nerc gads benchmarking workshop 2011 (1)
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Using Reliability Data to Improve Power PlantImprove Power Plant
PerformanceNERC-GADS Workshop
presented by Robert R. (Bob) Richwinepresented by Robert R. (Bob) RichwineReliability Management ConsultantRichwine Consulting Group, LLC
Oct 27, 2011
Workshop Agenda
I. Background and Case StudyI. Background and Case StudyII. Common Elements in Successful Programs
A. Awareness PhaseA. Awareness PhaseB. Identification PhaseC. Evaluation PhaseD. Implementation Phase
III. Transforming to a Market-Driven Business gEnvironment
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Background
• From a 2006 Wall Street Journal article
– Business today is awash in data and data hcrunchers
Only a few companies use data as a strategic– Only a few companies use data as a strategic weapon
– The ability to collect, analyze and act on data is the essence of a company’s competitive advantage
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Survey Results in WSJ
• 450 executives; 370 companies; 35 countries; 19 industries
• Identified a strong link between extensive and sophisticated use of analytics and sustained high performancehigh performance
T f i i 5 ti• Top performing companies were 5 times more likely to single out analytics as critical to their competitive edge
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to their competitive edge4
A substantial gap exists between actual and potential performance
Potential Performance
Actual PerformanceActual Performance
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The Worldwide Value of Closing the Gap (WEC estimate)
E i• Economic– US$80 Billion per Year
• Environmental– 1 Billion Tons of CO2 Reduction
per Year and Proportional Reductionsper Year and Proportional Reductions of Other Emissions
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Source of Performance Variation
• Variation of performance due toVariation of performance due to– Technology/mode of operation = 20-25 % – Human factors/management = 75-80 %– Human factors/management = 75-80 %
C fi d b• Confirmed by– Analytical studies (Reference 2)– Practical experiences
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Closing the Gap
Better Use of Reliability Data is a Key Factor in Achieving and Sustaining Top Performance
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NERC-GADS
• The NERC-GADS system is the premier reliability data collection and analysis system worldwidedata collection and analysis system worldwide
• NERC has been collecting power plant reliability data in the GADS format for 30 yearsin the GADS format for 30 years
• An increasing number of international companies have begun using the NERC-GADS system to collect and analyze their plant’s performance
• The World Energy Council has adapted NERC-GADS for international usefor international use
NERC-GADS
• Some companies have used the GADS database in innovative ways to help them achieve topinnovative ways to help them achieve top performance of their generating plants
• NERC created the Generating Availability Trend Evaluation (GATE) working group to analyzeEvaluation (GATE) working group to analyze reliability trends and their causes
• A new NERC-GATE type group is now being organizedorganized
• GATE’s publications can be found on NERC’s website at www.nerc.com
• The World Energy Council has summarized many of• The World Energy Council has summarized many of the GATE studies and has published them, along with other similar studies on its website at www worldenergy orgwww.worldenergy.org
30+ WEC Published Case Studies
• Objective – Demonstrate that the valueObjective Demonstrate that the value of performance data is far greater than the cost of collecting the data plus thethe cost of collecting the data plus the risk of sharing the datawww worldenergy org• www.worldenergy.org• Click on “Performance of Generating Plant”
under “Work Programme”under Work Programme• Click on “Performance of Generating Plant” on
right side of pageright side of page• Click on “Case Studies”
WEC Case Study Topics include the Use of Data in:
• Performance ImprovementB h ki
• Maintenance Planning• Risk Management
• Benchmarking• Configuration
Optimization
• Catastrophic Event Reduction
Optimization• Generation Planning
O ti
• Life Management• Equipment Design
• Operations• Goal Optimization
• Peak Season Performance
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Southern Company ExperienceMay 2004 WEC Case Study (Ref 4)
AlabamaPower
GeorgiaPower
MississippiPower
Gulf
SavannahElectric
GulfPower
Southern Company HeadquartersAtlanta, GA, USA
Southern CompanyCoal-Fired Power Stations
Equivalent Availability Factor (EAF) q y ( )Trend
9 39 5
S o u th e rn C o m p a n y W o rld
8 38 58 78 99 19 3
7 37 57 77 98 1
6 56 76 97 1
1 97 0 19 71 19 72 19 73 1 97 4 1 97 5 1 97 6 19 77 19 78 19 79 1 98 0 1 98 1 19 82 1 98 3 1 98 4 19 85 19 86 19 87 1 98 8 1 98 9 1 99 0 19 91
Reasons for Performance Decline
I bilit t id d t• Inability to provide adequate resources to power stations
• See May 2004 WEC-PGP case study and Ref 4 for detailed discussion ofand Ref 4 for detailed discussion of other reasons including:• Limited use of performance data p
Southern Company Coal-FiredPower Stations
Equivalent Availability Factor (EAF) Trend
9 5
1 0 0
S o u th e rn C o m p a n y W o rld
q y ( )
8 5
9 0
9 5
7 5
8 0
6 0
6 5
7 0
6 019 70 19 71 19 72 19 73 19 74 19 75 19 76 19 77 19 78 19 79 19 80 1 98 1 1 98 2 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91
Reasons for Improved Performance
H i ht d f ti t• Heightened awareness of executive management of the need for performance improvementC it t f dditi l f• Commitment of additional resources for performance improvementS M 2004 WEC PGP t d d R f 4 f• See May 2004 WEC-PGP case study and Ref 4 for other reasons including:• Improved data collection analysis and application of results• Improved data collection, analysis and application of results
Performance Improvement Benefits
Savings of >US$1 billion (1991$) per year equaled:~12 percent of Annual Revenue
~100 percent of Net Income (Profit)
• Annual avoided emissions included seven million tons of CO2e per year at2 p y
NO EXTRA COST!
Availability Improvement at Other Generating Companies
PREPA – Puerto Rico +25%
NEES – USA +13%
ESB – Ireland +10%
ESKOM – South Africa +20%
Observations
E h / t f it t f• Each company/country faces its own set of challenges, constraints, and opportunitiesNo single program is optimal for e er• No single program is optimal for every company/countryThere are common elements within each• There are common elements within each successful program
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Common Elements In Successful Improvement Programs
January – April 2003 WEC Case Studiesy p
Performance Improvement
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Performance Improvement Process Phase 1 - Awareness
• Benchmarking
• Forecasting
• Communications
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AwarenessJanuary 2003 WEC Case Study
• BenchmarkingApril 2002 WEC Case Study– April 2002 WEC Case Study
– August 2002 WEC Case Study– September 2003 WEC Case StudySeptember 2003 WEC Case Study– ASME technical papers (Ref 5,6)
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Unit Level Benchmarking Unit Level Benchmarking The First Step in Improving Plant PerformanceThe First Step in Improving Plant Performance
Wh B h ki ?Why Benchmarking?• Set realistic, achievable goals
Id if b f i l i• Identify best areas for potential improvement• Give advance warning of threats
T d k l d d i ith• Trade knowledge and experience with peers• Quantify and manage performance risks
Create increased awareness of the potential for• Create increased awareness of the potential for and the value of increased plant performance
Reliability Benchmarking Process
Id tif li bilit i bl t d th• Identify reliability variables to measure and the databases required
• Select peer power plants having similar design or modeSelect peer power plants having similar design or mode of operations characteristics
• Compare the candidate power plant’s reliability against these peer plants
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Reliability Benchmarking Process
Identify reliability variables to measure and the• Identify reliability variables to measure and the databases required:
Typical Reliability VariablesTypical Reliability Variables
– Equivalent Availability Factor (EAF)E i l t F d O t R t (EFOR)– Equivalent Forced Outage Rate (EFOR)
– Scheduled Outage Factor (SOF)and increasingly,and increasingly,
– Equivalent Forced Outage Rate Demand (EFORd)
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Benchmarking Process
• Select peer power plants having similar design or mode of operations
h t i ticharacteristics:
Selection Procedure (NERC/Richwine developed)– Selection Procedure (NERC/Richwine developed)• Advanced statistical methodology• Has been applied numerous times over theHas been applied numerous times over the
past 20 + years at companies and countries around the world
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Peer Selection Criteria
Large Population
NERC-GADS Data Base5000 + units
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Peer Selection Criteria
Exact Match
x xxx xx
xx
x xx
xxx
xx
xxx
x x xxx xNumber of Exact Matches 0Number of Exact Matches 0
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Peer Selection Criteria
Exact MatchesLarge Population
Must Balance CriteriaMust Balance Criteria30Richwine Consulting Group, LLC
Peer Selection Criteria
Etc Etc.Etc.
