Data-driven adaptive operation of soft open points in active distribution networks

Huo, Yanda, Li, Peng, Ji, Haoran, Yan, Jinyue, Song, Guanyu, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Wang, Chengshan 2021. Data-driven adaptive operation of soft open points in active distribution networks. IEEE Transactions on Industrial Informatics 17 (12) , pp. 8230-8242. 10.1109/TII.2021.3064370

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Abstract

The integration of soft open point (SOP) effectively improves the flexibility of active distribution networks (ADNs). However, in practical operation, accurate network parameters are difficult to obtain and the operation state changes rapidly with distributed generators (DGs). With the development of information technologies, massive operation data can be acquired in ADNs. How to utilize multi-source data has become the key to realize the intelligent operation of ADNs. This paper proposes a data-driven operation strategy of SOP based on model-free adaptive control (MFAC). First, considering the inaccurate parameters and frequent change of operation states, a data-driven framework is formulated for the real-time operation of SOP. Then, the operation strategies of multiple SOPs are further improved with inter-area coordination. The results of case studies show that driven by measurement data, the potential benefits of SOPs are explored to adaptively respond to system state changes and improve the operational performance of ADNs.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	© 2021 IEEE. 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.
Publisher:	IEEE
ISSN:	1551-3203
Date of First Compliant Deposit:	9 April 2021
Date of Acceptance:	28 February 2021
Last Modified:	06 May 2023 21:23
URI:	https://orca.cardiff.ac.uk/id/eprint/140116

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