Distributed tree-based model predictive control on an open water system

2012 ◽  
Author(s):  
J. M. Maestre ◽  
L. Raso ◽  
P. J. van Overloop ◽  
B. de Schutter
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Water System ◽  
Open Water

scholarly journals Model Predictive Control of a River Reach with Weirs

10.29007/1nnf ◽  
2018 ◽  
Author(s):  
Klaudia Horváth ◽  
Bart van Esch ◽  
Jorn Baayen ◽  
Ivo Pothof ◽  
Jan Talsma ◽  
...  
Keyword(s):  
Decision Support ◽  
Convex Optimization ◽  
Decision Support System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Support System ◽  
Water System ◽  
Open Water ◽  
Global Optimum ◽  
Water Levels

A decision support system for water management based on convex optimization, RTC-Tools 2, is applied for a water system containing river branches connected by weirs. The advantage of convex optimization is the ability of finding the global optimum, which makes the decision support system robust and deterministic. In this work the convex modeling of open water channels and weirs is presented. The decision support system is implemented for a river made of 12 river reaches divided by movable weirs. It is shown how the discharge wave is dispatched in the river without the water levels exceeding the bounds by controlling the weir heights. After this test the optimization can be applied to a realistic numerical model and model predictive control can be implemented.

scholarly journals Distributed tree-based model predictive control on a drainage water system

2012 ◽  
Vol 15 (2) ◽  
pp. 335-347 ◽  
Author(s):  
J. M. Maestre ◽  
L. Raso ◽  
P. J. van Overloop ◽  
B. De Schutter
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Drainage System ◽  
Water System ◽  
Open Water ◽  
Weather Forecast ◽  
Drainage Water ◽  
Programming Approach ◽  
Continuous Exposure ◽  
Dual Decomposition

Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. The control of systems under uncertainty is, in general, a challenging problem. In this paper, we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. Each branch of the tree corresponds to a possible disturbance realization and has a certain probability associated to it. A model predictive controller is used to optimize the expected value of the system variables taking into account the disturbance tree. This technique, tree-based model predictive control (TBMPC), is solved in a distributed fashion. In particular, we apply dual decomposition to get an optimization problem that can be solved by different agents in parallel. In addition, different possibilities are considered in order to reduce the communicational burden of the distributed algorithm without reducing the performance of the controller significantly. Finally, the performance of this technique is compared with others such as minmax or multiple MPC.

scholarly journals Time-instant optimization for hybrid model predictive control of the Rhine–Meuse delta

2013 ◽  
Vol 15 (2) ◽  
pp. 271-292 ◽  
Author(s):  
H. van Ekeren ◽  
R. R. Negenborn ◽  
P. J. van Overloop ◽  
B. De Schutter
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Sea Water ◽  
Water System ◽  
Open Water ◽  
Time Instant ◽  
Water Levels ◽  
Continuous Variables ◽  
Discrete Events ◽  
Control Structures

In order to ensure safety against high sea water levels, in many low-lying countries, water levels are maintained at certain safety levels, and dikes have been built, while large control structures have been installed that can also be adjusted dynamically after they have been constructed. Currently, these control structures are often operated purely locally, without coordination of actions being taken at different locations. Automatically coordinating these actions is difficult, as open water systems are complex, hybrid dynamical systems, in the sense that continuous dynamics (e.g. the evolution of the water levels) appear mixed with discrete events (e.g. the opening or closing of barriers). In low lands, this complexity is increased further due to bi-directional water flows resulting from backwater effects and interconnectivity of flows in different parts of river deltas. In this paper, we propose a model predictive control (MPC) approach that is aimed at automatically coordinating the actions of control structures. The hybrid dynamical nature of the water system is explicitly taken into account. In order to relieve the computational complexity involved in solving the MPC problem, we propose TIO-MPC, where TIO stands for time-instant optimization. Using this approach, the original MPC optimization problem that uses both continuous and integer variables is transformed into a problem involving only continuous variables. Simulation studies of current and future situations are used to illustrate the behavior of the proposed scheme.

