Date of Award

May 2018

Degree Type


Degree Name

Master of Science



First Advisor

Lingfeng Wang

Committee Members

David Yu, Chao Zhu





Wentao Zou

The University of Wisconsin-Milwaukee,2018

Under the Supervision of Dr. Lingfeng Wang

As a big consumer of energy, water and wastewater treatment used about 75000 to 100000 GWh electricity, which accounts for nearly 3% of U.S. annual energy [1]. Not only being energy-intensive, wastewater treatment plant (WWTP) also consumes a lot of electricity during peak hours, which makes WWTP a good candidate of DR (demand response). The main purpose of demand response is to improve the stability of the electric grid and reduce the use of electricity during peak period to lower the total system costs. Two kinds of strategies can be utilized to reduce electrical loads during peak periods, which are load shifting and load shedding. Load shedding strategy is to reduce the total electrical load during demand response event and load shifting is to reschedule the time of some electrical load to partial-peak or off-peak hours. In this work, both of them are used to reach a better financial benefit.

The process and energy consumption of WWTP have been analyzed. It is found that the aeration in secondary treatment and pumps for wastewater pumping and sludge pumping are two main processes which consume the majority of total electric power. Based on shifting loads of aerations and pumps, a load shifting model is formulated to shift load from on-peak hours to off-peak hours. Several constraints have been taken into consideration such the storage capacity, maximum holding time of wastewater when it stays in storage tanks, maximum treatment capacity of WWTP, etc. This model can effectively reduce the annual electricity cost while the quality of effluent and the reliability of WWTP are not compromised. In the case study analysis, 22% cost reduction is achieved by using the load shifting model.

A software tool has also been developed to help users calculate the amount of cost they can save when the load shifting model is applied. The software tool is user friendly and easy to use. The influent data and electricity price data need to be loaded by users, and some kinds of parameters need to be typed in depending on different situations. For instance, the size of the WWTP and the capacity of storage tank need to be loaded.

In addition to demand response, WWTP can save more money with the help of a microgrid. A microgrid is a smaller version of traditional power grid which can provide backup power to WWTP so that the power generated by a microgrid can be used during on-peak hours or sold back to the main grid if possible. A microgrid can also increase the reliability of WWTP. As a discrete energy system with distributed energy sources, a microgrid can operate in parallel with or independently from the main power grid. This feature of the microgrid makes sure WWTP can still receive reliable energy when no electricity can be provided by the main grid.

A microgrid model is developed. A battery bank is also involved in the formulation. Constraints including microgrid capacity, charge and discharge efficiency of battery bank, and battery capacity have been considered. The method used to solve this formulation is particle swarm optimization (PSO). A detailed description of the problem-solving process has been displayed step by step. The case study shows the microgrid model can increase the cost reduction further to 29% of total energy expense based on the load shifting model.