{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/111921"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/111921","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Machine Learning based Methods to Improve Power System Operation under High Renewable Pennetration","abstract":"In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. In the United States, the Biden ad- ministration has called for 100% clean energy by 2035. It is estimated that in order to meet that target, the energy production from solar and wind should increase to 50-70% from the current 11% share. Under higher penetration of solar and wind, the intermittency of the energy source poses critical problems in forecasting, uncertainty quantification, reserve man- agement, unit commitment, and economic dispatch, and presents unique challenges to the distribution system, including predicting solar adoption by the user as well as forecasting end-use load profiles. While these problems are complex, advances in machine learning and artificial intelligence provide opportunities for novel paradigms for addressing the challenges. The overall aim of the dissertation is to harness data-driven and model-based techniques and develop computationally efficient tools for improved power systems operation under high re- newables penetration in the next-generation electric grid. Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin.","abstract_html":"In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. In the United States, the Biden ad- ministration has called for 100% clean energy by 2035. It is estimated that in order to meet that target, the energy production from solar and wind should increase to 50-70% from the current 11% share. Under higher penetration of solar and wind, the intermittency of the energy source poses critical problems in forecasting, uncertainty quantification, reserve man- agement, unit commitment, and economic dispatch, and presents unique challenges to the distribution system, including predicting solar adoption by the user as well as forecasting end-use load profiles. While these problems are complex, advances in machine learning and artificial intelligence provide opportunities for novel paradigms for addressing the challenges. The overall aim of the dissertation is to harness data-driven and model-based techniques and develop computationally efficient tools for improved power systems operation under high re- newables penetration in the next-generation electric grid. Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin.","abstract_has_math":false,"creators":["Bhavsar, Sujal Pradipkumar"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Pitchumani, Ranga"],"committee_members":["Acar, Pinar","Rahman, Saifur","Tafti, Danesh K."],"year":2022,"date_issued":"2022-09-19","date_published":"2022-09-19","updated_at":"2026-07-22T22:18:53Z","subjects":["Applied machine learning","Stochastic optimization","Renewable integration"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:35611"],"render_values":[{"text":"vt_gsexam:35611","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/111921","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Pitchumani, Ranga"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Acar, Pinar","Rahman, Saifur","Tafti, Danesh K."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Bhavsar, Sujal Pradipkumar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-09-20T08:00:20Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-09-20T08:00:20Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-09-19"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Applied machine learning","Stochastic optimization","Renewable integration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:35611"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/111921"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. 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The overall aim of the dissertation is to harness data-driven and model-based techniques and develop computationally efficient tools for improved power systems operation under high re- newables penetration in the next-generation electric grid. Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. In the United States, the Biden ad- ministration has called for 100% clean energy by 2035. 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Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Machine Learning based Methods to Improve Power System Operation under High Renewable Pennetration"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Pitchumani, Ranga"],"dc:contributor.committeemember":["Acar, Pinar","Rahman, Saifur","Tafti, Danesh K."],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Bhavsar, Sujal Pradipkumar"],"dc:date.accessioned":["2022-09-20T08:00:20Z"],"dc:date.available":["2022-09-20T08:00:20Z"],"dc:date.issued":["2022-09-19"],"dc:description.abstract":["In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. 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The overall aim of the dissertation is to harness data-driven and model-based techniques and develop computationally efficient tools for improved power systems operation under high re- newables penetration in the next-generation electric grid. Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin."],"dc:description.abstractgeneral":["In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. In the United States, the Biden ad- ministration has called for 100% clean energy by 2035. 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Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:35611"],"dc:identifier.uri":["http://hdl.handle.net/10919/111921"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Applied machine learning","Stochastic optimization","Renewable integration"],"dc:title":["Machine Learning based Methods to Improve Power System Operation under High Renewable Pennetration"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:53Z"}