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Optimal operation of Internet Data center with PV and energy storage type of UPS clusters

Global Energy Interconnection 2024年第1期7卷 61-70页

作者： Man Chen Yuxin Zhao Yuxuan Li Peng Peng Xisheng Tang CSG PGC Energy Storage Research Institute Guangzhou 510000P.R.China Institute of Electrical Engineering Chinese Academy of SciencesBeijing 100190P.R.China University of Chinese Academy of Sciences Beijing 100049P.R.China

With the development of green data centers,a large number of Uninterruptible power Supply(UPS)resources in Internet Data center(IDC)are becoming idle assets owing to their low utilization *** revitalization of these idle UPS resources is an urgent problem that must be *** on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this ***,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control ***,the classified EUPS aggregation unit was used to determine the optimal operation of the *** IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was ***,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

关键词： Three-tier optimization framework Energy storage type of the UPS EUPS cluster classification method Quantum Particle Swarm Optimization

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Variation Characteristics of Regional Synchronous Wind in Hami,Xinjiang of Northwest china

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Journal of Meteorological Research 2015年第2期29卷 344-357页

作者：黄林宏宋丽莉李刚辛渝 School of Atmospheric Sciences Nanjing University of Information Science & Technology Public Meteorological Service center China Meteorological Administration Institute of Desert Meteorology China Meteorological Administration

From several towers in Hami, Xinjiang of Northwest china, built by the national wind power resources professional observation network, we selected three towers with synchronous 10-min average wind speed data for one year（May 2011-April 2012） under strict quality control. The towers are located where large-scale wind power development is projected. We analyzed the frequency and variation of extreme wind speed at low wind condition（LWC）, rated wind condition（RWC）, and cut-out wind condition（CWC）, which may significantly impact the electric power grid configuration in large-scale wind power development. The correlation between duration and frequency of LWC/RWC/CWC is obtained. Major findings are： 1） The frequency of CWC is the lowest among all conditions, its synchronous rate at all three towers tends to be zero, and the frequency of LWC is always greater than that of RWC. 2） Among the three towers, the synchronous rate of RWC steadily increases with height, and LWC differs little between different levels. The synchronous rate of LWC concentrates in winter, while that of RWC mainly occurs in spring and *** variation of LWC/RWC during the entire year is significantly different. 3） During the study year,the longest durations of synchronous LWC and RWC among the three towers are up to 640 and 700 min,respectively. The duration and frequency of LWC/RWC can be quantitatively well described by a logarithmic function. Consequently, the synchronous rates of LWC and RWC over any duration in the region can be easily calculated by using the fitting function equation from observed data. These results are of value to the planning of large-scale wind power transmission and grid dispatching in this area.

关键词： regional synchronous wind low wind rated wind variation characteristics Hami Xinjiang

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维普期刊数据库

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A long short-term memory based wind power prediction method

A long short-term memory based wind power prediction method

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第40届中国控制会议

作者： Yufeng Huang Min Ding Zhijian Fang Qingyi Wang Zhili Tan Danyun Li School of Automation China University of Geosciences Hubei Key Laboratory of Advanced control and Intelligent Automation for Complex Systems Engineering Research center of Intelligent Technology for Geo-Exploration Ministry of Education

Wind power prediction is the basis of power grid energy dispatching. However, wind instability increases the difficulty of wind power prediction. The paper proposes a wind power prediction method based on long and short-term memory network to improve the accuracy of wind power prediction. First, wind power sequence is decomposed by empirical mode decomposition（EMD） method, and the noise in the original sequence was removed by effective component reconstruction. Then, long shortterm memory（LSTM） with the ability of information memory predicts model of wind power sequence. The improved particle swarm optimization algorithm（IPSO） optimized the parameters of LSTM to solve the problem that the parameters of LSTM, such as the number of neurons, the learning rate and the number of iterations, are difficult to determine and thus affect the prediction accuracy of the model. Finally, the proposed EMD-IPSO-LSTM method makes rolling prediction of wind power series of actual wind farm, and the prediction results are compared with other prediction models. The results show that the prediction model has higher accuracy.

关键词： Wind power prediction Long short-term memory Improved particle swarm optimization

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Review of Wind power Forecasting Methods:From Multi-spatial and temporal Perspective

Review of Wind Power Forecasting Methods:From Multi-spatial ...

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第36届中国控制会议

作者： Zhaoyu Jiang Qing-Shan Jia Xiaohong Guan center for Intelligent and Networked Systems(CFINS) Department of AutomationTsinghua National Laboratory for Information Science and TechnologyTsinghua University MOE KLINNS Lab Xi'an Jiaotong University

ISBN: (纸本)9781538629185

Wind power is one of the most popular renewables and occupies a great proportion in the total capacity of the grid *** operations of the grid system such as scheduling and dispatching dependent much on the prediction accuracy of power *** paper reviews wind power forecasting methods in multi-spatial scales and multi-temporal *** different spatial scales,methods for a single turbine,a wind farm,and even a large wind power generation area are considered in three parts *** each part,firstly,forecasting methods are divided into methods with only historical wind information and methods with additional meteorological information,and then,classified up to four different temporal *** about the similarity and discrepancy of these methods are also presented at the end of each ***,this paper gives two examples of real applications where wind power forecasting methods are significant.

关键词： Wind power forecasting Forecast method Temporal scale Spatial scale Review

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