Historical Temperatures at Alice Springs

Paul Matthews has recently reported on the highly unstable versions of temperatures at Alice Springs (and elsewhere) in GHCN:

Instability of GHCN adjustment algorithm

Several years ago Roger Andrews raised doubts about GHCN temperature homogenisation at Alice Springs:

https://tallbloke.wordpress.com/2012/10/11/roger-andrews-chunder-down-under-how-ghcn-v3-2-manufactures-warming-in-the-outback/

These blog posts and ones related to them (apologies for not mentioning everyone) have inspired me to fire up once again my own temperature analysis tools. This post will develop over the next several weeks, but initial results and thoughts are as follows.

Besides the question of GHCN algorithm stability, there is also the question of what the right answer is, and whether or not one of the GHCN versions has come close. Step one is always to plot the raw data, and the data for close neighbours, as shown in the following figure for annual averages of daily maximum temperature (Tmax), downloaded from BoM Climate Data Online:

alice_a

The figure above shows the temperatures as they are (not anomalies), with Alice Springs Post Office in black, and data from nearest neighbours in various colours. Several tentative conclusions can be drawn just by eye-balling the data as follows:

  • Alice Springs is cooler than its neighbours (possibly due to higher elevation and differences in vegetation and cloud cover), which immediately creates an obstacle to easy homogenisation
  • The neighbours share a great deal of consistency in their temperature fluctuations, and in their gentle cooling trend to around 1960, seen elsewhere in Eastern Australia. It should be relatively easy to detect inhomogeneities amongst the neighbours, and correct them at the level of annual averages.
  • The elevated temperatures at Alice before around 1900 (marked A on the figure above) suggests that non-standard or damaged exposures were used in that period. Documentary evidence for a Stevenson screen in use at a certain date only provides evidence for its use from that date, not before.
  • Alice is missing a rise in temperature in 1931 (marked B on the figure above), which would trigger some algorithms to shift its temperatures at that time, especially as the site moved in 1932 from the Telegraph Office to the Post Office. But, the overall temperature trend of Alice is already consistent with the neighbours, and a major shift in its temperatures would create an inconsistent warming trend.
  • It is possible that Alice at the Post Office (after 1932) had urban heating, if so that would make homogenisation extremely difficult. The problem of urban heating at a Post Office site, prior to a shift to an airport site, is not obviously dealt with properly by govt. homogenisations, such as ACORN-SAT. Cobar may be a clear example of the Post Office heating problem. Simply splicing together airport and urban-warmed Post Office data gives an exaggerated warming trend, even if there is no urban warming at the airport.

The bottom line is that I don’t fancy trying to homegenise Alice Springs at all, it can be safely ignored, as it only covers the small (relative to the whole of Australia) area of its local range of hills. Beyond the hills the climate histories of the neighbours can be used.

More to follow later …

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Issues with AEMO Forecasting, Part 3

The AEMO models the future adequacy of the NEM electricity system, one of the key outputs being an annual “Electricity Statement of Opportunities” (ESOO), whose stated purpose is to alert industry to potential opportunities for new generators. This post is about whether or not the ESOO is achieving its stated purpose, the conclusion being that it largely fails because it focuses on a single number (average unserved energy) as a measure of system adequacy. This single number is usually zero, which does not convey any information about how close it is to being non-zero, and when it is non-zero it conveys no information about how bad the energy shortfall might be in a severe heatwave summer.

The key quantity modelled by AEMO is unserved energy (USE), the expected average number of MWh that the system will fail to provide (in the absence of special measures being taken), as a percentage of total annual consumption. The Reliability Standard for USE is 0.002% (around 250 MWh in South Australia), and the value of USE is quoted if the Reliability Standard is exceeded. To gain some insight into USE I have calculated actual sample values for it, as the amount of non-wind supply varies, for years 2009/10 to 2014/15, the ones for which AEMO provides wind trace data at 2016/17 levels.

The following figure shows how USE varies with non-wind supply in South Australia for 2013/14 weather and demand, for a system with no wind power (black), 2016/17 level wind power (red) and 150% of 2016/17 wind power (blue):

sa_use_a1

The figure above indicates what might happen in the future if non-wind supply drops below 3000 MW, but it obscures the situation around the very low percentage of the Reliability Standard.

