Clustering Approach for the Efficient Solution of Multiscale Stochastic Programming Problems: Application to Energy Hub Design and Operation under Uncertainty

The management of the supply chain for enterprise-wide operations generally consists of strategic, tactical, and operational decision stages dependent on one another and affecting various time scales. Their integration usually leads to multiscale models that are computationally intractable. The desi...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Processes Jg. 11; H. 4; S. 1046
Hauptverfasser:	Alkatheri, Mohammed, Alhameli, Falah, Betancourt-Torcat, Alberto, Almansoori, Ali, Elkamel, Ali
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.04.2023
Schlagworte:	Algorithms Alternative energy sources Case studies Clustering Costs Decision making Design optimization Efficiency Electricity Emissions Energy industry Energy resources Energy sources Energy storage Greenhouse gases Hydrogen storage Integer programming Intermittency Linear programming Mathematical programming Mixed integer Monte Carlo simulation Multiple objective analysis Optimization models Renewable energy Renewable resources Scale models Scheduling Statistical analysis Statistical methods Statistics Stochastic models Stochastic programming Superstructures Supply chains Uncertainty
ISSN:	2227-9717, 2227-9717
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	The management of the supply chain for enterprise-wide operations generally consists of strategic, tactical, and operational decision stages dependent on one another and affecting various time scales. Their integration usually leads to multiscale models that are computationally intractable. The design and operation of energy hubs faces similar challenges. Renewable energies are challenging to model due to the high level of intermittency and uncertainty. The multiscale (i.e., planning and scheduling) energy hub systems that incorporate renewable energy resources become more challenging to model due to an integration of the multiscale and high level of intermittency associated with renewable energy. In this work, a mixed-integer programming (MILP) superstructure is proposed for clustering shape-based time series data featuring multiple attributes using a multi-objective optimization approach. Additionally, a data-driven statistical method is used to represent the intermittent behavior of uncertain renewable energy data. According to these methods, the design and operation of an energy hub with hydrogen storage was reformulated following a two-stage stochastic modeling technique. The main outcomes of this study are formulating a stochastic energy hub optimization model which comprehensively considers the design and operation planning, energy storage system, and uncertainties of DRERs, and proposing an efficient size reduction approach for large-sized multiple attributes demand data. The case study results show that normal clustering is closer to the optimal case (full scale model) compared with sequence clustering. In addition, there is an improvement in the objective function value using the stochastic approach instead of the deterministic. The present clustering algorithm features many unique characteristics that gives it advantages over other clustering approach and the straightforward statistical approach used to represent intermittent energy, and it can be easily incorporated into various distributed energy systems.
AbstractList	The management of the supply chain for enterprise-wide operations generally consists of strategic, tactical, and operational decision stages dependent on one another and affecting various time scales. Their integration usually leads to multiscale models that are computationally intractable. The design and operation of energy hubs faces similar challenges. Renewable energies are challenging to model due to the high level of intermittency and uncertainty. The multiscale (i.e., planning and scheduling) energy hub systems that incorporate renewable energy resources become more challenging to model due to an integration of the multiscale and high level of intermittency associated with renewable energy. In this work, a mixed-integer programming (MILP) superstructure is proposed for clustering shape-based time series data featuring multiple attributes using a multi-objective optimization approach. Additionally, a data-driven statistical method is used to represent the intermittent behavior of uncertain renewable energy data. According to these methods, the design and operation of an energy hub with hydrogen storage was reformulated following a two-stage stochastic modeling technique. The main outcomes of this study are formulating a stochastic energy hub optimization model which comprehensively considers the design and operation planning, energy storage system, and uncertainties of DRERs, and proposing an efficient size reduction approach for large-sized multiple attributes demand data. The case study results show that normal clustering is closer to the optimal case (full scale model) compared with sequence clustering. In addition, there is an improvement in the objective function value using the stochastic approach instead of the deterministic. The present clustering algorithm features many unique characteristics that gives it advantages over other clustering approach and the straightforward statistical approach used to represent intermittent energy, and it can be easily incorporated into various distributed energy systems.
