Applied Energy 276 (2020) 115497
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Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
A new indicator for a fair comparison on the energy performance of data
centers☆
T
⁎
Jian Lia,b,1, Jakub Jurasza,c,1, Hailong Lia,d, , Wen-Quan Taoe, Yuanyuan Duanb, Jinyue Yana
a
School of Business, Society & Engineering, Mälardalen University, Vasteras, Sweden
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Tsinghua University, Beijing, PR China
c
AGH University, Cracow, Poland
d
School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, Hubei, PR China
e
Key Laboratory for Thermo-Fluids Science and Engineering of Ministry of Education, Xi’an Jiaotong University, PR China
b
H I GH L IG H T S
new indicator, coefficient of power usage effectiveness (COPUE) is proposed.
• AEnergy
performances of data centers in various climatic conditions is compared.
• A simplified
method is developed to present benchmark cooling technology.
•
A R T I C LE I N FO
A B S T R A C T
Keywords:
Data center
Power usage effectiveness (PUE)
Cooling technology
Coefficient of PUE (COPUE)
Energy efficiency
Climatic conditions
The power usage effectiveness (PUE) is commonly used as the key performance indicator to evaluate the energy
performance of data centers. However, using only PUE cannot enable a fair comparison when data centers are
operating in different regions, due to the unneglectable impacts of climatic conditions on the power consumption
of cooling systems. To solve this problem, a new indicator, coefficient of PUE (COPUE), is proposed, which is
defined as the ratio of the measured PUE of real data centers to the local benchmark PUE. The benchmark PUE is
reckoned based on the current most commonly used cooling technology, which consists of water-cooled chillers
and water cooling towers. A simplified method for calculating benchmark PUE is also developed. The degree
hour of water cooling is introduced to consider the impacts of local climatic conditions. It presents the annual
accumulated hours, in which chilled water is needed to satisfy the cooling demand of data centers. Through
several case studies, COPUE has been proved to be an effective indicator for comparing the energy performance
of data centers. When the same cooling technology is adopted, it can reflect how good the design and operation
are; while, when different cooling technologies are adopted, it can be used to demonstrate which one is superior.
1. Introduction
To support the information driven society, data centers (DCs) are
used to process, store, and transmit data and information. DCs have
become indispensable parts of modern societies [1]. With the fast
growth of the number of DCs, their electricity consumption increased
by 6% from 2010 to 2018 [2]; and it exceeded 200 TWh in 2018, accounting for 1% of the global electricity production [3]. Currently,
some reports predicted that in 2025, DCs would account for about 20%
of the global electricity production [4]. Therefore, improving the
energy performance of DCs is crucial to minimize their impact on the
environment and enhance global energy security.
The electricity demand of a DC mainly comes from IT equipment,
cooling systems, illuminating systems, and other safety related electrical equipment [5]. The IT equipment is the core of the DC operation,
whereas other subsystems are accessories but necessary to guarantee an
effective and stable operation of the DC. Many indicators have been
proposed to evaluate the energy performance of DCs [6], amongst
which the power usage effectiveness (PUE) is the most commonly used
key performance indicator (KPI) [7].
☆
The short version of the paper was presented at CUE2019, Oct 16–18, Xiamen, China. This paper is a substantial extension of the short version of the conference
paper.
⁎
Corresponding author.
E-mail addresses: hailong.li@mdh.se, lihailong@gmail.com (H. Li).
1
Contributed equally to this work as first authors.
https://doi.org/10.1016/j.apenergy.2020.115497
Received 24 March 2020; Received in revised form 13 June 2020; Accepted 4 July 2020
0306-2619/ © 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
Applied Energy 276 (2020) 115497
J. Li, et al.
Nomenclature
P
T
t
W
out
PPTD
rated
real
wb
wct
power capacity
temperature
time duration
power consumption
Abbreviations
Subscripts
bm
chiller
cs
DC
es
in
IT
max
min
mode
outlet
pinch point temperature difference
rated design condition
real data/real condition
wet bulb
water cooling tower
COP
COPUE
CRAH
DC
FAC
FC
HC
KPI
PUE
benchmark
water-cooled chiller
cooling system
data center
electrical system
indoor
IT equipment
maximal
minimal
operation mode
Coefficient of Performance
Coefficient of Power Usage Effectiveness
Computer Room Air Handler
Data Center
Free Air Cooling
Forced Cooling
Hybrid Cooling
Key Performance Indicator
Power Usage Effectiveness
significantly affect the energy performance of DCs. However, so far,
there has not been such a performance indicator that can take into
account such impacts. Even though PUE is a simple KPI to evaluate the
energy performance of DCs, it cannot enable a fair comparison and
assessment on the merits of different cooling technologies. For instance,
a lower PUE may be simply due to lower ambient temperatures, instead
of meaning a better cooling technology. Therefore, PUE cannot be used
for the selection of cooling technologies when DCs are operating in
different climatic conditions.
To bridge the knowledge gap, the objective of this work is to propose a new indicator, coefficient of PUE (COPUE), which can enable a
fair comparison between different cooling technologies for the DCs in
different climatic conditions, and also further assist the development of
new cooling technologies. This new indicator can also support decision
making regarding the selection of cooling technologies and the system
inspection to find out the potential problems in the design and operation. In addition to the new indictor, another major contribution of this
work is to develop a simplified method, which can be used to reckon the
PUE of a DC equipped with the benchmark cooling technology with the
consideration of the local climatic conditions.
PUE is defined as the ratio of total electricity consumption (including the electricity consumptions of IT equipment, cooling and illuminating systems, and other safety related electrical equipment) to
the electricity consumption of IT equipment in a DC, over a certain time
[8]. A lower PUE implies a more effective energy utilization in the DC.
A survey conducted by the U.S. Environmental Protection Agency regarding 120 existing DCs showed that the average PUE was 1.91 [9].
Currently, the newly built DCs can achieve a PUE less than 1.5, for
which a term “green DCs” has been coined [10]. Such impressively low
PUEs are observed mainly for hyperscale DCs owned by companies like
Google or Facebook where highly efficient and customized designs are
implemented.
The cooling system is an essential and crucial part in all DCs, which
dissipates the large amount of heat generated by IT equipment to ensure
its high performance and safe operation. It is also used to control the
indoor humidity to meet the requirement of IT equipment [11]. The
electricity consumption of the cooling system accounts for a big share of
the total electricity consumption of the DC, and quite often is the
second largest after the IT equipment [12]. For example, the results of
Garimella et al. [13] showed that electricity consumption of the cooling
system could account for 33% of the total electricity consumption of a
DC. Hence, the electricity consumption of the cooling system has a
substantial impact on PUEs of DCs.
The important role of the cooling system in the overall performance
of DCs has been well recognized, and different cooling technologies
have been developed [7,10,14]. While, the power consumption of the
cooling system is significantly affected by the local climatic conditions
(such as the ambient temperature and humidity). For example, Lee and
Chen [15] analyzed the potential energy saving from using free air
cooling in DCs and found that the saving was highly dependent on the
local climatic conditions. Similar conclusions have been found for DCs
in Europe [16] and Australia [17].
Building DCs in the locations with low ambient temperatures are
usually preferred for lowering PUEs. However, considering other factors, such as information security, political issues, data and power
transmission, and availability of power sources, placing DCs in locations with harsh climatic conditions cannot be avoided, such as in the
tropical and subtropical areas. Even though pursuing low PUE is always
the goal, setting the targeted PUE should consider the local climatic
conditions. For example, PUE of a state of the art Facebook DC located
in Oregeon (USA) has already approached 1.1 [10]; nevertheless, in
Singapore, the National Environment Agency only aims at an average
PUE of 1.78 [18]
It is well acknowledged that the local climatic conditions can
2. Methodology
2.1. Coefficient of PUE
According to Rasmussen white paper published in 2006 [19], PUE is
defined as:
PUE =
WIT + Wcs + Wes
,
WIT
(1)
where WIT, Wcs, and Wes present the power consumptions of IT equipment, cooling system, and other electrical systems (including the illuminating system and safety related electrical equipment), respectively.
