Abstract
Understanding the fluctuations in power demand is critical to the integration of variable renewable resources and the design of future electricity grids. We present an approach to determining the full statistical distribution of peak values of power demand based on extreme value statistics. We apply this method to characterising the tails of the consumer demand distribution and exploring how peak electricity demand scales with aggregation over increasing numbers of consumers and moving-average smoothing at increasing timescales for two very different consumer groups. The results show evidence of fat tail distributions for some consumers. For both consumer groups, extreme values scale as an inverse power law with aggregation over increasing numbers of consumers and as a decaying exponential with the timescale of moving-average smoothing. Peak reduction by moving-average smoothing is much more sensitive to different sets of consumers than aggregation. As smoothing about a moving average is the primary effect of battery storage, this means that, in general, battery storage cannot play the same role as aggregation in reducing peak demand.
• Method to apply Extreme Value Theory to non-stationary electricity data.
• Characterising consumer electricity demand using extreme value statistics.
• Quantifying peak consumer demand under aggregation and battery storage.