Online security assessment with load and renewable generation uncertainty: The iTesla project approach

File Description SizeFormat 
PMAPS_2016_Submitted_Version.pdfAccepted version879.54 kBAdobe PDFView/Open
Title: Online security assessment with load and renewable generation uncertainty: The iTesla project approach
Authors: Vasconcelos, MH
Carvalho, LM
Meirinhos, J
Omont, N
Gambier-Morel, P
Jamgotchian, G
Cirio, D
Ciapessoni, E
Pitto, A
Konstantelos, I
Strbac, G
Ferraro, C
Biasuzzi, C
Item Type: Conference Paper
Abstract: The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (HPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.
Issue Date: 5-Dec-2016
Date of Acceptance: 1-Sep-2016
URI: http://hdl.handle.net/10044/1/43078
DOI: https://dx.doi.org/10.1109/PMAPS.2016.7764166
Publisher: IEEE
Copyright Statement: “© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 283012
Conference Name: 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Publication Status: Published
Start Date: 2016-10-16
Finish Date: 2016-12-20
Conference Place: Beijing, China
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx