A Short-Term Nodal Voltage Phasor Forecasting Method Using Temporal and Spatial Correlation

This paper presents a new method for short-term nodal voltage phasor forecasting in power systems to which a large number of renewable resources and microgrids are connected. Its motivation stems from the observation that such systems are characterized by an unconventional topology along with the presence of a number of buses with bursty power injection patterns, which in turn significantly impacts the temporal and spatial correlation of the nodal voltage phasors. Simulation results carried out on three IEEE test systems reveal that this new pattern affects the number of dominant buses, termed electrical hubs, and, subsequently, the time and spatial correlation among nodal voltage angles. They also reveal that the nodal voltage magnitudes exhibit time correlation, but no spatial correlation. In this paper, time series are represented by autoregressive (AR) processes while multivariate time series with spatial correlation are represented by vector autoregressive (VAR) processes. Statistical tests show that an order one for both models is adequate. The non-zero parameters of the VAR(1) models are identified using metrics on electrical connectivity, centrality, and node significance. The good performance of the proposed method is demonstrated on the IEEE 57-, 118-, and 300-bus test systems in presence of large-scale distributed wind farms and microgrids, and under loss of system observability.