Using Reliability Data to
Improve Power Plant
Performance
NERC-GADS Workshop
presented by Robert R. (Bob) Richwine
Reliability Management Consultant
Richwine Consulting Group, LLC
Oct 28, 2010
Workshop Agenda
I.
II.
Background and Case Study
Common Elements in Successful Programs
A.
B.
C.
D.
Awareness Phase
Identification Phase
Evaluation Phase
Implementation Phase
III. Transforming to a Market-Driven Business
Environment
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Background
• From a 2006 Wall Street Journal article
– Business today is awash in data and data
crunchers
– 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 performance
• Top performing companies were 5 times
more likely to single out analytics as critical
to their competitive edge
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A substantial gap exists between actual
and potential performance
Potential Performance
Actual Performance
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The Worldwide Value of Closing the
Gap (WEC estimate)
• Economic
– US$80 Billion per Year
• Environmental
– 1 Billion Tonnes of CO2 Reduction
per Year and Proportional Reductions
of Other Emissions
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Source of Performance Improvement
• Variation of performance due to
– Technology/mode of operation = 20-25 %
– Human factors/management = 75-80 %
• Confirmed by
– Analytical studies
– 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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30+ WEC Published Case Studies
• www.worldenergy.org
Click on “Work Programme”
Click on “Performance of Generating Plant”
Click on “Case Studies”
• Each Case Study demonstrates actual value
received by use of performance data
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Case Studies Published by the WEC
Objective
Demonstrate that the value of performance
data is far greater than the combined cost of
collecting the data plus the risk of sharing the
data
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WEC Case Study Topics include the
Use of Data in:
• Benchmarking
• Configuration
Optimization
• Generation Planning
• Operations
• Goal Optimization
• Maintenance Planning
• Risk Management
• Catastrophic
Event Reduction
• Life Management
• Equipment Design
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NERC-GADS
• In North America NERC has been collecting power
plant reliability data in the GADS format for 28 + 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 use
• Some companies have used the GADS database in
innovative ways to help them achieve top performance
of their generating plants
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CASE STUDY
Performance Improvement in Power Stations
Southern Company’s Experience
May 2004 WEC Case Study
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Southern Company
Alabama
Power
Georgia
Power
Savannah
Electric
Mississippi
Power
Gulf
Power
Southern Company Headquarters
Atlanta, GA, USA
Currently ~ 35,000MW Capacity
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Fossil Steam Power Stations
Availability Trend 1970-1976
Equivalent Availability Factor (EAF) Trend
Southern Company
World
95
93
91
89
87
85
83
81
79
77
75
73
71
69
67
65
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991
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Decline In Power Station Availability
1970-1976
• Inability to provide adequate resources to power stations
– High load growth
– Extensive new environmental requirements (particulates)
– Beginning of nuclear plant construction program
• Lack of advanced decision support methods/tools to
determine best use of resources that were available
• Design philosophy of “lowest initial cost”
• Advanced technology plants specified without adequate
understanding of “learning curve” effects
• Reactive maintenance philosophy instead of proactive
• Lower quality fuel purchased based on “low delivered
cost” instead of “lowest total cost”
• Little use of performance data except for “reports”
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Fossil Steam Power Stations
Availability Trends 1970-1991
Equivalent Availability Factor (EAF) Trend
Southern Company
World
100
95
90
85
80
75
70
65
60
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991
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Increased Power Station Availability
Due To...
• Heightened awareness of executive
management of the need for availability
improvement
• Commitment of additional resources for
availability improvement
• Improved decision making addressing the
“what and how” of power plant management
• Many advanced programs and processes
including improved reporting and analysis
of plant performance data
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Availability Improvement
Benefit Areas
•
•
•
•
Replacement energy
Deferred construction
Reduced reserve margin requirements
Increased customer service reliability
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Availability Improvement
Benefits to Southern Company
US$1,235,000,000 per year
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Heat Rate Trend
(Inverse of Efficiency)
10,600
10,400
10,200
10,000
9,800
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991
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Heat Rate Benefits
to Southern Company
US$108,000,000 per year
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Additional Cost
US$325,000,000 per year
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Benefits versus Cost
Benefits
=
$1,235,000,000
+ 108,000,000
$1,343,000,000 per year
Cost
=
$ 325,000,000 per year
Net Savings =
$1,343,000,000
- 325,000,000
$1,018,000,000 per year
Benefit/Cost =
$1,343,000,000 = 4.3
$325,000,000
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Southern Company Perspective
• In 1991, net savings in excess of US$1 billion
per year equaled:
– ~12 percent of Annual Revenue
– ~100 percent of Net Income (Profit)
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Environmental Benefits
of Performance Improvement
Annual avoided emissions
included:
Seven million tons of CO2e per year
at essentially $0 cost!
