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

In this paper, a new model for stochastic congestion management considering system uncertainties has been developed. The model utilizes chance constrained programming(CCP) to propose the stochastic formulation for the congestion management problem. In this approach, transmissionconstraints are considered with stochastic models instead of deterministic models. Indeed, this approach considers network uncertainties with a specific level of probability in optimization process. Moreover, an efficient numerical approach based on the real-coded genetic algorithm and Monte-Carlo techniquehas been proposed to solve the CCP based congestion management scheme. Effectiveness of the proposed algorithmhas been evaluated by applying the method to the IEEE 30-bus test system. The results show that the flexibility of the stochasticcongestion management approach rises due to utilizing chance constrained programming

کلیدواژه ها:

Chance Constrained Programming (CCP) ، Congestion Management (CM) ، Monte-Carlo Simulation ، Real-Coded Genetic Algorithm ، System Uncertainties