Natural aggregation algorithm: A new efficient metaheuristic tool for power system optimizations

Fengji Luo; Zhao Yang Dong; Yingying Chen; Junhua Zhao

doi:10.1109/SmartGridComm.2016.7778759

Natural aggregation algorithm: A new efficient metaheuristic tool for power system optimizations

Fengji Luo, Zhao Yang Dong, Yingying Chen, Junhua Zhao

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

40 Citations (Scopus)

Abstract

With the increasing complexity of power systems in operation and planning, many engineering problems require a robust and efficient optimization solver which can handle non-convexity, discontinuity, and nonlinearity. We propose a new metaheuristic optimization algorithm - the natural aggregation algorithm (NAA) for power system optimization applications. NAA has very strong search capability and robustness compared with other algorithms. Economic dispatch and wind farm dispatch examples are given to illustration its superior performance. The demonstrated applications show that NAA is a promising tool for power system optimization problems. © 2016 IEEE.

Original language	English
Title of host publication	2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016
Publisher	IEEE
Pages	186-192
ISBN (Print)	9781509040759
DOIs	https://doi.org/10.1109/SmartGridComm.2016.7778759
Publication status	Published - 8 Dec 2016
Externally published	Yes
Event	7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 - Sydney, Australia Duration: 6 Nov 2016 → 9 Nov 2016

Publication series

Name	2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016

Conference

Conference	7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016
Place	Australia
City	Sydney
Period	6/11/16 → 9/11/16

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

biological intelligence
economic dispatch
evolutionary computation
Natural aggregation algorithm
wind farm dispatch

Access Output

10.1109/SmartGridComm.2016.7778759

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Luo, F., Dong, Z. Y., Chen, Y., & Zhao, J. (2016). Natural aggregation algorithm: A new efficient metaheuristic tool for power system optimizations. In 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 (pp. 186-192). Article 7778759 (2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016). IEEE. https://doi.org/10.1109/SmartGridComm.2016.7778759

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title = "Natural aggregation algorithm: A new efficient metaheuristic tool for power system optimizations",

abstract = "With the increasing complexity of power systems in operation and planning, many engineering problems require a robust and efficient optimization solver which can handle non-convexity, discontinuity, and nonlinearity. We propose a new metaheuristic optimization algorithm - the natural aggregation algorithm (NAA) for power system optimization applications. NAA has very strong search capability and robustness compared with other algorithms. Economic dispatch and wind farm dispatch examples are given to illustration its superior performance. The demonstrated applications show that NAA is a promising tool for power system optimization problems. {\textcopyright} 2016 IEEE.",

keywords = "biological intelligence, economic dispatch, evolutionary computation, Natural aggregation algorithm, wind farm dispatch",

author = "Fengji Luo and Dong, {Zhao Yang} and Yingying Chen and Junhua Zhao",

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doi = "10.1109/SmartGridComm.2016.7778759",

language = "English",

isbn = "9781509040759",

series = "2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016",

publisher = "IEEE",

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Luo, F, Dong, ZY, Chen, Y & Zhao, J 2016, Natural aggregation algorithm: A new efficient metaheuristic tool for power system optimizations. in 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016., 7778759, 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, IEEE, pp. 186-192, 7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, Sydney, Australia, 6/11/16. https://doi.org/10.1109/SmartGridComm.2016.7778759

Natural aggregation algorithm: A new efficient metaheuristic tool for power system optimizations. / Luo, Fengji; Dong, Zhao Yang; Chen, Yingying et al.
2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016. IEEE, 2016. p. 186-192 7778759 (2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Natural aggregation algorithm

T2 - 7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016

AU - Luo, Fengji

AU - Dong, Zhao Yang

AU - Chen, Yingying

AU - Zhao, Junhua

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2016/12/8

Y1 - 2016/12/8

N2 - With the increasing complexity of power systems in operation and planning, many engineering problems require a robust and efficient optimization solver which can handle non-convexity, discontinuity, and nonlinearity. We propose a new metaheuristic optimization algorithm - the natural aggregation algorithm (NAA) for power system optimization applications. NAA has very strong search capability and robustness compared with other algorithms. Economic dispatch and wind farm dispatch examples are given to illustration its superior performance. The demonstrated applications show that NAA is a promising tool for power system optimization problems. © 2016 IEEE.

AB - With the increasing complexity of power systems in operation and planning, many engineering problems require a robust and efficient optimization solver which can handle non-convexity, discontinuity, and nonlinearity. We propose a new metaheuristic optimization algorithm - the natural aggregation algorithm (NAA) for power system optimization applications. NAA has very strong search capability and robustness compared with other algorithms. Economic dispatch and wind farm dispatch examples are given to illustration its superior performance. The demonstrated applications show that NAA is a promising tool for power system optimization problems. © 2016 IEEE.

KW - biological intelligence

KW - economic dispatch

KW - evolutionary computation

KW - Natural aggregation algorithm

KW - wind farm dispatch

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U2 - 10.1109/SmartGridComm.2016.7778759

DO - 10.1109/SmartGridComm.2016.7778759

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509040759

T3 - 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016

SP - 186

EP - 192

BT - 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016

PB - IEEE

Y2 - 6 November 2016 through 9 November 2016

ER -

Luo F, Dong ZY, Chen Y, Zhao J. Natural aggregation algorithm: A new efficient metaheuristic tool for power system optimizations. In 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016. IEEE. 2016. p. 186-192. 7778759. (2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016). doi: 10.1109/SmartGridComm.2016.7778759

Natural aggregation algorithm: A new efficient metaheuristic tool for power system optimizations

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Research Keywords

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