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Availability Improvement
at Other Utilities
PREPA – Puerto Rico
+25%
NEES – USA
+13%
ESB – Ireland
+10%
ESKOM – South Africa
+20%
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Observations
• Each company/country faces its own set of
challenges, constraints, and opportunities
• No single program is optimal for every
company/country
• There are common elements within each
successful program
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Common Elements In Successful
Improvement Programs
January – April 2003 WEC Case Studies
Performance
Improvement
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Performance Improvement Process
Phase 1 - Awareness
• Benchmarking
• Forecasting
• Communications
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Awareness
January 2003 WEC Case Study
• Benchmarking
– April 2002 WEC Case Study
– August 2002 WEC Case Study
– September 2003 WEC Case Study
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Reliability Benchmarking - Why?
•
•
•
•
•
•
Set realistic, achievable goals
Identify areas for improvement
Give advance warning of threats
Determine appropriate incentives
Trade knowledge/experience with peers
Quantify and manage performance risks
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Reliability Benchmarking Process
• Identify reliability variables to measure and
the databases required
• Select 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 databases required:
Typical Reliability Variables
– Equivalent Availability Factor (EAF)
– Equivalent Forced Outage Rate (EFOR)
– Scheduled Outage Factor (SOF)
AND INCREASINGLY, EFOR(demand)
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Benchmarking Process
•
Select peer power plants having similar
design or mode of operations
characteristics:
– Selection Procedure (NERC/Richwine developed)
• Advanced statistical methodology
• Has 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 Base
5000
+ units
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Consulting
Group, LLC
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Peer Selection Criteria
Exact Match
x x
x
x
x
x
x
x
x
Number of Exact Matches
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x
x
x
x
x
x
0
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Peer Selection Criteria
Large Population
Exact Matches
Must Balance Criteria
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Peer Selection Criteria
Etc.
Firing
Fuel
Vintage
Boiler
Manufacturer
ASSUME
Size
Etc.
Etc.
Duty
Age
Criticality
Etc.
Etc.
Draft
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Turbine
Manufacturer
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Peer Selection Criteria
Etc.
Firing
Fuel
Vintage
Boiler
Manufacturer
ANALYSIS
Size
Etc.
Etc.
Duty
Age
Criticality
Etc.
Etc.
Draft
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Turbine
Manufacturer
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Peer Selection Criteria
Significance Testing
Subcritical
Supercritical
Baseload Duty Cyclic Duty
EFOR
EFOR
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Peer Groups Select Criteria
Fossil Units
All Fossil Units
CRITICALITY
Super
Sub
VINTAGE
<1972
MODE OF OPERATION
≥1972
Cycling
Baseload
Size
Draft Type
Fuel
Boiler Mfr.
Draft Type
Size
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Does Peer Selection Make a Difference?
SUPERCRITICAL TECHNOLOGY
EARLY VINTAGE
EFOR(mean)
EFOR(median)
EFOR(best quartile)
15.60%
12.17%
8.14%
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RECENT VINTAGE
9.68%
8.08%
5.47%
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Does Peer Selection Make a Difference?
EFOR - PLANT A
OLD CRITERIA
(Coal; 100-199MW)
NEW CRITERIA
% difference
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
(Coal; 800-1300MW)
NEW CRITERIA
% difference
mean
5.83%
7.63%
-31%
Median
4.55%
5.87%
+29%
best quartile
2.70%
3.97%
+47%
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Performance Benchmarking Results -30 Peer Units
• Peer unit selection criteria
– Subcritical
– Reserve shutdown hours less than 963 hours
per year
– Natural boiler circulation
– Primary fuel = coal
– Single reheat
– Net output factor greater than 85.6%
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Peer Unit EFOR Distribution
CUMULATIVE PERCENT
100
90
80
70
60
50
40
30
20
10
0
0
1
2
3
4
5
6
7
8
9
10
11
EFOR (%)
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Peer Unit SOF Distribution
MON EYPOIN T 1 PEER UN IT SO F
DISTRIB U TIO N
100
C U M UL A T IVE PERC EN T
90
80
70
60
50
40
30
20
10
0
0
5
10
15
20
SOF (%)
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Peer
Unit
EAF
Distribution
MONEYPOINT 1 PEER UNIT EAF
DISTRIBUTION
100
CUMULATIVE PERCENT
90
80
70
60
50
40
30
20
10
0
70
75
80
85
90
95
100
EA F (%)
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Conclusions
• Benchmarking is helping utilities
–
–
–
–
Set goals
Develop incentives
Identify improvement opportunities
Quantify and manage risks
• Proper peer group selection is essential
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Forecasting
Statistics Versus Probability
• Statistics – Yesterday’s actual results
• Probability – Tomorrow’s predicted results
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Forecasting Performance
• An embarrassing personal example
– September 2002 WEC Case Study
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EFORACTUAL - EFORPREDICTED
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Basic Principle
Past Conditions
Past Results
~
Future Conditions
Future Results
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Predicting EFOR
Most Important Parameters
• Lagging Equivalent Forced Outage Hours
• Lagging Service Factor
• Current Year Planned Outage Hours
• Lagging O&M Spending
• Current Year O&M Spending
• Fuel Type
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EFORACTUAL - EFORPREDICTED
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Forecasting Examples
• New Technology “learning curve” – Supercritical
• November 2002 WEC Case Study
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Outage Rates versus Year
of Initial Operation
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Communications
Communications to all stakeholders,
especially employees, is vital to clearly show
the “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
February 2003 Case Study
Identifying problem areas with best payback potential
– Component Benchmarking
– High Impact – Low Probability Event Reduction –
February 2002 Case Study
– Trend Analysis Case Studies:
• March 2002 – Peak Season Reliability
• June 2002 – Availability Following Planned Outages
• December 2002 – Reliability Versus Demand
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Component Benchmarking
• Two options
– #1 - Components from all groups but using
component design and operational data for
selecting peer groups
– # 2 - Components in peer group of unit-level
benchmarking
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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 the “best in class” in its peer group
should be a high priority system to study
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Is Your Power Plant Headed for a HILP??
How to Avoid, Detect or Mitigate
High Impact – Low Probability (HILP) Events
Robert Richwine – Richwine Consulting
Michael Curley – NERC
G. 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
plant)
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Typical HILPs
•
•
•
•
Turbine Water Induction
Boiler Explosions
Generator Winding Failures
Many, many others
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HILP Reduction Programs
• Some companies have successfully
reduced their HILP frequencies or
magnitudes with a formal HILP Reduction
Program using the North American Electric
Reliability Corporation’s (NERC) GADS data.
• NERC-GADS database contains 25+ years
of detailed design and reliability data from
over 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 major elements
– Routine expected events with small/medium
outage consequences
– Unexpected major events with large outage
consequences
• Should separate these two elements of FOR
when benchmarking reliability and
establishing reliability improvement
programs
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Benchmarking two units’ Forced
Outage Rate (FOR) - Example
Unit A
FOR
10%
Many small
Type of Outages
events
"Normal" FOR
10%
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Unit B
10%
Fewer, smaller
events but 1
major event of
3 weeks length
~4%
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Benchmarking two units’
Forced Outage Rate (FOR)
Implications
1) The two units have had very different failure
modes
2) We should adapt our benchmarking analysis
and improvements efforts to account for
these differences.
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HILP Reduction Program
• Step 1 – Select the best peer group for benchmarking
against your unit
• Step 2 – Find the peer group’s HILP contribution to EFOR
and compare to your unit’s HILP contribution
• 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 6 – Identify options to address HILPS
• Step 7 – Evaluate and select HILP reduction options
• Step 8 – Track results of implemented options, compare to
expectations and feedback into program to improve the
process
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Step 1 – Select Peer Group
• It 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
both fruit
• If you don’t go through an analytical selection
process 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”
define as a HILP (typically greater than 1 week
or longer)
• 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 yourRichwine
company’s
fleet
Step 3 – Prioritize the Peer Group’s
HILP Problem Areas
• Using pc-GAR-MT and excluding non-HILP
events (an option of the software) compile a
frequency chart of HILP cause codes that
the peer group has experienced
• Use the frequency chart to focus on the
most likely HILP areas for your unit
• Consider exporting the files from pc-GARMT 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 freeformat data field, often containing valuable data
regarding the outages.
