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A Topology-Guided Learning Framework for Security-Constraint Unit Commitment

https://doi.org/10.2139/ssrn.4718365
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Security constrained unit commitment of power systems by a new combinatorial solution strategy composed of enhanced harmony search algorithm and numerical optimization
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An improved dynamic programming algorithm for security-constrained unit commitment under spatial-temporal decomposition framework
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Integrated data‐driven framework for fast SCUC calculation
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Learning-Assisted Variables Reduction Method for Large-Scale MILP Unit Commitment
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Tight and Compact MILP Formulation of Start-Up and Shut-Down Ramping in Unit Commitment
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The IEEE Reliability Test System: A Proposed 2019 Update
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A Systematic Formulation Tightening Approach for Unit Commitment Problems
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A Unit Commitment Algorithm With Relaxation-Based Neighborhood Search and Improved Relaxation Inducement
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Combining Deep Learning and Optimization for Preventive Security-Constrained DC Optimal Power Flow
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Security-Constrained Unit Commitment for Electricity Market: Modeling, Solution Methods, and Future Challenges
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Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment
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Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations
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A Fast Solution Method for Large-Scale Unit Commitment Based on Lagrangian Relaxation and Dynamic Programming
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Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
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Deep Learning to Optimize: Security-Constrained Unit Commitment With Uncertain Wind Power Generation and BESSs
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Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems
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Variable and single neighbourhood diving for MIP feasibility
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A Review of Graph Neural Networks and Their Applications in Power Systems
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A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems
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Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
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CVXPY: A Python-Embedded Modeling Language for Convex Optimization
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