Real-time state estimation on micro-grids

This paper presents a new probabilistic approach of the real-time state estimation on the micro-grid. The grid is modeled as a factor graph which can characterize the linear correlations among the state variables. The factor functions are defined for both the circuit elements and the renewable energy generation. With the stochastic model, the linear state estimator conducts the Belief Propagation algorithm on the factor graph utilizing real-time measurements from the smart metering devices. The result of the statistical inference presents the optimal estimates of the system state. The new algorithm can work with sparse measurements by delivering the optimal statistical estimates rather than the solutions. In addition, the proposed graphical model can integrate new models for solar/wind correlation that will help with the integration study of renewable energy. Our state-of-art approach provides a robust foundation for the smart grid design and renewable integration applications.