A Low-Carbon and Economic Dispatch Strategy for a Multi-Microgrid Based on a Meteorological Classification to Handle the Uncertainty of Wind Power

In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertaint...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Sensors (Basel, Switzerland) Ročník 23; číslo 11; s. 5350
Hlavní autori:	Liu, Yang, Li, Xueling, Liu, Yamei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 05.06.2023 MDPI
Predmet:	adjustable robust optimization Air quality management Algorithms Alternative energy sources Analysis Carbon Case studies Classification Clustering Consumption Economic aspects economic operation Efficiency Electric power production Emissions (Pollution) Emissions trading Feature selection Global warming Green technology low-carbon operation meteorological classification Optimization Probability distribution Random variables Renewable Energy - economics Renewable resources Uncertainty Wind Wind power adjustable robust optimization low-carbon operation uncertainty economic operation meteorological classification
ISSN:	1424-8220, 1424-8220
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution.
AbstractList	In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution. In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution.In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution.
Audience	Academic
Author	Liu, Yamei Liu, Yang Li, Xueling
AuthorAffiliation	2 Key Laboratory of Intelligent Electric Power Grid of Sichuan Province, Sichuan University, Chengdu 610065, China 1 College of Electrical Engineering, Sichuan University, Chengdu 610065, China; yang.liu@scu.edu.cn (Y.L.); shirley_neenee@163.com (X.L.)
AuthorAffiliation_xml	– name: 2 Key Laboratory of Intelligent Electric Power Grid of Sichuan Province, Sichuan University, Chengdu 610065, China – name: 1 College of Electrical Engineering, Sichuan University, Chengdu 610065, China; yang.liu@scu.edu.cn (Y.L.); shirley_neenee@163.com (X.L.)
Author_xml	– sequence: 1 givenname: Yang orcidid: 0000-0002-7935-7146 surname: Liu fullname: Liu, Yang – sequence: 2 givenname: Xueling surname: Li fullname: Li, Xueling – sequence: 3 givenname: Yamei surname: Liu fullname: Liu, Yamei
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37300077$$D View this record in MEDLINE/PubMed
BookMark	eNptkk1vEzEQhleoiH7AgT-ALHGBw7b-XO-eUAiFVkoEElQcV7Ne78bVZh1shyp_g1_MJClRWyEfPLKfeWc8fk-zo9GPNsteM3ouREUvIheMKaHos-yESS7zknN69CA-zk5jvKWUCyHKF9mx0IJSqvVJ9mdCZv4un0Jo_EhgbMml8aNfOkM-ubiCZBbkewqQbL8hnQ8EyHw9JJfPnQm-D64lHyHalmyzydwm64MffO8MDGQ6QIyuwzg5vE-eXGGFwZK0sORmNDYkcGPaEN-Rnw5rf_N3NrzMnncwRPvqfj_Lbj5f_phe5bOvX66nk1luFK1S3ha0EVBI0FbyUhlTyU4KsLSUVaMkBhwa4Ea1DKTmsqJKtRVXjGvdSsXFWXa912093Nar4JYQNrUHV-8OfOhrCMmZwdaFLsquxFl3vJC0KkpRSdlQLapKMw2AWh_2Wqt1s7StsSPObHgk-vhmdIu6979rRrkqUAgV3t0rBP9rbWOqly4aOwwwWr-ONS-5LCquVYHo2yforV-HEWe1o6gqmWBIne-pHvAFbuw8Fja4Wou_iwbqHJ5PtNo2wKjChDcP33Bo_p9ZEHi_B_DnYwy2OyCM1lsj1gcjInvxhDUu7WyAXbjhPxl_ASb13LA
CitedBy_id	crossref_primary_10_1515_nleng_2025_0134 crossref_primary_10_3390_en17081954 crossref_primary_10_3390_en18174635 crossref_primary_10_3390_pr12010218 crossref_primary_10_3390_en18143679 crossref_primary_10_1038_s41598_025_05145_3 crossref_primary_10_3390_en17081878
Cites_doi	10.1016/j.epsr.2020.106412 10.1016/j.ijepes.2022.107963 10.1016/j.ijepes.2021.107898 10.1109/TSTE.2017.2728098 10.1109/TPWRS.2019.2891057 10.1016/j.ijepes.2022.108902 10.1049/joe.2018.5488 10.14710/ijred.2022.43838 10.1016/j.energy.2020.117273 10.1155/2022/9569224 10.1049/iet-gtd.2018.5239 10.1007/s40095-022-00503-7 10.1109/ACCESS.2018.2875936 10.1016/j.apenergy.2021.118034 10.1016/j.renene.2022.11.006 10.1016/j.ijepes.2022.108558 10.1016/S0306-4379(01)00008-4 10.1109/TPWRS.2021.3096144 10.1016/j.ijepes.2021.107891 10.3390/electronics12010214 10.1016/j.segan.2022.100969 10.1109/TIE.2017.2787605 10.1109/TPWRS.2016.2531739 10.1016/j.apenergy.2021.117024 10.1049/iet-rpg.2019.0263
ContentType	Journal Article
Copyright	COPYRIGHT 2023 MDPI AG 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 by the authors. 2023
Copyright_xml	– notice: COPYRIGHT 2023 MDPI AG – notice: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2023 by the authors. 2023
DBID	AAYXX CITATION NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA
DOI	10.3390/s23115350
DatabaseName	CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic PubMed Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1424-8220
ExternalDocumentID	oai_doaj_org_article_6768f8339f26409683944b07399717aa PMC10256073 A752560105 37300077 10_3390_s23115350
Genre	Journal Article
GroupedDBID	--- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M ALIPV NPM 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM
ID	FETCH-LOGICAL-c509t-d60b3a64a7e4285cc94f43ae0849b54ae02aba2c5d1a47249055d9251277d4523
IEDL.DBID	7X7
ISICitedReferencesCount	8
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001006414400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1424-8220
IngestDate	Tue Oct 14 19:04:05 EDT 2025 Tue Nov 04 02:06:55 EST 2025 Thu Sep 04 18:04:35 EDT 2025 Tue Oct 07 07:31:06 EDT 2025 Tue Nov 04 17:19:17 EST 2025 Thu Apr 03 07:13:25 EDT 2025 Sat Nov 29 07:17:08 EST 2025 Tue Nov 18 22:16:51 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Keywords	adjustable robust optimization low-carbon operation uncertainty economic operation meteorological classification
Language	English
License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c509t-d60b3a64a7e4285cc94f43ae0849b54ae02aba2c5d1a47249055d9251277d4523
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-7935-7146
OpenAccessLink	https://www.proquest.com/docview/2824058131?pq-origsite=%requestingapplication%
PMID	37300077
PQID	2824058131
PQPubID	2032333
ParticipantIDs	doaj_primary_oai_doaj_org_article_6768f8339f26409683944b07399717aa pubmedcentral_primary_oai_pubmedcentral_nih_gov_10256073 proquest_miscellaneous_2824692756 proquest_journals_2824058131 gale_infotracacademiconefile_A752560105 pubmed_primary_37300077 crossref_primary_10_3390_s23115350 crossref_citationtrail_10_3390_s23115350
PublicationCentury	2000
PublicationDate	2023-06-05
PublicationDateYYYYMMDD	2023-06-05
PublicationDate_xml	– month: 06 year: 2023 text: 2023-06-05 day: 05
PublicationDecade	2020
PublicationPlace	Switzerland
PublicationPlace_xml	– name: Switzerland – name: Basel
PublicationTitle	Sensors (Basel, Switzerland)
PublicationTitleAlternate	Sensors (Basel)
PublicationYear	2023
Publisher	MDPI AG MDPI
Publisher_xml	– name: MDPI AG – name: MDPI
References	Ning (ref_19) 2022; 37 Zhang (ref_22) 2020; 186 Jiang (ref_18) 2018; 6 Bauer (ref_5) 2023; 33 Guha (ref_25) 2001; 26 Zhang (ref_3) 2023; 144 Zhai (ref_14) 2022; 139 Li (ref_20) 2016; 31 Shayan (ref_2) 2022; 11 Wei (ref_7) 2021; 295 Wang (ref_17) 2022; 306 Qin (ref_21) 2019; 13 Yang (ref_24) 2023; 147 Liu (ref_1) 2022; 137 ref_23 Hou (ref_15) 2019; 13 Wu (ref_11) 2022; 138 Shayan (ref_9) 2022; 201 Li (ref_10) 2018; 9 Wang (ref_13) 2020; 198 ref_26 Chen (ref_12) 2019; 66 Shayan (ref_4) 2023; 14 Xu (ref_8) 2019; 2019 Ning (ref_16) 2019; 34 Gupta (ref_6) 2022; 2022
References_xml	– volume: 186 start-page: 106412 year: 2020 ident: ref_22 article-title: Bi-level distributed day-ahead schedule for islanded multi-microgrids in a carbon trading market publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2020.106412 – volume: 139 start-page: 107963 year: 2022 ident: ref_14 article-title: Distributed adjustable robust optimal power-gas flow considering wind power uncertainty publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2022.107963 – volume: 138 start-page: 107898 year: 2022 ident: ref_11 article-title: Data-driven adjustable robust Day-ahead economic dispatch strategy considering uncertainties of wind power generation and electric vehicles publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2021.107898 – volume: 9 start-page: 273 year: 2018 ident: ref_10 article-title: Optimal Stochastic Operation of Integrated Low-Carbon Electric Power, Natural Gas, and Heat Delivery System publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2017.2728098 – volume: 34 start-page: 2409 year: 2019 ident: ref_16 article-title: Data-Driven Adaptive Robust Unit Commitment Under Wind Power Uncertainty: A Bayesian Nonparametric Approach publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2019.2891057 – ident: ref_26 – volume: 147 start-page: 108902 year: 2023 ident: ref_24 article-title: Coordinated optimization scheduling operation of integrated energy system considering demand response and carbon trading mechanism publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2022.108902 – volume: 2019 start-page: 5423 year: 2019 ident: ref_8 article-title: Application of cluster analysis in short-term wind power forecasting model publication-title: J. Eng. doi: 10.1049/joe.2018.5488 – volume: 11 start-page: 471 year: 2022 ident: ref_2 article-title: Sustainable Design of a Near-Zero-Emissions Building Assisted by a Smart Hybrid Renewable Microgrid publication-title: Int. J. Renew. Energy Dev. doi: 10.14710/ijred.2022.43838 – volume: 198 start-page: 117273 year: 2020 ident: ref_13 article-title: A stochastic-robust coordinated optimization model for CCHP micro-grid considering multi-energy operation and power trading with electricity markets under uncertainties publication-title: Energy doi: 10.1016/j.energy.2020.117273 – volume: 2022 start-page: 9569224 year: 2022 ident: ref_6 article-title: Probabilistic Load Flow of an Islanded Microgrid with WTGS and PV Uncertainties Containing Electric Vehicle Charging Loads publication-title: Int. Trans. Electr. Energy Syst. doi: 10.1155/2022/9569224 – volume: 13 start-page: 896 year: 2019 ident: ref_15 article-title: Data-driven multi-time scale robust scheduling framework of hydrothermal power system considering cascade hydropower station and wind penetration publication-title: IET Gener. Transm. Distrib. doi: 10.1049/iet-gtd.2018.5239 – volume: 14 start-page: 35 year: 2023 ident: ref_4 article-title: A novel approach of synchronization of the sustainable grid with an intelligent local hybrid renewable energy control publication-title: Int. J. Energy Environ. Eng. doi: 10.1007/s40095-022-00503-7 – volume: 6 start-page: 62193 year: 2018 ident: ref_18 article-title: Scenario Generation for Wind Power Using Improved Generative Adversarial Networks publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2875936 – volume: 306 start-page: 118034 year: 2022 ident: ref_17 article-title: Wasserstein and multivariate linear affine based distributionally robust optimization for CCHP-P2G scheduling considering multiple uncertainties publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.118034 – volume: 201 start-page: 179 year: 2022 ident: ref_9 article-title: Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm publication-title: Renew. Energy doi: 10.1016/j.renene.2022.11.006 – volume: 144 start-page: 108558 year: 2023 ident: ref_3 article-title: An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2022.108558 – volume: 26 start-page: 35 year: 2001 ident: ref_25 article-title: Cure: An efficient clustering algorithm for large databases publication-title: Inf. Syst. doi: 10.1016/S0306-4379(01)00008-4 – volume: 37 start-page: 191 year: 2022 ident: ref_19 article-title: Deep Learning Based Distributionally Robust Joint Chance Constrained Economic Dispatch Under Wind Power Uncertainty publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2021.3096144 – volume: 137 start-page: 107891 year: 2022 ident: ref_1 article-title: Research on bidding strategy of virtual power plant considering carbon-electricity integrated market mechanism publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2021.107891 – ident: ref_23 doi: 10.3390/electronics12010214 – volume: 33 start-page: 100969 year: 2023 ident: ref_5 article-title: Analytical uncertainty propagation for multi-period stochastic optimal power flow publication-title: Sustain. Energy Grids Netw. doi: 10.1016/j.segan.2022.100969 – volume: 66 start-page: 1379 year: 2019 ident: ref_12 article-title: Adaptive Robust Day-Ahead Dispatch for Urban Energy Systems publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2787605 – volume: 31 start-page: 4330 year: 2016 ident: ref_20 article-title: A Two-Tier Wind Power Time Series Model Considering Day-to-Day Weather Transition and Intraday Wind Power Fluctuations publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2016.2531739 – volume: 295 start-page: 117024 year: 2021 ident: ref_7 article-title: An optimal scheduling strategy for peer-to-peer trading in interconnected microgrids based on RO and Nash bargaining publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.117024 – volume: 13 start-page: 3050 year: 2019 ident: ref_21 article-title: Weather division-based wind power forecasting model with feature selection publication-title: IET Renew. Power Gener. doi: 10.1049/iet-rpg.2019.0263
SSID	ssj0023338
Score	2.4410188
Snippet	In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy...
SourceID	doaj pubmedcentral proquest gale pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	5350
SubjectTerms	adjustable robust optimization Air quality management Algorithms Alternative energy sources Analysis Carbon Case studies Classification Clustering Consumption Economic aspects economic operation Efficiency Electric power production Emissions (Pollution) Emissions trading Feature selection Global warming Green technology low-carbon operation meteorological classification Optimization Probability distribution Random variables Renewable Energy - economics Renewable resources Uncertainty Wind Wind power
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Pa9VAEB6keNCD-NtolVEEvYTmZZNscnytlh5s6cFib8v-SPRBSSQvVfpv-Bc7s5uEPBS8eAvZDWwyMzvfR2a_AXgrHYHuxriYRus4M1VNMWdk3FjjNKWkvMhCswl5dlZeXlbni1ZfXBMW5IHDhzsoCA83pRBVQ6mb8HbJJzkN_1-qiIloD40SWU1kaqRagphX0BGiJ5ODbcqiMoIP1y-yjxfp_3MrXuSi3TrJReI5vg_3RsSI67DSB3Crbh_C3YWO4CP4tcZP3c_4SPema1G3DqfzxvhhQ1sGWQZHGdobJJSKGv3B2_iUy_G-9huHh5TNHPLTeEo4uuunTRF920wuKPI2xKHDE6_MgAQd8YJ8xtcUDDfYNfiFGD6ec-O1x3Bx_PHz0Uk8NluILWGGIXZFYoQuMi1rYiS5tVXWZELXSZlVJs_oItVGpzZ3K51JIm1JnruK0ZGULiM6-wT22q6tnwEWiU5KY6SkfZTwmTMU6AXdSOtSF1aUEbyfjKDsqETODTGuFDEStpea7RXBm3nq9yC_8bdJh2zJeQIrZvsb5Edq9CP1Lz-K4B37geK4psVYPR5PoFdihSy1lnlgr3kE-5OrqDHgt4qYK0HfciVWEbyehylU-f-LbuvuOswpKtbbj-Bp8Kx5zYL7BiRSRlDu-NzOS-2OtJtvXg585WGrFM__x2d4AXdSCh9fDJfvw97QX9cv4bb9MWy2_SsfZL8BEv4p3g priority: 102 providerName: Directory of Open Access Journals
Title	A Low-Carbon and Economic Dispatch Strategy for a Multi-Microgrid Based on a Meteorological Classification to Handle the Uncertainty of Wind Power
URI	https://www.ncbi.nlm.nih.gov/pubmed/37300077 https://www.proquest.com/docview/2824058131 https://www.proquest.com/docview/2824692756 https://pubmed.ncbi.nlm.nih.gov/PMC10256073 https://doaj.org/article/6768f8339f26409683944b07399717aa
Volume	23
