TY - JOUR
T1 - Online scheduling for hierarchical vehicle-to-grid system
T2 - Design, formulation, and algorithm
AU - Chen, Xiangyu
AU - Leung, Ka Cheong
AU - Lam, Albert Y.S.
AU - Hill, David J.
N1 - Funding Information:
Manuscript received January 16, 2018; revised June 17, 2018 and September 26, 2018; accepted November 19, 2018. Date of publication December 17, 2018; date of current version February 12, 2019. This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant 17261416, and in part by the National Natural Science Foundation of China, under Grant 51707170. The review of this paper was coordinated by Prof. M. Khodayar. (Corresponding author: Ka-Cheong Leung.) X. Chen, K.-C. Leung, and D. J. Hill are with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong (e-mail:, [email protected]; [email protected]; [email protected]).
Funding Information:
This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant 17261416, and in part by the National Natural Science Foundation of China, under Grant 51707170.
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2019/2
Y1 - 2019/2
N2 - Due to the increasing popularity of electric vehicles (EVs) and technological advancements of EV electronics, the vehicle-to-grid (V2G) technique, which utilizes EVs to provide ancillary services for power grid, stimulates new ideas in current smart grid research. When coordinating a large number of EVs distributed in different geographical locations, a single aggregator is not sufficient to oversee the whole system and a hierarchical V2G system is required. Therefore, how to design a hierarchical V2G system and how to coordinate large-scale EVs to provide ancillary services become critical issues. In this paper, a generic hierarchical framework for a V2G system, which aims to provide frequency regulation services, is proposed to address the issues. Smart V2G aggregators (SVAs) are designed and employed to control the V2G system in a tree-like manner. A multi-level online V2G (MLOV) algorithm is devised for hierarchical V2G scheduling and it requires no forecasting information on regulation signals. It can also deal with the scalability issue encountered by the centralized algorithms and incast issue arising in the distributed algorithms. The simulation results show that the proposed algorithm outperforms the existing methods for the tradeoff between the quality of frequency regulation services and computational time. Through the computational study of the proposed algorithm, we also find that the computational time of the MLOV algorithm can be reduced exponentially by employing more SVAs and distributing the computational burden to the SVAs, with slight sacrifice on the smoothing quality.
AB - Due to the increasing popularity of electric vehicles (EVs) and technological advancements of EV electronics, the vehicle-to-grid (V2G) technique, which utilizes EVs to provide ancillary services for power grid, stimulates new ideas in current smart grid research. When coordinating a large number of EVs distributed in different geographical locations, a single aggregator is not sufficient to oversee the whole system and a hierarchical V2G system is required. Therefore, how to design a hierarchical V2G system and how to coordinate large-scale EVs to provide ancillary services become critical issues. In this paper, a generic hierarchical framework for a V2G system, which aims to provide frequency regulation services, is proposed to address the issues. Smart V2G aggregators (SVAs) are designed and employed to control the V2G system in a tree-like manner. A multi-level online V2G (MLOV) algorithm is devised for hierarchical V2G scheduling and it requires no forecasting information on regulation signals. It can also deal with the scalability issue encountered by the centralized algorithms and incast issue arising in the distributed algorithms. The simulation results show that the proposed algorithm outperforms the existing methods for the tradeoff between the quality of frequency regulation services and computational time. Through the computational study of the proposed algorithm, we also find that the computational time of the MLOV algorithm can be reduced exponentially by employing more SVAs and distributing the computational burden to the SVAs, with slight sacrifice on the smoothing quality.
KW - Electric vehicles (EVs)
KW - frequency regulation
KW - hierarchical V2G system
KW - vehicle-to-grid (V2G)
UR - https://www.scopus.com/pages/publications/85058877689
U2 - 10.1109/TVT.2018.2887087
DO - 10.1109/TVT.2018.2887087
M3 - Article
AN - SCOPUS:85058877689
SN - 0018-9545
VL - 68
SP - 1302
EP - 1317
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 2
ER -
SDG 7 Affordable and Clean Energy