The water intake and water supply pump stations consume a large amount of energy every year, and their energy efficiency improvement has a significant impact on the operations of the water industry. In this study, a general model for simplifying a simulated two-stage system (i.e., water intake and water supply pumping stations) was established. Optimization strategies were developed based on a dynamic-level-feedback-control approach. Non-dominated sorted genetic algorithm-II (NSGA-II) was used to solve the multi-objective optimization problem. Both cost-driven and energy-driven optimizations were proposed from the perspective of reliability, economy, and durability of pumping station operation. Results show that, compared to the extant strategy currently used, the cost- and energy-driven optimization strategies developed in this study can reduce operating energy costs of the system by 7.0% and 6.2%, and have satisfactory stability under the condition of uncertain water demand. Cost-driven optimization improves the power demand response of the two-stage system by increasing the load transfer in peak periods. Energy-driven optimization reduces carbon dioxide emissions by reducing the total operational energy consumption of the system.

取水和供水泵站每年消耗大量能源，其能效的提高对水行业的运营产生重大影响。在这项研究中，建立了简化模拟两级系统（即进水和供水泵站）的通用模型。基于动态水平反馈控制方法开发了优化策略。使用非支配排序遗传算法-II（NSGA-II）解决了多目标优化问题。从可靠性，经济性和泵站运行的耐用性的角度，提出了成本驱动和能源驱动的优化方案。结果表明，与当前使用的现有策略相比，在这项研究中开发的以成本和能源为驱动的优化策略可以将系统的运行能源成本降低7.0％和6.2％，并且在不确定的需水条件下具有令人满意的稳定性。成本驱动的优化通过增加高峰时段的负载转移来改善两级系统的功率需求响应。能源驱动的优化通过减少系统的总运行能耗来减少二氧化碳的排放。