Johnston, John (2011-05). Minimizing Energy Consumption in a Water Distribution System: A Systems Modeling Approach. Master's Thesis. Thesis uri icon

abstract

  • In a water distribution system from groundwater supply, the bulk of energy consumption is expended at pump stations. These pumps pressurize the water and transport it from the aquifer to the distribution system and to elevated storage tanks. Each pump in the system has a range of possible operating conditions with varying flow rates, hydraulic head imparted, and hydraulic efficiencies. In this research, the water distribution system of a mid-sized city in a subtropical climate is modeled and optimized in order to minimize the energy usage of its fourteen pumps. A simplified model of the pipes, pumps, and storage tanks is designed using freely-available EPANET hydraulic modeling software. Physical and operational parameters of this model are calibrated against five weeks of observed data using a genetic algorithm to predict storage tank volume given a forecasted system demand. Uncertainty analysis on the calibrated parameters is performed to assess model sensitivity. Finally, the pumping schedule for the system's fourteen pumps is optimized using a genetic algorithm in order to minimize total energy use across a 24-hour period.
  • In a water distribution system from groundwater supply, the bulk of energy consumption is expended at pump stations. These pumps pressurize the water and transport it from the aquifer to the distribution system and to elevated storage tanks. Each pump in the system has a range of possible operating conditions with varying flow rates, hydraulic head imparted, and hydraulic efficiencies.

    In this research, the water distribution system of a mid-sized city in a subtropical climate is modeled and optimized in order to minimize the energy usage of its fourteen pumps. A simplified model of the pipes, pumps, and storage tanks is designed using freely-available EPANET hydraulic modeling software. Physical and operational parameters of this model are calibrated against five weeks of observed data using a genetic algorithm to predict storage tank volume given a forecasted system demand. Uncertainty analysis on the calibrated parameters is performed to assess model sensitivity. Finally, the pumping schedule for the system's fourteen pumps is optimized using a genetic algorithm in order to minimize total energy use across a 24-hour period.

publication date

  • May 2011