ISSN 1000-1239 CN 11-1777/TP

Special Issue: 2017能源互联网专题

### Energy Efficiency Optimization Based on Storage Scheduling and Multi-Source Power Supplying of Data Center in Energy Internet

Sun Chunlei1,2,3, Wen Xiangming1,2,3, Lu Zhaoming1,2,3, Sheng Wanxing4, Zeng Nan5, Li Yang4

1. 1(School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876); 2(Beijing Key Laboratory of Network System Architecture and Convergence (Beijing University of Posts and Telecommunications), Beijing 100876); 3(Beijing Laboratory of Advanced Information Networks (Beijing University of Posts and Telecommunications), Beijing 100876); 4(China Electric Power Research Institute, Beijing 100192); 5(State Grid Corporation of China, Beijing 100031)
• Online:2017-04-01

Abstract: Recently, the optimization problem of energy efficiency for data centers has been paid widespread attention. In this paper, we investigate this problem in a new idea under the background of energy Internet, where subscribers are equipped with storage and smart energy management devices, especially for industry subscribers. In addition, there are large scale of clean energy generation and electricity-sale companies, which means that industry subscribers can purchase electricity from multi-source suppliers to cut down their energy cost and improve their energy efficiency based on real-time price, pollution index, etc. It is assumed that data centers always attempt to choose cheaper and cleaner energy in each hour and buy more electricity in valley hours with lower price compared to peak hours to reduce the energy cost. Thus both of pollution index function and real-time price are adopted to formulate the multi-source energy-purchasing cost. And both of the operation cost and potential cost are adopted to model the charging and discharging cost of storage devices, or storage cost for short. Based on this, the energy cost model with storage is formulated and is compared with the one without storage. Then we give the related algorithm to solve these problems and give the analysis of performance. Simulation results confirm that the proposed model can greatly reduce the daily cost of electricity and encourage the utilization of renewable energy resources by choosing optimal strategies of energy source selecting and daily storage scheduling.

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