Chance-Constrained Based Voltage Control Framework to Deal with Model Uncertainties in MV Distribution Systems

Bashir Bakhshideh Zad, Jean-François Toubeau and François Vallée
Additional contact information
Bashir Bakhshideh Zad: Power Systems and Markets Research (PSMR) Group, University of Mons, B7000 Mons, Belgium
Jean-François Toubeau: Power Systems and Markets Research (PSMR) Group, University of Mons, B7000 Mons, Belgium
François Vallée: Power Systems and Markets Research (PSMR) Group, University of Mons, B7000 Mons, Belgium

Energies, 2021, vol. 14, issue 16, 1-16

Abstract: In this paper, a chance-constrained (CC) framework is developed to manage the voltage control problem of medium-voltage (MV) distribution systems subject to model uncertainty. Such epistemic uncertainties are inherent in distribution system analyses given that an exact model of the network components is not available. In this context, relying on the simplified deterministic models can lead to insufficient control decisions. The CC-based voltage control framework is proposed to tackle this issue while being able to control the desired protection level against model uncertainties. The voltage control task disregarding the model uncertainties is firstly formulated as a linear optimization problem. Then, model uncertainty impacts on the above linear optimization problem are evaluated. This analysis defines that the voltage control problem subject to model uncertainties should be modelled with a joint CC formulation. The latter is accordingly relaxed to individual CC optimizations using the proposed methods. The performance of proposed CC voltage control methods is finally tested in comparison with that of the robust optimization. Simulation results confirm the accuracy of confidence level expected from the proposed CC voltage control formulations. The proposed technique allows the system operators to tune the confidence level parameter such that a tradeoff between operation costs and conservatism level is attained.

Keywords: voltage control; distribution systems; model uncertainty; chance-constrained optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/16/5161/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/16/5161/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:16:p:5161-:d:618618

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().