Etc.VintageFiring
Fuel
ASSUME
Etc.g
DutyBoilerManufacturer
EtcAge
Manufacturer
TurbineCriticality
Etc.
Etc. SizeDraft
TurbineManufacturer
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Peer Selection Criteria
Etc Etc.Etc.
Etc.VintageFiring Fuel
ANALYSISEtc.
DutyBoilerManufacturer
EtcAge
Manufacturer
TurbineCriticality
Etc.
Etc. SizeDraft
TurbineManufacturer
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Peer Selection Criteria
Significance TestingSignificance TestingSubcritical Supercritical Cyclic DutyBaseload Duty
EFOR EFOREFOR EFOR33Richwine Consulting Group, LLC
Peer Groups Select Criteria Fossil Units
All Fossil Units
CRITICALITY
SubSuper
CRITICALITY
MODE OF OPERATIONVINTAGE
Cycling<1972 BaseloadSize
Draft TypeFuel
Boiler Mfr.Draft Type
Size
≥1972
Fuel Size
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Does Peer Selection Make a Difference?
SUPERCRITICAL TECHNOLOGY
EARLY VINTAGE RECENT VINTAGE
EFOR(mean) 15.60% 9.68%EFOR(mean) 15.60% 9.68%EFOR(median) 12.17% 8.08%EFOR(best quartile) 8.14% 5.47%
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Does Peer Selection Make a Difference?
EFOR - PLANT A
OLD CRITERIA NEW CRITERIA % differenceOLD CRITERIA NEW CRITERIA % difference (Coal; 100-199MW)
mean 6.47% 5.53% -14%mean 6.47% 5.53% 14%
median 4.78% 5.07% +6%
best quartile 2.65% 3.26% +23%
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Does Peer Selection Make a Difference?
EFOR - PLANT B
OLD CRITERIA NEW CRITERIA % difference(Coal; 800-1300MW)
mean 5.83% 7.63% +31%
Median 4.55% 5.87% +29%
best quartile 2.70% 3.97% +47%
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Reliability Benchmarking Results 30 Peer Units
P it l ti it i• Peer unit selection criteria– Subcritical
Reserve shutdown hours less than 963 hours– Reserve shutdown hours less than 963 hours per year
– Natural boiler circulationNatural boiler circulation– Primary fuel = coal– Single reheatg– Net output factor greater than 85.6%
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Peer Unit EFOR Distribution
100
70
80
90
100
CEN
T
40
50
60
70
ATI
VE P
ERC
10
20
30
40
CU
MUL
A
0
10
0 1 2 3 4 5 6 7 8 9 10 11
EFOR (%)EFOR (%)
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Peer Unit SOF Distribution
1 00
70
80
90
RCEN
T
40
50
60
ULA
TIVE
PER
10
20
30
CU
M
00 5 10 1 5 2 0
S O F ( % )
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Peer Unit EAF Distribution
1 00
70
80
90
RCEN
T
40
50
60
ULA
TIVE
PER
10
20
30
CU
M
07 0 7 5 80 8 5 9 0 9 5 1 0 0
EA F (% )
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Conclusions
• Benchmarking is helping utilities• Benchmarking is helping utilities– Set goals– Develop incentivese e op ce t es– Identify improvement opportunities– Quantify and manage risks– Create increased awareness of the potential for and
the value of increased plant performance
• Proper peer group selection is essential
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Forecasting
Statistics Versus Probability
• Statistics – Yesterday’s actual resultsP b bilit T ’ di t d lt• Probability – Tomorrow’s predicted results
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Forecasting Performance September 2002 WEC Case Study
• Southern Company’s historic plant EFOR’s trends were used as projections
• Comparison of actual EFORs to projected EFORs (statistical process control) showed a need for improved forecasting methodology
• New forecasting methodology was developed using multi-variable regression techniques that showed great potential
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EFORACTUAL - EFORPREDICTED
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Forecasting’s Basic Principle
Past Conditions Future ConditionsPast Conditions
Past Results
Future Conditions
Future ResultsPast Results Future Results
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Predicting EFOR
Most Important Parameters (from statistical l i )analysis)
• Lagging Equivalent Forced Outage Hours• Lagging Service Factor• Current Year Planned Outage Hours• Lagging O&M Spending• Current Year O&M Spendingp g• Fuel Type• Other Factors
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Other Factors47
EFORACTUAL - EFORPREDICTED
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Other Forecasting Example
• New Technology “learning curve” – Supercritical – Ref 7
• November 2002 WEC Case Studyy
• Applies learning curve theory to historic EFORsApplies learning curve theory to historic EFORs to forecast expected improved in new technology performances
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gy p
49
Outage Rates versus Yearof Initial Operation
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Other Forecasting Example
• Estimating a Generating Plant’s Future• Estimating a Generating Plant’s Future Maintenance Cost
• October 2003 WEC Case Study & Ref 8October 2003 WEC Case Study & Ref 8• Uses probabilistic forecasting techniques to
estimate the range of future cost (O&M + g (Retrofit Capital, recurring and non-recurring) and its probability distribution
• Results used by many organizations including operations & maintenance, resource planning, generation planning and financegeneration planning and finance
Communications
• Communications to all stakeholders, especially employees, is vital to clearly show p y p y , ythe “GAP” between your plant’s reliability compared to the best performers in their peer group
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Phase 2 - Identification February 2003 Case Study
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Identification Phase – Part 2Problem Area Identification
Goal – To identify problem areas with best payback potential
• Component Benchmarking
• High Impact – Low Probability Event Reduction –February 2002 WEC Case Study & Ref 9
• Trend Analysis WEC Case Studies:– March 2002 – Peak Season Reliability (Ref 10)y ( )– June 2002 – Availability Following Planned Outages (Ref 11)– December 2002 – Reliability Versus Demand
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Component Benchmarking
• Three options for peer group selection:# 1 U t i f it l l– # 1 – Use components in peer group from unit-level benchmarking peer group analysis (but this will exclude many valid peer components)
