A Fast Solution Method for Unit Commitment with Renewable Power via Ordinal Optimization

The high penetration of renewable power has led to an increasing demand for rapid solutions to large-scale security-constrained unit commitment (SCUC) problems. In this paper, we develop a fast solution method for SCUC problems with a significant number of renewable power scenarios based on Ordinal Optimization (OO). First, we propose a Crude Feasibility Model to efficiently generate estimated feasible solutions. The Crude Feasibility Model uses machine learning (ML) techniques to generate high-quality initial solutions, and then evaluates the feasibility of these solutions based on feasibility conditions with negligible computational effort. Second, we propose an OO-based method to rapidly seek good enough solutions while still providing probabilistic performance guarantees. The computational burden is reduced from the original large-scale mixed-integer programming (MIP) problem to a few linear programming (LP) problems that can be solved in parallel, significantly accelerating the solution process. Numerical experiments on the IEEE 118-bus system demonstrate that our method achieves the near-optimal solution with a performance gap of no more than 0.4%, while achieving a speedup of 8.39 to 27.99 times across different scales compared to Gurobi. Our method exhibits high computational efficiency and scales effectively to larger problems.