Behind-the-Meter Disaggregation of Residential Electric Vehicle Charging Load

With the rapidly evolving penetration of electric vehicles (EVs) in power distribution systems, a major issue that utilities face is the lack of visibility into the charging behaviors of the behind-the-meter (BTM) EVs. Knowing the BTM EV charging behaviors can greatly enhance utilities' system planning and operation efficacy. In this paper, the problem of disaggregating BTM EV load traces from smart meter data traces is studied. Based on the characteristics of typical EV charging traces, three interdependent sub-problems are formulated: a) Detecting the presence of BTM EVs, b) Estimating the EV charging rate, and c) Detecting the EV charging periods. A unified iterative algorithmic framework is developed to solve all three sub-problems. Importantly, the proposed algorithms do not assume or utilize the knowledge of ground truth EV load traces but estimate BTM EV load traces in an “unsupervised” fashion. Numerical evaluation is conducted based on real-world 15-minute interval smart meter data from Austin, TX, and demonstrates great performance achieved by the proposed algorithms.