Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation

The time-varying demand and stochastic power generation from renewable distributed generating resources necessitate an exhaustive assessment of distribution feeder parameters for the purpose of long-term planning. This paper proposes a novel formulation of probabilistic load flow for distribution feeders with high penetration of renewable distributed generation. The dependency between the load demand of different consumer classes and generation from different types of renewable resources is addressed in this study. In order to capture the coincidental variations of demand and generation, associated time series data for the same time instances are used. A transformation matrix based probabilistic load flow is formulated using the method of cumulants. Moreover, Pearson distribution functions are used to estimate the probability distribution of the line flows. The proposed load flow method is tested on a practical distribution feeder with high penetration of solar photovoltaic and wind energy conversion systems. The results demonstrate the aptitude of the proposed method for conducting probabilistic load flow studies with dependent non-Gaussian distribution of load and generation.