In practice, PUE is usually calculated based on the measure data,
whereas during the design of DCs, it can also be reckoned by doing
dynamic simulations.
Based on PUE, this work proposes a new indicator, coefficient of
PUE (COPUE), as below:
COPUE =
PUEreal
,
PUEbm
(2)
where PUEreal is the real PUE of a DC, and PUEbm is the benchmark PUE
when the benchmark cooling technology (Section 2.2) is adopted for
the same DC. Since both PUEreal and PUEbm are obtained under the
2
Applied Energy 276 (2020) 115497
J. Li, et al.
same climatic condition, using COPUE can eliminate the influences of
climatic conditions through the PUE ratio; hence, COPUE can be used to
fairly compare the energy performance of cooling technologies and DCs
in different locations.
When COPUE is higher than 1, it implies that the energy performance of the adopted cooling technology is worse than that of benchmark cooling technology. There could potentially be two reasons behind this: one is that a less efficient cooling technology is selected, and
the other is that the cooling system is not properly designed, such as
oversized capacity, or some devices are misoperated. On the contrary, a
COPUE lower than 1 means that the adopted cooling technology has a
better energy performance than the benchmark cooling technology. The
lower the COPUE, the better energy performance of adopted cooling
technology.
2.2. Benchmark cooling technology and its operation modes
Using COPUE (Eq. (2)) needs to calculate PUEbm. According to the
review work by Huang et al. [7], the cooling system, which consists of
water-cooled chillers and water cooling towers, is the most commonly
adopted cooling technology for modern commercial DCs. Therefore, it
is selected as the benchmark cooling system in this work. The typical
configuration of such a system is presented in Fig. 1, including other
auxiliary devices, such as pumps, waterside economizer (heat exchanger) and indoor ventilation systems. PUEbm represents the energy
performance of the same DC using such a cooling technology in the
same local climatic conditions.
Depending on the ambient conditions, the benchmark cooling
technology operates in different modes to provide the cooling demand
of the IT equipment. In general, the operation can be divided into three
modes, free air cooling, hybrid cooling, and forced cooling, which are
mainly determined by the wet bulb temperature of ambient air (Twb),
the required indoor temperature of the DC (TDC,in) and the pinch point
temperature difference for heat exchanging (ΔTPPTD):
• Free air cooling (FAC): when the ambient wet bulb temperature is
lower than the required indoor temperature of the DC, the environment can provide the cooling demand of the DC. Thus, watercooled chillers are shut-down and only water cooling towers are in
operation. The cooling capacity provided by water cooling towers is
determined by the heat exchange capacity of waterside economizers, which are heat exchangers. The design of heat exchanges is
usually based on a rated pinch point (ΔTPPTD,rated) [20], which is the
Fig. 2. Degree hour of water cooling for Beijing in 2017: (a) variation of the
hourly wet bulb temperature; (b) accumulated hours for various wet bulb
temperature ranges.
Fig. 1. Configuration of the benchmark cooling system.
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Applied Energy 276 (2020) 115497
J. Li, et al.
determined by the ambient wet bulb temperature. According to thermodynamics, the lowest operating temperature that can be achieved in
the water cooling tower is the ambient wet bulb temperature.
2.3. Simplified method for estimating the power consumption of the
benchmark cooling technology
In order to estimate the power consumption of the benchmark
cooling technology introduced in Section 2.2, a simplified method is
developed, which only needs the design capacities of water-cooled
chillers and water cooling towers, yearly wet bulb temperature profile
as inputs.
The rated capacities of water-cooled chillers (Pchiller) and water
cooling towers (Pwct) can be determined dependent on the IT load and
the performance of the cooling system in variable ambient conditions.
In this work, the cooling demand of non-IT related equipment (including building) is neglected as it usually only accounts for a very
small amount (less than 4%) according to the results of Kosik [21].
Once the capacities are obtained, in order to assess the yearly power
demand, the operation hours are needed for different operation modes.
