Evaluating Multitimescale Response Capability of EV Aggregator Considering Users’ Willingness
Abstract
With the wide deployment of charging piles and the development of vehicle-to-grid technology, electric vehicles (EVs) will have more opportunities to participate in the operation and scheduling of electric power system through the EV aggregator (EVA), an intermediate agent between the power grid and EV users. Quantitatively evaluating the response capability (RC) of EVA, i.e., charging and discharging power ranges, is of fundamental importance for its trading with the operator of power grid. This article proposes a user-aware method to obtain a more accurate range of multitimescale RC considering the response willingness of EV users. First, a temporal RC evaluation model of a single EV considering charge-discharge state and state of charge (SOC) is established. Second, based on the user psychology model, which reflects the relationship between users' responsivity and incentive price, a multitimescale RC evaluation model of EVA considering users' willingness is built. The day-ahead RC of EVA is evaluated by the state prediction data of EVs. According to the control strategy which considers response time and SOC indicators comprehensively, the RC evaluation is updated intraday. Finally, using the statistical data from the EU MERGE project, the effectiveness of the proposed evaluation model is verified, and the impact of incentive price and scheduling time scale on the RC of EVA are analyzed. The results indicate that the proposed model can effectively track the scheduling goals…
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Affine mathematics characterizes the uncertainty of fuzzy variables, supporting real-time flexibility supply-demand balance at FCS and enhancing the robustness of flexible scheduling compared with existing frameworks.
“Affine mathematics is used to characterize the uncertainty of fuzzy variables, supporting real-time flexibility supply-demand balance at FCS and enhancing the robustness of flexible scheduling compared with existing frameworks [19], [26] that typically address behavioral aspects or uncertainty handling separately.”
From Flexibility Region Quantification and Optimal Scheduling of Fast Charging Stations Considering EV User Discharge Willingness (2025) · cites this paper as [19]
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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
- Xu, X., Li, K., Wang, F., Mi, Z., Jia, Y., Wei, W., & Jing, Y.
- Affiliations
- North China Electric Power University (CN)
- Tsinghua University (CN)
- State Grid Corporation of China (China) (CN)
- Venue
- IEEE Transactions on Industry Applications
- Published
- 2021-07
- Volume
- 57
- Issue
- 4
- Pages
- 3366-3376
- ISSN
- 0093-9994 (ISSN-L)
- 1939-9367
- OpenAlex
- W3163572858
- Cited by
- 65
- References
- 0
- Citations by year
- 2026: 12 · 2025: 21 · 2024: 16 · 2023: 10 · 2022: 4 · 2021: 2
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industry Applications (publishedVersion)
- Funders
- National Key R&D Program of China (2018YFE0122200)
- Science and Technology Project of State Grid (SGHEYX00SCJS2000037)
- Major Science and Technology Achievements Conversion Project of Hebei Province (19012112Z)
- 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 aggregators, vehicle-to-grid, state of charge, day-ahead scheduling, intra-day scheduling
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