Characterizing energy flexibility of buildings with electric vehicles and shiftable appliances on single building level and aggregated level

Residential energy flexibility is considered one of the efficient concepts to alleviate the ever-increasing concerns of better balancing supply and demand. A positive assumption that all buildings have the same energy flexibility potential, is not applicable in a realistic situation especially when direct load control is not applied for each. This paper proposes a novel approach to characterize the energy flexibility of shiftable appliances and EVs (as two main sources of energy flexibility) protecting consumers’ data privacy and considering the usage behavior. First, an xg-boost regression is utilized to non-intrusively extract the consumption of appliances. Then, the uncertainty of the power values and the operation time of each appliance is computed based on the extracted consumption patterns. Finally, a price-based DR model is used to determine their energy flexibility and prioritize them according to the change in their operating time before and after optimization. Case studies are conducted and results prove that the proposed method is computationally cost-effective and outperforms other methods in terms of accuracy, customer privacy and comfort. Moreover, the results show that the proposed model can significantly decrease the flattening signal up to 3% for each residential building and up to 25% in the residential aggregated level.