Bi-level Stochastic Optimization Model for Coordination of Transmission and Distribution Networks Considering Generic Resources

In this paper, an optimization model for transmission and distribution network integration is proposed. The proposed model is a bi-level optimization model formulated as an upper and lower-level problem. The problem of unit commitment in the transmission network as the upper level problem with the aim of reducing operating costs and load shedding as a mixed integer linear programming model and the problem of optimal operation in the distribution network by considering renewable and non-renewable resources along with electric vehicle charging stations (EVCS), it has been considered as a linear model with the aim of reducing the cost of purchasing power from the transmission network and increasing the use of renewable energy and charging power of EVCS. According to the literature, the study gap can be seen in three key points; firstly, it is of great importance to provide a convex model that can guarantee global optimal solutions. The next point is related to the problem-solving time, according to the dimensions and number of variables and parameters in the problem of optimal coordination of the transmission and distribution network, providing a method to achieve global optimal solutions in the shortest time and the last point is related to the modeling of any number of distribution networks is integrated with the transmission network. To solve the proposed bi-level optimization problem, the method of rewriting the optimization problems using Karush–Kuhn–Tucker condition equations, which can model global optimal solutions as well as fast solution time and modeling a large number of distribution networks in the transmission network has been proposed. Several different networks have been considered for the validation of the proposed model and method, which proves the results obtained from the simulation of the efficiency of the proposed model and method in considering the coordinated operation of transmission and smart distribution networks. The results showed that the proposed method is about 40% faster than the decomposition method and 20% faster than the evolutionary method, and the results obtained are about 7% more optimal than the evolutionary method.