1. October 4, 2005 Stakeholder Meeting Calgary, AB Incremental Impact on System Operations with Increased Wind Power Penetration Phase 1 Report

2. Topics Introduction Wind Power Variability Study System Impact Study – Phase 1 Conclusions Next Steps

3. Introduction

4. Why are we concerned about wind power? STEAM TURBINE Steam From Boiler Headwater ROTOR Generator Bus AVR CT AUTO MANUAL Steam To Turbine Water To Turbine Speed Governor Generator (Supply) System Load Load Non-dispatchable, varies and can be reasonably forecasted Supply Primarily Dispatchable Before wind and interconnections

5. Add interconnections STEAM TURBINE Steam From Boiler Headwater ROTOR Generator Bus AVR CT AUTO MANUAL Steam To Turbine Water To Turbine Speed Governor Generator (Supply) System Load Interconnections Import or Export Interconnections have rules and timing for schedules

6. Add Wind STEAM TURBINE Steam From Boiler Headwater ROTOR Generator Bus AVR CT AUTO MANUAL Steam To Turbine Water To Turbine Speed Governor Non-Wind Power Generator (Supply) System Load Wind Power (Supply) Interconnections Import or Export

7. Add the AESO STEAM TURBINE Steam From Boiler Headwater ROTOR Generator Bus AVR CT AUTO MANUAL Steam To Turbine Water To Turbine Speed Governor Non-Wind Power Generator (Supply) System Load Wind Power (Supply) Interconnections Import or Export Keep the balance within prescribed bounds

8. Main Questions and Concerns How big is the variability? Variability causes uncertainty in real time operation What is the effect on system performance? Variability can effect system operation performance

9. Introduction - History 2003 Increased interest in wind power development in Alberta Raised questions on adequate standards, planning considerations and operating considerations AESO engaged ABB to conduct study 2004 ABB Report released in May Indicated concerns around wind power variability Concluded concerns can be managed via controls / monitoring, wind forecasting and market rules Many stakeholders had questions or concerns with assumptions on variability data used in the study and thus any conclusions on system impact

10. Introduction – History Continued 2004 AESO released Technical Requirements for wind power facilities in November Operational requirements not finalised pending further understanding of wind variability in Alberta 2005 Jan. - AESO initiated a variability study Aug. - AESO released the Wind Power Variability study to industry Sept. - Released draft of the system impact study

11. Consultation Process Sept. 23 Release draft phase study to stakeholders Sept. 27 Present to wind group Oct. 4 Industry wide stakeholder session Oct. 5-21 1-on-1 sessions with key stakeholders Oct. 21 Deadline for stakeholder comment on phase 1 Nov. 1 Finalize phase 1 and launch phase 2 (sensitivity studies list) Dec. 1 Release draft phase 2 results Jan. 2006 Begin finalizing options for solutions Mar. 2006 Communicate recommendations externally May 2006 Recommendations finalized and implementation timeline developed DOE Market Policy Implementation initiatives are coinciding with the technical process.

12. The Wind Power Variability Study

13. Wind Power Variability Study Used actual time-stamped measured wind speed data at existing and potential wind power facilities in Alberta Models to convert wind speed to MW Models were validated to ensure accuracy One and 10-minute time series MW data provided to AESO for the system impact studies

14. Development Scenarios Alberta SW divided up into 6 development areas There are four development scenarios to be studied: Scenario A – Existing Generation (254 MW) Used to benchmark accuracy of the models developed for the variability study Scenario B – 895 MW Scenario C – 1445 MW Scenario D – 1994 MW Pincher Creek Waterton Fort Macleod/ Magrath Taber Medicine Hat North

15. Accuracy of the Models to Simulate Wind Variability The simulated or predicted wind power from the study was compared to the actual wind power as measured at the AESO from SCADA data. The AESO and wind developers were satisfied with the accuracy of the models. Nov 21-27, 2004 Blue-Measured Red - Simulated

