Ultra-short term probabilistic transmission congestion forecasting considering wind power integration

Wind power continues its rapid growth in the world. High wind penetration brings significant uncertainty to transmission congestion management. For system security consideration, some monitoring indexes of transmission congestion management should be presented to system operators. In this paper, the transmission congestion probability forecasting for power systems with high wind power integration is discussed, and a probabilistic forecasting method for ultra-short term transmission congestion is introduced. Probability distribution is applied to express the uncertainty brought by wind power integration in this paper. The proposed forecasting method applies the Boundary Load Flow and Newton power flow algorithm as the section power flow calculation framework, and obtains the probability distribution of section power flow through Monte Carlo Simulation in which antithetic variable sampling is used to reduce sampling frequency, and then the congestion probability of transmission sections is predicted. Simulation results of IEEE 39-bus system validate the efficiency of the proposed probabilistic forecasting method. The ultra-short term probabilistic forecasts of transmission congestion would provide monitoring index and information to system operators, which is helpful to relieve congestion scenario on the transmission sections.