Open Collections

Model Predictive Control for real-time operation of a stormwater drainage system

University of British Columbia

Stormwater drainage system operators in lowland areas use weather forecast information, tide tables, a hydraulic model and heuristic experiences to balance the water table in the region close to the desired water level setpoints. Water can be discharged using pumps and gravity outflow flap gates, or can be stored in the system if the discharge capacity is limited. In the lower mainland of British Columbia (BC), climate change projections are showing an increasing trend in high-intensity, short-duration rainfall events, and sea level is expected to rise up to 1.0 m by the end of 2100. Given the uncertainties in climate change projections, the challenge is to build more resilient stormwater drainage system whilst reducing the cost of pumping operation or other capacity expansions. Experiences in the Netherlands have shown that algorithmic control of drainage system using model predictive control (MPC) can be a way to link water and energy objectives more cohesively. To maintain water level at the desired water level setpoint, MPC calculates water levels that need to be controlled using rainfall and sea tides forecasts, and computes optimal control actions for pump stations. This thesis aims to gain operational insights into algorithmic control of urban stormwater drainage system in Richmond BC, using a simplified drainage system model. Smart control strategies serving different objectives are explored to reduce pump energy costs while avoiding flood. Furthermore, an application of bottom-up vulnerability assessment under different control strategies aiming to maximize the operational flexibilities, illuminates the vulnerabilities and adaptation capacity of the (modeled) existing stormwater drainage system to plausible scenarios of climate change induced sea level rise, heavy rainstorms, and land-use changes. This provides necessary information to water systems operators and engineers about the smart, real-time control of such a system, and finding ways to combine engineering designs with operational flexibilities for better adapting to future conditions.

Text

2020-05-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/69409

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/