Abstract
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.
Highlights
In the last decades, awareness of the scarcity of energy sources and the impact of anthropogenic activities on the environment has increased
The Bayesian Monte Carlo (BMC) simulation was performed on a total of 10,000 function evaluations, optimizing three objective functions concerning the total management and operating costs of the water network (TC), the management of the pressures in the pipeline and the power produced by the turbines (PWI) and the gains from total energy production (TE) [22]
The BMC simulation was performed on a total of 10,000 function evaluations, being able to provide results that were arranged on a Pareto front so as to be able to relate the different objective functions to each other
Summary
Awareness of the scarcity of energy sources and the impact of anthropogenic activities on the environment has increased. Efforts have been made to this end, as interest in adopting a sustainable perspective is growing in this field. It is estimated that only 30–60% of the energy used is utilized for supply to end users; the remaining portion, 60–40%, is lost due to head losses [1]. There are numerous studies that propose analyses and solutions to improve energy efficiency [2,3,4] and the environmental impact in terms of CO2 produced [5,6,7]
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