{"id":"https://openalex.org/W2752800962","doi":"https://doi.org/10.1109/cdc.2017.8264321","title":"Solving optimal power flow with non-Gaussian uncertainties via polynomial chaos expansion","display_name":"Solving optimal power flow with non-Gaussian uncertainties via polynomial chaos expansion","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2752800962","doi":"https://doi.org/10.1109/cdc.2017.8264321","mag":"2752800962"},"language":"en","primary_location":{"id":"doi:10.1109/cdc.2017.8264321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2017.8264321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059695632","display_name":"Tillmann M\u00fchlpfordt","orcid":null},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Tillmann Muhlpfordt","raw_affiliation_strings":["Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054359566","display_name":"Timm Faulwasser","orcid":"https://orcid.org/0000-0002-6892-7406"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Timm Faulwasser","raw_affiliation_strings":["Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005034793","display_name":"Line Roald","orcid":"https://orcid.org/0000-0003-4378-7272"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Line Roald","raw_affiliation_strings":["Los Alamos National Laboratory, Los Alamos, NM, United States"],"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Los Alamos, NM, United States","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014228448","display_name":"Veit Hagenmeyer","orcid":"https://orcid.org/0000-0002-3572-9083"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Veit Hagenmeyer","raw_affiliation_strings":["Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Institute for Applied Computer Science, Karlsruhe Institute of Technology, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5059695632"],"corresponding_institution_ids":["https://openalex.org/I102335020"],"apc_list":null,"apc_paid":null,"fwci":4.2842,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.94960814,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4490","last_page":"4496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10454","display_name":"Optimal Power Flow Distribution","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10305","display_name":"Power System Optimization and Stability","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/polynomial-chaos","display_name":"Polynomial chaos","score":0.8671255111694336},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6668004989624023},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5789948105812073},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.49309417605400085},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4921671748161316},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.4566350281238556},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4524761438369751},{"id":"https://openalex.org/keywords/polynomial","display_name":"Polynomial","score":0.44639644026756287},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.4455238878726959},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4435361623764038},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.4347202479839325},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.3434731364250183},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3376815915107727},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.1171799898147583}],"concepts":[{"id":"https://openalex.org/C197656079","wikidata":"https://www.wikidata.org/wiki/Q17147719","display_name":"Polynomial chaos","level":3,"score":0.8671255111694336},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6668004989624023},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5789948105812073},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.49309417605400085},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4921671748161316},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.4566350281238556},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4524761438369751},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.44639644026756287},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.4455238878726959},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4435361623764038},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.4347202479839325},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.3434731364250183},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3376815915107727},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.1171799898147583},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cdc.2017.8264321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2017.8264321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 56th Annual Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},{"id":"pmh:oai:tore.tuhh.de:11420/46225","is_oa":false,"landing_page_url":"https://hdl.handle.net/11420/46225","pdf_url":null,"source":{"id":"https://openalex.org/S4306401751","display_name":"tub.dok (Hamburg University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I884043246","host_organization_name":"Hamburg University of Technology","host_organization_lineage":["https://openalex.org/I884043246"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8899999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W172596906","https://openalex.org/W1858422019","https://openalex.org/W1967729795","https://openalex.org/W2000587848","https://openalex.org/W2007423841","https://openalex.org/W2039404136","https://openalex.org/W2088917197","https://openalex.org/W2101836934","https://openalex.org/W2105235982","https://openalex.org/W2106424475","https://openalex.org/W2106836195","https://openalex.org/W2145455389","https://openalex.org/W2286169807","https://openalex.org/W2303654018","https://openalex.org/W2528940215","https://openalex.org/W2738684166","https://openalex.org/W2962850894","https://openalex.org/W2964287149","https://openalex.org/W3217167116","https://openalex.org/W4256550092","https://openalex.org/W4300600608","https://openalex.org/W6606965931","https://openalex.org/W6634666681","https://openalex.org/W6695842710"],"related_works":["https://openalex.org/W3215394929","https://openalex.org/W2108001913","https://openalex.org/W2517755966","https://openalex.org/W2014910883","https://openalex.org/W2789381754","https://openalex.org/W2981659597","https://openalex.org/W3158222746","https://openalex.org/W2998325883","https://openalex.org/W2811262943","https://openalex.org/W3038021552"],"abstract_inverted_index":{"The":[0,43,87,112],"increasing":[1],"penetration":[2],"of":[3,14,27,36,84,95],"renewable":[4],"energy":[5],"sources":[6],"to":[7,56,104,128],"power":[8,18,40],"grids":[9],"necessitates":[10],"a":[11,32,64,77,90,129],"structured":[12],"consideration":[13],"uncertainties":[15,22,44],"for":[16,81,132],"optimal":[17,39],"flow":[19,41],"problems.":[20],"Modeling":[21],"via":[23],"continuous":[24],"random":[25],"variables":[26],"finite":[28],"variance":[29],"we":[30],"propose":[31],"tractable":[33],"convex":[34,66],"formulation":[35],"the":[37,58,85,96,107,133],"uncertain":[38],"problem.":[42],"can":[45,72],"be":[46,73],"(non-)Gaussian,":[47],"multivariate":[48],"and/or":[49],"correlated.":[50],"We":[51],"employ":[52],"polynomial":[53,122],"chaos":[54,123],"expansion":[55],"rewrite":[57],"infinite-dimensional":[59],"random-variable":[60],"optimization":[61],"problem":[62,71],"as":[63],"finite-dimensional":[65],"second-order":[67],"cone":[68],"program.":[69],"This":[70],"solved":[74],"efficiently":[75],"in":[76,93,126],"single":[78],"numerical":[79],"run":[80],"all":[82],"realizations":[83],"uncertainty.":[86],"solution":[88,108,131],"provides":[89],"feedback":[91],"policy":[92],"terms":[94],"fluctuations.":[97],"No":[98],"Monte":[99],"Carlo":[100],"sampling":[101],"is":[102],"required":[103],"obtain":[105],"either":[106],"or":[109],"its":[110],"statistics.":[111],"reduced":[113],"computational":[114],"effort":[115],"and":[116],"yet":[117],"consistent":[118],"results":[119],"stemming":[120],"from":[121],"are":[124],"demonstrated":[125],"comparison":[127],"Monte-Carlo-based":[130],"ieee":[134],"300-bus":[135],"test":[136],"system.":[137]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
