A1 Journal article (refereed)
Robust Energy Scheduling in Vehicle-to-Grid Networks (2017)

Zhou, Z., Sun, C., Shi, R., Chang, Z., Zhou, S., & Li, Y. (2017). Robust Energy Scheduling in Vehicle-to-Grid Networks. IEEE Network, 31 (2), 30-37. doi:10.1109/MNET.2017.1600220NM

JYU authors or editors

- Chang, Zheng

Publication details

All authors or editors: Zhou, Zhenyu; Sun, Changhao; Shi, Ruifeng; Chang, Zheng; Zhou, Sheng; Li, Yang

Journal or series: IEEE Network

ISSN: 0890-8044

eISSN: 1558-156X

Publication year: 2017

Volume: 31

Issue number: 2

Pages range: 30-37

Publisher: Institute of Electrical and Electronics Engineers

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/MNET.2017.1600220NM

Open Access: Publication channel is not openly available

Abstract

The uncertainties brought by intermittent renewable generation and uncoordinated charging behaviors of EVs pose great challenges to the reliable operation of power systems, which motivates us to explore the integration of robust optimization with energy scheduling in V2G networks. In this article, we first introduce V2G robust energy scheduling problems and review the stateof- the art contributions from the perspectives of renewable energy integration, ancillary service provision, and proactive demand-side participation in the electricity market. Second, for each category of V2G applications, the corresponding problem formulations, robust solution concepts, and design approaches are described in detail based on the characteristics of problem structures and uncertainty sets. Then, an adjustable robust energy scheduling solution is proposed to address the over-conservatism problem by exploring chance-constrained methods. Results demonstrate that the proposed algorithm not only can efficiently shift the peak load and reduce the total operation cost, but also provide great flexibility in adjusting the trade-off between economic performance and reliable operation. Finally, we present key research challenges and opportunities.

Keywords: electric vehicles

Free keywords: smart electrical grids; vehicle-to-grid; scheduling

Fields of science: