A method for forecasting the spatial and temporal distribution of PEV charging load

Author

Hongcai Zhang ; Wenzuo Tang ; Zechun Hu ; Yonghua Song ; Zhiwei Xu ; Ling Wang

Author_Institution

Dept. of Electr. Eng., Tsinghua Univ., Beijing, China

fYear

2014

fDate

27-31 July 2014

Firstpage

1

Lastpage

5

Abstract

Forecasting the spatial and temporal distribution of plug-in electric vehicle (PEV) charging load is of great significance to study impacts of PEVs on power systems and plan charging facilities. A method for forecasting the spatial and temporal distribution of PEV charging load is proposed based on PEVs´ parking behaviors and the Parking Generation Rate Method (PGRM). Firstly, parking demands are quantified by PGRM and the PEVs´ spatial and temporal distributions are formulated considering various parking behaviors in blocks of different land usages. Then, PEV charging demands for different types of charging facilities are analyzed. An integrated procedure using Monte-Carlo Simulation method is designed to simulate PEVs´ parking, driving and charging behaviors. A case study of a typical urban area in China proves the proposed method is effective to forecast the spatial and temporal distribution of PEV charging load taking various factors, such as drivers´ charging preferences and the service abilities of charging facilities, into account.

Keywords

Monte Carlo methods; electric vehicles; load forecasting; secondary cells; China; Monte Carlo simulation; PEV charging load forecasting; PEV parking behavior; PGRM; parking generation rate method; plug-in electric vehicle; spatial distribution; temporal distribution; Forecasting; Monte Carlo methods; Smart grids; System-on-chip; Urban areas; Vehicles; Charging load; Monte-Carlo Simulation; Parking Generation Rate Method; Plug-in Electric Vehicles (PEVs); Spatial and Temporal Distribution;

fLanguage

English

Publisher

ieee

Conference_Titel

PES General Meeting | Conference & Exposition, 2014 IEEE

Conference_Location

National Harbor, MD

Type

conf

DOI

10.1109/PESGM.2014.6939167

Filename

6939167