Impact of Data Quality in Home Energy Management System on Distribution System State Estimation

As the coupling between home energy management system (HEMS) and distribution system state estimation (DSSE) becomes stronger for smart distribution grid operations, unexpected HEMS data change can, through the distortion of the HEMS solution, deteriorate the DSSE performance. In this paper, we investigate the impact of data changes in the HEMS for multiple homes on three-phase unbalanced DSSE. We develop a two-level sensitivity analysis framework based on the perturbed Karush-Kuhn-Tucker conditions from the HEMS and DSSE optimization formulations. The developed framework is used to assess the impact of the HEMS data changes in a low-voltage (LV) distribution network (at the first level) on the DSSE solutions in a medium-voltage (MV) distribution network (at the second level). Using the sensitivity framework, system operators can quantify the sensitivity of DSSE to changes in various types of HEMS data (e.g., demand response signals, appliance parameters, and consumer comfort). Along with the HEMS data impact analysis, the proposed sensitivity approach is tested under different measurement redundancy for DSSE in an IEEE 13-bus MV distribution system with 12 smart households in a radial LV network.