optimizations, the availability and cost of plant performance engineers, and the potential benefits at the plant and system levels. For example, if the optimization is to be repeated very frequently, the costs of such activity may increase to the same level or higher than on-line/advisory and closed-loop systems.

D. Cost-Benefit of Alternative Optimization Types As Figure 4-1 shows, higher performance may be achieved with on-line/advisory and closed-loop systems, but at a higher cost than stand-alone optimization. The cost effectiveness of each option depends on the cost versus benefit as measured by net present value (NPV), return on investment, payback period, and cost-benefit ratio. These measurements of cost effectiveness can be calculated using the Table 4-1. Box 4-1 (on the next page) provides guidance on the use of Table 4-1.

Closed-Loop On-Line/ Advisory

% Performance Improvement (e.g., % NOx Reduction)

Stand-Alone

Quick tuning to new operating mode

Costs ($) Figure 4-1 Optimization Cost Effectiveness

4-3


Box 4-1 Software Guidance on Table 4-1

For system requirements, installation and loading of the Excel files included on the floppy disk, see Appendix A. The Excel file “Table 4-1.xls” provided on the floppy disk should be used in Step 4 of the guidelines to carry out a cost-benefit analysis of alternative optimization types. A print out of the Excel spreadsheet appears on pages 4-5 to 4-6. The printed version is for illustration purposes and uses default values which are based on a typical 500 MW coal-fired boiler. Step 4 of the guidelines walks the user through the key questions which need to be addressed to adjust the default values in the electronic version of Table 4-1 to better reflect their own technical requirements and financial assumptions. In the Excel file, values appearing in a blue font are user-defined inputs which the spreadsheet uses to automatically calculate other parameters and cost-benefit measures. The calculated values and the text appear in black font and they are locked. To assure consistency, inputs which appear more than once in the spreadsheet are linked so that the user inputs the value only once (where it appears in blue). The spreadsheet begins with Up Front Costs and Annual Costs, continues with Up Front (one time) Benefits and Annual Benefits, and at the bottom provides Cost-Benefit Analysis Measures. The user can examine the formulas used throughout the tables in several different ways. The user can read a description of a formula in English by moving the Excel cursor on to cells in the left most column to pop up the memo function. Only cells with a small red spot in the upper right hand side of their cells contain memos. These formula descriptions also are all listed in the manual as Appendix C. The values that are used as defaults within Table 4-1 fall within a range that has been found to be typical based on optimization experience to date. This experience and the typical ranges are presented in this manual in shaded boxes. The general text in this section provides background information on all the inputs while the text in the shaded boxes provides step-by-step instructions.

4-4


Table 4-1 Power Plant Optimization Cost-Benefit Analysis COSTS

Optimization Types Stand-alone

O n-line/advisory

Closed-loop

$30,000

$130,000

$170,000

$5,000 $0 $5,000 $0 $0

$5,000 $0 $5,000 $0 $0

$5,000 $0 $5,000 $0 $0

$33,000 $14,600 12 $800 $5,000 $18,400 25 $320 2.3 $0

$44,040 $14,600 12 $800 $5,000 $29,440 40 $320 2.3 $0

$50,120 $17,000 15 $800 $5,000 $33,120 45 $320 2.3 $0

Training ($)

$0

$0

$0

Power Plant Hardware Modifications ($)

$0

$0

$0

$68,000

$179,040

$225,120

$5,000

$13,000

$17,000

$31,720 $17,000 15.0 $800 $5,000 $14,720 20 $320 2.3

$24,040 $13,000 10.0 $800 $5,000 $11,040 15 $320 2.3

$20,360 $13,000 10.0 $800 $5,000 $7,360 10 $320 2.3

$36,720

$37,040

$37,360

$0 $0 0 $0 $0 0

$0 $0 0 $0 $0 0

$0 $0 0 $0 $0 0

$0

$0

$0

$0

$0

$0

$130,165 9,000 0.40 1.27 500 65.0

$244,059 9,000 0.75 1.27 500 65.0

$406,765 9,000 1.25 1.27 500 65.0

Up Front Costs Up Front Software License Fees ($) Additional Computer Software and Hardware ($) Com puter so ftw are Com puter hardw are Instrum entation and co ntrols (I&C) Data acquisition system (DAS)

Installation and Calibration ($) O utside technical support total -Num ber of m an days -Average daily rate for outside technical support -Miscellaneous expenses (Trav el, reports, ov erhead, etc.) In-house technical su pport total -Num ber of m an days -Average daily rate for in-house technical support -Cost m ultiplier for ov erhead Pow er plant dow ntim e/non-econom ic dispatch

TOTAL UP FRONT COSTS

Annual Costs (O&M incl. Recalibration) Annual Software License & Maintenance Fees ($) Technical Support ($) O utside technical support total -Num ber of m an days -Av erage daily rate for outside technical support -Miscellaneous expenses (Trav el, reports, ov erhead, etc.) In-house technical su pport -Num ber of m an days -Average daily rate for in-house technical support -Cost m ultiplier for ov erhead

TOTAL ANNUAL COSTS

BENEFITS (Losses) Up Front (one time) Benefits NPV of Deferred Costs ($) Deferred costs How long does optim izatio n d elay investment? (in month s)

NPV of Avoided Costs ($) Avoided costs When w ould investm ent have been im plemented (in months)

TOTAL UP FRONT BENEFITS

Annual Benefits Annual Avoided Costs ($/yr) Fuel Cost Savings ($/yr) Baseline heat rate (Btu/kWh) Unit heat rate im provem ent (% Pts) Fu el cost ($/MBtu) Unit output (MW) Capacity factor (%)

4-5

EPRI Licensed Material Step Four: Identify the Most Cost-Effective Type of Optimization Table 4-1 Cont’d. O&M Impacts ($/yr)

$0 $0 $0

$0 $0 $0

$0 $0 $0

$0 0 0

$0 0 0

$0 0 0

$0 0 0.00 65 0

$0 0 0.00 65 0

$0 0 0.00 65 0

NOx Emissions Avoided fines/penalties ($/yr) Annual value of NOx emission credits ($/yr) -Value of NOx emission credits ($/ton removed) NOx emission credits (tons/yr) -NOx reference level for credits (lbs/MBtu) -NOx emission reduction below reference level (%) -Unit output (MW ) -Unit baseline heat rate (Btu/kWh) -Capacity factor (%)

$0 $0 0 320.3 0.500 5.0 500 9,000 65

$0 $0 0 640.6 0.500 10.0 500 9,000 65

$0 $0 0 960.9 0.500 15.0 500 9,000 65

SO2 Emissions Avoided fines/penalties ($/yr) Annual value of SO2 emission credits ($/yr) -Value of SO2 emission credits ($/ton remov ed) SO2 emissions reduction (tons/yr) -Baseline SO2 emissions (lbs/MBtu) -Baseline unit heat rate (Btu/kW h) -Unit heat rate improvement (%) -Unit output (MW ) -Capacity factor (%)

$0 $6,150 100 61.50 1.20 9,000 0.40 500 65

$0 $11,530 100 115.30 1.20 9,000 0.75 500 65

$0 $19,217 100 192.17 1.20 9,000 1.25 500 65

CO Emissions Avoided fines/penalties ($/yr)

$0

$0

$0

Opacity Avoided fines/penalties ($/yr)

$0

$0

$0

$0 0 10,686 208.52 9,000 0.40 500 65

$0 0 20,036 208.52 9,000 0.75 500 65

$0 0 33,393 208.52 9,000 1.25 500 65

Annual benefits before diminishing effectiveness factor ($/yr) Diminishing effectiveness factor

$136,314 0.50

$255,589 0.75

$425,982 0.90

ACTUAL ANNUAL BENEFITS ($/yr)

$68,157

$191,692

$383,384

5.0 4.9

5.0 4.9

5.0 4.9

$31,437 $136,484 $13,600

$154,652 $671,417 $35,808

$346,024 $1,502,254 $45,024

$68,484 26 26.0 1.0

$492,377 66 13.9 2.8

$1,277,134 134 7.8 5.7

Waterwall corrosion Operating flexibility

Value of Change in Unit Availability ($/yr) Change in equivalent availability (%) Value of an additional % point ($/%-yr)

Value of Change in Unit Output ($/yr) Change in output (MW) Incremental cost of replacement power ($/MWh) Capacity factor (%) Percentage of operating time affected (%)

Benefits Due to Emission Reductions

CO2 Emissions Annual value of CO2 emission credits ($/yr) -Value of CO2 credits ($/ton remov ed) CO2 emissions reduction (tons/yr) -Baseline CO2 emissions (lbs/MBtu) -Baseline unit heat rate (Btu/kWh) -Unit heat rate improv ement (%) -Unit output (MW) -Capacity factor (%)

FINANCIAL ASSUMPTIONS Book life of the project (default 5 years) Discount rate (%) (default 4.9%)

INTERMEDIARY FIGURES Net annual benefits ($) PV of future net annual benefits ($) Annual depreciation (straightline)

COST-BENEFIT ANALYSIS RESULTS Net Present Value (NPV) Return on Investment (ROI as %) Payback Period (simple payback in months) Benefit-Cost Ratio

4-6


Background and Instructions for Completing Table 4-1 COSTS Costs for optimization are broken down into two categories: Up front costs (including license fees, additional computer software and hardware, installation & calibration, training, and, when applicable, power plant hardware modifications) and Annual costs (including annual license fees and operating and maintenance costs for both outside and in-house technical support).

UP FRONT COSTS Up Front License Fees Up front license fees may apply to a unit, a multi-unit power plant, or power system in which the optimization software is used and may include other items such as: x

commercial software which are needed to operate the optimization system,

x

technical support to install and calibrate the optimization system, and

x

training.

Up Front License Fees Default Inputs: x x x

Stand-alone: $30,000 On-line/advisory: $130,000 Closed-loop: $170,000

Vendors have quoted up front fees for stand-alone systems as low as $15,000 and as high as $60,000 per boiler. Similarly, on-line/advisory and closed-loop systems have been quoted as low as $100,000 and as high as $250,000 per boiler. In most cases, these costs are for turn-key systems which include software license, computer hardware, outside technical support, and training. In such cases, avoid entering the costs twice; enter the cost once (e.g., as Up Front License Fees). Annual licensing and/or maintenance fees, which are usually in the range of 10% of the Up front fee, are recorded under Annual O&M Costs (see further explanation below). The buyer should clarify with the optimization software vendors what are the proposed prices and what is included.

4-7


Additional Computer Software and Hardware Depending on the circumstances, additional components may be needed such as: x

Computer software which is required to operate the optimization system or is a recommended add-on to make data analysis or electronic communication easier,

Computer Software x

Computer Hardware; In Table 4-1, it is assumed that an additional personal computer is needed which is not included in the Up front fee. The computer hardware is assumed to be a Pentium-based IBM-compatible system. A few optimization software packages may require a more powerful computer such as a Sun Microsystems Sparc. In the latter case, Table 4-1 should be updated to reflect higher costs. In this entry you may also include computer-related components such as modems and communication ports.

Computer Hardware x

Default Input: $5,000

Instrumentation and controls-related components; in most cases, especially in standalone optimization, there is no need for special instrumentation or controls. However, some instrumentation, such as CO monitor and LOI measurement device, may be considered essential depending on the optimization objective. CO monitor is essential in most cases. A LOI monitor may be necessary, especially in cases where LOI is expected to be affected significantly or where LOI is already a concern.

Instrumentation and Controls (I&C) x

Default Input: $0

Default Input: $0

Data acquisition-related software and hardware; in most cases, the existing DAS is adequate, or the optimization software includes the required DAS capabilities. However, the adequacy of the DAS-optimization system interface should be confirmed and if additional components are needed, they should be included in the cost-benefit analysis.

Data Acquisition System (DAS) -

Default Input: $0

Installation and Calibration Installation of the software: x

4-8

In the case of stand-alone applications, installation is limited to establishing data acquisition links which usually take less than a day,

EPRI Licensed Material Step Four: Identify the Most Cost-Effective Type of Optimization x

In the case of on-line/advisory and closed-loop applications, 1-2 weeks should be allowed for installation.

Calibration of the software: x

Stand-alone applications require a minimum of 60-80 “data sets” (complete sets of data from steady state plant operation) per load, which means a minimum of 10 working days, assuming the data are acquired electronically and 6-10 sets of data per (8-hour) day can be obtained. However, there are cases (especially units which require longer to achieve steady state performance or when manual data input is required) which performed only 3-4 tests per day. One factor which adds to the time required to obtain data and calibrate the model is the need for LOI measurements. If required, the test team may have to wait until adequate amount of ash has been collected to measure LOI. If the model needs to be calibrated in more than one load, the above estimate of time needs to be multiplied by the number of loads.

x

Experience has shown that initial calibration takes 3-4 calendar months. Typical level of effort is: ¡

30-50 working days for on-line/advisory, and

¡

35-55 working days for closed-loop.

Outside Technical Support: is usually included in the fixed fee charged for the optimization software. However, it is important to clarify how much support is provided (in terms of man-days in the field and support by phone or e-mail). Twothree weeks of on-site support seem to be an appropriate level at this point in the development of the software. In the future, these requirements may decrease. At present, if the vendor claims that installation/ calibration can be done in less than the average time (see Table 4-1), it should document this with data other utility sites. Reduced technical support by the vendor may compromise the objectives of the optimization program and/or may increase the work required by in-house staff. Outside Technical Support - Defaults Inputs: x x x

Stand-alone: On-line/advisory: Closed-loop:

12 man-days 12 man-days 15 man-days

Typical average daily rates for outside technical support are $800/day, and miscellaneous expenses for travel, report writing, etc. over the time period required typically add up to $5,000.

