Robust Energy Scheduling in Vehicle-to-Grid Networks

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, w...

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Bibliographic Details
Published in:IEEE network 2017-03, Vol.31 (2), p.30-37
Main Authors: Zhou, Zhenyu, Sun, Changhao, Shi, Ruifeng, Chang, Zheng, Zhou, Sheng, Li, Yang
Format: Article
Language:English
Subjects:
Algorithms
Batteries
Charging stations
Electric power systems
Electric vehicles
Electricity consumption
Energy management
Generators
Optimization
Peak load
Production scheduling
Renewable energy sources
Robustness
Robustness (mathematics)
Scheduling
Uncertainty
Vehicle-to-grid
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Summary: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.
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.2017.1600220NM