WOSCitedRecordID	wos001006414400001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: AUTh Library subscriptions: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BywEOvEsDZWUQElyiZvNyckK7ZasisasIUbGcIj-SEgklbTYF9cKP4Bcz43jTXYG4cIlWtqN15PH4--zxNwCvuEbQXUrtYm3hhjItcM5J7pZKaoFLUhSHfbIJvlgky2Wa2Q23lQ2rXPtE46h1o2iP_BCpAWKLZByM355fuJQ1ik5XbQqNm7BLabPJzvnymnAFyL96NaEAqf3hyidpmYCu2G-sQUaq_0-HvLEibUdLbiw_x_f-t-P34a4FnmzSW8oDuFHUD-HOhhzhI_g1YR-aH-6RaGVTM1Frtr62zN5V6HlwgJlVs71iCHaZYOb-rjunqL6zttJsiouiZvQ2myMcb9q1b2Um-ybFJRlTYF3DTozAA0MEyk7R9ExoQnfFmpJ9rvC_M8rf9hhOj2efjk5cm7PBVQg9OlfHngxEHApeILGJlErDMgxE4SVhKqMQf_hCCl9FeixCjtzPiyKdEsjiXIfIivdgp27qYh9Y7AkvkZJzdMcI87REfxFjgV8kIlZB4sCb9SjmygqaU16NbzkSGxrwfBhwB14OTc97FY-_NZqSKQwNSHjbFDTtWW7ncR4jPSsTfLFEJIn0L6GLxZKOO1MkxkI48JoMKSf3gJ1Rwt5ywE8ioa18wqOeBEcOHKztJbd-Y5VfG4sDL4ZqnPF0jCPqorns28QpyfY78KQ3zaHPAaUf8Dh3INky2q2P2q6pq69GVXxs0C8Pnv67X8_gto8zy0TLRQew07WXxXO4pb531aodmflnnskIdqezRfZxZLY58Dn_OcOy7P08-_IbWmlAYQ
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB5VBQk48H4YCgwIBBerjl9rHxBKW6pUTaIeWpGb2fXaxRKyi-NS5W_wQ_iNzKztNBGIWw_cLO9aXtvz-rwz3wC8EZqC7lxpm0Yz21dxRjqnhJ2nSktySUHot80mxHQazWbx0Qb86mthOK2yt4nGUOsq5X_k2wQNKLaIBt7g49l3m7tG8e5q30KjFYvDbHFBkG3-4WCPvu9b193_dLw7sruuAnZKzrGxdegoT4a-FBmF3kGaxn7uezJzIj9WgU8HrlTSTQM9kL4gdOIEgY45DBBC-wETHZDJv0Z2XDDYE7NLgOcR3mvZizwvdrbnLlPZeFzSv-LzTGuAPx3Aigdcz85ccXf7d_63F3UXbneBNQ5bTbgHG1l5H26t0C0-gJ9DHFcX9q6sVVWiLDX2Zdm4V5BlJQHGjq13gRTMo0RTn2xPOGvxtC407pDT18hX44TgRlX3vgNNd1HOuzKijk2FI0NggRRh4wmplkm9aBZY5fi5oHsfcX-6h3ByJS_lEWyWVZk9AQwd6URKCUHuhsJYrcgehnTCzSIZpl5kwfteapK0I2znviHfEgJuLGDJUsAseL2cetaylPxt0g6L3nICE4ubE1V9mnR2KgkJfuYRXZhTpEzwNuLCacXbuTEBfykteMeCm7D5o8WksqvioEdiIrFkKIIW5AcWbPXymXR2cZ5cCqcFr5bDZNF4m0qWWXXezgljbktgweNWFZZr9ri9giOEBdGakqw91PpIWXw1rOkDE90L7-m_1_USboyOJ-NkfDA9fAY3XdJqkxkYbMFmU59nz-F6-qMp5vULo_sIX65ah34DDJaUzA
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB5VLUJw4P0wFBgQCC5WHL_WPiCUNkSN2kQ5UFFOZtdrl0jILk5Klb_Bz-HXMbN-kAjErQdulnctr-15fOOd-QbgpdAEunOlbRrNbF_FGemcEnaeKi3JJQWhXzebENNpdHISz7bgZ1sLw2mVrU00hlqXKf8j71FoQNgi6nv9Xt6kRcyGo3dn32zuIMU7rW07jVpEDrPVBYVvi7fjIX3rV647ev9h_8BuOgzYKTnKpa1DR3ky9KXICIYHaRr7ue_JzIn8WAU-HbhSSTcNdF_6giIVJwh0zJBACO0HTHpA5n-HILnvbsPObDyZferCPY-iv5rLyPNip7dwmdjG4wL_NQ9oGgX86Q7W_OFmruaa8xvd_J9f2y240UBuHNQ6chu2suIOXF8jYrwLPwZ4VF7Y-7JSZYGy0NgWbONwTjaXRBsbHt8VEsxHiaZy2Z5wPuNpNde4R3BAI1-NEwpEyqr1Kmj6jnJGllECXJZ4YKgtkLA3HpPSmaSM5QrLHD_O6d4z7lx3D44v5aXch-2iLLKHgKEjnUgpIcgREcDViixlSCfcLJJh6kUWvGklKEkbKnfuKPI1oZCOhS3phM2CF93Us5q_5G-T9lgMuwlMOW5OlNVp0liwJKTANI_owpwwNAW-EZdUK97ojUVfSGnBaxbihA0jLSaVTX0HPRJTjCUDEdThf2DBbiurSWMxF8lvQbXgeTdMto43sGSRlef1nDDmhgUWPKjVoluzx40XHCEsiDYUZuOhNkeK-RfDp943uF94j_69rmdwlVQnORpPDx_DNZcU3KQMBruwvazOsydwJf2-nC-qp40hQPh82Ur0C1uHnxs
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=A+Low-Carbon+and+Economic+Dispatch+Strategy+for+a+Multi-Microgrid+Based+on+a+Meteorological+Classification+to+Handle+the+Uncertainty+of+Wind+Power&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Liu%2C+Yang&rft.au=Li%2C+Xueling&rft.au=Liu%2C+Yamei&rft.date=2023-06-05&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=23&rft.issue=11&rft.spage=5350&rft_id=info:doi/10.3390%2Fs23115350&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon