Title :

Confidence level optimization of DG piecewise affine controllers in distribution grids

Author(s) :

Buire, Jerome [Auteur]
L2EP - Équipe Réseaux
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Colas, Frédéric [Auteur]
L2EP - Équipe Réseaux
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Dieulot, Jean-Yves [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Méthodes et Outils pour la Conception Intégrée de Systèmes [MOCIS]
de Alvaro, Leticia [Auteur]
Guillaud, Xavier [Auteur]
L2EP - Équipe Réseaux
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]

Journal title :

IEEE TRANSACTIONS ON SMART GRID

Pages :

6126-6136

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2019-11

ISSN :

1949-3053

English keyword(s) :

Confidence level optimization
control tuning
distribution network
piecewise affine controller
stochastic power flow

HAL domain(s) :

Sciences de l'ingénieur [physics]/Automatique / Robotique

English abstract : [en]

Distributed generators (DGs) reactive powers are controlled to mitigate voltage overshoots in distribution grids with stochastic power production and consumption. Classical DGs controllers may embed piecewise affine laws ...
Show more >Distributed generators (DGs) reactive powers are controlled to mitigate voltage overshoots in distribution grids with stochastic power production and consumption. Classical DGs controllers may embed piecewise affine laws with dead-band terms. Their settings are usually tuned using a decentralized method which uses local data and optimizes only the DG node behavior. It is shown that when short-term forecasts of stochastic powers are Gaussian and the grid model is assumed to be linear, nodes voltages can either be approximated by Gaussian or sums of truncated Gaussian variables. In the latter case, the voltages probability density functions (pdf) that are needed to compute the overvoltage risks or DG control effort are less straightforward than for normal distributions. These pdf are used into a centralized optimization problem which tunes all DGs control parameters. The objectives consist in maximizing the confidence levels for which voltages and powers remain in prescribed domains and minimizing voltage variances and DG efforts. Simulations on a real distribution grid model show that the truncated Gaussian representation is relevant and that control parameters can easily be updated even when extra DGs are added to the grid. The DG reactive power can be reduced down to 50% or node voltages variances can be reduced down to 30%.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL