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Cited by 62Electric Vehicles and InfrastructureSmart Grid Energy ManagementOptimal Power Flow Distribution

Hierarchical distributed framework for EV charging scheduling using exchange problem

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Khaki, Behnam, Chu, Chicheng, Gadh, Rajit·2019·Applied Energy
DOI 10.1016/j.apenergy.2019.03.008
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  • Most mentioned studies overlook the potential benefits of an integrated DER environment, such as DERMS, where EVs could operate in coordination with other DERs to enhance overall grid stability and flexibility.

    “Second, most studies overlook the potential benefits of an integrated DER environment, such as DERMS, where EVs could operate in coordination with other DERs to enhance overall grid stability and flexibility.”

    From Improving distribution system flexibility using electric vehicles under the distributed energy resource management system framework (2026) · cites this paper as [19]

    The Conclusion (page unavailable) reports that coordinated EV charging reduces load variance and peak demand while respecting feeder capacity constraints. The Related Work section (page unavailable) discusses limitations of prior EV-charging methods, but our copy does not say that most studies overlook integration with other DERs or a DERMS. Its plug-and-play flexibility refers to agents changing objectives, not to grid flexibility from EV–DER coordination. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • 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 [19]

    The Conclusion (page unavailable) describes an EV charging-scheduling framework and uses “flexibility” to mean that agents can change their objective functions. The evaluation metrics passage (page unavailable) measures load-profile performance, not EV flexibility. I searched for flexibility quantification, but our copy does not assess whether this study introduces a proper method for quantifying EV flexibility. (LLM, conf=0.70)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 40

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Cited by

Sorted by citations

Authors
Khaki, B., Chu, C., & Gadh, R.
Affiliations
University of California, Los Angeles (US)
Corresponding author
Khaki, Behnam
ORCID
Venue
Applied Energy
Publisher
Elsevier BV
Published
2019-05
Volume
241
Pages
461-471
ISSN
0306-2619 (ISSN-L)
DOI
10.1016/j.apenergy.2019.03.008
OpenAlex
W2920436579
Cited by
62
References
0
Citations by year
2026: 5 · 2025: 6 · 2024: 6 · 2023: 9 · 2022: 14 · 2021: 10 · 2020: 9 · 2019: 3
FWCI
3.24
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Energy (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • arXiv (Cornell University)
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 › Optimal Power Flow Distribution
Keywords
electric vehicle charging scheduling, multi-agent systems, alternating direction method of multipliers, agent clustering, load flattening, charging cost reduction, scalability

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