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Emissions Policy Renewables

The capacity factor of wind

Guest Post by John Morgan. John is Chief Scientist at a Sydney startup developing smart grid and grid scale energy storage technologies.  You can follow John on twitter at @JohnDPMorgan.


A lot of ink is spilled on wind intermittency, and not necessarily based in data.  So I have extracted and analyzed a high resolution dataset of a year’s worth of Australian wind power for a number of interesting properties.  I previously wrote about the capacity factor as a limit to the share of electricity that wind and solar can acquire, so I also ask how wind capacity factor changes with time, place and season.  In particular, how does it change during sunlight hours and what does it mean for the capacity factor limit on renewable energy penetration?

Australian wind fleet data

The Australian Energy Market Operator (AEMO) publishes data on all generators connected to the National Electricity Market (NEM) grid, which covers the eastern states including Tasmania, but excludes Western Australia and the Northern Territory.  The data includes power generation every five minutes for every generator for the last year, their capacities as registered with the grid operator, and more.  It is not very accessible, being in the form of thousands of SCADA data files, many of which contain errors.  But with a bit of work the data can be extracted.  Here, for instance, is the output of all grid-connected wind farms at five minute resolution over one year:

Wind capacity factor

Here is the top level summary of the Australian wind farm fleet over the last year:

The nameplate capacity is the total capacity of all wind farms – 3753 MW.  But the whole fleet only manages 3238 MW at peak.The whole is less than the sum of its parts – half a gigawatt less in this case. Why is this?

The fleet is comprised of wind farms distributed over a large area of eastern Australia.  To achieve maximum theoretical power the wind would have to be blowing at the optimum speed for each wind farm, at all wind farms, simultaneously.  This is a statistical improbability and quite possibly a hydrodynamic impossibility, as it would require a high velocity correlated flow field over very large distances.

So while we often hear that the wind is always blowing somewhere, it is equally true that it is always not blowing somewhere else, and the fleet output never achieves full capacity.  Australia in theory has 3753 MW of wind capacity, but this will never be realized in practice.  Similarly, statements like: “The US added x GW of wind capacity last year”, overstate the capacity addition because the new wind build is unlikely to ever produce its maximum power in full correlation with the rest of the fleet.

In other words, national capacity statistics overstate the potential output of wind.  The flip side of this is that the capacity factor limit underestimates the potential for wind penetration.  We can push the penetration of wind a bit higher than the capacity factor – generation would start to exceed demand at 33% share, rather than 29% share.

Wind correlation times and the synoptic scale

Over what distances are wind farm outputs correlated?  Its actually easier to ask, over what period in time is wind power correlated?  This information is contained in the wind power autocorrelation function, which we can calculate from the dataset:

The autocorrelation function tells us how long the influence of a particular state of wind persists.  If its windy now, for how long will it remain windy?  Surely for the next five minutes.  But will it still be windy tomorrow?  The autocorrelation function is a “memory” function that tells us how long wind “remembers” how hard it was blowing.

The autocorrelation function decays in about 40 hours (since we have 5 minute data the x-axis is in units of 5 minute “lag”s).  This means wind, on average, bears no relation to its output 40 or more hours ago.  Wind has a “correlation time” of about 40 hours.

Its interesting to interpret this as the time for a body of moving air to pass over a windfarm.  If we knew how fast it was moving we’d know how big it was.  Wind resource maps suggest a velocity of about 7-8 ms-1, so that interpretation suggests a wind correlation distance of about 1200 km – the “synoptic scale”.  This seems a pretty reasonable estimate of the size of weather systems and can in fact be done for a single wind farm with a similar result.  Its remarkable to be able to pull large scale geographic information out of just the power fluctuations of a single wind farm!

http://www.seabreeze.com.au/Photos/View/2871107/Weather/Australia-wind-energy-map/

If we wanted to cover intermittency we would need to ensure our wind fleet is dispersed over distances of 1200 km and more, so that the output of at least some of its wind farms will be uncorrelated.  This smooths the output of the wind fleet, reducing maximum output below the nameplate capacity, but increasing the amount of energy that can be integrated without having to spill, store or manage excess generation.

Wind power ramp rates

Wind output is constantly changing and requires the rest of the grid to be flexible enough to ramp up power or shed load to balance wind fluctuation.  This rate of change is just the time derivative of wind power.  The plot below shows this “acceleration rate” throughout the year.  It’s a normal distribution, the symmetry showing that wind power picks up as fast as it drops off, and that the grid needs to be responsive at a rate of 20 MW per minute, in both directions, to cover most conditions.

As more wind is added, the flexibility of the rest of the grid will have to increase proportionately – double the wind energy would require about 40 MW/min ramp rate.  But this additional ramping ability must be delivered by the shrinking dispatcheable generator sector.  So the intrinsic flexibility of the rest of the grid must increase, and faster than in simple proportion to wind penetration.  Practically that means increasingly strong pressure to shift from coal generation to gas as wind share grows.

Low wind days

Lets look at the distribution of low wind days.  We can ask, for how many days was wind output below some level?  For instance, we can find 29 days in which output was below 10% of capacity, and 127 days below 20% capacity.  127 days is, incidentally, pretty close to the number of weekends and annual leave of most Australians – Australian wind is obviously governed by Australian workplace awards!

The plot below shows the number of days below a particular output level.  Interestingly, the daily average power output never exceeded 75% of capacity, or 2810 MW, almost 1 gigawatt less than installed.  The fleet was never totally becalmed, but the lowest recorded day in the year saw output of just 2.7%.

Also of interest is the number of consecutive low wind days.  This affects the strategies we might use to cover wind outages – whether we store energy in batteries, or with pumped hydroelectricity, or shed load, cut in gas generators, or coal.  For instance, of the 29 days of wind output below 10% of capacity, 15 are single isolated low wind days, and then there are 7 pairs of 2 day long low wind runs.  If we look for sequences of days with less than 20% output, we find 2 runs of 5 low wind days.  The full distribution is shown below.

The number and distribution of low wind days show that while wind contributes energy, it does not provide capacity.  Alternative generation capacity must be available to meet the near absence of wind about one day in ten, and for two or more days in sequence.  But many of these low wind events are of just a single day duration.  This is a difficult timescale for coal plants, so again, increasing wind penetration drives the residual mix towards gas.

Wind capacity factor by month and day

To get a handle on wind seasonality we can look at monthly output and capacity factors.  Each point in the plot below is the average power output for a day in the year.  The coloured blobs show the distribution of power output in each month.  Also shown is the capacity factor for each month.

The nameplate capacity factor varies from month to month (30%±5% covers it). Every month has low wind days and high wind days and everything in between with little seasonal structure.  The winter months have more high wind days, but they also have more low wind days, and one cannot confidently assert the monthly CF variation is greater than noise, in this year.

We can see this in a box plot of daily capacity factor – the data distribution is very wide and a capacity factor of 25% is consistent with the data for every month.  The highest daily capacity factor for the whole fleet was 75%, and the lowest was 2.7%.  These maximum and minimum output days both occurred in winter, when solar power is at a minimum.

Wind capacity factor by hour

We can push this through to a still finer grain by looking at wind output by hour of the day.  The following plot shows the average capacity factor by hour of the day, for each month.  We can see if wind picks up or drops off in any consistent way through the course of a day.  The summer months show some daily pattern, perhaps, but the rest of the year does not:

Does the wind output drop when the sun is shining?  It would be convenient if it did, as it would allow more solar  power on the grid.  Lets nominate solar hours as 10 am – 4 pm, and compare solar hours with non-solar hours.

There is no difference between wind capacity factor when the sun is high in the sky and when it is not.  Wind does not cooperate with solar by subsiding in the middle of the day.  Possibly wind blows harder when clouds block sunlight.  Unfortunately I don’t have a dataset for solar PV output and can’t test this.  My guess is that the number of days and the number of sites at which such anticorrelation occurs is not large enough to shift the average output of the total fleet enough to change the overall picture.

What does this mean for the capacity factor threshold?

As explained in detail by Jenkins and Trembath, it is increasingly difficult to build more wind or solar capacity as their market share approaches their capacity factor (CF) because they will then, at times, be producing energy in excess of demand.  The economic drag incurred by dealing with surplus generation by storage, curtailment or demand reduction undermines the economics of building additional capacity.  The capacity of wind and solar is thus limited to be roughly numerically equal to 100% of grid demand.

In “Less than the sum of its parts” I argued that adding solar to the mix actually reduces the combined amount of wind and solar energy that can be added to the grid.  This is because solar competes with wind for share of capacity, but contributes less actual energy due to its lower capacity factor.  Building solar thus reduces the maximum amount of renewable energy we can get onto the grid.

You can get around this if wind and solar generate at different times of the day, or year.  But from the data above we can say that wind does not drop during the day or pick up at night, to any significant degree.  The capacity factor of wind during “solar” hours is the same as during “non-solar” hours.

Turning to the seasonal variation, its possible wind has a higher capacity factor in winter when solar output is low, but the evidence of the last year is not compelling.  The lowest wind capacity factor in the year was actually in the winter month of June, and January in high summer was one of the higher producing wind months.  The winter months have more high wind days, but they also have more low wind days.

If there is some seasonal synergy between wind and solar, its not particularly strong, and the contention that the maximum share of renewable energy is achieved by building wind and not solar seems sound.

But the capacity factor threshold does require an adjustment.  Recall the peak wind output was only 3238 MW, less than the nameplate capacity of 3753 MW.  So we could build more capacity without fear of excess generation.  Instead of spillage or storage being required at 29% wind share, we can accommodate a more generous 33% share.  This greater share of wind energy is possible due to the geographic distribution of wind smoothing out some of the peaks.

Conclusions

There’s a lot of information in noise.  Deducing the size of large scale weather systems from the power fluctuations is pretty cool, as is seeing the signature of spatial distribution of wind farms in a one-dimensional time series.  Notably, a national wind fleet will not achieve full output due to geographical smoothing, but this smoothing also increases the capacity factor threshold for wind share a bit, from about 29% to 33%.

As expected, intermittency means wind contributes energy but not capacity to the grid, meaning wind acts as a fuel saver for fossil plants, which must increasingly shift to gas rather than coal as wind penetration grows, to accommodate higher ramp rates.

The capacity factor does not show strong consistent variation across hours, days or months, and share of renewable energy is limited as Jenkins and Trembath describe.  There is little evidence of a synergy between wind and solar in the Australian grid, supporting my earlier conclusion that a combination of wind and solar can displace less fossil energy than wind alone.  If we really wanted to push towards maximum renewable energy, we would build wind and not solar, and variable renewables share could grow to about 33%.

Data

The Australian Energy Market Operator Generation and Load data can be found at this page.

Five minute data for all generators can be extracted by parsing the SCADA data files in this directory.  Data goes back a little over a year.  Older data is not available – AEMO appear to delete the oldest files on this page on a daily basis.

AEMO lists all generators connected to the grid, by technology here, in the spreadsheet “Registration and Exemption List”, in the tab “generators and Scheduled Loads”.  Each generator has a unique identifier, the DUID, allowing it to be located in the SCADA files.

The SCADA files contain a number of errors – in many files the output of many generators is double counted.  A corrected data set was created by filtering out duplicate generator entries.

The data was extracted and analysed with python code using the excellent SciPy scientific python tools, iPython notebook, pandas data analysis library, with MatPlotLib and Seaborn data visualization libraries for plotting.

By Barry Brook

Barry Brook is an ARC Laureate Fellow and Chair of Environmental Sustainability at the University of Tasmania. He researches global change, ecology and energy.

355 replies on “The capacity factor of wind”

7.059?? How on earth did I come up with that figure. You’re absolutely correct, David – the wind speed increase should have been from 7 to 7.10 m/s. How embarrassment!

So yes, I get the 2% increase for 10% hub height increase you suggested. Still, I can’t help but think that we must be approaching an economic scaling limit for this sort of improvement, given the leverage that the drag on the blades and nacelle must place on the footing and tower base. I guess that is why the turbine tends to get scaled up in power capacity as tower height is increased.

As for wind shear, it seems logical that as the better, more cleared sites get used, then the more cluttered sites would demand higher hub heights since the larger wind shear would make lower heights ineffective. But I don’t see this leading to higher capacity factors since the increase in Hellman exponent would only make the bring the wind speed back up towards that of the uncluttered site as hub height increases.

singletonengineer, you refer to certain widespread policies on renewable generation as being “unfair”. The costs (both economic and opportunity) that result from the distortions caused by these policies must be quantifiable.

The NEM article is interesting and paradoxical – the displacement of gas generation by coal seems to defy the ramp requirements needed to cope with the increased penetration of renewable sources. This is especially surprising given that this occurred in SA as well as Qld and Victoria,
And according to the ACIL Allen Emissions Report used as the basis for the CO2 emissions, they are estimated from power generated so any effects from ramping and spinning in reserve would not be accounted for. This could well have led to an even larger increase in CO2 emissions with the increased wind generation penetration if it behaved as John describes.

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Greg Kaan, I generally speak english.

What language lends meaning to:

“…according to the ACIL Allen Emissions Report used as the basis for the CO2 emissions, they are estimated from power generated so any effects from ramping and spinning in reserve would not be accounted for. This could well have led to an even larger increase in CO2 emissions with the increased wind generation penetration if it behaved as John describes.”

I was not discussing “what if” or “could well have led to”, which are unjustified idle musings, given the rules for reporting performance in the NEM.

The paper that Greg Kaan cited presented actual numbers and they aren’t pretty. Wind power has not produced a reduction in any of emissions intensity, system stability or cost. What will it take to convince wind proponents to change their minds?

Is ACIL Allen’s data wrong? In which case, why does Greg Kaan rely on selective citation drawn from the same source?

John Bennetts +61 407 724 095.

Come on, Greg. Phone me and have a chat if you wish.

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@singletonengineer

“In a fair energy market wind power providers would bid for day-ahead supply like others and either provide at the bid price or pay someone else to do so in the event of inability to supply as bid.. Wind would carry the financial risks that arise from unplanned inability to supply, either via a direct market mechanism or by arranging, progressively through the day, for others to do so on their behalf.”

My understanding is that this is exactly what happens in the German day-ahead market. Providers quote for power in particular chunks and are then responsible for providing it.

http://www.businessspectator.com.au/sites/default/files/styles/full_width/public/forecasting%20error.png?itok=CAeleBij

Wind predictability is very good. It is generally better than 8% of rated wind capacity for 24 hours ahead, and better than 4% of rated wind capacity for 1 hour ahead. This is with the benefit of specialised weather forecasts. The forecasts do cost some money, so if you have large quantities of despatchable hydro available you might not have to bother.

In Germany they treat is as a grid exceptional event if the forecast for wind power by time slot 24 hours ahead is worse than 10% of the rated wind capacity, and it doesn’t happen very often.

When comparing wind predictability with CCGT or coal forecasting, then the size of CCGT generators is much larger than wind turbine generators. So if you lose one it makes much more difference. So the current time spinning reserve (not backup) requirement for wind power tends to be less purely because you are only going to lose 10 or 20 MW when you lose a couple of turbines to mechanical failure, whereas with CCGT or coal the generators are many 100s of MW.

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John Bennets, here it is in plain english, then.

If you read through the 2014 ACIL Allen Report used by the NEM to calculate the CO2 emissions “EMISSION FACTORS REVIEW OF EMISSION FACTORS FOR USE IN THE CDEII”, you will see from section 2.4 that they estimate average thermal efficiency and auxiliary consumption factors for each generator to produce an emissions factor and from this, they used the delivered electrical energy over the year (in MWh) to produce the estimated CO2 emissions for each generator.

They key is the the average thermal efficiency used to calculate the emissions factor. If the average used is that for a steady output operation but in actual operation, there is considerable ramping up and down to match demand fluctuations, then the operational thermal efficiency will be lower than the steady state efficiency, particularly for coal (and to a lesser extent CCGT) plants. This will lead to a higher actual emissions factor which means the actual CO2 output will be correspondingly higher for the same delivered energy.

Now from section 3 in the NEM report, hydro’s generated output fell in 2015 as did gas when compared with 2014. Meanwhile, wind and solar generated output increased as did brown and black coal.

Now without knowing the instantaneous output from all sources over both years, I am reduced to musings but it seems reasonable to assume that in 2015, the larger generation capacity of wind and solar would almost certainly have created larger demand fluctuations for the other generators. And since coal plants made up a larger proportion of “dispatchable” generation capacity, it also seems reasonable to assume that these needed to ramp more often to cover the fluctuations which would reduce their thermal efficiency, leading to more CO2 being produced per MWh generated/delivered.

This is what led me to propose that it is likely that the actual 2015 CO2 emissions are higher than that stated in the NEM report, since the same emissions factors are used for both years.

I will call you later today to discuss this further

Regards
Greg

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@dos74

Peter Davies, you say ‘provided the wind farm economics rewards maximum annual energy and not a higher capacity factor’.

Shouldn’t capacity factor be a baseline for assessing how a wind farm is built, given that most of the cost appears to be in the generator rather than the tower and base (not that those costs should be ignored). Or are the other costs, like of setting up transmission lines and maintenance, a greater factor so the AEP is given priority?

As someone pointed out, absolute maximum AEP would involve grossly oversizing the generator, so I should have said “a bias towards maximum AEP rather than a high CF”. Optimising return on investment is key criterion.

The cost of the generator itself turns out to be small. The other AC components (transformer, power converter and grid connection) costs must be added in as these should also be proportional to the nameplate capacity, for maybe a grand total of 25%. That the total is so low is a surprise to me.

Presumably if the price of copper and maybe other materials goes up the cost of these components rises as a proportion.

Even though the AC side is a low fraction of the costs, there is still an optimisation in which the major decision is to select the model of wind turbine. There’s no point in incurring costs of a bigger generator for the same rotor size if the high wind speeds at which it will be required rarely occur in the location.

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@ Peter Davies

Would you be able to tell us what items might be in the OTHER section of the first chart that you give above, seeing that it appears to be the largest single section of wind turbine cost.

I would also like to make the point that grid connection is a huge variable, the cost of which may preclude all other considerations.

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@singletonengineer,

Why do you think a fair market would require supply to be determined a day ahead? Wouldn’t it be easier for the price to be determined in real time to match demand (which they can’t accurately forecast a day ahead)?

Isn’t priority a good thing? Surely we should avoid a situation where we’re buying power from fossil fuels while forcing wind to curtail its output?

Frequency control is needed anyway because of fluctuating demand. Isn’t it unfair to attribute the cost to wind power?

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Either Greg Kaan adopts the findings of the 2015 ACIL Allen report which he cites or he does not.

Yes, ramping up and down while chasing increasing fractions of variable generation does have an impact on the carbon intensity of energy from the thermal power stations which are doing the work. The correct way to estimate this is not to guess from the 2015 report alone, but by reviewing statistics from previous years,maybe previous ACIL Allen reports. I agree with Greg that increased load following will indeed result in increased carbon intensity, if all other factors remain unchanged. That is not at issue.

My concern is that, having cited the report and adopted some of its findings, it is a bit rich to then set aside those findings which are not supportive of wind power. That is cherry-picking.

The figures I quoted from that report present a bleak picture of increasing emissions, despite increasing renewables. Maybe next year will be different, but that remains to be seen.

The two primary goals are to reduce carbon emissions and to minimise energy costs. Neither is going to be met at the necessary scale via efforts to increase the capacity factor and penetration of wind – stronger measures are needed.

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@ singletonengineer

Sorry but I did think that Greg Khan was making a valid point about using averages etc, as opposed to using say tons of coal burnt and/or the amount of gas burnt.

I also did not get the impression that what Greg Khan was saying supported wind or solar.

Further, the goal in terms of electricity generation is to eliminate the use of fossil fuels entirely, at minimal cost.

I am not a zealot so I will settle for 90 percent of the world’s electricity generation coming from non fossil fuels by 2100.

The science tells me that the major source of this generation will come directly from nuclear energy.

PS
That is unless we work out how to use the warp drive from the Enterprise.

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@ Aidan Stainger:
Q1:
“Why do you think a fair market would require supply to be determined a day ahead? Wouldn’t it be easier for the price to be determined in real time to match demand (which they can’t accurately forecast a day ahead)?”

Instant action requires infinite speed. I say 24 hours, in order for the market to be orderly. “Real time” implies requiring instantaneous action by others, which is not possible except with real cost.

Q2: “Isn’t priority a good thing? Surely we should avoid a situation where we’re buying power from fossil fuels while forcing wind to curtail its output?”

That is a very shallow and selfish way to view the market. It implies an infinite value on avoided CO2 emissions, plus an implied assumption that wind power actually avoids emissions, which is debatable when its negative effects on other generators is considered. A rational, structured approach to costing both electricity and emissions, combined with a merit order approach to scheduling of generating plant is the smart way to go.

