Hierarchical Clustering to Find Representative Operating Periods for Capacity-Expansion Modeling
Power system capacity-expansion models are typically intractable if every operating period is represented. This issue is normally overcome by using a subset of representative operating periods. For instance, representative operating hours can be selected by discretizing the load-duration curve, whic...
Uložené v:
| Vydané v: | IEEE transactions on power systems Ročník 33; číslo 3; s. 3029 - 3039 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 0885-8950, 1558-0679 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Power system capacity-expansion models are typically intractable if every operating period is represented. This issue is normally overcome by using a subset of representative operating periods. For instance, representative operating hours can be selected by discretizing the load-duration curve, which captures the effect of load levels on system-operation costs. This approach is inappropriate if system-operating costs depend on parameters other than load (e.g., renewable-resource availability) or if there are important intertemporal operating constraints (e.g., generator-ramping limits). This paper proposes the use of representative operating days, which are selected using clustering, to surmount these issues. We propose two hierarchical clustering techniques, which are designed to capture the important statistical features of the parameters (e.g., load and renewable-resource availability), in selecting representative days. This includes temporal autocorrelations and correlations between different locations. A case study, which is based on the Texan power system, is used to demonstrate the techniques. We show that our proposed clustering techniques result in investment decisions that closely match those made using the full unclustered dataset. |
|---|---|
| AbstractList | Power system capacity-expansion models are typically intractable if every operating period is represented. This issue is normally overcome by using a subset of representative operating periods. For instance, representative operating hours can be selected by discretizing the load-duration curve, which captures the effect of load levels on system-operation costs. This approach is inappropriate if system-operating costs depend on parameters other than load (e.g., renewable-resource availability) or if there are important intertemporal operating constraints (e.g., generator-ramping limits). This paper proposes the use of representative operating days, which are selected using clustering, to surmount these issues. We propose two hierarchical clustering techniques, which are designed to capture the important statistical features of the parameters (e.g., load and renewable-resource availability), in selecting representative days. This includes temporal autocorrelations and correlations between different locations. A case study, which is based on the Texan power system, is used to demonstrate the techniques. We show that our proposed clustering techniques result in investment decisions that closely match those made using the full unclustered dataset. |
| Author | Conejo, Antonio J. Liu, Yixian Sioshansi, Ramteen |
| Author_xml | – sequence: 1 givenname: Yixian surname: Liu fullname: Liu, Yixian email: liu.2441@osu.edu organization: Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA – sequence: 2 givenname: Ramteen orcidid: 0000-0002-1440-0158 surname: Sioshansi fullname: Sioshansi, Ramteen email: sioshansi.1@osu.edu organization: Department of Integrated Systems Engineering, The Ohio State University, Columbus, OH, USA – sequence: 3 givenname: Antonio J. orcidid: 0000-0002-2324-605X surname: Conejo fullname: Conejo, Antonio J. email: conejonavarro.1@osu.edu organization: Department of Integrated Systems Engineering, and the Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA |
