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.
Highlights
The smart grid infrastructure enables the integration of renewable energy resources at the individual consumer level [1]
Since classic clustering algorithms (e.g., K-means, DBSCAN) can be tailored to discover homogeneous energy community (HEC) by integrating the net energy (NE) amounts [10], we focus on the mixed energy community (MEC) discovery and sufficient energy community (SEC) discovery
Where the above two sets of constraints ensure that the overall outgoing energy of every microgrid with positive NE is no greater than its current excessive energy, and the overall incoming energy of every microgrid with negative energy is no less than its current demand, respectively [26]
Summary
The smart grid infrastructure enables the integration of renewable energy resources at the individual consumer level [1]. It creates a paradigm where any individual consumer in the grid can be a power supplier. Microgrids can provide energy independence to individual communities or entities who intend to manage their own power generation and distribution [3]. Microgrids can provide resilience against large-scale failures across the grid They can continue to operate if largescale blackouts occur [3]. Some energy communities with respect to time interval 1⁄2T1; T2 can be defined as follows
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