The following figure shows the same data as above, but with a focus on the very low percentages of the Reliability Standard:

sa_use_a2

The figure above reveals that the effect of 1500 MW of 2016/17 wind power on un-served energy would be equivalent to a few hundred MW of firm capacity, but 50% more wind power would not provide 50% more equivalent firm capacity.

The following figure shows the same data as above, but for all the 6 years used by AEMO in its assessment of future USE values, for South Australia. Note that for display purposes the value of un-served energy is limited to 0.004%, double the Reliability Threshold:

sa_use_a

The figure above shows a high degree of consistency for the effect of 2016/17 level wind power, and for 50% more wind power. The main difference between the years is simply the varying amount of demand, which is due in large part to the varying severity of heatwaves and to whether or not the severest ones culminated on working days.

Discussion

A prediction of future average USE figures involves modelling plant availability, including unexpected outages, and future demands, and this is what the AEMO does. Leaving aside issues of the validity of the various ingredients in the calculation, this post is suggesting that ESOO reports would benefit from much more information being provided, for example giving information for each separate year (2009/10 to 2014/15) that goes into the future year averages. It is likely that the future averages are dominated by one or two severe heatwave years in the past, whose effect gets considerably diluted by averaging over all six years examined.

Reliability standard breaches come from a few severe heatwave days with low wind power, it would be easy to do a poor job of modelling unexpected outages on these heatwave days, for example via a limited number of Monte Carlo runs that have a good chance of no outage falling on a heatwave day.

The Monte Carlo approach, and use of averages by AEMO may not be appropriate for this problem. An alternative approach would be to focus on credible worst case scenarios, such as the highest demand seen in the last 6 years, at the same time as an outage at a major generator or interconnector.

There are many obstacles to getting conventional generators built, and one such obstacle may be the limited amount of (and possibly misleading) information being provided in ESOO reports. Builders of wind farms do not have a problem with the quantification of system adequacy, the only relevant information for them is their capacity figure, which they can estimate themselves from information available elsewhere. Maybe ESOO reports are poor because nobody reads them, because nobody is currently contemplating building conventional generators.

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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:

https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/NEM-Electricity-Statement-of-Opportunities

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:

sa_windtraces_a

Source of the 5-minute data in the figure above: http://nemweb.com.au/Reports/ARCHIVE/Next_Day_Actual_Gen/

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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Issues with AEMO Forecasting, Part 1

The AEMO does a lot of modelling of the NEM, to assess such things as the adequacy of the future generating capacity to meet peak demands, and of the transmission system to cope with new and lost generators. A key quantity in the modelling is the expected maximum demand, and this post deals with how AEMO joins its estimates to the latest actual maximum demands, as shown in the following figure from the 2016 South Australian Electricity Report (SAER):

sa_maxdems_a

It appears that AEMO are assuming that the last two actual maximum demands (from summers 2014/15 and 2015/16) are somehow “average”levels of maximum demand. There are no references to any attempt to correct the actual maximum demands for demand reductions due to weekends/holidays or for heatwave severity.

I have examined the daily peak demands, temperatures and weekend/holiday dates for each summer from 2004/05 to 2015/16 and found that each summer can be assigned to one of two classes:

  • Class A: A major heatwave was unaffected by weekends/holidays
  • Class B: There was no major heatwave falling on working days

Examples of this kind of analysis are given in the previous two posts. The results for all years examined are shown in the following figure, showing Class A summers with black markers, and Class B ones with purple markers:

sa_maxdems_b

The following figure shows the same data in a different way:

sa_maxdems_c

The figure above suggests to the author that AEMO demand forecasting may be flawed by its failure to take account of weekends/holidays and of the variability of heatwave severity. The bottom line is that AEMO may be underestimating future maximum demands.

Circumstantial evidence for the prosecution lies in the fact that Queensland recently recorded its highest ever maximum demand, despite the proliferation of solar panels, increasingly efficient appliances and ever higher prices, all of which are meant to reduce demand:

http://www.wattclarity.com.au/2017/01/queensland-demand-today-exceeds-the-previous-all-time-record/

The record breaking Queensland heatwave culminated on a working day, and it may be that by chance this has not happened there for several years.

Conclusions

Maximum demand may not be as low and falling as fast as AEMO are assuming, and in fact may not be falling at all, casting doubt on claims by AEMO that system capacity is adequate over the next several years. There are other issues with AEMO modelling, also connected with system capacity, to be covered in future posts.

