Two-stage multi-objective unit commitment optimization under future load uncertainty

Bo Wang; You Li; Junzo Watada

doi:10.1109/ICGEC.2012.147

Two-stage multi-objective unit commitment optimization under future load uncertainty

Bo Wang^*, You Li, Junzo Watada

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. To define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. In addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.

Original language	English
Title of host publication	Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
Pages	128-131
Number of pages	4
DOIs	https://doi.org/10.1109/ICGEC.2012.147
Publication status	Published - 2012
Event	2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 - Kitakyushu Duration: 2012 Aug 25 → 2012 Aug 28

Other

Other	2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
City	Kitakyushu
Period	12/8/25 → 12/8/28

Keywords

Fuzzy set theory
Load uncertainty
Maximal blackout time
Particle swarm optimization algorithm
Two-stage multiobjective

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)

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10.1109/ICGEC.2012.147

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Wang, B, Li, Y & Watada, J 2012, Two-stage multi-objective unit commitment optimization under future load uncertainty. in Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012., 6457196, pp. 128-131, 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012, Kitakyushu, 12/8/25. https://doi.org/10.1109/ICGEC.2012.147

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abstract = "The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. To define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. In addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.",

keywords = "Fuzzy set theory, Load uncertainty, Maximal blackout time, Particle swarm optimization algorithm, Two-stage multiobjective",

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AU - Li, You

AU - Watada, Junzo

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AB - The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. To define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. In addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.

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KW - Maximal blackout time

KW - Particle swarm optimization algorithm

KW - Two-stage multiobjective

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BT - Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012

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Two-stage multi-objective unit commitment optimization under future load uncertainty

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Keywords

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