The growing popularity of renewable-based generations along with loads fluctuation and network topology variation has exposed distribution systems to high uncertainties, causing difficulties in operating and planning decisions. In addition, the correlation among various uncertain variables has introduced more complexity to this problem. The probabilistic assessment of power systems with various uncertain variables and with any correlation between them can be efficiently handled by Monte-Carlo simulation (MCS) method, but the calculation burden in this method is heavy and thus it is not appropriate in online applications. Keeping the accuracy of the results, data clustering techniques can be efficiently substituted for this method with much less calculation time and burden. In this study, two methods based on data clustering which can consider the correlation between different variables in a straightforward manner are presented for the probabilistic power flow of distribution systems. In order to demonstrate the efficiency of the proposed methods, IEEE 37 node test feeder and IEEE 123 node test feeder were selected as the case study. The results obtained by the proposed methods were compared with those of the MCS method in terms of accuracy and calculation time.
Modern distribution systems are equipped with various distributed energy resources (DERs) because of the importance of local generation. These distribution systems encounter more and more uncertainties because of the ever-increasing use of renewable energies. Other sources of uncertainty, such as load variation and system components' failure, will intensify the unpredictable nature of modern distribution systems. Integrating energy storage systems into distribution grids can play a role as a flexible bidirectional source to accommodate issues from constantly varying loads and renewable resources. The overall functionality of these modern distribution systems is enhanced using communication and computational abilities in smart grid frameworks. Robust operation of these systems is effectively taken into consideration to manage the uncertainty, which offers an explicit way to control the desired conservativeness. This paper presents an optimal operating program for smart grids equipped with wind generators, controllable distributed generators, energy storage systems, and reactive power compensators. In order to make the studies more practical, uncertainty about wind generators and grid loads is taken into account. Furthermore, the presented operating program is robust in various conditions, i.e. there is no need to change the operating program in a wide range of probable states. The point estimation method and fuzzy clustering method are used for probabilistic assessment of the distribution system in the presence of uncertainties. The IEEE 37-node standard test system, which is a highly unbalanced system, is selected for the case study and the results are discussed comprehensively.
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