Identification of charging behavior characteristic for large-scale heterogeneous electric vehicle fleet

This paper aims to accurately identify parameters of the natural charging behavior characteristic (NCBC) for plug-in electric vehicles (PEVs) without measuring any data regarding charging request information of PEVs. To this end, a data-mining method is first proposed to extract the data of natural aggregated charging load (ACL) from the big data of aggregated residential load. Then, a theoretical model of ACL is derived based on the linear convolution theory. The NCBC-parameters are identified by using the mined ACL data and theoretical ACL model via the derived identification model. The proposed methodology is cost-effective and will not expose the privacy of PEVs as it does not need to install sub-metering systems to gather charging request information of each PEV. It is promising in designing unidirectional smart charging schemes which are attractive to power utilities. Case studies verify the feasibility and effectiveness of the proposed methodology.