Optimality-Guaranteed Acceleration of Unit Commitment Calculation via Few-Shot Solution Prediction

Recently, data-driven approaches are widely used to predict and fix the values of integer variables in unit commitment (UC) problems to reduce the computational burden. However, learning the complex pattern between the UC model characteristics and integer solutions requires a huge number of samples, which is an obstacle in the practical application. Meanwhile, the prediction error is hard to control. Facing these challenges, this paper proposes a hybrid offline-online approach to predict the UC solution using few-shot samples (typically, no more than 5). To avoid the reliance on the sample scale, the prediction task is strategically decomposed into offline and online tasks. In the offline process, the internal solution information of the branch-and-bound process is collected to determine the candidate integer variables that can be predicted using online information. In the online process, an instance-specific root relaxation method is used to determine the values to which the candidate integer variables should be fixed. A parameter tuning method of the hybrid offline-online framework is presented to improve the performance. Based on the prediction result, an accompanying model is constructed and solved in parallel to provide a better estimation of upper bound and accelerate the branch-and-bound process without compromising any optimality. Test cases on public and utility test systems show that the proposed method can achieve up to 14.69 times acceleration under a variety of conditions with guaranteeing the optimality.