High‐Performance Integrated Control of water quality and quantity in urban water reservoirs

Abstract

This paper contributes a novel High‐Performance Integrated Control framework to support the real‐time operation of urban water supply storages affected by water quality problems. We use a 3‐D, high‐fidelity simulation model to predict the main water quality dynamics and inform a real‐time controller based on Model Predictive Control. The integration of the simulation model into the control scheme is performed by a model reduction process that identifies a low‐order, dynamic emulator running 4 orders of magnitude faster. The model reduction, which relies on a semiautomatic procedural approach integrating time series clustering and variable selection algorithms, generates a compact and physically meaningful emulator that can be coupled with the controller. The framework is used to design the hourly operation of Marina Reservoir, a 3.2 Mm3 storm‐water‐fed reservoir located in the center of Singapore, operated for drinking water supply and flood control. Because of its recent formation from a former estuary, the reservoir suffers from high salinity levels, whose behavior is modeled with Delft3D‐FLOW. Results show that our control framework reduces the minimum salinity levels by nearly 40% and cuts the average annual deficit of drinking water supply by about 2 times the active storage of the reservoir (about 4% of the total annual demand).

Urban Multi-Source Water Supply in China: Variation Tendency, Modeling Methods and Challenges

“…Zhao et al adopted a decentralized control model in the joint operation of water quality and quantity, and a water quality and quantity feedback mechanism was added in this model in order to give full play of the directive function of water quality simulation results [95]. A high-performance integrated control framework was proposed by Galelli et al to support the real-time operation of urban water supply storages affected by water quality problems, and Delft3D-FLOW was used to simulate nonsteady flow and transport phenomena in Marina Reservoir [96]. To sum up, researches on the joint operation of water quality and quantity pay more attention to the river basin, while research in the urban water supply systems are still lacking.…”

Estimating the Economic Value of Interannual Reservoir Storage in Water Resource Systems

“…Water Resources Research RESEARCH ARTICLE model predictive control (Anghileri et al, 2016;Galelli et al, 2015;Mayne et al, 2000;Raso & Malaterre, 2017). Other studies (Cai et al, 2002;Shiau, 2011) used nonlinear optimization formulations with constrained carryover storage volumes.…”

“…Most approaches for efficient allocation of reservoir storage are limited by the so‐called curse of dimensionality where computational time and memory to increase exponentially with the number of storage units (Bellman & Dreyfus, ; Giuliani et al, ). Examples include dynamic programming (Banihabib et al, ; Fontane & Labadie, ; Ji et al, ; Mansouri et al, ; Marino & Mohammadi, ; Tauxe et al, ; Yakowitz, ; Yeh & Becker, ), stochastic dynamic programming (Butcher, ; Scarcelli et al, ; Soleimani et al, ; Stedinger et al, ; Torabi & Mobasheri, ; Zhou et al, ), and model predictive control (Anghileri et al, ; Galelli et al, ; Mayne et al, ; Raso & Malaterre, ). Other studies (Cai et al, ; Shiau, ) used nonlinear optimization formulations with constrained carryover storage volumes.…”

A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

“…Surrogate modelling is an approach to develop a simpler and faster model emulating the outputs of a more complex simulator as a function of its inputs and parameters [1]. Surrogate models are useful when the simulators are computationally expensive for applications such as model-based real time control (RTC) [2], model calibration [3], design optimization [4], Monte Carlo based uncertainty propagation analysis [5], or sensitivity analysis [6]. In Urban Drainage Modelling (UDM), most of the urban drainage simulators are also among the computationally demanding modelling tools.…”

“…Afterwards this process is conditioned on certain selected design data to produce a posterior, which is called the emulator [3]. To date, several studies have confirmed the effectiveness of application of the emulators in acceleration of hydrological and hydrodynamic simulators for purposes such as uncertainty propagation analysis using the Monte Carlo method [5]; optimal dam operation [23,24]; and Real-time Model Predictive Control (RT-MPC) [2].…”