4-9


In-house Technical Support: is critical for the success of the project, but very often is overlooked in the evaluation of the various options. Activities which should be included are: x

time needed to collect coal and ash samples,

x

time and costs for analyzing coal and ash samples

x

increased emphasis on instrumentation calibration, and

x

consultations between performance engineers and plant operators regarding implementation of optimum setting.

Based on the above assessment regarding the duration of the installation and calibration of optimization software, the following estimates should be considered: In-house Technical Support - Default Inputs: x

x x

Number of man-days: varies for each type of optimization. ¡ Stand-alone: 25 days (range is 20-30 days), ¡ On-line/advisory: 40 days (range is 30-60 days), ¡ Closed-loop: 45 days (range is 40-70 days). Average daily rate for in-house technical support: $320/day Cost multiplier for overhead: 2.3

Power Plant Downtime/Non-Economic Dispatch: In most cases, calibration of the optimization software does not require a unit outage or load reduction. However, in some cases non-economic dispatch may be needed to obtain data from operating conditions which are outside the typical range. Power Plant Downtime/Non-Economic Dispatch -

Default Input: $0

Determining the cost of non-economic dispatch involves the following steps: x x x x

4-10

define clearly (along with the optimization vendor) the tests which result in non-economic operating conditions, determine their impact on key parameters, especially unit output (MW), heat rate, O&M costs, reliability, and emission rates, estimate the time the unit will be in each non-economic dispatch, and estimate the total cost due to non-economic dispatch and insert value of result in Table 4-1.


Training Training is usually included in the up front software license fee. The importance of training relates to the plant owner’s strategy regarding long-term utilization of the software, and the general plant O&M management approach. If the plant owner relies more on outsourcing, limited training is needed (e.g., less than one week). However, adequate “outside technical support” should be provided under the annual O&M costs. Many plant owners prefer to build in-house expertise to use the software, in which case, more training may be needed (e.g., two weeks). In terms of normal operation of the optimization software, the need for training is the highest for stand-alone and the lowest for closed-loop applications. Stand-alone systems require well-trained staff, because of the need to repeat the optimization frequently, if the desirable performance improvement is to be sustained. Online/advisory optimization requires similar training because the plant staff (performance engineers and plant operators) needs to be well-aware of how to use the optimization system because it decides whether or not to implement the optimum settings. Closed-loop systems require minimum training (relative to the other two options), they require more in-depth training for interfacing the optimization software with the control system. Training -

Default Input: $0

Assume that training costs are included in the up front license fee. Power Plant Hardware Modifications If hardware modifications are included in the optimization project (see Step 3), the capital costs associated with such modifications should be included. Because the objective of most optimization projects is to use optimization software to avoid the need for hardware modifications or reduce their scope, the default value of this entry is zero. Power Plant Hardware Modifications

Default Input: $0

ANNUAL COSTS (O&M Including Recalibration) Annual O&M costs are expenses which incur every year starting immediately after the initial installation and calibration has been completed. For the purposes of this costbenefit analysis, it is assumed that the up-front costs incur also at the end of calibration period.

4-11


Annual Software License and Maintenance Fees In addition to the up front license fee (discussed above), very often vendors charge an annual fee which varies considerably with the vendor and the market conditions. Usually, this fee provides the user with: x

updated versions of the software, including advanced (new) features

x

versions in new operating platforms (e.g., Windows NT), and

x

remote maintenance and user support.

It is important to clarify both the price and the scope of the annual license/AF fee. Annual Software License Fees - Default Inputs: Annual license fees are assumed to be: x x

$5,000 for stand-alone systems, and 10% of the up-front license fee for on-line/advisory and closed-loop systems.

Technical Support On-going technical support and maintenance depends on: x

the type of optimization; stand-alone and on-line/advisory usually require more involvement of plant personnel than closed-loop, and

x

recalibration frequency; plants which experience significant changes in the plant operating objectives or characteristics (e.g., after a combustion NOx control retrofit or fuel switching) will need more technical support, by both outside organizations and in-house staff.

Recalibration is a potentially time-consuming activity especially for stand-alone systems. From the on-line/advisory and closed-loop systems, some have automatic (dynamic) recalibration capabilities, while others require the user to specify when they are to be recalibrated (e.g., GNOCIS is recalibrated every 6 months). Outside Technical Support: This is on-site technical support above and beyond what is included in the annual license/maintenance fees (see above). Higher outside technical support is needed for stand-alone optimization software, especially if the utility does not have dedicated staff for such activity. 4-12


Outside Technical Support - Default Inputs: x x x x x

Stand-alone: 15 man-days On-line/advisory: 10 man-days Closed-loop: 10 man-days Average daily rate for outside technical support: $800/day Miscellaneous expenses: $5,000 for travel, report writing, etc.

In-house Technical Support: In particular, stand-alone and on-line/advisory system will require some commitment from the plant performance engineer, plant operator, and test support staff to: x

monitor the optimization system and keep it calibrated,

x

implement the optimum advice,

x

periodically check certain instrumentation, and

x

obtain flyash samples for LOI analysis, etc.

Provided that the optimization system works reliably and does not require major reconfiguration and maintenance, closed-loop systems require less on-going technical support than stand-alone or on-line/advisory systems. However, if the software experiences problems or resetting of the control loops is required, technical support from both in-house staff and outside organizations may be needed. Whether in-house staff will be used or not depends on the level of training provided, their availability, and preferences of the plant owner. In-house Technical Support – Default Inputs: x x x x x

Stand-alone: 20 man-days On-line/advisory: 15 man-days Closed-loop: 10 man-days Average daily rate for in-house technical support: $320/day Cost multiplier for overhead: 2.3

It should be noted that all annual O&M costs associated with optimization software are treated as fixed O&M.

4-13


OPTIMIZATION BENEFITS This section describes the assumptions that are used to determine the benefits of an optimization project. Similar to the costs, the benefits are broken down into two categories. Up front benefits consist of the benefits from deferring or avoiding the need to install capital equipment (usually NOx control equipment). All the other benefits are derived from more efficient plant operation and are thus counted annually. Annual benefits include fuel cost savings, O&M impacts, improved unit availability, increased unit output, and emission reductions.

UP FRONT (ONE TIME) BENEFITS NPV of Deferred Costs If an optimization package improves plant performance and thereby delays the installation of emission control equipment, the plant operator benefits by being able to use this capital for other purposes in the meantime. For example, an optimization package could defer the need for installation of a low NOx burner or selective catalytic reduction (SCR) system to year 2002 when maximum allowable NOx emission requirements are expected to be reduced further. Given the large cost of these environmental control options and the time frame involved, the value of these deferred costs can be substantial. Based on the input of the deferred costs, Table 4-1 determines the net present value of this deferment using the discount rate provided by the user. NPV of Deferred Costs -

Default Input: $0

NPV of Avoided Costs In some cases, the performance improvement from an optimization package may be enough to avert the need for a capital investment that would have otherwise been required. For example, an optimization package could eliminate the need to install NOx controls (e.g., low NOx burners, reburning, SNCR and SCR), or it could reduce the scope of the NOx control retrofit project (e.g., instead of low NOx burners with overfire air, install only low NOx burners). Table 4-1 takes into account the avoided costs and the point time this item would have taken place and calculates its net present value by taking into account the time value of money. NPV of Avoided Costs 4-14

Default Input: $0


ANNUAL BENEFITS Table 4-1 provides a step-by-step process to determine the annual benefits due to optimization. First the user is guided to provide inputs based on which the annual benefits (“ideal annual benefits” reflecting the initial performance improvements) due to optimization are estimated. Then, a “diminishing effectiveness factor” is applied to take into account potential degradation of performance with time, after initial optimization has been performed. These items are found at the bottom of the benefits section of the spreadsheet, just before financial assumptions. Such degradation is real and varies with the optimization type. Diminishing effectiveness factor Definition: percentage of the initial benefits which will actually be realized over time given the gradual deviation between actual plant performance and software predictions. Given industry experience, we estimate that the effectiveness of stand-alone optimization will achieve 40-70% of their initial benefits. This is mainly due to changes in the operating characteristics of the plant, including coal quality, plant operators’ preferences, equipment degradation, etc. On-line/advisory optimization is expected to maintain a higher level (60-90%) of the initial benefits, while closed-loop will maintain an even higher level (80-95%). Default inputs for the diminishing effectiveness factor used in Table 4-1 are as follows: Default Inputs: x x x


0.50 0.75 0.90

These default values will be updated based on further evaluation of actual performance achieved by power plant optimization in utility boilers. Annual Avoided Costs Annual Avoided Costs are annual savings due to an optimization project which avoided the implementation of an option with higher annual operating and maintenance costs. Examples of such avoided or deferred costs include: x

increase in annual O&M costs due to a low NOx burner and overfire air retrofit

x

annual O&M costs associated with urea or ammonia injection of an SNCR system which was avoided because of the utilization of optimization software 4-15


difference in annual O&M costs between (a) an SCR system which is designed to achieve a certain NOx reduction and (b) a combination of optimization software plus SCR system; the latter SCR has lower NOx emission at its inlet and therefore has smaller catalyst and lower consumption of ammonia.

Annual Avoided Costs -

Default Input: $0

Fuel Cost Savings Annual fuel cost savings are calculated from the inputs listed in the shaded box below. One of the key factors in the calculation is determination of the degree of heat rate improvement from an optimization system. Heat rate is affected by a number of operating parameters including: x

Excess O2,

x

unburned carbon (LOI),

x

CO emissions,

x

Other unit operating variables (e.g., overfire air, combustion air distribution and burners out of service) which may impact stack and steam temperatures,

x

steam outlet temperatures, and

x

auxiliary power consumption.

During the actual optimization, the impact of each of these parameters on heat rate needs to be assessed in detail. For the purposes of these guidelines, the results of Step 3 are adequate to assess the cost-effectiveness of the different optimization options. The baseline unit heat rate and the unit heat rate improvement used in the above formula should be based on a weighted average which reflects unit dispatch profile. The heat rate improvement range provided above is typical of coal-fired baseloaded units. In case of intermediate load or cycling units, the heat rate improvement may be higher (up to 4-5%).

4-16


Fuel Cost Savings - Default Inputs: x x x x x

Baseline unit heat rate (Btu/kWh): 9,000 Unit heat rate improvement (%): 0.40-1.25 (depending on type of optimization--see below) Fuel cost ($/MBtu): 1.27 3 Unit size (MW): 500 Capacity factor (%): 65

Unit heat rate improvements vary with optimization type and the specific operating conditions of each unit. The user should input the heat rate improvement predicted during Step 3 of these guidelines (see Tables 3-2a through 3-2d) or should override it if s/he has a better estimate (based on sitespecific considerations). From the range developed in Step 3 (e.g., heat rate improvement: 0.40 - 1.25%), stand-alone optimization is expected to achieve heat rate improvement closer to the lower end of the range (e.g., 0.40%), online/advisory in the middle (e.g., 0.75%), and closed-loop at the upper end of the range (e.g., 1.25%). Default inputs used in Table 4-1: x x x


0.40 percentage points, 0.75 percentage points, and 1.25 percentage points.

O&M Impacts In addition to impacts which directly affect the unit heat rate (which have been included in the previous input), there may be others impacts (both positive and negative) on operation and maintenance of the unit including: x

impact on waterwall corrosion, and

x

unit operating flexibility such as rate of load change and limited operating range of certain control variables.

Waterwall corrosion may occur especially in cases where the optimization results in lower (than baseline) excess O2 and/or staged combustion. However, there is not yet adequate information to quantify the impacts due to waterwall corrosion. EPRI is monitoring the impacts of staged combustion on waterwall corrosion and is expected to release guidelines on how to assess such impacts. EPRI is carrying our research on waterwall corrosion and will continue to provide the latest information in this field.

3

Default Fuel cost used $1.27/MBtu for Illinois #6 coal delivered to plants in the East/West Central US. Source: EPRI Technical Assessment Guide (TAG), P-6587L, Vol. 1, 1989.

4-17


Similarly, quantification of the benefits or costs due to reduced rate of load change or limited operating flexibility is site-specific and not easy to generalize. Considering that no such impacts have been documented in plants which have used optimization software, we assume that these impacts are negligible. O&M Impacts

Default Input: $0

Value of Change in Unit Availability Operating changes may have an impact on unit reliability and availability. Examples of impacts include: x

waterwall corrosion may increase the number of forced outages, and

x

coal-biasing increases the duty (amount of coal handled) of certain pulverizers which may experience increased forced outages, if appropriate predictive maintenance is not used.

In most cases, impacts on unit availability can be eliminated. Waterwall corrosion is the only impact which needs to be assessed in more detail, especially for units which have a history of corrosion. Value of Change in Unit Availability - Default Inputs: x x

Change in equivalent availability (%): Value of an additional % point ($/year-%):

0 0

Value of Change in Unit Output In most cases, unit output does not increase as a result of optimization. This is mainly because the objective of most optimization program has been to reduce NOx emissions and/or heat rate while maintaining the same unit output. However, there may be cases where a unit has been derated and optimization results in recovery of the lost capacity. Incremental cost of replacement power is the cost differential between this and the next unit in the dispatch order for obtaining the capacity change (gained or lost MW). In some cases, it may be the price at which this capacity can be purchased in the open market.

4-18


Value of Change in Unit Output ($/yr) - Default Inputs: x x x x

Change in output (MW): Incremental cost of replacement power ($/MWh): Capacity factor (%): Percentage of operating time affected (%):

0 0 65 0

Benefits Due to Emission Reductions NOx, SO2, CO2, CO and opacity may be reduced as a result of power plant optimization. In addition to the deferred and/or avoided costs which have been mentioned already, the benefits from reducing these pollutants fall into the following categories: x

Avoided Fines (Penalties); such fines may be imposed if the unit does not comply with environmental regulations,

x

Emission Credits; these are allowances which are accumulated as a result of emission reductions.

