Optimal Bi-level Planning of Autonomous MGs

In recent years some researchers have focused on dividing of large distribution grids to autonomous Microgrids (MGs). The benefits of MGs consist of their ability to increase the reliability of distribution networks and reduce the power losses. The distribution resources within the MGs can balance the gap between limited generation capacity and actively growing demands. In this paper, we have proposed new dynamic boundaries for MGs to gain the flexibility in the grid. The proposed method is based on finding the optimal state of switches, sizing and siting of distributed energy resources (DES) in an MG-based distribution network. A bi-level optimization approach is used to solving the proposed problem. In the upper level of the optimization problem, the sizing and siting of DER is implemented and the system is updated to optimal switching in lower level. The stochastic model of wind, solar and load demand is represented. The 94 buses distribution network is modified to the MG-based distribution network for testing and validating the proposed model. The Particle swarm optimization (PSO) is applied to minimize the objective function of upper level and Genetic algorithm (GA) is used for minimization of lower level. According to the results, optimal planning of the autonomous MGs can improve the performance of distribution network operation.