Homogeneous and Mixed Energy Communities Discovery with Spatial-Temporal Net Energy
For smart grid operators, this work provides methods to discover energy communities for improved energy management, though it is incremental as it extends existing community detection to energy-specific contexts.
The paper tackles energy community discovery in smart grids, proposing algorithms to identify homogeneous, mixed, and self-sufficient energy communities using spatial-temporal net energy data. Experiments on synthetic and real datasets validate algorithm performance.
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 the 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 multiple kinds of energy communities for the microgrids to facilitate energy management (e.g., power supply adjustment, load balancing, energy sharing) on the grid, such as homogeneous energy communities (HECs), mixed energy communities (MECs), and self-sufficient energy communities (SECs). Specifically, we present efficient algorithms to discover such communities of microgrids by taking into account not only their geo-locations but also their net energy over any period. Finally, we experimentally validate the performance of the algorithms using both synthetic and real datasets.