Feasibility Research on Establishment of Green Data Center Based on Combined Cooling Heating and Power System

Transcription

1 Feasibility Research on Establishment of Green Data Center Based on Combined Cooling Heating and Power System Li qifen 1, Li tao 1, Zhou zhitian 1, Sun weidong 1, Yang yongwen 1, Zhao Min 2 1. Department of Energy and Environment Engineer, Shanghai University Of Electric Power, Shanghai, , China 3. Nanyang Technological University, Singapore, Abstract Data center is the core of the operation of information industry. With the development of economy, it has experienced dramatic expansion, with its operational stability and economy receiving increasing attention from enterprises. The huge energy consumption and high cost of operation has seriously limited the development of enterprises. Therefore, it is an important way to cut down the energy consumption to explore new energy-saving technology and establish green data center. The establishment of green data center based on distributed cooling heating and power system is the new energy-saving technology based on user-oriented development, which could realize the gradual utilization of energy and enhance the operational efficiency of the motor room. The paper analyzed the causes and status quo of the current difficulties and regards the operating data center of China Unicom as an example to prove the adaptability of the distributed cooling heating and power system and the data center energy demand. At the same time, it analyzed the promotional value and feasibility of CCHP according to the current price war in our country. 1. Introduction Key words: Data Center, CCHP, PUE, Energy Saving Data center is the core of the operation of information industry. With the development of economy, it has experienced dramatic expansion, with its operational stability and economy receiving increasing attention from enterprises. The huge energy consumption and high cost of operation has seriously limited the development of enterprises. Therefore, it is an important way to cut down the energy consumption to explore new energy-saving technology and establish green data center. Establishment of a green data center based on distributed combined cooling heating and power system is a new energy-saving technology based on customer-oriented development. Its principle is based on data center energy consumption needs to establish a micro-grid system, and allocate the electricity and heat gas generator produces to users and realize cascaded utilization of energy, improving the economic feasibility of the engine room operation. 2. Difficulties Concerning Data Center The domestic running data centers in China are generally designed around 2000, so it was difficult to keep up with user s demand and the pace of business expansion in recent years, causing growing contradictions between the business, user, device (as shown in Figure 1). International Journal of Digital Content Technology and its Applications(JDCTA) Volume7,Number5,March 2013 doi: /jdcta.vol7.issue

2 2.1 The Increasing Operational Cost Figure 1 Difficulties concerning data center According to the International Association of uptime, Uptime Institute survey, currently the fees used for the electricity and cooling for servers within three years is 1.5 times that for server hardware purchase. To take a small Unicom IDC engine room in a city in North China for example, IT equipment and refrigeration and air conditioning are key aspects of the electric energy consumption. Through research, the main power in the room for about two months is shown in table 1. Table 1 Power consumption of a typical IDC engine room of Unicom in a north city of China (Unit: kw h yuan/kw h) Date IT Equipment Power Air-conditioner Power Unit Power Total Power consumption consumption Price price ~ ~ It can be seen that the enterprise needs to pay high cost for the room per month. In recent years, the energy situation has been increasingly tense with national electricity sector adjusting prices frequently. For example, in June 2011, the selling prices increased by 0.02 Yuan/kW h. With regard to each month's total power consumption of the room being about 150,000 kw h, an electricity price hike will bring additional expenditures of more than 40,000 yuan a year to enterprises, seriously increasing the burden on them. 2.2 Inadequate Power Supply Data center compare to general commercial building, has a high energy density (approximately 200~1000W/square meters). With the emergence of large capacity, high speed processors, high-density power equipment, marked increase of the overall load of the room appears, and even a local data center s load will amount to 5000W/square meters. The power demand of current data center construction and expansion stands at over 1MW of electricity [2]. In some circumstances, regional power system is unable to meet the growing capacity, which becomes the thorny problem limiting the development of enterprises. 2.3 Inadequate Equipment Space Whenever new project comes on-line, it is needed to set new server and the related storage system. However, traditional engine room generally uses air conditioning system which occupies large space. Therefore, with improving business, IT equipment and refrigeration equipment have more contradictions in space. Outdated cooling system and layout impact the new equipment and circuit expansion and layout [4]. When it is unable to add server and storage system, the only way is to establish new data center and increase the company s investment cost. 1216