Q3: “Frequency control is needed anyway because of fluctuating demand. Isn’t it unfair to attribute the cost to wind power?”

I was not discussing fluctuating demand, which is a red herring. However, a case can be made in favour of wind carrying its own share of the response to variable demand, both up and down.

It is irrational to permit wind power generators to freely and without penalty introduce additional variability to the system in the form of variability of supply. This results in additional costs for the other generators and for transmission and distribution operators who need to work together via either load following or load shedding to compensate for the additional variability that is attributable to wind.

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We seem to gone off track again – maybe the issue of generation vs demand, last year’s CO2 emissions etc should be taken up in the open thread.

In any case, I appear to have made a further error on the issue of capacity factor vs wind turbine hub height. Just to recap, the Vestas V117 was presented by Dave Osmond to me as an example of a turbine for analysis and I found that the AEP varied with yearly average wind speed in a largely linear manner approximately as
AEP =2000MWh/(m/s) – 4000MWh in the yearly average wind speed range of 6.0m/s to 8.5m/s
see https://www.vestas.com/en/products_and_services/turbines/v117-3_3_mw#!power-curve-and-aep

I used the wind speed of 7.0m/s as an example and assumed a Hellman exponent of 1/7 to calculate that a 10% increase in turbine hub height would result in a 0.0959m/s increase in wind speed which led to the AEP increasing from 10000MWh to 10192MWh equating to about a 2% power increase.

What I forgot was that the capacity factor depends also on the nameplate capacity of the turbine which is 3.45MW in the case of the V117. The original capacity factor was 10000MWh / 3.45MW * 24h * 365.25 * 100 which equals 33.066%. With the power increase from the 10% increase in hub height, then new capacity factor only rises to 33.701%.

This increase of 0.635% with the 10% hub height increase is well short of the 2% rule of thumb I was provided so taller towers (with attendant wider bases, both suitably strengthened for the greater leverage) doesn’t seem to help much with the underlying capacity factor issue at the heart of John Morgan’s post.

Maybe Jens Stubbe can shed more light on his claim of “The capacity factor of wind has been ever growing and will continue to do so for a good deal of years to come simply because wind turbines scale and improve. Higher hub height means stronger and steadier winds. Higher capacity factor means less variation in output and slower variation in output, so despite the shrinking dispatcheable generator sector it won’t have to ramp more up and down.” which I originally asked about.

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Hi Greg

An increase in capacity factor from 33.066% to 33.701% is an increase of 1.9%, or an increase of 0.635 percentage points. I tried to word my original rule of thumb to avoid this confusion, but it appears I wasn’t successful.

“A 10% increase in hub height will yield approximately a 2% increase in energy”

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@Tony Carden

Would you be able to tell us what items might be in the OTHER section of the first chart that you give above, seeing that it appears to be the largest single section of wind turbine cost.

The source didn’t give the breakdown of other, but some obvious things missing are customer acquisition costs (marketing), R&D and profit.

I would also like to make the point that grid connection is a huge variable, the cost of which may preclude all other considerations.

The latest US DoE LCOE document https://www.eia.gov/forecasts/aeo/electricity_generation.cfm puts average additional transmission costs at around 0.3 US cents / kWh for onshore wind and about double that for offshore wind. As a proportion of 7.36 that is 4% for onshore wind, so the 11% allocated in the pie chart appears relatively generous.

If you are doing good things with a very long distance transmission, such as transmitting both solar and independent region wind power from North Africa to London or Berlin to complement North Sea region onshore or offshore wind, then you would expect significant transmission costs. Desertec were quoting 1 to 2 US cents / kWh over distances of 1,300 miles / 2,000 km. But you would not expect to spend this much unless renewables generation in total is exceeding 50% of supply. It’s only worth supplying wind from a long way away when it reduces the proportion of generation from storage. See http://www.desertec.org/concept/questions-answers/
.

Why do you believe transmission upgrade costs might be more than 11% for wind in some cases?

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Peter,
I am not trying to be pedantic but in Australian conditions Grid Connection Costs can be significant.

John Morgan included in his article above a map showing Australia’s Wind Resources. Here is the reference again

http://www.seabreeze.com.au/Photos/View/2871107/Weather/Australia-wind-energy-map/

Many of the resources identified are at least 1000 klms from any significant population centre. The estimate for the current Australian Transmission line losses in the NEM is 10%. Therefore grid connection costs, let alone the resultant transmission line losses, may well exceed 11% in Australia.

My reference to the Other costs is not a criticism of you but more of IRENA. Assuming that the pie and bar chart are drawn to some kind of scale then the Other costs are one of the largest components. My experience of presenting proposals to decision makers is that the first thing that they will target in a presentation is a large non-descript figure.

I have had the phrase
‘In relation to the Other or miscellaneous costs what are the hooks in there.’ said to me before I started to break them down more informatively.

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DesertTec link worked for me just now.

However, the concept failed to gain traction with the Spanish and so is moribund. For a live CSP project in the Maghreb, see what the Moroccans are doing.

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@Greg Kaan

“Maybe Jens Stubbe can shed more light on his claim of “The capacity factor of wind has been ever growing and will continue to do so for a good deal of years to come simply because wind turbines scale and improve.”

There are three trends behind the ever growing wind capacity factor, which in turn grow AEP and limits both the required backup and the speed with, which the back up needs to ramp up and down.

The first is simple because it is just about elevating the hub height to reach stronger and steadier winds. You can get a better grasp of it here. http://cleantechnica.com/2015/08/04/wind-could-replace-coal-as-us-primary-generation-source-new-nrel-data-suggests/

The second is equally simple to understand because the idea is to continue the ongoing development of bigger and bigger rotors. This article covers the timespan of some recent years http://www.nawindpower.com/issues/NAW1301/FEAT_05_Turbine_Advances.html

The third is more complex because it involves interaction on wind park scale and over the entire lifespan of a wind turbine. The effects at play here are all minor with a few notable exceptions. Wind farm scale LIDAR technology enables the wind turbines in a wind farm to be individually yawned to reduce turbulence (3% higher AEP). Sharkskin structures on blades has been demonstrated to increase AEP by 6% but cannot be used due to cost mainly because of fast degradation. LIDAR based individual blade pitching, which is in its infancy because of cost involved and because the pitching action is too demanding for the standard pitching mechanisms. Plasma drive where the boundary layer over the blades is dynamically controlled by electrodes that subdue large vortices. Winglets with dynamic morphing characteristics that limits losses due to wing tip vortices. Generators with less friction. Dynamic and passive stabilization of tower movements.

At some point in the future the capacity factor for onshore wind turbines will settle but what it will settle at will be very much dependent upon how the grid infrastructure is built and how the strategy will be for managing the grid. For instance there could be a very good case for over provision if the electricity becomes very cheap and more demand could be time shifted, and in such conditions could favor turbines with cheapest possible electricity generation cost over turbines with high capacity factors.

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Thanks Jens.

Dave Osmond helped me with looking into increasing hub heights and the calculations I came up with did not look very promising – around 0.66% increase in capacity factor with each 10% increase in height. This would not seem to be worthwhile unless the area around the wind farm was particularly cluttered making for high wind shear in which case, the area would seem to be a poor site for a wind farm in the first place.

Do you know what the cost increases are as a function of rotor size? The greater weight of a larger rotor must require stronger gearboxes and bearings. Of course the larger rotor also demands a taller tower (which also helps with the capacity factor) which then needs to be more strongly constructed with a larger footing.

There are also quite a few articles about bearing and gearbox failures and their prevention which means they must be already optimised to minimise contact areas in the interests of minimising friction for increased efficiency. Improvements in metallurgy and mechanical design will almost certainly be marginal since this is such an important area in many industries.

Active blades to tune out vortices sounds very promising but they must be far more costly to make than the relatively simply constructed blades currently being used. The same applies to better controlled towers.

Given that one of the concerns about low capacity factor is the cost due to the large overbuild, we really need solid figures on the costs required to achieve each incremental increase in turbine efficiency. As you have said, if the costs are low enough, we don’t have to worry about spillage/curtailment but everything seems to be leading to higher costs.

As for time shifting demand, meaningful changes couldn’t be made in this area without being able to schedule the wind produced electricity with certainty. Since predictability is one of the issues demonstrated in John Morgan’s article, time shifting demand is not a practical solution.

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@Greg Kaan

Given that one of the concerns about low capacity factor is the cost due to the large overbuild, we really need solid figures on the costs required to achieve each incremental increase in turbine efficiency. As you have said, if the costs are low enough, we don’t have to worry about spillage/curtailment but everything seems to be leading to higher costs.

There seems to be some confusion here about the trade off between AEP and capacity factor.

The discussion above indicates that capacity factors automatically increase slightly with higher hub heights. These also give additional output power.

But there’s always an optimisation available, because you can constrain the output power and get a higher capacity factor. This reduces costs slightly, but beyond a certain point this does not earn you more revenue, however, because it reduces, rather than increases the total units of electricity generated.

So capacity factor is not the only thing to take into account.

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@Tony Carden

Many of the resources identified are at least 1000 klms from any significant population centre. The estimate for the current Australian Transmission line losses in the NEM is 10%. Therefore grid connection costs, let alone the resultant transmission line losses, may well exceed 11% in Australia.

With the exception of Darwin, most of the large Australian cities I have heard of seem to be within spitting distance of reasonable wind resource except Darwin.

Darwin has excellent solar resources so forget about wind there.

Just because you have huge high quality wind resources available thousands of miles from anywhere does not mean you have to use them if reasonable wind in the required quantities closer to the population centres is available instead.

Normally you would not include transmission losses in project costs, just the cost of building the extra transmission lines required to exploit a new generating resource. You would put them in as a reduced revenue item.

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@Peter Davies, your point about proximity of mean wind resources in proximity to population centres misses that made by John Morgan in the original article, immediately below the link reposted by Tony Carden: “If we wanted to cover intermittency we would need to ensure our wind fleet is dispersed over distances of 1200 km and more…” This is where the requirement to transmit wind-generated electricity thousands of kilometres comes from.

As you yourself said in a similar context above, ‘you would not expect to spend this much (on connection) unless renewables generation in total is exceeding 50% of supply…’, but isn’t that latter condition exactly what is being advocated?

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Peter,
Below is the reference to the wind map you have quoted.
It shows the resources in better detail.

Click to access mean-wind-speed-2008.pdf

A more detailed examination of this map shows that a major part of the wind resources are off shore.

Here is a reference to a report by The Australian Renewable Energy Agency.

Click to access Chapter-9-Wind-Energy.pdf

From page 246 first paragraph
‘Australia has some of the best wind resources in the world. Australia’s wind energy resources are located mainly in the southern parts of the continent (which lie in the path of the westerly wind flow known as the ‘roaring 40s’) and reach a
maximum around Bass Strait (figure 9.8).’

From page 240 first paragraph
‘Grid constraints – lack of capacity or availability – may limit further growth of wind energy in some areas with good wind resources,
particularly in South Australia. In such areas, upgrades and extensions to the current grid may be needed to accommodate significant further wind energy development. Elsewhere,current
grid infrastructure should be adequate for the levels of wind energy penetration projected for 2030’

Further from page 248 last paragraph
‘Factors that may limit development of wind energy on a localised basis are a lack of electricity transmission infrastructure to access remote wind resources, and the intermittency and variability of wind energy.’

Further here is a reference to a map entitled
‘Figure 9.18 Wind energy resources in relation to reserved land and prohibited areas and the transmission grid. A 25 km buffer zone is shown around the electricity transmission grid.’

There are many pristine coastal regions in NSW and Qld which appear to be good locations for Wind Farms. Good luck with that.

Here is a reference to a report by the Australian Bureau of Statistics showing population densities.
http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/1301.0~2012~Main%20Features~Geographic%20distribution%20of%20the%20population~49

The fact that you wish to contend my original statement is proving the necessity for making it.

If the average grid connection cost is 11% then mathematically there must be installation that are over 11%.
Australia is a major candidate for having grid connection costs above 11% based on the information above.
There will be other situations that have similar issues.

I am sorry but Solar is off topic.

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@duffer70

As you yourself said in a similar context above, ‘you would not expect to spend this much (on connection) unless renewables generation in total is exceeding 50% of supply…’, but isn’t that latter condition exactly what is being advocated?

You do have a point, but it’s a littleless of a point than it might be!

If you are using a second source of wind a long way away to fill in the gaps, then the transmission capital cost is incurred. To get up to the high levels of wind penetration you are now going to have twice the wind power. At a reasonable future 45% capacity factor for wind the 1200km remote wind farms will be filling somewhat more than 45% of 55% (gap) or about another 25% of the gap if wind power has a random time distribution (which it doesn’t in Europe). So effectively you now have 45%/70% of the long transmission line costs to bear averaged over all wind kWh. These costs are going to be significantly less than the cost of storage, and still only a faction of the US DoE LCOE of over 9 cents / kWh for nuclear so you are still saving.

If, on the other hand, you believe wind power does not have a random time distribution, then you get less benefit from the remote wind but you have to assume the capacity factor is seasonally higher in the winter months and daily when the sun is not shining. That is also pretty good, because it means future very cheap solar PV power is going to be better anti-correlated with both local and remote wind.

Either way, local and remote wind and local solar power can get up to a decent fraction of generation before you have to start implementing storage in a nearly all renewables Australian grid. Darwin looks as if it might need some nuclear though.

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@Mark Duffett

I’m not following you here, in particular the second para.

There are two cases. In Australia either solar anti-correlates with wind or it does not.

If solar anti-correlates with wind, then it must anti-correlate with wind in both remote (> 1200kms) and local wind areas.That means that the wind in the remote and local areas must correlate somewhat.

