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October 4, 2005Stakeholder MeetingCalgary, AB
Incremental Impact on System Operations with Increased Wind Power PenetrationPhase 1 Report
Topics
• Introduction
• Wind Power Variability Study
• System Impact Study – Phase 1
• Conclusions
• Next Steps
Introduction
Why are we concerned about wind power?
STEAM TURBINE
Steam From BoilerHeadwater
ROTOR
Generator Bus
AVR
CT
AUTO
MANUAL
Steam To TurbineWater To Turbine
Speed Governor
Generator (Supply)System Load
Load
Non-dispatchable, varies and can be
reasonably forecasted
Supply
Primarily Dispatchable
Before wind and interconnections
Add interconnections
STEAM TURBINE
Steam From BoilerHeadwater
ROTOR
Generator Bus
AVR
CT
AUTO
MANUAL
Steam To TurbineWater To Turbine
Speed Governor
Generator (Supply)
System Load
Interconnections Import or Export
Interconnections have rules and timing for schedules
Add Wind
STEAM TURBINE
Steam From BoilerHeadwater
ROTOR
Generator Bus
AVR
CT
AUTO
MANUAL
Steam To TurbineWater To Turbine
Speed Governor
Non-Wind PowerGenerator (Supply)
System Load
Wind Power(Supply)
Interconnections Import or Export
Add the AESO
STEAM TURBINE
Steam From BoilerHeadwater
ROTOR
Generator Bus
AVR
CT
AUTOMANUAL
Steam To TurbineWater To Turbine
Speed Governor
Non-Wind PowerGenerator (Supply)
System Load
Wind Power(Supply)
Interconnections Import or Export
Keep the balance within prescribed bounds
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
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
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
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.
The Wind Power Variability Study
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
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
TaberMedicine Hat
North
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
AESO System Impact Study – Phase 1
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)
Variability Statistics
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
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
Examples of Variability and Persistence
Stable MW Variable MW
Persistent MW
Event Based Statistical AnalysisScenario A (254 MW)
Period of Time the Change Took Place
Benchmark Scenario
Cha
nge
in M
W
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.
Event Based Statistical AnalysisComparison
C 1445 D 1995B 895A 254
Events in the light blue area would indicate ramping problems if these occurred during off-peak hours
Example Event
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
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.
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
Example Load Forecast
Data available on the AESO website
Converting this data to forecast error
Forecast Load
Actual LoadForecast
Error
Forecast Change In
Load
Actual Change in
Load
Forecast Error on
Change in Load
6,681.00 6,799.00 -1.7%
6,592.00 6,689.00 -1.5% (89.00) (110.00) 0.31%
6,548.00 6,701.00 -2.3% (44.00) 12.00 0.84%
6,537.00 6,669.00 -2.0% (11.00) (32.00) 0.31%
6,578.00 6,714.00 -2.0% 41.00 45.00 0.06%
6,735.00 6,843.00 -1.6% 157.00 129.00 0.42%
7,102.00 7,234.00 -1.8% 367.00 391.00 0.35%
7,549.00 7,684.00 -1.8% 447.00 450.00 0.04%
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
WindPower
Load
DispatchableGeneration
1Min
Later
1Hr
Later
1Day
Later
1Yr
Later
1%1.5% 5%
* 9 to 25%
100% 100%
0.5%
*Decreases with increased amount of wind penetration
+/- 5 MW as per AESO rules
Adding Wind to the Load Forecast
The perfect
load forecast
Forecast Change in LoadThe less
than perfect
load forecast
Adding Wind to the Load Forecast
Effect of wind power
variability to the load forecast
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.
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.
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
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.
