Impact of search space discretisation on water distribution system design optimisation
Date
2023
Authors
Zhao, Q.
Wu, W.
Simpson, A.R.
Willis, A.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 25th International Congress on Modelling and Simulation (MODSIM2023), 2023, pp.1116-1122
Statement of Responsibility
Q. Zhao, W. Wu, A.R. Simpson and A. Willis
Conference Name
25th International Congress on Modelling and Simulation (MODSIM) (13 Jul 2023 - 14 Jul 2023 : Darwin, Northern Territory)
Abstract
Water distribution systems (WDSs) are essential components of both agricultural and urban infrastructure systems. In recent years, there have been an increase in energy consumption and associated costs and greenhouse gas (GHG) emissions for water supply and distribution. This has led water utilities to optimise their systems not only for economic benefits but also to reduce environmental impact. Real-world WDSs are topologically and dimensionally complex systems with many interconnected components including pipes, pumps and storages. These systems are often associated with a large search space in the optimisation process. Therefore, the question arises as to whether the search space can be reduced, and yet effective optimisation still be achieved. In this study, a hydraulic-power-based search space reduction method (power-based SSR method) has been used to reduce the optimisation search space by grouping pipes with similar hydraulic power capacity. The impact of search space discretisation on the optimisation performance has been investigated using a real-world WDS with 432 pipes. A multi-objective optimisation (MOO) problem has been formulated. The objectives considered include the minimisation of both the total life cycle cost and total life cycle greenhouse (GHG) emissions over the system design life. Pipe diameters are the decision variables. Various problem formulations with decision variable numbers ranging from 5 to 432 have been compared against two performance indicators: (1) the number of evaluations needed to achieve convergence, where large values indicate lower optimisation efficiency; and (2) the Hypervolume Indicator (HI), where larger HI values indicate better optimisation convergence. Results show that first, trade-offs between the two objective function values with clear Pareto fronts have been observed. With the increase in the number of decision variables, better convergence and smaller minimum objective function values can be achieved. Second, for the two performance indicators, an increase in the number of decision variables in general leads to an increase in both the number of evaluations needed for convergence (i.e., reduced optimisation efficiency) and the Hypervolume Indicator (HI) value of the final optimal solutions (i.e., improved convergence). In addition, as shown in Figure 1, there are also trade-offs observed between the speed of convergence and associated performance. Better convergence requires more optimisation effort, with an increased degree of search space discretisation.
School/Discipline
Dissertation Note
Provenance
Description
Session: E. Energy, integrated infrastructure and urban planning. E5. Water supply systems under change.
Access Status
Rights
© 2023 Individual MODSIM papers are copyright of the Authors and Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ). MSSANZ is the publisher of the MODSIM Proceedings. These proceedings are licensed under the terms of the Creative Commons Attribution 4.0 International CC BY License (http://creativecommons.org/licenses/by/4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you attribute MSSANZ and the original author(s) and source, provide a link to the Creative Commons licence and indicate if changes were made. Images or other third party material are included in this licence, unless otherwise indicated in a credit line to the material.