| BookMark | eNp9kLtOwzAUhi0EEi3wArBEYk7xJRd7RBUFpKKiAmIMbnwMRsEOtovg7XFoxcDAdIbzfefyj9GudRYQOiZ4QggWZ_e3j8u7CcWkntC6qFgtdtCIlCXPcVWLXTTCnJc5FyXeR-MQXjHGVWqM0NOVAS99-2Ja2WXTbh0ieGOfs-iymbEqW0LvIYCNMpoPyBZ9wuMA3CbOqZBp57Op7GVr4ld-8dlLG4yz2Y1T0CXuEO1p2QU42tYD9DC7uJ9e5fPF5fX0fJ63VJQxLxkhtZBaElKosmZa8UpQXTFgtCByVfGCEaC6VVi1wCnRoJUqCFvRirVYsAN0upnbe_e-hhCbV7f2Nq1sKKaCFAyLgeIbqvUuBA-6SWenf5yNXpquIbgZ8mx-8myGPJttnkmlf9Temzfpv_6XTjaSAYBfgSekFJx9A4hrhNQ |
| CODEN | ITPSEG |
| CitedBy_id | crossref_primary_10_1016_j_enpol_2022_112844 crossref_primary_10_1016_j_ress_2024_110086 crossref_primary_10_1109_ACCESS_2023_3327640 crossref_primary_10_1016_j_apenergy_2021_117825 crossref_primary_10_1016_j_apenergy_2019_113603 crossref_primary_10_1049_rpg2_13018 crossref_primary_10_1109_TPWRS_2024_3523220 crossref_primary_10_3390_electronics12102326 crossref_primary_10_1109_ACCESS_2024_3472843 crossref_primary_10_1109_ACCESS_2020_3027435 crossref_primary_10_1016_j_ejor_2019_07_054 crossref_primary_10_1007_s40518_023_00229_y crossref_primary_10_1109_TPWRS_2019_2892619 crossref_primary_10_1109_TPWRS_2023_3257368 crossref_primary_10_1109_TPWRS_2019_2929276 crossref_primary_10_3390_su12093543 crossref_primary_10_1016_j_ijepes_2022_108767 crossref_primary_10_1016_j_ijepes_2025_110929 crossref_primary_10_1016_j_enpol_2023_113503 crossref_primary_10_1016_j_renene_2022_09_040 crossref_primary_10_1016_j_ijepes_2021_107697 crossref_primary_10_1007_s40518_020_00169_x crossref_primary_10_1016_j_solmat_2023_112559 crossref_primary_10_1109_TPWRS_2023_3240830 crossref_primary_10_1109_TSTE_2018_2881531 crossref_primary_10_1016_j_rser_2021_111984 crossref_primary_10_1109_TII_2020_3024922 crossref_primary_10_1109_TSTE_2023_3246592 crossref_primary_10_1109_JESTIE_2022_3198504 crossref_primary_10_1109_TPWRS_2023_3284854 crossref_primary_10_1109_TPWRS_2023_3327969 crossref_primary_10_1109_TSG_2022_3224900 crossref_primary_10_3390_su12031083 crossref_primary_10_1016_j_energy_2025_138272 crossref_primary_10_1109_JPROC_2020_3005284 crossref_primary_10_1016_j_apenergy_2020_115224 crossref_primary_10_1016_j_apenergy_2022_119029 crossref_primary_10_1016_j_eneco_2019_07_017 crossref_primary_10_1016_j_est_2025_117950 crossref_primary_10_3390_buildings15040648 crossref_primary_10_1109_TPWRS_2022_3146299 crossref_primary_10_1109_TPWRS_2019_2958850 crossref_primary_10_1016_j_epsr_2024_110267 crossref_primary_10_1016_j_ref_2025_100738 crossref_primary_10_1049_iet_rpg_2018_6264 crossref_primary_10_1016_j_apenergy_2020_114938 crossref_primary_10_1016_j_apenergy_2021_116719 crossref_primary_10_3390_en14227599 crossref_primary_10_1016_j_epsr_2021_107729 crossref_primary_10_1016_j_energy_2022_124467 crossref_primary_10_1109_TPWRS_2023_3236842 crossref_primary_10_1016_j_apenergy_2024_122965 crossref_primary_10_1007_s11750_019_00519_z crossref_primary_10_1002_ente_202401275 crossref_primary_10_1109_ACCESS_2019_2943498 crossref_primary_10_1007_s10287_023_00451_5 crossref_primary_10_1109_TSTE_2021_3077017 crossref_primary_10_1016_j_energy_2021_119989 crossref_primary_10_1007_s12667_018_00321_z crossref_primary_10_1016_j_compchemeng_2020_106785 crossref_primary_10_1109_TSTE_2024_3411577 crossref_primary_10_1049_enc2_12114 crossref_primary_10_3390_en13030641 crossref_primary_10_1109_TPWRS_2022_3141993 crossref_primary_10_1016_j_energy_2021_120491 crossref_primary_10_1016_j_segan_2025_101622 crossref_primary_10_1049_iet_gtd_2018_5863 crossref_primary_10_1016_j_rser_2022_112955 crossref_primary_10_1016_j_compchemeng_2022_108124 crossref_primary_10_1016_j_eneco_2024_107675 crossref_primary_10_1109_TPWRS_2022_3151062 crossref_primary_10_1016_j_asoc_2024_112107 crossref_primary_10_1016_j_apenergy_2022_119356 crossref_primary_10_1109_TPWRS_2024_3521250 crossref_primary_10_1007_s10287_023_00469_9 crossref_primary_10_1049_gtd2_12895 crossref_primary_10_1016_j_apenergy_2023_121207 crossref_primary_10_1109_TPWRS_2018_2842093 crossref_primary_10_1049_gtd2_12299 crossref_primary_10_1016_j_ijepes_2020_106560 |