A new parameter called the degree hour of water cooling is introduced,
which represents the annual accumulated duration (in hours) for various wet bulb temperature ranges. As the operation modes are determined by the wet bulb temperature, once the degree hour of water
cooling is known, the accumulated operating hours of different modes
can be determined. Using the degree hour of water cooling can take the
impacts of local climatic conditions into the consideration when estimating the power consumption of the cooling system.
One example is illustrated in Fig. 2. Fig. 2(a) shows the variation of
the hourly wet bulb temperature in Beijing for a typical year. Resorting
the data according to temperature, the accumulated hours for various
wet bulb temperature ranges can be obtained, which are shown in
Fig. 2(b). Referring to the temperature boundaries defined for different
operation modes, the accumulated operating hours for each mode can
be estimated, based on which the total yearly power consumption of
cooling system can be calculated as:
Fig. 3. Power capacities of the cooling system in different operation modes for
the DC in Beijing in 2017.
•
•
temperature difference between TDC,in and the water temperature at
the outlet of water cooling tower (Twct,out). It implies that only
Twct,out is lower than TDC,in–ΔTPPTD,rated, the cooling demand can be
fulfilled. In water cooling towers, when using the ambient air to cool
down the water, the minimum Twct,out is Twb. Therefore, for FAC, the
working condition is governed by the following equation: Twb
< TDC,in–ΔTPPTD,rated.
Hybrid cooling (HC): when the ambient wet bulb temperature is a
little higher whereas still lower than the required cooling temperature of the DC, TDC,in– ΔTPPTD,rated < Twb < TDC,in–ΔTPPTD,min,
the cooling demand cannot be only provided by the water cooling
towers. This is owing to that the water temperature from cooling
towers cannot satisfy the rated pinch point temperature difference
of waterside economizers, and consequently, waterside economizers
cannot work at the design capacity due to a smaller temperature
difference for heat transfer. To restore the needed ΔTPPTD,rated,
water-cooled chillers need to operate, which are running at partial
loads. The water from the IT room is first cooled by the waterside
economizers (water cooling towers), and then by the water-cooled
chillers. ΔTPPTD,min is included as the minimum temperature difference needed by heat transfer.
Forced cooling (FC): when the ambient wet bulb temperature is
much higher, Twb > TDC,in–ΔTPPTD,min, the cooling demand can only
be provided by the water-cooled chillers. Then the water-cooled
chillers are running at the full load, and the water cooling towers are
used as heat sinks for the water-cooled chillers.
Wcs =
Pchiller,max + Pchiller,min
Pchiller,min + Pwtc
tmode,1 +
tmode,2 + Pwtc tmode,3,
2
2
(3)
where Pchiller,max and Pchiller,min present the maximum and minimum
power consumptions of water-cooled chillers under different ambient
conditions, respectively; Pwtc presents the power consumption of water
cooling towers; and tmode,1, tmode,2, and tmode,3 present the accumulated
hours for the operation modes of FC, HC, and FAC, respectively. It
should be pointed out that Pchiller,max and Pchiller,min are mainly determined by the coefficient of performance (COP), which further implies that Pchiller,max and Pchiller,min are corresponding to the lowest and
highest COPs, respectively. For FAC, even though the wet bulb temperature can be much lower, the consumed power of water cooling
towers changes slightly. Once the power consumption of the cooling
system (Wcs) is determined by using Eq. (3), PUEbm of the DC can be
The ambient wet bulb temperature is used since it determines the
operation of the water cooling tower. For the water cooling tower, the
cooling is driven by the water evaporation; while, water evaporation is
Table 1
Geographic information of the selected cities.
City
Country
Location
Climate zone [25]
Reykjavik
London
Rome
Beijing
San Francisco
Seoul
Riyadh
Guangzhou
Kuala Lumpur
Sydney
Iceland
UK
Italy
China
USA
South Korea
Saudi Arabia
China
Malaysia
Australia
N 64.15°, W 21.97°
N 51.50°, W 0.12°
N 41.88°, E 12.47°
N 39.90°, E 116.30°
N 37.80°, W 122.42°
N 37.55°, E 126.97°
N 24.68°, E 46.72°
N 23.10°, E 113.25°
N 3.15°, E 101.70°
S 33.92°, E 150.88°
Polar, Tundra
Mild temperate, fully humid, warm summer
Mild temperate, dry summer, hot summer
Mild temperate, dry summer, warm summer
Mild temperate, dry summer, hot summer
Snow, dry winter, hot summer
Dry, desert, hot arid
Mild temperate, fully humid, hot summer
Tropical, fully humid
Mild temperate, fully humid, hot summer
4
Applied Energy 276 (2020) 115497
J. Li, et al.
Fig. 4. Comparisons of calculated benchmark PUE between the dynamic and simplified approaches for DCs located in various cities.
addition, the difference between PUEs from the simplified method and
the design data is only 0.5%.
