Abstract
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.
Highlights
Plug-in electric vehicles (PEVs) are becoming increasingly popular due to their potential to enhance energy security as well as to address environmental issues [1, 2]
If PEVs are popularized, the aggregated residential load will consist of three components, i.e., climate-sensitive load (CSL) [21], base load and aggregated charging load (ACL)
This paper proposes a novel methodology to identify the natural charging behavior characteristic (NCBC)-parameters of the large-scale heterogeneous PEVfleet
Summary
The sub-metering methods need to build expensive submetering systems to gather the data with regard to the charging request information of PEVs [6, 14]. These methods usually aim at designing demand management paradigms that are based on the real time pricing mechanisms. This paper aims to accurately identify the NCBC-parameters without measuring data regarding the charging request information of PEVs. If PEVs are popularized, the aggregated residential load will consist of three components, i.e., climate-sensitive load (CSL) [21], base load and ACL. Identification of charging behavior characteristic for large-scale heterogeneous electric
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
