Cooperative bidding-based robust optimal energy management of multimicrogrids

The increasing penetration of renewable energy sources (RESs) has led to the proliferation of microgrids (MGs) in the power system. Recently, the concept of multimicrogrid (MMG) systems has come into prominence due to the economic benefits accrued through the sharing of resources between the constit...

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Main Authors: Sampath, Lahanda Purage Mohasha Isuru, Krishnan, Ashok, Foo, Eddy Yi Shyh, Gooi, Hoay Beng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/142037
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1420372020-06-15T03:55:05Z Cooperative bidding-based robust optimal energy management of multimicrogrids Sampath, Lahanda Purage Mohasha Isuru Krishnan, Ashok Foo, Eddy Yi Shyh Gooi, Hoay Beng School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution Day-ahead Planning Distributed Optimization The increasing penetration of renewable energy sources (RESs) has led to the proliferation of microgrids (MGs) in the power system. Recently, the concept of multimicrogrid (MMG) systems has come into prominence due to the economic benefits accrued through the sharing of resources between the constituent MGs. The uncertainties caused by the penetration of RESs necessitate the usage of advanced optimization procedures to manage the MMG system. This article proposes a robust optimization (RO) framework to determine the day-ahead schedule of an MMG system. Unlike the existing works in the literature, the proposed RO framework preserves the nonanticipativity in reserve scheduling. The proposed RO framework also includes a cooperative bidding-based trading scheme to facilitate the sharing of energy and reserves between the constituent MGs in the MMG system. The results highlight the economic benefits obtained through the sharing of resources between the constituent MGs in an MMG system. Furthermore, the results also demonstrate that the proposed nonanticipative RO framework performs better in terms of robustness when compared with the existing RO frameworks for MMG systems in the literature. MOE (Min. of Education, S’pore) Accepted version 2020-06-15T03:55:05Z 2020-06-15T03:55:05Z 2019 Journal Article Sampath, L. P. M. I., Krishnan, A., Foo, E. Y. S., & Gooi, H. B. (2020). Cooperative bidding-based robust optimal energy management of multimicrogrids. IEEE Transactions on Industrial Informatics, 16(9), 5757-5768. doi:10.1109/TII.2019.2955991 1551-3203 https://hdl.handle.net/10356/142037 10.1109/TII.2019.2955991 9 16 5757 5768 en 2018-T1-002-093 IEEE Transactions on Industrial Informatics © 2019 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.2019.2955991. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
Day-ahead Planning
Distributed Optimization
spellingShingle Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
Day-ahead Planning
Distributed Optimization
Sampath, Lahanda Purage Mohasha Isuru
Krishnan, Ashok
Foo, Eddy Yi Shyh
Gooi, Hoay Beng
Cooperative bidding-based robust optimal energy management of multimicrogrids
description The increasing penetration of renewable energy sources (RESs) has led to the proliferation of microgrids (MGs) in the power system. Recently, the concept of multimicrogrid (MMG) systems has come into prominence due to the economic benefits accrued through the sharing of resources between the constituent MGs. The uncertainties caused by the penetration of RESs necessitate the usage of advanced optimization procedures to manage the MMG system. This article proposes a robust optimization (RO) framework to determine the day-ahead schedule of an MMG system. Unlike the existing works in the literature, the proposed RO framework preserves the nonanticipativity in reserve scheduling. The proposed RO framework also includes a cooperative bidding-based trading scheme to facilitate the sharing of energy and reserves between the constituent MGs in the MMG system. The results highlight the economic benefits obtained through the sharing of resources between the constituent MGs in an MMG system. Furthermore, the results also demonstrate that the proposed nonanticipative RO framework performs better in terms of robustness when compared with the existing RO frameworks for MMG systems in the literature.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Sampath, Lahanda Purage Mohasha Isuru
Krishnan, Ashok
Foo, Eddy Yi Shyh
Gooi, Hoay Beng
format Article
author Sampath, Lahanda Purage Mohasha Isuru
Krishnan, Ashok
Foo, Eddy Yi Shyh
Gooi, Hoay Beng
author_sort Sampath, Lahanda Purage Mohasha Isuru
title Cooperative bidding-based robust optimal energy management of multimicrogrids
title_short Cooperative bidding-based robust optimal energy management of multimicrogrids
title_full Cooperative bidding-based robust optimal energy management of multimicrogrids
title_fullStr Cooperative bidding-based robust optimal energy management of multimicrogrids
title_full_unstemmed Cooperative bidding-based robust optimal energy management of multimicrogrids
title_sort cooperative bidding-based robust optimal energy management of multimicrogrids
publishDate 2020
url https://hdl.handle.net/10356/142037
_version_ 1681056666473201664