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dc.contributor.author
Zheng, Kedi
dc.contributor.author
Chen, Qixin
dc.contributor.author
Wang, Yi
dc.contributor.author
Kang, Chongqing
dc.contributor.author
Xie, Le
dc.date.accessioned
2021-01-08T10:17:37Z
dc.date.available
2021-01-08T03:47:32Z
dc.date.available
2021-01-08T10:17:37Z
dc.date.issued
2021-01
dc.identifier.issn
1949-3053
dc.identifier.issn
1949-3061
dc.identifier.other
10.1109/TSG.2020.3011266
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/460398
dc.description.abstract
Having a better understanding of how locational marginal prices (LMPs) change helps in price forecasting and market strategy making. This paper investigates the fundamental distribution of the congestion part of LMPs in high-dimensional Euclidean space using an unsupervised approach. LMP models based on the lossless and lossy DC optimal power flow (DC-OPF) are analyzed to show the overlapping subspace property of the LMP data. The congestion part of LMPs is spanned by certain row vectors of the power transfer distribution factor (PTDF) matrix, and the subspace attributes of an LMP vector uniquely are found to reflect the instantaneous congestion status of all the transmission lines. The proposed method searches for the basis vectors that span the subspaces of congestion LMP data in hierarchical ways. In the bottom-up search, the data belonging to 1-dimensional subspaces are detected, and other data are projected on the orthogonal subspaces. This procedure is repeated until all the basis vectors are found or the basis gap appears. Top-down searching is used to address the basis gap by hyperplane detection with outliers. Once all the basis vectors are detected, the congestion status can be identified. Numerical experiments based on the IEEE 30-bus system, IEEE 118-bus system, Illinois 200-bus system, and Southwest Power Pool are conducted to show the performance of the proposed method.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
Locational marginal price (LMP)
en_US
dc.subject
data-driven
en_US
dc.subject
unsupervised learning
en_US
dc.subject
subspace clustering
en_US
dc.title
Unsupervised Congestion Status Identification Using LMP Data
en_US
dc.type
Journal Article
dc.date.published
2020-07-22
ethz.journal.title
IEEE Transactions on Smart Grid
ethz.journal.volume
12
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
IEEE trans. smart grid
ethz.pages.start
726
en_US
ethz.pages.end
736
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-01-08T03:47:40Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-01-08T10:17:45Z
ethz.rosetta.lastUpdated
2021-02-15T23:00:29Z
ethz.rosetta.versionExported
true
ethz.COinS
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