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@INPROCEEDINGS{Wenzel:1048774,
author = {Wenzel, Moritz and De Din, Edoardo and Benigni, Andrea},
title = {{S}tochastic {T}ube {M}odel {P}redictive {C}ontrol of
{M}edium {V}oltage {G}rids with {D}istributed {R}esources},
reportid = {FZJ-2025-04889},
year = {2025},
abstract = {The uncertainties associated with load and generation
forecasts, as well as voltage measurement, present
significant challenges for voltage control strategies that
employ Model Predictive Control (MPC) in its deterministic
formulation. This paper presents a Stochastic Model
Predictive Control (SMPC) formulation for the voltage
control of distribution grids that uses information on the
probability distribution of the uncertainties to frame the
control problem. The proposed SMPC applies a tube-based
approach, which is capable of handling arbitrary probability
distributions, can jointly evaluate different uncertainty
sources, and allows for the formulation of the uncertainty
as an additive disturbance. The test results show a
significant reduction in voltage violations across the
majority of uncertainty realizations with regard to both
amplitude and frequency of these occurrences.},
month = {Jun},
date = {2025-06-29},
organization = {2025 IEEE Kiel PowerTech, Kiel
(Germany), 29 Jun 2025 - 3 Jul 2025},
subtyp = {After Call},
cin = {ICE-1},
cid = {I:(DE-Juel1)ICE-1-20170217},
pnm = {1122 - Design, Operation and Digitalization of the Future
Energy Grids (POF4-112) / 1123 - Smart Areas and Research
Platforms (POF4-112)},
pid = {G:(DE-HGF)POF4-1122 / G:(DE-HGF)POF4-1123},
typ = {PUB:(DE-HGF)6},
doi = {10.1109/PowerTech59965.2025.11180238},
url = {https://juser.fz-juelich.de/record/1048774},
}