3 3. Distributed Cooling Heating Power System and Data Center Energy Demand Adaptability Analysis 3.1 Distributed Combined Cooling Heating and Power System A distributed combined cooling heating and power system (CCHP) refers to the comprehensive energy utilization systems distributed at the user end. At present, it mainly uses natural gas as primary energy, supplemented by renewable energy; secondary energy refers to the scientific distribution of cooling heating and power at the user-end. It mainly centers on joint use and through absorbing cooling unit to carry out heat and cool conversion and the realization of cascaded utilization of energy in order to meet the diverse demands of customers. The technology is receiving widespread attention in recent years, which is in line with China's long-term State policy of energy saving and emission reduction. 3.2 Advantages of Applicative Distributed Combined Cooling Heating and Power System Effective utilization of waste heat from gas engine is the main technical advantages of the system. Also that system is the key to achieving economy. Data center energy is characterized by large and stable demand for electricity and cooling, the easy interfacing with gas-fired units with stable thermal power output so the waste heat could be fully utilized. Take the typical JMS316 gas engine of GE companies for example. The ratio of generated power and cooling demand stands at 1:1.14. The corresponding data center is with 300 rack units, with the power and cooling demand ratio being 1:1.98. In the case of power generation meets customer needs, waste heat of the gas generator can be fully utilized, and inadequate cooling capacity could be supplemented by other means. It s clear that through the technology, it could transfer user s heavy dependence on power grid to more stable demand on gas so as to reduce operating risk; on the other hand, cascaded energy use significantly improves the economy of the engine room operation, and at the same time, it adopts absorbing type cooling unit to carry out heat and cooling conversion outside the engine room, avoiding space contradictory air conditioner and equipment. 3.3 Green Data Center Energy Consumption Evaluation Index PUE Calculation and Economy of the System Energy Consumption Our national data center energy saving has great potential. PUE refers to the internationally recognized energy consumption index of measurement data center ( as shown by figure 1). The higher PUE value indicates that more power has been consumed by power supply, UPS, air-conditioner and other supporting equipment. From physical perspective, the researched data center mainly consists of IT equipment, power supply system and air-conditioner [5].Currently, the energy consumption structure of most data center in our country is shown by figure 2. Figure2 Data center energy consumption overview 1217

4 In Figure 2, the data center PUE value is about 2, which belongs to the rare high operational level in the domestic. Apart from the master devices, the maximum energy consumption is generated by air conditioning system. Thus, it is the key to reduce the PUE value of the air conditioner in the data. It is understood that the efficient data center PUE abroad is usually less than 1.6, and some even below 1.3, and studies have shown that 2.0~2.5 PUE refer to the common value for various data centers in China with the average level standing at 2.2. So it s clear that our national data center also has great potential in green energy [6]. As shown in Figure 2, the data center uses combined cooling, heat and power system. According to energy consumption data of December, 2011 of the research data center, we carried out the following approximate calculations. By PUE values results, it could prove the technical advantages of combined cooling heating and power system and further analyze the energy demand adaptability of distributed combined cooling heating and power system. According to the power and cooling demand ratio 1:1.98, one could get the needed cooling amount: According to GE equipment power capacity and cooling conversion ratio 1:1.14, one could get the cooling capacity of the combined unit: The difference of the total cooling capacity: According to cooling air-conditioner s cooling work and power work ratio (about 3:1), one could get the power consumption for supplementing the needed cooling capacity: The total energy consumption of the data center: Current PUE value of the data center: Clearly, under ideal circumstance, in order to ensure stable equipment operation and reach the same cooling effect, it could reduce the PUE value of the data center to adopt the combine cooling, heating and power system. (from about 2.0 to about 1.5), dramatically enhancing the energy utilization efficiency of the primary energy. So it should be promoted. 4. Feasibility Analysis of Promoting Distributed Gas Combined Cooling, Heating and Power System in Green Data Center 4.1 Regional Power Supply Price Fluctuation Promotes the Wide Application of Combine Supply System Figure 3 Price statistics of national power price 1218

