WO2018119777A1 - 一种集中制冷能耗分摊方法及装置 - Google Patents

一种集中制冷能耗分摊方法及装置 Download PDF

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Publication number
WO2018119777A1
WO2018119777A1 PCT/CN2016/112707 CN2016112707W WO2018119777A1 WO 2018119777 A1 WO2018119777 A1 WO 2018119777A1 CN 2016112707 W CN2016112707 W CN 2016112707W WO 2018119777 A1 WO2018119777 A1 WO 2018119777A1
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energy consumption
cabinet
sharing
coefficient
room
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PCT/CN2016/112707
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English (en)
French (fr)
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王俊
张滔
汪云飞
郑红星
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深圳中兴力维技术有限公司
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Priority to CN201680025093.0A priority Critical patent/CN107736084B/zh
Priority to PCT/CN2016/112707 priority patent/WO2018119777A1/zh
Publication of WO2018119777A1 publication Critical patent/WO2018119777A1/zh

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    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/20—Modifications to facilitate cooling, ventilating, or heating
    • H05K7/20709—Modifications to facilitate cooling, ventilating, or heating for server racks or cabinets; for data centers, e.g. 19-inch computer racks
    • H05K7/20836—Thermal management, e.g. server temperature control

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  • the present invention relates to the field of equipment room management, and in particular, to a centralized cooling energy sharing method and device.
  • the ratio of the total equipment energy consumption of the data center to the energy consumption of the IT equipment (PUE) is an important indicator for measuring the monitoring and management system of the data center. Improving the indicator is important for the daily operation and maintenance management of the data center. Significance, in which the cooling energy consumption data of a single computer room is an important parameter in the PUE calculation process. Since most data centers adopt centralized cooling mode, this makes the energy sharing method an important factor in the PUE process of the computer room, which will directly affect the PUE result. Accuracy.
  • the traditional centralized cooling energy sharing method mainly includes two types: (1) empirical apportionment method; and (2) area apportionment method.
  • the experience apportionment method refers to the manual allocation of energy consumption values of various computer rooms according to past experience. This empirical apportionment method has certain subjectivity and the apportionment results are not accurate.
  • the area apportionment method refers to the proportion according to the size of each computer room. Make an assessment. This method of allocation has a reasonable basis, but it cannot be applied to data centers with inconsistent internal utilization in the equipment room.
  • the main purpose of the present invention is to provide a centralized cooling energy consumption sharing method and device, which can calculate the sharing coefficient of each cabinet through the cooling effect and the cooling range of each cabinet in the data center, and unify according to the coefficient in the machine room. Apportionment increases the accuracy of cooling energy consumption.
  • the present invention provides a centralized cooling energy sharing method, including:
  • the basic data of the data center in the preset time period is obtained by the power environment monitoring system, where the data center includes a plurality of equipment rooms, and the equipment room includes several cabinets;
  • the energy consumption value of each equipment room is obtained.
  • the basic data includes: an average temperature of the cold channel of the cabinet, an average temperature of the hot channel of the cabinet, a distance between the hot and cold channels of the cabinet, and a total energy consumption of the concentrated cooling.
  • the calculating the energy consumption sharing coefficient of each cabinet according to the basic data is specifically:
  • ⁇ i is the i-th coefficient of energy sharing cabinet of C i, t h average temperature of the hot aisle rack for rack, t hot aisle rack cabinet for the average temperature c, d i between the hot and cold channels enclosure the distance.
  • the calculating the energy consumption sharing coefficient of the equipment room according to the energy consumption sharing coefficient of each cabinet included in each equipment room is specifically:
  • ⁇ j is the j-th coefficient of energy sharing room R & lt j
  • n is the number included within the room R & lt j j-th enclosure.
  • the energy consumption allocation value of each equipment room according to the total energy consumption of the centralized cooling of the data center is specifically:
  • E j is the energy consumption value of the jth machine room R j
  • E is the total energy consumption of the centralized cooling
  • m is the number of the computer rooms included in the data center.
