4.6 Article

Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)

Journal

SUSTAINABILITY
Volume 13, Issue 21, Pages -

Publisher

MDPI
DOI: 10.3390/su132112318

Keywords

pump as turbine; energy recovery; water distribution system; Bayesian Monte Carlo method

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A generic water system is made up of a series of works to collect, transport, store and distribute water to meet user needs. In areas with high altitude differences, the potential energy of water is high and needs to be dissipated. The study evaluates the results of using a stochastic approach to solve the multi-objective optimization problem of PATs in water systems, with different methods performing better in different contexts.
A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited centres, the potential energy of the water is very high. This energy is attributable to high pressures, which could compromise the functionality of the pipelines; it is therefore necessary to dissipate part of this energy. A common alternative to dissipation is the possibility of exploiting this energy by inserting a hydraulic turbine. The present study aims to evaluate the results obtained from a stochastic approach for the solution of the multi-objective optimization problem of PATs (Pumps As Turbines) in water systems. To this end, the Bayesian Monte Carlo optimisation method was chosen for the optimization of three objective functions relating to pressure, energy produced and plant costs. The case study chosen is the Net 3 literature network available in the EPANET software manual. The same problem was addressed using the NSGA-III (Nondominated Sorting Genetic Algorithm) to allow comparison of the results, since the latter is more commonly used. The two methods have different peculiarities and therefore perform better in different contexts.

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