• Although not currently available in pc-GAR or pcGAR-MT, Mike Curley, Manger of NERC-GADS
Services, can advise you on how to retrieve this
information.
• Reviewing this data for HILP events can indicate
the root causes of events that your unit’s peer
group has experienced and can point you in
directions for assessing your unit’s susceptibility
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to those HILPs. Richwine Consulting Group, LLC
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
that could induce the HILP
• Evaluate your unit to determine if these
circumstances are present such as equipment
condition, O&M experiences & practices, QA, etc.
• Create a scorecard to quantify the level of HILP
risk
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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 damage
– 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
– Prioritize
• Using the option evaluations and considering
the fact of limited company resources (time,
money, manpower) the best set of options
should be chosen for implementation
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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 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
to prevent, detect or mitigate HILPs
• Addressing HILP causes and seeking
solutions “before a HILP occurs” is a proven
way to move from a fire-fighting to pro-active
style of management
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Conclusions & Recommendations
The Future
• Competition is here (or just around the corner)
• Market-based business environments using
terms like Commercial Availability to indicate
the effects of your plant’s outages on the
company’s profitability makes it crucial to
better manage your plant’s reliability to be
available when its value is greatest
• A good HILP reduction program can help move
you to becoming one of the industry leaders
(or if already a leader to staying there)
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EFOR After Scheduled Outages
Week Following Schedule Outage
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EFOR After Scheduled Outage Trend
• If your plants exhibit this trend you can seek
cost-effective ways to reduce this unreliability
• If you cannot find ways to reduce the
problem, you can incorporate this tendency
into 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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Forced Outage Rate Versus Demand
Trend
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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 more
failures but take less time to repair
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Common Elements Phase 3 - Evaluation
March 2003 Case Study
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Capital Project
Evaluation Process
• Elements of an Evaluation Analysis
– 1) IMPACT – A prediction of difference in future
plant performance if the project is implemented
versus if it is not implemented.
– 2) WORTH of PERFORMANCE IMPROVEMENT
– 3) COST – The total budget cost including
equipment procurement and installation costs
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Capital Project
Evaluation Process - Impacts
• Future with/without the project
(positive/negative)
–
–
–
–
–
–
–
–
Availability
Efficiency
O & M Savings (or increased cost)
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 !!!
Industry data !!!
Manufacturers and consultants
Other projects results
Test Results
Other
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Capital Project
Evaluation Process Part 2
• Timing – The second obstacle a project must
overcome
– 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 economic
risk profile
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Capital Project
Evaluation Process Part 3
• 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?”
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Operations & Maintenance
Economic Decision Analysis
• Company’s business economics applied to
day-to-day O&M decisions
• Helps identify the best economic option for
recovering from abnormal conditions
• 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 economics
– Evaluate results and incorporate into decision
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O&M Decision Example
Leaking Feedwater Heater
Problem
Tube failure in 7A feedwater heater
Requires isolation of 6A & 7A heaters
1% efficiency loss during isolation
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O&M Decision Example
Leaking 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 imbed
repair
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O&M Decision Example
Leaking Feedwater Heater
• Consequences
– Option 1- repair immediately
•
•
•
•
48 hour outage during high demand period
Overtime labor cost of $1000
Start-up cost of $20,000
Total cost = $217,000
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O&M Decision Example
Leaking Feedwater Heater
• Consequences
– 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 weekend
Total cost = $137,000
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O&M Decision Example
Leaking Feedwater Heater
• Consequences
– Option 3 -repair during next planned outage
• 1% efficiency penalty until next planned outage
• Total cost = $202,000
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O&M Decision Example
Leaking Feedwater Heater
• Consequences
– Option 4- repair during next forced outage
• 1% efficiency penalty until next 48 hour outage
– uncertain when next 48 hour outage will occur
• Total cost range = $0 - $202,000
– maximum cost of $202,000
– “break even” point in 8 weeks
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O&M Decision Example
Leaking Feedwater Heater
• What would YOU decide?
–
–
–
–
Option 1 (repair now)
Option 2 (repair during weekend)
Option 3 (repair during P. O.)
Option 4 (repair during F.O.)
