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More Reliable Wind Power Forecasting - OSIsoft Users Conference


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

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More Reliable Wind Power Forecasting - OSIsoft Users Conference

  1. 1. Reducing Wind Forecasting Uncertainty with PI and ECHO<br />Using Real-time Offsite Meteorological Observations<br />Marty Wilde – CEO, Principal EngineerWINData, Inc.<br />Gregg Le Blanc – Flunky<br />WINData, Inc.<br /> F5Direct, LP<br />
  2. 2. PI-enabled Real Time Off-site Meteorological Observations for Improved Short-term Wind Forecasting, Operating Economics and System Reliability<br />Marty Wilde – CEO, Principal EngineerWINData, Inc.<br />Gregg Le Blanc – Flunky<br />WINData, Inc.<br /> F5Direct, LP<br />
  3. 3. Topics<br />Brief introductions<br />The importance of forecasting<br />Wind power: ups and downs<br />The impact of remote observations<br />How WINData leverages ECHO and PI<br />Future plans<br />
  4. 4. WINData background<br />Started by Marty Wilde<br />In wind energy 1991<br />Wind energy specialists<br />From raw dirt to developed wind farm<br />Over 500 meteorological tower installs<br />Specializing in high-end instrumentation and tall towers<br />Goal: <br />Advance the “state-of-the-art” by using real time measurements for model and investment validation<br />
  5. 5. Why forecasting is important<br />
  6. 6. The critical role of forecasting<br />Wind penetration<br />California: <br />20% renewables by 2010<br />33% renewables by 2020<br />Europe:<br />20% renewables by 2020<br />Already in place<br />Denmark<br />20% of demand today<br />50% by 2025<br />Spain<br />20,000 MW capacity by 2010<br />&gt; 15,000 MW in place today<br />Peaked at 40% of demand (typically ~25% of demand)<br />
  7. 7. Working together to improve forecasts<br />Goal: Designing a program that results in better forecasts<br />WINData &AWS Truewind<br />Teaming up with utilities to improve system operations<br />Better wind energy integration<br />
  8. 8. Bonneville Power’s balancing act<br />1,500 MW<br />Balancing Area<br />19 Wind<br />Farms<br />
  9. 9. BPA’s Balancing Area<br />
  10. 10. Scheduling wind operations is complex<br />
  11. 11. A series of events at BPA<br /><ul><li>Wind power  15% of peak load
  12. 12. Large deviations power use of Portland, OR</li></ul>Theoretical Scheduled MW<br />730 MW<br />680 MW<br />400 MW<br />625 MW<br />0 MW<br />
  13. 13. Consequences<br /><ul><li>Wind power is backed by another generation asset
  14. 14. Unintended results</li></ul>Theoretical Scheduled MW<br />Violation of:<br /><ul><li>WECC Control Standards
  15. 15. EPA Regulations</li></ul>0 MW<br />
  16. 16. Ramp Events<br />Not your friend.<br />
  17. 17. Ramp events captured by ERCOT (1)<br />
  18. 18. Ramp events captured by ERCOT (2)<br />
  19. 19. Ramp effects at ERCOT<br />News article from Reuters (2/27/08):<br />Sudden 1,700 to 300 MW drop in wind power<br />Increased demand due to colder temperatures<br />Ordered 1,100 MW to be curtailed from interruptible customers within 10 min<br />Other suppliers were unable to meet commitments<br />Happy ending<br />No other customers lost power<br />Interruptible customers were back online 90 min later<br />
  20. 20. Scheduling around ramp events<br />Hour ahead schedules -forecast interpretation<br />Forecasts are good at predicting that there WILL be a ramp event<br />When will it arrive?<br />Phase error<br />How significant?<br />Amplitude error<br />Forecast error tends to increase around ramp events<br />
  21. 21. WINData’s Plan<br />Introducing WINDataNOW<br />
  22. 22. Goal: Reduce the forecast uncertainty<br />Idea<br />Can short-term forecasts be improved by using actual upstream measurements from physical instruments?<br />Requirements<br />Off-site meteorological data from predominant wind directions<br />Deliver measurements with enough resolution and timeliness to be useful<br />
  23. 23. WINData’s technology<br />Create a modern, remote “met” sensing system<br />Combine off the shelf technologies:<br />PI System infrastructure <br />Sensors<br />Logger equipment<br />Focus on configuration vs. programming<br />
  24. 24. WINDataNOW! – remote met observations<br />Towers are strategically located<br />Transmit high fidelity “line of site” data<br />Use this data:<br />Collaborate with forecaster<br />Refine short-term forecasts<br />Back-cast to develop more detailed climatology<br />“Line of Site” Locations<br />Wind Farm<br />60 – 100 minutes<br />of prior notice<br />Upwind Met Tower<br />30 – 60 miles<br />upwind<br />
  25. 25. Tower placement<br />Prevailing <br />Wind Direction<br />
  26. 26. Tower placement<br />Wind Speed<br />meters / sec<br />Wind Speed<br />meters / sec<br />Wind Speed<br />meters / sec<br />Wind Speed<br />meters / sec<br />
