The environmental need to curb distribution network losses and utilize renewable energy sources has created new challenges in estimation. High fidelity estimates are required even in the presence of significant uncertainty. Herein, we develop a new analytical probabilistic load flow method that, unlike existing analytical methods, is not based on a Taylor series approximation of the power equations. The method is exact for a set of distributions that includes the multivariate normal distribution. The method implementation is made scalable by casting all formulas into the framework of the popular backward/forward algorithm. The advantages of this approach are illustrated on a radial IEEE 32-bus test system. Significant improvements are observed in the presence of large power uncertainties and near the network power limits.
Uniformly better estimation of power losses is achieved.Index Terms-Load flow, power system analysis computing, power system modeling, uncertainty.
One of basic system services that an electricity transmission system operator guarantees is power balance maintenance. The aim of this service is to keep power export/import to/from surrounding interconnected electricity systems at a proposed value. This implies that the amplitude of a power balance deviation has to be kept in specified boundaries. The operator uses so called ancillary services for this purpose. In order to perform the overall volume and structure optimization of the ancillary services it is needed to know the magnitude and dynamics of the deviation that will be decayed. For this reason a deviation balance model has been designed and is presented in the paper. It describes electricity transmission system operation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.