1. PI-BASED PERFORMANCE MONITORING SYSTEM FOR COMBINED CYCLE POWER GENERATION TECHNOLGY 4/21/2004

2. 2004 OSI UC 1 About Calpine  Founded in 1984  Headquartered in San Jose, Calif.  Calpine has 87 energy centers in 21 states in the U.S., as well as in Canada and the United Kingdom with a total capacity of about 22,000 megawatts  Uses clean, proven technologies: natural gas combined-cycle and geothermal energy

3. 2004 OSI UC 2 A Large Portfolio Calpine offices  Boston, MA  Calgary, Canada  Dublin, CA  Ft. Collins, CO  Houston, TX  Jupiter, FL  Northbrook, IL  Portland, OR  Tampa, FL  Folsom, CA Corporate headquarters  San Jose, CA As of 4/21/2003

4. 2004 OSI UC 3 Growth History

5. 2004 OSI UC 4 Performance Monitoring Necessity Large, regionally diverse fleet requires central performance monitoring to provide the following;  Standard fleet reporting/comparison  Operational benchmarking  Asset improvement and degradation quantification Value  Economic decisions based on operating performance data  Internal knowledge and understanding of equipment  Efficient and reliable operation

6. 2004 OSI UC 5 Why Use PI? Calpine chose to use OSI-PI as the foundation for our Performance Monitoring System  Existing PI infrastructure eliminated additional capital expense  Internal reliance and control  PI provides all the required functional components of 3 rd party systems  Development team and end-users already familiar with PI tools/systems Value  Zero additional capital cost associated with PI  Zero new training  Zero external reliance  Zero risk

7. 2004 OSI UC 6  Overview of Calpine PI network  PI Performance Equations  PI Process Book as GUI  PI Datalink and Excel employed as on-line QC Consumable Products  Wholesale Energy Products Wholesale Energy Products  Retail Power Retail Power  Renewable Energy Renewable Energy PERFORMANCE MONITORING SYSTEM ARCHITECTURE

8. 2004 OSI UC 7 Calpine PI Infrastructure PI at Calpine  Calpine standardized on PI as their operations historian in early 2001  PI is installed at 86 of Calpine’s 89 generating facilities  The fleet PI infrastructure consists of 62 production PI servers and 24 PI API nodes  Calpine PI data for every site is available remotely to every Calpine employee, from plant personnel to executive management, because all Calpine PI systems are connected to the Calpine WAN

9. 2004 OSI UC 8

10. 2004 OSI UC 9 PI Performance Equations  PI Performance Equation Subsystem used as backbone to calculate performance data  Average of 350 Performance Equations per site constructed by Calpine Performance Engineers  Calculations based on standard calculations as well as plant specific calculations  Daily averaging functions used to easier identify performance trends and create summary reporting

11. 2004 OSI UC 10 PI Process Book as GUI  PI Process Book used to view raw and calculated performance data  13 Monitoring Displays and 14 Trend Displays  Multiple individual PDI files used as opposed to a single PIW file  Navigation created using VB Code

12. 2004 OSI UC 11 C O M P OU N D A N N U A L G R O W T H R A T E 1 9 9 7 T O 2 0 0 2

13. 2004 OSI UC 12

14. 2004 OSI UC 13 PI Datalink as On- Line Quality Control Data Quality Critical  PI Datalink used to ensure data integrity  Standard statistical evaluation of data used to evaluate data integrity  PI Datalink used to summarize data and draw conclusions

15. 2004 OSI UC 14  Text Based Filtering in Performance Equations  Getting the Most out of the Performance Equation Subsystem  Correction Calculation Challenges TECHNICAL CHALLENGES

16. 2004 OSI UC 15 Text Based Filtering  Identified a need to display why corrections were not being applied  Solution must not affect summary data  Early solution was to display “-1” when corrections not applied

17. 2004 OSI UC 16

18. 2004 OSI UC 17 Text Based Filtering Solution  Final solution was to display text when corrections not applied  Text to be used in the tags must be present in the “System” Digital Set within the PI System  Text is not taken into account when summary equations are applied

19. 2004 OSI UC 18

20. 2004 OSI UC 19 Maintaining the PI-PE Subsystem  Found that 200 calculations is the approximate limit for a Performance Equation Scheduler  Set up multiple schedulers to spread the load  With the number of equations, and the complexity of the equations, Performance Equation Schedulers would fail from time to time  Set up Performance Equation Schedulers to be restarted regularly to minimize impact.  Used 3 rd Party Software to notify when calculations are experiencing issues

21. 2004 OSI UC 20 Technical Challenges The Challenges Site instrumentation errors / lack of site instrumentation Each plant uses different tag naming conventions PI was originally configured primarily as a Data Historian – PI tags typically have various levels of compression and exception enabled Gas Turbine based performance indicators vary drastically with load and ambient conditions The Solutions Correction, re-calibration, procurement of critical site instrumentation Standard PI tag naming convention applied to primary site tags (fuel, power, ambient conditions) PI compression and exception removed on critical performance tags – filtering applied where required Load Normalization Apply OEM ambient corrections at normalized (base) load Use PI-based text substitution to allow functional averaging and Process Book trending

22. 2004 OSI UC 21 Performance Trend of CTG Corrected MW

23. 2004 OSI UC 22 Value Realization Value  Eliminate dedicated on-site test personnel and temporary test instrumentation – Estimated savings of $200K annually  Accurate recoverable degradation data allows the sites to optimize CTG water washing based on market conditions  In a low spark spread market, every optimization opportunity can significantly contribute to Calpine’s profitability Examples  Use PI based Performance Monitoring to conduct DWR contractual annual capacity tests on (15) simple cycle peakers in CA  Quantify recoverable CTG degradation by analyzing pre and post offline water wash performance  Remote monitoring identifies operational procedure mispractice and optimizes plant performance

24. 2004 OSI UC 23 Performance Monitoring System vs. Performance Test The Performance Monitor calculations fall within the 1% uncertainty band for power and slightly outside the 1.5% uncertainty band for heat rate. This is very good considering that the tests use different instrumentation and the acceptance test uses more measurements and more corrections in their calculations. Calpine 3 x 1 Combined Cycle Plant Performance Test Results Comparison Calpine Simple Cycle Peaker Plant Performance Test Results Comparison

25. 2004 OSI UC 24 Performance Quantification

26. 2004 OSI UC 25 Performance Quantification

27. 2004 OSI UC 26 Performance Quantification CTG 1 Water Wash CTG 2 Water Wash CTG 1 Water Wash CTG 2 Water Wash

28. 2004 OSI UC 27 Performance Reporting

29. 2004 OSI UC 28 Performance Reporting

30. 2004 OSI UC 29 Summary Calpine is successfully leveraging OSI-PI technology in a Performance Monitoring environment directly resulting in;  Increased plant availability and reliability  Predictive maintenance scheduling  A historical plant performance database that can be used to effectively optimize plant and fleet performance  Better decision support data available to dispatch and trading  INCREASED CALPINE PROFITABILITY !