  27. 27. Anticipate changes<br />Ramp up<br />Ramp down<br />Improved wind energy integration on the grid<br />More data: smoother operations<br />Apply “line of site” datato better understand near-term transients<br />Power Production vs.<br />Augmented forecast<br />MWh<br />Wind Speed vs. <br />Augmented forecast<br />meters / sec<br />
  28. 28. Example<br />A Site in Montana<br />
  29. 29. Today’s data expectations<br />(m/s)<br />Typical industry-standard data logger output<br />
  30. 30. The same time period, now in high fidelity<br />Actual Wind Speed (m/s)<br />
  31. 31. Comparison<br />Average vs. Actual Wind Speed (m/s)<br />Averages vs. higher fidelity data<br />
  32. 32. Using PI’s 10m Averages<br />
  33. 33. Using PI’s 5m Averages<br />
  34. 34. Zooming out a little more<br />High wind speed cut-off<br />
  35. 35. Increased meteorological insight<br />Re-forecast<br />Account for actual upstream observations<br />Decrease forecast error around ramp events<br />Advanced warning<br />Situational awareness<br />Reduce costs<br />Avoid penalties<br />Use this<br />Hour Ahead<br />To reduce this<br />
  36. 36. C-Gorge Project<br />WINData in <br />The Columbia Gorge<br />
  37. 37. Map of C-Gorge vs. existing sites<br />
  38. 38. WDN Locations vs. BPA towers<br />Goodnoe<br />7 Mile Hill<br />WDN1<br />WDN2<br />Wasco<br />
  39. 39. How WINDataNOW! works<br />From full-service to self-service<br />Ways of engaging WINData<br />Project management<br />Deployment & management of early warning met arrays<br />Enterprise-wide retrofit projects<br />“WindTelligence”<br />Analysis<br />Development of situational awareness tools and IP<br />Subscription service<br />Data from reference locations<br />Hosted central PI Server<br />Stream data to your local system<br />
  40. 40. Subscriber architecture<br />*<br />Subscriber 1<br />Connection<br />OffsitePI System<br />WINData<br />Subscriber 2<br />WINData Managed<br />Subscriber 3<br />WINData Managed<br />PI<br />SCADA<br />Your <br />Operations<br />* Not to scale<br />
  41. 41. Owner architecture<br /><ul><li>Installation by WINData
  42. 42. One-time setup and validation
  43. 43. Ownership and management by customer
  44. 44. Your own IP</li></ul>Connection<br />WINData<br />Hardware<br />Managed by you<br />PI<br />SCADA<br />Your <br />Operations<br />
  45. 45. Combination architecture<br />Other sites in the area<br />Subscriber 1<br />Connection<br />OffsitePI System<br />WINData<br />Hardware<br />Subscriber 2<br />WINData Managed<br />Subscriber 3<br />Managed by you<br />PI<br />SCADA<br />Your <br />Operations<br />
  46. 46. Acquisition architecture<br />Advantages<br />Higher resolution data<br />Rolls out quickly<br />OffsitePI System<br />Met Tower<br />Secure Connection<br />Solar Panel<br />Wireless<br />Connectivity<br />Architecture<br />Fits in place with existing PI infrastructure<br />Fault tolerant<br />Low power<br />WINData Acquisition<br />Hardware / Software<br />Data<br />Buffering<br />Data <br />Acquisition<br />Battery<br />
  47. 47. Data features<br />Focus on data confidence<br />Logger spools data if connectivity goes down<br />Supports “industry standard”<br />At least 2Hz data always streamed to PI<br />Keep the data at its original resolution<br />Tune the data with standard PI settings<br />10m averages always available via PI<br />
  48. 48. Benefits of working on OSIsoft’s platform<br />Component<br />PI Server<br />ECHO<br />New ECHO to PI Interface<br />Partner network<br />Advantage<br />Directly integrate with utilities and system operators<br />Spool data during connectivity outages<br />Speaks PINET over TCP/IP natively<br />Solution works immediately with partners like Transpara’sVisualKPI<br />
  49. 49. Demo<br />D.I.Y.<br /><br />
  50. 50. Future possibilities<br />Smart Grid<br />Provide “fuel insight” to consumers<br />Encourage informed choices – demand side management<br />Better investments<br />Prospecting – High Fidelity “Bankable Data”<br />Improved climatology for new wind energy sites<br />Advanced wind farm design<br />Turbulence, FFT Analysis, Matrix correlations<br />Integrate shiny new technologies<br />Vertical profiling<br />SODAR, LIDAR, etc.<br />
  51. 51. Looking forward<br />Upstream observations can dramatically improve forecasts (Papers by Kristin Larson, 3TIER)<br />Significant interest from utilities, operators, and forecasters in short-term ramp event prediction<br />WINData wants to work with you<br />Contact Marty Wilde, Dave Roberts, or Gregg<br />
  52. 52. Special Thanks<br />OSIsoft for its support<br />Johnson City office and the embedded team for ECHO work<br />Dave Roberts, Business Development, and Sales for advocacy<br />ProSoft for its continued support<br />Transpara for use of it’s VisualKPI software<br />
  53. 53. Find us after the talk!<br />Marty Wilde<br /> <br />Gregg Le Blanc<br /><br /><br />Dave Roberts – OSIsoft<br /><br />