Titre :

Embedding OLTC nonlinearities in predictive Volt Var Control for active distribution networks

Auteur(s) :

Morin, J. [Auteur]
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Colas, F. [Auteur]
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]
Grenard, S. [Auteur]
Guillaud, X. [Auteur]
Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]

Titre de la revue :

Electric Power Systems Research

Pagination :

225-234

Éditeur :

Elsevier

Date de publication :

2017-02

ISSN :

0378-7796

Mot(s)-clé(s) :

Distribution Network
Mixed Integer Continuous Programming
Model Predictive Control
On-Load-Tap-Changer
Reactive Power Management
Smart Grids
Voltage Control

Discipline(s) HAL :

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

Résumé en anglais : [en]

Volatile productions and consumptions generate a stochastic behavior of distribution grids and make its supervision difficult to achieve. Usually, the Distributed Generators reactive powers are adjusted to perform decentralized ...
Lire la suite >Volatile productions and consumptions generate a stochastic behavior of distribution grids and make its supervision difficult to achieve. Usually, the Distributed Generators reactive powers are adjusted to perform decentralized voltage control. Industrial controllers are generally equipped with a local affine feedback law, which settings are tuned at early stage using local data. A centralized and more efficient tuning method should aim to maximize the probability that all the node voltages of distribution grids remain within prescribed bounds. When the characteristics of the stochastic power forecasts are known, the centralized algorithm allows to update the settings on a regular time basis. However, the method requires to solve stochastic optimization problem. Assuming that stochastic variables have Gaussian distributions, a procedure is given which guarantees the convergence of the stochastic optimization. Convex problems drastically reduce the difficulty and the computational time required to reach the global minimum, compared to nonconvex optimal power flow problems. The linear controllers with optimized parameters are compared to traditional control laws using simulations of a real distribution grid model. The results show that the algorithm is reliable and moreover fast enough. Hence, the proposed method can be used to update periodically the control parameters.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL