Modelling the Microgrid and Its Parameter Estimations Considering Fading Channels

As the electricity demand continues to grow and renewable energy resources are incorporated into the grid, an effective dynamic state estimation is required for monitoring and grid synchronization. This paper proposes a microgrid state estimation approach considering the unreliable communication channels. Particularly, the renewable microgrid incorporating distributed energy resources, such as solar panels, are represented as a state-space linear model. Then, the wireless sensor network is adopted to sense the microgrid states. For long-distance transmission, we propose an innovative smart grid infrastructure, which is easy to design and offers a reliable two-way communication. In this infrastructure, the modulated signal is transmitted over an unreliable channel, which causes signal distortions. After demodulation and dequantization, the received signal at the energy management system is used the fading Kalman filter algorithm. Using the tunable forgetting factor in the predicted error covariance step, this algorithm aims to minimize the estimation errors so the estimated states reflect the true system states. Simulations results indicate that the developed approach estimates the system states within 0.25 seconds.