Rated load state performance assessment and analysis of ultra-supercritical coal-fired power plant

ABSTRACT

To address the problems of unclear assessment standards and incomplete assessment systems in the performance assessment of ultra-supercritical coal-fired power plants. This study utilized the running dataset from a 660 MW ultra-supercritical coal-fired power plant in Xinjiang, China. By combining the analytic hierarchy process and the entropy weight method, a system of assessment indicators was established that used three layers of criteria. A running line graph representing power plant performance and containing 149 moments was obtained, which provided a theoretical basis and optimization direction for improving the power plant. The results show that the 149 running states of the power plant performance are all at level I. The boiler running level had 143 moments (95.97%), the steam-turbine level had 135 moments (90.60%), the power consumption rate level had 148 moments (99.33%), and the environmental performance level had 143 moments (89.94%) of running states at level I.

KEYWORDS:

• Performance assessment and analysis
• power plant optimization
• fuzzy assessment method
• analytic hierarchy process
• entropy weight method

Acknowledgement

The measurement and collection of the power plant running data in this paper were accomplished with the great support of Zenning Cheng, Zhilong Yang and Yanxun Hou from Xinjiang TBEA Tianchi Energy Co, Ltd. We would like to express our heartfelt thanks to them.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work supported by Major Science and Technology Special Projects of Xinjiang Uygur Autonomous Region [2022A01002-2], Key Research and Development Tasks of Xinjiang Uygur Autonomous Region [2022B03028-5], National Natural Science Foundation of China. [12362021], Xinjiang Uygur Autonomous Region “Tianshan Talents” Training Program [2022TSYCCX0054, 2022TSYCJC0031].

Notes on contributors

Aopeng Cao

Aopeng Cao received the B.S. degree in Heating, Ventilation and Air Conditioning from Tianjinchengjian University, Tianjin, China, in 2019. He is currently studying toward the Master degree in Energy & Power Engineering with the School of Electrical Engineering, Xinjiang University, Urumqi 830047, China.

Xiaojing Ma

Xiaojing Ma received the B.S. degree in Energy & Power Engineering from Xi’an Jiaotong University, Xi’an, China, in 2005, the M.S. degree in Energy & Power Engineering from Xi’an Jiaotong University, Xi’an, China, in 2008 and the PH. D in Mechanical Engineering & Automation from the School of Electrical Engineering, Xinjiang University, Urumqi 830047, China, in 2016.

Zening Cheng

Zening Cheng received the B.S. degree in Energy & Power Engineering from Xi’an Jiaotong University, Xi’an, China, in 2012, the M.S. degree in Energy & Power Engineering from Xi’an Jiaotong University, Xi’an, China, in 2015 and the PH. D in Energy & Power Engineering from Xi’an Jiaotong University, Xi’an, China, in 2020.

Jiawang Zhang

Jiawang Zhang received the B.S. degree in Energy and Power Engineering from China University of Petroleum-Beijing, Beijing, China, in 2019. He is currently studying toward the Master degree in Energy and Power Engineering with the School of Electrical Engineering, Xinjiang University, Urumqi 830047, China.

Yanxun Hou

Yanxun Hou received the B.S. degree in Energy and Power Engineering from Inner Mongolia University of Technology University, Hohhot, China, in 2018.