ClusRed: Clustering and network reduction based probabilistic optimal power flow analysis for large-scale smart grids

The smart electric grid in the United States is one of the largest and most complex cyber-physical systems (CPS) in the world and contains considerable uncertainties. Probabilistic optimal power flow (OPF) analysis is required to accomplish the electrical and economic operational goals. In this paper, we propose a novel algorithm to accelerate the computation of probabilistic OPF for large-scale smart grids through network reduction (NR). Cumulant-based method and Gram-Charlier expansion theory are used to efficiently obtain the statistics of system states. We develop a more accurate linear mapping method to compute the unknown cumulants. Our method speeds up the computation by up to 4.57X and can improve around 30% accuracy when Hessian matrix is ill-conditioned compared to the previous approach.