Characterizing and mitigating extreme demand fluctuations in highly renewable electricity systems

Understanding and mitigating the extreme 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. This provides a much more powerful method of characterizing peak demand than traditional methods based on sample maximum. We apply this method to characterizing the extreme tails of the distribution of consumer electricity demand and exploring how the extreme values scale with aggregation over increasing numbers of consumers. The results show evidence of fat tail distributions for some consumers. Extreme values scale as an inverse power law with aggregation over increasing numbers of consumers for two very different consumer groups. One method of mitigating peaks in electricity demand is via battery storage. Assuming that the primary effect of battery storage is to smooth demand about a moving average determined by the battery storage capacity, we also explore the impact of moving-average smoothing at increasing timescales on extreme values. We find that extreme values scale as a decreasing exponential with increasing timescale of moving-average smoothing. We also find that as peak reduction by moving-average smoothing is much more sensitive to different sets of consumers than aggregation, we conclude that, in general, battery storage cannot play the same role as aggregation in reducing peak demand.