For all pollutants, but especially for NOx, the emission reduction requirements should be clarified to avoid double-counting. For example, if optimization helps the power plant achieve compliance, either an avoided fine or a deferred/avoided cost should be recorded, not both. NOx Emissions Avoided Fines/Penalties: NOx emissions are usually limited below a certain level (e.g., under CAAA requirements). While not always the case, penalties may be imposed by Federal EPA, State agencies or local authorities if compliance is not achieved. If combustion optimization achieves compliance and avoids penalties, they should be included in Table 4-1 as benefits. Avoided Fines/Penalties ($/yr) -

Default Input: $0

Annual Value of NOx Emission Credits: Credits may be generated by reducing NOx emissions below a certain level, usually the compliance level for the unit as dictated by unit, plant or power system NOx requirements.

4-19


Annual Value of NOx Emission Credits ($/yr) - Default Inputs: x x x x x x

Value of NOx emission credits ($/ton NOx removed) NOx reference level for credits (lbs/MBtu) NOx emission reduction below reference level (%) (depending on the type of optimization) Unit output (MW) Unit baseline heat rate (Btu/kWh) Unit capacity factor (%)

0 0.50 5-15% 500 9,000 65

NOx reference level could be either the baseline NOx emissions or a NOx emission level below which NOx credits are accumulated. If the user knows the value of NOx ($/ton) and wants to estimate the total value due to the NOx reduction achieved by the optimization project, s/he should use the baseline NOx emissions. Alternatively, NOx credits may be accumulated after a certain level of NOx has been achieved. For T-fired units, such NOx level may be 320 ppm (0.43 lbs/MBtu), which satisfies a presumed compliance level of 340 ppm (0.45 lbs/MBtu) and provides some safety margin. NOx emission reduction due to optimization has been estimated in Step 3 of these guidelines. The following clarifications should be made: x

The % NOx reduction entered into the spreadsheet should be in reference to “NOx reference level” which could be different from the baseline NOx emissions,

x

From the NOx reduction range predicted in Step 3, stand-alone optimization is expected to achieve NOx reduction at the lower end of the estimated range, online/advisory in the middle and closed-loop in the upper end.

SO2 Emissions Avoided Fines/Penalties: SO2 emissions are limited to a certain level either on a unitby-unit basis or through a system-wide maximum. Penalties which may be imposed by Federal EPA, State agencies, or local authorities are site specific. Usually, combustion optimization helps reduce SO2 emissions through improvements in plant efficiency and heat rate. Because such improvements are usually no more than 1-2% for full load and less than 5% at low loads, optimization is not expected to reduce SO2 emissions significantly enough to be the main reason for avoiding penalties. However, if such penalties are avoided through a combination of options with plant optimization being one of them, a portion of the avoided penalties should be included in Table 4-1. Avoided Fines/Penalties ($/yr) -

4-20

Default Input: $0


Annual Value of SO2 Emission Credits: Such credits can be generated by reducing SO 2 emissions below a certain level, usually the compliance level for the unit dictated by unit, plant or power system SO2 requirements. Annual Value of SO2 Emissions Credits ($/yr) - Default Inputs: x x x x

x x

Value of SO2 credits ($/SO2 ton removed): 100 Baseline SO2 emissions (lbs/MBtu) 1.20 Unit baseline heat rate (Btu/kWh) 9,000 Unit heat rate improvement (%) 0.4 - 1.25 (depending on optimization type and specific unit operating conditions) Unit output (MW) 500 Unit capacity factor (%) 65

CO Emissions CO emissions are usually regulated to 100 ppm. Most optimization projects are carried out in such a way that this limit is not exceeded. Where CO emissions are above the allowable limit, optimization could be used to reduce them. In this case, the project should be credited for avoiding fines. Avoided Fines/Penalties ($/yr) -

Default Input: $0

Opacity Similarly to CO emissions, opacity is regulated by local and federal standards. Limiting opacity below the allowable limit is usually a constraint in power plant optimization and therefore no deterioration is expected as a result of optimization. In cases where the opacity does not meet the regulatory limit, the optimization project should take credit for opacity reduction, including potential avoidance for fines. Avoided Fines/Penalties ($/yr) -

Default Input: $0

CO2 Emissions Presently, there are no regulations limiting the release of CO2 emissions. While it is unlikely that such regulations imposing fines would be enacted in the foreseeable future, it is possible that CO2 emission reduction may have a value ($/ton). In this case, power plant optimization which results in heat rate improvement should take into account reduced CO2 emissions as an additional benefit. Most international organizations (e.g the UN Inter-governmental Panel on Climate Change) have suggested the values ranging from 20 to 100 $/ton of CO2 removed. 4-21


Annual Value of CO2 Emission Credits ($/yr) - Default Inputs: x x x x

x x

Value of CO2 credits ($/ton CO2 removed): Unit baseline CO2 emissions (lbs/MBtu): Unit baseline heat rate (Btu/kWh): Unit heat rate improvement (%): (depending on optimization type and specific unit operating conditions) Unit output (MW): Unit capacity factor (%):

0 208.521 9,000 0.4 - 1.25

500 65

1

The Baseline CO2 emissions default 208.52 reflects the following assumptions: Illinois #6 coal (see EPRI TAG for coal analysis and heating value) and 20% excess air.

Cost-Benefit Analysis Results Because different accounting procedures and measures are used by different companies, care must be used in interpreting the cost-benefit measures estimated in Table 4-1. The beginning of the Cost-Benefit Results includes inputs for the book life of the project and the assumed discount rate. Table 4-1 uses a default book life for the investment (5 years) and a default discount rate (4.9%), but the users may input their own assumptions. Intermediary financial figures that may be useful in decision making are also provided. The cost-benefit analysis results are calculated at the bottom of the spreadsheet. Based on the above inputs, Table 4-1 calculates the following cost-benefit measures: x

Net present value of implementing each optimization option: NPV of optimization by type = NPV of Net annual benefits (Annual benefits - Annual costs) - Up front costs NPV is the most useful measure of a project’s net benefits because it takes into account the costs and benefits throughout the life of the project, as well as the time value of money. Being measured in present dollars, NPV can be added up and easily compared to other projects, even with fluctuations in revenues.

x

Return on Investment (%): ROI = Net annual benefits (Annual benefits - Annual costs) - annual depreciation / Up front costs ROI does not take in to account the time value of money or cash flow profile of the project. It also does not take into account the overall scale of a project relative to other projects.

4-22


Payback Period (simple payback in months): PP = Up front costs / Net annual benefits (Annual benefits - Annual costs which is the same as first-year net annual savings) The simple payback period is a useful tool for describing investment projects, but ignores the order in which cash flows come within the payback period and subsequent cash flows after the payback period.

x

Benefit-Cost Ratio: BC Ratio = PV of total future benefits / Up Front Costs Also known as the profitability index, the B-C ratio takes into account discounted cash flows but, as a ratio, cannot be added up in the same way as you can add up NPV values

4-23


5 STEP FIVE: SELECT THE BEST OPTIMIZATION PRODUCT FOR YOUR APPLICATION





YES







5-1

EPRI Licensed Material Step Five: Select the Best Optimization Product for your Application

Selecting an Optimization Package In most cases, selection of an optimization product involves: x

development of design and performance specifications, and

x

evaluation of alternative products.

The rigor of the selection process depends on: x

the type of optimization: a utility’s evaluation of software associated with one-time optimization may not be as rigorous as evaluation of on-line/advisory or closedloop applications, because the latter implies longer-term commitment and higher costs,

x

number of power plants which will utilize optimization software: the larger the number of plants and therefore the level of commitment, the more need for a rigorous assessment, and

x

the utility’s approach regarding procurement of products and services: some utilities may prefer establishing a close working relationship with the software vendors in which case informal evaluation and negotiations of contractual or joint venture terms dictate the final selection of an optimization tool. In other cases, utilities may prefer to thoroughly evaluate every optimization tool under consideration.

Independently of the level of detail, there are a number of key factors which should be considered in the evaluation. The purpose of this section is to identify these factors and provide general guidance on how to select the best optimization software for a given project.

Key Factors Key factors which should be considered in evaluating optimization products include: x

Demonstrated track record of the optimization product in similar applications,

x

Experience of the software supplier in supporting product deployment,

x

Total cost of applying the optimization product, and

x

Benefits from the utilization of the optimization product.

In most cases, the first three factors determine the final selection. The fourth factor (benefits) is very important too. However, it is often difficult to estimate the difference 5-2


in benefits between the various software. The few rigorous evaluations performed so far have concluded that the expected benefits from various software for each type of optimization are within r 5-10%, which is well within the level of accuracy of the analysis. Any larger differences were based on benefits claimed by the vendors, but not confirmed with actual field experience. Box 5-1 (see next page) provides an example of a detailed software specification and evaluation for neural network-based software which was carried out at Tennessee Valley Authority’s (TVA) for the Kingston power plant.

Comments on the Key Factors for Evaluating Optimization Software A. Demonstrated track record of the optimization product in similar applications If the optimization software has been used in a power plant similar to the one under consideration, the risks associated with the optimization project are lower and the performance benefits can be predicted with better accuracy. Furthermore, the personnel of the plant which utilizes the software can be contacted to provide feedback on the overall experience. Engineering judgement should be used to decide if certain experience is applicable to a plant under consideration. Of course, the more similar the power plant is to ones which have used plant optimization, the better. However, general optimization experience is applicable to a broad categories of power plants. For example, experience from the application of optimization software to a wall-fired boiler burning coal is directly relevant to a T-fired boiler burning coal. However, the same experience is not directly relevant to a coal-fired cyclone boiler or an oil-fired unit. How to assess the experience of an optimization software? x

Visit EPRI’s Power Plant Optimization Web Site (http://www.epriweb.com/ gg/98funders/ppo/index.html); it includes an up-to-date list of power plants which have used optimization software,

x

A list of optimization applications up to May 1998 is included in EPRI’s publication TR-111316 (Proceedings: Second Annual EPRI Workshop on Power Plant Optimization, held in St. Louis, MO on May 12-13, 1998),

5-3


Box 5-1 TVA’s Experience in Selecting a Neural Network-based Optimization Software for Kingston #9

In June 1997, TVA selected a neural network-based optimization software for its Kingston #9 coal-fired unit. The objectives of this project were to: x

demonstrate that optimization software is a feasible and practical option for performance improvement (especially NOx control)

x

assess the costs and benefits of such an option, and

x

build in-house expertise needed to carry out similar projects throughout the TVA power system (tentatively 59 units have been identified for optimization).

Because of the level of commitment and the potential benefits, TVA decided that the optimization software selection deserved detailed specification and evaluation. TVA took the following steps: A. Assessment of experience base of commercial optimization software An industry survey was carried out to familiarize the utility staff with the state of the technology and industry’s experience. This was carried out mainly through requests to software suppliers for information on their products and the industry sites using them. Based on this information, TVA narrowed down the list of qualified bidders to four and invited them to TVA’s offices for presentations and detailed discussions. Finally, TVA visited selected utility sites which use such optimization software and held extensive meetings with plant personnel. These meetings were considered to be particularly useful. B. Detailed specification document prepared and sent to the vendors The specification included what the TVA team considered to be the best features of commercially available software. These features were identified in the previous step and included: sensor validation, model retraining, financial predictor, and contribution analysis. Furthermore, the specification provided a description of the Kingston #9 unit, the vendor scope of work, DCS/communication requirements, optimization software capabilities, technical support requirements, implementation schedule, licensing agreement preferences, and evaluation criteria. C. Bid evaluation and vendor selection Key evaluation criteria included: power plant optimization experience, neural network experience, neural network support capabilities, training, documentation and overall costs. TVA requested the prices of various options in order to assess the cost of implementing optimization throughout the power system (enterprise license), at a

5-4


specific site (plant) and in a single unit. As part of the evaluation process, TVA sent to the vendors a set of operational data (approximately 30 megabytes of 10-minute averages representing 30 days plant operation) and asked the vendors to analyze the data and identify the relationships between the key variables. Most vendors responded within a week; one of them responded within one working day (a 3-day weekend followed the time the data were sent to the vendors). This “blind test” was particularly useful because it provided an insight as to the vendors’ experience and capabilities. Follow-up questions showed which vendors had extensive experience and which lacked understanding of power plant engineering (design and operation), the utility business, and in some cases, neural network capabilities. D. Contract Award A company-wide license was finally negotiated to purchase GNOCIS. E. Implementation Successful and timely implementation of the project required close coordination between TVA and the vendor, especially on the following activities: x

TVA provided plant data for process model building prior to delivery,

x

unit walk-downs and review of I/O points,

x

identification of key independent and dependent parameters, and problem formulation, and

x

installation and calibration of the model at the site.

As a result, GNOCIS was installed as a closed-loop system in only 10 weeks. During implementation, TVA is comparing the performance achieved with the optimization system to pre-optimization (baseline) performance. The TVA’s experience suggests that: x

the key factors which contributed to the final selection were: proven field experience of the software, experienced team, and costs,

x

the evaluation process, especially the “blind test,” was particularly useful in assessing the experience of the suppliers and their ability to support the application, and

x

the performance and expected benefits of the evaluated products was “too close to call.”

5-5

EPRI Licensed Material Step Five: Select the Best Optimization Product for your Application x

Request from the vendors to provide a list of the sites which have used their software, as well as key contacts,

x

Call selected utilities (at least two for each software being considered) and asked them about their overall experience,

x

Visit at least one site for each optimization software; ¡

select the sites which resemble more closely the application you are considering; for example, if you plan to install a closed-loop optimization system, select a site which utilizes optimization software in a closed-loop mode over one that is on-line/advisory; however, if you have not decided which operating mode is best for your application, it may be worth visiting both closed-loop and on-line/advisory sites to assess their advantages and disadvantages,

¡

during the plant visit, it is more important to spend time with key plant personnel rather than tour the site; especially in the case of closed-loop systems where there is not much available to see. In most cases, discussion with plant personnel provides invaluable insights into the process of planning and implementing optimization systems.

