Open AccessImpacts of the model uncertainty on the voltage regulation problem of medium‐voltage distribution systems
Author(s) -
Bakhshideh Zad Bashir,
Lobry Jacques,
Vallée François
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.1829
Subject(s) - transformer , voltage , voltage regulation , control theory (sociology) , computer science , monte carlo method , electric power system , mathematical optimization , engineering , power (physics) , control (management) , mathematics , electrical engineering , statistics , physics , quantum mechanics , artificial intelligence
In this paper, a novel framework is proposed in order to evaluate impacts of the uncertain models of the system components on the voltage regulation problem of the medium‐voltage distribution systems. The investigation focuses on the model uncertainty associated with voltage dependency of loads, power factor of loads, thermal dependency of lines, shunt admittances of lines and internal resistance of substation transformer. To this end, firstly, voltage constraints are managed using a centralised voltage control algorithm (VCA) by relying on the simplified models of the system components. The system loads and lines as well as the substation transformer are then modelled with the uncertain variables which are bounded in the predefined ranges. Monte Carlo (MC) simulations are used to create wide series of scenarios that cover the possible values that the parameters of the system components can take due to their uncertain nature. The model uncertainty impacts on the voltage regulation problem are finally evaluated by the load flow calculations considering the scenarios created by the MC simulations and the set‐point obtained by the VCA. The proposed investigation brings useful information regarding the possible deviations that the node voltages can have due to the uncertain models of the studied components.