Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power

doi:10.23919/JSEE.2022.000110

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1143-1150.doi: 10.23919/JSEE.2022.000110

• SYSTEMS ENGINEERING • Previous Articles Next Articles

Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power

Bing WANG*(), Pengfei ZHANG(), Yufeng HE(), Xiaozhi WANG(), Xianxia ZHANG

¹ School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China

Received:2020-11-26 Online:2022-10-27 Published:2022-10-27
Contact: Bing WANG E-mail:susanbwang@shu.edu.cn;zpf1997@shu.edu.cn;heyu_11@163.com;wxz95@163.com
About author:|WANG Bing was born in 1966. She received her B.E. degree in precision mechanism and precision instrument from University of Science and Technology of China in 1989, M.E. degree in operations research and control theory from Shandong University in 2001, and Ph.D. degree in control theory and control engineering from Shanghai Jiaotong University in 2005. She is currently a professor of system engineering at School of Mechatronic Engineering and Automation of Shanghai University. Her research interests include robust optimization and applications, scheduling theory and algorithm, and game-theoretic approach and application. E-mail: susanbwang@shu.edu.cn||ZHANG Pengfei was born in 1997. He received his B.E. degree in automation from Changzhou University in 2020, and he is currently working toward his master’s degree at School of Mechatronic Engineering and Automation of Shanghai University. His research interests include robust optimization and machine scheduling problems. E-mail: zpf1997@shu.edu.cn||HE Yufeng was born in 1996. She received her B.E. degree in electrical engineering and automation from University of Shanghai for Science and Technology in 2017, and M.E. degree in power system and automation from Shanghai University in 2020. She is currently a technician of China Southern Power Grid. Her research interests include robust optimization and economic dispatch. E-mail: heyu_11@163.com||WANG Xiaozhi was born in 1995. He received his B.E. degree in electronic information science and technology from East China Normal University. He is currently a research assistant at School of Mechatronic Engineering and Automation of Shanghai University. His research interests include robust scheduling theory and algorithm. E-mail: wxz95@163.com||ZHANG Xianxia was born in 1975. She received her B.E. degree in automatic control from University of Science and Technology Beijing, Beijing, China, in 1998, M.E. degree in measurement techniques and instrumentation from Shanghai University, Shanghai, China, in 2003, and Ph.D. degree in control theory and control engineering from Shanghai Jiao Tong University, Shanghai, China, in 2008. From March 2005 to March 2006, she was a Research Assistant in the Department of Manufacturing Engineering and Engineering Management (MEEM), City University of Hong Kong. Since January 2008, she has been with the School of Mechatronics and Automation, Shanghai University. From May to August in 2008, she was a Senior Research Assistant in the Department of MEEM, City University of Hong Kong. Currently, she is a professor in the School of Mechatronics and Automation, Shanghai University. Her research interests include spatio-temporal system modeling, intelligent control for spatially distributed system, machine learning algorithm, vision-based robot control, and scheduling algorithm. E-mail: xianxia_zh@shu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (62173219;62073210)

Abstract

Abstract:

An economic dispatch problem for power system with wind power is discussed. Using discrete scenario to describe uncertain wind powers, a threshold is given to identify bad scenario set. The bad-scenario-set robust economic dispatch model is established to minimize the total penalties on bad scenarios. A specialized hybrid particle swarm optimization (PSO) algorithm is developed through hybridizing simulated annealing (SA) operators. The SA operators are performed according to a scenario-oriented adaptive search rule in a neighborhood which is constructed based on the unit commitment constraints. Finally, an experiment is conducted. The computational results show that the developed algorithm outperforms the existing algorithms.

Key words: wind power, robust economic dispatch, scenario, simulated annealing (SA), particle swarm optimization (PSO)

Bing WANG, Pengfei ZHANG, Yufeng HE, Xiaozhi WANG, Xianxia ZHANG. Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1143-1150.

Figures/Tables 5

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Table 1

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References 30

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Instance type	Algorithm	BR(×10⁵)			CPU time/s
Instance type	Algorithm	Maximum value	Minimum value	Mean value	CPU time/s
10-unit system	PSO	1031	201.7	357.2	23.00
	HPSOCC	980.6	179.4	296.6	84.00
	HPSONC	784.5	174.5	287.4	71.00
	MIW-PSO	680.4	170.6	273.7	43.00
	SHPSO	631.0	157.1	256.4	55.00
13-unit system	PSO	95.81	16.58	40.25	9.000
	HPSOCC	85.90	13.58	38.26	65.00
	HPSONC	82.84	11.83	37.22	45.00
	MIW-PSO	82.05	10.91	35.95	27.00
	MIW-PSO	79.79	9.819	35.53	30.00
40-unit system	PSO	151.7	35.50	69.54	120.0
	HPSOCC	103.3	29.23	60.05	456.0
	HPSONC	99.64	27.46	58.18	389.0
	MIW-PSO	97.43	24.58	56.95	280.0
	SHPSO	95.93	23.82	56.83	304.0

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Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power

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