Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads.
|Original language||English (US)|
|Number of pages||14|
|Journal||IEEE Transactions on Parallel and Distributed Systems|
|State||Published - Dec 1 2016|
Bibliographical noteFunding Information:
This work was supported by National Natural Science Foundation of China (61174127, 61573245, 61221003, 61521063, 61273181), Shanghai Municipal Science and Technology Commission (14511107903). The work of B. Yang was also supported by Shanghai Rising-Star Program (15QA1402300). The work of T. He was also supported by NSF CNS-1446640 and Shanghai Qianren Talent Program. The work of C. Chen was also supported by the Program of New Century Talents in University of China (NCET-13-0358).
© 2016 IEEE.
Copyright 2016 Elsevier B.V., All rights reserved.
- Electric vehicle
- Lyapunov optmization
- charging scheduling
- renewable energy
- smart grid