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 consu...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.展开更多
基金partially supported by the National Science Foundation(NSF)(No.CNS-1745894)the WISER ISFG grant+1 种基金partly sponsored by the Air Force Office of Scientific Research(AFOSR)(No.YIP FA9550-17-1-0240)the Maryland Procurement Office(No.H98230-18-D-0007).
文摘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.