If solar does not anti-correlate with wind, then there is more chance that wind in two areas 1200 km apart is independent. This is not guaranteed but appears to be John’s finding from the correlation times. If the wind is independent with a capacity factor of C, then a crude sum says the chance of no wind in either place is (1 – C) * (1 – C). It’s not as simple as this, however, because wind is often blowing at intermediate speeds between maximum power output and zero power output, so the effect is better than you might expect.

The question of installing wind and solar together is more complicated than you suggest.

Firstly in the 2030-2050 timeframe both onshore wind and solar PV are expected to be very cheap indeed. Solar PV is expected by the IEA to be lower than 2c US/kWh. Maybe not in UK or Germany with no sun, but certainly in Australia. Wind a little higher, but below 4c / kWh in reasonable locations. The costs of an all-renewable grid are then determined more by the requirement to fill in the gaps with storage than by the cost of wind or solar power. In this scenario you have to optimise and it will be better to overconfigure wind and solar somewhat, to avoid more expensive stored power costs.

Secondly most grids have a higher load during the (sunny) daytime rather than the nighttime, so it makes sense to configure solar PV as well as wind. There’s often an evening period after the sun goes down where load remains high, particularly domestic load. If this is due to a residual air-conditioning requirement then it may reduce somewhat. By 2030-2050 buildings are likely to be much better insulated. If it doesn’t reduce then there’s a role for solar CSP, heating salt during daylight, but not generating until the sun goes down – because it’s cheaper to generate with solar PV than solar CSP during the day.

So the conclusion that wind and solar capacity is restricted by economics to peak load is too simple, predicated on current, not future, prices and not taking account of daytime bias of the load.

And lastly I’ve no idea why wind should be more or less constant during 24 hours in Australia, but nowhere else (France, Germany, Texas). It seems odd. Most of the Australian population lives near the coast. During the day the land heats up faster than the sea surface and you get an offshore breeze, in the morning. Somewhere after noon the wind drops. Then in the evening you get onshore breezes as the land starts to cool down rapidly, particularly in cloudless skies. So you would expect a significant evening breeze. Where is it?

Maybe it’s because Australia is big and nearly circular and there are lags in the effect because of the large distances which cancel out. But why doesn’t that apply in Texas?

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@Tony Carden

Do you have an idea of how much nuclear Darwin might need?

Not a lot, from the population statistics of the city itself in the link you provided. But I’m not familiar with the degree of industrialisation or the size of the surrounding population.

If it was the only nuke in Australia, it would not be worth the bother.

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About the daily cycle… In the absence of vertical mixing, the layer of air near the ground slows down overnight, so that it is often still in the mornings. With a clear cold night sky, it radiates its heat to space, forming an inversion layer that may persist all day. Most often, the mid-morning sun warms the ground, causing thermals to rise through the layer to the geostrophic wind aloft, diverting momentum downwards and clearing the layer. John Morgan’s graphs shows this effect in Nov-Dec-Jan-Feb-Mar, with the ground wind rising between 0600 and 1800. In each case, the ground wind dies off between 1800 and 2400 as the next evening’s calm layer develops.

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@ Peter Davies,

If solar anti-correlates with wind, then it must anti-correlate with wind in both remote (> 1200kms) and local wind areas.That means that the wind in the remote and local areas must correlate somewhat.

Not necessarily. If it only anticorrelated with the wind in local areas, there could still be an overall anticorrelation.

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The wind in the Mid-North region of South Australia is nicely anti-correlated with solar, as you can see from the average daily generation curve over a 3 year period below :

And now the surprise – the South East of South Australia correlates (not anti-correlates) with sunlight :

These are charts 3-2 and 3-4 from the “SOUTH AUSTRALIAN WIND STUDY REPORT 2012” (for which Word Press does not seem to accept the URL and has rejected the full post a few times, so will try it in bits).

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Sorry I still can’t get the URL for the “SOUTH AUSTRALIAN WIND STUDY REPORT 2012”, so please Google the document.

The wind in the Mid-North region of South Australia is nicely anti-correlated with solar, as you can see from the top average daily generation curve in my post above.

My understanding is that South Australia represents half of all Australian electricity consumption and generation. Probably other Australian regions also have the same mix of wind profiles correlated and anti-correlated with solar PV.

The only reason John reports no anti-correlation between wind and solar PV is that he, very reasonably, performed only the obvious analysis, aggregating together all Australian wind farms before doing the time analysis. Since there are regions with both anti-correlation and correlation, the two types of daily profiles cancel and you lose the information you were looking for.

To spot the regional variation which gives strong wind / solar PV anti-correlation in the Mid North you have to analyse windy regions separately not together.

Not only is there anti-correlation with solar in the Mid North region, but also a high wind capacity factor at night (in excess of 50% for some wind farms), which is ideal.

Thus South Australia can achieve synergy of onshore wind and solar PV power by installing most of its wind farms in the Mid Northern region, probably at the cost of a few hundred km of transmission lines. Then it can incorporate another nameplate capacity of solar PV equal (or more) to the daytime peak load.

If the solar PV capacity factor is 25-30% over the course of a year, then for only daytime hours the capacity factor over a year is actually 50-60%, since everywhere on earth averages 12 hours of sunlight per day.

This combination of Mid North wind and solar PV can thus achieve a penetration of wind and solar PV power usefully and economically which is higher than the capacity factor of wind or solar PV alone. It would give something like a 45% (plus) wind capacity factor at night when load is low. During the peak day, the wind capacity factor of around a third is going to plug one third of the 40-50% gap left by solar, to give a 27-34% gap, which means 66-73% solar + wind coverage during the day, for the price of an excess of 50-60% of the wind power generated during the day. That means the solution can achieve high renewables penetration economically when wind and solar prices drop just a little below the cost of current fossil fuel generation, which is not likely to take too long to happen.

In practice analyses for all four seasons are needed, rather than the equinox calculation above. But the principle stands.

So it looks like the capacity factor of Australian wind does not represent a hard economic barrier after all, thanks to the emergence of new information on the daily profile of South Australia wind.

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“My understanding is that South Australia represents half of all Australian electricity consumption and generation”

Whoa, no. I’d be surprised if it’s as much as a tenth (googles…yes: http://www.energymatters.com.au/energy-efficiency/australian-electricity-statistics/). This is fundamentally why SA has been able to achieve nominally high wind+solar penetration – it’s embedded in a much larger grid consisting largely of dispatchable baseload. “If the solar PV capacity factor is 25-30% over the course of a year” is also a gross overstatement, it’s barely half that: http://www.businessspectator.com.au/article/2015/9/11/energy-markets/how-do-power-generator-capacity-factors-vary-across-world

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When talking about the capacity factor of solar in Australia, it is important to note if you are talking about residential/commercial or utility scale.

It is true that CF of residential solar in Australia (which represents the vast majority of Australia’s installation of ~4.8 GW) is around 15%-16%. However this capacity factor is based on the DC (panel) size of the system, and has a relatively low average due to non-optimal orientation, tilts and issues with shading. According the APVI website (link below) it seems that CF of commercial systems doesn’t do much better, with an average CF of 16%.

