Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/100540
| Title: | Model predictive control based ramp minimization in active distribution network using energy storage systems | Authors: | Li, J Xu, Z Zhao, J Chai, S Yu, Y Xu, X |
Issue Date: | 2019 | Source: | Electric power components and systems, 2019, v. 47, no. 3, p. 201-211 | Abstract: | The growing integration of renewable energy sources, especially the residential photovoltaic (PV) systems, in the distribution networks (DNs) aggravates the ramp-events in transmission system. To address this issue, we propose a novel look ahead dispatch model for the ramp minimization in DNs using distributed energy storage systems (ESSs). The dispatch problem considering the scheduling of ESSs is modeled as a finite-horizon optimization problem and is carried out using model predictive control (MPC) method that takes both current and future information into account. In addition, the optimal power flow in DN is formulated as a second-order cone programing problem to guarantee the global optimality. Numerical results on IEEE 37-bus distribution network show that our proposed model not only brings about 74% reduction of maximum ramp but also yields the near-minimum system operating cost. | Keywords: | Active distribution network Energy storage system Model predictive control Photovoltaic systems Ramp-event |
Publisher: | Taylor & Francis | Journal: | Electric power components and systems | ISSN: | 1532-5008 | EISSN: | 1532-5016 | DOI: | 10.1080/15325008.2019.1577929 | Rights: | © Taylor & Francis Group, LLC This is an Accepted Manuscript of an article published by Taylor & Francis in Electric Power Components and Systems on 23 Feb 2019 (published online), available at: http://www.tandfonline.com/10.1080/15325008.2019.1577929. |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Li_Model_Predictive_Control.pdf | Pre-Published version | 664.99 kB | Adobe PDF | View/Open |
Page views
69
Citations as of Apr 14, 2025
Downloads
38
Citations as of Apr 14, 2025
SCOPUSTM
Citations
5
Citations as of Sep 12, 2025
WEB OF SCIENCETM
Citations
5
Citations as of Oct 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.



