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The transmission-constrained unit commitment (TC-UC) problem is one of themost relevant problems solved by independent system operators for the dailyoperation of power systems. Given its computational complexity, this problem isusually not solved to global optimality for real-size power systems. In thispaper, we propose a data-driven method that leverages historical information toscreen out network constraints in the TC-UC problem. First, past data on demandand renewable generation throughout the network are used to learn thecongestion status of transmission lines. Then, we infer the lines that will notbecome congested for upcoming operating conditions based on such learning anddisregard their capacity constraints. This way, we formulate a reduced TC-UCproblem that is easier to solve. Numerical results on a medium- and alarge-size power system show that the proposed approach outperforms existingones by significantly reducing the computational time while obtaining solutionsthat are equal or close to the one obtained with the original TC-UC problem.Furthermore, the purely data-driven method we propose can be seamlesslycomplemented with a constraint generation procedure to guarantee that theoptimal solution to the original TC-UC problem is eventually recovered.

Data-Driven Screening of Network Constraints for Unit Commitment