{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1845"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1845","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Distributed online algorithms for energy management in smart grids","abstract":"The 21st-century electric power grid is transitioning from a centralized structure designed for bulk-power transfer to a distributed paradigm that integrates the variable renewable energy (VRE) resources spatially distributed across the grid. This work proposes algorithmic solutions for distributed economic dispatch based on Subgradient method and Alternating Direction Method of Multipliers (ADMM), both designed to be agnostic with any initialization vector. The proposed distributed online solutions leverage a dynamic average consensus algorithm to track the time-variant linearly coupled constraint that allows an abrupt change in power demand of the network because of the high penetration of VRE resources. The problems are modeled as discrete dynamic systems to investigate the stability and convergence of the algorithm. The update procedures are designed such that the iterates converge to the optimal solution of the original optimization problem, steered by the gain parameter corresponding to the second largest eigenvalue of the system matrix.","abstract_html":"The 21st-century electric power grid is transitioning from a centralized structure designed for bulk-power transfer to a distributed paradigm that integrates the variable renewable energy (VRE) resources spatially distributed across the grid. This work proposes algorithmic solutions for distributed economic dispatch based on Subgradient method and Alternating Direction Method of Multipliers (ADMM), both designed to be agnostic with any initialization vector. The proposed distributed online solutions leverage a dynamic average consensus algorithm to track the time-variant linearly coupled constraint that allows an abrupt change in power demand of the network because of the high penetration of VRE resources. The problems are modeled as discrete dynamic systems to investigate the stability and convergence of the algorithm. The update procedures are designed such that the iterates converge to the optimal solution of the original optimization problem, steered by the gain parameter corresponding to the second largest eigenvalue of the system matrix.","abstract_has_math":false,"creators":["Wasti, Shailesh"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Disfani, Vahid R.","Karrar, Abdelrahman A.; Ofoli, Abdul R.; Ahmed, Raga","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-12-31T08:00:00Z","date_published":"2021-12-31T08:00:00Z","updated_at":"2026-07-24T05:46:59Z","subjects":["Distributed algorithms","Modal analysis","Smart power grids","Subgradient methods"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/684","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Disfani, Vahid R.","Karrar, Abdelrahman A.; Ofoli, Abdul R.; Ahmed, Raga","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Wasti, Shailesh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-12-01T08:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-12-31T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Distributed algorithms","Modal analysis","Smart power grids","Subgradient methods"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/684"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["The 21st-century electric power grid is transitioning from a centralized structure designed for bulk-power transfer to a distributed paradigm that integrates the variable renewable energy (VRE) resources spatially distributed across the grid. This work proposes algorithmic solutions for distributed economic dispatch based on Subgradient method and Alternating Direction Method of Multipliers (ADMM), both designed to be agnostic with any initialization vector. The proposed distributed online solutions leverage a dynamic average consensus algorithm to track the time-variant linearly coupled constraint that allows an abrupt change in power demand of the network because of the high penetration of VRE resources. The problems are modeled as discrete dynamic systems to investigate the stability and convergence of the algorithm. The update procedures are designed such that the iterates converge to the optimal solution of the original optimization problem, steered by the gain parameter corresponding to the second largest eigenvalue of the system matrix."]},{"key":"dc:title","label":"Title","values":["Distributed online algorithms for energy management in smart grids"]}]}],"canonical_facts":{"dc:contributor":["Disfani, Vahid R.","Karrar, Abdelrahman A.; Ofoli, Abdul R.; Ahmed, Raga","College of Engineering and Computer Science"],"dc:creator":["Wasti, Shailesh"],"dc:date":["2020-12-01T08:00:00Z"],"dc:date.available":["2021-12-31T08:00:00Z"],"dc:description":["Dept. of Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["The 21st-century electric power grid is transitioning from a centralized structure designed for bulk-power transfer to a distributed paradigm that integrates the variable renewable energy (VRE) resources spatially distributed across the grid. This work proposes algorithmic solutions for distributed economic dispatch based on Subgradient method and Alternating Direction Method of Multipliers (ADMM), both designed to be agnostic with any initialization vector. The proposed distributed online solutions leverage a dynamic average consensus algorithm to track the time-variant linearly coupled constraint that allows an abrupt change in power demand of the network because of the high penetration of VRE resources. The problems are modeled as discrete dynamic systems to investigate the stability and convergence of the algorithm. The update procedures are designed such that the iterates converge to the optimal solution of the original optimization problem, steered by the gain parameter corresponding to the second largest eigenvalue of the system matrix."],"dc:identifier":["https://scholar.utc.edu/theses/684"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Distributed algorithms","Modal analysis","Smart power grids","Subgradient methods"],"dc:title":["Distributed online algorithms for energy management in smart grids"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:46:59Z"}