Probabilistic Congestion Management Considering Power System Uncertainties Using Chance-constrained Programming

In this article, a new model for stochastic congestion management considering system uncertainties has been developed. The model utilizes chance-constrained programming to propose the stochastic formulation for the congestion management problem. In this approach, transmission constraints are considered with stochastic models instead of deterministic models. Indeed, this approach considers network uncertainties with a specific level of probability in the optimization process. Moreover, an efficient numerical approach based on the real-coded genetic algorithm and Monte Carlo technique has been proposed to solve the chance-constrained programming based congestion management scheme. Effectiveness of the proposed algorithm has been evaluated by applying the method to the IEEE 30-bus test system.