Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Considering system uncertainties in developing power systems, algorithms such as congestion management (CM) are vital in power system analysis and studies. This paper proposes a new model for power system CM by considering power system uncertainties based on chance-constrained programming (CCP). In the proposed approach, transmission constraints are taken into account by stochastic, instead of deterministic, models. The proposed approach considers network uncertainties with a specific level of probability in the optimization process, and then an analytical approach is used to solve the new model of stochastic congestion management. In this approach, the stochastic optimization problem is transformed into an equivalent deterministic problem. Moreover, an efficient numerical approach based on a real-coded genetic algorithm and Monte Carlo technique is proposed to solve the CCP-based congestion management problem in order to make a comparison to the analytical approach. The effectiveness of the proposed approach is evaluated by applying the method to the IEEE 30-bus test system. The results show that the proposed CCP model and the analytical solving approach outperform the existing models.