The simplified method is further tested through the comparison to
the design model for calculating PUEbm in different climatic conditions,
in order to justify its robustness. Using the same reference DC, 10 locations from different climate zones, are considered, as listed in
Table 1. The climate zones are definied based the Köppen climate
classification [25]. Results are visualized in Fig. 4. The mean absolute
error between the dynamic and simplified approaches is about 0.3%.
Therefore, it can be concluded that the simplified method enables a
good estimation about PUEs of DCs.
The impacts of climatic conditions on PUEs of DCs can also be
clearly seen from the variation of PUE in Fig. 4. According to the dynamic approach, the calculated PUE changes from 1.244 in Reykjavik to
1.417 in Kuala Lumpur. For the same cooling technology, such a difference is caused by the remarkably different climatic conditions and
their resulting substantial influences on the power consumptions of
cooling systems. The wet bulb temperature for Reykjavik averages
around 3.3 °C (standard deviation 4.9 °C) whereas for Kuala Lumpur is
found to be 23.8 °C (standard deviation 0.9 °C). Reykjavik enables a
longer operating time for free air cooling, compared to Kuala Lumpur in
which the forced cooling accounts for 100% of the operating time.
Additionally, the simplified method is also used to estimate the PUE
of an existing DC located in Frankfurt (Germany) which utilizes the
same cooling technology [10]. The calculated benchmark PUE is 1.29
for 2013, which is very close to the reported PUE (1.3) [10]. This result
further validates the proposed simplified method.
calculated by using Eq. (1). It has been assumed that the IT load is
constant [22].
2.4. Feasibility analysis of the simplified method
To validate the proposed simplified method for calculating PUEbm,
the calculated power consumption of the cooling system is compared
with the design data of a real DC [23], which is located in Beijing.
The reference DC has four floors and its IT rooms are located on the
second to fourth floors. The dimension of each IT room is
93 m × 20.4 m, containing 340 racks. The total power of IT equipment
(WIT) is 12.9 MW; and the total power of other electrical systems not
including the cooling system (Wes), such as the illuminating system and
safety related electrical equipment, is approximately 0.97 MW.
The cooling system consists of four water-cooled chillers, each of
which is equipped with a primary pump, an in-series waterside economizer (a plate heat exchanger), a secondary pump, a cooling water
pump, and a water cooling tower. The rated refrigeration capacity of
each water-cooled chiller is 4220 kW, and its COP is 5.5 at the design
condition. The power capacities of the primary pump, secondary pump,
and cooling water pump are 55 kW, 75 kW, and 90 kW, respectively.
The rated cooling capacity of each cooling tower is 4880 kW.
The inlet temperature of IT room (TDC,in) is 18 °C, and its outlet
temperature is 24 °C. The ΔTPPTD,min and ΔTPPTD,rated are set as 1.5 °C
and 7 °C, respectively. Correspondingly, for this DC, the mode of FAC is
operated when the ambient wet bulb temperature is lower than the
11 °C, and the mode of FC is operated when the ambient wet bulb
temperature is higher than 16.5 °C. The design data is obtained by
doing hourly dynamic simulations about the building and the cooling
system based on real performance curves of the water-cooled chillers
and heat exchangers [24]. Results show that the annual power consumption of the cooling system is 24.9 GWh and the annual PUE of this
DC is 1.295.
When using the proposed simplified method to calculate the PUE,
the power capacities of the cooling system in different operation modes
are illustrated in Fig. 3. The accumulated working hours for each operation mode can also be read from Fig. 3, which are 2222 h, 1258 h,
and 5280 h for the operation modes of FC, HC, and FAC, respectively.
By using Eq. (3), the total annual power consumption of the cooling
system is 25.7 GWh. Compared to the design data, it is 3.06% higher. In
3. Applications of COPUE
COPUEs have been calculated for real commercial DCs collected in
Ref. [26]. As the cooling demand is obviously affected by the local wet
bulb temperature, which varies year by year, the benchmark PUEs are
calculated over the years of 2000–2015 and the results are listed in
Table 2. As an example, the variability of PUEbm for the DC in Ontario
(Canada) over the considered period is visualized in Fig. 5. It can be
seen that the benchmark PUE varies from 1.27 to 1.29, with a mean
value of 1.28. In order to illustrate this variability in the cases of other
DCs, the boxplots are used, and included in Table 2.
As aforementioned, COPUE can be used to evaluate the energy
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J. Li, et al.
Table 2
Coefficient of PUE (COPUE) for real commercial DCs.
#
Name
Location
PUE most
recent
update time
Real PUE
Average
COPUE
1
Tulip Data City
India/
Bangalore
2012
1.9
1.36
COPUE box plot over the years of 2000–2015
1.366
Cooling technology
Chillers
1.364
COPUE
1.362
1.360
1.358
1.356
2
Data Logistics
Center DC4
Lithuania/
Vilnius
2012
1.8
1.420
1.41
Free air cooling + Chilled water
1.416
COPUE
1.412
1.408
1.404
1.400
3
Mid-Range
Canada/
Ontario
2017
1.6
1.24
1.255
Free air cooling + Closed loop chilled
water system
COPUE
1.250
1.245
1.240
1.235
1.230
4
Telekom
DataCenter
Slovakia/
Bratislava
2012
1.5
1.172
1.16
Free air cooling + Chillers
1.170
COPUE
1.168
1.166
1.164
1.162
1.160
5
New York NY
(Peer1 Faciliy)
USA/New
Jersey
2014
1.37
1.054
1.05
Chillers + Evaporative condensing units
1.052
COPUE
1.050
1.048
1.046
1.044
1.042
6
H5 Data Centers
Quincy
USA/
Washington
2018
1.35
1.032
1.03
Free air cooling + Chillers
1.030
COPUE
1.028
1.026
1.024
1.022
BT Data Center
FrankfurtSossenheim
Germany/
Frankfurt
2013
1.3
1.020
1.02
Free air cooling + Chilled water
aggregates + Rainwater for recooling
1.018
COPUE
7
1.016
1.014
1.012
(continued on next page)
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Applied Energy 276 (2020) 115497
J. Li, et al.
Table 2 (continued)
#
Name
Location
PUE most
recent
update time
Real PUE
Average
COPUE
8
Osaka 5 Data
Center
Japan/Osaka
2015
1.3
0.98
COPUE box plot over the years of 2000–2015
0.985
Cooling technology
Free air cooling + Water-cooled air
conditioner + Wall injection air
conditioning
0.984
COPUE
0.983
0.982
0.981
0.980
9
Spark Digital
Takanini DC
New Zealand/
Auckland
2014
1.25
0.95
Free air cooling + Chillers
(supplementary)
0.956
0.954
COPUE
0.952
0.950
0.948
0.946
0.944
Tokyo No. 6 Data
Center
Japan/Tokyo
2015
1.2
Free air cooling + Highly-efficient watercooler air conditioner
0.91
0.910
COPUE
10
0.908
0.906
0.904
Fig. 5. Variability of the benchmark PUE for a DC in Ontario over the years of
2000–2015.
Fig. 6. Comparison of DCs’ PUE and COPUE indices with the results interpretation for three cases (A-C).
performance of DCs more comprehensively, since it can eliminate the
impacts of local climatic conditions. Fig. 6 compares COPUEs of DCs
listed in Table 2 with some highlights.