  • a centralized cooling energy sharing device including:
  • the basic data acquisition module is configured to acquire, by using the dynamic environment monitoring system, basic data of a data center in a preset time period, where the data center includes a plurality of equipment rooms, and the equipment room includes a plurality of cabinets;
  • a cabinet coefficient calculation module configured to calculate an energy consumption sharing coefficient of each cabinet according to the basic data
  • a calculation module of the engine room coefficient configured to calculate an energy consumption sharing coefficient of the equipment room according to an energy consumption sharing coefficient of each cabinet included in each equipment room;
  • the energy consumption value calculation module is configured to obtain the energy consumption value of each equipment room according to the total energy consumption of the centralized cooling of the data center.
  • the basic data includes: an average temperature of the cold channel of the cabinet, an average temperature of the hot channel of the cabinet, a distance between the hot and cold channels of the cabinet, and a total energy consumption of the concentrated cooling.
  • the cabinet coefficient calculation module is specifically:
  • ⁇ i is the energy consumption sharing coefficient of the i-th cabinet C i
  • t h is the average temperature of the cabinet hot channel of the cabinet
  • t c is the average temperature of the cabinet hot channel of the cabinet
  • d i is between the cabinet hot and cold channels the distance.
  • the computer room coefficient calculation module is specifically:
  • ⁇ j is the j-th coefficient of energy sharing room R & lt j
  • n is the number included within the room R & lt j j-th enclosure.
  • the energy consumption value calculation module is specifically:
  • E j is the energy consumption value of the jth machine room R j
  • E is the total energy consumption of the centralized cooling
  • m is the number of the computer rooms included in the data center.
  • the present invention provides a centralized cooling energy consumption sharing method and device.
  • the method includes: acquiring, by a dynamic environment monitoring system, basic data of a data center in a preset time period, where the data center includes a plurality of computer rooms, and the computer room includes Calculating the energy consumption sharing coefficient of each cabinet according to the basic data; calculating the energy consumption sharing coefficient of the equipment room according to the energy consumption sharing coefficient of each cabinet included in each equipment room; according to the data center
  • the total energy consumption of the centralized cooling system is calculated by the energy consumption of each equipment room.
  • the cooling factor and cooling range of each cabinet in the data center can be used to calculate the sharing coefficient of each cabinet, and the equipment can be uniformly distributed according to the coefficient. Increased cooling The accuracy of energy sharing.
  • FIG. 1 is a flowchart of a method for distributing energy consumption of a centralized refrigeration according to Embodiment 1 of the present invention
  • FIG. 2 is a block diagram showing an exemplary structure of a centralized cooling energy sharing device according to Embodiment 2 of the present invention.
  • a centralized cooling energy sharing method includes:
  • the basic data of the data center in the preset time period is obtained by the power environment monitoring system, where the data center includes a plurality of equipment rooms, and the equipment room includes several cabinets;
  • the sharing coefficient of each cabinet is calculated by the cooling effect and the cooling range of each cabinet in the data center, and the sharing is performed according to the coefficient in the machine room, thereby improving the accuracy of the energy sharing, thereby achieving Improve the accuracy of PUE in the equipment room, and provide a scientific basis for reasonable monitoring and management of the computer room to achieve energy saving and emission reduction.
  • the preset time period is a time range. For example, if the data center needs to formulate an energy sharing plan within one month, then the preset time period is set to one month, and the data center is read. One month of historical basis data, based on historical empirical data, each data center is calculated The equipment room needs to allocate the energy consumption value and apply it to the next month's energy sharing plan.
  • the preset time period may also be one week, one quarter, and the like.
  • the basic data includes: the average temperature of the cold channel of the cabinet, the average temperature of the hot channel of the cabinet, the distance between the hot and cold channels of the cabinet, and the total energy consumption of the centralized cooling; the temperature difference between the hot and cold air passages passing through the cabinet You can know the cooling effect of each cabinet, and the cooling range of the cabinet can be known by the distance between the hot and cold air channels of the cabinet.