B. Experience of the software vendor in supporting product deployment The software vendor should have adequate experience in all aspects of power plant optimization, including: x

power plant design and operation,

x

utility business, and

x

key components of the optimization software system, especially data acquisition, data validation, neural networks or statistical analysis modules, and optimization algorithms.

Most importantly, the vendors should understand the unit’s flexibility and capabilities to establish realistic goals for the optimization program. Considering that some of the power plant optimization vendors are small companies with limited track records, it may be necessary to assess their financial depth and business viability. A first assessment of the vendors’ experience can be done by asking them to submit information on prior projects, as well as resumes of key personnel who will be involved in an optimization project. A more thorough assessment requires direct (person-toperson) interaction. Opportunities to do so, include: 5-6


a working meeting between the utility and the optimization vendor to review the requirements of the project, identify the key issues associated with the project, and discuss the potential approach which could be followed,

x

performing a “blind test” (see Box 5-1),

x

carrying out a demonstration project in one unit before committing to a multi-unit or system-wide optimization program.

C. Total cost of applying the optimization product Table 4-1 can be used to evaluate alternative software products by revising the default inputs to reflect data provided by the software vendors or developed by the utility. A few comments on the Table 4-1 inputs follow. x

License Fee: It is important to clarify: What is provided under the license fee. Is it a run-time version of the model or a tool which allows utility staff to solve a variety of optimization problems? Does it include such details regarding item such as: ¡

First year’s annual fee,

¡

Additional software which may be needed,

¡

Computer hardware,

¡

Number of days on-site for installation, calibration and technical support,

¡

Additional hours or days of technical support either on-site or via telephone or email, and

¡

Days and number of people to be trained.

If these items are not included in a fixed price fee provided by the vendor, they should be added to the total project cost estimate (Table 4-1). x

Computer hardware, I&C and DAS: to the extent that additional computer hardware, instrumentation, controls (e.g., new DCS) and data acquisition system are needed, the user should make sure that there is consistency in assumptions between software; for example, if the utility decides to add an on-line LOI analyzer, its cost should be added to all software being evaluated. 5-7


Installation & Calibration: In addition to the cost of outside technical support, which is usually provided by the vendors as a fixed price, the utility should ask vendors to estimate and document the duration of the installation and package calibration. For example, a software package which requires a separate calibration at various loads, will need more time to be calibrated than a model which treats load as a variable. Particular attention should be paid to the data provided by the vendors in case they differ significantly from the default values in Table 4-1.

x

Training: Most optimization software packages are user friendly in terms of normal utilization. Therefore, training needs and costs provided by the various vendors should be similar. If not similar, it is worth clarifying further the differences. The utility should consider asking the vendor to provide the costs of training as a separate cost item. This is particularly important if the utility plans to build inhouse capability to do future optimization projects, in which case it may need more training.

x

Power Plant Hardware Modifications: usually plant hardware modifications do not differ with optimization software being considered.

x

Annual O&M Costs: The vendors should be asked to clarify the following: ¡

What does the annual fee include? Does it provide all new releases of the software?

¡

How often and long does it take to recalibrate the model? Is it done automatically or does the software requires “retraining” similar to the original calibration? Note: some models “learn on-line,” but others need to be off-line to

be recalibrated, some models require separate calibration at each load which

is more time-consuming, ¡

Does recalibration require technical support from the vendor or can it be completed by in-house staff?

The licensing fee and, in general, the contract with the vendor is negotiable and depends on market conditions (level of competition, how badly the vendor wants the job, potential future business as a result of the project under consideration, etc.). Each vendor has some flexibility to reduce the price being offered (after all software development expenses are sunk costs which the vendor hopes to recover), but a significant percentage of the costs are labor costs associated with installation, 5-8


calibration, and technical support which can not be reduced significantly. In fact, utilities should be careful to ensure that adequate level of technical support is included in the vendor’s proposal; otherwise, they may have to increase the estimated level of effort for in-house staff.

D. Benefits from the utilization of the optimization product In general, differentiation between the expected benefits from one software versus another is difficult because: x

there is a lack of adequate experience and information,

x

the limited experience available suggests that there is no significant difference in the final outcome between various models of the same optimization type, and

x

utilities which have undertaken a comprehensive evaluation of different products found that the product’s demonstrated experience, the capability of the vendor’s team, and the costs were the dominant factors in the selection process.

If the utility decides to assess the potential performance improvements based on information provided by the vendors, it should: x

x

make sure that the claimed benefits are due to the optimization software and not due to other factors, for example: ¡

the baseline performance should reflect “well-tuned” operation; a number of optimization projects have experience NOx emission reductions in the 30-40% range, mainly because the combustion system had not been tuned for many years,

¡

heat rate improvements may be influenced by weather conditions (especially when plant testing includes both winter and summer months) which affect the condenser backpressure and cooling water temperature,

¡

the moisture of the coal is reduced significantly either because of weather changes or because of changes in coal quality,

performance improvements should represent acceptable operating conditions. In some cases, results have been published which either include the best data points from a series of different tests or include unacceptable operating conditions (e.g., excess air below the level most utilities consider safe for operation, or CO and LOI above acceptable limits). 5-9


One well structured approach to estimate performance improvements is to perform a “blind test.” However, such a test should be designed to be unbiased and as representative as possible of actual plant operation. Important considerations for a blind test: x

Select a representative set of operating data covering at least 30-days; the more complete the data-set, the better; typical number of variables: 100-400; data provided in 5-10 minute averages reflecting steady-state (not transient) conditions,

x

State clearly the operating constraints and equipment limitations, so all vendors abide by the same constraints,

x

Provide clear optimization objectives, and

x

Send the data set to vendors and ask them to provide the optimum settings of the key control variables, the expected outcome (performance improvements), the control variables in order of decreasing priority relative to the optimization objective and the confidence level of the process models developed using the data.

While potentially useful, the blind test could be time-consuming for both the utility and the vendors. If the plant does not have DCS, collecting such data may be a very timeconsuming activity. Also, evaluation of the results developed by the vendors requires checking that all the operating constraints have been observed and the claimed performance improvements can be confirmed by actual field experience. Some vendors have designed service packages which are intended to assess the performance improvement potential. The drawback for utilities is that they have to pay for these assessments (usually priced at $10,000 to $30,000). However, in most cases these costs are subtracted from future optimization projects. Furthermore, the vendors may be willing to guarantee performance improvements. Such assessments may be particularly suitable when a utility has to reduce the number of optimization products it considers to two and needs more information on all aspects of the products (costs, benefits, and experience of the vendor’s team) to make a decision.

5-10


A SOFTWARE USER’S GUIDE

System Requirements Hardware Requirements x

Industry Standard Architecture (ISA) computer, such as IBM PC/AT or compatible.

x

A graphics card compatible with Microsoft Windows version 3.0 or later, such as IBM VGA, EGA, or Hercules graphics cards

x

At least 2 MB of RAM

Software Requirements x

Operating system: MS-DOS version 3.1 or later and Microsoft Windows 95 or later running in standard or 386 enhanced mode.

x

Microsoft Excel Version 7.0 or later

Installation 1. You must have Microsoft Excel version 7.0 or later installed on your computer to run the PPO Guidelines tables. 2. The PPO Guidelines disk containing the tables has been checked for viruses with McAfee VirusScan Version 3.1.9 Deluxe and has been found to be free of viruses. Due to some differences in Excel 7.0 and Excel 97, two sets of the Excel tables are provided on the disk, each set in its own folder (subdirectory). The user should select and install the appropriate version of the Excel tables. In addition to being saved into separate folders, the Excel 97 files can be differentiated from those in Excel 7.0 by an “E97” added to the name of the Excel file, e.g. Table 3-1E97 is Excel 97 while Table 3-1 is Excel 7.0. To optimize computer performance using the tables, copy the appropriate tables (e.g. Table 3-1.xls, Table 4-1.xls, Table B-3.xls, Table B-5.xls, Table B-7.xls, Table A-1

EPRI Licensed Material Software User’s Guide

B-8.xls, Table B-9.xls, Table B-11.xls) from the disk (drive A:\) to your hard (usually drive C:\). This can be accomplished by using Microsoft Windows Explorer: x

click on drive A:\

x

open the folder containing the files saved in the version of Excel installed on your computer,

x

select all files, click Ctrl-A (all the files will be highlighted)

x

click Edit

x

Go to the new folder you have created on drive C:\ and double click on it

x

Click Edit

x

Click Paste

Close the Excel files if you opened them from disk initially and relaunch them as needed from the copies that now reside on your hard drive.

Using the Software The 3.5” disk that accompanies the Power Plant Optimization (PPO) Guidelines contains tables in the form of Excel spreadsheets that can be used by the Guidelines user to: 1) better understand how the Guidelines work and 2) actually carry out a costbenefit analysis including the potential performance improvements and resulting financial savings that might be realized using PPO software on a specific boiler, given specific technical data and financial assumptions. The PPO Guidelines disk is meant to be used in tandem with the PPO Guidelines manual. Both are tools utilized to lead the Guidelines user through a five step project assessment process. To facilitate using both tools, a hard copy of every Excel table from the disks is included in the manual at the point where it is discussed or used. The tables on disk are not sequentially numbered because there are also numbered tables that appear only in the manual. In all the tables, values or text that appear in blue can be changed, while other values and formulas that appear in black are locked. Default values which reflect typical values are already entered, but the user should work their way through each table, changing these default values to better reflect their own financial assumptions and the technical circumstances of the boiler they are considering for optimization.

A-2


To help the user avoid mistakes, all the tables have built in warnings that appear when a questionable input is entered. When an input outside a reasonable range for a given cell is entered, a message appears warning the user (In Table 4-1 and the case study tables based on 4-1 the message “Input outside typical range” appears in the first empty cell below the entered input. In Table 3-1 and the case study tables based on it the message “Bad Input” appears to the right of the input cell). Likewise, when an alphanumeric is accidentally entered instead of a numeric value, the tables warn the user. In Table 4-1 the message “Input Value, not Text” appears in the first empty cell below the most recently entered input. In Table 3-1 the message “Input Value” appears to the right of the input cell. These warning messages refer to the last input that has been changed. For either type of error, the tables will not compute final results, costbenefit analysis results in Table 4-1 and Total Score for Table 3-1. The ranges that have been programmed into the table are broad and should accommodate all plant optimization scenarios. If for some reason, the user wishes to use inputs outside the range limitations, he/she should contact EPRI Project Manager and ask for the table password which is needed to unprotect the table and change the range limitations. The following provides an overview of each Excel file:

Table 3-1 This table should be used during Step 3 of the guidelines. A print out of this table appears on page 3-3. Based on site-specific considerations and the guidance provided in the PPO manual, the user enters the appropriate score from 1 to 3. Using these input scores, Table 3-1 calculates a Total Score between 100 and 300. Based on the Total Score, a proposed project will fall into one of three categories (A, B, or C) which indicates the potential range of improved boiler performance that can be achieved from boiler optimization. Tables 3-2 and 3-2a to 3-2d on pages 3-24 and 3-25 of the manual (they are not included on the disk) provide the performance improvements that can be expected based on the Total Score from Tables 3-1.

Table 4-1 This table is a cost-benefit analysis of the potential boiler optimization project and should be used during Step 4 of the Guidelines. A print out of the Excel file, appears on pages 4-5 to 4-6 of the Guidelines. Step 4 of the manual walks the user through the fine details related to refining the cost and benefit inputs to Table 4-1. Values that can be changed appear in blue. To assure consistency, when the same value appears more than once in a table, the value only appears in blue (i.e. is changeable) the first time it appears. Subsequent appearances are written in black (i.e. are locked) and are linked to the initial appearance.

A-3


Given the values entered, Table 4-1 automatically calculates the cost-benefit results (see bottom of the table). The user can examine the formulas used throughout the table in several different ways: x

read a description of the formulas in English by moving the Excel cursor on to cells in the left most column to pop up the memo function. Only cells with a small red dot in the upper right hand corner of the cell contain a memo,

x

read the Excel math formulas by placing the Excel cursor directly on the resulting cost-benefit values and looking at the Excel formula bar,

x

read formula descriptions which are also listed in Appendix C of the manual.

