Investigation of the Optimal Scheduling Strategy for an Intake Pump Station Based on Surrogate Models of the Differential Evolution Algorithm

At the Second Water Intake Pump Station of the Chenhang Reservoir in Shanghai, suboptimal pump scheduling resulted in electricity consumption cost attributable to pump-motor equipment accounting for an exceptionally large proportion of the total power expenditure. In response to the economical operation issues, a mathematical model of power consumption cost for the pump station was established by introducing time-of-use electricity pricing and constraint suppression terms. Taking the minimum cost as the research objective, the differential evolution (DE) algorithm was employed to establish a fitness function for electricity cost, aiming to find the most economical and reliable scheduling strategy. However, owing to its low computational speed and high complexity, machine learning was introduced to establish neural network surrogate models of the DE algorithm. By comparing three surrogate models, the Multilayer Perceptron (MLP) neural network model was adopted as the most appropriate surrogate model. It was optimized for robustness improvement and verified on site. The results demonstrate that implementing the surrogate model achieves over 25% savings in electricity cost per thousand cubic meters of water, while slashing the solution time by 88.53% compared to the standard DE algorithm. Furthermore, the overall power consumption is reduced by 2.20% under a cost-priority strategy and by 15.89% under a power-priority strategy, thereby directly mitigating the carbon footprint of the pump station. The proposed hybrid computational framework in this study bridges the gap between the computationally expensive heuristic optimization and the strict real-time control requirements in engineering, highlighting its significant contribution to the sustainable and low-carbon operation of water infrastructure.