Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. Most of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM), such as system...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on power systems Vol. 37; no. 6; pp. 4605 - 4617
Main Authors: Liu, Likai, Hu, Zechun, Mujeeb, Asad
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automatic control
Automatic generation control
Control methods
Damping
Data models
distributionally robust optimization
Electric vehicles
Electronic devices
Estimation
Frequency control
Frequency response
Mathematical models
model predictive control
Optimization
Parameter identification
Predictive models
probability estimation
Proportional integral derivative
Regulation
Turbines
Uncertainty
ISSN:0885-8950, 1558-0679
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. Most of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM), such as system inertia and load damping factors. This paper proposes an automatic generation control (AGC) method considering the uncertainties of these key parameters. First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline. Second, the offline identification results and the normal operation data right before the large power disturbance are used to train the online probability estimation model of the FRM key parameters. Third, the online estimation results of the FRM key parameters are used as the input, and the model predictive-based AGC optimization method is developed based on distributionally robust optimization (DRO) theory. Case studies conducted on the IEEE 118-bus system show that the proposed AGC method outperforms the widely utilized PI-based and PID-based control methods in terms of performance and efficiency.
AbstractList The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. Most of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM), such as system inertia and load damping factors. This paper proposes an automatic generation control (AGC) method considering the uncertainties of these key parameters. First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline. Second, the offline identification results and the normal operation data right before the large power disturbance are used to train the online probability estimation model of the FRM key parameters. Third, the online estimation results of the FRM key parameters are used as the input, and the model predictive-based AGC optimization method is developed based on distributionally robust optimization (DRO) theory. Case studies conducted on the IEEE 118-bus system show that the proposed AGC method outperforms the widely utilized PI-based and PID-based control methods in terms of performance and efficiency.
Author Mujeeb, Asad
Liu, Likai
Hu, Zechun
Author_xml – sequence: 1
  givenname: Likai
  orcidid: 0000-0003-3142-9865
  surname: Liu
  fullname: Liu, Likai
  email: llk17@mails.tsinghua.edu.cn
  organization: Department of Electrical Engineering, Tsinghua University, Beijing, China
– sequence: 2
  givenname: Zechun
  orcidid: 0000-0002-1870-3375
  surname: Hu
  fullname: Hu, Zechun
  email: zechhu@tsinghua.edu.cn
  organization: Department of Electrical Engineering, Tsinghua University, Beijing, China
– sequence: 3
  givenname: Asad
  surname: Mujeeb
  fullname: Mujeeb, Asad
  email: asd20@mails.tsinghua.edu.cn
  organization: Department of Electrical Engineering, Tsinghua University, Beijing, China
BookMark eNp9kMtOAjEUhhuDiYC-gG6auB487UznsiRE0IiRIMTlpMyc0ZKhxbYseHvLJS5cuDrX_1y-Huloo5GQWwYDxqB4WMw-5u8DDpwPYiZiAcUF6TIh8gjSrOiQLuS5iPJCwBXpObcGgDQUuqQd7rzZSK8qOkGNNnhG05HR3pr2YJ2q0Sr9SZe6Quul0l6ho6ah_gvpC-7pTFq5QY_WUaWP2bHF7x3qak_n6LZhBtJXU2N7TS4b2Tq8Ods-WY4fF6OnaPo2eR4Np1EVp8xHgnMmocpZIhPGY0hWgq2EEHUNKwAsMIU0j7NqJbBJ86RAEWJMWF1z2dQc4z65P83dWhMOcb5cm53VYWXJM55BEl7noYufuiprnLPYlFurNtLuSwblgWp5pFoeqJZnqkGU_xFVyh-heStV-7_07iRViPi7q8g4sCKJfwAGz4ho
CODEN ITPSEG
CitedBy_id crossref_primary_10_3390_fractalfract7110807
crossref_primary_10_1109_TPWRS_2024_3453945
