DOI:https://doi.org/10.1007/s40565-019-0543-4 |
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Discovering communities for microgrids with spatial-temporal net energy |
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Net amount: 517 |
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Author:
Shangyu XIE1, Han WANG1, Shengbin WANG2, Haibing LU3, Yuan HONG1 , Dong JIN1, Qi LIU4
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Author Affiliation:
1 Department of Computer Science, Illinois Institute of Technology, 10 W 31st Street, Chicago, IL 60616, USA 2 School of Business, College of New Jersey, 2000 Pennington Rd., Ewing, NJ 08628, USA 3 Department of Information Systems and Analytics, Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053, USA 4 College of Business, University of Rhode Island, 7 Lippitt Road, Kingston, RI 02881, USA
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Foundation: |
This work is partially supported by the National Science Foundation (NSF) (No. CNS-1745894) and the WISER ISFG grant. It is also partly sponsored by the Air Force Office of Scientific Research (AFOSR) (No. YIP FA9550-17-1-0240) and the Maryland Procurement Office (No. H98230-18-D-0007). |
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Abstract: |
Smart grid has integrated an increasing number of distributed energy resources to improve the efficiency and flexibility of power generation and consumption as well as the resilience of the power grid. The energy consumers on the power grid, e.g., households, equipped with distributed energy resources can be considered as ‘‘microgrids’’ that both generate and consume electricity. In this paper, we study the energy community discovery problems which identify energy communities for the microgrids to facilitate energy management, e.g., load balancing, energy sharing and trading on the grid. Specifically, we present efficient algorithms to discover such communities of microgrids considering both their geo-locations and net energy (NE) over any period. Finally, we experimentally validate the performance of the algorithms using both synthetic and real datasets. |
Keywords: |
Smart grid, Microgrid, Community discovery, Net energy (NE), Clustering |
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Online Time:2019/11/27 |
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