Here are the average capacity factors of every Danish offshore wind farm, newly updated to include data to the end of 2017. The Anholt 1 windfarm, which only opened in 2013, had a storming year, and reached an average capacity factor of 53.7% for the full year 2017. Compare these with the capacity factors for German, and UK, and Belgian offshore wind farms.
Here are the average capacity factors for offshore wind farms in UK waters, newly updated to include data to the end of December 2017 (though there are still some figures to come through for the last couple of months of last year, for the smaller windfarms). And you might be interested in comparing these with the capacity factors and load-duration curves for Belgium, Denmark and Germany.
NEW updated with data for 2017 too.
Germany has been phasing out its nuclear power, as policy, since 1999. It’s also been expanding its renewable generation. The nuclear phase-out provides the renewables market with a clear, long-term positive signal. As does Germany’s commitment to decarbonisation.
I’ve previously written about German PV and German offshore wind capacity factors, Now, let’s take a look at how quickly the rollout of renewables has happened, relative to the decline of nuclear. The chart below Read more…
Previously I’ve provided capacity factors for Danish, German and UK offshore windfarms. Now here are the numbers for the larger of the Belgian offshore windfarms. The ones shown here are the only ones I’ve been able to get detailed data for, so far.
Belgian offshore wind
|All numbers are to 27 November 2017. Analysis by EnergyNumbers.info.||Latest|
|quantiles TO HIDE|
Load duration curves
I’ve constructed for each of the offshore windfarms for which there is sufficient detailed hourly data (this will usually mean at least a year’s worth). Use the pause and play buttons to stop and start the sequential display of curves. Click on the windfarm name in the legend to toggle the display of that farm’s curve.
Note that for each individual windfarm, its curve is based on data starting from the date that the windfarm was fully commissioned, or from 1 Jan 2015, whichever is later: data is only available from 2015 onwards. The windfarm’s age is calculated from the date it was fully commissioned.
The Thornton Bank windfarm was built out in three phases, I-II-III. However, the hourly data is broken down into two groups, SW and NE. As you can see from this map, courtesy of 4COffshore, most of phase II is in the NE section. The SW section contains the rest of phase II, as well as all of phases I and III.
My thanks to Rémi_C2W on Twitter for solving the mystery of the Northwind time-series in the ENTSOE data: he worked out that the reported data is the sum of output from both Northwind and Nobelwind: so the real Northwind output can be calculated by subtracting the reported Nobelwind output from it.
Previously I’ve provided the figures for Danish offshore windfarms, for the UK , and for Belgium too. Here are the numbers for the larger German offshore windfarms. The ones shown here are the only ones I’ve been able to get detailed data for, so far.
German offshore wind capacity factors
|All numbers are to the end of September 2017. Analysis by EnergyNumbers.info.||Latest|
|Amrumbank West||42.0%||42.1%||1.9||302||2 043||3.9|
|Bard Offshore 1||28.1%||0.5||400||470|
|Borkum Riffgrund I||40.4%||40.4%||2.0||312||2 180||3.5|
|Gode Wind I||33.8%||0.3||330||329|
|Gode Wind II||33.2%||0.3||252||247|
|Nordsee Ost 1||36.5%||34.8%||2.4||144||1 040||2.8|
|Nordsee Ost 2||37.3%||35.2%||2.4||144||1 062||2.8|
|Windpark Baltic 1 & 2||47.0%||45.2%||1.9||336||2 555||4.1|
Load duration curves
I’ve constructed for each of the offshore windfarms for which there is detailed hourly data. Use the pause and play buttons to stop and start the sequential display of curves. Click on the windfarm name in the legend to toggle the display of that farm’s curve.
Note that for each individual windfarm, its curve is based on data starting from the date that the windfarm was fully commissioned, and the windfarm’s age is calculated as starting at that date. There is one exception: the Bard Offshore Windfarm was fully commissioned in August 2013, but detailed data on its generation is only available from spring 2017 onwards – so the “age” shown for this windfarm is the age of the oldest available data, not the age of the windfarm itself.
The numbers for DanTysk did look strange: I had to clean a stretch of five months of data that was clearly wrong. A small kink remains in its load duration curve; this may be an artefact of some of the problematic data.
Windfarms less than a year old are excluded from the calculations of the power density per unit area spanned. The figure for total power density is a weighted average of the windfarms that are a year older or more: this is weighted by size, but not by time. So a windfarm that’s twice as large contributes twice as much to the total; whereas a windfarm that’s twice as old, does not.
