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A Multiobjective Approach to Multimicrogrid System Design

Chiu, Wei-Yu; Sun, Hongjian; Poor, H. Vincent

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Authors

Wei-Yu Chiu

H. Vincent Poor



Abstract

The main goal of this paper is to design a market operator (MO) and a distribution network operator (DNO) for a network of microgrids in consideration of multiple objectives. This is a high-level design and only those microgrids with nondispatchable renewable energy sources are considered. For a power grid in the network, the net value derived from providing power to the network must be maximized. For a microgrid, it is desirable to maximize the net gain derived from consuming the received power. Finally, for an independent system operator, stored energy levels at microgrids must be maintained as close as possible to storage capacity to secure network emergency operation. To achieve these objectives, a multiobjective approach is proposed. The price signal generated by the MO and power distributed by the DNO are assigned based on a Pareto optimal solution of a multiobjective optimization problem. By using the proposed approach, a fair scheme that does not advantage one particular objective can be attained. Simulations are provided to validate the proposed methodology.

Citation

Chiu, W., Sun, H., & Poor, H. V. (2015). A Multiobjective Approach to Multimicrogrid System Design. IEEE Transactions on Smart Grid, 6(5), 2263-2272. https://doi.org/10.1109/tsg.2015.2399497

Journal Article Type Article
Acceptance Date Jan 25, 2015
Online Publication Date Feb 26, 2015
Publication Date Sep 1, 2015
Deposit Date Feb 20, 2015
Publicly Available Date Mar 16, 2015
Journal IEEE Transactions on Smart Grid
Print ISSN 1949-3053
Electronic ISSN 1949-3061
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 6
Issue 5
Pages 2263-2272
DOI https://doi.org/10.1109/tsg.2015.2399497
Keywords Distribution network operator (DNO), Pareto optimality, Market operator (MO), Microgrids, Multimicrogrid design, Multiobjective approach, Smart grid, Utility maximization.

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Accepted Journal Article (311 Kb)
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