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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. [ABSTRACT FROM AUTHOR]

In this article we tried to control electrical loads in a small scales and a distributed generation with fuzzy logic controller. In this article the distributed generationis considered biomass power plant. The distributed generationis provides the power for electrical loads. In this simulation, the fuzzy system has the role of controling electrical loads connected to biomass power plant and when increasing electrical loads and biomass power plant not being able to provide the power, it separates the loads from the power plant and connects it to power network. The simulation in this paper was implemented on an IEEE standard 14 base load system and a fuzzy system operates between a small scale power plant and a throughout power network and controls the electrical loads. [ABSTRACT FROM AUTHOR]