Optimizing a Distributed Wind-Storage System Under Critical Uncertainties Using Benders Decomposition

A method for calculating the optimal size of an energy storage system (ESS) under wind uncertainties is presented based on Benders decomposition for stochastic programming in this paper. The ESSs are becoming essential components in microgrids due to significantly higher penetration of renewable energy sources. Integrating renewable energy sources coupled with ESSs in a power system enhances the power system reliability by increasing its availability and reducing its total cost of operation and maintenance. In addition, the ESS connected to a microgrid should be optimally sized to be able to provide the necessary power and minimize the total cost of investment and operation. In order to optimally size a storage system, a constrained optimization problem is solved using a probabilistic optimization method because the forecast of their output power cannot be determined accurately. In this paper, a probabilistic optimization problem is solved using the Benders decomposition for stochastic programming method to optimally size an ESS. This ESS will be integrated and connected to a grid-connected microgrid that has wind power generation. The simulation results prove the effectiveness of the proposed optimal sizing methodology.