Discipline: Technology and Engineering
Subcategory: Electrical Engineering
Warren Abrams - University of the District of Columbia
Co-Author(s): Sasan Haghani, University of the District of Columbia, Washington, DC
Microgrids are localized groupings of distributed power generation sources that allow users to achieve specific local goals, such as reliability, cost reduction, and better utilization of local renewable energy sources. Microgrids and their applications in real world energy infrastructures have the potential to transform Washington D.C.’s commercial energy usage, which accounts for 70% of the total power usage in Washington DC. Analysis of the Washington DC’s power usage has shown that there is great demand in the commercial sector for the more sustainable and efficient energy solution where microgrid can greatly contribute to. In this paper, the Distributed Energy Resources Customer Adoption Model (DER-CAM), a distributed generation analytics model developed at Berkeley Labs, was used to study the feasibility of various microgrid configurations that can reduce energy consumption in the District’s commercial sector. Ten buildings of varying load capacities, including offices, restaurants and supermarkets, was used as our commercial sector model. Our simulation results showed that a 15% saving in ‘high capacity’ power transmission costs can be achieved by the implementation of microgrids. It was also shown that during the cold months (September-February) microgrids (powered by solar energy) can supplement more than half of the energy demand. In the next step of the project, HOMER, a commercial package for the design and optimization of multiple energy resources, will be used to verify the results of 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, firstname.lastname@example.org
Role: I was the only student working on this project and was responsible for all aspects of it, including literature survey, learning to use the software, writing code, acquiring data and writing the abstract.