Abstract
This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R2) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.
Highlights
Power system stability has gained more attention in modern power system analysis due to the increased penetration of intermittent renewable energy sources (RES) and the operation of the electricity markets
These results clearly show the importance of considering the uncertainties in the power network and their impact on voltage stability
Various sampling techniques are implemented to generate random samples with different sizes to be compared with the reference data to identify an accurate and efficient sampling technique to be used for probabilistic voltage stability analysis
Summary
Power system stability has gained more attention in modern power system analysis due to the increased penetration of intermittent renewable energy sources (RES) and the operation of the electricity markets. For the economic operation of the power systems, the network has been operated close to its voltage stability boundary [1,2]. This has arisen from the growing usage of distributed generation of RES, and the intrinsic heterogeneity of grid loads, which increases the number of uncertainties in power system networks [3]. Probabilistic voltage stability analysis considering a wide range of the variability of the system parameters is imperative in power system planning and operation [6]. The probabilistic method can accurately provide a better reflection of the actual system behavior by explicitly taking into account the uncertainties of the system parameters and stochastic variability in parameters, disturbances, and operating conditions [7,8]
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