crossref_primary_10_1109_TSG_2024_3493124
crossref_primary_10_1016_j_apenergy_2023_120994
crossref_primary_10_1016_j_chaos_2024_115444
crossref_primary_10_1049_rpg2_12842
crossref_primary_10_1109_TPWRS_2023_3321847
crossref_primary_10_1016_j_epsr_2023_109916
crossref_primary_10_1109_TSTE_2025_3540599
Cites_doi 10.1016/j.ijepes.2019.05.034
10.1162/neco.1997.9.8.1735
10.1109/TPWRS.2019.2908988
10.1049/iet-gtd.2016.1734
10.1109/TPWRS.2012.2209901
10.1109/TPWRS.2015.2434837
10.1109/TPWRS.2010.2051168
10.1109/TPWRS.2014.2375918
10.1109/TPWRS.2018.2872868
10.1109/TPWRD.2014.2306062
10.1109/59.709084
10.1109/TPWRS.2015.2501458
10.3182/20140824-6-ZA-1003.02615
10.1109/ITOEC.2017.8122408
10.1109/TPWRS.2018.2881359
10.2307/1913267
10.1109/TPWRS.2014.2333776
10.1109/TPWRS.2017.2649579
10.1109/TPWRS.2017.2773531
10.1257/jep.15.4.143
10.1109/JSYST.2015.2444893
10.1109/TPWRS.2019.2894769
10.1016/j.epsr.2009.10.026
10.1016/j.ijepes.2012.06.032
10.1016/j.rser.2021.111176
10.1109/TPWRS.2017.2705761
10.1109/TSG.2016.2615473
10.1109/TII.2017.2764800
10.1109/TSG.2020.3022563
10.1007/978-0-387-84878-5
10.1109/TPWRS.2015.2412614
10.1109/TPWRS.2021.3134811
10.21314/JOR.2000.038
10.1109/TPWRS.2018.2846744
10.1109/TPWRS.2018.2843381
10.1109/TPWRS.2019.2915249
10.1109/TPWRS.2019.2905037
10.1287/opre.2014.1314
10.1109/ISAP.2017.8071383
10.1016/j.epsr.2018.04.008
10.1109/TPWRS.2019.2934318
10.1201/b10869
10.1016/j.orl.2021.01.012
10.1109/TPWRS.2018.2824823
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID 97E
RIA
RIE
AAYXX
CITATION
7SP
7TB
8FD
FR3
KR7
L7M
DOI 10.1109/TPWRS.2022.3153509
DatabaseName IEEE Xplore (IEEE)
IEEE All-Society Periodicals Package (ASPP) 1998-Present
IEEE Electronic Library (IEL)
CrossRef
Electronics & Communications Abstracts
Mechanical & Transportation Engineering Abstracts
Technology Research Database
Engineering Research Database
Civil Engineering Abstracts
Advanced Technologies Database with Aerospace
DatabaseTitle CrossRef
Civil Engineering Abstracts
Engineering Research Database
Technology Research Database
Mechanical & Transportation Engineering Abstracts
Advanced Technologies Database with Aerospace
Electronics & Communications Abstracts
DatabaseTitleList
Civil Engineering Abstracts
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1558-0679
EndPage 4617
ExternalDocumentID 10_1109_TPWRS_2022_3153509
9720194
Genre orig-research
GrantInformation_xml – fundername: Key Research and Development Program of Inner Mongolia
  grantid: 2021ZD0039
GroupedDBID -~X
.DC
0R~
29I
3EH
4.4
5GY
5VS
6IK
85S
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABFSI
ABQJQ
ABVLG
ACGFO
ACGFS
ACIWK
ACKIV
AENEX
AETIX
AGQYO
AGSQL
AHBIQ
AI.
AIBXA
AKJIK
AKQYR
ALLEH
ALMA_UNASSIGNED_HOLDINGS
ASUFR
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
E.L
EBS
EJD
HZ~
H~9
ICLAB
IFIPE
IFJZH
IPLJI
JAVBF
LAI
M43
MS~
O9-
OCL
P2P
RIA
RIE
RNS
TAE
TN5
VH1
VJK
AAYXX
CITATION
7SP
7TB
8FD
FR3
KR7
L7M
ID FETCH-LOGICAL-c361t-5221a0c814a412304b51b555dd0b00e9e606837cb5ef6849e5068e41dd2afd2e3
IEDL.DBID RIE
ISICitedReferencesCount 14
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000871076600040&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 0885-8950
IngestDate Fri Jul 25 18:54:53 EDT 2025
Tue Nov 18 22:30:52 EST 2025
Sat Nov 29 02:52:27 EST 2025
Wed Aug 27 02:18:49 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 6
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
https://doi.org/10.15223/policy-029
https://doi.org/10.15223/policy-037
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c361t-5221a0c814a412304b51b555dd0b00e9e606837cb5ef6849e5068e41dd2afd2e3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0003-3142-9865
0000-0002-1870-3375
PQID 2727046792
PQPubID 85441
PageCount 13
ParticipantIDs crossref_primary_10_1109_TPWRS_2022_3153509
proquest_journals_2727046792
crossref_citationtrail_10_1109_TPWRS_2022_3153509
ieee_primary_9720194
PublicationCentury 2000
PublicationDate 2022-Nov.
