Non-Intrusive Load Monitoring for Energy Management in Smart Grids Incorporating EVs

In the current smart grid, managing high-energy consumption loads, particularly electric vehicles (EVs), become increasingly complex. This paper introduces a framework that employs non-intrusive load monitoring (NILM) techniques for enhanced energy management in smart grids. Our approach encompasses a broader range of common flexible loads and energy related to EV charging. The NILM technology enables the detailed analysis of individual appliance consumption patterns from aggregated smart meter data, addressing privacy concerns and technical limitations. In detail, we propose a new NILM algorithm, Serial Multi-task Transformer with Attention (SMTA), to address the problem of long-term household load disaggregation. SMTA uses a Serial-Correlation mechanism with period-based dependencies and cyclical information aggregation for effective period time series analysis and utilizes the Task-Specific Attention strategy to find the power influence between appliances. Afterwards, based on the NILM results, we develop an energy management model aiming to optimize the net operation cost in the smart grid, after all active customers participation in ancillary service programs. The proposed framework has been verified on a modified IEEE 57-bus radial distribution system based on real-world household data. The results show that the SMTA algorithm significantly improves load disaggregation accuracy. Furthermore, the proposed framework in comparison to basic energy management reduces the net operation costs by up to 26.9%, highlighting the substantial economic benefits of integrating NILM into smart grid operations.