Hydrodynamic model-driven evolutionary algorithm-based operation optimization of an experimental drainage pumping station

ABSTRACT The effective operation of pumping stations plays a crucial role in urban flood management. However, challenges persist in optimizing pumping station operations, including inaccuracies in characterizing flood propagation and the high computational costs associated with optimization. This study introduces a novel optimization approach for pumping station operation that integrates a hydrodynamic model with evolutionary algorithms, leveraging data-driven technology. The method iteratively computes operation rules using the adaptive particle swarm optimization (APSO) algorithm to identify optimal solutions. The hydrodynamic model accurately simulates flood propagation and provides hydraulic parameters for the objective function and constraints of the APSO algorithm. With the predictive capability of the Kriging model, the optimization enhances efficiency by reducing the frequency of calls to the hydrodynamic model. A study case of a flood management digital twin experimental platform was then taken for the application. Compared to initial operation rules, the objective function value of the proposed method is reduced by 28.7, 32.5, and 25%, respectively, under varying magnitudes of unsteady flood inflows, demonstrating high performances in both flood mitigation and operation cost control. Moreover, the method only requires 70 calls to the hydrodynamic model to formulate the decision operation rule.