Date of Graduation

5-2015

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering (MSEE)

Degree Level

Graduate

Department

Electrical Engineering

Advisor/Mentor

Mantooth, H. Alan

Committee Member

Balda, Juan C.

Second Committee Member

Ang, Simon S.

Third Committee Member

McCann, Roy A.

Keywords

Applied sciences; Automated energy management; Power electronics; Smart grid

Abstract

With the increase in energy demand by the residential community in this country and the diminishing fossil fuel resources being used for electric energy production there is a need for a system to efficiently manage power within a residence. The Smart Green Power Node (SGPN) is a next generation energy management system that automates on-site energy production, storage, consumption, and grid usage to yield the most savings for both the utility and the consumer. Such a system automatically manages on-site distributed generation sources such as a PhotoVoltaic (PV) input and battery storage to curtail grid energy usage when the price is high. The SGPN high level control features an advanced modular algorithm that incorporates weather data for projected PV generation, battery health monitoring algorithms, user preferences for load prioritization within the home in case of an outage, Time of Use (ToU) grid power pricing, and status of on-site resources to intelligently schedule and manage power flow between the grid, loads, and the on-site resources.

The SGPN has a scalable, modular architecture such that it can be customized for user specific applications. This drove the topology for the SGPN which connects on-site resources at a low voltage DC microbus; a two stage bi-directional inverter/rectifier then couples the AC load and residential grid connect to on-site generation. The SGPN has been designed, built, and is undergoing testing. Hardware test results obtained are consistent with the design goals set and indicate that the SGPN is a viable system with recommended changes and future work

Share

COinS