{"id":{"repo_id":"gatech","oai_identifier":"oai:repository.gatech.edu:1853/41188"},"canonical_url":"https://search.dev.ndltd.org/etd/gatech/oai:repository.gatech.edu:1853/41188","repository":{"repo_id":"gatech","name":"Georgia Tech","base_url":"https://repository.gatech.edu/server/oai/request"},"display":{"title":"Design and implementation of a software tool for day-ahead and real-time electricity grid optimal management at the residential level from a customer's perspective","abstract":"This thesis focuses on the design and implementation of a software tool able to achieve electricity grid optimal management in a dynamic pricing environment, at the residential level, and from a customer's perspective. The main drivers encouraging a development of energy management at the home level are analyzed, and a system architecture modeling power, thermodynamic and economic subsystems is proposed. The user behavior is also considered. A mathematical formulation of the related energy management optimization problem is proposed based on the linear programming theory. Several cases involving controllable and non-controllable domestic loads as well as renewable energy sources are presented and simulation scenarios illustrate the proposed optimization strategy in each case. The performance of the controller and the changes in energy use are analyzed, and ideas for possible future work are discussed.","abstract_html":"This thesis focuses on the design and implementation of a software tool able to achieve electricity grid optimal management in a dynamic pricing environment, at the residential level, and from a customer's perspective. The main drivers encouraging a development of energy management at the home level are analyzed, and a system architecture modeling power, thermodynamic and economic subsystems is proposed. The user behavior is also considered. A mathematical formulation of the related energy management optimization problem is proposed based on the linear programming theory. Several cases involving controllable and non-controllable domestic loads as well as renewable energy sources are presented and simulation scenarios illustrate the proposed optimization strategy in each case. 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