Tables B-3, B-5, B-7, B-8, B-9, and B-11 The remaining Excel files are versions of Tables 3-1 and 4-1 that have been used for two case studies described in Appendix B of the Guidelines. Simply reading the manual and referring to the printed tables in the manual should be adequate to understand the case studies. However, for users who would like to experiment with variations of the case studies to better understand the dynamics of the analysis, all the Excel tables from Appendix B are included on the disk. The following tables are based on Table 3-1: x

Table B-3 Case Study 1, Option 1

x


x

Table B-9 Case Study 2, single option analyzed

The following tables are based on Table 4-1 and finalize the analyses initiated in the above options: x


x


x

Table B-11 Case Study 2, single option analyzed

User Tutorial Appendix B provides two case studies illustrating how utilities used the PPO Guidelines to carry out evaluation of different optimization types. To familiarize the user with how these utilities used the software tables and how the user can utilize the tables to carry out his/her own evaluation of optimization options for a specific boiler, this tutorial walks the user through a few changes in the two primary tables, 3-1 and A-4


4-1. By continuing the same process of changing one input at a time from the default value in these tables to a value that more accurately reflects the circumstances of the specific boiler and the financial assumptions of the user’s company, the tables calculate the costs and benefits of various options. While the case studies in Appendix B go into detail regarding technical parameters and how to determine specific table inputs, this tutorial focuses instead on the mechanical steps of inputting this data into the tables. For any boiler, the user would first carry out Step 1 of the Guidelines by undertaking a quick tuning of the boiler and establishing its baseline performance. The user would then carry out Step 2 by setting optimization objectives. Step 3 is the first step in the Guidelines to use a software tool, Table 3-1. The user would open this table from his/her hard drive (see installation above) and save it under a different name, e.g. “Table 3-1 Boiler XYZ” (for example, in Case Study 1, the table is saved as “Table B-3” to easily identify it as the third table referenced in Appendix B). Then, the user will modify one input at a time to reflect the requirements and consideration of he specific boiler being evaluated. Figure A-1 below shows the top and bottom parts of Table 3-1 (the jagged break in vertical lines represents a jump in the table). The top box in Figure A-1, labeled “Before,” shows Table 3-1 as it appears on the Guidelines disk. The bottom box in Figure A-1, labeled “After,” shows how it should look after the user has saved it as “Table 3-1 Boiler XYZ” and entered new inputs. Breaking these changes down step-by-step, under the first topic “Information on the last tune up,” the first category requiring an input is “When was unit last tuned?” Reading from the shaded box presenting this question on page 3-4 of Guidelines manual, the user will see this question as the first line requiring a score input in Table 3-1. In this hypothetical case, boiler XYZ was tuned 18 months before which the Guidelines indicate is a score of 2. In the table, since the default value that appears in blue in the “Score” column is set at 1, the user should: x

click on this cell,

x

delete the default value “1,”

x

enter the number “2,” and

x

delete the other options listed in the cell the right in the “Clarifications” column so that the table clearly identifies the specific circumstances of this boiler. A-5


In the bottom box of Figure A-1, we see that based solely on this change, the table has recalculated the ”Total Score” of boiler XYZ, changing from 100 to 130. Before: Criteria

Score Clarifications

Information on the last tune up - When was the unit last tuned? - Tuning Objective/Tuned for: Unit Operating Flexibility (at full load) - Fuel flow biasing capacitlity (e.g. mill capacity)

TOTAL SCORE

1 Options: less than a year (enter 1); 1-2 years (enter 2); more than 2 years (enter 3) 1 Options: safe operation (enter 1); heat rate (enter 2); NOx (enter 3); heat rate & NOx (enter 4) 1 Options: no biasing (enter 1); some biasing, but no BOOS (enter 2); BOOS possible (enter 3)

100 Range: 100-300

After: Criteria


Information on the last tune up - When was the unit last tuned? - Tuning Objective/Tuned for: Unit Operating Flexibility (at full load)

2 18 months ago 1 Options: safe operation (enter 1); heat rate (enter 2); NOx (enter 3); heat rate & NOx (enter 4)

- Fuel flow biasing capacitlity (e.g. mill capacity)

1 Options: no biasing (enter 1); some biasing, but no BOOS (enter 2); BOOS possible (enter 3)

TOTAL SCORE

130 Range: 100-300

Figure A-1 A Portion of Table 3-1 Before and After a Change is entered

The second category under the first topic is “Tuning Objective/Tuned for:” (see Figure A-2 below). In the case of the boiler XYZ, let us assume that the objective was “safe operation” which the shaded box on page 3-5 of the Guidelines indicates is a score of 1. Since the default value in the “Score” column is already set at 1, the user does not need to change the score. However, the user may delete the other options in the “Clarifications” cell to the right of his/her input and add any comments they he/she wishes pertaining to the tuning objective. Continuing on with Step 3, the next topic in Table 3-1 is “Unit Operating Flexibility (at full load).” The first category requiring an input is “Fuel flow biasing capability, (e.g. mill capacity).” In the case of boiler XYZ, let us assume that some biasing is possible, but there are no burners-out-of-service (BOOS). The Guidelines indicate this as a score of 2. The user should:

A-6

x

click on this cell,

x

delete the default value “1,”

x

and enter the correct value “2,” and

EPRI Licensed Material Software User’s Guide x

change the Clarifications” column to reflect the specific circumstances of boiler XYZ.

As shown in the “After” box of Figure A-2, with this change, the table has automatically recalculated the Total Score for the boiler XYZ, changing from 130 to 133. Before: Criteria



2 18 months ago


1 Options: no biasing (enter 1); some biasing, but no BOOS (enter 2); BOOS possible (enter 3)

TOTAL SCORE

1 Options: safe operation (enter 1); heat rate (enter 2); NOx (enter 3); heat rate & NOx (enter 4)

130 Range: 100-300

After: Criteria



2 18 months ago 1 Safe operation


2 Some biasing, but no BOOS

TOTAL SCORE

133 Range: 100-300

Figure A-2 A Portion of Table 3-1 Before and After a Second Input is Entered

By continuing on through all the topics and categories listed in Step 3 in this manner, the user will tailor every row in the table to the specific boiler they are evaluating, thereby completing the table. Based on the final Total Score, a proposed project will fall into one of three categories (A, B, or C) which indicates the potential range of improved boiler performance that can be achieved from boiler optimization. In the hypothetical case of boiler XYZ, let us assume a final “Total Score” of 170 so that we can see how the user would use this result from Table 3-1 to how he/she would use Table 4-1. With a Total Score of 170, Table 3-2 on page 3-24 classifies the project as Group B. Given the previous tuning objective of “safe operation,” Table 3-2a (on page 3-25) indicates the range of potential improvements in heat rate and NOx reduction. The low end of the range can be reached using stand-alone optimization, the middle end of the range by on-line/advisory, and the high end of the performance improvement range can be met with closed-loop type optimization. A-7


To determine the cost of reaching these improvements, the user should use Table 4-1 to carry out a cost-benefit analysis. The cost for each type of optimization can then be weighed against that project’s estimated performance improvements. The mechanics of using Table 4-1 are the same as for Table 3-1. The user should open the file from his/her hard drive, save it under a different name, and then work step-by-step through the shaded boxes in Step 4 of the Guidelines manual to tailor all the inputs to the boiler under consideration and the financial assumptions of his/her company. Figure A-3 below shows the top and bottom parts of Table 4-1. The first box in Figure A-3, labeled “Before,” shows Table 4-1 as it appears on the Guidelines disk. The second box in Figure A-3, labeled “After,” shows how it should look after the user has saved it as “Table 4-1 Boiler XYZ” and entered new inputs. Breaking these change down step-by-step, under the first topic “Up Front Costs,” the first category requiring an input is “Up Front Software License Fees.” Reading from the shaded box presenting this cost category on page 4-7 of Guidelines manual, the user will see this category as the first line in Table 4-1 with a value that appears in blue, i.e. it is changeable.

A-8


Before: COSTS


On-line/advisory

Closed-loop

Up Front Costs Up Front Software License Fees ($)

$30,000

$130,000

$170,000

Additional Computer Software and Hardware ($)

$5,000 $0 $5,000

$5,000 $0 $5,000

$5,000 $0 $5,000

Computer software Computer hardware


$68,484 26 26.0 1.0

$492,377 66 13.9 2.8

$1,277,134 134 7.8 5.7

After: COSTS


On-line/advisory

Closed-loop

$20,000

$130,000

$170,000

$5,000 $0 $5,000

$5,000 $0 $5,000

$5,000 $0 $5,000

$78,484 34 22.1 1.4

$492,377 66 13.9 2.8

Up Front Costs Up Front Software License Fees ($) Additional Computer Software and Hardware ($) Computer software Computer hardware


$1,277,134 134 7.8 5.7

Figure A-3 A Portion of Table 4-1 Applied to Boiler XYZ; Before Use and After a Change is Entered

For boiler XYZ, let us assume that the utility is considering application of optimization to several boilers at the same time and that a optimization vendor that sells stand alone optimization has offered a reduced up front software fee of $20,000 if applied to several boilers. Since this amount is lower than default value in the “Stand alone” column, the user should: x

click on this cell,

x

delete the default value of $30,000,

x

enter the correct value of $20,000,

x

If the user has any comments to add to regarding this line item, the user can input comments to the right of the optimization types columns (off the edge of A-3). A-9


In Figure A-3, all four measurements of project value listed under “Cost-Benefit Analysis Results” have now been recalculated in the Stand-alone column to reflect this improvement in price. Continuing on with Step 4 (see Figure A-4), the next cost category is “Additional Computer Software and Hardware ($).” In the case of boiler XYZ, let us assume that the utility has an older computer system that is inadequate to deal with the processing requirements of any of the optimization types and that a total of $10,000 is required to purchase new computer equipment. In Figure A-4, we see that Table 4-1 provides a default value of $5,000 for sub-category “Computer hardware.” Since the case of boiler XYZ requires a greater investment, the user should: x

click on the cell in the stand-alone column,

x


x

input the correct value of $10,000.

By doing so, the user will note that the table automatically recalculates the total cost for “Additional Computer Software and Hardware ($),” increasing it from $5,000 to $10,000 (appears in bold which above the Computer hardware sub-category). Since Computer hardware is the only sub-category with a value entered, the “Additional Computer Software and Hardware ($)” total equals the total computer hardware entry. The user must repeat these changes in the cells for computer hardware in the columns for “On-line/advisory” and “Closed-loop” as well. In the electronic version of the table, the user will note that the values in these cells appear in blue (i.e. are changeable) and are not linked to each other. The table is designed in this way so that varying requirements for different optimization types can be entered and cost-benefit results calculated accurately for each. The user should: x

click on the Computer hardware cell row in the On-line/advisory column,

x


x

input the correct value of $10,000, and

x

repeat these steps in the Computer hardware cell row in the Closed-loop column

As shown in the bottom box of Figure A-4, these changes have automatically been used to recalculate the project value of the three optimization types. In this case, since the A-10


cost of all three optimization types was increased from the default values, the project values presented under Cost-Benefit Analysis Results have decreased. The projected numbers for of Net Present Value, Return on Investment, and Benefit-Cost Ratio have all dropped somewhat. The number for Payback Period has gone up, indicating a longer period required for recouping the initial investment and consistent with a lower project value. Before: COSTS


On-line/advisory

Closed-loop

$20,000

$130,000

$170,000

$5,000 $0 $5,000

$5,000 $0 $5,000

$5,000 $0 $5,000

$78,484 34 22.1 1.4

$492,377 66 13.9 2.8

Up Front Costs Up Front Software License Fees ($) Additional Computer Software and Hardware ($) Computer software Computer hardware


$1,277,134 134 7.8 5.7

After: COSTS


On-line/advisory

Closed-loop

Up Front Software License Fees ($)

$20,000

$130,000

$170,000


$10,000 $0 $10,000

$10,000 $0 $10,000

$10,000 $0 $10,000

$73,484 30 24.0 1.2

$487,377 64 14.3 2.6

$1,272,134 130 8.0 5.5

Up Front Costs

Computer software Computer hardware


Figure A-4 A Portion of Table 4-1 Applied to Boiler XYZ; After a Second Group of Changes is Entered

By continuing on through all the categories and sub-categories of costs and benefits listed in Step 4 in this manner, the user will tailor every row in the table to the specific boiler they are evaluating, thereby completing the table. With an estimate of the potential performance improvements of each optimization type and an estimate of the value of each optimization type, the utility will be well prepared to make a decision regarding optimization on their boiler units. A-11


The user is advised to review the case studies in Appendix B to familiarize him/herself with the Guidelines before applying them to his/her own project.

A-12

EPRI Licensed Material Case Studies

B CASE STUDIES

Case Study 1: Allegheny Power’s Armstrong 1 Unit Background Allegheny Power has been a leader in utilizing combustion NOx controls and power plant optimization software. Most of its power stations have been retrofitted with low NOx burners including: x

Albright 1 and 2 utilizing DB Riley's CCV burners

x

Albright 3 retrofitted with ABB/CE's LNCFS III

x

Armstrong 1 and 2 retrofitted with Foster Wheeler's IFS burners and overfire air

x

Harrison units 1, 2, and 3 retrofitted with Foster Wheeler's IFS burners and overfire air

x

Smith 11 retrofitted with ABB/CE's LNCFS III.

These projects were driven mainly by the need to comply with the requirements of Phase I of the Clean Air Act Amendment (CAAA). However since 1995, Allegheny has focused its efforts in assessing the potential for further NOx emission reductions and performance improvements utilizing power plant optimization software. Such improvements are important in fine-tuning its future NOx compliance plans (CAAA Title I and Phase II/ Title IV) and increasing its competitiveness in the postderegulation era. As of May 1998, Allegheny has utilized optimization software in four power stations. As shown in Table B-1, the results have been very encouraging. Simultaneous NOx reduction and heat rate improvement was achieved in all units by utilizing optimization software in stand-alone mode.

B-1


Table B-1 Allegheny's Experience with Power Plant Optimization Station Name

Burner Type Front-wall

Optimization Software Used Ultramax

% NOx Reduction Achieved 20

% Heat Rate Improvement 0.5

Harrison 2 Hatfield 1-3

Cell burners

Ultramax

7

0.3

Ft. Martin 1

T-fired

Ultramax

17

1.5

Smith 4

T-fired

Boiler OP

NA (ongoing)

NA (ongoing)

In deciding what type of optimization to employ at Allegheny's Armstrong power station, it was necessary to carry out a more detailed assessment. The reasons for such assessment were: x

the number of control variables in this boiler may be much higher that in the units shown in Table B-1; this is mainly due to the fact that coal flow rate may be monitored in each of the 12 coal pipes;

x

the potential for NOx emission trading requires increased flexibility and online optimization;

x

also, industry deregulation puts additional pressure to reduce production costs, and

x

it was considered important to employ software which are capable of optimization at low loads and, if possible, during load following.

