A robust data clustering method for probabilistic load flow in wind integrated radial distribution networks

Abstract

Data clustering incorporated in Monte Carlo Simulation (MCS) proves efficient in Probabilistic Load Flow (PLF) of the power grids under uncertainty of renewable energy resources. Fixed cluster agents are assumed for the clusters in the investigations reported in literature. This assumption ignores the changeable characteristics of Normal Data Clustering (NDC). This implies that the agents may change in another execution of the NDC. Under such circumstances, providing precise results for the PLF during frequent executions is not practical and there is high error in the executions. This paper presents a robust data clustering (RDC) scheme to overcome the problem arising from varying results of the NDC, and provides closer solutions to MCS. The proposed RDC method obtains the average of solutions by performing numerous NDCs under a so-called normal to robust (N2R) factor so as to solve the PLF problem in wind-integrated radial distribution systems. The proposed method is applied to various IEEE test systems, and the results are discussed. The results demonstrate the efficacy of the proposed RDC method for probabilistic load flow.