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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
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
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