– #2 – Perform a similar analysis to that described in the unit-level benchmarking but using component design and operational data for analyzing and selecting peer
( b i d igroups (can become very expensive and time consuming)
– #3 – Use a combination of experience and engineering judgment with unit-level peer group analysis results (recommended)
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Component Benchmarking
• Compare the performance of each /system/equipment to its peer distribution
• The system/equipment with the largest “percentile gap” between its performance and th “b t i l ” i it h ld bthe “best in class” in its peer group should be a high priority system to study
Component UnavailabilitySteam Turbine
S te am T u r b in e U n a va ila b i l ity
5 06 0
7 0
8 0
9 0
1 0 0
Perc
ent
0
1 0
2 0
3 0
4 0
5 0
Cum
ulat
ive
0 2 4 6 8 1 0 1 2 1 4 1 6
Un ava ilab ilit y (P e r ce n t )
Component UnavailabilityAll Boiler Tube Leaks
B o ile r T u b e L e a k U n a va ila b i l ity
5 06 0
7 0
8 0
9 0
1 0 0
Perc
ent
0
1 0
2 0
3 0
4 0
5 0
Cum
ulat
ive
0 2 4 6 8 1 0 1 2 1 4 1 6
Un ava ilab ilit y (P e r ce n t )
Component BenchmarkingBoiler Tube Leaks vs. Steam Turbine
B o ile r Tu b e s an d S tm Tu rb in e U n av a ila b ility
8 0
1 0 0
cent
2 0
4 0
6 0
Cum
ulat
ive Pe
rc
0
0 2 4 6 8 1 0 1 2 1 4 1 6
Un av a ilab ilit y (P e r ce n t )
C
S tea m Tu r b i n e B o i l e r Tu b es
Component BenchmarkingBoiler Tube Leaks vs. Steam Turbine
C t U l d I tComponent Unplanned ImprovementUnavailability Potential Rank
BT leaks 1% #1
Turbine 0.5% #2Turbine 0.5% #2
Component BenchmarkingBoiler Tube Leaks vs. Steam Turbine
B o ile r Tu b e s an d S tm Tu rb in e U n av a ila b ility
8 0
1 0 0
cent
2 0
4 0
6 0
Cum
ulat
ive Pe
rc
0
0 2 4 6 8 1 0 1 2 1 4 1 6
Un av a ilab ilit y (P e r ce n t )
C
S tea m Tu r b i n e B o i l e r Tu b es
Component BenchmarkingBoiler Tube Leaks vs. Steam Turbine
Improvement Potential Ranking
Component Unplanned Improvement Peer ImprovementUnavailability Potential Rank Percentile Potential RankUnavailability Potential Rank Percentile Potential Rank
BT leaks 1% #1 50th #2
Turbine 0.5% #2 80th #1
Component Unavailabilityp yWaterwall Tube Leaks
B o ile r W a te rw a ll T u b e L e a k s
5 0
6 0
7 0
8 0
9 0
1 0 0
Per
cent
0
1 0
2 0
3 0
4 0
5 0
ACu
mul
ative
0 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2
Un ava ilab lit y (P e r ce n t )
Component Benchmarking p gNuclear Reactor Only Nuclear Reactors Only Vs. Total Nuclear Unitsy
2005-2009
90100
60708090
t, %
Total Unit
20304050
Perc
ent Total Unit
Reactor Only
010
0 5 10 15 20 25 30
U l d O t F t (UOF)Unplanned Outage Factor (UOF)
Component Benchmarking p gBalance of Plant
Balance of Plant Vs. Total Nuclear Units2005-2009
90100
607080
90
t, %
Total Unit
20304050
Perc
ent Total Unit
Balance of Plant
010
20
0 5 10 15 20 25 30
Unplanned Outage Factor (UOF)
Component Benchmarking p gHydro Turbines
Hydro Turbines Only Vs Total Hydro Units (No PS)Hydro Turbines Only Vs. Total Hydro Units (No PS)2005-2009
90
100
60
70
80
90
t, %
Total Unit
20
30
40
50
Perc
ent Total Unit
Turbine only
0
10
0 10 20 30 40 50 60 70 80 90 100
Unplanned Outage Factor (UOF)Unplanned Outage Factor (UOF)
Component Benchmarking B l f Pl tBalance of Plant
Balance of Plant Only Vs. Total Hydro Units (No PS)2005-2009
90100
50607080
ent,
%
Total Unit
10203040
Perc
e Balance of Plant
010
0 10 20 30 40 50 60 70 80 90 100
Unplanned Outage Factor (UOF)
Component Benchmarking p gGas Turbines in Combined Cycle Blocks
Combined Cycle Blocks Vs. CC Gas Turbinesy2005-2009
90100
506070
80
nt, % Block
1020
304050
Perc
e GT Only
010
0 5 10 15 20 25 30 35 40
Unplanned Outage Factor (UOF)
Component Benchmarking p gHeat Recovery Steam Generators
Combined Cycle Blocks Vs HRSGsCombined Cycle Blocks Vs. HRSGs2005-2009
90100
607080
90
nt, % Block
20304050
Perc
en HRSG
010
0 5 10 15 20 25 30 35 40
Unplanned Outage Factor (UOF)
Component Benchmarking p gCC Steam Turbines
Combined Cycle Blocks Vs. Steam Turbines200 20092005-2009
90100
50607080
ent,
%
Block
St T bi
10203040
Perc
e Steam Turbine
00 10 20 30 40
Unplanned Outage Factor (UOF)
Is Your Power Plant Headed for a HILP?? How to Avoid, Detect or Mitigate High Impact – Low Probability (HILP) Events
R b t Ri h i Ri h i C ltiRobert Richwine – Richwine Consulting Michael Curley – NERCG. Scott Stallard – Black & Veatch
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What is a HILP?
• High Impact – Low Probability Event• Happens infrequently but results in extended
unplanned outages• Sometimes called “First Time Event”
(at least the first time it has happened at your ( pp yplant)
• Similar to the concept described in Nassim pTaleb’s book “The Black Swan: The Impact of the Highly Improbable”
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Typical HILPsTypical HILPs
• Turbine Water Induction• Boiler Explosions• Generator Winding Failures• Many, many others
HILP Reduction Programs
• Some companies have successfully reduced their HILP frequencies and/or magnitudes with a formal HILP Reduction P i NERC GADS d tProgram using NERC-GADS data.
• NERC-GADS database contains ~30 years of detailed design and reliability data from
5 100 ti it ith idover 5,100 generating units with a wide variety of technologies.
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HILP Effect on Forced Outage Rate (FOR)
• FOR made up of two type of events– Routine expected events with small/medium
outage consequences– Unexpected major events with large outageUnexpected major events with large outage
consequences
• Should separate these two elements of FOR when benchmarking reliability and establishing reliability improvementestablishing reliability improvement programs
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Benchmarking two units’ Forced Outage Rate (FOR) - Example
Unit A Unit B
FOR 10% 10%FOR 10% 10%Fewer, smaller
events but 1
Type of OutagesMany small
events
events but 1 major event of 3 weeks lengthType of Outages events 3 weeks length
"Normal" FOR 10% ~4%
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Benchmarking two units’ Forced Outage Rate (FOR)
Implications1) The two units have had very different failure
modes2) We should adapt our benchmarking analysis
and improvements efforts to account for th diffthese differences.
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HILP Reduction Program
• Step 1 – Select the best peer group for benchmarking against your unity
• Step 2 – Find the peer group’s HILP contribution to FOR or EFOR and compare to your unit’s HILP contributionSt 3 P i iti th ’ HILP bl• Step 3 – Prioritize the peer group’s HILP problem areas
• Step 4 – Review GADS root cause information• Step 5 – Assess your plant’s susceptibility to HILPs• Step 5 – Assess your plant s susceptibility to HILPs• Step 6 – Identify options to address HILPS• Step 7 – Evaluate and select HILP reduction optionsp p• Step 8 – Track results of implemented options, compare to
expectations and feedback into program to improve the processprocess
Step 1 – Select Peer Group
• It is vital to select the best peer groupIt is vital to select the best peer group• You don’t want to be comparing apples to
oranges• Actually, the best we can usually do is
compare apples to oranges – at least they are b th f itboth fruit
• If you don’t go through an valid peer selection process you might be comparing apples toprocess you might be comparing apples to zebras
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Step 2 – Compare Unit to Peer Group’s HILP Contribution to FOR
• Using NERC’s pc-GAR software calculate FOR• Using NERC’s pc-GAR-MT software determine
the number of full forced outage hours with outage durations greater that the value “you”outage durations greater that the value you define as a HILP (typically greater than 1 week or longer)g )
• Using the HILP full forced outage hours calculate the FOR due to HILPs
• Compare the unit’s HILP contribution to FOR to its peer groups
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• Repeat for your company’s fleet 80
Step 3 – Prioritize the Peer Group’s HILP Problem Areas
• Using pc-GAR-MT and excluding non-HILP t ( ti f th ft ) ilevents (an option of the software) compile a
frequency chart of HILP cause codes that the peer group has experiencedthe peer group has experienced
• Use the frequency chart to focus on the most likely HILP areas for your unity y
• Consider exporting the files from pc-GAR-MT to a spreadsheet for easier manipulation and more detailed analysis as well as graphical reports
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Step 4 – Review GADS Root Cause Data
• GADS input contains an optional 80 character free-format data field, often containing valuable data regarding the outages.Alth h t tl il bl i GAR• Although not currently available in pc-GAR or pc-GAR-MT, Mike Curley, Manger of NERC-GADS Services can advise you on how to retrieve thisServices, can advise you on how to retrieve this information.
• Reviewing this data for HILP events can indicate gthe root causes of events that your unit’s peer group has experienced and can point you in di ti f i it’ tibilit
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directions for assessing your unit’s susceptibility to those HILPs. 82
Step 5 – Assess Your Unit’s Susceptibility to HILPs
• HILP susceptibility is usually the result of several factors occurring together
• Assessing HILP risk must rely on a structured process focusing on if these factors could exist
• Catalogue key HILP events and the circumstances th t ld i d th HILPthat could induce the HILP
• Evaluate your unit to determine if these circ mstances are present s ch as eq ipmentcircumstances are present such as equipment condition, O&M experiences & practices, QA, etc.
• Create a scorecard to quantify the level of HILPRichwine Consulting Group, LLC
• Create a scorecard to quantify the level of HILP risk 83
Step 6 – Identify Options to Address HILPs
• HILP reduction options are usually very specific to the issue
• HILP reduction options should consider ways to:– Prevent the HILP– Detect the HILP event early so as to minimize
downstream damageMitigate the impact of an undetected HILP– Mitigate the impact of an undetected HILP
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Step 7 – Evaluate and Select HILP Reduction Options
• Sufficient information should be gathered to be able to forecast the effect of each option
• An economic analysis for each option should be done to: – Justify– Time
P i iti– Prioritize• Using the option evaluations and considering
the fact of limited company resources (timethe fact of limited company resources (time, money, manpower) the best set of options should be chosen for implementation
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should be chosen for implementation85
Step 8 – Track Results, Compare to Expectations & Feedback
• Monitor the actual results of each implemented HILP improvement option and compare to expected results
• Compare the fleet’s FOR trend due to HILPs over time
• Feedback successes and failures into the HILP reduction program to learn from past
i d i thexperiences and improve the process
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Conclusions & Recommendations
HILPs Happen!!• No power plant in immune to HILPs• While your staff must react to the “problems
of the day” some resources should be devoted to searching for cost-effective ways t t d t t iti t HILPto prevent, detect or mitigate HILPs
• Addressing HILP causes and seeking l ti “b f HILP ” isolutions “before a HILP occurs” is a proven
way to move from a fire-fighting to pro-active style of management
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style of management87
Trend Analysis
• EFOR Following Planned Outages– WEC June 2002 Case Study– NERC-GATE Study
R f 11– Ref 11
EFOR Following Planned Outages
Week Following Schedule Outage
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EFOR Following Planned Outage Trend
• If your plants exhibit this trend you can seek y p ycost-effective ways to reduce this unreliability
• If you cannot find ways to reduce the problem, you can incorporate this tendency p , y p yinto the dispatch optimization process (perhaps by not scheduling outages at two major units back-to-back or some other planning/scheduling method)
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Trend Analysis
• Forced Outage Rate versus Demand• WEC December 2002 Case Study
Forced Outage Rate Versus Demand Trend
Forced Outage Rate Versus Demand Trend
• High Output Factor (maximum generation most of the time) units have fewer failures but take more time to repair
• Low Output Factor (often generating at minimum and load-following) units have morefailures but take less time to repair
• If your staff is aware of these trends they may have ideas to address these issues
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Trend Analysis
• Peak Season ReliabilityWEC M h 2002 C St d– WEC March 2002 Case Study
– NERC-GATE Study– Based upon a study originally performed at Southern p y g y p
Company that resulted in a 1% reduction in its reserve margin criteria with no reduction in customer service reliability (a $100 million savings)
• Aging Versus Reliability– WEC July 2004 Case Study
• Aging or Vintage – which is most responsible for differences in boiler tube leak rates?– WEC November December 2003 Case StudyWEC November, December 2003 Case Study
Identification Phase - Part 2 Solution Option Identification
For Each Problem Area
• Conduct Failure Modes and Effects AnalysisD t i C• Determine Consequences
• Identify Technologically Feasible AlternativesPl t & G l Offi St ff– Plant & General Office Staff
– Successful implementations at other plants– Vendor recommendationsVendor recommendations– Research organizations recommendations (EPRI)– Other outside sources
Common Elements Phase 3 - EvaluationMarch 2003 Case Study
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Retrofit Capital ProjectEvaluation Process
• Elements of an Evaluation Analysis1) IMPACT A di ti f diff i f t l t– 1) IMPACT – A prediction of difference in future plant performance if the project is implemented versus if it is not implemented (this is the primary place in an evaluation
h NERC GADS i i l bl )where NERC-GADS is invaluable).
– 2) WORTH of PERFORMANCE IMPROVEMENT – An estimate of the value to the company resulting from a change in the unit’s performance
– 3) COST – The total budget cost including equipment procurement and installation costs and should include all financing chargesfinancing charges
Capital Project Evaluation Process – Typical Impacts
• Future with/without the project (positive/negative)– Availability– Availability– Efficiency– O & M Savings (or increased cost)
A ili P R i t– Auxiliary Power Requirements– Maximum or Minimum Capacity– Environmental– Other quantifiable impacts– Intangibles
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Impact Data Sources
• Knowledgeable plant and support staff• Engineering staff• Plant data !!! (NERC-GADS)• Industry data !!! (NERC-GADS)• Manufacturers and consultantsManufacturers and consultants• Other projects results• Test Results
Oth• Other
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Capital Project Evaluation Process
• Justification• Justification• Timing
P i iti ti• Prioritization
Capital Project Evaluation Process Part 1
• Justification - The first (and easiest)Justification The first (and easiest) obstacle a project must pass– Addresses the question “Are the total Benefits
f thi j t t th it t t l C t ?”of this project greater than its total Costs?”– A variety of financial terms can be used
• Net Present ValueNet Present Value• Internal Rate of Return• Payback Period
Life Cycle Benefit to Cost Ratio• Life Cycle Benefit to Cost Ratio• Others
Capital Project Evaluation Process Part 2
• Timing – The second obstacle a project must overcomeovercome– Addresses the question “If a project is justified, when
should it be implemented”.– Many “wear-out” project need to have this analysis
performed based on their technical risk profile – All projects should be timed based on their economicAll projects should be timed based on their economic
risk profile
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Capital Project Evaluation Process Part 3
• Prioritization - The third (and hardest)Prioritization The third (and hardest) obstacle a project must overcome
– Addresses the question “ If the company does not have all of the resources (money, time, manpower) necessary to implement all of the justified projects that should be done this year, which projects will hurt the least to delay?”which projects will hurt the least to delay?
