TY - GEN
T1 - Optimal electric vehicle charging scheduling with time-varying profits
AU - Wang, Boyu
AU - Yang, Jing
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/21
Y1 - 2018/5/21
N2 - EV (electric vehicle) charging scheduling to maximize the revenue of charging station while satisfying customer demands is challenging, mainly due to the time-varying nature of user demands and profits. In this paper, we develop an admission control and scheduling mechanism to jointly consider the revenue of charging stations and the service requirements of customers. We consider an offline setting. Given a set of inflexible service demands from customers, our objective is to design an admission control and scheduling scheme, so that the admitted demands can be satisfied while the revenue of the charging station is maximized. We first propose a calculus based scheduling algorithm, and show that if the system is underloaded, it maximizes the revenue of the charging station. We then consider the general overloaded case, and prove that it is NP-complete. We then develop a heuristic algorithm to greedily decline a subset of demands until the remaining demands can be satisfied. We evaluate the performances of the proposed algorithms through simulations.
AB - EV (electric vehicle) charging scheduling to maximize the revenue of charging station while satisfying customer demands is challenging, mainly due to the time-varying nature of user demands and profits. In this paper, we develop an admission control and scheduling mechanism to jointly consider the revenue of charging stations and the service requirements of customers. We consider an offline setting. Given a set of inflexible service demands from customers, our objective is to design an admission control and scheduling scheme, so that the admitted demands can be satisfied while the revenue of the charging station is maximized. We first propose a calculus based scheduling algorithm, and show that if the system is underloaded, it maximizes the revenue of the charging station. We then consider the general overloaded case, and prove that it is NP-complete. We then develop a heuristic algorithm to greedily decline a subset of demands until the remaining demands can be satisfied. We evaluate the performances of the proposed algorithms through simulations.
UR - http://www.scopus.com/inward/record.url?scp=85048539484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048539484&partnerID=8YFLogxK
U2 - 10.1109/CISS.2018.8362285
DO - 10.1109/CISS.2018.8362285
M3 - Conference contribution
AN - SCOPUS:85048539484
T3 - 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
SP - 1
EP - 6
BT - 2018 52nd Annual Conference on Information Sciences and Systems, CISS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 52nd Annual Conference on Information Sciences and Systems, CISS 2018
Y2 - 21 March 2018 through 23 March 2018
ER -