Probabilistic load flow in distribution networks: An updated and comprehensive review with a new classification proposal

Abstract

Due to the increased penetration of distributed energy resources and the presence of uncertainties in power networks, it is essential to evaluate the uncertainty of system performance. As a result, effective technologies for load flow analysis are required. The stochastic properties of power systems can be discovered via probabilistic load flow analysis. To model the uncertainties and construct the parametric tools of probabilistic power flow, several works recently utilize ensembles of probability density functions. Nevertheless, the uncertainties may not fit into any of the conventional classes of probability density functions. Thus, nonparametric tools are required. Various studies on uncertainty modeling techniques in probabilistic load flow have recently been undertaken, although there is no single best uncertainty modeling methodology to date. The load flow problem and the type of uncertain input variables determine the best strategy. There have been positive evaluations in this area, but the main focus of this research is firstly to present an overview and classification of the available load flow strategies in distribution networks from various perspectives and also, investigate the stochastic correlation or dependency of a random variable's variation relative to others. Then, based on a fresh perspective, we present a review, taxonomy, and discussion of the strengths and shortcomings of existing uncertainty modeling techniques in stochastic load flow as probability methods (parametric, semi- and non-parametric approaches) and non-probability methods. Moreover, for first time, a comprehensive comparison and assessment between these uncertainty methods based on accuracy, complexity, and simulation time presents in the uncertainties handling of the power system. The authors suggest this review to engineers, scientists and researchers who are doing their research work in this field.