A New Formulation for the Probabilistic Congestion Management Using Chance Constrained Programming

In this paper, a new method for probabilistic congestion management considering power system uncertainties is proposed. Chance constrained programming (CCP) is used to formulate the probabilistic congestion management as an efficient approach for stochastic optimization problems. The CCP based probabilistic congestion management is solved utilizing a numerical approach by applying the Monte-Carlo technique into the real-coded genetic algorithm. The effectiveness of the proposed method is evaluated applying the method to the modified IEEE 9-bus test system. The results of the proposed approach are compared with those of the expected method to have a comprehensive study. The simulation results reflect the flexibility of the proposed approach in transmission congestion management.