Genetic optimization toward operation of water intake-supply pump stations system

Wanpeng Chen, Tao Tao, Aijiao Zhou, Lu Zhang, Lei Liao, Xumeng Wu, Kai Yang, Chenxiu Li, Tian C. Zhang, Zhi Li

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


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.

Original languageEnglish (US)
Article number123573
JournalJournal of Cleaner Production
StatePublished - Jan 10 2021


  • Energy efficiency
  • Load transfer
  • Modeling
  • Optimization
  • Water intake-supply pump stations

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • General Environmental Science
  • Strategy and Management
  • Industrial and Manufacturing Engineering


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