| Cites_doi | 10.2172/1031955 10.1016/j.apenergy.2013.02.057 10.1287/moor.16.1.119 10.1109/JPHOTOV.2017.2695328 10.1080/01621459.1963.10500845 10.1093/bioinformatics/bti201 10.1016/j.eneco.2008.10.005 10.1016/S1040-6190(99)00071-8 10.1109/TPWRS.2014.2300697 10.1109/TPWRS.2016.2614368 10.1109/TPAS.1982.317636 10.1145/331499.331504 10.1109/TPWRS.2016.2609538 10.1016/j.energy.2012.07.059 10.1109/TASSP.1978.1163055 10.1016/j.apenergy.2012.06.002 10.1109/TPWRS.2017.2694612 10.1016/j.enpol.2011.06.062 10.1109/TPWRS.2016.2596803 10.1198/jasa.2011.tm10183 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018 |
| DBID | 97E RIA RIE AAYXX CITATION 7SP 7TB 8FD FR3 KR7 L7M |
| DOI | 10.1109/TPWRS.2017.2746379 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore 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 | 3039 |
| ExternalDocumentID | 10_1109_TPWRS_2017_2746379 8017598 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Science Foundation grantid: 1029337; 1548015 |
| 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-c295t-531179afa114d573fd8692f63e3241ab68431e2fcd0dce821fefdd413b263c093 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 98 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000430733300063&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 12:27:08 EDT 2025 Tue Nov 18 22:30:52 EST 2025 Sat Nov 29 02:52:12 EST 2025 Wed Aug 27 02:49:35 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| 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-c295t-531179afa114d573fd8692f63e3241ab68431e2fcd0dce821fefdd413b263c093 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-1440-0158 0000-0002-2324-605X |
| PQID | 2029143099 |
| PQPubID | 85441 |
| PageCount | 11 |
| ParticipantIDs | crossref_citationtrail_10_1109_TPWRS_2017_2746379 ieee_primary_8017598 crossref_primary_10_1109_TPWRS_2017_2746379 proquest_journals_2029143099 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-May 2018-5-00 20180501 |
| PublicationDateYYYYMMDD | 2018-05-01 |
| PublicationDate_xml | – month: 05 year: 2018 text: 2018-May |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on power systems |
| PublicationTitleAbbrev | TPWRS |
| PublicationYear | 2018 |
| 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 ref15 (ref24) 2014 ref14 ref11 ref2 ref1 ref17 ref16 ref19 macqueen (ref10) 0; 1 liu (ref21) 2017 mai (ref26) 2012 ref23 ref20 masiello (ref27) 2010 ref22 (ref25) 2012 ref28 ref8 liu (ref18) 2016 ref7 ref9 ref4 ref3 ref6 johnson (ref12) 2008 ref5 |
| References_xml | – ident: ref2 doi: 10.2172/1031955 – ident: ref23 doi: 10.1016/j.apenergy.2013.02.057 – ident: ref28 doi: 10.1287/moor.16.1.119 – ident: ref16 doi: 10.1109/JPHOTOV.2017.2695328 – ident: ref13 doi: 10.1080/01621459.1963.10500845 – ident: ref14 doi: 10.1093/bioinformatics/bti201 – ident: ref20 doi: 10.1016/j.eneco.2008.10.005 – ident: ref19 doi: 10.1016/S1040-6190(99)00071-8 – year: 2012 ident: ref25 article-title: Cost and performance data for power generation technologies – ident: ref5 doi: 10.1109/TPWRS.2014.2300697 – ident: ref8 doi: 10.1109/TPWRS.2016.2614368 – ident: ref1 doi: 10.1109/TPAS.1982.317636 – year: 2017 ident: ref21 article-title: A vector autoregression weather model for electricity supply and demand modeling publication-title: J Modern Power Syst Clean Energy – ident: ref11 doi: 10.1145/331499.331504 – year: 2010 ident: ref27 article-title: Research evaluation of wind generation, solar generation, and storage impact