On 17 December 2015 Britain passed a new landmark on the road to cleaner electricity: for four hours, from 03.00 to 07.00, the grid had a plurality of wind: wind was the largest generation type on the grid: there was more wind generation than generation in Britain from gas, or from coal, or from nuclear, or any other domestic source. That was the case for the four hours as a whole, and for each half-hour within that four-hour period.
Is land area a barrier, or a significant constraint, to achieving 100% renewables?
- New renewable capacity outstripped new coal capacity;
- additional output from renewables (the increase in output in 2014 over 2013) was greater than the total output from nuclear;
- total generation from hydro, wind and solar all went up;
- total generation from fossil thermal plant went down;
- coal & gas consumption for electricity went down.
And so to the detail.
Half of the 104 GW of new capacity is renewables; 47 GW is thermal plant (predominantly coal and fossil gas) and 5 GW is nuclear. 22GW of the renewable capacity is hydro, 20 GW wind, and 11 GW solar.
I’ve updated this post on 2015-03-12 with the more specific statistics now available from China.
Generation from wind increased to 17.88GW (average electricity production) this year, from an estimated 16GW last year. Hydro generation increased by about 17% to 122 GW. China now has 27 GW of grid-connected solar capacity, and 20 GW of nuclear capacity.
Output from thermal (coal & gas) plants dropped in 2014, because although thermal plant capacity increased by 5.9%, this was outweighed by the relative decrease of 6.3% in its capacity factor (from 57.3% to 53.7%, an absolute change of -3.6%, which is a relative change of -6.3%). In addition, the efficiency of coal & gas plants went up, meaning that the consumption of coal & gas for electricity generation decreased in 2014.
Total electricity generation was about 632 GW, of which 476 GW was thermal, 122 GW was hydro, 17.8 GW was wind, 14.4 GW was nuclear, and 2.6 GW was solar.
So coal & gas generation decreased slightly; hydro generation increased by about 17 GW; solar generation increased by about 1 GW; and wind generation increased by about 2 GW; giving a net increase in renewables generation of about 20 GW.
It’s been a remarkable few months for wind generation in Britain, Feb 2014 – Jan 2015, and the last of those two months in particular. Several records were broken, and re-broken.
You can see the live British grid data, including wind generation, here; and here’s a version for mobiles (cellphones) and other small-screen devices.
February 2014 saw the highest monthly average metered wind power generation that Britain’s ever achieved: in that month, average generation from metered windfarms was 4.09 GW.
The half-hour starting at 06.00 on the morning of 18 October 2014 saw the highest percentage contribution of wind (penetration) to total demand: 23.5% from metered windfarms; 32.9% from all windfarms.
The half-hour starting at 19.30 on 9 December 2014 saw the highest half-hourly wind generation: 6.80 GW from metered windfarms; 9.42 GW from all windfarms.
And until January 2015, December 2014 also had the highest amount of wind-generated electricity of any month: 3.90 TWh (of which a record 2.85 TWh was from metered windfarms); and the highest monthly contribution from wind to total demand – 13.9% from all windfarms (the highest contribution from metered windfarms was 10.5%, in February 2014). But January 2015 outdid the preceeding month, with 14.4% of demand being met by metered and embedded wind; 4.13 TWh of wind in total, which was equivalent to an average power of 5.56 GW; and 2.95 TWh from the metered windfarms.
Records for electricity generation from wind in Britain
|analysis by EnergyNumbers.info||All windfarms||Metered windfarms only|
|Monthly||Max wind penetration||14.4 %||Jan 2015||10.5 %||Feb 2014|
|Maximum energy||4.13 TWh||Jan 2015||2.95 TWh||Jan 2015|
|Max average power||5.56 GW||Jan 2015||4.09 GW||Feb 2014|
|Half-hourly||Max wind penetration||32.9 %||2014-10-18 06.00-06.30||23.5 %||2014-10-18 06.00-06.30|
|Max average power||9.42 GW||2014-12-09 19.30-20.00||6.80 GW||2014-12-09 19.30-20.00|
(thanks to BMReports and Elexon for the raw data I used for this analysis)
The capacity factor is the average power generated, divided by the rated peak power. Let’s take a five-megawatt wind turbine. If it produces power at an average of two megawatts, then its capacity factor is 40% (2÷5 = 0.40, i.e. 40%).
To calculate the average power generated, just divide the total electricity generated, by the number of hours.
You could do an equivalent calculation for a car. Let’s say your car’s top speed is Read more…
Following on from this question on the Sustainability Stack Exchange about decarbonisation in Eire, and a discussion on the Claverton Energy Group about the British and Irish grids, I took a quick look at the data on carbon intensity and wind generation in the Irish grid. This uses all the available data at time of writing – 38 months, from November 2008 to the end of December 2013.
