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The load flow accessibility of the power system is carefully modeled by considering the dependence and uncertainty factors. Considering the real discrete data, the ILHS is adopted. 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.
In probabilistic research approaches, the dominant interest is to achieve appropriate modelling of input random variables and reduce the computational burden. 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. To handle this uncertainty, it is a provocation for the power system control, planning, and operation engineers. The emerging trend of distribution generation with existing power system network leads uncertainty factor.
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