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Operations & MaintenanceEconomic Decision Analysis
• Company’s business economics applied to day-to-day O&M decisions
• Helps identify the best economic option for recovering from abnormal conditionsHelps identif the best economic option for• Helps identify the best economic option for establishing normal O&M programs
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O&M Decision Analysis
• Problem solution steps
– Define problem and identify viable options
– Quantify technical consequences of options
– Combine technical consequences with company’s economics
– Evaluate results and incorporate into decision
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O&M Decision ExampleLeaking Feedwater Heater
P blProblem– Tube failure in 7A feedwater heater
– Requires isolation of 6A & 7A heaters
– 1% efficiency loss during isolation
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O&M Decision ExampleLeaking Feedwater Heater
• Solution Options 1) Remove unit from service immediately, locate and plug leaking
tube
2) Wait until weekend to repair
3) Wait until next planned outage and imbed repair
4) Wait until next forced outage of sufficient duration to imbed4) Wait until next forced outage of sufficient duration to imbed repair
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O&M Decision ExampleLeaking Feedwater Heater
• ConsequencesConsequences– Option 1- repair immediately
• 48 hour outage during high demand periodg g g p• Overtime labor cost of $1000• Start-up cost of $20,000
T t l t $217 000• Total cost = $217,000
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O&M Decision ExampleLeaking Feedwater Heater
• ConsequencesConsequences– Option 2- repair during weekend
• 48 hour outage during lower demand period• Overtime labor cost of $1000• Start-up cost of $20,000• 1% efficiency penalty until weekend1% efficiency penalty until weekend• Total cost = $137,000
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O&M Decision ExampleLeaking Feedwater Heater
• Consequences• Consequences– Option 3 -repair during next planned outage
• 1% efficiency penalty until next planned outage1% efficiency penalty until next planned outage• No outage cost since repair will be embedded in
planned outageT t l t $202 000• Total cost = $202,000
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O&M Decision ExampleLeaking Feedwater Heater
• Consequences– Option 4- repair during next forced outage
1% ffi i lt til t 48 h t• 1% efficiency penalty until next 48 hour outage– uncertain when next 48 hour outage will occur– NERC-GADS data for your unit & peer units can help
quantify the uncertainty range
• Total cost range = $0 - $202,000Total cost range $0 $202,000– maximum cost of $202,000– “break even” point in 8 weeks
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O&M Decision ExampleLeaking Feedwater Heater
• What would YOU decide?– Option 1 (repair now) - $217,000
O ti 2 ( i d i k d) $137 000– Option 2 (repair during weekend) - $137,000– Option 3 (repair during P. O.) - $202,000
Option 4 (repair during F O ) $0 $202 000– Option 4 (repair during F.O.) - $0-$202,000
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O&M Decision ExampleLeaking Feedwater Heater
• If the same event happened at a different time the following economic results could happen:– Option 1- repair now $ 75,000– Option 2- repair during weekend $ 90,000– Option 3- repair during next PO $450,000Option 3 repair during next PO $450,000– Option 4- repair during next FO $0-$450,000
N h t ld d id ???Now what would you decide???
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Operations & MaintenanceEconomic Decision Analysis
• Applications
– Maintenance• Reactive (e g planned outage extension)• Reactive (e.g. planned outage extension)• Proactive (e.g. condition directed maintenance)
– Operations• Reactive (e.g. tube leaks)• Proactive (e.g. pump operations)Proactive (e.g. pump operations)
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Common Elements Phase 4 Implementation April 2003 Case Study
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Implementation
• Project choice (economic plus intangibles)j ( p g )• Financing• Goal selection• Monitor actual results and compare against
expected results
Awareness
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Implementation
• Goal Setting Case Studies• Goal Setting Case Studies
– May & June 2003 – Are Reliability MeasuresMay & June 2003 Are Reliability Measures Unreliable?? (Ref 12)
– July 2003 – Planned vs. Unplanned Outages -Effects on Goals (Ref 13)
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Are Reliability Measures Unreliable?
• Factors – EAF, FOF, etc.Factors use the entire time period as the
denominator without regard to unit demand
EXAMPLE: Peaking Gas Turbine100 hrs/year demand00 s/yea de a d25 forced hours during demandEAF = 99.71% FOF = 00.29%
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Are Reliability Measures Unreliable?
• FOR EFOR• FOR, EFORIn Example
FOR = EFOR = 25%FOR = EFOR = 25%
In reality the GT is likely to have had many moreIn reality the GT is likely to have had many more FOH reported since GADS counts all forced outage hours, not just ones during demand periods. Therefore act al EFOR statistics are m ch higherTherefore, actual EFOR statistics are much higher, often 60% +.
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Equivalent Forced Outage Rate –Demand (EFORd)
• Markov equation developed in 1970’s• Markov equation developed in 1970 s• Used by the industry for many years
PJM Interconnection (20 years)– PJM Interconnection (20 years)– Similar to that used by the Canadian Electricity
Association (20 years)Association (20 years) – Being use by the New York ISO, ISO New
England, and California ISO.– Now a part of IEEE standard 762 & NERC-GADS
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EFORd Equation:
EFORd= [f(FOH) + fp(EFDH)] * 100%EFORd [f(FOH) + fp(EFDH)] 100%[SH + f(FOH)]
Where: f = (1/r)+(1/T) (1/r)+(1/T)+(1/D)(1/r)+(1/T)+(1/D)
fp = SH/AHr= FOH/(# of FOH occur.)r FOH/(# of FOH occur.)T= RSH/(# of attempted Starts)D= SH/(# of actual starts)
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EFORd Concept
• Equation is complex, but concept is simpleO “• Reported Forced Outage Hours are “reduced”
• Reduction % is the ratio of Reserve Shutdown Hours to the Service Hours in the time periodHours to the Service Hours in the time period
• This is an approximation (since actual demand hours are not collected by NERC)demand hours are not collected by NERC) that estimates the hours of forced outage during demand periods
• Advantage – We can calculate historic EFORd without collecting new data!!!
Example of EFORd
EFOR vs EFORd
30 0035.0040.0045.00
, %
EFOR range from 3 9 to 42 4%
10 0015.0020.0025.0030.00
OR
& E
FOR
d, EFOR, range from 3.9 to 42.4%
EFORd, range from 3.9 to 10.6%
0.005.00
10.00
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
21.7
0
EFO
762
712
662
612
562
512
462
412
362
312
262
212
162
112 62 12
Reserve Shutdown Hours
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EFOR - demand
• For units with zero Reserve Shutdown HoursFor units with zero Reserve Shutdown Hours (RSH), EFOR and EFORd have the same value.
• For units with high RSH, EFOR will have an unrealistically high value compared to EFORd
• Because EFORd comes closer than EFOR to ti th it’ i bilit t drepresenting the unit’s inability to produce
power when needed, I recommend that EFORd be used for all generating units in your fleetbe used for all generating units in your fleet
Goal SettingPlanned versus Unplanned Outages
WEC JULY 2003 Case Study & Ref 13
F t d f l d t l d• For every extra day of planned outage, unplanned outages only were reduced by 0.6 of a day
• This suggests that planned outages should be gg p gminimized in order to maximize availability
• However, planned outages almost always occur during the non-peak season when financial consequences arethe non-peak season, when financial consequences are much lower by as much as 75% (compared to an average peak season day)Th f th t t th t ill lt i th l t t• Therefore, the strategy that will result in the lowest cost of electricity is to maximize planned outages (within reason) so as to minimize the expensive forced outages
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Implementation
• Monitor Actual Results and CompareMonitor Actual Results and Compare Against Expected Results– September 2002 Case Study – Predicting Unit p y g
Reliability• Feedback Into Awareness, Identification &
Evaluation Phases
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Common Elements January–April 2003 Case Studies
P f Performance Improvement
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Performance Improvement Programs
• Successful Performance Improvement Programs (PIP) utilize a systemic approach to improving plant(PIP) utilize a systemic approach to improving plant performance, tailored to each company’s unique set of constraints and opportunities
• There are many diverse elements that must be integrated together
Th NERC GADS d t t i k t f• The NERC-GADS data system is a key aspect of many of these elements
GADS in Successful Performance Improvement Programs
• Awareness– Unit BenchmarkingUnit Benchmarking– Forecasting– Communications
• IdentificationIdentification– Component Benchmarking– HILP Benchmarking– Trend AnalysisTrend Analysis
• Evaluation– Proposed Project’s Impact
• ImplementationImplementation– Goal Selection– Results Monitoring and Comparison to Expectations– Feedback into ProcessFeedback into Process
The Future
Transforming to a Market-Driven Business Environment
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The Future Is Not What It U d TUsed To BBe
IncreasingIncreasing CompetitionIs The FutureOf O I d tOf Our Industry
Past Business Environment
Regulated with Suppressed Competition
Basic Equation was Cost (prudent) + profit (mandated) = Price
Management Decision Focus was toAVOID RISK!!AVOID RISK!!
Evolving Business Environment
Market-Driven with Increased Competition
Basic Equation is Price (market) – Cost (total) = Profit
Management Decision Focus is toIdentify Quantify and Manage RiskIdentify, Quantify and Manage Risk
Example 1 of Reward/Risk Decisions
• You are playing a video poker “jacks or better” gamep y g p j g• You bet $5• You are dealt the 10, jack, king and ace of hearts and the
queen of spades• The reward for a straight is $20• The reward for a royal flush is $2000• Should you
1) keep the five cards you are dealt for a sure $20 payoff?– 1) keep the five cards you are dealt for a sure $20 payoff? – 2) discard the queen of spades, hoping for the queen of hearts
for a possible $2000 payoff?
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Example 1 of Reward/Risk Decisions
Video poker example analysis
Risk = $5RewardReward
Option 1 - $20 @ 100% probability = $20.00Option 2 - $2000 @ 1/47 probability = $42.55
Reward/RiskOption 1 - $20/$5 = 4Option 2 $42 55/ $5 = 8 51 (Actually better sinceOption 2 - $42.55/ $5 = 8.51 (Actually better since other winning cards could be drawn; i.e. a different heart for a flush or a different queen for a straight)
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Example 1A of Reward/Risk Decisions
• You are playing a video poker “jacks or better” gamep y g p j g• You bet $5• You are dealt the 9, 10, jack, king of hearts and the
queen of spades• The reward for a straight is $20• The reward for a straight flush is $250• Should you
1) keep the five cards you are dealt for a sure $20 payoff?– 1) keep the five cards you are dealt for a sure $20 payoff? – 2) discard the queen of spades, hoping for the queen of hearts
for a possible $250 payoff?
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Example 1A of Reward/Risk Decisions
Video poker example analysis
Risk = $5Reward
$ $Option 1 - $20 @ 100% probability = $20.00Option 2 - $250 @ 1/47 probability = $5.32
Reward/RiskReward/RiskOption 1 - $20/$5 = 4Option 2 - $5.32/ $5 = 1.06 (Actually, slightly better since other winning cards could be drawn; i e asince other winning cards could be drawn; i.e. a different heart for a flush or a different queen for a straight)
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Example 2 of Reward/Risk Decisions
• You are at a horse track with $100 in your pocket
• Your instructions are to bet $10 on one horse to win in each of 10 raceseach of 10 races
• You will be graded on how many times you cash a winning ticket (this is analogous to a regulated, risk-adverse business environment)
What is your best betting strategy??
Horse Track Reward/Risk ExampleRisk-Adverse Winning Strategy
Below are the horses in the first race and the odds that they will win the race (as set by the track handicapper)( y pp )
Horse OddsSecretariat (favorite) 2-1Whirlaway 4-1yCitation 5-1Gallant Fox 7-1Alysheba 8-1Alysheba 8 1Seattle Slew 10-1Northern Dancer 12-1Swaps 15-1Swaps 15 1War Admiral 17-1Aristides 20-1
Which horse would you bet on?Which horse would you bet on?
Horse Track Reward/Risk ExampleRisk-Adverse Winning Strategy
• As the famous sportswriter, Grantland Rice said, the race doesn’t always go to the swiftest, but that’s therace doesn t always go to the swiftest, but that s the way to bet!
• The best strategy is maximize the number of times you pick a winning horse is to “bet the favorite”!pick a winning horse is to bet the favorite !
• This virtually guarantees that you will cash a winning ticket the most times (versus other betting options)
• However you will almost certainly walk out of the track• However, you will almost certainly walk out of the track with less than $100 in your pocket (the total payoff on the times when the favorite wins is not enough to counteract the times it doesn’t win). But how muchcounteract the times it doesn t win). But how much money you end up with is not the way you will be graded, so you don’t care.
Horse Track Reward/Risk ExampleRisk-Manage Winning Strategy
• This time you will be graded on how much f 10money you have at the end of the 10 races
• In this case you must first “handicap” ( ti t th t dd ) h h ’ h(estimate the true odds) each horse’s chance of winning its race using all available and relevant data about each horse such as pastrelevant data about each horse, such as past performance, previous workout times, jockey’s success rate track conditions etcjockey s success rate, track conditions, etc.
• But you will not simply bet on the horse with the best chance of winning its race!!the best chance of winning its race!!
Horse Track Reward/Risk ExampleRisk-Manage Winning Strategy
• Instead, you will now compare each horse’s “ f “ ff“true odds” of winning against the “pay-off” as noted on the tote board just prior to the raceY h ld th b t th h ith th• You should then bet on the horse with the highest pay-off to true odds ratio – the Reward to Risk ratioto Risk ratio
Horse Track Reward/Risk ExampleRisk-Management Winning Strategy
Below are the horses in the first race and the odds that they will win the race (as set by the track handicapper) and the payoff rates (as set by the betting
public)public)Horse Odds Payoff Reward/Risk Ratio
Secretariat (favorite) 2-1 1.5/1 0.75/1Whi l 4 1 6/1 1 5/1Whirlaway 4-1 6/1 1.5/1Citation 5-1 10-1 2/1Gallant Fox 7-1 10-1 1.43/1Alysheba 8-1 4-1 0.5/1ySeattle Slew 10-1 17-1 1.7/1Northern Dancer 12-1 20-1 1.67/1Swaps 15-1 25-1 1.67/1War Admiral 17 1 30 1 1 76/1War Admiral 17-1 30-1 1.76/1Aristides 20-1 35-1 1.75/1
Which horse would you bet on?
