Potential of model predictive control of a polder water system including pumps, weirs and gates. (November 2022) - Horváth, K.; van Esch, B.; Vreeken, T.; Piovesan, T.; Talsma, J.; Pothof, I.
Abstract: This work presents an assessment of the potential of model predictive control (MPC) of a Dutch polder system. The system drains to the Linge river and includes 13 weirs, 4 hydraulic gates and 4 large pumping stations each equipped with multiple pumps, managed by the Water Board Rivierenland. The management of the system must comply with several goals: keep the water levels within the bounds of safety, pump out the excess water at minimum cost or CO2 emission, but always have enough water for irrigation and shipping. To achieve these goals there are weirs regulating the water level in different pools, pumping stations to pump water in and out and gates to let water in and out by free flow when possible. These pumping stations consume large amounts of energy. We propose multi-objective mixed-integer optimization by using goal programming to prioritize different operational objectives. For the control of the pumps mixed-integer optimization is used, which makes it possible to not only model the energy consumption of the pumps while in operation, but also to model if the pumps are turned on or off. The control system is implemented using RTC-Tools, an open-source software tool to implement MPC. It is demonstrated that the proposed control system implementation can comply with the operational goals of the water board: keeping the water levels within the bounds while reducing the operational costs. The proposed control system has been tested numerically on data from the year 2013, and it is shown that it highly outperforms the current operation. Highlights: A case study on minimization of operational costs of pumps in a polder system Mathematical optimization and MPC lead to significantly lower operational costs Resulting insights in optimized control of pumps can also be used in manual operation RTC-Tools toolbox is used to enable sustainable operational water management.