on the California grid – ident: ref7 doi: 10.1109/TPWRS.2016.2609538 – volume: 1 start-page: 281 year: 0 ident: ref10 article-title: Some methods for classification and analysis of multivariate observations publication-title: Proc 5th Berkeley Symp Math Statist Probab – year: 2012 ident: ref26 article-title: Renewable electricity futures study: Executive summary – ident: ref22 doi: 10.1016/j.energy.2012.07.059 – year: 2008 ident: ref12 publication-title: Applied multivariate statistical analysis – ident: ref17 doi: 10.1109/TASSP.1978.1163055 – year: 2014 ident: ref24 publication-title: Annual Energy Outlook 2014 – ident: ref4 doi: 10.1016/j.apenergy.2012.06.002 – ident: ref9 doi: 10.1109/TPWRS.2017.2694612 – year: 2016 ident: ref18 article-title: Electricity capacity investments and cost recovery with renewables – ident: ref3 doi: 10.1016/j.enpol.2011.06.062 – ident: ref6 doi: 10.1109/TPWRS.2016.2596803 – ident: ref15 doi: 10.1198/jasa.2011.tm10183 |
| SSID | ssj0006679 |
| Score | 2.5933778 |
| Snippet | Power system capacity-expansion models are typically intractable if every operating period is represented. This issue is normally overcome by using a subset of... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 3029 |
| SubjectTerms | <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> k</tex-math> </inline-formula> </named-content>-means clustering Cluster analysis Clustering Clustering algorithms Clustering methods Correlation Dynamic time warping hierarchical clustering Investment Load modeling Mathematical models Operating costs Parameters Planning power system planning representative days Time series analysis |
| Title | Hierarchical Clustering to Find Representative Operating Periods for Capacity-Expansion Modeling |
| URI | https://ieeexplore.ieee.org/document/8017598 https://www.proquest.com/docview/2029143099 |
| Volume | 33 |
| WOSCitedRecordID | wos000430733300063&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/eLvHCXMwlV1LS8NAEB6qeNCDb7G-2IM3XU12m93NUYrFg2ipit7iZh8glLb0hT_f2U1aBEXwFshsCPmyM98k880AnHOvWkqknmpMl2mrFIxqn2qaGJbwsjSl51EofC8fHtTbW95twOVSC-Oci8Vn7iocxn_5dmhm4VPZNXpTmeVqBVaklJVWa-l1haj66imVUZVnyUIgk-TXz93X3lOo4pJXmIMJHsq2vgWhOFXlhyuO8aWz9b8724bNmkeSmwr4HWi4wS5sfOsuuAfvdx9BXRyHnfRJuz8LPRHwDJkOSQdTcdKLVbCV-GjuyOMoNFgOBl20G9oJQUJL2hhNDVJ1evuJjiN8WyNhflpQse_DS-f2uX1H64EK1LA8m1Lcb7j_tNeYBNlMcm-VyJkX3CGtSnUpFNIJx7yxiTVOsdQ7by2GuZIJbhDMA1gdDAfuEIhIjWGa-5YyvGVsWWqJxEZjqEPC46RqQrp4woWpu42HoRf9ImYdSV5EVIqASlGj0oSL5ZpR1WvjT-u9gMPSsoagCScLIIt6O05wHcuRGCIbPvp91TGs47VVVcl4AqvT8cydwpqZTz8m47P4pn0B-_fSDA |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS-tAEB48Khx98C7Wy3EffPOsJrvJZvMoxVI5tRatHN_iZi8glFZsK_58ZzdpERTBt0BmSciXnfkmmW8G4IQ7mUgRO6owXaZJKRhVLlY00iziZalLx4NQuJN1u_LhIe8twN-5FsZaG4rP7Jk_DP_yzUhP_aeyc_SmWZrLX7CUJgmLK7XW3O8KUXXWkzKlMk-jmUQmys_7vf-3d76OKzvDLExwX7j1IQyFuSqfnHGIMK31n93bBqzVTJJcVNBvwoIdbsHqh_6C2_DYfvL64jDuZECag6nvioBnyGREWpiMk9tQB1vJj14tuXn2LZa9QQ_tRmZMkNKSJsZTjWSdXr6h6_Bf14ifoOZ17Dtw37rsN9u0HqlANcvTCcUdhztQOYVpkEkz7owUOXOCWyRWsSqFREJhmdMmMtpKFjvrjMFAVzLBNcK5C4vD0dDuARGx1kxxl0jNE23KUmVIbRQGO6Q8NpMNiGdPuNB1v3E_9mJQhLwjyouASuFRKWpUGnA6X_Ncddv41nrb4zC3rCFowOEMyKLekGNcx3KkhsiH979edQy_2_3rTtG56v47gBW8jqzqGg9hcfIytUewrF8nT-OXP-GtewcQcdVT |
| 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=Hierarchical+Clustering+to+Find+Representative+Operating+Periods+for+Capacity-Expansion+Modeling&rft.jtitle=IEEE+transactions+on+power+systems&rft.au=Liu%2C+Yixian&rft.au=Sioshansi%2C+Ramteen&rft.au=Conejo%2C+Antonio+J.&rft.date=2018-05-01&rft.issn=0885-8950&rft.eissn=1558-0679&rft.volume=33&rft.issue=3&rft.spage=3029&rft.epage=3039&rft_id=info:doi/10.1109%2FTPWRS.2017.2746379&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TPWRS_2017_2746379 |
| 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 |