Probabilistic load flow methods to estimate impacts of distributed generators on a LV unbalanced distribution grid

Abstract : —The aim of this paper is to apply probabilistic load flow methods on a three phases, unbalanced low voltage distribution network. We use a point estimate method and a Monte Carlo simulation based method to estimate the electrical characteristics (buses voltage, phases and neutral conductors currents) of a distribution grid in presence of a large number of small size photovoltaic generators. Probabilistic load flow allows us to take into account the uncertainty of photovoltaic production and load consumption in load flow computation. The literature shows that PEM method gives good accuracy results while requiring less time simulation than Monte Carlo simulation. In this paper, we aim to check if this assumption is still right with different kinds of probability density function and for a large size electrical network. Usually, random parameters are modeled as a normal distribution. In this work, a generalized extreme value is used to model load consumption behaviour instead of a normal one. The uncertainty of photovoltaic production is supposed to be directly linked to the sky clear index which is modeled as a beta distribution.
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Communication dans un congrès
POWERTECH 2017, Jun 2017, Manchester, United Kingdom. pp.1 - 6, 2017, PowerTech, 2017 IEEE Manchester. 〈10.1109/PTC.2017.7981107〉
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Fallilou Diop, Martin Hennebel. Probabilistic load flow methods to estimate impacts of distributed generators on a LV unbalanced distribution grid. POWERTECH 2017, Jun 2017, Manchester, United Kingdom. pp.1 - 6, 2017, PowerTech, 2017 IEEE Manchester. 〈10.1109/PTC.2017.7981107〉. 〈hal-01572463〉

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