Here’s the impact that its wind generation has on the carbon intensity of the grid: each MW of wind power that’s generating, reduces the carbon intensity of electricity by 0.138 gCO2/kWh: 1GW of generation reduces the carbon intensity by 138 gCO2/kWh. For context, average demand is about 2.9 GW, and peak demand is about 5 GW.
I’ve used Robust regression, as there are some reporting errors in there (further cleaning has refined the estimate to 0.136 from 0.138).
The y-axis is baselined at 200 gCO2/kWh, because there’s very little real data below that line at present.
I note that Eirgrid has heat-curves for every thermal plant on the grid (which is how they calculate the carbon intensity). Does National Grid have anything like that for GB? Do you? Would you like to share them with me?
And re the data-cleaning – just in case anyone else downloads the wind forecast and generation data, note that every year on the last Sunday in October, the wind data for each of the four quarter-hours when the clocks go back is duplicated.
The UK is the Saudi Arabia of wind
Dale Vince of Ecotricity said the same thing back in 2011:
Mr. Vince continued by saying that the UK is the ‘Saudi Arabia’ of wind energy, which makes Britain a potential to become an independent producer of energy.
And back in October 2009, I said the same thing at the Claverton Energy Conference. Anyway, enough of the source of this: let’s look at the numbers.
2011 was something of a boom year for Saudi Arabian energy production. The Arab Spring uprisings resulted in reduced output from other countries, meaning Saudi Arabia could significantly boost production without trashing the oil price. So let’s use its 2011 production as our benchmark. From the OPEC Annual Statistical Bulletin 2012, and converting from millions of barrels of oil per year into gigawatts, and from millions of cubic feet of gas per day into gigawatts, we see that Saudi Arabia’s annual rate of energy production was just under 800GW. By comparison, in 2011, UK average final electricity demand was 36GW and total final energy demand (including all gas for heating, and all transport fuels) was 183GW.
So, as the chart above shows, the UK’s annual average offshore wind resource is somewhere between 1.5 times larger, and 11 times larger, than 2011 Saudi Arabian energy production. And the great thing is that the wind won’t run out. It will vary, at all scales from seconds to decades, but it won’t run out as long as the sun keeps shining.
My own earlier estimate (shown above as Smith 2011) of over two terawatts as the UK offshore wind resource is documented on the Claverton Energy website. Using the same method as described there, and considering all UK waters, the resource is given above as Smith (2012).
Stuart Gatley, in his Masters of Engineering thesis at the University of Nottingham, models a range of potential future scenarios both for turbine density and turbine technology. His Scenario A-T1, assuming current technologies, is given as Gatley 1 above; whereas Gatley 2 is his Scenario C-T5, which assumes advances in turbine technology and the opening up of all UK sea depths as accessible to wind.
Giorgio Dalvit, in his Masters thesis “UK Offshore Wind Source”, produced a set of estimates, for different constraints. His estimate for the resource at less than 200 metres depth, and within 200km of shore, is given above as Dalvit 1; his all-area resource is Dalvit 2.
Why are there such different forecasts for the UK offshore wind resource? Because each analysis uses different assumptions. And they each use a different estimation method. Though, notably, they all use the same underlying data set: the Renewables Atlas. A future paper (being written now, in Summer 2012), will set out the different assumptions for each figure, and propose a new protocol for such assessments.
Introducing the EnergyNumbers 2050 Pathways calculator
The DECC 2050 calculator is a good start at producing a toy model to give some ideas of the trade-offs, and approximate orders of magnitude of costs involved in converting Britain’s energy systems into a low-carbon system.
But it has its flaws.
So I’ve revised some of the model’s weakest parts, and re-released it. Here’s the EnergyNumbers 2050 Pathways calculator
A summary of the changes (most recent, first)
- Added a new option to change the amount of fossil-fuels (coal, gas, oil) extracted in the UK. This option exists in the DECC spreadsheet, but wasn’t previously available in the web interface.
- Added a whole new section with performance against national and international targets. In the top-left corner of every page, you’ll find indicators showing progress against targets. Click on them to read an explanation of each target, and how well the selected pathway performs against each.
