A Scheme for Resource Sharing in Distributed DC Microgrids with Minimal System Losses

Abstract

Neighborhood level power-sharing is a key feature in DC microgrids (DCMGs) for resource balancing among distributed users having intermittent distributed generators (DGs) and varying load consumption. For resource sharing in DCMGs, power losses predominantly consist of distribution losses and power electronic conversion losses. Depending on the power scheduling matrix, under varying load consumption and DGs output, both of these losses have varying contributions to the system’s overall losses. For optimal energy sharing in a distributed DCMG, the power scheduling decisions need to be made considering the system’s overall power losses. The traditional optimal power flow algorithms do not account for the power electronic losses, therefore, they fail to guarantee the overall system loss minimization. In this work, we first presented a detailed analysis of semiconductor losses. Subsequently, the main components of converter losses which have influence on converter efficiency with varying output power are discussed. Further, we proposed a non-linear optimization framework that allows the users to share their resources (surplus generation or loads) in a DCMG network while keeping the overall system’s losses to a minimum. Distribution losses are calculated using a Newton-Raphson method and power electronic conversion losses are modeled as a non-linear function of converter output power against its nominal power rating. Both of these losses are collectively used in optimization framework to minimized the system’s losses. The proposed model is formulated in the standard form of optimization using OptimProblem available in Matlab 2020 and applied to a DCMG having multiple DGs, energy storage systems (ESS) and load consumption units. Results show that the total system losses can be significantly reduced up to 30-40% with the proposed optimization framework in comparison to the traditional standalone distribution losses based optimization framework.