The increasing frequency of torrential rainfall due to global warming has resulted in a significant rise in urban flooding and river overflows. Rainwater pumping stations, typically located near rivers, serve as buffers between sewer systems and receiving water bodies, helping to mitigate flood risks. A primary challenge in operating these stations is optimizing pump performance to prevent flooding while minimizing energy consumption and costs. Various computational methods, including meta-heuristics and deep learning, have been proposed to tackle this optimization problem. However, most studies either overlook or inadequately address pump maintenance costs, which are essential for long-term operational efficiency. This gap stems from the lack of a comprehensive model that accurately captures the full spectrum of costs involved in pump operation. This paper introduces a cost estimation model that integrates both deterministic and probabilistic elements to enhance the energy-efficient operation of rainwater pumping stations. The model focuses on pumps with capacities of 100 m3/min and 170 m3/min, which are commonly used. It takes into account electricity consumption costs as well as maintenance costs arising from frequent on/off cycles and dry-run events. Predictions of failures due to these operational stresses are modeled using the Crow–AMSAA non-homogeneous Poisson process (NHPP) and Weibull distributions—probabilistic models widely used in mechanical failure analysis. To evaluate the proposed model, simulations were conducted using the Storm Water Management Model (SWMM), comparing a deep reinforcement learning-based control strategy with the current operational method at the Gasan Pumping Station in Seoul, South Korea. The pump operating costs associated with each method were calculated and analyzed using the proposed model, demonstrating its potential for ensuring cost-effective and reliable pump operation.
Joo et al. (Fri,) studied this question.