Accelerated SCUC Solution: Integrating Cutting Planes, Interior Points, and Rounding Heuristics

In this research contribution, we present a novel approach aimed at accelerating online mixed-integer optimization by leveraging heuristic ROUNDING techniques and interior point methods. The cornerstone of our method lies in the construction of a Cardinality cutting plane. This Cardinality cutting plane method effectively harnesses the solution of linear programming (LP) without incorporating the CROSSOVER procedure within the interior point method. It ingeniously forms a feasible area for the segmentation of cutting planes while meticulously configuring the relevant parameters to ensure their viability. Notably, our method seamlessly integrates into any Mixed-Integer Programming (MIP) solver. Our empirical findings decisively demonstrate the superior performance of the proposed algorithm compared to conventional direct solution methods, particularly in terms of its agility in rapidly exploring feasible regions. Additionally, our algorithm exhibits a remarkable acceleration in improving the lower bound. This enhancement significantly enhances the efficiency of solving optimization problems, offering a promising avenue for effectively addressing the complexities of energy system scheduling.