Abstract
The operational management of potable water distribution networks presents a great challenge to water utilities, as reflected by the complex interplay of a wide range of multidimensional and nonlinear factors across the water value chain including the network physical structure and characteristics, operational requirements, water consumption profiles and the structure of energy tariffs. Nevertheless, both continuous and discrete actuation variables can be involved in governing the water network, which makes optimizing such networks a mixed-integer and highly constrained decision-making problem. As such, there is a need to situate the problem holistically, factoring in multidimensional considerations, with a goal of minimizing water operational costs. This paper, therefore, proposes a systematic optimization methodology for (near) real-time operation of water networks, where the operational strategy can be dynamically updated using a model-based predictive control scheme with little human intervention. The hydraulic model of the network of interest is thereby integrated and successively simulated with different trial strategies as part of the optimization process. A novel adapted mixed-integer differential evolution (DE) algorithm is particularly designed to deal with the discrete-continuous actuation variables involved in the network. Simulation results on a pilot water network confirm the effectiveness of the proposed methodology and the superiority of the proposed mixed-integer DE in comparison with genetic algorithms. It also suggests that 23.69% cost savings can be achieved compared with the water utility's current operational strategy, if adaptive pricing is adopted for all the pumping stations.
Highlights
This paper has proposed and developed a systematic discrete-continuous optimization methodology for water network real-time operation, where a wide range of connected multidimensional factors are intrinsically considered as a whole, including the physical structure and characteristics of the network, operational requirements, water consumption profiles and the structure of energy tariff
An adapted Differential evolution (DE) algorithm was proposed to deal with the mixed-integer and constrained nature of the network operational optimization problem
A converted hydraulic model for a pilot area in the U.K. was integrated into the optimization process to achieve real-time prediction of control strategies based on the monitoring of the water network
Summary
In the operation of potable water distribution networks [1], energy cost from pumping accounts for the major proportion of total expenditure [2,3,4]. The minimization of energy cost over an operational horizon considering constraints of water hydraulics, nodal pressures and tank levels was usually pursued These traditional methods are able to quickly find operational solutions, they generally suffer from poor optimization performance and/or oversimplified networks. (i) A systematic optimization methodology is designed for the real-time operation of water networks by dynamically producing control strategies, while being able to holistically take into account various complexities and constraints (e.g. network characteristics, energy tariff, consumption profiles and nodal demand pressure). Such a control strategy is periodically optimized for actuation considering a finite rolling horizon of predicted network statuses upon the updating of the field monitoring.
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