Bellizio, F, Karagiannopoulos, S, Aristidou, P orcid.org/0000-0003-4429-0225 et al. (1 more author) (2018) Optimized Local Control for Active Distribution Grids using Machine Learning Techniques. In: Proceedings of the IEEE Power & Energy Society General Meeting (PESGM 2018). PESGM 2018: IEEE Power & Energy Society General Meeting, 05-10 Aug 2018, Portland, OR, USA. IEEE ISBN 978-1-5386-7703-2
Abstract
Modern distribution system operators are facing a changing scenery due to the increasing penetration of distributed energy resources, introducing new challenges to system operation. In order to ensure secure system operation at a low cost, centralized and decentralized operational schemes are used to optimally dispatch these units. This paper proposes a decentralized, real-time, operation scheme for the optimal dispatch of distributed energy resources in the absence of extensive monitoring and communication infrastructure. This scheme uses an offline, centralized, optimal operation algorithm, with historical information, to generate a training dataset consisting of various operating conditions and corresponding distributed energy resources optimal decisions. Then, this dataset is used to design the individual local controllers for each unit with the use of machine learning techniques. The performance of the proposed method is tested on a low-voltage distribution network and is compared against centralized and existing decentralized methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. This is an author produced version of a paper published in Proceedings of the IEEE Power & Energy Society General Meeting (PESGM 2018). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
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: | 04 Apr 2018 13:59 |
Last Modified: | 21 Feb 2019 15:58 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/PESGM.2018.8586079 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129152 |