Stochastic optimization for AC optimal transmission switching with generalized Benders decomposition

Optimal transmission switching is proposed in recent years to optimize the power system operational cost in deterministic studies. With the rapid growth of renewable generations, the grid uncertainties have greatly increased, which cannot be ignored in the decision-making of optimal transmission switching problems. This paper proposes a novel two-stage stochastic optimization formulation with a convex relaxation for AC optimal transmission switching problems. A generalized Benders decomposition based algorithm, including an inner loop and an outer loop, is proposed to solve the AC optimal transmission switching problem with grid uncertainties. The optimal switching plan and the expected system cost will be found in the iterative calculation without any sacrifice of accuracy. Numerical studies on the IEEE 118-bus system and the South Carolina 500-bus confirm the effectiveness of the proposed decomposition approach in solving the AC optimal transmission switching problems with grid uncertainties. The scalability analysis shows the proposed approach is efficient in dealing with a large number of scenarios.

Automated summary

This study proposes a novel stochastic optimization method to solve AC optimal transmission switching problems with grid uncertainties. Through a generalized Benders decomposition algorithm, the optimal switching plan and system cost can be found iteratively while maintaining accuracy. The scalability analysis confirms the effectiveness of the proposed approach in dealing with a large number of scenarios.