Modeling the Effect of Renewable Energy and Smart Appliances in Energy Reduction of Residential Homes Using GridLab-D
- Conference
- Location
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Columbus, Ohio
- Publication Date
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June 24, 2017
- Start Date
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June 24, 2017
- End Date
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June 28, 2017
- Conference Session
- Tagged Division
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Energy Conversion and Conservation
- Page Count
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12
- DOI
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10.18260/1-2--28678
- Permanent URL
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https://peer.asee.org/28678
- Download Count
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902
Paper Authors
Nitt Chuenprateep University of the District of Columbia
Nitt Chuenprateep is a third-year Mechanical Engineering major at the University of the District of Columbia. His research interests include renewable energy and aviation safety modeling and analysis. He is also interested in the pedagogy of non-traditional engineering students who may be re-entering undergraduate-level study after time in the workforce.
Warren Orlando Abrams University of the District of Columbia
Warren Abrams is a junior electrical engineering major at the University of the District of Columbia. His research interests include micro-grid/smart-grid modelling and implementation, low-cost photovoltaic cells, and high-capacity battery compositions.
Alicia Marshall University of the District of Columbia
Sasan Haghani University of the District of Columbia
Sasan Haghani, Ph.D., is an Associate Professor of Electrical and Computer Engineering at the University of the District of Columbia. His research interests include the application of wireless sensor networks in biomedical and environmental domains and performance analysis of communication systems over fading channels.
Abstract
This paper details a senior design project that quantifies the energy savings achievable by using solar power and smart thermostats in the Washington DC residential homes, during the summer and winter seasons. The project was conducted by a group of three students from the Departments of Electrical and Computer Engineering and Civil Engineering at the University of X. GridLab-D, a simulation software developed by the Department of Energy, was used to conduct the simulations.
To simulate a residential area, 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.
The project was conducted by our senior undergraduate students in the Spring of 2016 and was funded by the Xerox Corporation.
Chuenprateep, N., & Abrams, W. O., & Marshall, A., & Haghani, S. (2017, June), Modeling the Effect of Renewable Energy and Smart Appliances in Energy Reduction of Residential Homes Using GridLab-D Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28678
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