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Determining the flexibility of power systems with high share of wind generation using artificial neural networks
Citation
Glazunova, A and Aksaeva, E and Semshikov, E and Negnevitsky, M, Determining the flexibility of power systems with high share of wind generation using artificial neural networks, Proceedings of 31st Australasian Universities Power Engineering Conference (AUPEC), 26-30 September 2021, Perth, Australia, pp. 1-6. ISBN 9781665434515 (2021) [Refereed Conference Paper]
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Official URL: https://ieeexplore.ieee.org/xpl/conhome/9597674/pr...
DOI: doi:10.1109/AUPEC52110.2021.9597838
Abstract
Integration of renewable energy sources in the electric power system (EPS) can introduce some uncertainties to the EPS operation. In this case, normal and secure operation of the system requires information about its flexibility. This paper explores the flexibility of the EPS with high share of wind generation supported by the battery energy storage system using artificial neural networks. The flexibility metric is expressed as the difference between the desired (target) loads at the considered nodes and the largest loads that the EPS can handle. In this study, the flexibility of a 6-node EPS is calculated for the ten minutes ahead operational point.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | electric power system, flexibility metric, artificial neural networks, wind farm, battery energy storage system |
Research Division: | Engineering |
Research Group: | Electrical engineering |
Research Field: | Electrical energy generation (incl. renewables, excl. photovoltaics) |
Objective Division: | Energy |
Objective Group: | Renewable energy |
Objective Field: | Renewable energy not elsewhere classified |
UTAS Author: | Semshikov, E (Mr Evgenii Semshikov) |
UTAS Author: | Negnevitsky, M (Professor Michael Negnevitsky) |
ID Code: | 155281 |
Year Published: | 2021 |
Deposited By: | Engineering |
Deposited On: | 2023-02-08 |
Last Modified: | 2023-02-08 |
Downloads: | 0 |
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