Real-time feedback based online aggregate EV power flexibility characterization
Abstract
As an essential measure to combat global warming, electric vehicles (EVs) have witnessed rapid growth. Flexible EVs can enhance power systems' ability to handle renewable generation uncertainties. How EV flexibility can be utilized in power grid operation has captured great attention. However, the direct control of individual EVs is challenging due to their small capacity and large number. Hence, it is the aggregator that interacts with the grid on behalf of the EVs by characterizing their aggregate flexibility. In this article, we focus on the aggregate EV power flexibility characterization problem. First, an offline model is built to obtain the lower and upper bounds of the aggregate EV power flexibility region. It ensures that any trajectory within the region is feasible. Then, considering that parameters such as real-time electricity prices and EV arrival/departure times are not known in advance, an online algorithm is developed based on Lyapunov optimization techniques. We provide a theoretical bound for the maximum charging delay under the proposed online algorithm. Furthermore, real-time feedback is designed and integrated into the proposed online algorithm to better unlock EV power flexibility. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed method.
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Studies in Refs [11 -17,19 -22] do not introduce a proper flexibility quantification method to effectively exploit the flexibility of EVs.
“Furthermore, the studies in Refs. [11 -17,19 -22] do not introduce a proper flexibility quantification method to effectively exploit the flexibility of EVs”
From Improving distribution system flexibility using electric vehicles under the distributed energy resource management system framework (2026) · cites this paper as [11]
Cites
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
Wan, Z., Li, H., He, H., & Prokhorov, D. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2879572 · Cited by 548 · cited 1× · see citing works
“A deep Q-network (DQN) was used to approximate the optimal action-value function to determine the EV charging strategy [27].”
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Lee, S., & Jung, J. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116292 · Cited by 13 · see citing works
Zhang, X., Zhao, Q., Guo, L., & Wang, S. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3594615 · Cited by 1 · see citing works
- Authors
- Yan, D., Huang, S., & Chen, Y.
- Affiliations
- Chinese University of Hong Kong (HK)
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2024-01
- Volume
- 15
- Issue
- 1
- Pages
- 658-673
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W4387665580
- Cited by
- 51
- References
- 1
- Citations by year
- 2026: 20 · 2025: 20 · 2024: 10 · 2023: 1
- FWCI
- 3.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Sustainable Energy (publishedVersion)
- Funders
- National Natural Science Foundation of China (52307144)
- Shun Hing Institute of Advanced Engineering
- Chinese University of Hong Kong (RNE-p2-23, 8115071)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- electric vehicle charging, electric vehicle aggregators, Lyapunov optimization, real-time feedback, renewable energy uncertainty, power system operation
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