2022-11-00
20221101
PublicationDateYYYYMMDD 2022-11-01
PublicationDate_xml – month: 11
  year: 2022
  text: 2022-Nov.
PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
PublicationTitle IEEE transactions on power systems
PublicationTitleAbbrev TPWRS
PublicationYear 2022
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref12
ref15
ref14
ref11
ref10
ref17
ref16
ref19
ref18
kisiala (ref43) 2015
ref45
ref48
ref47
ref42
wood (ref46) 2013
ref41
ref44
ref49
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref35
ref34
ref36
ref31
ref30
ref33
ref32
ref2
ref1
ref39
kingma (ref38) 2017
p (ref40) 2017; 171
ref24
ref23
ref26
ref25
ref20
ref22
ref21
goodfellow (ref37) 2016; 1
ref28
ref27
ref29
References_xml – ident: ref21
  doi: 10.1016/j.ijepes.2019.05.034
– ident: ref35
  doi: 10.1162/neco.1997.9.8.1735
– ident: ref26
  doi: 10.1109/TPWRS.2019.2908988
– ident: ref5
  doi: 10.1049/iet-gtd.2016.1734
– ident: ref13
  doi: 10.1109/TPWRS.2012.2209901
– year: 2015
  ident: ref43
  article-title: Conditional value-at-risk: Theory and applications
– ident: ref48
  doi: 10.1109/TPWRS.2015.2434837
– ident: ref44
  doi: 10.1109/TPWRS.2010.2051168
– ident: ref9
  doi: 10.1109/TPWRS.2014.2375918
– ident: ref24
  doi: 10.1109/TPWRS.2018.2872868
– ident: ref20
  doi: 10.1109/TPWRD.2014.2306062
– ident: ref2
  doi: 10.1109/59.709084
– ident: ref32
  doi: 10.1109/TPWRS.2015.2501458
– ident: ref14
  doi: 10.3182/20140824-6-ZA-1003.02615
– ident: ref23
  doi: 10.1109/ITOEC.2017.8122408
– ident: ref6
  doi: 10.1109/TPWRS.2018.2881359
– ident: ref17
  doi: 10.2307/1913267
– ident: ref33
  doi: 10.1109/TPWRS.2014.2333776
– ident: ref25
  doi: 10.1109/TPWRS.2017.2649579
– ident: ref47
  doi: 10.1109/TPWRS.2017.2773531
– ident: ref36
  doi: 10.1257/jep.15.4.143
– ident: ref45
  doi: 10.1109/JSYST.2015.2444893
– ident: ref31
  doi: 10.1109/TPWRS.2019.2894769
– ident: ref4
  doi: 10.1016/j.epsr.2009.10.026
– ident: ref8
  doi: 10.1016/j.ijepes.2012.06.032
– ident: ref12
  doi: 10.1016/j.rser.2021.111176
– ident: ref11
  doi: 10.1109/TPWRS.2017.2705761
– ident: ref27
  doi: 10.1109/TSG.2016.2615473
– ident: ref28
  doi: 10.1109/TII.2017.2764800
– ident: ref30
  doi: 10.1109/TSG.2020.3022563
– ident: ref3
  doi: 10.1007/978-0-387-84878-5
– ident: ref10
  doi: 10.1109/TPWRS.2015.2412614
– ident: ref15
  doi: 10.1109/TPWRS.2021.3134811
– year: 2013
  ident: ref46
  publication-title: Power Generation Operation and Control
– ident: ref42
  doi: 10.21314/JOR.2000.038
– year: 2017
  ident: ref38
  article-title: Adam: A Method for Stochastic Optimization
– ident: ref29
  doi: 10.1109/TPWRS.2018.2846744
– ident: ref22
  doi: 10.1109/TPWRS.2018.2843381
– ident: ref34
  doi: 10.1109/TPWRS.2019.2915249
– volume: 171
  start-page: 115
  year: 2017
  ident: ref40
  article-title: Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations
  publication-title: Math Program
– ident: ref16
  doi: 10.1109/TPWRS.2019.2905037
– ident: ref39
  doi: 10.1287/opre.2014.1314
– volume: 1
  year: 2016
  ident: ref37
  publication-title: Deep Learning
– ident: ref18
  doi: 10.1109/ISAP.2017.8071383
– ident: ref19
  doi: 10.1016/j.epsr.2018.04.008
– ident: ref7
