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dc.contributor.advisorMunther A. Dahleh and Mardavij Roozbehani.en_US
dc.contributor.authorSchneider, Ian Michael.en_US
dc.contributor.otherMassachusetts Institute of Technology. Institute for Data, Systems, and Society.en_US
dc.date.accessioned2020-11-24T17:32:28Z
dc.date.available2020-11-24T17:32:28Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/128639
dc.descriptionThesis: Ph. D. in Social and Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, February, 2020en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 117-126).en_US
dc.description.abstractThe rapid growth of renewable energy is transforming the electric power sector. Wind and solar energy are non-dispatchable: their energy output is uncertain and variable from hour-to- hour. New challenges arise in electricity markets with a large share of uncertain and variable renewable energy. We investigate some of these challenges and identify economic opportunities and policy changes to mitigate them. We study electricity markets by focusing on the preferences and strategic behavior of three different groups: producers, consumers, and load-serving entities. First, we develop a game-theoretic model to investigate energy producer strategy in electricity markets with high levels of uncertain renewable energy. We show that increased geographic dispersion of renewable generators can reduce market power and increase social welfare. We also demonstrate that high-quality public forecasting of energy production can increase welfare. Second, we model and explain the effects of retail electricity competition on producer market power and forward contracting. We show that increased retail competition could decrease forward contracting and increase electricity prices; this is a downside to the general trend of increased access to retail electricity competition. Finally, we propose new methods for improving demand response programs. A demand response program operator commonly sets customer baseline thresholds to determine compensation for individual customers. The optimal way to do this remains an open question. We create a new model that casts the demand response program as a sequential decision problem; this formulation highlights the importance of learning about individual customers over time. We develop associated algorithms using tools from online learning, and we show that they outperform the current state of practice.en_US
dc.description.statementofresponsibilityby Ian Michael Schneider.en_US
dc.format.extent126 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.titleMarket design opportunities for an evolving power systemen_US
dc.typeThesisen_US
dc.description.degreePh. D. in Social and Engineering Systemsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.identifier.oclc1223507045en_US
dc.description.collectionPh.D.inSocialandEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Societyen_US
dspace.imported2020-11-24T17:32:27Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentESDen_US
mit.thesis.departmentIDSSen_US


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