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- $217,000
- $137,000
- $202,000
- $0-$202,000
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O&M Decision Example
Leaking Feedwater Heater
• If the same event happened at a different time the
following economic results could happen:
–
–
–
–
Option 1- repair now
Option 2- repair during weekend
Option 3- repair during next PO
Option 4- repair during next FO
$ 75,000
$ 50,000
$450,000
$0-$450,000
Now what would you decide???
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Planned versus Unplanned Outages
• 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
minimized in order to maximize availability
• However, planned outages almost always occur during
the non-peak season, when financial consequences are
much lower by as much as ¼
• 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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Operations & Maintenance
Economic Decision Analysis
• Applications
– Maintenance
• Reactive (e.g. planned outage extension)
• Proactive (e.g. condition directed maintenance)
– Operations
• Reactive (e.g. tube leaks)
• 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)
Financing
Goal selection
Monitor actual results and compare against
expected results
Awareness
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Implementation
• Goal Setting Case Studies
– May & June 2003 – Are Reliability Measures
Unreliable??
– July 2003 – Planned vs. Unplanned Outages Effects on Goals
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Problems with Current Indices
• Factors – EAF, FOF, UCF, UCLF, etc.
Factors use the entire time period as the
denominator without regard to unit demand
EXAMPLE: Peaking Gas Turbine
100 hrs/year demand
25 forced hours during demand
EAF = 99.71%
FOF = 00.29%
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Problems with current indices
• FOR, EFOR
In Example
FOR = EFOR = 25%
In 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, actual EFOR statistics are much higher,
often 60% +.
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Equivalent Forced Outage Rate –
Demand (EFORd)
• Markov equation developed in 1970’s
• Used by the industry for many years
– PJM Interconnection (20 years)
– Similar to that used by the Canadian Electricity
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%
[SH + f(FOH)]
Where: f =
(1/r)+(1/T)
(1/r)+(1/T)+(1/D)
fp = SH/AH
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 simple
• Reported Forced Outage Hours are “reduced”
• Reduction % is the ratio of Reserve Shutdown
Hours to the Service Hours in the time period
• This is an approximation (since actual
demand hours are not collected by NERC)
that estimates the hours on forced outage
during demand
• Advantage – We can calculate historic EFORd
without collecting new data!!!
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Example of EFORd
45.00
40.00
35.00
EFOR, range from 3.9 to 42.4%
30.00
25.00
20.00
15.00
EFORd, range from 3.9 to 10.6%
121.70
621.70
1121.70
1621.70
2121.70
2621.70
3121.70
3621.70
4121.70
4621.70
5121.70
5621.70
6121.70
6621.70
7121.70
10.00
5.00
0.00
7621.70
EFOR & EFORd, % ,
EFOR vs EFORd
Reserve Shutdown Hours
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Implementation
• Monitor Actual Results and Compare
Against Expected Results
– September 2002 Case Study – Predicting Unit
Reliability
• Feedback Into Awareness, Identification &
Evaluation Phases
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Common Elements
January–April 2003 Case Studies
Performance
Improvement
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The Future
Transforming to a Market-Driven
Business Environment
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The Future
Is Not
What It
Used To
Be
Increasing
Competition
Is The Future
Of Our Industry
Past Business Environment
• Regulated – Suppressed Competition
Cost (Prudent) + Profit (Mandated) = Price
Avoid Risk
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Evolving Business Environment
• Market Driven – Increased Competition
Price (Market) – Cost (Total) = Profit
Identify, Quantify, and Manage Risk
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Example of Risk/Reward Decisions
• You are playing a video poker “jacks or better” game
• 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?
– 2) discard the queen of spades, hoping for the queen of hearts
for a possible $2000 payoff?
