Probabilistic Analysis To Analyze Uncertainty Incorporating Copula Theory

Abstract

The emerging trend of distribution generation with existing power system network leads uncertainty factor. To handle this uncertainty, it is a provocation for the power system control, planning, and operation engineers. Although there are numerous techniques to model and evaluate these uncertainties, but in this paper the integration of Copula theory with Improved Latin-hypercube Sampling (ILHS) are incorporated for Probabilistic load Flow (PLF) evaluation. In probabilistic research approaches, the dominant interest is to achieve appropriate modelling of input random variables and reduce the computational burden. To address the said problem, Copula theory is applied to execute the modelling and interaction among input random variables of the active power system network. Considering the real discrete data, the ILHS is adopted. The load flow accessibility of the power system is carefully modeled by considering the dependence and uncertainty factors. Modified IEEE 14-bus system is employed to analyze the efficiency and performance of the proposed model using active power system network. Output power of two wind energy farms situated in New Jersey are obtained for accuracy comparison. The proposed technique shows the superiority in PLF evaluation.