Short-Term Variability Effect on Peak Demand: Assessment Based on a Microcomponent Stochastic Demand Model

Peak demand plays a major role in water distribution system design. Peak demand assessment is currently facing a double challenge: shifting toward a probabilistic approach, and considering the effect of different spatial and temporal resolutions. Most probabilistic approaches so far have focused on inferring scaling laws by analyzing measurement time series for different spatial and temporal resolutions. The aim of this paper is two-fold: (a) to present a novel analytical approach that enables to assess the short-term variability effect on probabilistic peak demands for different spatial and temporal scales, and (b) to ease the understanding of peak demand factors thanks to a physically based (rather than an empirical) approach. This is possible by combining the principles of peak demand analysis with a microcomponent-based (i.e., end-use oriented) demand model that enables assessment of short-term variability. The methodology here proposed is applied to two case studies with the purpose of validating the approach and showing its full potential. Results prove that stochastic demand models can be a powerful tool for peak demand assessment.