Such assessment was carried out using a beta version of the EPRI Power Plant Optimization Guidelines. Based on the outcomes of this assessment, Allegheny is now selecting specific software through competitive solicitation (note: this step is not included in the PPO Guidelines).

Armstrong Unit 1 Armstrong 1 and 2 are front wall-fired boilers rated at 180 MWs each designed to burn Eastern Bituminous coal (see Table B-2). They were originally designed and built by Foster Wheeler in the mid-1950s, but were refurbished in the early 1990s (see Figure B-1). Refurbishment included replacement of pressure parts and installation of Foster Wheeler's IFS low NOx burners.

B-2


Table B-2 Characteristics of Eastern Bituminous Coal Burned in Armstrong 1 Grindability

55

Proximate Analysis (as received) Moisture

7.80

Volatile matter

31.82

Fixed carbon

49.05

Ash

11.33

Ash softening temperatures at reducing/oxidizing conditions qC (qF) Ultimate Analysis (% by weight)

1283/1378

Ash

11.33

Sulfur

1.94

H2

5.22

C

66.54

H2O

7.80

N2

1.35

O2

5.82

KJ/Kg. (Btu/lb)

27,928 (12,007)

Each unit utilizes two ball mills (see Figure B-2) feeding 12 burners which are arranged in three rows along the front wall of the boiler. Significant imbalance has been measured among coal pipes resulting in increased LOI (Loss On Ignition ). In most cases, LOI averages at 12-14%. Plant engineers estimate that LOI could be reduced to the 5-6% range with better coal flow balancing and unit optimization. Coal flow balancing is expected to be achieved through the utilization of an electrostatic or microwave-based coal flow measurement technique and control of primary air flow rate. Depending on the effectiveness of coal flow balancing and optimization, a coal additive may be used, too.

B-3


Figure B-1 Armstrong Units 1 & 2 Boiler Arrangement

B-4


Burners

1

3

5

2

4

1

2

3

4

12

5

6

11

7

8

9

10

6

7

9

11

8

10

12

Coal Conduits

Coal Conduits 3 way distributor

Ball mill pulverizer A

Ball mill pulverizer B

Figure B-2 Burner and Mill Arrangement

After the IFS burner installation, Armstrong 1 operates as an intermediate load unit and generates an average 300 ppm (0.40 lbs/MBtu) of NOx emissions; as Figure B-3 shows, NOx as low as 225 ppm (0.30 lbs/MBtu) and as high as 360 ppm (0.48 lbs/MBtu) have been measured. Such performance satisfies present CAAA requirements, but further tightening of NOx regulations is likely. For this reason, Allegheny is interested to assess the potential for NOx reduction at Armstrong to assess its NOx compliance options including system averaging. As Figure B-4 shows, boiler efficiency varies considerably suggesting that heat rate improvement of the order of 1% is feasible. However, such improvement may have to be balanced against potential LOI and NOx emission reductions.

B-5


375

0.5

300

0.40

0.35

0.30

N O x (ppm )

N O x (Lb/M btu)

0.45

225

0.25

0.20

150 50

150

100

200

Lo ad (G M W )

Boiler Efficiency

Boiler Efficiency

Figure B-3 Armstrong Unit 1 NOx Emissions After IFS Burner Installation

Load (G MW ) Figure B-4 Armstrong Unit 1 Boiler Efficiency After IFS Burner Installation

B-6


Evaluation of Alternative Power Plant Optimization Options As a first step, it was decided to evaluate the different types of optimization (standalone, on-line/advisory and closed-loop) using the beta version of the EPRI Power Plant Optimization Guidelines. Based on this evaluation, specific software will be selected through competitive solicitation. The 5-steps outlined in the Guidelines as applied to Armstrong 1 are described in the following paragraphs. Step 1: Quick Tuning and Establishment of Baseline Performance Following the guidance in Step 1, a plant performance engineer finds that all plant components are in good operating condition and decides that no changes should be made. As a result, the baseline performance (NOx and boiler efficiency) is reflected by the regression curves in Figures B-3 and B-4. Baseline LOI was measured at 13.2%. Step 2: Objectives for Power Plant Optimization Based on the above, it was determined that the primary objective would be to reduce heat rate and LOI while maintaining NOx emissions at present levels. Preliminary targets for heat rate and LOI reductions were set at: x

1% improvement in heat rate, and

x

LOI reduction from 13.2% to 5-6%.

While NOx emission reduction was not the primary objective, it was recognized that NOx trading might increase the importance of NOx reduction in the future. To reflect this, the value of NOx emissions for Allegheny was determined at an average of $400 per ton. This was based on average compliance costs and the potential value of NOx if a trading system is established. Other parameters, which relate to the objectives and were set at this step include: x

Incremental cost for replacement power: $20 per MWh; this was important because Armstrong 1 is derated by 10 MWs due to opacity limits approximately 30% of the time;

x

Value of SO2 emissions: $100 per ton; note: baseline SO2 emissions were 473 ppm (1.1 lbs/MBtu);

x

Project book life: 5 years; and

x

Discount rate: 10.6%. B-7


Step 3: Determine Optimization Potential/Can Optimization Alone Meet Established Objectives? It was determined that two alternatives would be evaluated: x

Case 1: with monitoring of coal flow in each coal pipe and control of imbalance through modulation of the secondary air sleeve dampers, and

x

Case 2: no coal flow monitoring and control.

For each case, the electronic version of Table 3-1 of the Guidelines was used to determine the potential performance improvement. Case 1: Monitoring and controlling coal flow Table B-3 is the result of tailoring Table 3-1 to the specific circumstances of Armstrong 1. The following bullets summarize the key observations and assumptions: x

Armstrong 1 has a Data Acquisition System (DAS) and Digital Control System (DCS), so it can utilize any of the three optimization types

x

The combustion system was tuned by Foster Wheeler within the last year. Tuning objective was NOx emission reduction

x

Some air flow biasing (more air to the upper elevation and lower air to the lower elevation of burners) is possible

x

Excess O2 could be reduced from the present level of 3.2% to 2.0-2.5%. To achieve such reduction, a CO monitor should be installed

x

Classifier settings and position of the coal nozzle could be adjusted to optimize LOI and NOx emissions

x

Re-orificing of coal pipes, monitoring of coal flow and control of primary air are included in this option.

As shown in Table B-3, the Armstrong optimization project scores 168 points (out of 300 points) which Table 3-2 on page 3-24 of the Guidelines manual classifies as a Group B project. Given the fact that the previous tuning (within the last year) focused on NOx emission reduction, Table 3-2c on page 3-25 provides an even more refined estimate of potential performance improvements

B-8


Table B-3 Project Classification to Determine Potential Performance Improvements Due to Optimization Armstrong 1/Advanced Control for Coal Flow Distribution Criteria Information on the last tune up - When was the unit last tuned? - Tuning Objective/Tuned for: Unit operating plexibility (at full load) - Fuel flow biasing capability (e.g., mill capacity) - Air flow biasing capability - Excess O2 range (min-max) - Burner tilts (for T-fired units) - Air and Gas dampers - PA/Fuel ratio - Other control variables Ability to change equipment settings - Burner settings (e.g., registers, yaw, etc.) - Pulverizer settings (e.g., spring tension; classifier; exit temp - Other equipment settings Hardware modifications - Air distribution modifications - Coal pipe orificing - Mill Modifications - Other TOTAL SCORE (weighted average)

Score

Clarifications

1 Boiler retrofitted with IFS burners and tuned 3 In addition to FW tuning, Allegheny did some tuning to reduce CO emissions 2 Some biasing; three levels of burners fed by two mills 2 Some biasing 2 Flexibility limited by lack of CO monitor; running at 3.2%; possible reduction to 2-2.5% 1 No tilt 3 Secondary air sleeve dampers 1 Tight range 3 Mill outlet temperature and tempering air 3 Inner and outer sleeve dampers 3 Coal nozzle position could be adjusted 2 Mill classifier settings could be adjusted 1 No air mods 3 Significant coal flow imbalance; re-orificing and control of coal flow for each coal pipe 1 No mill mods 1 No other mods 168 Group B

B-9


x

NOx Reduction:

15.0-22.5%, and

x

Heat Rate Improvement:

0.75-1.25 percentage points.

Within these ranges, the projected improvements for the various types of optimization are shown in Table B-4. (Note: it is assumed that closed-loop will achieve the upper end of the projected range, the stand-alone will achieve the lower end and the online/advisory the average). Table B-4 Expected Performance Improvements with Optimization (Case 1) Optimization Type

NOx Emission Reduction (%)

Heat Rate Reduction (% pts)

Stand-alone

15.00

0.750

On-Line/Advisory

18.75

1.000

Closed-loop

22.50

1.250

Also, it was estimated that the stand-alone option could regain 5 out of the 10 MWs lost due to opacity limitations, while on-line/advisory and closed-loop could regain all 10 MWs. Case 2: No Coal Flow Monitoring and Control Case 2 is different from Case 1 in that no re-orificing of coal pipes and monitoring of coal flow are included in this option. As a result (see Table B-5), the Total Score is lower (153 out of 300 points). Table 3-2 on page 3-24 classifies this optimization project as Group A and Table 3-2c on page 3-25 provides an even more refined estimate of potential performance improvements: x

NOx Reduction:

5.0-10.0%, and

x

Heat Rate Improvement:


Within these ranges, the projected improvements for the various types of optimization are shown in Table B-6

B-10


Table B-5 Project Classification to Determine Potential Performance Improvements due to Optimization Armstrong 1/ No Coal Flow Distribution Control Criteria Information on the last tune up - When was unit last tuned? - Tuning Objective/Tuned for: Unit operating flexibility (at full load) - Fuel flow biasing capability (e.g., mill capacity) - Air flow biasing capability - Excess O2 range (min-max) - Burner tilts (for T-fired units) - Air and Gas dampers - PA/Fuel ratio - Other control variables Ability to change equipment settings - Burner settings (e.g., registers, yaw, etc.) - Pulverizer settings (e.g., spring tension; classifier; exit temp - Other equipment settings Hardware modifications - Air distribution modifications - Coal pipe orificing - Mill Modifications - Other TOTAL SCORE (weighted average)

Score

Clarifications

1 Boiler retrofitted with IFS burners and tuned 3 In addition to FW tuning, Allegheny did some tuning to reduce CO emissions 2 Some biasing; three levels of burners fed by two mills 2 Some biasing 2 Flexibility limited by lack of CO monitor; running at 3.2%; possible reduction to 2-2.5% 1 No tilt 3 Secondary air sleeve dampers 1 Tight range 3 Mill outlet temperature and tempering air 3 Inner and outer dleeve dampers 3 Coal nozzle position could be adjusted 2 Mill classifier settings could be adjusted 1 No air mods 1 Significant coal flow imbalance; No control of coal flow for each coal pipe 1 No mill mods 1 No other mods 153 Group A

B-11


Table B-6 Expected Performance Improvements with Optimization (Case 2) Optimization Type



Stand-alone

5.0

0.250

On-Line/Advisory

7.5

0.375

Closed-loop

10.0

0.500

Step 4: The Most Cost-effective Type of Optimization For Case 1, all the optimization options come close to or surpass the preliminary targets (objectives). For Case 2, all options fall short of achieving the desirable 1.0% heat rate reduction. However, the projected NOx reduction and potential for LOI improvement were sufficient to justify carrying out a cost-effectiveness analysis of all optimization types for Armstrong 1. Case 1 Table B-7 shows the inputs and results of the cost-benefit analysis for the three optimization types given the assumption of coal flow monitoring and control. This analysis takes into account: x

All the up front and annual costs, including in-house technical support needed to install, calibrate, and maintain the software

x

Benefits such as fuel savings due to heat rate improvement, increase unit output (regaining lost MWs), and reduction of NOx and SO2 emissions.

Based on this analysis, closed-loop is the most cost-effective option for Armstrong 1. This option is also the most appropriate considering the relatively high number of control variables (a total of 20-30 including the coal flow rate or primary air in 12 coal pipes). With such high number of variables, stand-alone optimization would be timeconsuming and impractical. Case 2 The cost-benefit analysis and the results for Case 2 (no coal flow distribution control) are shown in Table B-8 indicating again that the most cost-effective option for Armstrong 1 is the closed-loop optimization.

