TY - JOUR
T1 - Waiting Time Minimized Charging and Discharging Strategy Based on Mobile Edge Computing Supported by Software-Defined Network
AU - Tang, Qiang
AU - Wang, Kezhi
AU - Song, Yun
AU - Li, Feng
AU - Park, Jong Hyuk
N1 - Funding information: This work was supported in part by the National Natural Science Foundation of China under Grant 61772087, in part by the Outstanding Youth Project of Hunan Province Education Department under Grant 18B162, in part by the Double First-Class International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology under Grant 2018IC23, and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea Government under Grant NRF-2019R1A2B5B01070416.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - With the increasing number of electric vehicles (EVs), temporary charging demands grow rapidly. Unlike charging at home or workplace, temporary charging requires less waiting time. In this article, a mobile edge computing (MEC)-enabled charging and discharging networking system algorithm (CDNSA) is proposed to minimize the waiting time for EVs in charging stations (CSs). A software-defined network (SDN) paradigm is adopted to enhance the data transmission efficiency for MEC servers. In CDNSA, the optimization problem is formulated as a mixed-integer nonlinear programming (MINLP). A heuristic algorithm is proposed to solve the optimal CS selection variables for EVs that needs to be charged (EVCs) and EVs that can be discharged (EVDs), and then a remaining problem nonlinear programming (NLP) is obtained. By verifying the convexity of each continuous variable, the NLP is solved by adopting the block coordinate descent (BCD) method. In simulation, the optimality of CDNSA is verified by comparing with the exhaustive algorithm in terms of minimizing maximal waiting time (MMWT) of CSs. We also compare CDNSA with other benchmarks to illustrate its advantage.
AB - With the increasing number of electric vehicles (EVs), temporary charging demands grow rapidly. Unlike charging at home or workplace, temporary charging requires less waiting time. In this article, a mobile edge computing (MEC)-enabled charging and discharging networking system algorithm (CDNSA) is proposed to minimize the waiting time for EVs in charging stations (CSs). A software-defined network (SDN) paradigm is adopted to enhance the data transmission efficiency for MEC servers. In CDNSA, the optimization problem is formulated as a mixed-integer nonlinear programming (MINLP). A heuristic algorithm is proposed to solve the optimal CS selection variables for EVs that needs to be charged (EVCs) and EVs that can be discharged (EVDs), and then a remaining problem nonlinear programming (NLP) is obtained. By verifying the convexity of each continuous variable, the NLP is solved by adopting the block coordinate descent (BCD) method. In simulation, the optimality of CDNSA is verified by comparing with the exhaustive algorithm in terms of minimizing maximal waiting time (MMWT) of CSs. We also compare CDNSA with other benchmarks to illustrate its advantage.
KW - Charging and discharging
KW - minimizing maximal waiting time (MMWT)
KW - mobile edge computing (MEC)
KW - software-defined network (SDN)
UR - http://www.scopus.com/inward/record.url?scp=85081106286&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2019.2957124
DO - 10.1109/JIOT.2019.2957124
M3 - Article
AN - SCOPUS:85081106286
SN - 2327-4662
VL - 7
SP - 6088
EP - 6101
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 7
M1 - 8930011
ER -