- Change nuclear level 1 to phaseout by 2020; bumped all the other levels up by one (so old level 1 is new level 2; old level 3 is new level 4), and updated all the “expert” pathways accordingly. Old level 4 wasn’t plausible, wasn’t used in any of the “expert” pathways, and so has been removed
- Added estimated damage costs for greeenhouse gases: low £70/tCO2e; medium £100/tCO2e; high £200/tCO2e
- Added estimates of nuclear liability costs: low 0p/kWh; medium 11p/kWh; high 100p/kWh
- The choice of car and van techology, between fuel cells and electric batteries, is a category scale (A,B,C,D), not ascending order of difficulty (1-4)
- Ensure coal capacity has a floor of zero
- Selecting biomass plant will not drive up coal use
- Updated nuclear build costs: high £4.548/Wp rising to £5.072/Wp; medium £3.50/Wp; low £2.478/Wp
- Onshore wind, level 4 upgraded to hit 50GWp by 2020 and stay steady
- Offshore wind fixed-foundation, level 4, from 2020 onwards, upgraded to 10GWp annual installation rate
A while ago, the French Chamber of Commerce in Great Britain invited me to write an article for their magazine’s (INFO) special edition on the Green Economy. Here’s the article, updated (Feb 2013) and with links added to further information
Why the Green Economy? Summary
Markets that exclude the impact on natural capital are distorted. Markets that exclude the costs of pollution are distorted. Markets that allow the Tragedy of the Commons are distorted. These distorted markets represent economic efficiency, and leave us all worse off.
The polluter-pays principle corrects the market distortion caused by unpriced pollution. Joint-stewardship agreements allow us to sustainably manage common resources, preventing the Tragedy of the Commons. Tracking changes to the value of our natural capital base is just as important as tracking transaction values: both represent changes to our wealth.
The Green Economy, in all those forms, is here because it fixes problems that have been accumulating for decades. Why the Green Economy? Because in the long run, the Green Economy leaves us better off, environmentally and economically.
Background to the Green Economy
The Green Economy is worth hundreds of billions of pounds (euro / dollars) each year; it spans many sectors including the most fundamental ones of energy, food and water supplies; and in the last fifty years, it’s gone from fringe to mainstream, growing in value and coverage each year. For example, in 2011, global investment in renewable energy was US$257bn; and there are electric cars on the market that can out-run a Porsche.
But the Green Economy has been around for quite some time, and its academic foundations, in the “polluter-pays principle” dates back to the early decades of the twentieth century. However, more recently, the problems have become global in scale, and the solutions have required international co-operation, for example in banning the industrial production of some of the worst ozone-depleting chemicals. The next wave of problems and solutions dwarfs what has gone before, requiring revolutionary changes to how we generate electricity, how we heat our homes and offices, how we power our transport systems, how we manage our livestock and fertilise our crops. The economic risks (and opportunities) are orders of magnitude greater than what has gone before.
By 2008, Matilda was the world’s most productive wind turbine, having generated 61.4 GWh of energy by the end of its life.
But by the end of March 2010, this record had been broken four times over, Read more…
The map below shows the live positions of ships working on the Walney offshore wind farm, off the coast of Blackpool and Barrow-in-Furness. It’s not as good as sitting on the dock of the bay, watching the ships roll in and roll out again, but it’s better than a slap in the face with a wet cod.Read more…
“Why are you using those units for power? Why don’t you use something else?”
Is that what you’re asking? Is it?
So how about a detailed explanation of why gigawatts just rock, and all the others just get on my tit? And if that is what you are thinking, then (1) you might be a bit odd, but (2) you’re in luck:
Why gigawatts rock
It’s part of the standardised SI system of units understood around the world, and it enables numbers from different countries and different times to be compared.
Standardising on one unit of power makes a lot of sense. That is what the SI system does. Switching between mtoe/y, mboe/d, TWh/y, EJ/y, quads/y, kWh/d is a pain – I guess we all agree on that.
Recently, commentators have suggested that the German experience of providing feed-in tariffs to subsidise photovoltaics [PV] has been a Bad Thing, but much of it is based on a flawed study from the RWI think-tank .
Chris Goodall, George Monbiot (twice), and the Low Carbon Kid have all joined in. Jeremy Leggett has picked up the defence several times now. Let’s look at the RWI claims. But first, a trip to the shop …
Before we had lots of natural gas, nitrogen fertiliser was produced using the Haber-Bosch process. In our low-carbon future, we’ll be making use of it, or something like it, once more. It uses Hydrogen and Nitrogen with energy and an iron catalyst to produce ammonia, which can then form the basis of nitrogen fertilisers. And this process will be of assistance in the move to decarbonise. We can build coastal energy-intensive plants, right on the transmission network, for example at the points where offshore wind farms come ashore, and just operate them when energy is the cheapest. This would help balance demand and supply, at those times when electricity production from wind, wave, tidal and solar energy is highest, and at the same time it helps decarbonise the agriculture sector.