Optimized Scheduling of Fixed and Mobile Pumps for Urban Drainage Under Extreme Rainfall

When extreme rainfall intensity exceeds the design capacity of urban drainage systems, fully operating all drainage facilities may still fail to eliminate urban flooding risks. In such scenarios, mobile pumps play a crucial role in supplementing the drainage system. However, coordinating mobile and fixed pumps within a complex drainage network to achieve optimal efficiency at minimal cost remains a significant challenge. This study proposes a virtual pump station-based optimization method for the real-time coordinated control of mobile pump deployment, activation, deactivation, and fixed pump operation. The objective is to minimize system overflow while accounting for pump activation and operational costs. The Storm Water Management Model (SWMM) is employed to predict drainage system performance under varying rainfall return periods, while Model Predictive Control (MPC) and Genetic Algorithms (GA) are utilized to determine optimal pump control strategies. Simulation results demonstrate that the proposed modeling and control approach effectively mitigates overflow risks in extreme rainfall conditions.