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South Australian response today to 2014/15 weather

Introduction

This post repeats the analysis of the previous one, this time for the summer weather of 2014/15 in South Australia (SA), with 2016/17 wind farms.

The maximum demand in SA in summer 2014/15 was quite low, an outcome that the AEMO seems to give an unwarranted significance in its forecasting of future maximum demand. Also relevant to the AEMO demand forecasting is the fact that Queensland recently had its highest ever demand, something not meant to happen in a world of solar PV, increasing efficiency and prices, and “smart” consumers:

http://www.wattclarity.com.au/2017/01/queensland-demand-today-exceeds-the-previous-all-time-record/

The heatwaves of summer 2014/15 in SA fell mostly on weekends and the New Year holiday period, suggesting that intrinsic consumer demand was not as low as the raw demand data suggests.

Data Sources: see the previous post

Demand Data

The following figure shows the daily peak demands (blue curve) and the two major influences on them, the maximum temperature (at Adelaide Kent Town) in red and the times of weekends and holidays, marked with purple dots:

sa_2014_15_a

The four heatwaves in February 2015 all fell on weekends at critical times, in particular the 22nd February, the fourth in a sequence of very hot days, would have very likely produced a much higher peak demand if it had fallen on a working day. The only heatwave that was unaffected by the weekend demand reduction was in early January, but that was during the New Year holiday period, with schools and some businesses still on vacation. Intrinsic consumer demand was probably a lot higher than is suggested by the actual demand data.

Wind Data

The AEMO-estimated total wind power today from the 2014/15 weather is shown in the following figure, in terms of the daily maximum (red) and minimum (black):

sa_2014_15_b

The figure above shows the typical wind power variations in South Australia, often going from very high to very low in many days of summer. It is the deep wind power lulls, many lower than 50 MW, that causes the severe limitations on the impact of additional wind farms on peak demands on non-wind sources. The wind power data provided by the AEMO are 30-minute averages, and the minima will go to even lower values than shown above at times during each 30-minute interval.

Wind Power Impact

The following figure shows the daily peak demands (red squares), and the peak residual demands on non-wind sources (black curve):

sa_2014_15_c

The figure above shows that the highest peak demands on non-wind sources would be reduced by around 200 MW on average, i.e. by only around 10 MW for each of the 18 wind farms, but the reduction is only around 50 MW for what could have been the highest demand day, the 22nd February 2015. A simple way of seeing the impact of additional wind farms is to repeat the calculation of non-wind peak demands (black squares) with 50% more wind power (purple curve), shown in the following figure:

sa_2014_15_d

The figure above shows the barely perceptible effect of 50% more wind power on peak non-wind demands. The deep wind lulls of around 50 MW are the explanation, as they imply that each of the 18 existing wind farms are averaging a contribution of only several MW at the times of highest demand on non-wind sources.

CONCLUSIONS

Peak demand is not falling as fast as the AEMO may be assuming, increasing wind (and solar PV) cannot contribute significantly to additional firm capacity, conventional generators are closing down at a rapid rate, and no new ones are being built. This combination of circumstances can only end in tears.

 

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South Australian response today to 2013/14 weather

Summary

The AEMO uses past wind power and demand data to model the future adequacy of the NEM, and they make the data used available to the public. This post presents some of the data available for the summer of 2013/14, of particular interest because of a major heatwave in South Australia that caused peak demands greater than 3000 MW.

The focus of this post is on the amount, and on the impact, of wind power that would be produced today if there was a repeat of the summer weather of 2013/14. No attempt is made here to adjust the actual demands of 2013/14 to the present time.

The estimated wind power that would be produced today from the 2013/14 weather is found to reduce peak demands on non-wind sources of electricity by around 200 MW, around 10 MW for each of the 18 wind farms. In common with other years examined the impact on this figure (200 MW) of additional wind power is very small, because the large amount of existing wind power pushes many of the non-wind peak demand times to deep wind lulls, such that adding another wind farm to the 18 already present would reduce the highest non-wind peak demands by only several MW.

Increasing wind power in South Australia will have a large impact on the economic viability of non-wind sources, whose market share is reducing, but at the same time cannot reduce the need for around 3000 MW of firm capacity for the foreseeable future.