5 Figure 4 Natural gas price of various provinces in China Table 2 National natural gas price area statistics Unit: yuan/m³ Region Northeast North East Center South Southwest Northwest Price The energy saving measures of the data center remains centering on the optimization of engine room management and the improving of the equipment of higher energy consumption. Although it invested huge amount of money, the effect is limited. So it could not get rid of the limit from the power grid department and the dependence on the power system. With the worsening energy situation, the rise of power price will force enterprises to seek combined cooling, heating and power to address the problems concerning the fees [7]. Combined cooling heating and power system s extension is subject to the investment restrictions. It is still confined to the commercial area. Through the research on the commercial power price in major cities in China, one could see that the power price is shown by figure 3. Seen from the figure, commercial electricity prices in China have big regional differences. The power prices of 31 major cities in China stand at about 0.82 dollars. 14 cities are above the line and southern provinces face particularly serious situation. Higher electricity prices not only increases business costs, but also reduced the competitiveness of enterprises. Therefore, it is urgent to reform the energy saving of enterprises with high energy consumption in areas with high power price. 4.2 Impact of Natural Gas Price on Combined Cooling, Heating and Power System Combined cooling, heating and power system regards natural gas as the primary energy, so it economy is directly subject the local natural gas prices. The pricing system of our country s natural gas is related to the piping and transportation cost. So the natural gas price varies from regions to regions. Through research on natural gas supply price of 31 key cities, we get the statistic result as shown by figure 4 and table

6 Figure 5 The ratio of major cities power and gas supply price According to statistic result, the average price of natural gas stands at 2.40yuan/m³, with 18 provinces below the number. The price is low in most regions but the southern part. So it s clear that gas combined cooling, heating and power system enjoy wide application regions in China, which is conducive to displaying the system s energy saving advantage. 4.3 Competition Between Power and Gas Price and the Analysis of Value for Promotion It aims to obtain the region with best potential for promotion for the combined cooling, heating and power system. Given the gas system and major power, gas prices as the index for the feasibility research for the technology, the higher the ratio of power and gas price indicates the higher power price and lower gas price, so it is more conducive to promotion. According to the above power and gas price, one could get the power and gas price ratio of different areas as shown by figure 5. As the figure shows, Southwest China, East and the Northwest have better promotion environment, such as using gas-fired generation unit for data center transformation, which can reverse the passive situation in data center with high power consumption, significantly reduce the PUE value, and improve economic behavior in operation of the room. 5. Conclusion The costs generated by operation, maintenance of the data center enjoy a big portion of the total cost. In addition energy saving problem concerns construction of the engine room and the whole process of maintenance. So the traditional transformation could not fully address the energy saving problem of the data center. Only the combined cooling, heating and power system is the basic method to reduce the huge cost for operation. Combined cooling heating and power generation has been applied in China in recent years. Technology becomes mature, suitable for transformation of data center with stable demand of power and cooling. It is calculated that this technology significantly reduces data center PUE parameter, improves energy efficiency, and is very important to the frog-leap development of the energy saving and transformation technology of data center. With increasingly severe situation of China's energy, the rise of power price cannot be changed. Electricity, gas prices will also rise. So, the advantages of the combined cooling, heating and power system will be more highlighting. Therefore, gradual promotion of the gas combined cooling, heating and power transformation of the data center and construction of green and efficient data center will sure be the development trend for energy saving of enterprises. 1220

7 6. Acknowledgement This paper is supported by National Natural Science Fund Project ( ) and the Shanghai City Board of education, education development foundation (09SG51). 7. References [1] Peichang Shi, Huaimin Wang, Gang Yin, Fengshun Lu, Tianzuo Wang, "Prediction-based Federated Management of Multi-scale Resources in Cloud", AISS, Vol. 4, No. 6, pp. 324 ~ 334, 2012 [2] Yong Khai Leong, Khin Mi Mi Aung, Pantelis Sophoclis Alexopoulos, "Storage System Architecture for Data Centers of the Future", IJACT, Vol. 4, No. 9, pp. 184 ~ 192, 2012 [3] Renuga Kanagavelu, Bu-Sung Lee, Francis, Vasanth Ragavendran, Khin Mi Mi Aung, "Adaptive Routing for Layer-2 Load Balancing in Data Center Networks", AISS, Vol. 4, No. 15, pp. 1 ~ 12, 2012 [[4] Dianxuan Gong, Chuanan Wei, Ling Wang, Xiaoqiang Guo, "Research on the Center Sets for Radial Basis Functions Interpolation", JDCTA, Vol. 6, No. 16, pp. 79 ~ 86, 2012 [5] Dulyawit Prangchumpol, Siripun Sanguansintukul, Panjai Tantasanawong, "Improving Heterogeneous Workload Performance in Server Virtualization Based on User Behaviors", JCIT, Vol. 7, No. 16, pp. 544 ~ 552, 2012 [6] Jun Liang, TingXue Sun, MeiFang Xue, Zhou Ji, LiZhong Zhang, BaoLuo Li, "Research on Regional Electronic Health Records Data Center Based on Cloud Computing", IJACT, Vol. 4, No. 11, pp. 389 ~ 397, 2012 [7] Zeliu Ding, Wei Wei, Deke Guo, Xueshan Luo, "Measuring the Reliability of Data Center Network", AISS, Vol. 3, No. 7, pp. 82 ~ 91, 2011 [8] Yiqun Li, Qing Nie, Xuezheng Zhang, "Study on System Application and Efficiency Analysis of China E-Port", JCIT, Vol. 6, No. 2, pp. 50 ~ 58, 2011 [9] M. R. Aliabadi, Mohammad Reza Ahmadi, "Proposing a Comprehensive Storage Virtualization Architecture with Related Verification for Data Center Application", AISS, Vol. 2, No. 3, pp. 68 ~ 75, 2010 [10] Yan Liping,,, Song Kai, Wang Chao, "Design and Realization of Forest Fire Monitoring System Based on Image Analysis", JDCTA, Vol. 5, No. 12, pp. 452 ~ 458, 2011 [11] Daoan Huo,Qiang Cao, "RTO Optimization for TCP Incast Scenarios", IJACT, Vol. 3, No. 8, pp. 119 ~ 126, 2011 [12] Li Dequan, Wang Ying, Xiong Anyuan, Tinghuai Ma, "High Performance Computing Model for Processing Meteorological Data in Cluster System", JCIT, Vol. 6, No. 4, pp. 92 ~ 98, 2011 [13] Siyuan Jing, Kun She, "A Novel Model for Load Balancing in Cloud Data Center", JCIT, Vol. 6, No. 4, pp. 171 ~ 179, 2011 [14] Wang xiao-hua, Zhang yong-mei, "Chaotic quantum-behaved PSO Algorithm for Power System Economic Load Dispatch", JDCTA, Vol. 5, No. 8, pp. 290 ~ 297, 2011 [15] Fang Fang, "Wind Power Pricing Strategy based on Accounting Cost Analysis Approach", JDCTA, Vol. 5, No. 8, pp. 348 ~ 354, 2011 [16] Amrita Dey, Nabendu Chaki, Sugata Sanyal, "Modeling Smart Grid using Generalized Stochastic Petri Net", JCIT, Vol. 6, No. 11, pp. 104 ~ 114, 2011 [17] LI Jian, "Modified Plasmodium Evolutionary Algorithm for Economic Dispatch", IJACT, Vol. 3, No. 11, pp. 333 ~ 339, 2011 [18] Carrasco, M.C. Romero-Ternero, F. Sivianes, M.D. Hernandez, J.I. Escudero, "Multi-Agent and Embedded System Technologies Applied to Improve the Management of Power Systems", JDCTA, Vol. 4, No. 1, pp. 79 ~ 85, 2010 [19] M. ELSayed Youssef, Moataz H.Khalil, Khairia E.AL-NAdi, "Neural Network Modeling for Proton Exchange Membrane Fuel Cell (PEMFC)", AISS, Vol. 2, No. 2, pp. 61 ~ 66, 2010 [20] Mustarum Musaruddin,, Rastko Zivanovic, "Web Services for Automated Fault Analysis in Electrical Power Systems", AISS, Vol. 2, No. 3, pp. 76 ~ 81, 2010 [21] Yonggui He, Tianxiang Huang, Chang Liu, "Study of Wind Power Forecasting Based on Combination Method", IJACT, Vol. 3, No. 10, pp. 38 ~ 45,

8 [22] Yonghua Wu, Li Xu, "Research on Fuzzy Linguistic Synthetic Evaluation of Renewable Distributed Energy Generation Investment", AISS, Vol. 4, No. 18, pp. 513 ~ 518, 2012 [23] ZHANG Wei, FAN Lei, LIU Yujuan, "Study on Improving Power System Transient Stability by Superconducting Magnetic Energy Storage Device", JDCTA, Vol. 6, No. 15, pp. 306 ~ 314, 2012 [24] Huiting Chen, Xiaolin Luo, Yongquan Li, Huachen Fu, Xiaolei Zheng, Xiaotao Yuan, "Ill-conditioned Load Flow Study Based on Asynchronous Parallel Computing in Rural Power Distribution Networks", JCIT, Vol. 7, No. 19, pp. 261 ~ 268, 2012 [25] Mat Syai'in, Adi Soeprijanto, I Made Yulistya Negara, Muhammad Mahfud., "Incremental Particle Swarm Optimizer with local search for Optimal Power Flow Subjected to Digital GCC based on Neural Network", JDCTA, Vol. 6, No. 7, pp. 242 ~ 252,