Online security assessment with load and renewable generation uncertainty: The iTesla project approach
File(s)PMAPS_2016_Submitted_Version.pdf (879.54 KB)
Accepted version
Author(s)
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.
Date Issued
2016-12-05
Online Publication Date
2016-12-05
2016-12-09T12:08:53Z
Date Acceptance
2016-09-01
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.
Source Database
manual-entry
Sponsor
Commission of the European Communities
Grant Number
283012
Source
2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Publication Status
Published
Start Date
2016-10-16
Finish Date
2016-12-20
Country
Beijing, China