Title:
Optimization of residential microgrids using black-box modeling methods

dc.contributor.advisor Garimella, Srinivas
dc.contributor.author Green, Christy
dc.contributor.committeeMember Bras, Berdinus
dc.contributor.committeeMember Simmons, Richard
dc.contributor.department Mechanical Engineering
dc.date.accessioned 2020-09-08T12:47:03Z
dc.date.available 2020-09-08T12:47:03Z
dc.date.created 2020-08
dc.date.issued 2020-07-06
dc.date.submitted August 2020
dc.date.updated 2020-09-08T12:47:03Z
dc.description.abstract 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%.
dc.description.degree M.S.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/63638
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Energy optimization
dc.subject Black-box model
dc.subject Data-driven model
dc.subject Machine learning
dc.subject Demand response
dc.subject Residential microgrid
dc.title Optimization of residential microgrids using black-box modeling methods
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Garimella, Srinivas
local.contributor.corporatename George W. Woodruff School of Mechanical Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 7c74399b-6962-4814-9d2a-51f8b9c41e1f
relation.isOrgUnitOfPublication c01ff908-c25f-439b-bf10-a074ed886bb7
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Masters
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