TY - GEN
T1 - Behind-the-Meter Disaggregation of Residential Electric Vehicle Charging Load
AU - Pu, Kang
AU - Zhao, Yue
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85144206708
U2 - 10.1109/SmartGridComm52983.2022.9961024
DO - 10.1109/SmartGridComm52983.2022.9961024
M3 - Conference contribution
AN - SCOPUS:85144206708
T3 - 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2022
SP - 426
EP - 431
BT - 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2022
Y2 - 25 October 2022 through 28 October 2022
ER -