Bottom-Up Generation of Peak Demand Scenarios in Water Distribution Networks

Enrico Creaco, Giacomo Galuppini, Alberto Campisano and Marco Franchini
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Enrico Creaco: Dipartimento di Ingegneria Civile ed Architettura, Università degli Studi di Pavia, Via Ferrata 3, 27100 Pavia, Italy
Giacomo Galuppini: Dipartimento di Ingegneria Civile ed Architettura, Università degli Studi di Pavia, Via Ferrata 3, 27100 Pavia, Italy
Alberto Campisano: Dipartimento di Ingegneria Civile ed Architettura, Università degli Studi di Catania, Viale Andrea Doria 6, 95125 Catania CT, Italy
Marco Franchini: Dipartimento di Ingegneria, Via Saragat 1, 44100 Ferrara, Italy

Sustainability, 2020, vol. 13, issue 1, 1-18

Abstract: This paper presents a two-step methodology for the stochastic generation of snapshot peak demand scenarios in water distribution networks (WDNs), each of which is based on a single combination of demand values at WDN nodes. The methodology describes the hourly demand at both nodal and WDN scales through a beta probabilistic model, which is flexible enough to suit both small and large demand aggregations in terms of mean, standard deviation, and skewness. The first step of the methodology enables generating separately the peak demand samples at WDN nodes. Then, in the second step, the nodal demand samples are consistently reordered to build snapshot demand scenarios for the WDN, while respecting the rank cross-correlations at lag 0. The applications concerned the one-year long dataset of about 1000 user demand values from the district of Soccavo, Naples (Italy). Best-fit scaling equations were constructed to express the main statistics of peak demand as a function of the average demand value on a long-time horizon, i.e., one year. The results of applications to four case studies proved the methodology effective and robust for various numbers and sizes of users.

Keywords: water distribution network; peak demand; scenarios; stochastic generation; beta probability distribution; correlation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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