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More Reliable Wind Power Forecasting - OSIsoft Users Conference


The plan for improving wind power forecasting by using upwind meteorological observations.

The plan for improving wind power forecasting by using upwind meteorological observations.

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  • In order to turn wind into a fuel source, it has to be forecast reliably and reliablyMaintain Supply > DemandWind energy adds uncertaintyWhen will the wind blow?How fast?When will it stop?How do I balance it?System operator headachesUnanticipated demandUnanticipated supply problemsParticipants do not meet commitmentsUnplanned outagesHigh spot market pricesEnronIntermittent renewables
  • Bonneville Power Authority19 wind farms interconnected (1,000 turbines)Potentially 6,000 MW online by 2009 11 years ahead of schedule
  • In the spring of 2008 BPA’s balancing area:Generated 400MW over schedule for several hoursIn August 2008 BPA’s balancing area:Generated 730 MW over schedule in 1hrGenerated 680 MW over schedule in 1hrIn September 2008 BPA’s balancing area:Generated 625 MW under schedule in 1hr
  • Wind power is “backed” by another type of utilityCombined cycle gas turbinesHydroelectric plantsEtc.Spring 2008 excess power event resulted inWECC control standard violations (fines)Hydro dam over-spill; endangering fish (EPA fines)
  • Sudden increases or decreases in wind speedIncreases usually better than decreasesSubject to seasonal variationDifficult to forecast in the short-termEffects:Operating at reduced or conservative capacityInability to participate in day and hour-ahead marketsPurchase of make-up power at high pricesDispatcher headaches
  • Another example:News article from Reuters (2/27/08):Sudden 1,700 to 300 MW drop in wind powerIncreased demand due to colder temperaturesOrdered 1,100 MW to be curtailed from interruptible customers within 10 minOther suppliers were unable to meet commitmentsHappy endingNo other customers lost powerInterruptible customers were back online 90 min later
  • Most utilities and operators own a PI SystemIs it deployed?
  • “Line of Site”: WINData’s met tower configurations are to be in line with your operations site in the predominant wind corridors.
  • More than a year of development and testingSeveral trial sites to test equipmentComparisons and validation against existing market playersNow in an industry validation phase with utility partnersWINDataNOW installation in the Columbia River Valley in OregonStarted January, 2009Upstream from large wind farm installationsPhase 1 includes installations in 2 locationsPhase 2 & 3 brings online 3 more locations


