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October 4, 2005 Stakeholder Meeting Calgary, AB

Incremental Impact on System Operations with Increased Wind Power Penetration Phase 1 Report. October 4, 2005 Stakeholder Meeting Calgary, AB. Topics. Introduction Wind Power Variability Study System Impact Study – Phase 1 Conclusions Next Steps. Introduction. Generator Bus. Speed

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October 4, 2005 Stakeholder Meeting Calgary, AB

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

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

  3. Introduction

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

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

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

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

  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. North Pincher Creek Fort Macleod/ Magrath Taber Medicine Hat Waterton 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

  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 Methods 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

  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 Variable MW Stable MW Persistent MW

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

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

  25. Correlation of Wind Power Variability and System Load Variability + ∆Load + ∆Wind - ∆Load - ∆Wind 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

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

  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 HourRelationship 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 2.3x 2.6x Wind power variability increases, but not in proportion to wind power development. It is smaller at shorter time periods.

  35. Inter- 20 Minute and Inter- 60 Minute Results 2.4x 2.7x Wind power variability increases, but not in proportion to wind power development. It is smaller at shorter time periods.

  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) Operating Transfer Capability Violations OTC violation analysis examines events that exceed the hourly TTC TTC TRM ATC MW TRM analysis examines events to determine a TRM level that would have prevented the OTC violation 0 MW 0 MW

  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 ModelAssumptions • 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 ModelAssumptions 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 ModelAssumptions 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 ModelAssumptions 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. Frequency of minute OTC violations at different hour endings 300 Scenario B 250 Scenario C Scenario D 200 150 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour Ending Results Time-Simulation AnalysisOTC Violations by the hour OTC violations were more often off-peak when system ramp rate was low

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