Abstract:

Traditional home energy management methods mainly focus on the energy management within individual households, overlooking considerations of comfort and the interaction with the grid. To enhance the capabilities of home energy management, a two-layered home energy management strategy is proposed. The upper layer considers the operational constraints and costs of the distribution network and the lower layer aims to fulfill users' electricity consumption preferences and cost requirements, achieving optimal household energy management under the premise of interacting with the grid. Acknowledging the limitations of conventional power flow calculations for distribution networks and indoor thermal response algorithms, data-driven methods are introduced to improve the accuracy in assessing both the grid's state and indoor thermal conditions. Furthermore, a natural aggregation algorithm is employed to increase the precision of the optimization process. Extensive experiments are conducted to evaluate the accuracy of the proposed algorithm, and the results convincingly demonstrate the effectiveness and scientific rigor of the proposed strategy.

Key words: user preference；data-driven, home energy management, thermal effect estimation