  • step S20 is specifically:
  • ⁇ i is the energy consumption sharing coefficient of the i-th cabinet C i
  • t h is the average temperature of the cabinet hot channel of the cabinet
  • t c is the average temperature of the cabinet hot channel of the cabinet
  • d i is between the cabinet hot and cold channels the distance.
  • step S30 is specifically:
  • ⁇ j is the j-th coefficient of energy sharing room R & lt j
  • n is the number included within the room R & lt j j-th enclosure.
  • step S40 is specifically:
  • E j is the energy consumption value of the jth machine room R j
  • E is the total energy consumption of the centralized cooling
  • m is the number of the computer rooms included in the data center.
  • the number of cabinets in the equipment room of the data center is relatively large, usually between several tens and hundreds.
  • this embodiment assumes that a data center has R 1 and R 2 .
  • R 1 includes two cabinets C 1 , C 2 ;
  • R 2 includes three cabinets C 3 , C 4 , C 5 .
  • the channel average temperature values are shown in Table 1 below:
  • step S20 it can be calculated that the calculation processes of the energy consumption sharing coefficients ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 , and ⁇ 5 corresponding to the five cabinets are as follows:
  • step S30 it can be calculated that the calculation process of the energy consumption sharing coefficients ⁇ 1 and ⁇ 2 corresponding to the equipment rooms R 1 and R 2 is as follows:
  • step S40 the engine room R 1, R 2 corresponding to the assessed value of power consumption is calculated as follows:
  • the R 1 cooling energy consumption value of the equipment room is 350kw.h
  • the R 2 cooling energy consumption value of the equipment room is 650kw.h.
  • a centralized cooling energy sharing device includes:
  • the basic data acquisition module 10 is configured to acquire, by using the dynamic environment monitoring system, basic data of a data center in a preset time period, where the data center includes a plurality of equipment rooms, and the equipment room includes a plurality of cabinets;
  • the cabinet coefficient calculation module 20 is configured to calculate an energy consumption sharing coefficient of each cabinet according to the basic data.
  • the machine room coefficient calculation module 30 is configured to calculate an energy consumption sharing coefficient of the equipment room according to an energy consumption sharing coefficient of each cabinet included in each equipment room;
  • the energy consumption value calculation module 40 is configured to obtain the energy consumption value of each equipment room according to the total energy consumption of the centralized cooling of the data center.
  • the sharing coefficient of each cabinet is calculated by the cooling effect and the cooling range of each cabinet in the data center, and the sharing is performed according to the coefficient in the machine room, thereby improving the accuracy of the energy sharing, thereby achieving Improve the accuracy of PUE in the equipment room, and provide a scientific basis for reasonable monitoring and management of the computer room to achieve energy saving and emission reduction.
  • the preset time period is a time range. For example, if the data center needs to formulate an energy sharing plan within one month, then the preset time period is set to one month, and the data center is read.
  • One month's historical basic data based on historical experience data, calculate the energy consumption value that each computer room needs to allocate in the data center, and apply it to the next month's energy sharing plan.
  • the preset time period may also be one week, one quarter, and the like.
  • the basic data includes: the average temperature of the cold channel of the cabinet, the average temperature of the hot channel of the cabinet, the distance between the hot and cold channels of the cabinet, and the total energy consumption of the centralized cooling;
  • the temperature difference between the channels can be used to know the cooling effect of each cabinet.
  • the distance between the hot and cold air channels of the cabinet can be known.
  • the cabinet coefficient calculation module is specifically:
  • ⁇ i is the energy consumption sharing coefficient of the i-th cabinet C i
  • t h is the average temperature of the cabinet hot channel of the cabinet
  • t c is the average temperature of the cabinet hot channel of the cabinet
  • d i is between the cabinet hot and cold channels the distance.
