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001048774 0247_ $$2doi$$a10.1109/PowerTech59965.2025.11180238
001048774 037__ $$aFZJ-2025-04889
001048774 041__ $$aCzech
001048774 1001_ $$0P:(DE-Juel1)203428$$aWenzel, Moritz$$b0$$eCorresponding author
001048774 1112_ $$a2025 IEEE Kiel PowerTech$$cKiel$$d2025-06-29 - 2025-07-03$$wGermany
001048774 245__ $$aStochastic Tube Model Predictive Control of Medium Voltage Grids with Distributed Resources
001048774 260__ $$c2025
001048774 3367_ $$033$$2EndNote$$aConference Paper
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001048774 520__ $$aThe 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.
001048774 536__ $$0G:(DE-HGF)POF4-1122$$a1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112)$$cPOF4-112$$fPOF IV$$x0
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001048774 7001_ $$0P:(DE-Juel1)201161$$aDe Din, Edoardo$$b1
001048774 7001_ $$0P:(DE-Juel1)179029$$aBenigni, Andrea$$b2
001048774 773__ $$a10.1109/PowerTech59965.2025.11180238
001048774 8564_ $$uhttps://ieeexplore.ieee.org/document/11180238
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001048774 9141_ $$y2025
001048774 920__ $$lno
001048774 9201_ $$0I:(DE-Juel1)ICE-1-20170217$$kICE-1$$lModellierung von Energiesystemen$$x0
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