Data Sources

The AEMO has done the estimation of wind power from past weather, using actual measured data for the wind farms present at the time, with interpolation/extrapolation for recent additions, for example all 3 Snowtown wind farms are assumed to have identical capacity factors. The resulting data is called “Wind Traces” and can be downloaded from here:

https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/NEM-Electricity-Statement-of-Opportunities

Demand data is used from a different source, the actual historical demands available from here:

http://nemweb.com.au/Reports/ARCHIVE/HistDemand/

Demand Data

The following figure shows the daily peak demands (blue curve) and the two major influences on them, the maximum temperature (at Adelaide Kent Town) in red and the times of weekends and holidays, marked with purple dots:

sa_2013_14_a

The figure above shows several heatwaves, the one from 13th to 17th January being particularly long and hot and producing a major spike in peak demand.

Wind Power

The AEMO-estimated total wind power today from the 2013/14 weather is shown in the following figure, in terms of the daily maximum (red) and minimum (black):

sa_2013_14_b

The figure above shows the typical wind power variations in South Australia, often going from very high to very low in many days of summer. It is the deep wind power lulls, many lower than 50 MW, that causes the severe limitations on the impact of additional wind farms on peak demands on non-wind sources. The wind power data provided by the AEMO are 30-minute averages, and the minima will go to even lower values than shown above at times during each 30-minute interval.

Wind Power Impact

The following figure shows the daily peak demands (red squares), and the peak residual demands on non-wind sources (black curve):

sa_2013_14_c

The figure above shows that the highest peak demands on non-wind sources would be reduced by around 200 MW, i.e. by only around 10 MW for each of the 18 wind farms. A simple way of seeing the impact of additional wind farms is to repeat the calculation of non-wind peak demands (black squares) with 50% more wind power (purple curve), shown in the following figure:

sa_2013_14_d

The figure above shows the barely perceptible effect of 50% more wind power on peak non-wind demands. The deep wind lulls of around 50 MW are the explanation, as they imply that each of the 18 existing wind farms are averaging a contribution of only several MW at the times of highest demand on non-wind sources.

Increasing wind power in South Australia will have a large impact on the economic viability of non-wind sources, whose market share is reducing, but at the same time cannot reduce the need for around 3000 MW of firm capacity for the foreseeable future.

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Quantifying Capacity Credit

This post proposes a method of characterising the “Capacity Credit” of renewable electricity generation methods, covering both the credit of the existing generators, and how the credit will increase with additional generators.

Solar power is a good way to introduce the issues involved, as the “Law of Diminishing Returns” is quite well known for this method of electricity generation. In hot sunny regions (such as South Australia), with peak demand in summer heatwaves, the early solar PV generators are more effective in reducing peak demand than later additions because the early generators push the time of peak demand towards dusk. Thus, capacity credit for solar PV cannot be represented by a single number, it requires two numbers:

  • The credit for the existing generators
  • The rate of increase of credit with additional generators

Many specifications of capacity credit are somewhat misleading as they usually give a single number, expressed as a percentage of nameplate capacity, leaving some readers to assume (incorrectly) that this percentage applies to future additions.

Solar PV is difficult to quantify as it tends to be installed on domestic rooftops and is not metered by the transmission operator, hence this post will focus on wind power, using South Australian data from AEMO archives.

EXISTING GENERATORS

The following figure shows the daily peak demand (red curve) in South Australia for summer 2015/16, together with the daily peak demand (black curve) on non-wind sources of electricity:

sa_quant_credit_a

The data shown above provides some samples of the random distribution of the reduction, due to existing (in 2015/16) wind power, in peak demand on conventional sources of electricity. Further work is required to deal with the problem of demand modulation due to weekends and holidays, before a number can be produced for existing wind power capacity credit in South Australia, but the figure above provides a ball-park estimate.

FUTURE GENERATORS

The following figure shows the daily peak demand on non-wind sources (black curve, repeated from above), together with the same quantity but with 50% more wind power (purple curve):

sa_quant_credit_b

The most striking feature of the data shown in the figure above is the close similarity of the two curves, except at the minima, which are the windy days. To a good first approximation future wind farms in South Australia will not give any more reduction in peak demand on non-wind sources.

Discussion

The existing wind power capacity in South Australia is sufficient to push the times of peak demand on non-wind sources to deep wind lulls, except on very windy days, which tend not to be very hot (high demand) days. Many of these deep wind lulls are in the late afternoon or early evening, so additional solar PV will have little impact.

In assessing the amount of conventional generation required to meet future peak demands the focus must be on the limited capacity credit of additional renewable generation, as well as on that of the existing generators.

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