All ETDs from UAB

Advisory Committee Chair

Hessam Taherian

Advisory Committee Members

Gregoty Franklin

Shuhui Li

David Littlefield

Pradeep Vitta

Richard Watson

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) School of Engineering


Renewable energy continues to proliferate throughout the world as costs decrease and the public desire for clean energy rises. These sources are intermittent by nature, providing added complexity to its integration with existing grid infrastructure. This paper proposes an iterative pricing and energy consumption strategy between a utility and home energy management (HEM) system, acting on behalf of homeowners, with the goal of easing this integration while also decreasing homeowner energy costs, increasing utility profit and minimizing utility energy storage requirements. The strategy begins with a day-ahead energy cost profile developed by the utility using a mix of traditional and renewable resources. The HEM receives this cost profile and develops a schedule for the home based on preprogrammed preferences. The HEM then estimates its energy usage profile and sends it to the utility, who receives one aggregated profile from all participating homes. The utility then calculates an updated pricing scheme to encourage a shift of usage towards times where generation is in excess of demand. This process iterates until the supply and demand of energy are reasonably aligned, annual homeowner energy costs are decreased and utility profit is increased. Finally, a new fifteen-minute ahead price is sent to the HEM throughout the day to address forecasting errors and is associated only with energy storage devices within the home. The algorithm’s effectiveness was simulated with a group of ten homes. Implementation resulted in an increased correlation between usage and renewable generation from 12% to 40%, reduced the annual energy costs to consumers by 4%, increased utility profit by 2% and decreased energy storage requirements by 46%. A second simulation was performed with highly efficient homes, which resulted in an increased correlation coefficient of 43%, decreased energy costs of 5%, an increase in profit by 6% and a decrease in energy storage requirements of 32%.

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