Hydraulic Optimization of RTC Systems: Number, Placement, and PID Control Rules of FCDs in Distributed Flood Routing

Climate change, characterized by an increase in the frequency and intensity of extreme weather events, combined with rapid urbanization, has significantly strained urban drainage networks by exacerbating surface runoff and increasing flood risk. Traditional strategies, such as upsizing conduits or constructing additional detention facilities, often require substantial financial investment and space, which are not always feasible. This study presents an alternative, cost-effective solution by optimizing Real-Time Control (RTC) systems applied to a real case study (i.e., the New Orleans drainage network model) to enhance the drainage performance without major infrastructure expansion. RTC is a promising strategy for dynamic flood mitigation; however, its effectiveness depends heavily on the optimal placement, number, and control rules of Flow Control Devices (FCDs). To address this, a fully integrated Python-based optimization framework was developed, coupling Multi Objective Genetic Algorithms (MOGA) or Multi Objective Particle Swarm Optimization (MOPSO) with the SWMM hydraulic model. The framework simultaneously determines the optimal locations, quantities, and Proportional-Integral-Derivative (PID) control parameters for FCDs under two different optimization methods. The methodology was applied to a real-world case study in New Orleans, Louisiana, USA. Performance was evaluated under a range of design storms (1-, 2-, 5-, 10-, 25-, and 50-year return periods). Results demonstrated that the optimized RTC configuration could reduce peak discharge, delay peak flow timing, and significantly reduce flooding, achieving up to 80% improvement in network efficiency under certain conditions. Shifting towards dynamic modeling represents a significant advancement in the field of urban stormwater management, offering more reliable and robust solutions for mitigating flooding in complex urban environments. This study aims to address the gaps in previous research by conducting the entire optimization process (i.e., find the optimal quantity, placement, and PID controllers) within a Python environment, including the reading, writing, manipulating, and optimizing the SWMM file.