Advisor

Robert Bass

Date of Award

6-7-2019

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Physical Description

1 online resource (x, 125 pages)

DOI

10.15760/etd.6909

Abstract

The increased penetration of renewable energy sources poses new challenges for grid stability. The stochastic and uncontrollable generation of solar and wind power cannot be adjusted to match the load profile, and the transition away from traditional synchronous generators is reducing the grid capacity to arrest and recover from frequency disturbances.

Additionally, the distributed nature of many renewable energy sources makes centralized control of generation more complicated. The traditional power system paradigm balances the supply and demand of electricity on the grid by regulating generation. As this becomes more difficult, one alternative is to adjust the load instead. This is not entirely novel, and utilities have incentivized large industrial customers to reduce consumption during peak hours for years. However, the residential sector, which constitutes 37% of electricity consumption in the U.S., currently has very little capacity for load control.

Smart electric water heaters provide utilities with an appliance that can be remotely controlled and serves as a form of energy storage. They have very fast response times and make up a large amount of residential energy consumption, making them useful for load peak shifting as well as other ancillary grid services. As smart appliances become increasingly widespread, more and more devices can be brought into the utility's control network and aggregated into a flexible resource on a megawatt scale.

This work demonstrates the usefulness of aggregated electric water heaters for peak shifting and frequency response. Because a large number of assets are required, emulators are developed based on observations of real devices. Emulated water heaters are then connected to an energy resource aggregator using an internet-of-things network. The aggregator successfully uses these assets to shift consumption away from peak hours. An algorithm was developed for detecting upward frequency disturbances in real-time. The aggregator uses this algorithm to show that an aggregation of water heaters is well-suited to respond to these frequency disturbances by quickly adding a large amount of load to the grid.

Persistent Identifier

https://archives.pdx.edu/ds/psu/29091

Share

COinS