B-12

EPRI Licensed Material Case Studies Table B-7 Power Plant Optimization Cost-Benefit Analysis--Armstrong 1 Advanced Control for Coal Flow Distribution COSTS


O n-line/advisory

Closed-loop

$70,000

$130,000

$170,000

$5,000 $0 $5,000 $0 $0

$5,000 $0 $5,000 $0 $0

$5,000 $0 $5,000 $0 $0

$57,000 $25,000 25 $800 $5,000 $32,000 40 $400 2.0 $0

$46,600 $14,600 12 $800 $5,000 $32,000 40 $400 2.0 $0

$53,000 $17,000 15 $800 $5,000 $36,000 45 $400 2.0 $0

Training ($)

$0

$0

$0


$0

$0

$0

$132,000

$181,600

$228,000

Annual Software License & Maintenance Fees ($)

$5,000

$13,000

$17,000

Technical Support ($)

$33,000 $17,000 15.0 $800 $5,000 $16,000 20 $400 2.0

$25,000 $13,000 10.0 $800 $5,000 $12,000 15 $400 2.0

$21,000 $13,000 10.0 $800 $5,000 $8,000 10 $400 2.0

$38,000

$38,000

$38,000

$0 $0 0 $0 $0 0

$0 $0 0 $0 $0 0

$0 $0 0 $0 $0 0

$0

$0

$0

$0

$0

$0

$88,080 10,000 0.750 1.12 171 70.0

$117,440 10,000 1.000 1.12 171 70.0

$146,800 10,000 1.250 1.12 171 70.0

Up Front Costs Up Front Software License Fees ($) Additional Computer Software and Hardware ($) Computer software Computer hardw are Instrumentation and controls (I&C) Data acquisition system (DAS)

Installation and Calibration ($) Outside technical support total -Num ber of man days -Av erage daily rate for outside technical support -Miscellaneous expenses (Trav el, reports, overhead, etc.) In-house technical support total -Num ber of man days -Av erage daily rate for in-house technical support -Cost m ultiplier for ov erhead Power plant downtime/non-economic dispatch


Annual Costs (O&M incl. Recalibration)

Outside technical support total -Num ber of man days -Av erage daily rate for outside technical support -Miscellaneous expenses (Trav el, reports, overhead, etc.) In-house technical support -Num ber of man days -Av erage daily rate for in-house technical support -Cost m ultiplier for ov erhead

TOTAL ANNUAL COSTS

BENEFITS (Losses) Up Front (one time) Benefits NPV of Deferred Costs ($) Deferred costs How long does optimization delay investment? (in months)

NPV of Avoided Costs ($) Avoided costs When would investm ent have been implemented (in m onths)


Annual Benefits Annual Avoided Costs ($/yr) Fuel Cost Savings ($/yr) Baseline heat rate (Btu/kWh) Unit heat rate improvement (% Pts) Fuel cost ($/MBtu) Unit output (MW) Capacity factor (%)

B-13

EPRI Licensed Material Case Studies Table B-7 Cont’d. O&M Impacts ($/yr)

$0 $0 $0

$0 $0 $0

$0 $0 $0

$0 0.0 0

$0 0.0 0

$0 0.0 0

$183,960 5 20.00 70 30

$367,920 10 20.00 70 30

$367,920 10 20.00 70 30

$0 $141,557 400 353.9 0.450 15.00 171 10,000 70

$0 $176,947 400 442.4 0.450 18.75 171 10,000 70

$0 $212,336 400 530.8 0.450 22.50 171 10,000 70

$0 $4,325 100 43.25 1.100 10,000 0.75 171 70

$0 $5,767 100 57.67 1.100 10,000 1.00 171 70

$0 $7,209 100 72.09 1.100 10,000 1.25 171 70


$0

$0

$0


$0

$0

$0

$0 0 8,199 208.52 10,000 0.75 171 70

$0 0 10,932 208.52 10,000 1.00 171 70

$0 0 13,666 208.52 10,000 1.25 171 70

$417,923 0.50

$668,074 0.75

$734,265 0.90

$208,961

$501,055

$660,838

5.0

5.0

5.0

10.6

10.6

10.6

$170,961 $638,265 $26,400

$463,055 $1,728,766 $36,320

$622,838 $2,325,298 $45,600

$506,265 110 9.3 3.8

$1,547,166 235 4.7 8.5

$2,097,298 253 4.4 9.2

Waterwall corrosion ($/yr) Operating flexibility ($/yr)



Benefits Due to Emission Reductions NOx Emissions Avoided fines/penalties ($/yr) Annual value of NOx emission credits ($/yr) -Value of NOx emission credits ($/ton removed) NOx emission credits (tons/yr) -NOx reference lev el for credits (lbs/MBtu) -NOx emission reduction below reference lev el (%) -Unit output (MW) -Unit baseline heat rate (Btu/kW h) -Capacity factor (%) SO2 Emissions Avoided fines/penalties ($/yr) Annual value of SO2 emission credits ($/yr) -Value of SO2 emission credits ($/ton remov ed) SO2 emissions reduction (tons/yr) -Baseline SO2 emissions (lbs/MBtu) -Baseline unit heat rate (Btu/kW h) -Unit heat rate improvement (%) -Unit output (MW) -Capacity factor (%)

CO2 Emissions Annual value of CO2 emission credits ($/yr) -Value of CO2 credits ($/ton removed) CO2 emissions reduction (tons/yr) -Baseline CO2 emissions (lbs/MBtu) -Baseline unit heat rate (Btu/kWh) -Unit heat rate improvement (%) -Unit output (MW ) -Capacity factor (%) Annual benefits before diminishing effectiveness factor ($/yr) Diminishing effectiveness factor

ACTUAL ANNUAL BENEFITS ($/yr) FINANCIAL ASSUMPTIONS Book life of the project (default 5 years) Discount rate (%) (default 4.9%)



B-14


Table B-8 Power Plant Optimization Cost-Benefit Analysis--Armstrong 1 Without Coal Distribution Control COSTS


On-line/advisory

Closed-loop


$50,000

$130,000

$170,000


$5,000 $0 $5,000 $0 $0

$5,000 $0 $5,000 $0 $0

$5,000 $0 $5,000 $0 $0

$34,600 $14,600 12 $800 $5,000 $20,000 25 $400 2.0 $0

$46,600 $14,600 12 $800 $5,000 $32,000 40 $400 2.0 $0

$53,000 $17,000 15 $800 $5,000 $36,000 45 $400 2.0 $0

Training ($)

$0

$0

$0


$0

$0

$0

$89,600

$181,600

$228,000


$5,000

$13,000

$17,000


$33,000 $17,000 15.0 $800 $5,000 $16,000 20 $400 2.0

$25,000 $13,000 10.0 $800 $5,000 $12,000 15 $400 2.0

$21,000 $13,000 10.0 $800 $5,000 $8,000 10 $400 2.0

$38,000

$38,000

$38,000

$0 $0 0 $0 $0 0

$0 $0 0 $0 $0 0

$0 $0 0 $0 $0 0

$0

$0

$0

$0

$0

$0

$29,360 10,000 0.250 1.12 171 70.0

$44,040 10,000 0.375 1.12 171 70.0

$58,720 10,000 0.500 1.12 171 70.0

Up Front Costs

Computer software Computer hardware Instrumentation and controls (I&C) Data acquisition system (DAS)

Installation and Calibration ($) Outside technical support total -Number of man days -Average daily rate for outside technical support -Miscellaneous expenses (Trav el, reports, overhead, etc.) In-house technical support total -Number of man days -Average daily rate for in-house technical support -Cost multiplier for ov erhead Pow er plant dow ntime/non-economic dispatch



Outside technical support total -Number of man days -Average daily rate for outside technical support -Miscellaneous expenses (Trav el, reports, overhead, etc.) In-house technical support -Number of man days -Average daily rate for in-house technical support -Cost multiplier for ov erhead

TOTAL ANNUAL COSTS

BENEFITS (Losses) Up Front (one tim e) Benefits NPV of Deferred Costs ($) Deferred costs How long does optimization delay investment? (in months)

NPV of Avoided Costs ($) Avoided costs When would investment have been implemented (in months)



B-15


Table B-8 Cont’d. O&M Impacts ($/yr)

$0 $0 $0

$0 $0 $0

$0 $0 $0

$0 0.0 0

$0 0.0 0

$0 0.0 0

$183,960 5 20.00 70 30

$367,920 10 20.00 70 30

$367,920 10 20.00 70 30

NOx Emissions Avoided fines/penalties ($/yr) Annual value of NOx emission credits ($/yr) -Value of NOx emission credits ($/ton removed) NOx emission credits (tons/yr) -NOx reference level for credits (lbs/MBtu) -NOx emission reduction below reference level (%) -Unit output (MW ) -Unit baseline heat rate (Btu/kWh) -Capacity factor (%)

$0 $47,186 400 118.0 0.450 5.00 171 10,000 70

$0 $70,779 400 176.9 0.450 7.50 171 10,000 70

$0 $94,371 400 235.9 0.450 10.00 171 10,000 70

SO2 Emissions Avoided fines/penalties ($/yr) Annual value of SO2 emission credits ($/yr) -Value of SO2 emission credits ($/ton removed) SO2 emissions reduction (tons/yr) -Baseline SO2 emissions (lbs/MBtu) -Baseline unit heat rate (Btu/kW h) -Unit heat rate improvement (%) -Unit output (MW ) -Capacity factor (%)

$0 $1,442 100 14.42 1.100 10,000 0.25 171 70

$0 $2,163 100 21.63 1.100 10,000 0.38 171 70

$0 $2,884 100 28.84 1.100 10,000 0.50 171 70


$0

$0

$0


$0

$0

$0

$0 0 2,733 208.52 10,000 0.25 171 70

$0 0 4,100 208.52 10,000 0.38 171 70

$0 0 5,466 208.52 10,000 0.50 171 70

$261,948 0.50

$484,901 0.75

$523,895 0.90

$130,974

$363,676

$471,506

Book life of the project (default 5 years)

5.0

5.0

5.0

Discount rate (%) (default 4.9%)

10.6

10.6

10.6

$92,974 $347,107 $17,920

$325,676 $1,215,875 $36,320

$433,506 $1,618,445 $45,600

$257,507 84 11.6 2.9

$1,034,275 159 6.7 5.7

$1,390,445 170 6.3 6.1




Benefits Due to Emission Reductions

CO2 Emissions Annual value of CO2 emission credits ($/yr) -Value of CO2 credits ($/ton removed) CO2 emissions reduction (tons/yr) -Baseline CO2 emissions (lbs/MBtu) -Baseline unit heat rate (Btu/kW h) -Unit heat rate improvement (%) -Unit output (MW) -Capacity factor (%) Annual benefits before diminishing effectiveness factor ($/yr) Diminishing effectiveness factor

ACTUAL ANNUAL BENEFITS ($/yr) FINANCIAL ASSUMPTIONS



B-16


Step 5: Select the Best Optimization Product for Your Application In conclusion, it seems that the closed-loop optimization is the most appropriate and cost-effective option for Armstrong 1. This is true especially in the case of monitoring the coal flow in the individual coal pipes (Case 1), where the total number of control variables is relatively high and continuous control and optimization is needed. If coal flow monitoring is not included, all optimization types are suitable, but closed-loop is the most cost-effective. Selection of specific power plant optimization software will be carried out by Allegheny following the guidance provided in Step 5 of the Guidelines manual. Such evaluation is not described here because of the proprietary nature of the information included.

Case Study 2: Northern States Power Riverside 7 Unit Background Riverside 7 consists of two front wall-fired boilers, vintage 1950s, and a steam turbine/generator. The unit operates near full load and generates approximately 675 ppm (0.90 lbs/MBtu) of NOx emissions. While NSP plans to use NOx averaging to comply with the Clear Air Act Amendment of 1990, system considerations suggest that an additional NOx reduction of 20-40% from Riverside 7 is desirable. Before considering hardware modifications, NSP decided to assess the possibility of using PPO software to achieve this NOx reduction using the Beta Version of EPRI’s Power Plant Optimization Guidelines. The results are revealing in terms of the importance of the tuning and the need to consider site-specific features in the evaluation of optimization options. NSP’s NOx Compliance Plans and Future Needs Northern States Power Co. (NSP) has used a combination of combustion NOx control projects and system averaging to comply with requirements of the Clean Air Act Amendment (CAAA) of 1990. Key NOx control retrofit projects include: x

Sherco 2 (a 725 MW T-fired boiler): retrofitted with low NOx burners and overfire air

x

King (a 550 MW cyclone boiler): to be retrofitted with overfire air in October of 1999, and

B-17

EPRI Licensed Material Case Studies x

Riverside 8 (a 245 MW cyclone boiler): to be retrofitted with overfire air in October of 1999.

While these projects are expected to satisfy CAAA requirements, both at the unit and power system level, NSP would like to have some additional means for controlling NOx emissions as an "insurance policy" for unexpected events which can affect NOx compliance. Such events include: x

forced outage of a nuclear plant would require higher utilization of NSP’s coal-fired plants; hence increasing the total NOx emissions released by the system;

x

NOx reduction at King and Riverside 8 may be less than expected, especially if the units experience waterwall corrosion problems due to reducing atmosphere in the cyclone boilers; and

x

so far, CAAA Title I has not imposed any requirements which can not be met with NSP’s latest NOx compliance program, but it is possible that these requirements may be more stringent in the future.

For this reason, power plant optimization is being considered for a number of units including Riverside 7, Sherco 3, and Black Dog 3 and 4.

Description of Riverside 7 Riverside 7 is a 153 MW coal-fired unit consisting of two wall-fired boilers and one steam turbine. The wall-fired boilers were built by Babcock & Wilcox in the 1950s and burn subbituminous coal. The unit has two pulverizers feeding six burners arranged in two rows (four burner in the bottom row and two in the top). Also, it is equipped with a baghouse and a Westinghouse (WDPF) Digital Control System (DCS). The unit is used for intermediate load, but recently has been operating more as baseloaded. Full load NOx emissions average 675 ppm (0.90 lbs/MBtu). LOI averages less than 1%, but in recent years has been highly variable. Occasionally, this variability is not acceptable to buyers of the flyash and requires that it be trucked 53 miles (90 km from Riverside) to Sherco for disposal.

Evaluation of Power Plant Optimization Software As a first step, it was decided to evaluate the different types of optimization (standalone, on-line/advisory and closed-loop) using the Beta Version of EPRI’s Power Plant Optimization Guidelines. The specific software will be selected later (note: it is not

B-18


included in this paper). The 5 steps outlined in the Guidelines are described in the following paragraphs. Step 1: Quick Tuning and Establishment of Baseline Performance The first step was a quick walk-down/checking of the operating condition of key plant components and control variable set-points. This review reveal the following: x

excess O2 is set at 2.3%; most likely, the boiler can operate at a lower level, but for safety purposes and because of the absence of a CO monitor, 2.3% has been set as a minimum operating level

x

the air registers are set visually by operations. During this quick tuning exercise, the burners were found to be set uniformly (in a scale of 1 to 9, the registers were set at 6)

x

potential parameters affecting NOx emissions and boiler performance which can be changed during a tuning or optimization program include: ¡

primary air to fuel ratio, and

¡

improvement of coal fineness through adjustment of classifiers ball loading.

