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

Considering system uncertainties in developing power system algorithms such as congestion management (CM) is a vital issue in power system analysis and studies. This paper proposes a new model for the power system congestion management, considering power system uncertainties based on the chance constrained programming (CCP). In the proposed approach, transmission constraints are taken into account by stochastic models instead of deterministic models. The proposed approach considers network uncertainties with a specific level of probability in the optimization process. Then, an analytical approach is used to solve the new model of the 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. 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.

Keywords:

Congestion Management (CM) , System Uncertainties , Chance constrained programming (CCP) , Monte Carlo Simulation , Stochastic optimization