Research on Adaptive Control and Optimal Scheduling Algorithm Under Mixed Energy Architecture of Data Center
An approximate dynamic programming optimization scheduling strategy combining independent microgrid day-ahead scheduling system and adaptive weighted sum algorithm is proposed. The strategy aims to minimize energy consumption and maximize energy utilization efficiency of data centers through intelli...
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| Veröffentlicht in: | International Conference on Information Systems and Computer Aided Education (Online) S. 1159 - 1164 |
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| Sprache: | Englisch |
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IEEE
27.09.2024
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| ISSN: | 2770-663X |
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| Abstract | An approximate dynamic programming optimization scheduling strategy combining independent microgrid day-ahead scheduling system and adaptive weighted sum algorithm is proposed. The strategy aims to minimize energy consumption and maximize energy utilization efficiency of data centers through intelligent control means. Firstly, this paper designs a day-ahead scheduling system of an independent micro-grid, which can integrate a variety of energy inputs, including solar energy, wind energy, traditional grid power supply and energy storage equipment, to ensure the stability and reliability of the energy supply of data centers. Secondly, the adaptive weighted sum algorithm is introduced, which can dynamically adjust the proportion of each energy input according to the changes of real-time energy market price and environmental conditions, so as to achieve the optimization of energy cost. Finally, through the approximate dynamic programming algorithm, the adaptive ability of the system is further improved, so that it can automatically learn and optimize the scheduling decision in the complex and changeable energy environment. In order to verify the effectiveness of the proposed strategy, a system simulation experiment is carried out. The experimental results show that compared with the traditional fixed scheduling strategy, the proposed adaptive control and optimal scheduling algorithm can significantly reduce the energy cost of data centers, and improve the flexibility and response speed of energy use. |
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| AbstractList | An approximate dynamic programming optimization scheduling strategy combining independent microgrid day-ahead scheduling system and adaptive weighted sum algorithm is proposed. The strategy aims to minimize energy consumption and maximize energy utilization efficiency of data centers through intelligent control means. Firstly, this paper designs a day-ahead scheduling system of an independent micro-grid, which can integrate a variety of energy inputs, including solar energy, wind energy, traditional grid power supply and energy storage equipment, to ensure the stability and reliability of the energy supply of data centers. Secondly, the adaptive weighted sum algorithm is introduced, which can dynamically adjust the proportion of each energy input according to the changes of real-time energy market price and environmental conditions, so as to achieve the optimization of energy cost. Finally, through the approximate dynamic programming algorithm, the adaptive ability of the system is further improved, so that it can automatically learn and optimize the scheduling decision in the complex and changeable energy environment. In order to verify the effectiveness of the proposed strategy, a system simulation experiment is carried out. The experimental results show that compared with the traditional fixed scheduling strategy, the proposed adaptive control and optimal scheduling algorithm can significantly reduce the energy cost of data centers, and improve the flexibility and response speed of energy use. |
| Author | Zhang, Wancai Wang, Tao Xia, Xuwei Mu, Jun Yang, Wenqing Zhang, Nan |
| Author_xml | – sequence: 1 givenname: Wancai surname: Zhang fullname: Zhang, Wancai organization: State Grid Electric Power Research Institute Co., Ltd.,Nanjing,China – sequence: 2 givenname: Xuwei surname: Xia fullname: Xia, Xuwei organization: Electric Power Research Institute of State Grid Ningxia Electric Power Co., Ltd.,Yinchuan,China – sequence: 3 givenname: Nan surname: Zhang fullname: Zhang, Nan email: zhangnan2@sgepri.sgcc.com.cn organization: State Grid Electric Power Research Institute Co., Ltd.,Nanjing,China – sequence: 4 givenname: Wenqing surname: Yang fullname: Yang, Wenqing organization: State Grid Electric Power Research Institute Co., Ltd.,Nanjing,China – sequence: 5 givenname: Jun surname: Mu fullname: Mu, Jun organization: State Grid Electric Power Research Institute Co., Ltd.,Nanjing,China – sequence: 6 givenname: Tao surname: Wang fullname: Wang, Tao organization: State Grid Electric Power Research Institute Co., Ltd.,Nanjing,China |
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| Snippet | An approximate dynamic programming optimization scheduling strategy combining independent microgrid day-ahead scheduling system and adaptive weighted sum... |
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| SubjectTerms | Accuracy adaptive control approximate dynamic programming Approximation algorithms Data centers Dynamic scheduling Heuristic algorithms In dependent micro-network Machine learning Optimal scheduling optimal scheduling algorithm optimized scheduling Power system stability Scheduling Stability analysis |
| Title | Research on Adaptive Control and Optimal Scheduling Algorithm Under Mixed Energy Architecture of Data Center |
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