  • the computer room coefficient calculation module is specifically:
  • ⁇ j is the j-th coefficient of energy sharing room R & lt j
  • n is the number included within the room R & lt j j-th enclosure.
  • the energy consumption value calculation module is specifically:
  • E j is the energy consumption value of the jth machine room R j
  • E is the total energy consumption of the centralized cooling
  • m is the number of the computer rooms included in the data center.
  • the number of cabinets in the equipment room of the data center is relatively large, usually between several tens and hundreds.
  • this embodiment assumes that a data center has R 1 and R 2 .
  • R 1 includes two cabinets C 1 , C 2 ;
  • R 2 includes three cabinets C 3 , C 4 , C 5 .
  • Table 2 The average channel temperature values are shown in Table 2 below:
  • step S20 it can be calculated that the calculation processes of the energy consumption sharing coefficients ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 , and ⁇ 5 corresponding to the five cabinets are as follows:
  • step S30 it can be calculated that the calculation process of the energy consumption sharing coefficients ⁇ 1 and ⁇ 2 corresponding to the equipment rooms R 1 and R 2 is as follows:
  • step S40 the engine room R 1, R 2 corresponding to the assessed value of power consumption is calculated as follows:
  • the R 1 cooling energy consumption value of the equipment room is 350kw.h
  • the R 2 cooling energy consumption value of the equipment room is 650kw.h.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better.
  • Implementation Based on such understanding, the technical solution of the present invention, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a cell phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present invention.
  • the present invention provides a centralized cooling energy consumption sharing method and device.
  • the method includes: acquiring, by a dynamic environment monitoring system, basic data of a data center in a preset time period, where the data center includes a plurality of computer rooms, and the computer room includes Calculating the energy consumption sharing coefficient of each cabinet according to the basic data; calculating the energy consumption sharing coefficient of the equipment room according to the energy consumption sharing coefficient of each cabinet included in each equipment room; according to the data center
