LAPSE:2020.0102
Published Article
LAPSE:2020.0102
Multi-Time-Scale Rolling Optimal Dispatch for Grid-Connected AC/DC Hybrid Microgrids
Zhao Luo, Zhendong Zhu, Zhiyuan Zhang, Jinghui Qin, Hao Wang, Zeyong Gao, Zhichao Yang
January 19, 2020
In order to reduce the impact of the randomness and volatility of renewable energy on the economic operation of AC/DC hybrid microgrids, a multi-time-scale rolling optimization strategy is proposed for the grid-connected AC/DC hybrid microgrids. It considers the source-load uncertainty declined with time scale reduction, and the scheduling cooperation problem of different units on different time scales. In this paper, we propose a three-time-scale optimal strategy of the day-ahead, intraday and real-time dispatching stage and a two-level rolling optimal strategy of the intraday and real-time stage, aiming at minimizing the operating cost. We added the power penalty cost in the rolling optimization model to limit the energy state of the energy storage system in the constraint, and improve the power correction and tracking effect of the rolling optimization. A typical-structure AC/DC hybrid microgrid is analyzed in this paper and the simulation results are shown to demonstrate the feasibility and effectiveness of the proposed multi-time-scale rolling optimal dispatch.
Keywords
AC/DC hybrid, energy management, microgrids, multi-time-scale, rolling optimization
Suggested Citation
Luo Z, Zhu Z, Zhang Z, Qin J, Wang H, Gao Z, Yang Z. Multi-Time-Scale Rolling Optimal Dispatch for Grid-Connected AC/DC Hybrid Microgrids. (2020). LAPSE:2020.0102
Author Affiliations
Luo Z: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China [ORCID]
Zhu Z: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
Zhang Z: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
Qin J: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
Wang H: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
Gao Z: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China
Yang Z: School of Electrical Engineering, Southeast University, Nanjing 210096, China
Journal Name
Processes
Volume
7
Issue
12
Article Number
E961
Year
2019
Publication Date
2019-12-16
Published Version
ISSN
2227-9717
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PII: pr7120961, Publication Type: Journal Article
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LAPSE:2020.0102
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doi:10.3390/pr7120961
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Jan 19, 2020
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Calvin Tsay
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