However, utility scale solar is a different matter all together. It will usually be designed with optimal orientation, tilts, will have next to no shading issues, is probably located in a place with very good solar resource, and the CF will be based on the AC (inverter) size of the system. For example, the Nyngan solar has been at full power since June 2015, and in the 6 months Jul-Dec has had an average CF of about 26%. I’m not sure what the DC size is of Nyngan, but it is likely to be atleast 20% larger than the AC capacity of 102 MW. As an example, Royalla has an AC size of 20 MW, but a DC size of 24 MW.

In the future, we may see more large utility solar with tracking, which will get even higher CFs than Nyngan’s ~26% (Nyngan uses fixed panels).

http://pv-map.apvi.org.au/historical#4/-26.67/134.12

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Fair enough, however are you aware of any indications/drivers that will change the relative proportion of residential vs utility-scale PV in the foreseeable future, and thereby significantly raise the PV sector’s aggregate CF?

Comparing http://www.bom.gov.au/web03/ncc/www/awap/solar/solarave/6month/colour/latest.ns.hres.gif with the long term average for the solar plant location (from the grid data at http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp), it seems the Jul-Dec 2015 period has been on the high side of long term average daily insolation for the Nyngan plant location (possibly consistent with a strong El Niño), ~20 vs 19.45 MJ/m2. It will be interesting to see if that apparently small difference translates in linear proportion to PV output.

The distance of good sites like Nyngan from markets also goes back to the transmission issues thoroughly canvassed above.

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Peter Davies said “My understanding is that South Australia represents half of all Australian electricity consumption and generation”

Whoa, no. I’d be surprised if it’s as much as a tenth

Thanks, you are right. I had spotted the problem earlier from somewhere else which said the main Australia population centres were the South East and South West.

50% looks like the South Australia wind capacity as a fraction of the wind capacity in the whole of Australia. I did check the numbers added up to 100% before writing down the statement at the top!!

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Why use the 2012 report?
Here is a reference to the 2015 report
http://www.aemo.com.au/~/media/Files/Other/planning/SAWR%202014/2015_SAWSR.ashx.

Here is also a reference to AEMO
http://www.aemo.com.au/Electricity

‘AEMO operates Australia’s National Electricity Market (NEM), the worlds largest interconnected power system. NEM infrastructure is comprised of both state and privately owned assets and is managed by a variety of entities under the overall direction of AEMO’

By large I think they mean geographically.

I would suggest that you research this site.

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Why use the 2012 report?

Here is a reference to the [South Australian Wind Study Report] 2015 report…

Having scanned it, the 2015 report does not break down wind generation daily profile into the 3 different regions of South Australia. The information that Mid North wind power is anti-correlated with solar PV power is thus only in the 2012 report for some reason.

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@Greg Kane

You are wrong in concluding that capacity factors does not grow significantly with hub height.

I think you better look it up yourself or assume these guys know what they are talking about. http://cleantechnica.com/2015/08/04/wind-could-replace-coal-as-us-primary-generation-source-new-nrel-data-suggests/ https://www.google.dk/search?q=wind+speed+as+function+of+hub+height&client=safari&rls=en&tbm=isch&tbo=u&source=univ&sa=X&ved=0ahUKEwjC9uXf3cLKAhWKFCwKHe9qAl0QsAQIRw&biw=1427&bih=696

The problems with bearings and gear boxes has absolutely no consequence for the PPA contracts signed because they all fix the cost of electricity for typically 20 years into the future. Usually the owners of the wind turbines sign insurance and service contracts that cover their risk exposure.

The major wind turbine manufacturers tinker with adaptive pitch and none of them have products in the market. The main reason is that the idea only became practical when LIDARS became cheap. The challenge lies in the getting the mechanism, the predictive software and the LIDAR fine tuned to work consistently over the lifetime of wind turbines.

As for dampened towers the concept is validated both in calculations and trials but has not found its way into mainstream turbines yet. This can change once wind turbines grow because the gains then become greater.

Your assumption that you have to predict wind electricity production to make use of excess power is simply not correct. In the first place wind is predictable and in the second place excess power usage options can be entirely stand by options.

Wind power is less than half the cost of coal power in North America and Europe (I realize that for some reasons wind is quite expensive in Australia and China) and the cost is going down fast so over provision with with high capacity factor turbines is very cheap irrespectively whether you decide to curtail the production or sell it of just above the marginal cost of operating the wind turbines.

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Duffer70,

Australia often gets special mention as ideal for solar generation, so you would expect inherently pretty good capacity factors.

The issue of transmission cost in sparsely populated Australia is likely to raise the capacity factors of utility-scale solar to be as high as possible.

There are references to 20% overconfiguring of DC (panel) vs AC (inverter, lines) capacity for other countries. If the distances and thus transmission line costs in Australia are higher than elsewhere then you would expect more DC overconfiguration as the cost balance favours achieving a very good capacity factor. My guess would be 30-50% DC overconfiguration, particularly as panel prices continue to reduce. You would also expect 2 axis tracking arrays to be pretty standard in Australia, as this seems to be a finely balanced decision elsewhere.

This would mean you end up wasting DC power around mid day, but the advantage is that you get higher power all the time the inverter and lines are not maxed out, including the beginning and end of the day.

I read somewhere that 15% of Australian homes already have rooftop solar, which is already high. If you went to 6X this figure you would be nearly at 100%. City properties may not have sensible area of North-facing roof. Hence there is a very definite limit to residential rooftop solar, and the enthusiasts will have installed it already.

Some wind sites at least are anti-correlated with solar PV. So you would expect the economic solar capacity fraction to be around the solar PV capacity factor of 25-30% as prices reduce. Rooftop solar is likely to max out well below this, so most of this has to come from utility-scale solar PV.

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Slight correction to the above. The penulatimate sentence should read :

So you would expect the economic solar electricity units generated fraction to be around the solar PV capacity factor of 25-30% as prices reduce.

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The Eastern Australian grid, with the exception of South Australia, tends currently to have generation capacity located close to centres of population and to rely on radial transmission to remoter areas. Summer daytime peaks dominate in the remoter areas.

Thus, eg for the large scale PV plant at Nyngan, it seems to me that it has the advantages of (a) high insolation figures and (b) feeding into the system closer to some of the load which otherwise would be towards the end of long transmission lines. The AEMO web site has good maps and system diagrams here: http://www.aemo.com.au/Electricity/Planning/Related-Information/Maps-and-Diagrams

Additional supply from Nyngan feeds backwards to the city of Dubbo and thus potentially acts to reduce the peak flows needed from the existing base load generators to the east.

This is a good thing, but may necessitate upgrading of control systems, switching and protection, perhaps also voltage correction. My point is that locating a moderate amount, ie a couple of hundred MW maneplate of PV at Nyngan at the end of the HV grid is not necessarily a bad thing and could well bring a suite of benefits.

