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.
Original language | English |
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Title of host publication | 2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509019700 |
DOIs | |
Publication status | Published - 1 Dec 2016 |
Externally published | Yes |
Event | 2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 - Beijing, China Duration: 16 Oct 2016 → 20 Oct 2016 |
Publication series
Name | 2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 - Proceedings |
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Conference
Conference | 2016 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2016 |
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Country/Territory | China |
City | Beijing |
Period | 16/10/16 → 20/10/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- High Performance Computing
- Machine Learning
- Online Static/Dynamic Security Assessment
- Renewable Power
- Uncertainty Modeling