Distributed Robust Energy Management of a Multimicrogrid System in the Real-Time Energy Market
Abstract
In this paper, a distributed robust energy management scheme for multiple interconnected microgrids (MGs) is developed. It aims to optimize the total operational cost of the MGs through energy trading with neighboring MGs and the main grid in the real-time energy market. Various uncertainties including renewable generation, load consumption, and buying/selling prices of the main grid are handled using an adjustable robust optimization technique. To keep consistent with the distributed nature of the multiple MGs, we propose a distributed adjustable robust optimal scheduling algorithm. Within the framework, each MG energy management system determines its own selling price and operation schedule via distributed communication of noncritical information with its neighboring MGs. Robust optimal scheduling and fair energy trading can be collectively achieved. A case study of a 4-MG system is conducted to validate the effectiveness of the proposed approach.
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Centralized schemes may jeopardise the privacy of charging stations by gathering their private data to make a central decision.
“The above centralized schemes may jeopardise the privacy of charging stations by gathering their private data to make a central decision [16].”
From Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility (2023) · cites this paper as [16]
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Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility
Yan, D., Ma, C., & Chen, Y. (2023) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2022.3213173 · Cited by 96 · see citing works
“The above centralized schemes may jeopardise the privacy of charging stations by gathering their private data to make a central decision [16].”
BackgroundFeng, Z.-K., Liu, S., Niu, W.-J., Liu, Y., Luo, B., Miao, S.-M., & Wang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2949582 · Cited by 20 · see citing works
Self-citation
- Authors
- Liu, Y., Li, Y., Gooi, H. B., Jian, Y., Xin, H., Jiang, X., & Pan, J.
- Affiliations
- Shenzhen University (CN)
- Huazhong University of Science and Technology (CN)
- Nanyang Technological University (SG)
- Zhejiang University (CN)
- Western Washington University (US)
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2019-01
- Volume
- 10
- Issue
- 1
- Pages
- 396-406
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W2774133674
- Cited by
- 238
- References
- 0
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 30 · 2023: 28 · 2022: 39 · 2021: 47 · 2020: 35 · 2019: 20 · 2018: 7
- FWCI
- 10.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Sustainable Energy (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Funders
- National Natural Science Foundation of China (51707069)
- Ministry of Education Key Laboratory of Image Processing and Intelligence Control, Wuhan, China (IPIC2015-01)
- DNV GL Energy (formerly KEMA) Technology Centre
- Energy Innovation Research Programme (NRF2014EWT-EIRP002-005)
- NSFC (51477103)
- Guangdong Natural Science Foundation (S2014A030313564, 2015A010106017, 2016KZDXM007)
- Shenzhen Government (JCYJ20160308104825040)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- distributed energy management, adjustable robust optimization, real-time energy market, renewable energy uncertainty, load uncertainty, operational cost minimization
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