Stochastic monitoring of distribution networks including correlated input variables

Gustavo Valverde, Andrija T. Saric, Vladimir Terzija

    Research output: Contribution to journalArticlepeer-review


    The evolving complexity of distribution networks with higher levels of uncertainties is a new challenge faced by system operators. This paper introduces the use of Gaussian mixtures models as input variables in stochastic power flow studies and state estimation of distribution networks. These studies are relevant for the efficient exploitation of renewable energy sources and the secure operation of network assets. The proposed formulation is valid for both power flow and state estimation problems. The method uses a combination of the Gaussian components used to model the input variables in the weighted least square formulation. In order to reduce computational demands, this paper includes an efficient optimization algorithm to reduce the number of Gaussian combinations. The proposed method was tested in a 69-bus radial test system and the results were compared with Monte Carlo simulations. © 1969-2012 IEEE.
    Original languageEnglish
    Article number6231710
    Pages (from-to)246-255
    Number of pages9
    JournalIEEE Transactions on Power Systems
    Issue number1
    Publication statusPublished - 2013


    • Distributed power generation
    • Load flow
    • Power system measurements
    • Probability distribution
    • Random variables
    • State estimation
    • Uncertainty


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