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Research on Improving Voltage Transient Characteristics of Energy Storage System with Improved MPC
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Jian BAO1, Peihao YANG2
Journal of Power Supply | 2024, 22(2) : 242 - 249
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Journal of Power Supply | 2024, 22(2): 242-249
Battery and Energy Storage
Research on Improving Voltage Transient Characteristics of Energy Storage System with Improved MPC
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Jian BAO1, Peihao YANG2
Affiliations
  • 1 Intelligent Manufacturing College Shandong Polytechnic Jinan 250104 China
  • 2 Xi'an Thermal Power Research Institute Co., Ltd. Xi'an 710054 China
Published: 2024-03-30 doi: 10.13234/j.issn.2095-2805.2024.2.242
Outline
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The grid-connected operation of an energy storage system is realized by a converter. Due to the low switching frequency of the conventional converter, there exists time delay in sampling and calculation, which will lead to poor transient characteristics of the energy storage system and even instability of the whole power grid. In this paper, model predictive control(MPC) is used to achieve a fast power response of energy storage system and avoid the influence of time delay. A power weight value function is introduced to calculate the optimal output voltage of energy storage converter in the MPC control of active and reactive power. To solve the problem of inaccurate MPC model caused by the parameter deviation of filter inductor, inductance error compensation control is used to improve the model accuracy. Through Matlab/Simulink simulations and experimental results, it is verified that the proposed scheme can improve the transient characteristics of energy storage system and effectively eliminate the influence of error on the MPC control performance.

Energy storage system  /  transient characteristics  /  model predictive control(MPC)  /  power weight value function  /  inductance error voltage compensation
Jian BAO, Peihao YANG. Research on Improving Voltage Transient Characteristics of Energy Storage System with Improved MPC[J]. Journal of Power Supply, 2024 , 22 (2) : 242 -249 . DOI: 10.13234/j.issn.2095-2805.2024.2.242
  • National Key R & D Plan"973"(2017YFB0902102)
Year 2024 volume 22 Issue 2
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Article Info
doi: 10.13234/j.issn.2095-2805.2024.2.242
  • Receive Date:2021-03-30
  • Online Date:2025-07-21
  • Published:2024-03-30
Article Data
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History
  • Received:2021-03-30
  • Revised:2021-05-31
  • Accepted:2021-06-09
Funding
National Key R & D Plan"973"(2017YFB0902102)
Affiliations
    1 Intelligent Manufacturing College Shandong Polytechnic Jinan 250104 China
    2 Xi'an Thermal Power Research Institute Co., Ltd. Xi'an 710054 China
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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
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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