{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/45328"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/45328","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Reinforcement learning techniques for controlling resources in power networks","abstract":"Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-07-05T19:19:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Kowli_AnupamaSunil.pdf: 19755114 bytes, checksum: 5440ed4a7dde9654960c339f165fff66 (MD5)","abstract_html":"Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-07-05T19:19:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Kowli_AnupamaSunil.pdf: 19755114 bytes, checksum: 5440ed4a7dde9654960c339f165fff66 (MD5)","abstract_has_math":false,"creators":["Kowli, Anupama"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Meyn, Sean P.","Sauer, Peter W.","Domínguez-García, Alejandro D.","Sanders, William H."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08-22T16:36:46Z","date_published":"2013-08-22T16:36:46Z","updated_at":"2026-07-22T22:25:34Z","subjects":["smart grid","ancillary services","approximate dynamic programming","demand response","economic dispatch","energy storage","Markov decision theory","model predictive control","power networks","reinforcement learning","renewable integration","stochastic control","unit commitment","wind power"],"languages":["en"],"rights":["Copyright 2013 Anupama Sunil Kowli"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/45328","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Meyn, Sean P.","Sauer, Peter W.","Domínguez-García, Alejandro D.","Sanders, William H."]},{"key":"dc:creator","label":"Author","values":["Kowli, Anupama"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-08-22T16:36:46Z","2013-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["smart grid","ancillary services","approximate dynamic programming","demand response","economic dispatch","energy storage","Markov decision theory","model predictive control","power networks","reinforcement learning","renewable integration","stochastic control","unit commitment","wind power"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Anupama Sunil Kowli"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/45328"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-07-05T19:19:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Kowli_AnupamaSunil.pdf: 19755114 bytes, checksum: 5440ed4a7dde9654960c339f165fff66 (MD5)","Made available in DSpace on 2013-08-22T16:36:46Z (GMT). No. of bitstreams: 2 Anupama_Kowli.pdf: 19755114 bytes, checksum: 5440ed4a7dde9654960c339f165fff66 (MD5) license.txt: 4062 bytes, checksum: a47e4687a9b9683aff59dca52f17b4d4 (MD5)","\"As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be \"\"learned\"\" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.\""]},{"key":"dc:title","label":"Title","values":["Reinforcement learning techniques for controlling resources in power networks"]}]}],"canonical_facts":{"dc:contributor":["Meyn, Sean P.","Sauer, Peter W.","Domínguez-García, Alejandro D.","Sanders, William H."],"dc:creator":["Kowli, Anupama"],"dc:date":["2013-08-22T16:36:46Z","2013-08"],"dc:description":["Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-07-05T19:19:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Kowli_AnupamaSunil.pdf: 19755114 bytes, checksum: 5440ed4a7dde9654960c339f165fff66 (MD5)","Made available in DSpace on 2013-08-22T16:36:46Z (GMT). No. of bitstreams: 2 Anupama_Kowli.pdf: 19755114 bytes, checksum: 5440ed4a7dde9654960c339f165fff66 (MD5) license.txt: 4062 bytes, checksum: a47e4687a9b9683aff59dca52f17b4d4 (MD5)","\"As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be \"\"learned\"\" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.\""],"dc:identifier":["http://hdl.handle.net/2142/45328"],"dc:language":["en"],"dc:rights":["Copyright 2013 Anupama Sunil Kowli"],"dc:subject":["smart grid","ancillary services","approximate dynamic programming","demand response","economic dispatch","energy storage","Markov decision theory","model predictive control","power networks","reinforcement learning","renewable integration","stochastic control","unit commitment","wind power"],"dc:title":["Reinforcement learning techniques for controlling resources in power networks"],"dc:type":["text"],"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":"2026-07-22T22:25:34Z"}