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Data centres are heavily entrenched in our everyday lives since they are an essential element of the web/cloud based economy. In fact, as the demand for data processing and warehousing is increasingly growing, data centres are subjected to deep scrutiny due to their high-energy consumptions and costs. In addition, the impact of data centres is worsened by the use of polluting diesel generators to provide backup power, which contributes directly to increase the emission levels of CO 2 and NOx. In that context, the aim of this work is to investigate the performance of a novel zero-emission cryogenic energy system serving a 1MW el data centre; the cryo-system uses liquid Nitrogen for the combined generation of cooling and power during the sudden utility grid events (power outage & peak demand). A steady-state thermodynamic model is developed covering single-stage and multistage designs; the proposed model considers electrical-load-following as the control strategy for the cryo-system and the consumption of liquid Nitrogen as the objective function to be minimized. Exergy analysis is used to assess the irreversibility distribution in the whole system. The results show that the cryogenic polygeneration for data centre leads to promising values of Power Usage Effectiveness. The results also indicate that the multistage process reduces the hourly consumption of liquid Nitrogen of about 30% compared to the single-stage process.

Conventional approaches for modelling energy consumption for data center are limited to deal with the randomness, emergence and interdependence in energy systems. To fill the gap, an agent-based method was proposed and implemented to simulate the electricity consumption of a data center and to explore the potential of energy saving. The study found that a large amount of energy can be saved by dynamically operating the cooling load according to the practical load of the servers, namely 13% of the total energy consumption can be saved in the dynamic energy saving case (DESC) and 17% in the enhanced dynamic energy saving case (EDESC). The power usage effectiveness (PUE) of the data center can decrease from 1.71 in the original situation to 1.49 in the DESC and 1.42 in the EDESC. By contrast, the frequency of data backup has a heavy impact on the electricity consumption of the data center, while the frequency of computing tasks do not.

One of most important concerns in electrical power markets and distribution network is supplying the customer demands. It is necessary to forecast the usage of electrical power in distribution network. The pattern of electrical power usage depends on many different parameters such as the density of population in those areas, the week days, weather condition and etc. However, the electric utilities and regulators face difficult challenges evaluating new energy efficiency and smart grid programs prompted by recent state. In this paper, it is tried to forecast the electrical power usage according to above parameters by using artificial neural network base on service sharing strategies regarding that can help meet their energy efficiency policy goals. It is tried to find out the pattern of electrical power usage with the dataset which is prepared by real data. Therefore, it has been used here is useful in all kind of power forecasting such as short term, middle term, and long term. It can be helpful to manage the distributed generators production schedule and also correction of electrical power usage.

In data centers, about 40% of the total energy is consumed for cooling the IT equipment. Cooling costs are thus one of the major contributors to the total electricity bill of large data centers. This paper studies two factors affecting data center cooling energy consumption, namely air flow management and data center location selection. A unique rack layout with a vertically cooling air flow is proposed. Two cooling systems, computer room air conditioning (CRAC) cooling system and airside economizer (ASE), have been studied. Based on these two cooling systems, four cities have been selected from the worldwide data center locations. A number of energy efficiency metrics are explored for data center cooling, such as power usage effectiveness (PUE), coefficient of performance (COP) and chiller hours. By analyzing the effects of chiller hours and economizer hours, comparative economic results of cooling power consumption are provided in both systems. The results show that the cooling efficiency and operating costs vary significantly with different climate conditions, energy prices and cooling technologies. As climate condition is the major factor which affects the airside economizer, employing the airside economizer in the cold climate yields much lower energy consumption and operation costs.