Micro-grid State Estimation Using Belief Propagation on Factor Graphs

Smart grid envisions the potential to manage diverse energy resources and enable a future self-dispatch and selfhealing grid. This would first require the micro-grid visibility of node behavior (i.e. electrical parameters). In this paper we propose a novel approach to construct a stochastic model that makes global inference on every node at the micro-grid level. The micro-grid system can be modeled as a factor graph addressing proper correlation functions including distributed renewable generation correlation. We conduct statistical inference on the factor graph using Belief Propagation (BP) algorithm. The purpose is that given incomplete measurements, marginal probability distribution for unmetered node behavior can be derived. Simulation of the BP algorithm is performed on a simplified micro-grid model with linear local correlations. The results demonstrate that loopy BP can converge to optimal state estimates efficiently.