Using PUE to compare DC1 and DC2 shows that DC1 should have a
worse performance than DC2. However, DC1 is located in India where
the yearly average temperature is 23.6 °C; while, DC2 is located in
Lithuania where the yearly average temperature is 6.7 °C. A higher
ambient temperature implies a larger cooling demand, which should
further result in a higher PUE. It is unfair to claim that DC2 has a better
performance than DC1 from a technical perspective. When COPUE is
employed, it is clear to see that DC1 has a lower COPUE than DC2. This
implies that the real PUE of DC1 is closer to the PUEbm compared with
DC2. As both DCs adopt similar cooling technologies, it can further
imply that the design of DC2 may be not optimized or misoperation
may exist.
The PUE of DC10 is 1.2, which is amongst the normal PUE of new
DCs. However, it has a quite low COPUE that is well below 1. According
to the given information about DC10, a highly-efficient water-cooled air
conditioner is applied. Therefore, using COPUE can effectively demonstrate the merits of the advanced cooling technologies.
4. Discussion
Using the simplified method for calculating the benchmark PUE has
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Applied Energy 276 (2020) 115497
J. Li, et al.
Acknowledgements
uncertainties (as shown in Fig. 4). When using it further for the calculation of COPUE, the uncertainty might propagate. This is especially
problematic when the COPUE is very close to “1” and it is hard to draw
a definite conclusion. For example, comparing DC7 and DC8 shows that
they have the same PUE. They are located in similar climatic conditions
and adopt similar cooling technologies. However, DC7 has a higher
COPUE, which is a little over 1, than DC8, which has a COPUE of
slightly below 1. According to COPUE, DC7 should have a worse performance than DC8. Nevertheless, due to the uncertainty of the simplified method, it may be not proper to draw such a conclusion, which
is the limitation of using COPUE.
A DC located in Osaka is used as an example to conduct tests to
understand how uncertainties of PUEbm estimation affect COPUE. Such
an analysis is necessary to estimate how errors associated with PUEbm
calculation may propagate in calculating COPUE. To answer this
question, the Monte Carlo approach is applied. Based on PUEbm for all
DCs shown in Fig. 4 over the year 2017, the statistic about the deviations of PUEbm compared to the dynamic approach shows a normal
distribution with a mean of 0.3% and a standard deviation of 0.18%.
After performing the Monte Carlo analysis for 200 runs, it is found that
considering the benchmark PUE potential errors the mean COPUE is
0.991 (0.35% lower than reported in Table 2) and can potentially range
from 0.986 to 0.997. The above analysis indicates that the estimation
error of PUEbm calculated based on the simplified approach has a relatively small impact on the calculation of COPUE.
This work is supported by the KKS Research Profile ‘Future Energy’
project: CH2018-7844: Energy management of fuel cell powered data
centers in the acknowledgement, which is gratefully acknowledged.
Wen-Quan Tao thanks the grants from the International Exchange
Cooperation Project of NSFC-STINT (51911530157).
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5. Conclusions
This work proposes a new indicator, coefficient of power usage effectiveness (COPUE), to enable the fair comparison and evaluation for
the energy performance of data centers operating in the different climatic conditions. A simplified method is also developed based on the
degree hour of water cooling to reckon the PUE of data centers
equipped with the benchmark cooling technology, which consists of
water-cooled chillers and water cooling towers.
Based on the results, it can be concluded that:
• The developed simplified method can give a good estimation for the
•
PUE of a data center equipped with the benchmark cooling technology.
COPUE has been proved as an effective indicator for comparing and
evaluating the energy performance of data centers in different climatic conditions.
In addition, many new cooling technologies and design schemes are
emerging in the field of data centers. Using COPUE can clearly demonstrate their advantages in terms of energy performance.
Furthermore, COPUE can also be used to assist the selection of cooling
technologies and be used for process diagnosis.
CRediT authorship contribution statement
Jian Li: Formal analysis, Investigation, Writing - original draft.
Jakub Jurasz: Visualization, Validation, Writing - original draft.
Hailong Li: Conceptualization, Methodology, Writing - original draft.
Wen-Quan Tao: Supervision, Funding acquisition. Yuanyuan Duan:
Supervision. Jinyue Yan: Funding acquisition, Writing - review &
editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence the work reported in this paper.
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