Performance of Generating Plant

1. Performance of Generating Plant Using Benchmarking for Competitive Advantage Presented by Robert R. Richwine Reliability Management Consultant USA

2. Agenda • Benchmarking Background • Unit Level Benchmarking • Component Benchmarking • High Impact – Low Probability Benchmarking • Optimum Economic Availability Benchmarking

3. Benchmarking 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

4. 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 5times more likely to single out analytics as critical to their competitive edge

5. WEC PGP Case Studies • Over 30 case studies published on WEC website • www.worldenergy.org • Focus is on practical use of data, including benchmarking, to improve plant performance

6. Benchmarking - Why? • Set realistic, achievable goals • Identify opportunities for improvement • Give advance warning of threats • Determine appropriate incentives • Trade knowledge/experience with peers • Quantify and manage performance risks • Estimate supply adequacy risks • Close the gap between actual and potential plant performance

7. The Gap Potential Performance Actual Performance

8. Worldwide Value of Closing the Gap(WEC Estimate) • Economic • US$80 Billion per Year • Environmental • 1 Billion Tonnes of CO2e Reduction per Year and Proportional Reductions of Other Emissions

9. Primary Causes of the Gap • May 2002 Case Study • From Analytical Studies Plus Practical Experiences • Only 20% - 25% of the Gap is Due to “Technology” Issues • 75% - 80% is Due to Management Practices

10. Benchmarking plays a key role Many companies have applied benchmarking in their performance improvement efforts to: 1) Identify gaps in their unit’s performance 2) Identify areas with best opportunity for cost-effective improvement initiatives 3) Quantify the economic value of closing those gaps

11. NERC GADS Benchmarking Process • Identify performance 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 performance and cost against these peer plants

12. WEC Data Base • Reliability data from around the world • Web-based analytical tools

13. NERC GADS Peer Selection Criteria Large Population WEC Data Base

14. Number of Exact Matches 0 NERC GADS Peer Selection Criteria x x x x x x Exact Match x x x x x x x x x

15. NERC GADS Peer Selection Criteria Large Population Exact Matches Must Balance Criteria

16. ASSUME NERC GADS Peer Selection Criteria Etc. Etc. Fuel Vintage Firing Etc. Boiler Manufacturer Duty Age Criticality Turbine Manufacturer Etc. Size Draft Etc.

17. NERC GADS Peer Selection Criteria Etc. Etc. Fuel Vintage Firing Etc. ANALYSIS Boiler Manufacturer Duty Age Criticality Turbine Manufacturer Etc. Size Draft Etc.

18. All Fossil Units CRITICALITY Supercritical Subcritical VINTAGE MODE OF OPERATION Cycling Baseload <1972 <1972 Size Draft Type Fuel Boiler Mfr. Draft Type Size NERC GADS Peer Groups Selection Criteria Fossil Units

19. NERC GADS Performance Benchmarking Results -- 30 Peer Units • Peer unit selection criteria • Subcritical • Base-loaded • Natural boiler circulation • Primary fuel - coal • Net output factor greater than 85.6%

20. NERC GADS Peer Unit EAF Distribution

21. NERC GADS Conclusions • Benchmarking is helping utilities • Set goals • Develop incentives • Identify improvement opportunities • Proper peer group selection is essential • August 2002 Case Study • September 2003 Case Study • Benchmarking is a key first step • The WEC is actively providing support

22. Component Benchmarking • Similar to unit level benchmarking but focused on individual: • Systems • Sub-systems • Components

23. Component Benchmarking • Still need to ensure that the peer selection process results in as close a match as possible while keeping sufficient units in the population for statistical accuracy • The component peer selection criteria is likely to be different from the unit criteria (e. g. the turbine is likely to be indifferent to fuel type)

24. HILP Benchmarking February, 2002 Case Study

25. 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) • Includes turbine water induction, boiler explosions, generator winding failure, etc.

26. 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 root cause information • Step 5 – Assess your plant’s susceptibility to HILPs • Step 6 – Identify options to avoid, detect and/or mitigate 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

27. HILPs Happen!! • No power plant in immune to HILPs • While a plant’s 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 • Benchmarking HILP unreliability, 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

28. Optimum Economic Availability

29. O&M Costs Availability 100% Optimum Economic Availability

30. Top Quartile Frontier O&M Costs Availability 100% Optimum Economic Availability

31. Top Quartile Frontier Total O&M Cost = Proactive + Reactive O&M Costs Availability 100% Optimum Economic Availability

32. $ Cost of Total O&M Cost Unavailability O&M Costs $ Cost Of Unavailability Availability 100% Optimum Economic Availability

33. Total O&M Cost + Unavailability Cost $ Cost of Total O&M Cost Unavailability O&M Costs $ Cost Of Unavailability Availability 100% Optimum Economic Availability

34. Total O&M Cost + Unavailability Cost $ Cost of Total O&M Cost Unavailability O&M Costs $ Cost Of Unavailability Optimum Economic Availability Availability 100% Optimum Economic Availability

35. Total O&M Cost + Unavailability Cost $ Cost of Total O&M Cost Unavailability O&M Costs $ Cost Of Total O&M Cost Target Unavailability Optimum Economic Availability Availability 100% Optimum Economic Availability

36. BENCHMARKING CONCLUSIONS • Benchmarking has been an integral part of many successful Performance Improvement Programs worldwide (WEC Jan–April 2003 & May 2004 Case Studies) • Proper peer selection is vital (WEC August 2002 & September 2003 Case Studies) • While traditional performance metrics can show important trends, new advanced concepts such as Optimum Economic Availability are becoming increasingly important in the evolving market-driven business environment (WEC July 2002)

37. WEC-PGP CommitteeBenchmarking Support • For more information contact Elena Nekhaev at nekhaev@worldenergy.org or Robert Richwine at richwine@worldenergy.org