  • the total energy consumption of the centralized cooling system is calculated by the energy consumption of each equipment room.
  • the cooling factor and cooling range of each cabinet in the data center can be used to calculate the sharing coefficient of each cabinet, and the equipment can be uniformly distributed according to the coefficient. Improve the accuracy of cooling energy consumption.

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Abstract

一种集中制冷能耗分摊方法及装置，该方法包括：通过动力环境监控系统获取预设时间段内的数据中心的基础数据，数据中心包括若干个机房，机房包括若干个机柜；根据基础数据计算每个机柜的能耗分摊系数；根据每个机房内所包括的每个机柜的能耗分摊系数计算机房的能耗分摊系数；根据数据中心的集中制冷总能耗得到每个机房的能耗分摊值，能够通过数据中心中每个机柜的降温效果和降温范围计算每个机柜的分摊系数，并根据该系数以机房为单位进行统一分摊，提高了制冷能耗分摊的准确率。

Description

一种集中制冷能耗分摊方法及装置 技术领域
本发明涉及机房设备管理领域，尤其涉及一种集中制冷能耗分摊方法及装置。
背景技术
机房数据中心总设备能耗与IT设备能耗的比值PUE(Power Usage Effectiveness)的计算结果精确度是衡量数据中心机房监控管理系统的一个重要指标，提升该指标对于数据中心日常运维管理有着重要意义，其中单个机房制冷能耗数据是PUE计算过程中一个重要参数，由于大部分数据中心都采用集中制冷方式，这使得能耗分摊方法成为计算机房PUE过程中一个重要因素，将直接影响PUE结果精确度。
传统的集中制冷能耗分摊方法主要包括两种：(1)经验分摊法；(2)面积分摊法。经验分摊法是指运维人员根据以往经验，手动分配各个机房能耗值，这种经验性分摊法带有一定主观性，分摊结果不太准确；面积分摊法是指根据各个机房面积大小按比例进行分摊。这种分摊法有一定合理依据，但是无法适用于机房内部利用率不一致的数据中心。
发明内容
本发明的主要目的在于提出一种集中制冷能耗分摊方法及装置，能够通过数据中心中每个机柜的降温效果和降温范围计算每个机柜的分摊系数，并根据该系数以机房为单位进行统一分摊，提高了制冷能耗分摊的准确率。
为实现上述目的，本发明提供的一种集中制冷能耗分摊方法，包括：
通过动力环境监控系统获取预设时间段内的数据中心的基础数据，所述数据中心包括若干个机房，所述机房包括若干个机柜；
根据所述基础数据计算每个机柜的能耗分摊系数；
根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数；
根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值。
可选地，所述基础数据包括：机柜冷通道平均温度、机柜热通道平均温度、机柜冷热通道之间的距离和集中制冷总能耗。
可选地，所述根据所述基础数据计算每个机柜的能耗分摊系数具体为：
通过以下公式计算机柜的能耗分摊系数：
Figure PCTCN2016112707-appb-000001
其中，λi为第i个机柜Ci的能耗分摊系数，th为该机柜的机柜热通道平均温度，tc为该机柜的机柜热通道平均温度，di为机柜冷热通道之间的距离。
可选地，所述根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数具体为：
通过以下公式计算机房的能耗分摊系数：
Figure PCTCN2016112707-appb-000002
其中，θj为第j个机房Rj的能耗分摊系数，n为第j个机房Rj内包括的机柜的数量。
可选地，所述根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值具体为：
通过以下公式计算每个机房的能耗分摊值：
Figure PCTCN2016112707-appb-000003
其中，Ej为第j个机房Rj的能耗分摊值，E为集中制冷总能耗，m为所述数据中心包括的机房的数量。
作为本发明的另一个方面，提供的一种集中制冷能耗分摊装置，包括：
基础数据获取模块，用于通过动力环境监控系统获取预设时间段内的数据中心的基础数据，所述数据中心包括若干个机房，所述机房包括若干个机柜；
机柜系数计算模块，用于根据所述基础数据计算每个机柜的能耗分摊系数；
机房系数计算模块，用于根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数；
能耗分摊值计算模块，用于根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值。
可选地，所述基础数据包括：机柜冷通道平均温度、机柜热通道平均温度、机柜冷热通道之间的距离和集中制冷总能耗。
可选地，所述机柜系数计算模块具体为：
通过以下公式计算机柜的能耗分摊系数：
Figure PCTCN2016112707-appb-000004
其中，λi为第i个机柜Ci的能耗分摊系数，th为该机柜的机柜热通道平均温度，tc为该机柜的机柜热通道平均温度，di为机柜冷热通道之间的距离。
