Determining optimal location and capacity of DG units based on system uncertainties and optimal congestion management in transmission network

Congestion has always been defined as one of the primary problems in transmission grids and creating a competitive based power grid. Considering the effect of congestion on stability, thermal and other power system constraints, acquiring a suitable congestion management method is essential. In this paper, a new management scheme based on determining generation capacity and optimum placement of Distributed Generations (DGs), employing system's uncertainty modelling, has been proposed. By utilizing the Monte-Carlo technique, probabilistic load flow has been executed for determining lines' congestion state. Consequently, weight factors are calculated for the goal of finding optimum DG placement. Furthermore, the designated DG units' dispatched generation must be assigned. Genetic Algorithm (GA) has been chosen to carry out this responsibility, where the appropriate fitness function is designed based on the means of reducing loss and improving voltage profile. The DG units' influence on Accessible Transferable Capacity (ATC), general congestion probability, reliability and losses have also been studied. In addition, simulation results obtained from the proposed method have been compared with the classic approach, while typical system uncertainty are neglected. Rigorous simulations, performed on a partial test system of Khorasan power grid, consistent of 180 busbars, have shown the effectiveness of the proposed method in previously mentioned scenarios. The presented scheme illustrated to be a more practical solution, despite the relative higher computational burden.