Date of Award

May 2017

Degree Type


Degree Name

Master of Science



First Advisor

Lingfeng Wang

Committee Members

Guangwu Xu, Chiu-Tai Law


Demand Response, Electric Water Heater, Operational Reliability


The electricity consumption has increased dramatically in past decades due to the improvement of people’s life standard and the increase of their incomes. Some uncertainties have occurred because of an increasing electricity consumption at the household level. As a result, the high power consumption of massive households will affect power system reliability. Recently, the traditional power grid is being transformed to the smart grid, which is an effective way to deal with these issues. The electricity utility could manage the demand side resources using different kinds of Demand Response (DR) methods. Residential resource is an important part besides industrial resource and commercial resource. With the deployment of Home Energy Management System (HEMS) and smart household devices, users’ behavior could be adjusted to respond to the utility signal. Electric Water Heaters (EWHs) account for a huge percentage of energy consumption among all the home appliances. Aggregated EWHs are idea candidates as demand response resources whose power consumption pattern can be modified because they not only consume lots of energy but also have heat storage capability. Therefore, EWHs can react to the optimal operation signal without affecting customers’ daily needs. In this way, electricity utility could treat EWHs as a kind of interruptible load to provide operating reserves to improve power system reliability.

In this thesis, a Binary Particle Swarm Optimization (BPSO) algorithm is utilized to perform the optimization of EWHs. The goal of each EWH optimization using BPSO is to minimize the customers’ electricity cost. Therefore, Time-Of-Use (TOU) electricity rate is utilized as the DR incentive. Meanwhile, the customers’ daily need for hot water should be guaranteed, so a comfort level index is enforced in the optimization process. The thermal model of EWH and water usage profile are used to calculate the real-time hot water temperature. Aggregating thousands of EWHs will have positive influences on power system reliability when massive EWHs are utilized as interruptible loads. EWHs could compensate for the Unit Commitment Risk (UCR) considering the operating reserve capacity they can provide. The UCR reduction is used to calculate and analyze the influence of aggregated EWHs.

A Reliability Test System is modified to test the capacity of aggregated EWHs in this study. Based on the simulation results, the proposed optimization strategy for EWHs is proved to be practical. The customers’ electricity bill has declined effectively and the user’s comfort level, considering different water temperature set point ranges, is ensured. This thesis provides a practicable scheme for residential customers to arrange their EWHs more reasonably. The simulation results show the aggregated EWHs’ load curve and indicate that the proposed method shifts aggregated EWHs load effectively during some peak hours. According to the calculation results of UCR reduction, the aggregated EWHs is turned out to be a great candidate for power system to improve the reliability during peak-hours.