System Flexibility and AI Computational Enhancement for Day-Ahead Power System Operations

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dc.contributor.advisorLi, Xingpeng
dc.contributor.committeeMemberRajashekara, Kaushik
dc.contributor.committeeMemberKrishnamoorthy, Harish S.
dc.contributor.committeeMemberFan, Lei
dc.contributor.committeeMemberShi, Jian
dc.creatorRamesh, Arun Venkatesh
dc.creator.orcid0000-0001-9539-3991
dc.date.accessioned2023-06-02T18:42:21Z
dc.date.createdDecember 2022
dc.date.issued2022-12-14
dc.date.updated2023-06-02T18:42:22Z
dc.description.abstractSmart grids play a critical part in today’s world and is paramount to optimize the usage of energy and to address the increasing penetration of renewable energy sources (RES). The power system is a complicated network of electrical elements and requires efficient operation. The day-ahead operations use the security-constrained unit-commitment (SCUC) to provide a reliable, secure, and least-cost solution while clearing the market for forecasted demand. However, existing power system operations do not use available system flexibility in the form of transmission network or demand response exhaustively. Operators rely on experience to use these resources and disregard the economic benefit of such technologies. Hence, the importance of strategies such as corrective network reconfiguration (CNR) and corrective demand response (CDR) as an economic tool are initially explored. Network reconfiguration is considered for superior economic incentive. In addition, it enables integration of RES, efficient utilization of energy storage, and reducing carbon emission in high penetration systems. This is implemented by reducing curtailment of RES and relieving system congestion, while also addressing reducing carbon emissions. However, due to the complexity added to existing SCUC model, such solutions are not scalable to practical systems. To address computational efficiency to SCUC, considering substantial economic incentive tools like CNR, two novel remedies are identified in this thesis: (1) A purely optimization-based technique is shown by utilizing benders decomposition by breaking a large SCUC model into master problem and sub-problems. The proposed approach is iteratively solved by effectively screening non-critical sub-problems to handle the computational complexity. Simulation results points to scalability to large practical power system networks. (2) A novel approach by leveraging machine learning (ML) to learn patterns between system demand profile and generator commitment schedule using historical information is developed. The ML would assist with innovative post-processing methods and create a feasibility layer to improve predictions that would result in a reduced model for problem size reduction of SCUC. The proposed approach with selective utilizing of ML predictions can bring substantial computational benefits. This is achieved without loss in solution quality while being easily extendible to any decomposed, heuristic, or sped-up algorithms for SCUC.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Ramesh, Arun Venkatesh, and Xingpeng Li. "Security constrained unit commitment with corrective transmission switching." In 2019 North American Power Symposium (NAPS), pp. 1-6. IEEE, 2019; and in: Ramesh, Arun Venkatesh, and Xingpeng Li. "Enhancing system flexibility through corrective demand response in security-constrained unit commitment." In 2020 52nd North American Power Symposium (NAPS), pp. 1-6. IEEE, 2021; and in: Tuo, Mingjian, Arun Venkatesh Ramesh, and Xingpeng Li. "Benefits and Cyber-Vulnerability of Demand Response System in Real-Time Grid Operations." In 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1-6. IEEE, 2020; and in: Ramesh, Arun Venkatesh, and Xingpeng Li. "Reducing congestion-induced renewable curtailment with corrective network reconfiguration in day-ahead scheduling." In 2020 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5. IEEE, 2020; and in: Ramesh, Arun Venkatesh, and Xingpeng Li. "Network reconfiguration impact on renewable energy system and energy storage system in day-ahead scheduling." In 2021 IEEE Power & Energy Society General Meeting (PESGM), pp. 01-05. IEEE, 2021; and in: Ramesh, Arun Venkatesh, Xingpeng Li, and Kory W. Hedman. "An accelerated-decomposition approach for security-constrained unit commitment with corrective network reconfiguration." IEEE Transactions on Power Systems 37, no. 2 (2021): 887-900; and in: Ramesh, Arun Venkatesh, and Xingpeng Li. "Machine Learning Assisted Model Reduction for Security-Constrained Unit Commitment." In 2022 North American Power Symposium (NAPS), pp. 1-6. IEEE, 2022; and in: Ramesh, Arun Venkatesh, and Xingpeng Li. "Feasibility layer aided machine learning approach for day-ahead operations." IEEE Transactions on Power Systems (2023).
dc.identifier.urihttps://hdl.handle.net/10657/14411
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMixed Integer Programming
dc.subjectUnit Commitment
dc.subjectMachine learning
dc.subjectArtificial intelligence
dc.subjectSecurity Constrained
dc.subjectRenewable Energy System
dc.subjectEnergy storage
dc.subjectNetwork Reconfiguration
dc.subjectDemand Response
dc.subjectPower System
dc.subjectDay Ahead Operations
dc.subjectEnergy Markets
dc.subjectCongestion Management
dc.subjectSystem Flexibility
dc.subjectNeural networks
dc.subjectGraph Neural Networks
dc.titleSystem Flexibility and AI Computational Enhancement for Day-Ahead Power System Operations
dc.type.dcmiText
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2024-12-01
local.embargo.terms2024-12-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentElectrical and Computer Engineering, Department of
thesis.degree.disciplineElectrical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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