Karagiannopoulos, S, Dobbe, R, Aristidou, P orcid.org/0000-0003-4429-0225 et al. (2 more authors) (2019) Data-driven Control Design Schemes in Active Distribution Grids: Capabilities and Challenges. In: Proceedings of the 2019 IEEE PowerTech conference. 2019 IEEE Milan PowerTech, 23-27 Jun 2019, Milan, Italy. IEEE ISBN 9781538647226
Abstract
Today, system operators rely on local control of distributed energy resources (DERs), such as photovoltaic units, wind turbines and batteries, to increase operational flexibility. These schemes offer a communication-free, robust, cheap, but rather sub-optimal solution and do not fully exploit the DER capabilities. The operational flexibility of active distribution networks can be greatly enhanced by the optimal control of DERs. However, it usually requires remote monitoring and communication infrastructure, which current distribution networks lack due to the high cost and complexity. In this paper, we investigate data-driven control algorithms that use historical data, advanced off-line optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We elaborate on the suitability of various schemes based on different local features, we investigate safety challenges arising from data-driven control schemes, and we show the performance of the optimized local controls on a three-phase, unbalanced, low-voltage, distribution network.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This conference paper is protected by copyright. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | —optimal control, data-driven control design, active distribution networks, OPF, machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 May 2019 14:00 |
Last Modified: | 23 Oct 2019 15:08 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/PTC.2019.8810586 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146460 |