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Multi-objective optimization of water distribution systems based on a real options approach
Affiliation:1. Departamento de Engenharia Civil, Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Coimbra, Portugal;2. Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter, United Kingdom;1. Civil and Environmental Engineering Department, Imperial College London, SW7 2AZ London, United Kingdom;2. School of Computing & Communications, Lancaster University, LA1 4WA Lancaster, United Kingdom;3. Potsdam Institute for Climate Impact Research, Climate Impacts & Vulnerabilities, P.O. Box 60 12 03, 14412 Potsdam, Germany;4. The James Hutton Institute, Information and Computational Sciences Group, Craigiebuckler, Aberdeen AB15 8QH, Scotland, United Kingdom;1. Università di Enna Kore, Cittadella Universitaria, Enna - 94100, Italy;2. Università di Palermo, Viale delle Scienze, Palermo - 90128, Italy;1. LHC - FEC, University of Campinas, Campinas, Brazil;2. EDEn - Department of Architecture and Civil Eng., University of Bath, Bath, UK;3. Fluing - IMM, Universitat Politècnica de València, Valencia, Spain
Abstract:This paper describes a multi-objective optimization model including Real Options concepts for the design and operation of water distribution networks. This approach is explained through a case study with some possible expansion areas defined to fit different future scenarios. A multi-objective decision model with conflicting objectives is detailed. Also, environmental impacts are considered that take into account not only the life cycle carbon emissions of the different materials used during the construction of the networks but also the emissions related to energy consumption during operation. These impacts are translated by giving a cost to each tonne of carbon dioxide emitted. This work presents a new multi-objective simulated annealing algorithm linked to a hydraulic simulator to verify the hydraulic constraints, and the results are represented as points on the Pareto front. The results show that the approach can deal explicitly with conflicting objectives, with environmental impacts and with future uncertainty.
Keywords:Carbon emissions  Multi-objective optimization  Real options  Simulated annealing  Uncertainty  Water networks
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