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A two-level decision making approach for optimal integrated urban water and energy management

Vakilifard, Negar; A. Bahri, Parisa; Anda, Martin and Ho, Goen (2018). A two-level decision making approach for optimal integrated urban water and energy management. Energy, 155 pp. 408–425.

DOI (Digital Object Identifier) Link: https://doi.org/10.1016/j.energy.2018.04.191
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Abstract

A spatial-temporal model is proposed for optimal integrated water and energy resource management in urban areas, considering daily surplus output from residential grid-connected rooftop photovoltaics as an energy source for sustainable supply. The model addresses optimal investment and operational decisions of a desalination-based water supply system driven by surplus photovoltaic output and grid electricity. The two-level mixed integer linear programming model considers demands, systems configuration, resources capacity and electricity tariffs and gives the solution such that the highest compatibility with available renewable energy is achieved. The model is then applied to Perth, Australia and solved for three operational scenarios. The results show, for a given year, hourly (flexible) basis scenario leads to $9 521 425 and $18 673 545 economic benefits over seasonal (semi-flexible) and yearly (fixed) basis scenarios, respectively. They also indicate 19.9% better economic performance in terms of annualised unit cost of water production over existing Southern seawater desalination plant in Perth. Additionally, it is shown that the seasonal change on the optimal solutions mainly corresponds to the share of each energy resource to meet water-related energy demand. Finally, the results indicate higher sensitivity to the variation of the photovoltaic installation density compared to financial rate.

Item Type: Journal Item
ISSN: 0360-5442
Keywords: Optimisation; Photovoltaics; Grid electricity; Desalination; Urban water supply
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Environment, Earth and Ecosystem Sciences
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 59810
Depositing User: Negar Vakilifard
Date Deposited: 15 Jul 2019 09:04
Last Modified: 15 Jul 2019 09:19
URI: http://oro.open.ac.uk/id/eprint/59810
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