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.

Recent technological progresses in the wide-area measurement systems (WAMS) are realizing the centralized controls as a breakthrough for improving the power systems stability. The most challenging deficiency against WAMS technology is related to communication delays. If this latency is neglected, it can deteriorate the damping performance of closed loop control or even degrade the system stability. This paper investigates a conventional Wide Area Damping Controller (WADC) for a static synchronous series compensator (SSSC) to damp out the Sub-Synchronous Resonance (SSR) and also investigation of the destructive effect of time delay in remote feedback signal. A new optimization algorithm called teaching-learning-based-optimization (TLBO) algorithm has been implemented to normalize and optimize the parameters of the global SSR damping controller. The IEEE Second Benchmark Model is considered as the system under study and all simulations are carried out in MATLAB/SIMULINK environment.

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