  • 1. Reducing Wind Forecasting Uncertainty with PI and ECHO
    Using Real-time Offsite Meteorological Observations
    Marty Wilde – CEO, Principal EngineerWINData, Inc.
    Gregg Le Blanc – Flunky
    WINData, Inc.
    F5Direct, LP
  • 2. PI-enabled Real Time Off-site Meteorological Observations for Improved Short-term Wind Forecasting, Operating Economics and System Reliability
    Marty Wilde – CEO, Principal EngineerWINData, Inc.
    Gregg Le Blanc – Flunky
    WINData, Inc.
    F5Direct, LP
  • 3. Topics
    Brief introductions
    The importance of forecasting
    Wind power: ups and downs
    The impact of remote observations
    How WINData leverages ECHO and PI
    Future plans
  • 4. WINData background
    Started by Marty Wilde
    In wind energy 1991
    Wind energy specialists
    From raw dirt to developed wind farm
    Over 500 meteorological tower installs
    Specializing in high-end instrumentation and tall towers
    Advance the “state-of-the-art” by using real time measurements for model and investment validation
  • 5. Why forecasting is important
  • 6. The critical role of forecasting
    Wind penetration
    20% renewables by 2010
    33% renewables by 2020
    20% renewables by 2020
    Already in place
    20% of demand today
    50% by 2025
    20,000 MW capacity by 2010
    > 15,000 MW in place today
    Peaked at 40% of demand (typically ~25% of demand)
  • 7. Working together to improve forecasts
    Goal: Designing a program that results in better forecasts
    WINData &AWS Truewind
    Teaming up with utilities to improve system operations
    Better wind energy integration
  • 8. Bonneville Power’s balancing act
    1,500 MW
    Balancing Area
    19 Wind
  • 9. BPA’s Balancing Area
  • 10. Scheduling wind operations is complex
  • 11. A series of events at BPA
    • Wind power  15% of peak load
    • 12. Large deviations power use of Portland, OR
    Theoretical Scheduled MW
    730 MW
    680 MW
    400 MW
    625 MW
    0 MW
  • 13. Consequences
    • Wind power is backed by another generation asset
    • 14. Unintended results
    Theoretical Scheduled MW
    Violation of:
    • WECC Control Standards
    • 15. EPA Regulations
    0 MW
  • 16. Ramp Events
    Not your friend.
  • 17. Ramp events captured by ERCOT (1)
  • 18. Ramp events captured by ERCOT (2)
  • 19. Ramp effects at ERCOT
    News article from Reuters (2/27/08):
    Sudden 1,700 to 300 MW drop in wind power
    Increased demand due to colder temperatures
    Ordered 1,100 MW to be curtailed from interruptible customers within 10 min
    Other suppliers were unable to meet commitments
    Happy ending
    No other customers lost power
    Interruptible customers were back online 90 min later
  • 20. Scheduling around ramp events
    Hour ahead schedules -forecast interpretation
    Forecasts are good at predicting that there WILL be a ramp event
    When will it arrive?
    Phase error
    How significant?
    Amplitude error
    Forecast error tends to increase around ramp events
  • 21. WINData’s Plan
    Introducing WINDataNOW
  • 22. Goal: Reduce the forecast uncertainty
    Can short-term forecasts be improved by using actual upstream measurements from physical instruments?
    Off-site meteorological data from predominant wind directions
    Deliver measurements with enough resolution and timeliness to be useful
  • 23. WINData’s technology
    Create a modern, remote “met” sensing system
    Combine off the shelf technologies:
    PI System infrastructure
    Logger equipment
    Focus on configuration vs. programming
  • 24. WINDataNOW! – remote met observations
    Towers are strategically located
    Transmit high fidelity “line of site” data
    Use this data:
    Collaborate with forecaster
    Refine short-term forecasts
    Back-cast to develop more detailed climatology
    “Line of Site” Locations
    Wind Farm
    60 – 100 minutes
    of prior notice
    Upwind Met Tower
    30 – 60 miles
  • 25. Tower placement
    Wind Direction
  • 26. Tower placement
    Wind Speed
    meters / sec
    Wind Speed
    meters / sec
    Wind Speed
    meters / sec
    Wind Speed
    meters / sec
  • 27. Anticipate changes
    Ramp up
    Ramp down
    Improved wind energy integration on the grid
    More data: smoother operations
    Apply “line of site” datato better understand near-term transients
    Power Production vs.
    Augmented forecast
    Wind Speed vs.
    Augmented forecast
    meters / sec
  • 28. Example
    A Site in Montana
  • 29. Today’s data expectations
    Typical industry-standard data logger output
  • 30. The same time period, now in high fidelity
    Actual Wind Speed (m/s)
  • 31. Comparison
    Average vs. Actual Wind Speed (m/s)
    Averages vs. higher fidelity data
  • 32. Using PI’s 10m Averages
  • 33. Using PI’s 5m Averages
  • 34. Zooming out a little more
    High wind speed cut-off
  • 35. Increased meteorological insight
    Account for actual upstream observations
    Decrease forecast error around ramp events
    Advanced warning
    Situational awareness
    Reduce costs
    Avoid penalties
    Use this
    Hour Ahead
    To reduce this
  • 36. C-Gorge Project
    WINData in
    The Columbia Gorge
  • 37. Map of C-Gorge vs. existing sites
  • 38. WDN Locations vs. BPA towers
    7 Mile Hill
  • 39. How WINDataNOW! works
    From full-service to self-service
    Ways of engaging WINData
    Project management
    Deployment & management of early warning met arrays
    Enterprise-wide retrofit projects
    Development of situational awareness tools and IP
    Subscription service
    Data from reference locations
    Hosted central PI Server
    Stream data to your local system
  • 40. Subscriber architecture
    Subscriber 1
    OffsitePI System
    Subscriber 2
    WINData Managed
    Subscriber 3
    WINData Managed
    * Not to scale
  • 41. Owner architecture
    • Installation by WINData
    • 42. One-time setup and validation
    • 43. Ownership and management by customer
    • 44. Your own IP
    Managed by you
  • 45. Combination architecture
    Other sites in the area
    Subscriber 1
    OffsitePI System
    Subscriber 2
    WINData Managed
    Subscriber 3
    Managed by you
  • 46. Acquisition architecture
    Higher resolution data
    Rolls out quickly
    OffsitePI System
    Met Tower
    Secure Connection
    Solar Panel
    Fits in place with existing PI infrastructure
    Fault tolerant
    Low power
    WINData Acquisition
    Hardware / Software
  • 47. Data features
    Focus on data confidence
    Logger spools data if connectivity goes down
    Supports “industry standard”
    At least 2Hz data always streamed to PI
    Keep the data at its original resolution
    Tune the data with standard PI settings
    10m averages always available via PI
  • 48. Benefits of working on OSIsoft’s platform
    PI Server
    New ECHO to PI Interface
    Partner network
    Directly integrate with utilities and system operators
    Spool data during connectivity outages
    Speaks PINET over TCP/IP natively
    Solution works immediately with partners like Transpara’sVisualKPI
  • 49. Demo
  • 50. Future possibilities
    Smart Grid
    Provide “fuel insight” to consumers
    Encourage informed choices – demand side management
    Better investments
    Prospecting – High Fidelity “Bankable Data”
    Improved climatology for new wind energy sites
    Advanced wind farm design
    Turbulence, FFT Analysis, Matrix correlations
    Integrate shiny new technologies
    Vertical profiling
    SODAR, LIDAR, etc.
  • 51. Looking forward
    Upstream observations can dramatically improve forecasts (Papers by Kristin Larson, 3TIER)
    Significant interest from utilities, operators, and forecasters in short-term ramp event prediction
    WINData wants to work with you
    Contact Marty Wilde, Dave Roberts, or Gregg
  • 52. Special Thanks
    OSIsoft for its support
    Johnson City office and the embedded team for ECHO work
    Dave Roberts, Business Development, and Sales for advocacy
    ProSoft for its continued support
    Transpara for use of it’s VisualKPI software
  • 53. Find us after the talk!
    Marty Wilde
    Gregg Le Blanc
    Dave Roberts – OSIsoft