Demand Response-based Energy Management Model for Energy Communities considering Data Privacy of the Members.

This paper proposes a demand response-based energy management model for energy communities, considering the respective members’ data privacy. Through forecasting and clustering algorithms, this model can identify demand response opportunities for the next day, rank and select the participants for the event and monitor and evaluate the respective event. The paper’s novelty lies in increasing the energy community members’ active participation by allowing them to submit one or more proposals to participate in a demand response event. In this way, a member can, on the one hand, have more freedom to choose how to participate and, on the other hand, have more opportunities to contribute to the DR event with their energy flexibility. This model was tested with real data from an energy community with 50 buildings, which can provide flexibility through reductions and shifting. The results showed that the model’s efficiency could increase by considering the multiple proposals per member. Moreover, it was verified that with just one event, it is possible to reduce the CO 2 emissions and the energy cost by 50% and 73%, respectively, as well increasing the energy community sustainability by 10%.