Comprehensive Optimal Management System of Distributed Resources Using Dynamic Neural Network in Modeling of Electricity Consumption Uncertainty for Grid-Connected Microgrids

In this paper, to enhance the optimal planning for power management of micrigrids, a strategy is proposed using power sharing through coordination between microgrids and the neighborhood system, which has no additional costs for generating units. The uncertainty values of electrical consumers are modeled by dynamic neural network, considering the implementation process and high accuracy of forecasting. In another view, to supply the electrical energy of microgrid, diesel generator, renewable energies such as solar energy and wind energy and so, battery energy storage are used, in addition to the upstream grid connection. As well as, using of the reliability factors, along with a detailed assessment of current costs will improve the performance of microgrid. Hence, the loss of power supply probability (LPSP) and loss of load expectations (LOLE) are expressed as factors for assessing the accuracy of current costs. The proposed model is implemented in GAMS and MATLAB environment and the simulation results clearly demonstrate the desired performance of the proposed algorithm, and leads to gaining revenue for the under-study system.