Audience	Academic
Author	Alhameli, Falah Elkamel, Ali Alkatheri, Mohammed Betancourt-Torcat, Alberto Almansoori, Ali
Author_xml	– sequence: 1 givenname: Mohammed surname: Alkatheri fullname: Alkatheri, Mohammed – sequence: 2 givenname: Falah surname: Alhameli fullname: Alhameli, Falah – sequence: 3 givenname: Alberto surname: Betancourt-Torcat fullname: Betancourt-Torcat, Alberto – sequence: 4 givenname: Ali orcidid: 0000-0002-0789-5105 surname: Almansoori fullname: Almansoori, Ali – sequence: 5 givenname: Ali orcidid: 0000-0002-6220-6288 surname: Elkamel fullname: Elkamel, Ali
BookMark	eNptkd9KwzAUxoNMcOpufIKAd8Jm2mTN6t2Y8w8oE9TrkqYnXUab1CS92Mv4rGaboIg5gZyE7_eFc84pGhhrAKGLhEwozcl155KEsLizIzRM05SPc57wwa_8BI2835C48oTOptkQfS6a3gdw2tR43nXOCrnGyjoc1oCXSmmpwQT8aps-aGuwVfi5b4L2UjSAX4OVa-GDlvjF2dqJtt0ZxbxsoPU3O8tGS7FHg8VLA67e4oe-xLfgdW2wMBVedeAOkt5U4PC7keCC0CZsz9GxEo2H0fd5ht7vlm-Lh_HT6v5xMX8aS0pZGFc8z0tWcSZUMkvyrOQktqKiGVOSUKAMShlvZEYgm3IGFHiqSDaFClhOmKBn6PLgGzvw0YMPxcb2zsQvi3RGsoxwntOomhxUdSy-0EbZ4ISMUUGrZRyG0vF9zlkMkk55BMgBkM5670AVUod9qRHUTZGQYje54mdyEbn6g3ROt8Jt_xN_Abv_nPA
CitedBy_id	crossref_primary_10_3390_pr11072008 crossref_primary_10_3390_su17073001
Cites_doi	10.3390/en15249572 10.1021/ie404165c 10.1016/j.est.2023.106937 10.1016/j.neucom.2013.03.017 10.1016/j.energy.2015.10.079 10.1016/j.renene.2017.10.017 10.1016/j.scs.2022.104354 10.1016/j.energy.2020.117052 10.1016/B978-0-12-397219-4.00017-5 10.1016/j.rser.2018.03.003 10.1080/01621459.1969.10500990 10.1007/s11590-012-0469-5 10.1021/acs.iecr.6b01264 10.1016/j.jenvman.2009.11.009 10.1016/j.ejor.2005.04.048 10.1016/j.ijhydene.2015.08.038 10.2478/amcs-2014-0012 10.1016/j.egyr.2023.02.051 10.1016/j.enbuild.2019.04.004 10.1016/j.scs.2020.102578 10.1007/978-1-4614-0237-4 10.1016/j.enconman.2017.04.074 10.1016/j.energy.2020.117130 10.1016/j.ejor.2005.04.049 10.1002/aic.16578 10.1016/j.energy.2022.125219 10.1016/j.enconman.2017.11.007 10.1016/j.energy.2015.10.137 10.1109/BigData.2015.7364011 10.1016/j.enconman.2016.12.011 10.1016/j.egypro.2017.12.539 10.1109/TEM.2013.2284386 10.1016/j.energy.2017.06.003 10.1007/978-3-540-88908-3 10.1016/j.enconman.2018.12.073 10.3390/en10070868
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.
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.
DBID	AAYXX CITATION 7SR 8FD 8FE 8FG 8FH ABJCF ABUWG AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU D1I DWQXO GNUQQ HCIFZ JG9 KB. LK8 M7P PDBOC PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS
DOI	10.3390/pr11041046
DatabaseName	CrossRef Engineered Materials Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One Community College ProQuest Materials Science Collection ProQuest Central Korea ProQuest Central Student SciTech Premium Collection Materials Research Database Materials Science Database ProQuest Biological Science Collection Biological Science Database Materials Science Collection Proquest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China
DatabaseTitle	CrossRef Publicly Available Content Database Materials Research Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials Materials Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection ProQuest Central Korea Biological Science Collection Materials Science Database ProQuest Central (New) ProQuest Materials Science Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Technology Collection Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: KB. name: Materials Science Database url: http://search.proquest.com/materialsscijournals sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Sciences (General) Statistics
EISSN	2227-9717
ExternalDocumentID	A747470257 10_3390_pr11041046
GroupedDBID	5VS 8FE 8FG 8FH AADQD AAFWJ AAYXX ABJCF ACIWK ACPRK ADBBV ADMLS AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BBNVY BCNDV BENPR BGLVJ BHPHI CCPQU CITATION D1I HCIFZ IAO IGS ITC KB. KQ8 LK8 M7P MODMG M~E OK1 PDBOC PHGZM PHGZT PIMPY PQGLB PROAC RNS 7SR 8FD ABUWG AZQEC DWQXO GNUQQ JG9 PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c334t-d799b4d74af18196b70110d364fc03e34ebcd36080e6574e3e72f065ede4904a3
IEDL.DBID	KB.
