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



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Smart 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.



Mixed Integer Programming, Unit Commitment, Machine learning, Artificial intelligence, Security Constrained, Renewable Energy System, Energy storage, Network Reconfiguration, Demand Response, Power System, Day Ahead Operations, Energy Markets, Congestion Management, System Flexibility, Neural networks, Graph Neural Networks


Portions 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).