Technical Brief

Optimal Generation Maintenance Schedule for Bundled Wind–Thermal Generation System

[+] Author and Article Information
Yinghao Ma

State Key Laboratory of Power Transmission Equipment
and System Security,
Chongqing University,
Chongqing 400044, China
e-mail: yinghao_ma@126.com

Kaigui Xie

State Key Laboratory of Power Transmission Equipment
and System Security,
Chongqing University,
Chongqing 400044, China
e-mail: kaiguixie@vip.163.com

Jizhe Dong

Power Economic Research Institute of State Grid Jilin
Electric Power Company Ltd.,
Changchun 130062, China
e-mail: djzccforward@163.com

Heng–Ming Tai

Department of Electrical and Computer Engineering,
University of Tulsa,
Tulsa, OK 74104
e-mail: tai@utulsa.edu

Bo Hu

State Key Laboratory of Power Transmission Equipment
and System Security,
Chongqing University,
Chongqing 400044, China
e-mail: hboy8361@163.com

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 25, 2017; final manuscript received July 26, 2017; published online August 22, 2017. Assoc. Editor: Ryo Amano.

J. Energy Resour. Technol 140(1), 014501 (Aug 22, 2017) (7 pages) Paper No: JERT-17-1178; doi: 10.1115/1.4037536 History: Received April 25, 2017; Revised July 26, 2017

Bundled wind–thermal generation system (BWTGS) is an effective way to utilize remote large–scale wind power. The optimal generation maintenance schedule (GMS) for BWTGS is not only helpful to improve the system reliability level but also useful to enhance the system economic efficiency and extend the lifetime of components. This paper presents a model to optimize the GMS for BWTGS. The probabilistic production simulation technique is employed to calculate the system costs, and a sequential probabilistic method is utilized to capture the sequential and stochastic nature of wind power. A hybrid optimization algorithm (HOA) based on the simulated annealing (SA) and multipopulation parallel genetic algorithm (GA) is developed to solve the proposed model. Case studies demonstrate the effectiveness of this proposed model. Effects of the reliability deterioration of thermal generating units (TGUs) and the pattern of BWTGS transmission power are also investigated.

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Grahic Jump Location
Fig. 1

The flowchart of the hybrid algorithm

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Fig. 2

Wind profile of the typical day in each month used in the case studies

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Fig. 3

Results of iteration processes of the HOA and conventional GA

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Fig. 4

Transmission power in January of four scenarios in case 3 study



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