Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145059
Title: Decentralized local energy trading in microgrids with voltage management
Authors: Paudel, Amrit
Khorasany, Mohsen
Gooi, Hoay Beng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Paudel, A., Khorasany, M., & Gooi, H. B. (2021). Decentralized local energy trading in microgrids with voltage management. IEEE Transactions on Industrial Informatics, 17(2), 1111-1121. doi:10.1109/TII.2020.2980160
Project: A1990b0060
Journal: IEEE Transactions on Industrial Informatics
Abstract: Local energy trading in microgrids is one of the emerging concepts in the area of distribution networks. A proper business model is required to manage local energy trading. The pricing mechanism is crucial because the agreed energy price determines the benefits of local energy trading. Designing a proper pricing mechanism with a specific objective considering the privacy of agents and respecting physical network constraints is a challenging task. This article proposes a decentralized algorithm for local energy trading in microgrids with an integrated pricing mechanism considering welfare maximization and network voltage management through local information exchange among neighbors. The proposed algorithm guarantees that the energy transactions do not violate voltage constraints in a physical network and agents’ privacy is preserved. A two-stage approach is proposed to achieve fast convergence and increase the practicability of the algorithm. The simulation results are presented to verify the effectiveness of the proposed approach.
URI: https://hdl.handle.net/10356/145059
ISSN: 1551-3203
DOI: 10.1109/TII.2020.2980160
Rights: © 2020 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. The published version is available at: https://doi.org/10.1109/TII.2020.2980160
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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