Issues with AEMO Forecasting, Part 2

The previous post looked at a demand-side issue with AEMO electricity forecasting, this post deals with an issue concerning the contribution of wind power to meeting peak demands. In summary, it appears that AEMO decouples historical wind power from historical demand in its Monte-Carlo modelling of future system adequacy. If so this significantly over-estimates the contribution of wind power to peak demands, and hence over-estimates system adequacy. There is a strong association between heatwaves and wind power lulls in South Australia, and this association is partly lost by the decoupling that AEMO appears to have done with demand and wind data.

The basis of AEMO’s Monte Carlo modelling is a set of historical data on demand and wind power, at 30-minute resolution, which are available to the public at this link:

The AEMO’s approach to the modelling is excellent, being based on actual demand and wind power data, but an examination of the data used reveals a potential problem. The problem can be seen in the following two example CSV files, which can be opened with a spreadsheet:

  • LKBONNY1-REF-2014.csv
  • 2016 SA Neutral 10POE REF-YR 2013-14.csv

The LKBONNY file contains wind power data as 30-minute averages from July 2013 to June 2014, which I have verified against NEMWEB archive data at 5-minute resolution in the following figure:


Source of the 5-minute data in the figure above:

The strange thing about the 30-minute wind traces data files is that they contain around 25 identical copies of the actual 2013/14 data, one copy for each year to 2040 (for leap years the data for 29th February is an exact copy of the data for the 28th). Thus, when the future is modeled, it appears that the wind power data retains its original dates, but the same thing does not happen with the demand data.

The demand data files for future years shift the actual demand dates so as to preserve their days of the week. This can be seen clearly by looking at the demand spikes of heatwaves, in particular the very severe one of Monday 13th to Friday 17th January 2014; the 5 consecutive days of very high demands can be seen moving in date from year to year so as to preserve them always as Monday to Friday.

Thus, it appears that there is a good chance that AEMO has decoupled wind power data from demand data, partly diluting the key correlation between very high demand and very low wind power, thereby over-estimating system adequacy for dealing with heatwaves. The dilution would be only partial, rather than total, because the demand data oscillates around correct alignment with the wind power data.


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