scholarly journals A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3608
Author(s):  
Yang Yuan ◽  
Neng Zhu ◽  
Haizhu Zhou ◽  
Hai Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Water System ◽  
Energy Performance ◽  
Effective Control ◽  
New Model ◽  
Chilled Water ◽  
Chilled Water System ◽  
Hydraulic Oscillation

To enhance the energy performance of a central air-conditioning system, an effective control method for the chilled water system is always essential. However, it is a real challenge to distribute exact cooling energy to multiple terminal units in different floors via a complex chilled water network. To mitigate hydraulic imbalance in a complex chilled water system, many throttle valves and variable-speed pumps are installed, which are usually regulated by PID-based controllers. Due to the severe hydraulic coupling among the valves and pumps, the hydraulic oscillation phenomena often occur while using those feedback-based controllers. Based on a data-calibrated water distribution model which can accurately predict the hydraulic behaviors of a chilled water system, a new Model Predictive Control (MPC) method is proposed in this study. The proposed method is validated by a real-life chilled water system in a 22-floor hotel. By the proposed method, the valves and pumps can be regulated safely without any hydraulic oscillations. Simultaneously, the hydraulic imbalance among different floors is also eliminated, which can save 23.3% electricity consumption of the pumps.

scholarly journals Continuous nonlinear model predictive control of a hybrid water system

2012 ◽  
Vol 15 (2) ◽  
pp. 246-257 ◽  
Author(s):  
Eelco Nederkoorn ◽  
Jan Schuurmans ◽  
Joep Grispen ◽  
Wytze Schuurmans
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Land Surface ◽  
Real Life ◽  
Water System ◽  
Water Levels ◽  
Nonlinear Model Predictive Control ◽  
Weather Forecasts ◽  
Highly Nonlinear

Incorporating weather forecasts in the control of land surface water levels requires predictions of the net inflow to the water system. This net inflow is the combined flow of an incoming load (rain, evaporation, etc.) and outgoing pump rates. Because the pump costs are considerable, optimal pump schedules have minimal energy consumption. Model predictive control (MPC) is able to compute, revise and apply such optimized schedules by incorporating a model of the water system. The pumps typically cause discontinuities in the model, which leads to mathematical complications. Avoiding advanced solving techniques for these hybrid systems, this paper introduces an alternative that enables pure continuous MPC by smoothing the jumps. Although the resulting underlying model is continuous, it is also highly nonlinear. This requires use of the specialized class of nonlinear model predictive control (NMPC), which is able to cope with the arising nonlinearities. Control inputs computed by these methods can be translated to the original hybrid system by a final post-processing step. This paper presents the outlined scheme, and verifies it by applying an optimized NMPC implementation (the DotX nonlinear predictive controller, DNPC), equipped with the approximated continuous nonlinear model, to a real-life hybrid water system.

scholarly journals A Model Predictive Control Framework for Real-Time Optimisation of Water System Operations

10.29007/fg7g ◽  
2018 ◽  
Author(s):  
Henrik Madsen ◽  
Anne Katrine Falk ◽  
Rasmus Halvgaard
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Case Studies ◽  
Predictive Control ◽  
Large Scale ◽  
Water System ◽  
River System ◽  
Quadratic Program ◽  
Real Time Control ◽  
Time Control

We have developed a versatile Model Predictive Control (MPC) framework, which can handle real-time control of a large variety of water systems. The framework combines a fast-solvable optimisation model (a quadratic program) with evaluation and realignment by a detailed hydrological-hydrodynamic model. The flexibility of the MPC framework is highlighted by two case studies: (1) a large-scale river system with several weeks of travel time, and (2) an urban storm and wastewater system with a concentration time of about half an hour to one hour. Both case studies demonstrate a large potential for improving operations by system-wide real-time optimisation.

Sign in / Sign up

Export Citation Format

Share Document