TY - JOUR
T1 - Optimal Sizing of PEV Fast Charging Stations with Markovian Demand Characterization
AU - Yang, Qiang
AU - Sun, Siyang
AU - Deng, Shuiguang
AU - Zhao, Qinglin
AU - Zhou, Mengchu
N1 - Funding Information:
Manuscript received February 6, 2018; revised May 14, 2018; accepted July 21, 2018. Date of publication July 27, 2018; date of current version June 19, 2019. This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1400601, in part by the National Natural Science Foundation of China under Grant 51777183 and Grant 61772461, in part by the Key Research and Development Project of Zhejiang Province under Grant 2015C01027, in part by the Natural Science Foundation of Zhejiang Province under Grant LZ15E070001 and Grant LR18F020003, in part by the Deanship of Scientific Research at King Abdulaziz University, Jeddah, under Grant G-415-135-38, in part by the Jiangsu Province under Grant BK20161142, and in part by the Macao FDCT under Grant 056/2017/A2. Paper no. TSG-00205-2018. (Corresponding author: Qiang Yang.) Q. Yang and S. Sun are with the College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China (e-mail: qyang@zju.edu.cn; sunsiyang911@zju.edu.cn).
Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1400601, in part by the National Natural Science Foundation of China under Grant 51777183 and Grant 61772461, in part by the Key Research and Development Project of Zhejiang Province under Grant 2015C01027.
PY - 2019/7
Y1 - 2019/7
N2 - Fast charging stations are critical infrastructures to enable high penetration of plug-in electric vehicles (PEVs) into future distribution networks. They need to be carefully planned to meet charging demand as well as ensure economic benefits. Accurate estimation of PEV charging demand is the prerequisite of such planning, but a nontrivial task. This paper addresses the sizing (number of chargers and waiting spaces) problem of fast charging stations and presents an optimal planning solution based on an explicit temporal-state of charge characterization of PEV fast charging demand. The characteristics of PEV charging demand are derived through a vehicle travel behavior analysis using available statistics. The PEV dynamics in charging stations is modelled with a Markov chain and queuing theory. As a result, the optimal number of chargers and waiting spaces in fast charging stations can be jointly determined to maximize expected operator profits, considering profit of charging service, penalty of waiting and rejection, as well as maintenance cost of idle facilities. The proposed solution is validated through a case study with mathematical justifications and simulation results.
AB - Fast charging stations are critical infrastructures to enable high penetration of plug-in electric vehicles (PEVs) into future distribution networks. They need to be carefully planned to meet charging demand as well as ensure economic benefits. Accurate estimation of PEV charging demand is the prerequisite of such planning, but a nontrivial task. This paper addresses the sizing (number of chargers and waiting spaces) problem of fast charging stations and presents an optimal planning solution based on an explicit temporal-state of charge characterization of PEV fast charging demand. The characteristics of PEV charging demand are derived through a vehicle travel behavior analysis using available statistics. The PEV dynamics in charging stations is modelled with a Markov chain and queuing theory. As a result, the optimal number of chargers and waiting spaces in fast charging stations can be jointly determined to maximize expected operator profits, considering profit of charging service, penalty of waiting and rejection, as well as maintenance cost of idle facilities. The proposed solution is validated through a case study with mathematical justifications and simulation results.
KW - Markov model
KW - Monte Carlo simulation
KW - Plug-in electric vehicle (PEV)
KW - charging station planning
KW - queuing theory
KW - state of charge (SoC)
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U2 - 10.1109/TSG.2018.2860783
DO - 10.1109/TSG.2018.2860783
M3 - Article
AN - SCOPUS:85050760353
SN - 1949-3053
VL - 10
SP - 4457
EP - 4466
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 4
M1 - 8421601
ER -