Peak electricity demand in the UK occurs between 5-30pm to 6pm each weekday evening. I have been monitoring daily power generation on an hourly basis for several years. During 2018 extra wind capacity has been added to the grid and a new interconnection between Scotland and England has improved deployment. As a result the net average power contribution of wind has increased since last year’s result. Note that my figures also include an estimated increase in metered wind power to include smaller embedded onshore wind farms using the procedure described here.
Figure 1 shows the latest overall result.
Figure 1. Contribution of different fuels to UK daily peak demand
Figure 2 shows the yearly average contributions to daily maximum and minimum demand for different fuels. Note how at night (minimum power) the contribution of both wind and nuclear increase dramatically, although for different reasons. Nuclear is always on producing a fixed output while wind output depends only on weather conditions. The demand balance is always met with dispatchable fuels – gas, imports, coal in winter, or Bio (DRAX – wood burners). Solar output is minimal in winter.
UK electricity generation by fuel for red – peak demand blue – low demand at night.
Wind supplies an average 13% of peak demand and 18% of low demand at night. Our ageing nuclear stations still provide 19% of peak demand and 28% of low demand night-time energy.
We can see how crucial gas generation plays in smoothing out the erratic power generation from wind in the following plot.
Comparison of daily peak power supply from Gas and Wind. Gas is tuned to smooth out the surges and falls in power generation by UK’s fleet of wind turbines.
In 2019 roughly half the electricity supply was from low carbon sources and half from fossil fuels (gas and coal). Further expansion of wind capacity always needs an equivalent amount of gas capacity to offset days with no wind.
I wanted to check whether the choice of baseline can affect the calculation of global temperature anomalies from station data. Each temperature index (GISS, Berkeley, CRU) uses different normalisation periods for calculating weather station temperature anomalies. I was surprised to discover that this choice makes no difference whatsoever to the results.
I used the new GHCN V4 which contains 27315 weather stations, and calculated the global average temperature anomaly relative to 5 different 30-year baseline periods using Spherical triangulation. Selecting different baselines restricts the analysis to those stations with sufficient data falling within those periods. Here are the results.
Global Land temperature anomalies calculated relative to 5 different baselines. The numbers in brackets are the number of stations contributing for each baseline period.
All the trends are very similar despite a factor of up to 8 difference in the number of stations used. We can compare them all directly by offsetting each onto the same 1961-1990 baseline. To do this I simply scale each one by the offset difference between 1961-1990 (shown in ‘calc’ brackets).
All 5 baselines offset to the same 1961-1990 normalisation. The offsets are shown as Calc.
The results are surprisingly similar. This means that the choice of baseline period is essentially arbitrary and does not affect the end result.
Global averaged surface temperature for January 2019 was 0.73C using my spherical triangulation method merging GHCNV3 with HadSST3. This is unchanged since December 2018. The baseline used is always 1961-1990.
Monthly temperatures since 1998.
The Northern Hemisphere is shown here.
Temperature distribution Northern Hemisphere. Siberia is warmer than December while N.America is cooler.
and here is the Southern Hemisphere.
Souther Hemisphere shows high Australia temperatures while Antarctica is actually colder than normal.