Multi-time Scale Stochastic Optimization for Hybrid AC-DC Distribution Networks with PET Based on E-C-Kmeans Clustering

This paper addresses the uncertainty of distributed power supply in a hybrid AC-DC distribution network with multi-port Power Electronic Transformers (PET). A multi-time scale stochastic optimization model based on E-C-Kmeans clustering is proposed. First, the E-C-Kmeans clustering and reduction mod...

Full description

Saved in:
Bibliographic Details
Published in:2025 4th International Conference on Smart Grid and Green Energy (ICSGGE) pp. 223 - 226
Main Authors: Sun, Yaobin, Liu, Shourui, Lu, Jiashuo, Du, Yonggang, Zheng, Huankun
Format: Conference Proceeding
Language:English
Published: IEEE 28.02.2025
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract This paper addresses the uncertainty of distributed power supply in a hybrid AC-DC distribution network with multi-port Power Electronic Transformers (PET). A multi-time scale stochastic optimization model based on E-C-Kmeans clustering is proposed. First, the E-C-Kmeans clustering and reduction model for new energy scenarios is constructed by integrating the CBFSAFODP algorithm (a fast search and density peak discovery algorithm) with the K-means algorithm. Then, the model aims to minimize wind curtailment penalties, operational and maintenance costs of energy storage devices, electricity purchasing costs, and micro gas turbine generation costs. A random optimization operation model for the day-ahead and intra-day scheduling of the hybrid AC-DC distribution network with PET based on E-C-Kmeans clustering is built. Finally, the case study on the hybrid AC-DC distribution network with PET verifies that the proposed model can reduce system operating costs and effectively address the uncertainty in the distribution of renewable energy generation.
AbstractList This paper addresses the uncertainty of distributed power supply in a hybrid AC-DC distribution network with multi-port Power Electronic Transformers (PET). A multi-time scale stochastic optimization model based on E-C-Kmeans clustering is proposed. First, the E-C-Kmeans clustering and reduction model for new energy scenarios is constructed by integrating the CBFSAFODP algorithm (a fast search and density peak discovery algorithm) with the K-means algorithm. Then, the model aims to minimize wind curtailment penalties, operational and maintenance costs of energy storage devices, electricity purchasing costs, and micro gas turbine generation costs. A random optimization operation model for the day-ahead and intra-day scheduling of the hybrid AC-DC distribution network with PET based on E-C-Kmeans clustering is built. Finally, the case study on the hybrid AC-DC distribution network with PET verifies that the proposed model can reduce system operating costs and effectively address the uncertainty in the distribution of renewable energy generation.
Author Lu, Jiashuo
Sun, Yaobin
Zheng, Huankun
Liu, Shourui
Du, Yonggang
Author_xml – sequence: 1
  givenname: Yaobin
  surname: Sun
  fullname: Sun, Yaobin
  email: 572828676@qq.com
  organization: State Grid Baoding Electric Power Company,Baoding City,Hebei Province,China
– sequence: 2
  givenname: Shourui
  surname: Liu
  fullname: Liu, Shourui
  organization: State Grid Baoding Electric Power Company,Baoding City,Hebei Province,China
– sequence: 3
  givenname: Jiashuo
  surname: Lu
  fullname: Lu, Jiashuo
  organization: State Grid Baoding Electric Power Company,Baoding City,Hebei Province,China
– sequence: 4
  givenname: Yonggang
  surname: Du
  fullname: Du, Yonggang
  organization: State Grid Baoding Electric Power Company,Baoding City,Hebei Province,China
– sequence: 5
  givenname: Huankun
  surname: Zheng
  fullname: Zheng, Huankun
  organization: North China Electric Power University,School of Electrical and Electronic Engineering,Baoding City,Hebei Province,China
BookMark eNo1kM1OAjEAhGuiB0XewEN9gGJ_tt32iGVdiCgmcCfdblcal13SlhB4ejf-XGaS-ZJJZu7Addd3DoBHgieEYPW00OuyLEQmRD6hmPLJEMpM5vwKjFWuJGOEZ5QwdQtOb8c2eZT83sG1Ne2gqbc7E5O3cHUYcn8xyfcdbPoA5-cq-BpONZppOPMxBV8df-i7S6c-fEV48mkHP4oNfDbR1XBABdLode9MF6FujzG54LvPe3DTmDa68Z-PwOal2Og5Wq7KhZ4ukVcsISMaVWPLjKkJaTIpKaVG5EpVTkhpDSWqwZbXOMfUDsMU4YJXFousbqzkho3Aw2-td85tD8HvTThv_-9g32w6WzQ
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/ICSGGE64667.2025.10984875
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library Online
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9798331542139
EndPage 226
ExternalDocumentID 10984875
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i93t-a6f9d0c3aad11f488222a6799be688ca219f0c5d0702c79891565bc064dfc85a3
IEDL.DBID RIE
IngestDate Thu May 29 05:57:27 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i93t-a6f9d0c3aad11f488222a6799be688ca219f0c5d0702c79891565bc064dfc85a3
PageCount 4
ParticipantIDs ieee_primary_10984875
PublicationCentury 2000
PublicationDate 2025-Feb.-28
PublicationDateYYYYMMDD 2025-02-28
PublicationDate_xml – month: 02
  year: 2025
  text: 2025-Feb.-28
  day: 28
PublicationDecade 2020
PublicationTitle 2025 4th International Conference on Smart Grid and Green Energy (ICSGGE)
PublicationTitleAbbrev ICSGGE
PublicationYear 2025
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.9006406
Snippet This paper addresses the uncertainty of distributed power supply in a hybrid AC-DC distribution network with multi-port Power Electronic Transformers (PET). A...
SourceID ieee
SourceType Publisher
StartPage 223
SubjectTerms Clustering algorithms
Costs
Distribution networks
E-C-Kmeans clustering algorithm
Hybrid AC-DC distribution network
Hybrid power systems
Optimization models
PET
Power electronics
Processor scheduling
stochastic optimization
Stochastic processes
Transformers
Uncertainty
Title Multi-time Scale Stochastic Optimization for Hybrid AC-DC Distribution Networks with PET Based on E-C-Kmeans Clustering
URI https://ieeexplore.ieee.org/document/10984875
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1dS8MwFA1uiPik4sRvIviambVNkzxqVzdR5mB72NvI8oEDt8nWKf57b9JO8cEHX0poA4Ebbu_JxzkHoWuqqUhiaohOBCWJjlIiJ9QRFXn4zoy0KriWPPFeT4xGsl-R1QMXxlobLp_Zpm-Gs3yz0Gu_VQYZLoUH2DVU45yXZK0ddFXpZt48ZINOJ0-TNOWw8ItYc9P_l3NKKBz3e_8cch81fih4uP9dXA7Qlp0foo9AlyXeDx4PILjwLBb6RXmtZfwMyT-rWJUYoCjufno2Fr7NSDvDbS-QW3lb4V5593uF_S4s7udDfAfFzGD4lJOMPM4sFDCcva69igIM30DD-3yYdUnlnECmMi6ISp00VMdKmVbLQYoCCFApl3JiUyG0gr-Uo5oZSPdIcykkLOLYRAM6MU4LpuIjVJ8v5vYYYX_sagAz2Uh75BSpJKFOMW2hjrmYiRPU8EEbv5XaGONNvE7_eH-Gdv3UlKTwc1Qvlmt7gbb1ezFdLS_DjH4BqT-i6A
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwGG0UjXpSI8bf1sRrsWzt1h51IBBwkrADN1LaLpIIGBga_3u_bkPjwYOXpdnSNPmab9_rj_ceQrdUU8F8aohmghKmvYDIMU2J8hx850ZalbuW9MI4FsOh7Jdk9ZwLY63NL5_ZmmvmZ_lmrlduqwwyXAoHsDfRFmfMqxd0rR10Uypn3nWiQavVDFgQhLD083ht3eOXd0peOh73_znoAar-kPBw_7u8HKINOztCHzlhljhHeDyA8MIzm-sX5dSW8TOk_7TkVWIAo7j96fhY-D4ijQg3nERu6W6F4-L29xK7fVjcbyb4AcqZwfCpSSLSnVooYTh6XTkdBRi-ipLHZhK1SemdQCbSz4gKUmmo9pUy9XoKSQowQAWhlGMbCKEV_KdSqrmBhPd0KIWEZRwfa8AnJtWCK_8YVWbzmT1B2B28GkBN1tMOO3mKMZoqri1UstTn4hRVXdBGb4U6xmgdr7M_3l-j3Xby1Bv1OnH3HO25aSoo4heoki1W9hJt6_dsslxc5bP7BU9gpi8
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2025+4th+International+Conference+on+Smart+Grid+and+Green+Energy+%28ICSGGE%29&rft.atitle=Multi-time+Scale+Stochastic+Optimization+for+Hybrid+AC-DC+Distribution+Networks+with+PET+Based+on+E-C-Kmeans+Clustering&rft.au=Sun%2C+Yaobin&rft.au=Liu%2C+Shourui&rft.au=Lu%2C+Jiashuo&rft.au=Du%2C+Yonggang&rft.date=2025-02-28&rft.pub=IEEE&rft.spage=223&rft.epage=226&rft_id=info:doi/10.1109%2FICSGGE64667.2025.10984875&rft.externalDocID=10984875