Stochastic modeling of distribution networks under uncertainty

The recent developments in power systems, as consequence of the market deregulation and the international treaties, as the ones originated by the Kyoto Protocol, have serious repercussions in power networks. Particularly on distribution networks, given that a large amount of distributed generation units are connected in the grid. For instance, renewable energy sources, that are used as distributed generation, are well-known for being distributed in nature and highly unpredictable. This fact adds a strong constraint on planning and operating the distribution networks that were not originally designed to accommodate distributed generation on a large scale. To this aim, this thesis examines the impact of uncertainties on classical power system planning studies, where classical static and dynamic planning studies are carried out in several power networks taking into account some sources of uncertainty. These uncertainties are modeled in the static studies using a probabilistic and a possibilistic approach. The possibilistic approach offers good advantages over the probabilistic method in terms of time consumption and precision. The maximum wind power penetration is determined for a small mesh network by the probabilistic method using dynamic and static stability simulations of the power system.

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Source https://theses.hal.science/tel-00845650
Author Briceño Vicente, Wendy Carolina, Briceno Vicente
Maintainer CCSD
Last Updated May 10, 2026, 07:46 (UTC)
Created May 10, 2026, 07:46 (UTC)
Identifier NNT: 2012GRENT053
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Laboratoire de Génie Electrique de Grenoble (G2ELab) ; Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
creator Briceño Vicente, Wendy Carolina, Briceno Vicente
date 2012-09-20T00:00:00
harvest_object_id 2e232c33-81ff-4d95-83b0-eae19955be29
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-31T00:00:00
set_spec type:THESE