An Analytical Polytope Approximation Aggregation of Electric Vehicles Considering Uncertainty for the Day-Ahead Distribution Network Dispatching
Abstract
The integration of large-scale heterogeneous electric vehicles (EVs) into the distribution network increases the system management complexity significantly. Due to the flexible charging/discharging operation and shiftable energy consumption during the parking time, EVs possess a great dispatching potential for the distribution network. This article develops an analytical polytope approximation (APA) aggregation model to efficiently depict the dispatchable regions of large-scale EVs with uncertainties and proposes a bilevel cooperative optimization approach for EV aggregators to participate in the distribution network day-ahead optimal scheduling. Uncertainties of EVs are modeled by chance constraints with different confidence levels to accommodate various regulatory requirements. The proposed approach can obtain large-scale EVs dispatchable capabilities with minimum flexibility loss and realize the regulation command disaggregation without violations. Numerical case studies are conducted on IEEE 33-bus and 141-bus distribution networks based on real data sets, which show that the proposed approach outperforms other state-of-art methods in terms of low operation flexibility conservatism, high system economics, and computational efficiency.
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- EV and system Dataset
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- Authors
- Jian, J., Zhang, M., Xu, Y., Tang, W., & He, S.
- Affiliations
- Tsinghua–Berkeley Shenzhen Institute (CN)
- China Southern Power Grid (China) (CN)
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2024-01
- Volume
- 15
- Issue
- 1
- Pages
- 160-172
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W4376481118
- Cited by
- 56
- References
- 20
- Citations by year
- 2026: 21 · 2025: 20 · 2024: 11 · 2023: 4
- FWCI
- 4.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Sustainable Energy (publishedVersion)
- Funders
- China Southern Power Grid Co., Ltd. (SZKJXM20210138)
- 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 vehicles, day-ahead scheduling, chance constraints, uncertainty modeling, bi-level optimization, electric vehicle aggregators, operational flexibility
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