Horse Track Reward/Risk ExampleRisk-Management Winning Strategy
• Example – You estimate that the horse with the best chance of winning g
(Secretariat) will win one out of two times (against this field of horses on this track on this night)
– However, since many people are betting on this horse to win, the payoff will only be 1.5-1 if it winsp y y
– Therefore, since the reward to risk ratio is only 0.75, this would not be a good bet
– Another horse that you have handicapped (Citation) at 5-1 has a payoff of 10-1 since not many people bet on itof 10 1 since not many people bet on it
– Therefore, its reward to risk ratio is 10/5 (or 2/1) making it an excellent bet
– You should anticipate having $200 in your pocket after 10 races if you bet on Citation ($100 profit) but only $75 if you bet on Secretariat ($25bet on Citation ($100 profit) but only $75 if you bet on Secretariat ($25 loss)
– This is actually how people make a living at a horse track – they are betting that they can make better choices of where to invest their money than the other people at the track that daymoney than the other people at the track that day
Power Plant Managementusing Reward/Risk Decision-Making
• Identify multiple solution options for any particular issueparticular issue
• Use all relevant data (especially NERC-GADS data for reliability effects) to estimate eachdata for reliability effects) to estimate each option’s impact
• Estimate each option’s reward/risk ratio p• Consider Reward/Risk Ratio in decision-
making• Compare results against expectations and
improve future decision-making
Need Better
• Key Performance Indicators (KPI)– EFOR (demand)( )– Commercial Availability
• Un-weighted• weighted
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Market-Based KPIsMay & June 2003 Case Studiesy
• Demand EFOR – EFOR (demand)• Demand EFOR – EFOR (demand)– Developed for non-base loaded units– Approximates the reliability of a unit during
demand periodsdemand periods
• Commercial Availabilityy– Un-weighted – measures a unit’s availability
only during demand periods– Weighted – measures a unit’s availability onlyWeighted measures a unit s availability only
during demand periods and “weights” each hour’s impact by the unit’s net value during that hour
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Commercial Availability
• Commercial Availability (CA) is a relatively new y ( ) yperformance metric that has become very important at many market-based generating companies and other companies who wish to minimize their cost of electricitycompanies who wish to minimize their cost of electricity
• CA combines the generating plant’s technical availability with the economic consequences of its availability (or unavailability)
• For more information see the Synopsis of the 2004 WEC-PGP paper “Performance of Generating Plant: NewPGP paper Performance of Generating Plant: New Realities, New Needs” published on the WEC website and the May and June 2003 WEC-PGP case studies
Why was Commercial Availability Why was Commercial Availability Created?Created?
• Traditional availability indicators such as EquivalentTraditional availability indicators such as Equivalent Availability Factor are sometimes inadequate or even misleadingA il bilit i di t d d th t id d• An availability indicator was needed that provided a direct linkage between the plant’s availability and the company’s electricity costs (or profits)p y y ( p )
• Commercial Availability (CA) was created to meet that need
Why are Traditional Availability Why are Traditional Availability Indicators Sometimes Inadequate?Indicators Sometimes Inadequate?
• There are often large differences in the economic consequences of an unexpected outage at a power plantp
• They economic consequences vary from season to season, day to day, and from hour to hour.
• This is most obvious in generators used primarily in peaking applications, but is also true for more base loaded fossil, hydro and even nuclear plants oaded oss , yd o a d e e uc ea p a ts(although to a lesser degree).
Variation in Financial Impact of Variation in Financial Impact of OutagesOutages
E lExampleA pin hole leak is detected in a boiler tube at a base-loaded unit on a weekday
Option 1 – Remove the unit from service immediately in order to minimize the damage and outage timeoutage timeOption 2 – Wait until the weekend to remove the unit (when the hourly economic consequences are less) but expect to have a longer outageless) but expect to have a longer outage
Variation in Variation in Technical Impactof Outagesof Outages
High or Low Demand Period
Option 1 1 day outage 0 27% reduction in EAFOption 1 – 1 day outage - 0.27% reduction in EAF
Option 2 – 2 day outage – 0.54% reduction in EAFp y g
Variation in Variation in Financial Financial ImpactImpactof Outagesof Outages
• Low Demand Period Increased Cost or Lost Profits• Option 1 (1 day outage during week)- $115,000 p ( y g g ) ,• Option 2 ( 2 day outage on weekend)-$184,000
• High Demand Period Increased Cost or Lost Profits• Option 1 (1 day outage during week)- $354 000• Option 1 (1 day outage during week)- $354,000
• Option 2 (2 day outage on weekend)- $265,000
Traditional IndicesTraditional Indices
• None of the traditional performance metrics are adequate to indicate or reflect the economic optimal d i idecision
• The term Commercial Availability (CA) was created to• The term Commercial Availability (CA) was created to address the problem
Commercial AvailabilityCommercial Availability
• Simple Concept
O d fi iti i th ti f t l fit (– One definition is the ratio of actual profit (gross margin) delivered by a plant relative to its potential profit if it had been able to deliver p pevery MW-HR required
– Other definitions are being used at different companies around the worldcompanies around the world
Commercial Availability Calculation Commercial Availability Calculation
• Example 1 ( 300MW unit for 10 random hours in year)
Hour G M EAF G MHour G. M. EAF G. M.potent actual CA = $72000/$73500 X 100 = 98.0%
1 $ 3000 1 $ 3000 EAF = 7/10 X 100 = 70%2 $ 0 1 $03 $ 1500 0 $0 Lost Margin = $73500-$720004 $ 6000 1 $6000 = $15005 $12000 1 $120005 $12000 1 $120006 $24000 1 $24000 EFORd = 1/7 X 100 = 14.3%7 $18000 1 $180008 $ 9000 1 $ 90009 $ 0 0 $ 010 $ 0 0 $ 0Total $73500 $72000
Commercial Availability CalculationCommercial Availability Calculation
• Example 2 (same as example except available in all hours but hour 6)
Hour G. M. EAF G. M.potent actual CA = $49500/$73500 = 67.3%
1 $ 3000 1 $ 3000 EAF = 9/10 = 90%2 $ 0 1 $ 03 $ 1500 1 $ 1500 Lost Margin = $73500-$495004 $ 6000 1 $ 6000 $ 24 0004 $ 6000 1 $ 6000 = $ 24,0005 $12000 1 $120006 $24000 0 $ 07 $18000 1 $18000 EFORd = 1/7 = 14.3%$ $8 $ 9000 1 $ 90009 $ 0 1 $ 010 $ 0 1 $ 0Total $73500 $49500Total $73500 $49500
Commercial Availability Commercial Availability vs. EAF & EFORdvs. EAF & EFORd
• EXAMPLE 1 EXAMPLE 2
Lost Margin = $1500 Lost Margin = $24000
EAF 70% EAF 90%EAF = 70% EAF = 90%
CA = 98% CA = 67.3%
EFORd = 14.3% EFORd = 14.3%
Benchmarking Commercial Benchmarking Commercial AvailabilityAvailability
• Cannot benchmark directly except against your own units and their trends
• Can benchmark indirectly using “ diti l b biliti ” l“conditional probabilities” plus yourplant’s actual economics
Conditional ProbabilityConditional Probability
When required (that’s the conditional part) what is the likelihood (that’s the probability part) that the unit will be able to generate at its rated capacity
Conditional Probability has been shown to vary depending upon the plant’s economic necessity!(WEC March 2002 case study)
(1-EFORd) is a good metric for Conditional Probability
Conditional ProbabilityConditional Probability
• Using reliability data from the GADS g ydatabase we can determine frequency distributions of Conditional Probability (C.P.) for the peer group of each individual ( ) p g punit (1-EFORd)
• There will be different probability distributions during different demanddistributions during different demand periods (peak/non-peak season, day/night, weekday/weekend day, etc.)Selecting each unit’s optimum CA goal will• Selecting each unit’s optimum CA goal will start with these C.P. distributions
Setting Commercial Availability Goals Setting Commercial Availability Goals Using Conditional ProbabilitiesUsing Conditional Probabilities
From the example of 10 random hours we might chooseFrom the example of 10 random hours we might choose Conditional Probability Goals CPG as:
Hours 1,2,3,4 = 92% (hours of mid expected value)
Hours 5 6 7 8 = 98% (hours of high expected value)Hours 5,6,7,8 = 98% (hours of high expected value)
Hours 9,10 = 90% (hours of low expected value)
These might be the best quartile reliabilities (1-EFORd) of the peer group for each demand periodof the peer group for each demand period
Setting Commercial Availability Goals Setting Commercial Availability Goals U i C di i l P b bili iU i C di i l P b bili iUsing Conditional ProbabilitiesUsing Conditional Probabilities
The gross margin goal (GMG) for each hour is calculated b lti l i h h ’ C diti l P b bilit G lby multiplying each hour’s Conditional Probability Goal (CPG) by that hour‘s Gross Margin Potential
Note: If the unit has other value to the company in addition to gross margin (such as ancillary power) that value should be incorporatedvalue should be incorporated
Setting Commercial Availability Goals Setting Commercial Availability Goals U i C di i l P b bili iU i C di i l P b bili iUsing Conditional ProbabilitiesUsing Conditional Probabilities
H GMP $ CPG GMG $Hour GMP-$ CPG GMG-$1 3000 .92 27602 0 .92 03 1500 92 13803 1500 .92 13804 6000 .92 5520 CA Goal = 71400/73500 5 12000 .98 11760 = 97.1%6 24000 .98 235206 24000 .98 235207 18000 .98 176408 9000 .98 88209 0 .90 010 0 .90 0Total 73500 71400
Commercial Availability GoalCommercial Availability Goal
• EXAMPLE 1 EXAMPLE 2EXAMPLE 1 EXAMPLE 2
Lost Margin = $1500 Lost Margin = $24000
EAF = 70% EAF = 90%
EFORd = 14.3% EFORd = 14.3%
CA = 98% CA = 67 3%CA 98% CA 67.3%
Goal CA = 97.1% Goal CA = 97.1%
Setting Commercial Availability Goals Using Conditional Probabilities
1) Id if i ’ d i & i l1) Identify your unit’s design & operational peers2) Calculate the probability distribution of these unit’s
Conditional Probabilities during their demand periods that are similar to yours.
3) Estimate your unit’s Optimum Economic Conditional Probabilities during each demand gperiod (often the top quartile or top decile C.P. of your peers).
4) Apply those Conditional Probability goals to your ) pp y y g yunit’s economics (forecast or actual) using whatever definition of Commercial Availability you choose
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Setting Commercial Availability Goals Using Conditional Probabilities
Although this process might seem complicated remember the following:g
For every complex, difficult to understand, hard problem,there is a simple, easy to understand,
WRONG solution!!
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Commercial Availability ImplicationsCommercial Availability Implications
• See WEC Case Studies for May & June 2003• Benchmarking• Design Changes• Goals Systems
D i i A l i ( ti & ti )• Decision Analysis (proactive & reactive)• Impact on traditional performance metrics• Perception by other stakeholders• Perception by other stakeholders
Commercial Availability Implications
– Technology tells us what can be done
Commercial Availability Implications
– Technology tells us what can be done
WHILE
– Economics tells us what should be done
Commercial Availability Implications
– Technology tells us what can be doneWhile
– Economics tells us what should be doneBUTBUT
– Politics tells us what will be done
Optimum Economic AvailabilityWEC July 2002 case study
• In order to maximize profitability each generating plant must seek to optimize (not simply maximize) its performance
• In addition to the WEC July 2002 case study an• In addition to the WEC July 2002 case study an article by Robert Richwine was published in Power Magazine in 2004 that more fully describes this concept (Ref 14)
Optimum Economic AvailabilityOptimum Economic Availability
Availability 100%Availability 100%
Optimum Economic AvailabilityOptimum Economic Availability
Top QuartileFrontier
A il bilit 100%Availability 100%
Optimum Economic AvailabilityOptimum Economic Availability
Total O&M Cost $ Cost ofUnavailability
$ Cost OfUnavailability
Availability 100%
Optimum Economic AvailabilityOptimum Economic Availability
Total O&M Cost +Unavailability Cost
Total O&M Cost $ Cost ofUnavailability
Unavailability Cost
$ Cost OfU il bilitUnavailability
Availability 100%
Optimum Economic AvailabilityOptimum Economic Availability
Total O&M Cost +Unavailability Cost
Total O&M Cost $ Cost ofUnavailability
y
$ Cost OfUnavailability
Optimum Economic Availability
Availability 100%
Optimum Economic AvailabilityOptimum Economic Availability
Total O&M Cost +Unavailability Cost
Total O&M Cost $ Cost ofUnavailability
$ Cost OfUnavailability
Total O&M Cost TargetUnavailability
Optimum Economic Availability
Availability 100%
Optimum Economic AvailabilityOptimum Economic Availability
Top QuartileFrontier
ReactiveProactive
A il bilit 100%Availability 100%
Optimum Economic AvailabilityOptimum Economic Availability
Total O&M Cost +Unavailability Cost
Total O&M Cost $ Cost ofUnavailability
Proactive Cost Target
Optimum Economic AvailabilityReactive
ProactiveProactive Cost Target
Reactive Cost Target
Availability 100%
Future - Market Oriented System
• Consequences– More revenue uncertainties– More cost uncertainties– More risk– More opportunities
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Summary
• Change is occurring everywhere• Changes are not the same everywhere• Company specific programs should be
developed and implemented that will allow each company to anticipate and respond
i kl t it i t f hquickly to its unique set of changes• The companies that are best able to respond
t k t i d d ill b thto market-induced pressures will be the survivors
Performance Improvement
Better Use of Reliability Data will be a Key Factor in Achieving and Sustaining
Optimal Performance
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Using Reliability Data to Improve Power Plant Performance
References (copies available upon request)
1) World Energy Council case studies www.worldenergy.orgwww.worldenergy.org
2) Optimizing O&M Cost to Maximize Profitability3) NERC-GATE trend studies www.nerc.com4) Performance Improvement at Southern Company5) Peer Unit Benchmarking6) Establishing Goals using Statistical Benchmarking6) Establishing Goals using Statistical Benchmarking7) Predicting Future Plant’s Reliability Using Learning
Curve Theory
Using Reliability Data to Improve Power Plant Performance
References (copies available upon request)
8) Predicting Long Range Plant Maintenance Cost9) Is Your Plant Headed for a HILP?)9) Seasonal Performance Trends10) Availability Following Planned Outages12) R li bilit M U li bl It Ti f12) Reliability Measures Unreliable – Its Time for a
Change13) Planned vs. Unplanned Outages: The Economic ) p g
Impact of Selecting Availability Goals14) Maximizing Plant Availability May Not Optimize Plant
EconomicsEconomics
Using Reliability Data to Improve Power Plant Performance
Presented by
Robert R. (Bob) RichwineR li bilit M t C lt tReliability Management ConsultantRichwine Consulting Group, LLC
[email protected] 678 231 3606+1-678-231-3606
Atlanta, Georgia, USA 30076
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