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A self-guided presentation on Wind Insight Dr Nicholas Cutler February 2012

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Presentation on theme: "A self-guided presentation on Wind Insight Dr Nicholas Cutler February 2012"— Presentation transcript:

1 A self-guided presentation on Wind Insight Dr Nicholas Cutler February 2012

2 Overview This presentation is intended as a guide to interpreting and using ROAMs wind power forecasting tool, Wind Insight Presentation structure: –Overview and advantages of Wind Insight –Performance statistics of Wind Insight –Interactive examples –Subscription options

3 Overview of Wind Insight Wind Insight provides: –Point forecasts of wind power (expected generation)

4 Overview of Wind Insight Wind Insight provides: –Point forecasts of wind power (expected generation) –Alerts of potential large rapid changes in wind power, with likelihoods

5 Overview of Wind Insight (contd) Wind power field forecast animations provide additional insight into potential scenarios during uncertain periods

6 Overview of Wind Insight (contd) Can be used for single wind farms or the aggregated wind power from groups of wind farms Based on novel approach using numerical weather prediction systems, developed by Dr Nick Cutler during his research at the University of New South Wales and in consultation with the Australian Energy Market Operator

7 Advantages of Wind Insight - compared with other wind power forecasting systems such as the Australian Wind Energy Forecasting System (AWEFS) By considering that significant wind features may deviate from their forecast positions, Wind Insight provides a superior assessment of possible wind generation scenarios compared to most wind power forecasting tools. –Due to wind feature forecast displacement, point forecasts (such as those that AWEFS provides) may not capture possible large rapid changes Wind Insight provides alerts with likelihoods of possible large rapid changes, covering high wind speed cut-outs, and rapid changes in wind speed caused by large synoptic systems as well as periods of localised high variability Wind power fields: graphical animations of the underlying wind patterns allow forecast users to visualise potential scenarios of wind power and respond accordingly

8 Wind power fields Estimated speed and direction of the most prominent moving wind features Australian coastline Forecast hub height wind directions Wind farm location

9 Wind speed forecasts are transformed Local elevation and surface roughness affect local wind speeds, making displacing wind features not a simple task Thus, the wind power fields use transformed wind speed forecasts where local modelled terrain effects are made equivalent to the terrain of the wind farm site This allows feature displacements to be used and visualised directly Site-equivalent wind speedsRaw wind speed forecasts Wind speeds over the ocean are reduced Wind speed transformation over land is more complex

10 Other wind power field display formats (1) 3D Wind power field can be displayed in 2D format Press Page Up and Page Down to play again. 2D wind power formatStandard 3D field format

11 Other wind power field display formats (2) Coloured changes format colours according to the severity of large rapid changes This example also includes locations of high wind speed alerts Click to start animation Coloured changes format2D wind power format

12 Alerting results from demo Wind Insight forecasting tool Canunda and Lake Bonney 1 wind power changes by > 65 MW in 30 minutes in 12- month period using ACCESS-A (BoM) Performance measureChange in wind speed events High wind speed cut-out events All events Number of events Number of events correctly alerted Percentage of events correctly alerted 67%80%70% Percentage of time-steps alerted5%6%10% 126 MW (These results are based only on AEMO wind power dispatch data – results would improve if turbine availability or wind speed information were available)

13 Alerting results from Wind Insight prototype developed for AEMO in 2010 Total SA wind power changes by > 200 MW in 30 minutes in 18-month period (548 days) using reduced alerts tuning 868 MW Performance measureChange in wind speed events High wind speed cut-out events All events Number of events15621 Number of events correctly alerted Percentage of events correctly alerted 73%100%81% Percentage of time-steps alerted7%3%10% (These results are based only on AEMO wind power dispatch data – results would improve if turbine availability or wind speed information were available)

14 How the likelihoods work Likelihood values apply to the hour time window E.g. The change in wind speed alert here gives: –20% likelihood of event occurring in the period 1:30 to 2:30 –20% likelihood of event occurring in the period 2:30 to 3:30 –4% likelihood for 3:30 to 4:30 and 4:30 to 5:30 –41% of event occurring in the full alerted period from 1:30 to 5:30 = 1 – likelihood no event occurs in this period = 1 – 0.8*0.8*0.96*0.96 = 41%

15 Interactive examples for using Wind Insight Example 1: point forecast and alerts Imagine the time is midnight on 12 th October 2010

16 Interactive examples for using Wind Insight Example 1: point forecast and alerts Imagine the time is midnight on 12 th October 2010 Wind power production is around 270 MW Point forecast shows rapid decrease at around 4:00

17 Interactive examples for using Wind Insight Example 1: point forecast and alerts Imagine the time is midnight on 12 th October 2010 Wind power production is around 270 MW Point forecast shows rapid decrease at around 4:00 High wind speed alert raised with 10% likelihood at midnight to 1:00 Change in wind speed alert raised for period 2:00 to 5:00 with 40% likelihood around 2-3:00

18 Press Page Up and Page Down to play again. What kind of wind power changes do you think are most likely to occur in the alerted period? (See next slide for what happened) Example 1: animations

19 Example 1: with observations A rapid decrease in wind power occurred at 2:00 on 12 October 2010 This was 1-2 hours earlier and more rapid than the point forecast suggested However the alerts and wind power fields did suggest this possibility During the 10% likelihood high wind speed cut-out alert, an actual event did not occur this time

20 Interactive examples for using Wind Insight Example 2: point forecast and alerts Imagine the time is 9:45am on 19 th December 2010 Wind power production has been around MW for the past 4 hours Point forecast is rated power for next 12 hours followed by rapid decrease High wind speed and change in wind speed alerts raised over next 12 hours

21 Example 2: animations Press Page Up and Page Down to play again. What kind of wind power changes do you think are most likely to occur in the alerted period? (See next slide for what happened)

22 Example 2: with observations Two high wind speed cut-out events occurred around 11:00 and 13:30. A large decrease in wind power occurred at 17:00. None of these events were suggested in the point forecast

23 Wind power fields for South Australia Wind power fields in any format can be displayed for multiple wind farms sites in large regions such as South Australia – click to start animation Four regions covering SA wind power in 2D format Wind speed forecast contours outside wind farm regions (ms -1 at hub height) Cathedral Rocks and Mt Millar: 136 MW Mid-north region: 321 MW Wattle Point and Starfish Hill: 125 MW SESA: 325 MW

24 Subscription Options Wind Insight forecasts can be provided up to 48 hours ahead for –single wind farm sites –aggregations of groups of wind farms, such as total wind generation in South Australia. The alerts can be tuned to the users needs –User can specify the size and duration for a large rapid change as well as the threshold for alerts Wind power fields can be provided in different formats The point forecast can be provided for –any time resolution desired (eg. 30 minutes or 5 minutes) –tailored for its use (i.e. minimised root mean square error, or optimised for wind fluctuations) Other specific features of Wind Insight are also possible Any further questions or enquiries, please contact Nick Cutler on , or


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