We use a k-means clustering algorithm to partition national electricity demand data for Great Britain and apply a novel profiling method to obtain a set of representative demand profiles for each year over the period 1994-2005. We then use a simulated dispatch model to assess the accuracy of these daily profiles against the complete dataset on a year-to-year basis. We find that the use of data partitioning does not compromise the accuracy of the simulations for most of the main variables considered, even when simulating significant intermittent wind generation. This technique yields 50-fold gains in terms of computational speed, allowing complex Monte Carlo simulations and sensitivity analyses to be performed with modest computing resource.

Physical laws mean that it is generally impossible to identify which power stations are exporting to another country, but economic logic offers strong clues. On windy days, Denmark tends to export electricity to its neighbours, and to import power on calm days. Storing electricity in this way thus allows the country to deal with the intermittency of wind generation. We show that this kind of behaviour is theoretically optimal when a region with wind and thermal generation can trade with one based on hydro power. However, annual trends in Denmark’s trade follow its output of thermal generation and are inversely related to Nordic production of hydro power and the amount of water available to Scandinavian generators, with no correlation with wind generation. We estimate the cost of volatility in Denmark’s wind output to equal between 4% and 8% of its market value.