Optimal energy recovery by means of pumps as turbines (PATs) for improved WDS management

Journal Article (2018)
Author(s)

Carla Tricarico (University of Cassino and Southern Lazio)

Mark S. Morley (KWR Water Research Institute)

Rudy Gargano (University of Cassino and Southern Lazio)

Zoran Kapelan (University of Exeter)

Dragan Savić (University of Exeter)

Simone Santopietro (University of Cassino and Southern Lazio)

Francesco Granata (University of Cassino and Southern Lazio)

Giovanni de Marinis (University of Cassino and Southern Lazio)

DOI related publication
https://doi.org/10.2166/ws.2017.202 Final published version
More Info
expand_more
Publication Year
2018
Language
English
Issue number
4
Volume number
18
Pages (from-to)
1365-1374
Downloads counter
166

Abstract

In water networks characterized by a significant variation in ground elevations the necessity of pumping water in some areas is complicated by a conflicting requirement to reduce excess pressures in other areas. This and the increasing cost of electricity has led to the use of Pumps-operating-As-Turbines (PATs) devices that can reduce pressure (and leakage) whilst harvesting energy. This paper presents a methodology for optimal water distribution system (WDS) management, driving the optimization by minimizing the surplus pressure at network nodes and the operational pumping costs and maximizing the income generated through energy recovery. The method is based on a highly parallelized Evolutionary Algorithm, employing an hydraulic solver to evaluate hydraulic constraints. Water demands at network nodes are considered as uncertain variables modelled by using a probabilistic approach in order to take into account unknown future demands. The approach is demonstrated in different case studies. Results obtained highlight that the economic benefits of installing PATs for energy recovery in conjunction with a combined pump-scheduling and pressure management regime is especially related to the input network characteristics. Further analysis of the importance of the probabilistic approach and of the influence of the interval time step adopted for the optimization has been evaluated.