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000982795800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2227-9717
IngestDate	Fri Jul 25 11:55:26 EDT 2025 Tue Nov 04 18:15:31 EST 2025 Tue Nov 18 21:53:10 EST 2025 Sat Nov 29 07:17:49 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	4
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c334t-d799b4d74af18196b70110d364fc03e34ebcd36080e6574e3e72f065ede4904a3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-6220-6288 0000-0002-0789-5105
OpenAccessLink	https://www.proquest.com/docview/2806607793?pq-origsite=%requestingapplication%
PQID	2806607793
PQPubID	2032344
ParticipantIDs	proquest_journals_2806607793 gale_infotracacademiconefile_A747470257 crossref_citationtrail_10_3390_pr11041046 crossref_primary_10_3390_pr11041046
PublicationCentury	2000
PublicationDate	2023-04-01
PublicationDateYYYYMMDD	2023-04-01
PublicationDate_xml	– month: 04 year: 2023 text: 2023-04-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Processes
PublicationYear	2023
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Lyu (ref_28) 2013; 119 Sengupta (ref_38) 2018; 89 Elkamel (ref_23) 2023; 89 ref_36 ref_10 Koltsaklis (ref_8) 2014; 53 Alhameli (ref_21) 2019; 65 ref_30 Salman (ref_34) 2006; 173 Nojavan (ref_20) 2017; 145 ref_39 ref_37 Maroufmashat (ref_9) 2016; 55 Maroufmashat (ref_18) 2016; 41 Vinod (ref_31) 1969; 64 Liu (ref_24) 2019; 182 Mirzaesmaeeli (ref_33) 2010; 91 (ref_25) 2010; 5 Yilmaz (ref_2) 2019; 61 (ref_35) 2006; 173 Guo (ref_7) 2023; 9 Zhang (ref_14) 2019; 194 Sabo (ref_29) 2014; 24 ref_43 ref_42 Zhang (ref_12) 2022; 261 Turk (ref_22) 2020; 196 ref_41 ref_1 Majidi (ref_16) 2017; 134 Lu (ref_5) 2020; 195 Wang (ref_19) 2017; 142 Moghaddam (ref_3) 2016; 94 Faraji (ref_15) 2020; 65 Kotzur (ref_11) 2018; 117 Green (ref_27) 2014; 61 Maroufmashat (ref_40) 2015; 93 ref_26 Mangasarian (ref_32) 2013; 7 Amry (ref_13) 2023; 62 Ma (ref_4) 2017; 133 Nojavan (ref_17) 2018; 156 ref_6
References_xml	– ident: ref_6 doi: 10.3390/en15249572 – volume: 53 start-page: 16905 year: 2014 ident: ref_8 article-title: Design and Operational Planning of Energy Networks Based on Combined Heat and Power Units publication-title: Ind. Eng. Chem. Res. doi: 10.1021/ie404165c – volume: 62 start-page: 106937 year: 2023 ident: ref_13 article-title: Optimal sizing and energy management strategy for EV workplace charging station considering PV and flywheel energy storage system publication-title: J. Energy Storage doi: 10.1016/j.est.2023.106937 – volume: 119 start-page: 413 year: 2013 ident: ref_28 article-title: A comparison of typical ℓ minimization algorithms publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.03.017 – volume: 93 start-page: 2546 year: 2015 ident: ref_40 article-title: Modeling and optimization of a network of energy hubs to improve economic and emission considerations publication-title: Energy doi: 10.1016/j.energy.2015.10.079 – volume: 117 start-page: 474 year: 2018 ident: ref_11 article-title: Impact of different time series aggregation methods on optimal energy system design publication-title: Renew. Energy doi: 10.1016/j.renene.2017.10.017 – volume: 89 start-page: 104354 year: 2023 ident: ref_23 article-title: Multi-agent modeling for linking a green transportation system with an urban agriculture network in a food-energy-water nexus publication-title: Sustain. Cities Soc. doi: 10.1016/j.scs.2022.104354 – volume: 195 start-page: 117052 year: 2020 ident: ref_5 article-title: Optimal household energy management based on smart residential energy hub considering uncertain behaviors publication-title: Energy doi: 10.1016/j.energy.2020.117052 – ident: ref_37 doi: 10.1016/B978-0-12-397219-4.00017-5 – volume: 5 start-page: 1721 year: 2010 ident: ref_25 article-title: The comparison of L11 and L22-norm minimization methods publication-title: Int. J. Phys. – volume: 89 start-page: 51 year: 2018 ident: ref_38 article-title: The National Solar Radiation Data Base (NSRDB) publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2018.03.003 – volume: 64 start-page: 506 year: 1969 ident: ref_31 article-title: Integer Programming and the Theory of Grouping publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1969.10500990 – volume: 7 start-page: 625 year: 2013 ident: ref_32 article-title: Absolute value equation solution via dual complementarity publication-title: Optim. Lett. doi: 10.1007/s11590-012-0469-5 – volume: 55 start-page: 8950 year: 2016 ident: ref_9 article-title: Multi-objective Optimization for Design and Operation of Distributed Energy Systems through the Multi-energy Hub Network Approach publication-title: Ind. Eng. Chem. Res. doi: 10.1021/acs.iecr.6b01264 – ident: ref_39 – volume: 91 start-page: 1063 year: 2010 ident: ref_33 article-title: A multi-period optimization model for energy planning with CO2 emission consideration publication-title: J. Environ. Manag. doi: 10.1016/j.jenvman.2009.11.009 – volume: 173 start-page: 866 year: 2006 ident: ref_34 article-title: A mixed-integer programming approach to the clustering problem with an application in customer segmentation publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2005.04.048 – volume: 41 start-page: 7700 year: 2016 ident: ref_18 article-title: Mixed integer linear programing based approach for optimal planning and operation of a smart urban energy network to support the hydrogen economy publication-title: Int. J. Hydrogen Energy doi: 10.1016/j.ijhydene.2015.08.038 – ident: ref_42 – ident: ref_1 – volume: 24 start-page: 151 year: 2014 ident: ref_29 article-title: Center-based l1–clustering method publication-title: Int. J. Appl. Math. Comput. Sci. doi: 10.2478/amcs-2014-0012 – volume: 9 start-page: 3683 year: 2023 ident: ref_7 article-title: Multi-energy collaborative optimization of park integrated energy system considering carbon emission and demand response publication-title: Energy Rep. doi: 10.1016/j.egyr.2023.02.051 – volume: 194 start-page: 113 year: 2019 ident: ref_14 article-title: Bi-stage stochastic model for optimal capacity and electric cooling ratio of CCHPs—A case study for a hotel publication-title: Energy Build. doi: 10.1016/j.enbuild.2019.04.004 – volume: 65 start-page: 102578 year: 2020 ident: ref_15 article-title: Stochastic operation and scheduling of energy hub considering renewable energy sources’ uncertainty and N-1 contingency publication-title: Sustain. Cities Soc. doi: 10.1016/j.scs.2020.102578 – ident: ref_43 doi: 10.1007/978-1-4614-0237-4 – volume: 145 start-page: 117 year: 2017 ident: ref_20 article-title: Stochastic optimization of energy hub operation with consideration of thermal energy market and demand response publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2017.04.074 – volume: 196 start-page: 117130 year: 2020 ident: ref_22 article-title: Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing publication-title: Energy doi: 10.1016/j.energy.2020.117130 – volume: 61 start-page: 125 year: 2019 ident: ref_2 article-title: Reducing energy time series for energy system models via self-organizing maps publication-title: Inf. Technol. – volume: 173 start-page: 910 year: 2006 ident: ref_35 article-title: A mixed-integer programming approach to multi-class data classification problem publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2005.04.049 – volume: 65 start-page: e16578 year: 2019 ident: ref_21 article-title: A mixed-integer programming