Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm

The decision support model of mean-lower semi-absolute deviation (MLSAD) and the optimization algorithm of group search optimizer with intraspecific competition and lévy walk (GSOICLW) are presented to solve wind-thermal power system dispatch. MLSAD model takes the profit and downside risk into acco...

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Main Authors: Li, Yuan Zheng, Wu, Q. H., Wang, Ping, Gooi, Hoay Beng, Li, K. C., Liu, Y. Q., Jiang, L., Lu, P., Cao, M., Imura, J.
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2017
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Online Access:https://hdl.handle.net/10356/84994
http://hdl.handle.net/10220/42069
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-849942020-03-07T13:57:23Z Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm Li, Yuan Zheng Wu, Q. H. Wang, Ping Gooi, Hoay Beng Li, K. C. Liu, Y. Q. Jiang, L. Lu, P. Cao, M. Imura, J. School of Computer Science and Engineering School of Electrical and Electronic Engineering Power system dispatch Wind power The decision support model of mean-lower semi-absolute deviation (MLSAD) and the optimization algorithm of group search optimizer with intraspecific competition and lévy walk (GSOICLW) are presented to solve wind-thermal power system dispatch. MLSAD model takes the profit and downside risk into account simultaneously brought by uncertain wind power. Using a risk tolerance parameter, the model can be converted to a single-optimization problem, which is solved by an improved optimization algorithm, GSOICLW. Afterwards, both the model and the algorithm are tested on a modified IEEE 30-bus power system. Simulation results demonstrate that the MLSAD model can well solve wind-thermal power system dispatch. The study also verifies GSOICLW obtains better convergent dispatching solutions, in comparison with other evolutionary algorithms, such as group search optimizer and particle swarm optimizer. NRF (Natl Research Foundation, S’pore) EDB (Economic Devt. Board, S’pore) Accepted version 2017-02-03T07:34:45Z 2019-12-06T15:55:06Z 2017-02-03T07:34:45Z 2019-12-06T15:55:06Z 2017 Journal Article Li, Y. Z., Jiang, L., Wu, Q. H., Wang, P., Gooi, H. B., Li, K. C., et al. (2017). Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm. Knowledge-Based Systems, 116, 94-101. 0950-7051 https://hdl.handle.net/10356/84994 http://hdl.handle.net/10220/42069 10.1016/j.knosys.2016.10.028 en Knowledge-Based Systems © 2016 Elsevier B.V. This is the author created version of a work that has been peer reviewed and accepted for publication by Knowledge-Based Systems, Elsevier B.V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.knosys.2016.10.028]. 8 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Power system dispatch
Wind power
spellingShingle Power system dispatch
Wind power
Li, Yuan Zheng
Wu, Q. H.
Wang, Ping
Gooi, Hoay Beng
Li, K. C.
Liu, Y. Q.
Jiang, L.
Lu, P.
Cao, M.
Imura, J.
Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm
description The decision support model of mean-lower semi-absolute deviation (MLSAD) and the optimization algorithm of group search optimizer with intraspecific competition and lévy walk (GSOICLW) are presented to solve wind-thermal power system dispatch. MLSAD model takes the profit and downside risk into account simultaneously brought by uncertain wind power. Using a risk tolerance parameter, the model can be converted to a single-optimization problem, which is solved by an improved optimization algorithm, GSOICLW. Afterwards, both the model and the algorithm are tested on a modified IEEE 30-bus power system. Simulation results demonstrate that the MLSAD model can well solve wind-thermal power system dispatch. The study also verifies GSOICLW obtains better convergent dispatching solutions, in comparison with other evolutionary algorithms, such as group search optimizer and particle swarm optimizer.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Li, Yuan Zheng
Wu, Q. H.
Wang, Ping
Gooi, Hoay Beng
Li, K. C.
Liu, Y. Q.
Jiang, L.
Lu, P.
Cao, M.
Imura, J.
format Article
author Li, Yuan Zheng
Wu, Q. H.
Wang, Ping
Gooi, Hoay Beng
Li, K. C.
Liu, Y. Q.
Jiang, L.
Lu, P.
Cao, M.
Imura, J.
author_sort Li, Yuan Zheng
title Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm
title_short Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm
title_full Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm
title_fullStr Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm
title_full_unstemmed Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm
title_sort wind-thermal power system dispatch using mlsad model and gsoiclw algorithm
publishDate 2017
url https://hdl.handle.net/10356/84994
http://hdl.handle.net/10220/42069
_version_ 1681047069998972928