Development of probabilistic load flow considering transmission line congestion in the presence of distributed generation

Transmission lines are generally loaded close to their rated capacity in order to meet the increased electric power demand and trades due to augmentation of the uncertain power flow. If the exchanges are not controlled, some lines located on particular paths may exceed their rated capacity. This phenomenon is known as congestion. Integration of renewable distributed generation (DG) units into power systems is a widely used technique in relieving congestion of lines, reducing line losses, improving voltage and overall improving system reliability. However, the use of DG to relieve congestion depends on identifying the optimal location to install them. This thesis will present a new probabilistic forecasting method of line congestion that can be utilized to determine the candidate lines (most congested) to install DG with the objective function of relieving line congestion. The optimal allocation and sizing of Renewable DG units in power networks is a vital factor for the efficient operation of a reliable and secure power network. Hence, in this thesis, the optimal location and size of renewable DG units to relieve line congestion will be implemented. A probabilistic load flow considering load uncertainty will be conducted to demonstrate the effectiveness of DG integration on system performance