Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment

dc.contributor.advisorEdgar, Thomas F.en
dc.creatorCole, Wesley Josephen
dc.date.accessioned2014-06-30T19:46:54Zen
dc.date.accessioned2018-01-22T22:26:08Z
dc.date.available2018-01-22T22:26:08Z
dc.date.issued2014-05en
dc.date.submittedMay 2014en
dc.date.updated2014-06-30T19:46:54Zen
dc.descriptiontexten
dc.description.abstractThis work considers how various integrated energy systems can be managed in order to provide economic or energetic benefits. Energy systems can gain additional degrees of freedom by incorporating some form of energy storage (in this work, thermal energy storage), and the increasing penetration of smart grid technologies provides a wealth of data for both modeling and management. Data used for the system models here come primarily from the Pecan Street Smart Grid Demonstration Project in Austin, Texas, USA. Other data are from the Austin Energy Mueller Energy Center and the University of Texas Hal C. Weaver combined heat and power plant. Systems considered in this work include thermal energy storage, chiller plants, combined heat and power plants, turbine inlet cooling, residential air conditioning, and solar photovoltaics. These systems are modeled and controlled in integrated environments in order to provide system benefits. In a district cooling system with thermal energy storage, combined heat and power, and turbine inlet cooling, model-based optimization strategies are able to reduce peak demand and decrease cooling electricity costs by 79%. Smart grid data are employed to consider a system of 900 residential homes in Austin. In order to make the system model tractable for a model predictive controller, a reduced-order home modeling strategy is developed that maps thermostat set points to air conditioner electricity consumption. When the model predictive controller is developed for the system, the system is able to reduce total peak demand by 9%. Further work with the model of 900 residential homes presents a modified dual formulation for determining the optimal prices that produce a desired result in the residential homes. By using the modified dual formulation, it is found that the optimal pricing strategy for peak demand reduction is a critical peak pricing rate structure, and that those prices can be used in place of centralized control strategies to achieve peak reduction goals.en
dc.description.departmentChemical Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/24908en
dc.language.isoenen
dc.subjectThermal energy storageen
dc.subjectSmart griden
dc.subjectModel predictive controlen
dc.subjectResidential air conditioningen
dc.subjectPeak demanden
dc.titleDynamic modeling, optimization, and control of integrated energy systems in a smart grid environmenten
dc.typeThesisen

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