Cutting plane methods for Lagrangian relaxation‐based unit commitment algorithm

Abstract

SUMMARYIn this paper, we study cutting plane methods for a Lagrangian relaxation-based unit commitment algorithm. In the algorithm, nondifferentiable optimization methods can be applied to optimize the dual function, and a subgradient method which needs parameter tuning and has some drawbacks such as computational inefficiency and oscillating behavior is commonly used. The cutting plane method and the central cutting plane method are applied to the algorithm and implemented using reoptimization techniques. A numerical example shows that both methods are accelerated by the reoptimization techniques and have good convergence without parameter tuning.