Your SlideShare is downloading. ×
More Reliable Wind Power Forecasting - OSIsoft Users Conference
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

More Reliable Wind Power Forecasting - OSIsoft Users Conference

1,180
views

Published on

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

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

Published in: Technology, Business

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,180
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • 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
  • Transcript

    • 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
      Goal:
      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
      California:
      20% renewables by 2010
      33% renewables by 2020
      Europe:
      20% renewables by 2020
      Already in place
      Denmark
      20% of demand today
      50% by 2025
      Spain
      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
      Farms
    • 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
      Idea
      Can short-term forecasts be improved by using actual upstream measurements from physical instruments?
      Requirements
      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
      Sensors
      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
      upwind
    • 25. Tower placement
      Prevailing
      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
      MWh
      Wind Speed vs.
      Augmented forecast
      meters / sec
    • 28. Example
      A Site in Montana
    • 29. Today’s data expectations
      (m/s)
      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
      Re-forecast
      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
      Goodnoe
      7 Mile Hill
      WDN1
      WDN2
      Wasco
    • 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
      “WindTelligence”
      Analysis
      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
      Connection
      OffsitePI System
      WINData
      Subscriber 2
      WINData Managed
      Subscriber 3
      WINData Managed
      PI
      SCADA
      Your
      Operations
      * 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
      Connection
      WINData
      Hardware
      Managed by you
      PI
      SCADA
      Your
      Operations
    • 45. Combination architecture
      Other sites in the area
      Subscriber 1
      Connection
      OffsitePI System
      WINData
      Hardware
      Subscriber 2
      WINData Managed
      Subscriber 3
      Managed by you
      PI
      SCADA
      Your
      Operations
    • 46. Acquisition architecture
      Advantages
      Higher resolution data
      Rolls out quickly
      OffsitePI System
      Met Tower
      Secure Connection
      Solar Panel
      Wireless
      Connectivity
      Architecture
      Fits in place with existing PI infrastructure
      Fault tolerant
      Low power
      WINData Acquisition
      Hardware / Software
      Data
      Buffering
      Data
      Acquisition
      Battery
    • 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
      Component
      PI Server
      ECHO
      New ECHO to PI Interface
      Partner network
      Advantage
      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
      D.I.Y.
      http://demo.transpara.com/
    • 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
      marty.wilde@windata-inc.com
      Gregg Le Blanc
      gregg.leblanc@windata-inc.com
      gregg@f5direct.com
      Dave Roberts – OSIsoft
      droberts@osisoft.com

    ×