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Example of Risk/Reward Decisions
Video poker example analysis
Risk = $5
Reward
Option 1 - $20 @ 100% probability = $20.00
Option 2 - $2000 @ 1/47 probability = $42.55
Reward/Risk
Option 1 - $20/$5
=4
Option 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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Low Cost Producer
• To be successful in a competitive business
environment a company must become the
Low Cost Producer
• Becoming the Low Cost Producer will only be
achieved when all employees are making
better day-to-day decisions
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Need Better
• Resource Management
–
–
–
–
People
Plants
Money
Time
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Transform People from Risk Avoidance
to Risk Management Mindset
• Any company’s only long-term sustainable
competitive advantage is the quality of it’s
people and the quality of it’s leadership
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Management Impact
• Only 20 - 25 percent of the variation in
reliability can be explained due to
design/mode of operation differences
• The remaining 75 - 80 percent of the
variation in reliability is due to
differences in management
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4 Pillars Of Change
Leadership
Climate
&
Culture
Selection
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Training
126
Decision Making
• Push decision making authority and responsibility
down to the lowest appropriate level (decisions
must be made quickly)
Executive Management
Plant Management
Plant Supervisor
Individuals
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Need Better
• Decision Tools
– Identify viable decision
options
– Combine technical
consequences with
economics
– Evaluate options based
on financials results
– Monitor results and refine
process
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Need Better
• Key Performance Indicators
– EFOR (demand)
– Commercial Availability
• Un-weighted
• weighted
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Market-Based KPI’s
May & June 2003 Case Studies
• Demand EFOR – EFOR (demand)
– Developed for non-base loaded units
– Approximates the reliability of a unit during
demand periods
• Commercial Availability
– Un-weighted – measures a unit’s availability
only during demand periods
– Weighted – measures a unit’s availability only
during demand periods and “weights” each
hour’s impact by the unit’s gross margin
during that hour
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Commercial Availability
• Cannot benchmark directly except against
your own units and their trends
• Can benchmark indirectly using
conditional probabilities plus
plant’s actual economics
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Conditional Probability
When required (conditional)
what is the likelihood (probability)
that the unit will be able to generate
at its rated capacity?
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Conditional Probability...
…has been shown to vary
depending upon the plant’s
economic necessity
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Conditional Probability
• Using NERC-GADS data we can determine
probability distributions of Conditional
Probability (C.P.) for the peer group of each
individual unit
• There will be different probability distributions
during different demand periods (peak
season, day/night, weekday/weekend day, etc.
• Selecting your unit’s optimum goal will start
with these C.P. distributions
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Commercial Availability Benchmarking
•
Example using Conditional Probabilities
Hour
G. M.
potent
Avail
G.M.
actual
1
2
3
4
5
6
7
8
9
10
Total
$ 3000
$
0
$ 1500
$ 6000
$12000
$24000
$18000
$ 9000
$
0
$
0
$73500
y
y
n
n
y
y
y
y
n
n
$ 3000
$0
$0
$0
$12000
$24000
$18000
$ 9000
$
0
$
0
$66000
C. P.
.92
.92
.92
.92
.98
.98
.98
.98
.90
.90
Richwine Consulting Group, LLC
G.M.
goal
$ 2760
$
0
$ 1380
$ 5520
$11760
$23520
$17640
$ 8820
$
0
$
0
$71400
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Commercial Availability
Benchmarking
From Example
Potential Gross Margin
Actual Gross Margin
Goal Gross Margin
G.M. achieved above goal
Traditional Availability
Forced Outage Rate (d)
Commercial Availability
Goal C. A.
Richwine Consulting Group, LLC
= $73500
= $66000
= $71400
=($5400)
= 60.0%
= 28.6%
= 89.8%
= 97.1%
136
Commercial Availability
Benchmarking
From Example but available in hour 4
Potential Gross Margin
= $73500
Actual Gross Margin
= $72000
Goal Gross Margin
= $71400
G.M. achieved above goal
= $ 600
Traditional Availability
= 70.0%
Forced Outage Rate (d)
= 14.3%
Commercial Availability
= 98.0%
Goal C. A.
= 97.1%
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Commercial Availability
Benchmarking
From Example but available in all hours except 6
Potential Gross Margin
= $73500
Actual Gross Margin
= $49500
Goal Gross Margin
= $71400
G.M. achieved above goal
= ($21900)
Traditional Availability
= 90.0%
Forced Outage Rate (d)
= 14.3%
Commercial Availability
= 67.3%
Goal C. A.
= 97.1%
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Commercial Availability
Benchmarking
1)
2)
3)
4)
Identify your unit’s design & operational peers
Calculate the probability distribution of these unit’s
Conditional Probabilities during their demand
periods that are similar to yours.
Estimate your unit’s Optimum Economic
Conditional Probabilities during each demand
period (often the top quartile or top decile C.P. of
your peers).
Apply those Conditional Probability goals to your
unit’s economics (forecast or actual) using
whatever definition of Commercial Availability you
choose
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Commercial Availability
Benchmarking
• Although this process might seem
complicated remember the following adage:
For every complex,
difficult to understand, hard problem,
there is a simple, easy to understand,
WRONG solution!!
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Commercial Availability Implications
• Benchmarking
– Selection
– Comparisons
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Commercial Availability Implications
• Design Impacts
– More or less redundancy?
– More or less Condition Monitoring Systems?
– More or less flexibility to respond to changing
economic conditions in the future?
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Commercial Availability Implications
• Goals Systems – Commercial Availability helps
provide a direct linkage between a plant’s
performance results and its company’s financial
results
• Human Factors – it has been proven that only 20%25% of the variation in a plant’s performance is due
to technical factors, while the remaining 75%-80% is
due to human factors (May 2002 WEC case study)
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Commercial Availability Implications
• Maximizing Commercial Availability
– How decisions are affected–plant & executive
– Impact on current indices- most will look worse
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Commercial Availability Implications
• Perception by other stakeholders
–
–
–
–
–
–
–
Company executives and board members
Regulatory agencies
Insurance Companies
Bank Engineers
Wall Street
Stockholders
Customers
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Need Better
• Goals Systems
– Direct linkage between
• Plant results
• Corporate objectives
Plant Goals
Corporate Goals
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#1 Problem Worldwide
Goals Conflict
Corporate Goals
Plant Goals
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Optimum Economic Availability
October 2004 Case Study
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Optimum Economic Availability
Availability
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100%
149
Optimum Economic Availability
Top Quartile
Frontier
Availability
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100%
150
Optimum Economic Availability
Top Quartile
Frontier
Total O&M Cost
Availability
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100%
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Optimum Economic Availability
Total O&M Cost
$ Cost of
Unavailability
$ Cost Of
Unavailability
Availability
100%
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Optimum Economic Availability
Total O&M Cost +
Unavailability Cost
Total O&M Cost
$ Cost of
Unavailability
$ Cost Of
Unavailability
Availability
100%
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Optimum Economic Availability
Total O&M Cost +
Unavailability Cost
Total O&M Cost
$ Cost of
Unavailability
$ Cost Of
Unavailability
Optimum Economic Availability
Availability
100%
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Optimum Economic Availability
Total O&M Cost +
Unavailability Cost
Total O&M Cost
$ Cost Of
Unavailability
$ Cost of
Unavailability
Total O&M Cost Target
Optimum Economic Availability
Availability
100%
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Optimum Economic Availability
Top Quartile
Frontier
Proactive
Reactive
Availability
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156
Optimum Economic Availability
Total O&M Cost +
Unavailability Cost
Total O&M Cost
$ Cost of
Unavailability
Proactive Cost Target
Proactive
Reactive
Optimum Economic Availability
Availability
100%
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Reactive Cost Target
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Need Better
• Goals Systems
– Direct linkage between
• Plant results
• Corporate objectives
Plant Goals
Corporate Goals
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Traditional Plant Goals System
• Goal Area
–
–
–
–
–
Weighting
Availability
Efficiency
O&M Budget Control
Safety
Other
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25%
15%
30%
10%
20%
100%
159
Market-Based Goals System
• Objective –
– Minimize a plant’s total controllable production
cost (or maximize its contribution to corporate
profitability)
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Market-Based Goals System
• Method
– Convert a plant’s technical goals (availability,
efficiency, etc.) to the company’s economic goals
– Develop economic forecasts of the worth of
performance improvement and incorporate them into
decision tools provided to production staff
– Train production employees in the use of these tools,
integrating their local “technical” knowledge with
corporate economics
– Give production management the flexibility to make
tradeoffs between individual performance/spending
goals in order to minimize the cost of electricity
and/or maximize the corporate profitability
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Market-Based Goals System
• Goal Area
–
–
–
–
Expectation
EFOR deviation
SOF deviation
Efficiency Deviation
Other performance areas
5%
7%
2%
Cost
$500,000
$140,000
$400,000
Total Performance Deviation Cost
$1,040,000
Operations & Maintenance Budget
$5,000,000
Total Controllable Production Cost
$6,040,000
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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 quickly to its unique set of
changes
• The companies that are best able to
respond to market-induced pressures
will be the survivors
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Performance Improvement
Better Use of Reliability Data will be a Key
Factor in Achieving and Sustaining Top
Performance
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Using Reliability Data to Improve
Power Plant Performance
Presented by
Robert R. (Bob) Richwine
Reliability Management Consultant
Richwine Consulting Group, LLC
[email protected]
+1-678-231-3606
Atlanta, Georgia, USA 30076
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