A secure, reliable and relatively affordable electricity supply is an essential part of modern life in many parts of the world. When charging an iPad, for example, one expects that power will be available at the flick of a switch and rarely considers the complex chain of events involved in producing and conveying the electricity to the device.
The production of energy – including electricity – accounts for a large part of national and global greenhouse gas emissions. Decarbonizing the energy sector is therefore a key ingredient in meeting climate change targets both in the UK and worldwide. Electricity – widely seen as the easiest form of energy to decarbonize – has seen a rapid shift towards renewables, particularly wind and solar photovoltaic (PV).
In a national-scale electricity system it is difficult to store large volumes of power efficiently and as such a near-instantaneous balance between supply and demand must be maintained. Thuswhile the growing use of weather-sensitive renewable generation is clearly beneficial in terms of reducing carbon emissions, it presents new challenges for electricity system management as electricity production cannot be controlled to the same extent as that from, for example a traditional gas-fired power station.
Figure 1: Simulated changes in weather sensitivity of the electricity system in Great Britain as increasing amounts of wind power capacity is installed. The red line shows the strength of the negative relationship between temperature and annual total demand-net-wind (primarily associated with cold periods driving high demand), which decreases as installed wind capacity increases. The blue line, in contrast, shows the increasing strength of the relationship between wind-speed and demand-net-wind as installed wind capacity increases (the sign is also negative because more wind implies a lower demand-net-wind). Recent installed wind capacities for the GB electricity system are marked in grey lines, suggesting that the system’s dominant weather sensitivity changed from being temperature to wind sometime between 2009 and 2015. The analysis is based on a 36-year modelled “reconstruction” of the power system’s behaviour derived from weather records. Adapted from Bloomfield et al. (2018)1
This can be illustrated with an example. It has long been known that electricity demand is strongly sensitive to temperature: as temperatures fall in winter, electricity demand rises. Recent research, however, suggests that in the UK the impact of year-to-year variations in temperature via demand are now typically smaller than year-to-year variations in wind power supply (as shown in the figure 1). That is, in terms of the amount of electricity that must be supplied by coal, gas and nuclear power stations, the impact of a calm winter now exceeds a cold winter1 while winter-time weather patterns associated with both cold and still conditions lead to a double whammy (both high demand and low supply2,3,4). This suggest an important but subtle shift in how the weather risk should be viewed. To assess how much non-renewable generation capacity is required, it makes more sense to ask what the maximum demand-net-renewables is (i.e., the demand remaining once the contribution from renewables is removed). Such a calculation is easier than quantifying the minimum renewable generation available during times of peak demand (the so called “capacity credit” of renewables).
The need for high-quality tools for assessing and understanding the impact of weather and a changing climate on the energy system has never been greater. The Energy-Meteorology group5, working closely with its industrial and academic partners, is leading the development of many such tools: from the first use of multi-decadal “energy system data reconstructions”6, to the development of new European climate services for energy (weeks-to-months ahead7,8,9), and the exploitation of state-of-the-art high-resolution climate data for informing the design of climate-resilient power systems in the coming decade10,11.
- Bloomfield, H. C., Brayshaw, D. J., Shaffrey, L. C., Coker, P. J., Thornton H. E., 2018: The changing sensitivity of power systems to meteorological drivers: a case study of Great Britain. Res. Lett., 13 (5), 054028, https://doi.org/10.1088/1748-9326/aabff9.
- Thornton, H. E., Scaife, A. A., Hoskins, B. J., Brayshaw, D. J., 2017: The relationship between wind power, electricity demand and winter weather patterns in Great Britain. Res. Lett., 12 (6), 064017, https://doi.org/10.1088/1748-9326/aa69c6.
- Ely, C. R, Brayshaw, D. J., Methven, J., Cox, J., Pearce, O., 2013: Implications of the North Atlantic Oscillation for a UK–Norway renewable power system. Energy Policy, 62, 1420-1427, https://doi.org/10.1016/j.enpol.2013.06.037.
- Brayshaw, D. J., Dent, C., Zacharay, S., 2012: Wind generation’s contribution to supporting peak electricity demand: meteorological insights. Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 226 (1), 44-50, https://doi.org/10.1177/1748006X11417503.
- University of Reading, 2018: Research Themes – Energy Meteorology. Accessed 6 December 2018, https://research.reading.ac.uk/met-energy/
- Cannon, D. J., Brayshaw, D. J., Methven, J., Coker, P. J., Lenaghan, D., 2015: Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain. Renewable Energy, 75, 767-778, https://doi.org/10.1016/j.renene.2014.10.024
- Thornton, H. E., Scaife, A., Hoskins, B. J., Brayshaw, D. J., Smith, D., Dunstone, N., Stringer, N., Bett, P. E., 2018: Skilful seasonal prediction of winter gas demand. Environmental Research Letters. Res. Lett., (in press).
- C3S, 2018: European Climatic Energy Mixes. Accessed 6 December 2018, http://ecem.climate.copernicus.eu.
- Earth System Services, 2018: S2S4E Climate Services for Clean Energy. Accessed 6 December 2018, https://s2s4e.eu.
- Santos-Alamillos, F. J, Brayshaw, D. J., Methven, J., Thomaidis, N. S., Ruiz-Arias, J. A., Pozo-Vázquez, D., 2017: Exploring the meteorological potential for planning a high performance European Electricity Super-grid: optimal power capacity distribution among countries. Res. Lett., 12 (11), 114030, https://doi.org/10.1088/1748-9326/aa8f18.
- PRIMAVERA, 2018: Primavera: User Interface Platform. Accessed 6 December 2018, https://uip.primavera-h2020.eu