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Reactive power coordination optimization technology for source-network-load-storage in new distribution network based on adaptive learning rate convolutional neural network
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Jinbao Qian1, Xiaoguang Liu2, Xi Cai2, Yi Liu2, Jianfeng Dai3
Renewable Energy Resources | 2024, 42(2) : 267 - 275
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Renewable Energy Resources | 2024, 42(2): 267-275
Reactive power coordination optimization technology for source-network-load-storage in new distribution network based on adaptive learning rate convolutional neural network
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Jinbao Qian1, Xiaoguang Liu2, Xi Cai2, Yi Liu2, Jianfeng Dai3
Affiliations
  • 1 Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd. Lanzhou 730000 China
  • 2 Gansu Tongxing Intelligent Technology Development Co., Ltd. Lanzhou 730050 China
  • 3 College of Au tomation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing 210023 China
Published: 2024-02-20
Outline
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With the promotion of the "dual carbon" goal, the capacity of distributed new energy connected to the power grid has significantly increased. The use of distribution network source network load storage coordination optimization strategy is an important method to achieve distributed new energy consumption, among which reactive power optimization can ensure the safe and stable operation of the power grid. This article proposes an adaptive learning rate convolutional neural network based optimization technique for load storage and reactive power coordination in distribution networks. Firstly, a reactive power optimization model is constructed with the goal of minimizing network loss and voltage offset. Secondly, utilizing the powerful nonlinear fitting ability of convolutional neural networks, the mapping relationship between power grid operation scenarios, reactive power regulation equipment, and energy storage charging and discharging strategies is excavated. Adaptive learning rate is introduced to update network parameters and improve network training efficiency. Finally, by controlling the charging and discharging conditions of reactive power regulation equipment and energy storage devices to coordinate the output of distributed power sources, active optimization control of reactive power and voltage in new distribution network is achieved. After simulation verification of the IEEE33 node power grid model, the results show that the proposed optimization method for load storage and reactive power coordination in the distribution network source network improves the voltage regulation ability of the power system, laying a good foundation for the safe and reliable operation of the distribution network.

distributed new energy  /  optimization of source network load storage coordination  /  reactive power optimization  /  adaptive learning rate  /  convolutional neural network
Jinbao Qian, Xiaoguang Liu, Xi Cai, Yi Liu, Jianfeng Dai. Reactive power coordination optimization technology for source-network-load-storage in new distribution network based on adaptive learning rate convolutional neural network[J]. Renewable Energy Resources, 2024 , 42 (2) : 267 -275 .
Year 2024 volume 42 Issue 2
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Article Info
  • Receive Date:2023-10-17
  • Online Date:2025-07-22
  • Published:2024-02-20
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  • Received:2023-10-17
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Affiliations
    1 Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd. Lanzhou 730000 China
    2 Gansu Tongxing Intelligent Technology Development Co., Ltd. Lanzhou 730050 China
    3 College of Au tomation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing 210023 China
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表12种不同金属材料的力学参数

Family
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Number of
genus
种数
Number of
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占总种数比例
Percentage 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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