  doi: 10.1109/TPWRS.2019.2934318
– ident: ref1
  doi: 10.1201/b10869
– ident: ref41
  doi: 10.1016/j.orl.2021.01.012
– ident: ref49
  doi: 10.1109/TPWRS.2018.2824823
SSID ssj0006679
Score 2.486772
Snippet The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. Most of them are connected to the grid via...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 4605
SubjectTerms Automatic control
Automatic generation control
Control methods
Damping
Data models
distributionally robust optimization
Electric vehicles
Electronic devices
Estimation
Frequency control
Frequency response
Mathematical models
model predictive control
Optimization
Parameter identification
Predictive models
probability estimation
Proportional integral derivative
Regulation
Turbines
Uncertainty
Title Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model
URI https://ieeexplore.ieee.org/document/9720194
https://www.proquest.com/docview/2727046792
Volume 37
WOSCitedRecordID wos000871076600040&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  customDbUrl:
  eissn: 1558-0679
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0006679
  issn: 0885-8950
  databaseCode: RIE
  dateStart: 19860101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwED624YM--GuK0yl58E3rmrZpm8chDkEYY264t9KmVxiMVfZD2H_vJenGQBF8KW1JSujX5O5L774DuJcFelERS4e8b-kEigiKxJQTS-FcKr8II7TFJqJ-P55M5KAGj7tcGEQ0wWf4pE_Nv_y8VGu9VdaREZkrGdShHkWhzdXarbphaHX14lg4sRTuNkHGlZ3R4GP4TlTQ84ihCl_o4MM9I2SqqvxYio196Z38b2SncFz5kaxrgT-DGs7P4WhPXbAJs-56VRpFVmbFpTUG7NnGprNtpU5qysaEvIkM0OqqrCwYeYXsDTdskOrYLS3AyaZzc7e3sLHXGza00bXIdDm12QWMey-j51enKq7gKD_kKyKgHk9dFfMgDbjeGc4Ez4QQee7STESJxGyIvKpMYBHGgURB1xjwPPfSIvfQv4TGvJzjFbA0K4RSuuZRRmQvCtJc5koVKpAp0jFrAd--7URVyuO6AMYsMQzElYlBKNEIJRVCLXjY9fm0uht_tm5qTHYtKzha0N6CmlRTc5l45LG5ZB6kd_17rxs41M-2CYdtaKwWa7yFA_W1mi4Xd-ar-wbtYtXS
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_8AvXBb3F-5sE3rTZZ0jaPYzgUdQzd0LfSplcQxiZzE_zvvSTdGCiCL6UtCQ39Nbn7pXe_AzjXJYq4THRA3rcOpCGCojHjxFI416ZeRjH6YhNxu528vurOAlzOcmEQ0QWf4ZU9df_yi6GZ2K2yax2TudJyEZaVlCL02VqzdTeKvLJekqgg0SqcpsiE-rrbeXl6JjIoBHFUVVc2_HDODLm6Kj8WY2dhWpv_G9sWbFSeJGt46LdhAQc7sD6nL7gL_cZkPHSarMzLS1sUWNNHp7NprU5qynqEvYsNsPqqbFgy8gvZPX6xTmajt6wEJ3sbuLutkY--_mJPPr4WmS2o1t-DXuum27wNqvIKgalHfEwUVPAsNAmXmeR2bzhXPFdKFUVIcxE1Erch-mpyhWWUSI2KrlHyohBZWQis78PSYDjAA2BZXipjbNWjnOheLLNCF8aURuoM6ZjXgE_fdmoq7XFbAqOfOg4S6tQhlFqE0gqhGlzM-rx75Y0_W-9aTGYtKzhqcDwFNa0m50cqyGcLyUBocfh7rzNYve0-PqQPd-37I1izz_Hph8ewNB5N8ARWzOf47WN06r7AbzJE2Rk
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%3Ajournal&rft.genre=article&rft.atitle=Automatic+Generation+Control+Considering+Uncertainties+of+the+Key+Parameters+in+the+Frequency+Response+Model&rft.jtitle=IEEE+transactions+on+power+systems&rft.au=Liu%2C+Likai&rft.au=Hu%2C+Zechun&rft.au=Mujeeb%2C+Asad&rft.date=2022-11-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0885-8950&rft.eissn=1558-0679&rft.volume=37&rft.issue=6&rft.spage=4605&rft_id=info:doi/10.1109%2FTPWRS.2022.3153509&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0885-8950&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0885-8950&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0885-8950&client=summon