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Optimization of automatic generation control response performance of flywheel-thermal power system based on load forecasting
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Le WEI1, Shaoxin SU1, Fang FANG1, Jun LI2, Feng HONG1
Thermal Power Generation | 2023, 52(5) : 92 - 99
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Thermal Power Generation | 2023, 52(5): 92-99
Thermal energy and science research
Optimization of automatic generation control response performance of flywheel-thermal power system based on load forecasting
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Le WEI1, Shaoxin SU1, Fang FANG1, Jun LI2, Feng HONG1
Affiliations
  • 1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
  • 2.State Grid Shandong Electric Power Company Research Institute of Electric Power, Jinan 250000, China
Published: 2023-05-25 doi: 10.19666/j.rlfd.202209238
Outline
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As the proportion of new energy power generation continues to increase, the stability of grid frequency is severely challenged, and the role of conventional thermal power units in grid frequency regulation has become increasingly prominent. However, the adjustment rate and accuracy of some thermal power units are difficult to meet the demand of grid load fluctuations. Therefore, a response performance optimization strategy for flywheel-thermal power system automatic generation control based on load forecasting was proposed. Firstly, the load is predicted, using the tree-based pipeline optimization tool TPOT library to automatically machine learning to match and train the load regression prediction model, and the automatic generation control day-ahead planned value is introduced into the training data to reduce the prediction error. Then, according to the load prediction value and the current flywheel system, with the optimization goal of minimizing the regulation rate of thermal power units, the flywheel energy storage system is acted firstly in load distribution, and the state of charge of the flywheel is adjusted meanwhile. Finally, a simulation experiment is carried out based on the actual operation data of a power plant in Hubei, and the experimental results prove that the proposed method can effectively improve the frequency modulation performance of thermal power units.

flywheel energy storage  /  thermal power unit  /  automatic generation control  /  automatic machine learning  /  load forecast
Le WEI, Shaoxin SU, Fang FANG, Jun LI, Feng HONG. Optimization of automatic generation control response performance of flywheel-thermal power system based on load forecasting[J]. Thermal Power Generation, 2023 , 52 (5) : 92 -99 . DOI: 10.19666/j.rlfd.202209238
  • Science and Technology Project of State Grid Corporation of China(52060021N00P)
Year 2023 volume 52 Issue 5
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Article Info
doi: 10.19666/j.rlfd.202209238
  • Receive Date:2022-09-20
  • Online Date:2026-01-23
  • Published:2023-05-25
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  • Received:2022-09-20
Funding
Science and Technology Project of State Grid Corporation of China(52060021N00P)
Affiliations
    1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
    2.State Grid Shandong Electric Power Company Research Institute of Electric Power, Jinan 250000, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202209238
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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