The first operating adjustment made was to change the settings of the air registers as follows: x

open the registers of the top row from position 6 to 8, and

x

close the registers of the bottom row from 6 to 3.

The result of these changes was an immediate NOx reduction of approximately 8%. Following this quick tuning, baseline NOx emissions were measured at approximately 600 ppm (0.80 lbs/MBtu). In addition, the following steps were identified to improve performance in order of priority: 1. Improvement of coal fineness through mill adjustments 2. Air and coal flow balancing including biasing of air towards the upper elevation of burners 3. Application of optimization software B-19


4. Simple hardware modifications to achieve further NOx emission reduction, if needed. The main hardware modification recommended includes switching two coal pipes (see Figure B-5) to allow some degree of fuel biasing (more fuel to the lower elevation, Mill 1) in addition to air biasing. Such hardware modification is easy and inexpensive and can result in 10-20% NOx reduction.

Present Arrangement Mill 2 Mill 1

Modification Mill 2

Mill 2 Mill 1

Mill 1

Alternative 1

Alternative 2

Figure B-5 Alternative Coal Pipe Arrangements for Improved NOx Control

B-20


Step 2: Objectives for Power Plant Optimization Based on the above, it was determined that the primary objective is to achieve the maximum NOx reduction without adverse impacts on heat rate. A secondary objective was to reduce the LOI variability. Additional parameters which relate to the objective and affect the cost-benefit analysis include: x

Value of NOx emission reduction 190 $/ton4, which is an industry average for LNB retrofits,

x

No emission credit will be provided for SO2 and CO2

x

Project book life: 5 years; and

x

Discount rate: 4.9%.

Step 3: Determine Optimization Potential/Can Optimization Alone Meet Established Objectives? Table B-9 is the result of tailoring Table 3-1 to the specific circumstances of Riverside 7. The following bullets summarize the key observations and assumptions: x

No systematic tuning of the boilers had been performed in the last two years

x

The last tuning had focused on combustion efficiency (heat rate)

x

Air biasing is possible through adjustment of the air register settings

x

Excess air could be reduced from 2.3% to 2.0%; further reduction may be possible, but a CO monitor needs to be added

x

Switching of two coal pipes to allow for fuel biasing is being included.

As shown in Table B-9, the Riverside optimization project scores 191 points (out of 300 points) which Table 3-2 on page 3-24 of the Guidelines manual classifies as a Group B project. Given the fact that the previous tuning was done more than two years ago and

4

EPRI's "Retrofit NOx Controls for Coal-fired Utility Boilers-1996 Update Addendum" (TR-102906-A dendum, May 1997), pp. I-11 and 6-2.

B-21


focused on heat rate improvement, Table 3-2b on page 3-25 provides an even more refined estimate of potential performance improvements:

B-22

x

NOx reduction:

22.5-30.0%

x

Heat rate improvement:



Table B-9 Project Classification to Determine Potential Performance Improvements Due to Optimization—Riverside 7 Criteria Information on the last tune up - When was the unit last tuned? - Tuning Objective/Tuned for: Unit operating flexibility (at full load) - Fuel flow biasing capability (e.g., mill capacity) - Air flow biasing capability - Excess O2 range (min-max) - Burner tilts (for T-fired units) - Air and Gas dampers - PA/Fuel ratio - Other control variables Ability to change equipment settings - Burner settings (e.g., registers, yaw, etc. etc.) - Pulverizer settings (e.g., spring tension; classifier; exit temp - Other equipment settings Hardware modifications - Air distribution modifications - Coal pipe orificing - Mill Modifications - Other TOTAL SCORE (weighted average)

Score

Clarifications

3 Last tuning more than two years ago 2 Focused on combustion efficiency (heat rate) 1 Presently no fuel biasing is possible because the two mills feed both elevations of burne 2 Some air flow biasing possible 1 Potential O2 reduction from 2.3 to 2.0% 1 No tilt avalaible 1 No additional dampers available (except air registers) 1 Plant engineers suggested that the PA/Fuel ratio should not be adjusted 1 No other control variables available 2 Changes in air registers possible 2 Some adjustments possible 1 No additional adjustments possible 1 None 1 None 1 None 3 Switching of coal pipes 191 Group B

B-23


The projections for the various types of optimization are shown in Table B-10. (Note: it is assumed that closed-loop will achieve the upper end of the projected range, the stand-alone will achieve the lower end and the on-line/advisory the average). Table B-10 Expected Performance Improvements with Optimization (Case 1) Optimization Type



Stand-alone

22.50

0.500

On-Line/Advisory

26.25

0.625

Closed-loop

30.00

0.750

Step 4: The Most Cost-effective Type of Optimization All optimization types satisfy the objectives set in Step 2. For this reason, the costeffectiveness of all optimization types will be evaluated to decide which type is most suitable for Riverside 7. Table B-11 is built on the template of Figure 4-1 and based on inputs for the Riverside 7 boiler, shows estimated cost-benefit analysis results for the three optimization types. This analysis takes into account: x

All the up front and annual costs, including in-house technical support needed to install, calibrate and maintain the software,

x

Benefits such as fuel savings due to heat rate improvement, and reduction of NOx and SO2 emissions.

Based on this analysis, closed-loop is the most cost-effective option for Riverside 7.

B-24


Table B-11 Power Plant Optimization Cost-Benefit Analysis—Riverside 7 COSTS


On-line/advisory

Closed-loop


$20,000

$130,000

$170,000


$5,000 $0 $5,000 $0 $0

$5,000 $0 $5,000 $0 $0

$5,000 $0 $5,000 $0 $0

$33,000 $14,600 12 $800 $5,000 $18,400 25 $320 2.3 $0

$44,040 $14,600 12 $800 $5,000 $29,440 40 $320 2.3 $0

$50,120 $17,000 15 $800 $5,000 $33,120 45 $320 2.3 $0

Training ($)

$0

$0

$0


$0

$0

$0

$58,000

$179,040

$225,120


$8,000

$13,000

$17,000


$31,720 $17,000 15.0 $800 $5,000 $14,720 20 $320 2.3

$19,040 $8,000 10.0 $800 $0 $11,040 15 $320 2.3

$15,360 $8,000 10.0 $800 $0 $7,360 10 $320 2.3

$39,720

$32,040

$32,360

$0 $0 0

$0 $0 0

$0 $0 0

$0 $0 0

$0 $0 0

$0 $0 0

$0

$0

$0

$0

$0

$0

$48,036 10,240 0.500 1.00 153 70.0

$60,045 10,240 0.625 1.00 153 70.0

$72,053 10,240 0.750 1.00 153 70.0

Up Front Costs

Computer software Computer hardware Instrumentation and controls (I&C) Data acquisition system (DAS)

Installation and Calibration ($) Outside technical support total -Num ber of m an days -Average daily rate for outside technical support -Miscellaneous expenses (Trav el, reports, overhead, etc.) In-house technical support total -Num ber of m an days -Average daily rate for in-house technical support -Cost multiplier for overhead Power plant downtim e/non-economic dispatch ($)



Outside technical support total -Num ber of m an days -Average daily rate for outside technical support -Miscellaneous expenses (Trav el, reports, overhead, etc.) In-house technical support -Num ber of m an days -Average daily rate for in-house technical support -Cost multiplier for overhead

TOTAL ANNUAL COSTS

BENEFITS (Losses) Up Front (one time) Benefits NPV of Deferred Costs ($) Deferred costs How long does optimization delay investment? (in months)

NPV of Avoided Costs ($) Avoided costs When would investment have been im plemented (in months)



B-25


Table B-11 Cont’d. O&M Impacts ($/yr)

$0 $0 $0

$0 $0 $0

$0 $0 $0

$0 0.0 0

$0 0.0 0

$0 0.0 0

$0 0 0.00 70 0

$0 0 0.00 70 0

$0 0 0.00 70 0

$0 $164,282 190 864.6 0.800 22.50 153 10,240 70

$0 $191,662 190 1008.7 0.800 26.25 153 10,240 70

$0 $219,042 190 1152.9 0.800 30.00 153 10,240 70

$0 $0 0 28.82 1.200 10,240 0.50 153 70

$0 $0 0 36.03 1.200 10,240 0.63 153 70

$0 $0 0 43.23 1.200 10,240 0.75 153 70


$0

$0

$0


$0

$0

$0

$0 0 5,008 208.52 10,240 0.50 153 70

$0 0 6,260 208.52 10,240 0.63 153 70

$0 0 7,512 208.52 10,240 0.75 153 70

$212,318 0.50

$251,707 0.75

$291,096 0.90

$106,159

$188,780

$261,986

Book life of the project (default 5 years)

5.0

5.0

5.0

Discount rate (%) (default 4.9%)

4.9

4.9

4.9

$66,439 $288,442 $11,600

$156,740 $680,482 $35,808

$229,626 $996,916 $45,024

$230,442 95 10.5 4.0

$501,442 68 13.7 2.8

$771,796 82 11.8 3.4




Benefits Due to Emission Reductions NOx Emissions Avoided fines/penalties ($/yr) Annual value of NOx emission credits ($/yr) -Value of NOx emission credits ($/ton removed) NOx emission credits (tons/yr) -NOx reference lev el for credits (lbs/MBtu) -NOx emission reduction below reference lev el (%) -Unit output (MW ) -Unit baseline heat rate (Btu/kW h) -Capacity factor (%) SO2 Emissions Avoided fines/penalties ($/yr) Annual value of SO2 emission credits ($/yr) -Value of SO2 em ission credits ($/ton remov ed) SO2 emissions reduction (tons/yr) -Baseline SO2 emissions (lbs/MBtu) -Baseline unit heat rate (Btu/kW h) -Unit heat rate improvement (%) -Unit output (MW ) -Capacity factor (%)

CO2 Emissions Annual value of CO2 emission credits ($/yr) -Value of CO2 credits ($/ton removed) CO2 emissions reduction (tons/yr) -Baseline CO2 emissions (lbs/MBtu) -Baseline unit heat rate (Btu/kW h) -Unit heat rate im provement (%) -Unit output (MW ) -Capacity factor (%) Annual benefits before diminishing effectiveness factor ($/yr) Diminishing effectiveness factor

ACTUAL ANNUAL BENEFITS ($/yr) FINANCIAL ASSUMPTIONS



B-26


Step 5: Select the Best Optimization Product for Your Application The conclusions reached based on the above analysis are: x

Quick tuning which should be carried out in Step 1 has the potential to reduce NOx emissions by approximately 10-15%. Such tuning involves mainly: ¡ Resetting the air registers in such a way that air biasing towards the top elevation of burners is created, ¡ Tuning of the mills to improve coal fineness, and ¡ Addition of CO monitor to reduce the excess O2 level ,

x

Simple hardware modifications should be considered, if additional NOx reduction is required. For example, switching two coal pipes (see Figure B-6) may reduce NOx by another 10-15% without significant costs and performance penalties,

x

Optimization: employ an optimization program, preferably in closed-loop mode, to achieve up to 30% NOx reduction (10% above and beyond the levels achieved by hardware modifications), 0.50-0.75 percentage point heat rate improvement , and reduction in LOI variability being experienced presently.

Selection of specific power plant optimization software will be carried out by Northern States Power following the guidance provided in Step 5 of the Guidelines manual. Such evaluation is not described here because of the proprietary nature of the information included. The projections for the various types of optimization are shown in Table B-10. (Note: it is assumed that closed-loop will achieve the upper end of the projected range, the stand-alone will achieve the lower end and the on-line/advisory the average).

B-27


C FORMULAS

Fuel Cost Savings Annual fuel cost savings ($/yr) = (876/106) x Baseline Heat Rate (Btu/kWh) x Heat Rate Improvement (%) x Fuel Price ($/MBtu) x Unit Size (MW) x Capacity Factor (%) Value of Change in Unit Availability Value of change in unit availability ($/yr) = Increase in equivalent availability (%) x Value of additional % point ($/yr-%) Value of Change in Unit Output This benefit category quantifies the incremental energy value of increased maximum unit output. No credit is taken for “capacity value” in the system asset sense. If capacity is created, enter the decrease in unit output (maximum continuous rating) as a negative capacity addition. Value of change in unit output ($/yr) = Change in output (MW) x Incremental cost of replacement power ($/MWh) x Capacity factor (%/100) x Hours to year conversion factor 8760 x Percentage of operating time affected (%/100) NOx Emissions Annual NOx reduction (tons/yr) = NOx compliance level or level below which NOx credits are being accumulated (lbs/MBtu) x % NOx emission reduction below compliance level due to optimization (%) x Unit output (MW) x Unit heat rate (Btu/kWh) x Unit capacity factor (%) x (876 x 0.5/10 9) Value of NOx emission credits ($/yr) = Annual NOx reduction (tons/yr) x Value of NOx credits ($/ton of NOx removed)

C-1

EPRI Licensed Material Formulas

SO2 Emissions Annual SO2 reduction (tons/yr) = Baseline SO2 emissions (lbs/MBtu) x Baseline heat rate (Btu/kWh) x Heat rate improvement (%) x Unit output (MW) x Unit capacity factor (%) x (876 x 0.5/109) Value of SO2 emission credits ($/yr) = Annual SO2 reduction (tons/yr) x Value of SO2 credits ($/ton of SO2 removed) CO2 Emissions Annual CO2 reduction (tons/yr) = Baseline CO2 emissions (lbs/MBtu) x Unit heat rate (Btu/kWh) x heat rate improvement (%) x Unit output (MW) x Unit capacity factor (%) x Pounds to million tons conversion factor (876 X 0.5/109) Value of CO2 credits ($/yr) = Annual CO2 reduction (tons/yr) x Value of CO2 credits ($/ton of CO2 removed)

C-2

EPRI Power Plant Optimization Guidelines

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