A probabilistic power flow algorithm based on semi-variable and series expansion

Abstract

Traditional power flow algorithm such as the trend for front-and-spoke networks and backward substitution method and a cyclic structure NR method cannot fully reflect the uncertainties affecting the system, and a lot of new energy sources access to change the original properties of the grid, so that a single flow algorithm has significant limitations, the study calculated adapt to the trend of large-scale new energy future grid connection characteristics is important. A method of probabilistic power flow algorithm based on semi-variable and Gram-Charlier series expansion was proposed in this paper. Wind power output, load fluctuation, fault lines and forced outages of generator and other uncertainties were considered. Based on node voltage and branch power expectations and sensitivity matrix, each order semi-variable of load, conventional generators, wind turbine output and node injection power were calculated. Probability density function and probability distribution function were obtained through Gram-Charlier series expansion. IEEE-30 node test shows that: The algorithm can reflect the uncertainty of large-scale new energy accessing, simplified convolution of calculating probability density function of random variables for algebra of semi-invariant. Greatly reduce the computation time and it has a good convergence.