Discipline: Technology and Engineering
Subcategory: Electrical Engineering
Alicia Marshall - University of the District of Columbia
Co-Author(s): Nitt Chuenprateen and Sasan Haghani, University of the District of Columbia, Washington, DC
Load balancing refers to the storage of excess power generated during lower demand periods for use during later periods of high demand. With the advent of smart appliances and new game-theory centered algorithms for load balancing, there is now a greater possibility for smart grid adoption on the residential scale. Smart appliances are connected to one another and to the Internet, communicate with the smart grid to indicate power usage levels, and can respond to the current state of the smart grid. While this can serve to make the home more comfortable, as with automated thermostats or lighting systems, there is massive possibility to allow smart appliances to modulate the times that they perform their tasks, and support the balancing process. This paper uses GridLab-D to test the effects of smart thermostats in Washington, DC in the summer and winter, for load balancing and reduction in power usage. GridLab-D, developed by the U.S. Department of Energy, uses multiple differential equations to model the power use of various elements within a smart grid system over time. The system handles a variety of systemic parameters that affect power use. For this simulation, a model consisting of twenty residences, including single family homes and apartments was created using XML. Each home was assigned several attributes, including the number of inhabitants, type of appliances present, size and insulation of the homes, possible solar panel installation, and the number of stories, ceiling height and the direction of windows in each home. This information was combined with climate data provided by the U.S. National Solar Radiation Database, as well as daily power use schedules. The data produced by this simulation compares the overall load on the smart grid at various times of day. The results of the simulation show that during the winter, there is a 20% reduction in peak load for the morning peak and a 10% reduction in load for the evening peak. During the summer, there is a 33% to 50% reduction in peak loads using the smart thermostat versus the traditional thermostat. Summer reduction is far more pronounced due to the influence of solar panels on the model.
Funder Acknowledgement(s): This research was supported by the STEM Center for Research and Development, NSF/HRD1531014 and NSF/HRD1435947.
Faculty Advisor: Sasan Haghani, email@example.com
Role: I was involved in all aspects of the project, including research on GridLab-D, smart appliances and smart grid and helped with writing the abstract.