Binary variables in unit commitment (UC) problems invalidate gradient-based directional information, often causing computational bottlenecks. Existing binary algorithms ignore a tendency of these variables towards 0 or 1, which affects efficiency. To improve performance, this letter formalizes this tendency as preference and leverages it to guide the optimization process. A solution-set-based global optimization algorithm is introduced to handle to non-convexity arising from complex operational constraints. The results show that the guided algorithm has improved efficiency and robust global convergence ability.
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Zeng et al. (2025) studied this question.
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