State forecasting of power systems with intermittent renewable sources using Viterbi Algorithm

This paper presents a new stochastic method for state forecasting in electrical power systems with high intermittent renewable energy penetration. The method utilizes Markov Models (MM) and the Viterbi Algorithm (VA) with a grid of power system states. Only feasible states of the MM are used to model the transition matrix, which significantly reduces the amount of data needed. We simulated a 4-bus and the IEEE 14-bus system using wind and load data available from the Bonneville Power Administration (BPA). The results show good correlation between the predictions and the actual data.