Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/100499
| Title: | DRO-MPC-based data-driven approach to real-time economic dispatch for islanded microgrids | Authors: | Lyu, C Jia, Y Xu, Z |
Issue Date: | Dec-2020 | Source: | IET generation, transmission & distribution, Dec. 2020, v. 14, no. 24, p. 5704-5711 | Abstract: | Rechargeable battery banks have been widely utilised in islanded microgrids as energy storage systems to complement the instant power imbalance in real-time. However, the cycle degradation becomes an unavoidable concern of the battery energy storage systems (BESSs) in achieving microgrid economic dispatch (ED). In this study, a novel degradation cost model based on an online auction algorithm is proposed for real-time management of BESS. To settle the intermittent distributed sources in real-time operation, a Wasserstein ambiguity set is adopted to characterise the uncertainties. Meanwhile, the authors newly reformulate the real-time microgrid ED as a two-stage distributionally robust optimisation (DRO) problem. To improve the tractability and scalability of the DRO problem, a model predictive control (MPC)-based data-driven approach is proposed, in which a novel affine policy namely extended event-wise affine adaption is properly employed. Through extensive case studies, the numerical results demonstrate the effectiveness of the proposed approach. | Publisher: | Institution of Engineering and Technology | Journal: | IET generation, transmission & distribution | ISSN: | 1751-8687 | EISSN: | 1751-8695 | DOI: | 10.1049/iet-gtd.2020.0849 | Rights: | © The Institution of Engineering and Technology 2020 This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission & Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library. |
| Appears in Collections: | Journal/Magazine Article |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| Lyu_DRO-MPC-based_Data-driven_Approach.pdf | Pre-Published version | 2.27 MB | Adobe PDF | View/Open |
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