16. AESO System Impact Study – Phase 1

17. Objectives Use wind power data that the wind industry can support as realistic from the variability study Examine variability statistics Examine the incremental effects of wind power penetration on system operation Scenario B to A, Scenario C to A, Scenario D to A Provide a more accurate assessment on operational impact: (CPS2, OTC, TRM) Provide strong analytical tools that can be used to lead to appropriate solutions (the second phase)

18. Variability Statistics

19. Statistical Analysis Event Based persistent behaviour General Statistics Variability and uncertainty relationships (>10 minutes) Standard deviation and correlation factor Studied between wind and combined system load (load - wind) Studied at 10, 20, 60, 120, 180 and 240 minutes Magnitude of Variability – short term (<20 minutes) 95% percentile Studied between wind and net demand (load + interchange - wind) Studied at 1-minute, intra 20-minute, 20-minute, and 60- minute Statistical Methods

20. Findings from Statistical Analysis Wind power variability has a persistence or ramping effect On an annual basis, there is low correlation between system load variability and wind power variability Increasing wind power development increases operational uncertainty In the 20-minute and less time frame, wind power variability increases with wind power development, but not in proportion to the wind power development

21. Examples of Variability and Persistence Stable MW Variable MW Persistent MW

22. Event Based Statistical Analysis Scenario A (254 MW) Period of Time the Change Took Place Benchmark Scenario Change in MW

23. Event Based Statistical Analysis Scenario C (1445 MW) In this scenario, there are 20 events periods where a significant portion of wind power capacity is ramped over a 2-6 hour time period.

24. Event Based Statistical Analysis Comparison C 1445 D 1995 B 895 A 254 Events in the light blue area would indicate ramping problems if these occurred during off- peak hours Example Event

25. Correlation of Wind Power Variability and System Load Variability Example where wind power changes and system load changes do correlate. Example where wind power changes and system load changes do not correlate. Example where wind power changes and system load changes have random correlation. + ∆Load + ∆Wind - ∆Load - ∆Wind + ∆Load - ∆Wind - ∆Load + ∆Wind

26. Study Indicates Low Correlation between Wind Power and System Load Correlate (1 Hour Period) On an annual basis, there is low correlation between system load variability and wind variability. As wind penetration increases, system load variability becomes less dominant.

27. Operational Uncertainty The AESO provides a day ahead load forecast to our system controller The AESO system controller uses the forecast in conjunction with what occurred: The day before The same day a week earlier A similar day during the previous half-year The difference between the forecast and actual is the operational uncertainty experienced during the real time

28. Example Load Forecast Data available on the AESO website Converting this data to forecast error

29. Uncertainty in Real-Time What will the resource do 1 minute from now, 1 hour from now, 1 day from now and 1 yr from now Wind Power Load Dispatchable Generation 1 Min Later 1 Hr Later 1 Day Later 1 Yr Later 1% 1.5% 5% * 9 to 25% 100% 0.5% * Decreases with increased amount of wind penetration +/- 5 MW as per AESO rules

30. Adding Wind to the Load Forecast The perfect load forecast Forecast Change in Load The less than perfect load forecast

31. Adding Wind to the Load Forecast Effect of wind power variability to the load forecast

32. What Does Variability Look Like Without Forecasting 1 Hour? The aggregation of wind power plus system load results with increased operational uncertainty with increased wind power penetration.

33. General Statistical Analysis 4 Hour Relationship between combined system (load - wind) forecast error and system load forecast error (4 hour) The aggregation of wind power plus system load results with increased operational uncertainty with increased wind power penetration.

34. 1 Minute and Intra- 20 Minute Results Wind power variability increases, but not in proportion to wind power development. It is smaller at shorter time periods. 2.3x 2.6x

35. Inter- 20 Minute and Inter- 60 Minute Results Wind power variability increases, but not in proportion to wind power development. It is smaller at shorter time periods. 2.4x2.7x

36. System Performance

37. Why is system performance important? Alberta is interconnected to the BC / Western US systems that form the Western Electricity Coordinating Council (WECC) Poor performance effects all members on the interconnected system The system is planned and operated on the basis that each control area meets operating criteria Violations are reported and appropriate actions initiated

38. What are the operational measures? CPS2, TRM, OTC violation CPS2 – Control performance standard measures ACE (area control error – supply/demand deviation) performance. NERC establishes a specific limit for CPS2 that the AESO must meet : The AESO is required to operate such that its average ACE for at least 90% of clock-ten-minute periods during a calendar month is within the NERC specified limit. TRM – Transmission Reliability Margin capacity on the interconnection with B.C. that is not used for market based interchange schedules and is available to keep the interconnected network secure under system uncertainties. An Operational Transfer Capability (OTC) violation occurs when the power on the interchange is greater, for a period of more than 20 minutes, than the sum of Available Transmission Capacity (ATC) plus TRM. TRM is currently set at 65 MW. Available Transfer Capability (ATC) is maximum amount of transfer capacity that can be scheduled on the inter-tie. It is continually changing usually by the hour as per the current system conditions

39. System Performance on the AB-BC Interconnection (CPS2) Illustrative Example showing two CPS2 violations

40. System Performance on the AB-BC Interconnection (OTC and TRM) 0 MW Operating Transfer Capability Violations TTC TRM ATC MW 0 MW OTC violation analysis examines events that exceed the hourly TTC TRM analysis examines events to determine a TRM level that would have prevented the OTC violation

41. Time-Simulation Model Developed to: Simulate 2004 system operation with the four wind power scenarios Calculate system performance with wind power variability Conduct sensitivity studies for ‘what if” questions Uses generator ramp-rate limited modeling Uses 2004 actual historical data for; Internal Alberta load, BC and Saskatchewan interchange schedules, Regulation reserve range, Available transfer capability (ATC) limits

42. Time-Simulation Model Assumptions Assumptions in the time-simulation model include The energy market based on observed historical data; 600 MW/hr on peak / 300MW/hr off peak ramp rate limit ramps in a linear fashion has a 5-minute delay representing system controller and plant operator dispatch response time

43. Time-Simulation Model Assumptions cont. Assumptions in the time-simulation model include Regulating reserves market ramp rate limited to 10% per minute of regulating range provided as per AESO’s ancillary services technical requirements volume is set at the top of the hour as per the AESO’s current ancillary service market rules will target to be in the middle of its range based on observations of historical data

44. Time-Simulation Model Assumptions cont. Assumptions in the time-simulation model include: Questionable data excluded from study results Any periods where system or wind power data quality was questionable were excluded from the analysis Wind power data was interpolated between two good data points when data quality was questionable Periods of supply or load contingencies were excluded from the analysis

45. Time-Simulation Model Assumptions cont. The time-simulation studies do not: predict energy (MWhrs) production of wind power facilities consider transmission capability or development consider system variability as a result of contingencies internal or external to the AIES examine variability of dispatchable generators examine variability of individual wind power facilities examine volatility of the energy market merit order

46. Validation of the Time-Simulation Analysis

47. Findings from Time-Simulation Analysis The 254 MW scenario produced results similar in characteristic and behavior to the actual system performance measures in 2004 There are no violations to the three reliability criteria at the 254 MW penetration level All three growth scenarios resulted in one or more performance violations Increased wind power variability reduced all three system performance measures There is an observable relationship between CPS2 performance and OTC violations or changes in TRM. Changing one effects the others Increased regulating reserves will improve system performance, but will neither totally eliminate OTC violations nor eliminate increases in TRM

48. Effects on System Performance with No Change in System Operation Effect on CPS2 Incremental TRM to prevent OTC Violations Number of OTC Violations with no change in TRM

49. Sensitivity Study on Effects on System Performance with Increased Regulating Reserves Incremental TRM to prevent OTC Violations Number of OTC Violations with no change in TRM

50. Results Time-Simulation Analysis OTC Violations by the hour Frequency of minute OTC violations at different hour endings 0 50 100 150 200 250 300 123456789101112131415161718192021222324 Hour Ending Scenario B Scenario C Scenario D OTC violations were more often off-peak when system ramp rate was low

51. Results Time- Simulation Analysis Simulation Example – Scenario A 20 Minutes Per Division 200 MW Per Division LOAD Simulated BC Tie TTC ATC Scheduled BC Tie Scenario Wind MW Simulated Energy Market

52. Results Time-Simulation Analysis Simulation Example – Scenario B 20 Minutes Per Division 200 MW Per Division

53. Conclusions

54. Increased wind power development will increase wind power variability, wind power persistence, and operational uncertainty Increased wind power variability reduced all three system performance measures 895 MW scenario has operational concerns Given the installed wind capacity in the province and the capacity of wind power at advanced stages of development mitigating measures will need to be developed

55. Conclusions continued… Further sensitivity studies are required in a phase 2 to assess the merit and effectiveness of various considerations including: Effectiveness of wind power forecasting Increasing the available ramp-rate in the energy market Increasing the ramp-rate requirement of regulating reserves Ramp-rate limiting on wind power facilities The impact of an increased load profile Others

56. Next Steps

57. Dealing With Variability Reduce the variability, live with the variability or a bit of both Reducing Variability converges on; predicting variability and have adequate ramp rate to mitigate it or preventing the variability Living with Variability converges on understanding its magnitude of variability you can withstand and leaving ‘enough room’ on the system for it.

58. Reducing Variability Predicting variability Forecasting or other prediction tools Operators can anticipate change and dispatch the system accordingly Ramp rate of non-wind power resources to reduce variability Ramp rate of wind power resources to prevent variability

59. Principle of Controlling Variability The originating variability which is left unchanged Control other resources to counter the variability Net result is reduced system variability

60. Principle of Preventing Variability Add Facility Controls The original variability Add facility controls to limit the variability Net result is the original variability is reduced

61. Living With Variability 0 MW Time Total Transfer Capability- Export Total Transfer Capability- Import Avail. Transfer Capability- Export Avail. Transfer Capability- Import Transmission Reliability Margin Operating Transfer Capability Violations Scheduling Limits

62. Goals - Next Steps Ensure that by year end there is sufficient information available to start looking at options that lead to solutions and recommendations during 2006 To do this, by year end we need to: Finalize the Phase 1 report Conduct additional studies to answer ‘what if’ questions as identified during industry consultation Finalize Phase 2 report with results of the ‘what if’ questions

63. Next Steps – Sensitivity Studies End zone 1 – What is the effect on CPS2 and TRM if Regulating Reserve volumes did not increase End zone 2 – What is the effect on Regulating reserve volumes if we cannot forecast Sensitivity – If we could forecast Sensitivity – What is the effect on Regulating reserve ramp rate Sensitivity – What is the effect on Regulating reserve volumes The actual answer is within the end zones

64. Steps over the next few months Finalize the Phase 1 Report Any comments and questions greatly appreciated All comments submitted to the AESO are to written form All written comments will be posted to website Phase 1 report should stimulate “what if” questions AESO will conduct one-on-one sessions on Phase 1 report These sessions will lead to concerns or questions that could be answered in the Phase 2 report Issue Phase 2 report

65. Stakeholder Involvement Provide written comments to the AESO on the Phase 1 report by Oct. 21 Participate in the one-on-one sessions to discuss the report and what additional information would you like to see before we start looking at options Contact the AESO with any questions or concerns you have

66. ` QUESTIONS?

67. CONTACTS John Kehler – 403-539-2622 john.kehler@aeso.ca Darren McCrank – 403-539-2623 darren.mccrank@aeso.ca@aeso.ca