Microgrid state estimation and control for smart grid and Internet of Things communication network

Abstract

Given the significant concerns regarding carbon emissions from fossil fuels, global warming and energy crises, the renewable distributed energy resources (DERs) are having to be integrated in the smart grid (SG). The SG can spread intelligence of the energy distribution and control system from the central unit to long-distance remote areas, thus enabling accurate state estimation (SE) and wide-area real-time monitoring of these intermittent energy sources. In contrast to the traditional methods of SE, a novel approach for SG SE is proposed based on concatenated coding structures, where the grid state is treated as a dynamic outer code and the recursive systematic convolutional code is seen as a concatenated inner code for protecting and redundancy in the system state. Furthermore, Kalman filter (KF)-based online DERs SE and a discrete-time linear quadratic regulation method are proposed to control these state deviations for the SG and the Internet of Things (IoTs) communication network, which can exploit the far-reaching connectivity and privacy of DERs messages. Simulation results show that the proposed algorithm achieves 3 dB performance improvement compared with the existing KF.