2.4x 2.7x
System Performance
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
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
System Performance on the AB-BC Interconnection (CPS2)
Example of ACE and CPS2 Violations
-200
-100
0
100
200
Time (10 minutes per division)
MW
Area Control Error 10 Minute Average Ld
Illustrative Example showing twoCPS2 violations
System Performance on the AB-BC Interconnection (OTC and TRM)
0 MW
Operating Transfer Capability Violations
TTCTRMATCMW
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
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
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
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
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
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
Validation of the Time-Simulation AnalysisComparison of Actual and Simulated Generation in the
Energy Market with Existing Wind Power
6000
6500
7000
7500
8000
1 Hour Per Division (Oct 9)
MW
Simulated Generation in the Energy Market
Actual Generation in the Energy Market
Comparison of Actual ACE versus Simulated ACE for Existing Wind Power
-150
-100
-50
0
50
100
150
1 Hour Per Division (Oct 9)
MW
Actual Area Control Error (ACE)
Simulated Area Control Error (ACE)
The actual CPS2 versus simulated CPS2 for 2004
97.0%
97.5%
98.0%
98.5%
99.0%
99.5%
100.0%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Simulated
Actual
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
Effects on System Performance with No Change in System Operation
TRM
-100
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
Month
Incr
ease
in
TR
M
A - Existing B - 850MW C -1500MW D -2100MW
CPS2 Performance
85%
90%
95%
100%
1 2 3 4 5 6 7 8 9 10 11 12
Month
CP
S2
D -2100MW C -1500MW B - 850MW A - ExistingEffect on CPS2
Incremental TRM to prevent OTC Violations
Number of OTC Violations with no change in TRM
Number of OTC Violations
0
5
10
15
1 2 3 4 5 6 7 8 9 10 11 12
Month
Nu
mb
er
C - 1500MW - OTC Number of Violation
C - 1500MW - OTC Number of Violation
D - 2100MW - OTC Number of Violation
Sensitivity Study on Effects on System Performance with Increased Regulating Reserves
Effects on TRM
-100
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
Month
MW
A - Existing B - 850MW C -1500MW D -2100MW
Effects on Number of OTC Violations
0
5
10
15
1 2 3 4 5 6 7 8 9 10 11 12
MonthN
um
ber
B - 850MW - OTC Number of Violation
C - 1500MW - OTC Number of Violation
D - 2100MW - OTC Number of Violation
Incremental TRM to prevent OTC Violations
Number of OTC Violations with no change in TRM
Results Time-Simulation AnalysisOTC Violations by the hour
Frequency of minute OTC violations at different hour endings
0
50
100
150
200
250
300
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
Scenario BScenario C
Scenario D
OTC violations were more often off-peak when system ramp rate was low
Results Time- Simulation AnalysisSimulation Example – Scenario A
6600
6800
7000
7200
7400
7600
7800
8000
Jul28 2200 Jul28 2220 Jul28 2240 Jul28 2300 Jul28 2320 Jul28 2340 Jul29 0000 Jul29 0020 Jul29 0040 Jul29 0100 Jul29 0120 Jul29 0140 Jul29 0200 Jul29 0220 Jul29 0240 Jul29 0300
Time
MW
-400
-200
0
200
400
600
800
1000System simulation based on wind data of scenario [A - Existing], dataSet of [Version_1] for 2004/Jul
WindVariability-ScaleFactor: 1.00, RR-ScareFactor: 1.00, RR-Ramping: 10% \ 10% of range
simulated wind generation
simulated BC tie
Export ATC
load
simulated energy market
dispatch
scheduled BC tie
Export ATC plus current
TRM (65MW)
TRM requirement is within current
65MW
20 Minutes Per Division
200
MW
Per
Div
isio
n
200
MW
Per
Div
isio
nLOADSimulated
BC Tie
TTC
ATC
Scheduled
BC Tie
Scenario
Wind MW
Simulated
EnergyMarket
Results Time-Simulation AnalysisSimulation Example – Scenario B
6600
6800
7000
7200
7400
7600
7800
8000
Jul28 2200 Jul28 2220 Jul28 2240 Jul28 2300 Jul28 2320 Jul28 2340 Jul29 0000 Jul29 0020 Jul29 0040 Jul29 0100 Jul29 0120 Jul29 0140 Jul29 0200 Jul29 0220 Jul29 0240 Jul29 0300
Time
MW
-400
-200
0
200
400
600
800
1000System simulation based on wind data of scenario [B - 850MW], dataSet of [Version_1] for 2004/Jul
WindVariability-ScaleFactor: 1.00, RR-ScareFactor: 1.00, RR-Ramping: 10% \ 10% of range
TRM
requirem
ent
simulated wind generation
simulated BC tie
Export ATC
load
simulated energy market
dispatch
scheduled BC tie
20 minutes20 minutesExport ATC plus current
TRM (65MW)
227 MW
20 Minutes Per Division
200
MW
Per
Div
isio
n
200
MW
Per
Div
isio
n
Conclusions
Conclusions
• 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
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
Next Steps
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.
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
Principle of Controlling VariabilityVariability
-1.5
-1
-0.5
0
0.5
1
1.5
Time
MW
Ramp rate of resources to counter Variability
-1.5
-1
-0.5
0
0.5
1
1.5
Time
MW
Net Result of Variability Combined With Ramp Rate Resources
-1.5
-1
-0.5
0
0.5
1
1.5
Time
MW
The originating variability which is left unchanged
Control other resources to counter the variability
Net result is reduced system variability
Principle of Preventing VariabilityVariability
-1.5
-1
-0.5
0
0.5
1
1.5
Time
MW
Preventing Variability
-1.5
-1
-0.5
0
0.5
1
1.5
0 20 40 60 80
Time
MW
Add Facility Controls
The original variability
Add facility controls to limit the variability
Net result is the original variability is reduced
Living With Variability
0 MW 0 MW0 MW 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
Operating Transfer Capability Violations
Scheduling Limits
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
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
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
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
`
QUESTIONS?
CONTACTS
John Kehler – 403-539-2622
Darren McCrank – 403-539-2623
