Online Security Assessment with Load and Renewable Generation Uncertainty: the iTesla Project Approach

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Maria Helena Vasconcelos (Author) FEUP

Conference proceedings International

Title: 2016 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS)

International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

Beijing, PEOPLES R CHINA, OCT 16-20, 2016

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ISI Web of Knowledge - 0 Citations

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Other information

Authenticus ID: P-00M-EMD

DOI: 10.1109/pmaps.2016.7764166

Abstract (EN): 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 (IIPC), 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8