Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Abstract

The reliability of the smart grid is adversely affected due to system uncertainties. Also, the steadily growing deployment of renewable distributed generation (DG) units increases the uncertainties of smart grids. Hence, it is essential to concern the uncertainties in the field of reliability evaluation of smart grids. Although the Monte Carlo simulation (MCS) has received a significant deal of consideration in the literature, there is a research gap in using the clustering algorithms to assess smart grids' reliability. This article aims to fill such a research gap by proposing a new reliability assessment method, using various clustering algorithms. The benefits from the proposed method's accuracy and fast computation are highlighted, while optimal operation, optimal short-term planning, and repetitive problems should be studied. In this paper, the performance and accuracy of various classic (k-means, fuzzy c-means, and k-medoids) and metaheuristic (genetic algorithm, particle swarm optimization, differential evolutionary, harmony search, and artificial bee colony) clustering algorithms are studied. Comparing different scenario reduction algorithms in the proposed reliability evaluation method is one of the most contributions. The proposed method is applied to two realistic test systems. Test results infer that the proposed method is adequately precise, while the required computation time is less than MCS-based approaches. Test results for both test systems imply that the accurate expected energy not supplied (EENS) with less than 2.1% is achievable applying the proposed method. The fuzzy c-means clustering algorithm results in the best accuracy among the studied classic and nonclassic (metaheuristic) algorithms.