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.
Zeng, C., Zhu, J., Borghetti, A., Chen, Y. (2026). A Preference-Driven UC Optimization Paradigm. IEEE TRANSACTIONS ON POWER SYSTEMS, 41(1), 781-784 [10.1109/TPWRS.2025.3631305].
A Preference-Driven UC Optimization Paradigm
Borghetti A.;
2026
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
