Toward intelligent demand-side energy management via substation-level flexible load disaggregation

Non-intrusive load monitoring is a prominent part of demand-side energy management that provides visibility of flexible loads to support real-time electricity market pricing strategies and intelligent demand response programs. Compared with household-level load disaggregation, substation-level load disaggregation can significantly preserve residential privacy and reduce facility costs while providing sufficient information of flexible loads for intelligent demand-side energy management from the area scale. Especially, among various flexible loads, thermostatically controlled loads are highlighted due to their large proportion and high demand response elasticity. However, due to the variation and complexity of residential routines on a large scale, disaggregation of flexible loads from the substation level remains unsolved. To this end, focusing on thermostatically controlled loads, this paper proposes a contrastive sequence-to-point learning algorithm for substation-level flexible load disaggregation to fill the research gap. In the first stage, the theory of the effect of load aggregation and thermal inertia effect is introduced, and significant impact factors on flexible loads are summarized. Secondly, a substation-level flexible load disaggregation algorithm based on contrastive sequence-to-point learning is proposed, where pair-wise comparison and residual mechanism are combined in a semi-supervised structure to extract deep features and track fluctuations in flexible loads. Then, SHapley Additive exPlanations are utilized to ensure the optimization and interpretability of the algorithm. The proposed algorithm is tested and verified on public datasets under low frequency, reducing the disaggregation Mean Absolute Percentage Error of thermostatically controlled loads to as low as 8.78% and 11.26% for bi-directional and unidirectional structures separately. Additionally, it is generalizable to disaggregate other flexible loads, including photovoltaic and electric vehicles, demonstrating satisfactory performance. The algorithm has proved to be robust to data sparsity problems and practical for substation-level demand response potential estimation.