{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117802"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117802","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Risk-sensitive optimization for power systems","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Avinash Madavan, accepted the attached license on 2022-11-29 at 15:18.","The student, Avinash Madavan, submitted this Dissertation for approval on 2022-11-29 at 15:23.","This Dissertation was approved for publication on 2022-11-30 at 17:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18676 on 2023-04-12 at 07:35:59","In this thesis, we study methods of incorporating uncertainty, whether it be discrete component failures or continuous variability in renewable energy, explicitly in decision-making for power systems operations and planning. We model this uncertainty in a risk-sensitive fashion using the conditional value at risk measure, presenting formulations to capture these uncertainties, as well as market design around them. We then study algorithms to solve such problems, in a more general form, exploring decomposition-based approaches such as critical region exploration and Benders' decomposition to handle discrete uncertainty, and a stochastic approximation approach to handle continuous uncertainty."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Risk-sensitive optimization for power systems"]}]}],"canonical_facts":{"dc:contributor":["Bose, Subhonmesh","Basar, Tamer","Srikant, Rayadurgam","Dominguez-Garcia, Alejandro D","Tong, Lang"],"dc:creator":["Madavan, Avinash N."],"dc:date":["2022-12","2022-11-30"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Avinash Madavan, accepted the attached license on 2022-11-29 at 15:18.","The student, Avinash Madavan, submitted this Dissertation for approval on 2022-11-29 at 15:23.","This Dissertation was approved for publication on 2022-11-30 at 17:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18676 on 2023-04-12 at 07:35:59","In this thesis, we study methods of incorporating uncertainty, whether it be discrete component failures or continuous variability in renewable energy, explicitly in decision-making for power systems operations and planning. We model this uncertainty in a risk-sensitive fashion using the conditional value at risk measure, presenting formulations to capture these uncertainties, as well as market design around them. We then study algorithms to solve such problems, in a more general form, exploring decomposition-based approaches such as critical region exploration and Benders' decomposition to handle discrete uncertainty, and a stochastic approximation approach to handle continuous uncertainty."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117802"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Avinash Madavan"],"dc:subject":["Risk-sensitive Optimization","Conditional Value At Risk","Power Systems","Electricity Markets"],"dc:title":["Risk-sensitive optimization for power systems"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}