The Woodruff School

SUMMARY

The optimization of a residential microgrid to respond to demand response signals that increase the dispatchability of locally generated solar photovoltaic (PV) energy while reducing total energy cost is investigated. The study is conducted using data gathered from a 62-home neighborhood located in Birmingham, AL. The HVAC system and water heater, which are the most significant residential electric loads, are designated as the controllable loads in this study. A comparison of system identification method accuracy for the HVAC and water heating systems is made between black-box models and grey-box models found in the literature. A multi-objective optimization problem is then developed with the objectives of minimizing energy cost and consumption while maximizing thermal comfort and the consumption of locally generated PV energy. Results show a maximum total energy savings of 12.9%, a maximum peak energy reduction of 41.3%, and a maximum total cost reduction of 16.6%.

SUBJECT:	M.S. Thesis Presentation

BY:	Christy Green

TIME:	Monday, June 22, 2020, 1:00 p.m.

PLACE:	https://bluejeans.com/271547461, NA

TITLE:	Optimization of Residential Microgrids Using Black-Box Modeling Methods

COMMITTEE:	Dr. Srinivas Garimella, Chair (ME) Dr. Berdinus Bras (ME) Dr. Richard Simmons (ME)

SUMMARY The optimization of a residential microgrid to respond to demand response signals that increase the dispatchability of locally generated solar photovoltaic (PV) energy while reducing total energy cost is investigated. The study is conducted using data gathered from a 62-home neighborhood located in Birmingham, AL. The HVAC system and water heater, which are the most significant residential electric loads, are designated as the controllable loads in this study. A comparison of system identification method accuracy for the HVAC and water heating systems is made between black-box models and grey-box models found in the literature. A multi-objective optimization problem is then developed with the objectives of minimizing energy cost and consumption while maximizing thermal comfort and the consumption of locally generated PV energy. Results show a maximum total energy savings of 12.9%, a maximum peak energy reduction of 41.3%, and a maximum total cost reduction of 16.6%.