approach for clustering demand data for multiscale mathematical programming applications publication-title: AIChE J. doi: 10.1002/aic.16578 – volume: 261 start-page: 125219 year: 2022 ident: ref_12 article-title: Modelling and analysis of offshore energy hubs publication-title: Energy doi: 10.1016/j.energy.2022.125219 – volume: 156 start-page: 34 year: 2018 ident: ref_17 article-title: Optimal scheduling of heating and power hubs under economic and environment issues in the presence of peak load management publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2017.11.007 – volume: 94 start-page: 157 year: 2016 ident: ref_3 article-title: A comprehensive model for self-scheduling an energy hub to supply cooling, heating and electrical demands of a building publication-title: Energy doi: 10.1016/j.energy.2015.10.137 – ident: ref_26 doi: 10.1109/BigData.2015.7364011 – ident: ref_41 – volume: 133 start-page: 292 year: 2017 ident: ref_4 article-title: Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2016.12.011 – ident: ref_36 – volume: 142 start-page: 1615 year: 2017 ident: ref_19 article-title: Optimal design and operation of CHPs and energy hub with multi objectives for a local energy system publication-title: Energy Procedia doi: 10.1016/j.egypro.2017.12.539 – volume: 61 start-page: 251 year: 2014 ident: ref_27 article-title: Divide and Conquer? ${k}$-Means Clustering of Demand Data Allows Rapid and Accurate Simulations of the British Electricity System publication-title: IEEE Trans. Eng. Manag. doi: 10.1109/TEM.2013.2284386 – volume: 134 start-page: 157 year: 2017 ident: ref_16 article-title: A cost-emission framework for hub energy system under demand response program publication-title: Energy doi: 10.1016/j.energy.2017.06.003 – ident: ref_30 doi: 10.1007/978-3-540-88908-3 – volume: 182 start-page: 126 year: 2019 ident: ref_24 article-title: Standardized modelling and economic optimization of multi-carrier energy systems considering energy storage and demand response publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2018.12.073 – ident: ref_10 doi: 10.3390/en10070868
SSID	ssj0000913856
Score	2.2292078
Snippet	The management of the supply chain for enterprise-wide operations generally consists of strategic, tactical, and operational decision stages dependent on one...
SourceID	proquest gale crossref
SourceType	Aggregation Database Enrichment Source Index Database
StartPage	1046
SubjectTerms	Algorithms Alternative energy sources Case studies Clustering Costs Decision making Design optimization Efficiency Electricity Emissions Energy industry Energy resources Energy sources Energy storage Greenhouse gases Hydrogen storage Integer programming Intermittency Linear programming Mathematical programming Mixed integer Monte Carlo simulation Multiple objective analysis Optimization models Renewable energy Renewable resources Scale models Scheduling Statistical analysis Statistical methods Statistics Stochastic models Stochastic programming Superstructures Supply chains Uncertainty
Title	Clustering Approach for the Efficient Solution of Multiscale Stochastic Programming Problems: Application to Energy Hub Design and Operation under Uncertainty
URI	https://www.proquest.com/docview/2806607793
Volume	11
WOSCitedRecordID	wos000982795800001&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: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources (ISSN International Center) customDbUrl: eissn: 2227-9717 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913856 issn: 2227-9717 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database (ProQuest) customDbUrl: eissn: 2227-9717 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913856 issn: 2227-9717 databaseCode: M7P dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database customDbUrl: eissn: 2227-9717 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913856 issn: 2227-9717 databaseCode: KB. dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest - Publicly Available Content Database customDbUrl: eissn: 2227-9717 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913856 issn: 2227-9717 databaseCode: PIMPY dateStart: 20130301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2227-9717 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913856 issn: 2227-9717 databaseCode: BENPR dateStart: 20130301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3BbtQwEB1By6EcgC5UbCmrkUCCHkLT2hvHXNC2pCoHlgioVE6RYzviUDbbTRaJC5_CtzKTeLsgVVy4RIpsWZZmPPPsmXkD8NxUQlrjZDQubRVJ63SUulJFJvZaVD6NtUm7ZhNqOk0vLnQeHtyakFa5somdoXa15TfyA44AJrEidXozv4q4axRHV0MLjduweXhEWJ-Dssevrt9YmPMyHSc9K6mg2_3BfEHuTnJc8y8_dLM17lzM6f3_3dwDuBfAJU56bdiGW342gLt_UA4OYDsc5gZfBsbp_QFsMeTsGZsfwq-TyyWzJ9B0nATGcSRoiwQVMesYJ8hR4eo9DesKuyrehqTt8VNb26-Gl8K8T_36xgvlfd-a5jVO1hFzbGvMutpDPFuW-LZLJkEzc_hh7nvNRK5xW-A57bhLXWh_PILz0-zzyVkUujhEVgjZRk5pXUqnpKkITeikVAw5nEhkZWPhhfSlpT9Crj4ZK-mFV0cVASPvvNSxNGIHNmb1zD8G1FYfVmRiXaoMrViWZTom9-q91Ym0Wg9hfyXTwgaKc-60cVnQVYflX6zlP4Rn13PnPbHHjbNesGoUfNppJWtC0QLth3mzigndxqQi3KiGsLdSjSKYgaZY68Xuv4efwBb3se9TgvZgo10s_VO4Y7-T9BYj2DzOpvnHUafdI05Pzfn7M6OR_N37_MtvC7QKIA
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9NAEB2VFIlyABpABAqMBAh6sGqyG68XCaGoTZWobbBEK7Uns18WhxKH2AH1z_AT-I3M-qMBqeLWA0fLq5G1-zzzdnfmDcBLlTFulOXBQJss4MbKILZaBCp0kmUuDqWKq2YTYjqNT09lsga_2loYn1bZ-sTKUdvc-DPyHX8DGIWC4PRh_i3wXaP87WrbQqOGxYG7-EFbtuL9ZI_W91W_vz863h0HTVeBwDDGy8AKKTW3gquMopuMtPAh0LKIZyZkjnGnDT0Rk3LRQHDHnOhnFKiddVyGXDGyewPWOYE97MB6MjlKzi5PdbzKZjyIah1UxmS4M1-Qde5vUv-KfFf7_yqo7d_936bjHtxp6DMOa7xvwpqbdeH2H6KKXdhs3FWBbxpN7e0ubHhSXWtS34efu-dLrw9Bw3HYaKojkXckMoyjSlODQjG2J4aYZ1jVKReEZ4efytx8Ud4UJnVy21dvKKk78xTvcLjKCcAyx1FVXYnjpca9Kl0G1czix7mr_z30VXwLPKEvrpIzyosHcHItU_gQOrN85h4BSiPfZhREbCwUWdRaxwMiEM4ZGXEjZQ-2WwylphFx971EzlPazHm8pSu89eDF5dh5LV1y5ajXHoqp92dkyaimLIO-xyuDpUPab3JBzFj0YKuFYto4uiJd4fDxv18_h1vj46PD9HAyPXgCG33iinUC1BZ0ysXSPYWb5jut5OJZ808hfL5u3P4G5gBh0Q
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1fb9MwED-NDqHxAKyAVhhwEiDYQ9RQu3GMhFBZW20aKhEwaW_BsR3tYTSlSUH7MnwQPh3nxFlBmnjbA49RrJNl_3x_7LvfATxTOeNaGR4MM50HXBsZxCYTgQqtZLmNQ6niutmEmM3ikxOZbMCvthbGpVW2OrFW1KbQ7o68714Ao1AQnPq5T4tIxtO3i2-B6yDlXlrbdhoNRI7s-Q8K38o3h2Pa6-eDwXTyef8g8B0GAs0YrwIjpMy4EVzlZOlklAlnDg2LeK5DZhm3maYv8qpsNBTcMisGORltayyXIVeM5F6DTcEo6OnA5rvJLPl4ccPjGDfjYdRwojImw_5iSdK5e1X9ywpebgtqAze9_T8vzR245d1qHDXnYBs27LwLN_8gW-zCtldjJb70XNt7XdhyznbDVX0Xfu6frRxvBA3HkedaR3LqkZxknNRcG2Sisb1JxCLHun65JJxb_FQV-lQ5UZg0SW9fnaCk6dhTvsbROlcAqwInddUlHqwyHNdpNKjmBj8sbHMm0VX3LfGYZlwnbVTn9-D4SpbwPnTmxdzuAEotX-VkXEwsFEnMsiwekmNhrZYR11L2YK_FU6o9ubvrMXKWUpDnsJeusdeDpxdjFw2lyaWjXjhYpk7PkSStfLkGzccxhqUjikO5II9Z9GC3hWXqFWCZrjH54N-_n8ANAmv6_nB29BC2BuRCNnlRu9Cpliv7CK7r77SRy8f-eCF8uWrY_gYRPGpr
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=Clustering+Approach+for+the+Efficient+Solution+of+Multiscale+Stochastic+Programming+Problems%3A+Application+to+Energy+Hub+Design+and+Operation+under+Uncertainty&rft.jtitle=Processes&rft.au=Alkatheri%2C+Mohammed&rft.au=Alhameli%2C+Falah&rft.au=Betancourt-Torcat%2C+Alberto&rft.au=Almansoori%2C+Ali&rft.date=2023-04-01&rft.issn=2227-9717&rft.eissn=2227-9717&rft.volume=11&rft.issue=4&rft.spage=1046&rft_id=info:doi/10.3390%2Fpr11041046&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_pr11041046
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2227-9717&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2227-9717&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2227-9717&client=summon