Chance-constrained programming approach to stochastic congestion management considering system uncertainties

Considering system uncertainties in developing power system algorithms such as congestion management (CM) are a vital issue in power system analysis and studies. This study proposes a new model for network CM based on chance-constrained programming (CCP), accounting for the power system uncertainties. In the proposed approach, transmission constraints are taken into account by stochastic rather than deterministic models. The proposed approach considers network uncertainties with a specific level of probability in the optimisation process. Then, single and joint chance-constrained models are implemented on the stochastic CM. Finally, an analytical approach is used to derive the new model of the stochastic CM. In both models, the stochastic optimisation problem is transformed into an equivalent easy-to-solve deterministic problem. 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 outperforms the existing models as the analytical solving approach applies fewer approximations and moreover, may have less complexity and computational burden in some special situations.