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Hi guys,

I’ve been reading recently about some startups that intend to store renewable energy using thermal storage. For example, one has a website here: http://www.isentropic.co.uk/. They’re working on something called “Pumped Heat Electricity Storage” or something similar. Another is here: http://www.highview-power.com/. They’re storing energy using liquified air. There are also a few other approaches.

It’s claimed that these devices they intend to build will allow roundtrip efficiencies of 60%+. In all cases they are using only abundant materials like liquified air.

Does anyone see any drawback to the approach of thermal storage, or any reason why those ideas are unfeasible? Couldn’t that be used to overcome the intermittency of wind, at least somewhat?

-Tom S

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Tom S
Perhaps it could, but why bother?
Even if some not unreasonably expensive storage method is invented, it will still be an extra expense. Other things being equal we want a mix of energy sources that minimizes the need for storage & over building of the energy producing/collecting equipment.

In the regions of the world where air conditioning is a bigger energy use than space heating, I expect solar would complement nuclear nicely by providing power just when demand peaks. I don’t see a situation where wind power is much better than a useless distraction.

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Tom S,

I can’t comment on Highview Power.

Isentropic is a very promising technology, and they are going to a lot of trouble to make it efficient. Basically this means keeping the hot and cold gases produced separate from the lukewarm gas which filled the space before the hot and cold stuff gets there. I’ve seen a couple of presentations by researchers at the UK Energy Storage Conference analysing and modelling the Isentropic technology and advising on how to keep it efficient.

For short-term storage of less than 24 hours it seems to be great, and potentially fairly cheap. But it isn’t appropriate for the situation where there are clouds and no wind for 2-15 days, and the hot and cold rocks have a chance to cool off.

It’s the usual problem for long-term storage – you need some physical property that doesn’t degrade over time, like the potential energy in a mass of water at altitude in pumped hydro, or the chemical energy in hydrogen in power to gas using electrolysis and back to power using CCGT equipment or fuel cells.

Short-term storage technologies like Isentropic may be able to reduce the gaps in wind and solar generation down from 30% to 10-15%, allowing pumped storage hydro to take it down to the 5-10% range at which point power to gas to power kicks in for the long-term or seasonal storage backup.

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@Jens Stubbe
Thanks for replying but your figures are still highly speculative. If you examine the NREL report that the CleanTechica article bases its predictions on, “Near future” turbine technology for a large portion of the 65% capacity factor predicted for the 140m hub height turbines.

Plus the intermittency of wind generation is still not addressed and the massive transmission infrastructure upgrade required to transmit the remote inland generated power to the coastal usage centers is glossed over (as usual) in the CleanTechnica article. Increasing the capacity factor may utilise this infrastructure more effectively but it still needs to be built, along with the turbines themselves.

BTW, I did look up wind shear effects and did some calculations on how increasing hub height would affect AEP and capacity factor and the 2% power increase per 10% hub height increase does not make hub height increase alone a major factor in the NREL prediction.

All the discussion about storage for renewables ignores that storage also helps increase the capacity factor of current baseload thermal generators as well. If it was technically and economically feasible, it would have been done already

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@Greg Kaan

Greg,

It looks like the CleanTechnica article has misinterpreted the NREL chart :

My reading of this is that there is close to zero area of the USA where the “near future” technology wind turbines could achieve a capacity factor of 65%. That is because the purple line hits zero on the y axis at about 66% and is almost zero at 65%.

The graph should be interpreted a little differently. It is showing that there is approximately 1.8m square km in the USA at which “near future” technology wind turbines could reach a 60% capacity factor or higher (but dropping to zero at 65%), and around 3m sq km at which they could reach a capacity factor of 50% or higher (i.e. an additional 1m sq km with a potential for capacity factors between 50 and 60%).

The world expert on the “near future” or Silent Wind Power Revolution wind turbines is Bernard Chabot, and here is his latest presentation about the state of play in the USA.

Click to access 132SWRUSAq13.pdf

The charts on pages 12 through 16 show how the turbines installed increased in sq m/kW as time went on, and this more than compensated for the fact that the more recent sites tended to have lower quality wind resource. The net was that capacity factors for new installations rose by around 3% per year. But if you think about it carefully, this is because there is less generator capacity for a given rotor diameter, which means overall the turbine generates fewer units of power, so you need a higher area to generate the same power. And the hub heights are higher, so a little more wind energy is available.

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@Greg Kaan

All the discussion about storage for renewables ignores that storage also helps increase the capacity factor of current baseload thermal generators as well.

and this quote from the CleanTechnica article :

Using ‘near future’ technology wind power’s CF will exceed the CF of both coal (61%) and natural gas (48%) achieved nationwide in recent years.

These capacity factors of current baseload thermal generators were set by how much they are required to generate, not how much they could actually produce if the demand for it were there. So the CleanTechnica article comparing 65% (which should read 60% – see post above) potential capacity factor for “near future” technology wind against actual lower capacity factors for thermal generation is misleading.

In other words thermal generators do not need storage backup because they can generate on demand and those in the USA are no-where near the possible limit.

This is almost certainly a CleanTechnica misunderstanding rather than a problem with NREL documents.

If it was technically and economically feasible, it would have been done already.

That argument could have been applied to any one of a huge number of technology developments in the past which are now regarded as essential to modern life!

Technology advances as time goes on, particularly if governments throw research and development money at an area because they know it will become important in the future. Not all such research bets come off, but some do and government usually gets a solution to a problem it needed to solve.

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I only brought the Cleantechnica article as a link because it supports my claim that capacity factors are improving for wind power.

The likelyhood of 65% capacity factor being an average value for US wind power is low though it is definitively not defined by technical limitations or limitations of wind resources. The defining properties will be economics where an optimal balance between rotor size and generator capacity will be sought.

A large number of design variables have been tried and will be tried.

As for the claim that the needed infrastructure is not accounted for then please bear in mind that wind is $0.035/kWh on an unsubsidized basis on average for 20 years PPA contracts in USA per 2014 with a strong trend towards still lower costs. This means there is room for quite substantial investments and much needed investments in the grid.

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@Jens Stubbe

…bear in mind that wind is $0.035/kWh on an unsubsidized basis on average for 20 years PPA contracts in USA per 2014 with a strong trend towards still lower costs

Jens,

Where have you found a reference to 3.5 c/kWh? The 2014 subsidised price average seems to be the 2.2 c/kWh PPA price on slide 49 of https://emp.lbl.gov/sites/all/files/lbnl-188167_presentation.pdf . To that you must add 2.3 c/kWh PTC, giving a total unsubsidised price of 4.5 c/kWh. Or is it a typo?

I agree about the trend to lower wind power costs, but wind isn’t quite at 3.5 c/kWh unsubsidised yet.

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John take the many years of your wind data and hourly demands and the AEMO fleet of generators and plug the data into the hdata and gdata files and run the RTS program I have written. It will calculate the LOLP every hour and you can see of those wind gaps are significant or not. The program is posted here in a zip file link inside this document: http://www.egpreston.com/RTS.pdf
I am using this program to study ERCOT, a 71,000 MW peaking system. It should work ideally for AEMO.

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