可选地，所述机房系数计算模块具体为：
通过以下公式计算机房的能耗分摊系数：
Figure PCTCN2016112707-appb-000005
其中，θj为第j个机房Rj的能耗分摊系数，n为第j个机房Rj内包括的机柜的数量。
可选地，所述能耗分摊值计算模块具体为：
通过以下公式计算每个机房的能耗分摊值：
Figure PCTCN2016112707-appb-000006
其中，Ej为第j个机房Rj的能耗分摊值，E为集中制冷总能耗，m为所述数据中心包括的机房的数量。
本发明提出的一种集中制冷能耗分摊方法及装置，该方法包括：通过动力环境监控系统获取预设时间段内的数据中心的基础数据，所述数据中心包括若干个机房，所述机房包括若干个机柜；根据所述基础数据计算每个机柜的能耗分摊系数；根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数；根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值，能够通过数据中心中每个机柜的降温效果和降温范围计算每个机柜的分摊系数，并根据该系数以机房为单位进行统一分摊，提高了制冷 能耗分摊的准确率。
附图说明
图1为本发明实施例一提供的一种集中制冷能耗分摊方法流程图；
图2为本发明实施例二提供的一种集中制冷能耗分摊装置示范性结构框图。
本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
在后续的描述中，使用用于表示元件的诸如“模块”、“部件”或“单元”的后缀仅为了有利于本发明的说明，其本身并没有特定的意义。因此，"模块"与"部件"可以混合地使用。
实施例一
如图1所示，在本实施例中，一种集中制冷能耗分摊方法，包括：
S10、通过动力环境监控系统获取预设时间段内的数据中心的基础数据，所述数据中心包括若干个机房，所述机房包括若干个机柜；
S20、根据所述基础数据计算每个机柜的能耗分摊系数；
S30、根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数；
S40、根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值。
在本实施例中，通过数据中心中每个机柜的降温效果和降温范围计算每个机柜的分摊系数，并根据该系数以机房为单位进行统一分摊，提高了能耗分摊的准确率，从而达到提高机房PUE精确度，为合理的监控、管理机房，实现节能减排提供了科学依据。
在本实施例中，所述预设时间段是一个时间范围，比如数据中心需要制定一个月内的能耗分摊计划，那么就将预设时间段设置为一个月，并读取该数据中心上一个月的历史基础数据，根据历史经验数据计算出数据中心内每 个机房需要分配的能耗分摊值，并应用到下一个月的能耗分摊计划中。
作为另一种实施例，所述预设时间段也可以是一周、一个季度等。
在本实施例中，所述基础数据包括：机柜冷通道平均温度、机柜热通道平均温度、机柜冷热通道之间的距离和集中制冷总能耗；通过机柜冷热风通道之间的温度差可以得知每个机柜的降温效果，通过机柜冷热风通道之间的距离可以得知机柜的降温范围。
在本实施例中，所述步骤S20具体为：
通过以下公式计算机柜的能耗分摊系数：
Figure PCTCN2016112707-appb-000007
其中，λi为第i个机柜Ci的能耗分摊系数，th为该机柜的机柜热通道平均温度，tc为该机柜的机柜热通道平均温度，di为机柜冷热通道之间的距离。
在本实施例中，所述步骤S30具体为：
通过以下公式计算机房的能耗分摊系数：
Figure PCTCN2016112707-appb-000008
其中，θj为第j个机房Rj的能耗分摊系数，n为第j个机房Rj内包括的机柜的数量。
在本实施例中，所述步骤S40具体为：
通过以下公式计算每个机房的能耗分摊值：
Figure PCTCN2016112707-appb-000009
其中，Ej为第j个机房Rj的能耗分摊值，E为集中制冷总能耗，m为所述数据中心包括的机房的数量。
在本实施例中，数据中心的机房内机柜的数量比较多，通常在几十到几百之间，为了能够完整的描述具体实施方案，本实施例假设假设某数据中心有R1、R2两个机房，并且采用统一的集中制冷系统；R1中包括两个机柜C1、C2；R2中包括三个机柜C3、C4、C5。
首先，确定需要计算的时间范围是某个月内，从动力环境系统中收集到该月内总共制冷能耗E＝1000kw.h；C1、C2对应的冷热风通道之间距离d1、 d2都是1.8米；C3、C4、C5对应的冷热风通道距离d3、d4、d5都是1.2米；根据动力环境监控系统收集到这5个机柜冷、热通道平均温度值如下表一所示：
机柜名称 热通道平均值(单位℃) 冷通道平均值(单位℃)
C1 17 15
C2 18 15
C3 20 17
C4 21 16
C5 21 15
表一
根据上述步骤S20可以计算得知，这5个机柜对应的能耗分摊系数λ1、λ2、λ3、λ4、λ5计算过程如下：
Figure PCTCN2016112707-appb-000010
Figure PCTCN2016112707-appb-000011
Figure PCTCN2016112707-appb-000012
Figure PCTCN2016112707-appb-000013
Figure PCTCN2016112707-appb-000014
根据上述步骤S30可以计算得知，机房R1、R2对应的能耗分摊系数θ1、θ2计算过程如下：
Figure PCTCN2016112707-appb-000015
Figure PCTCN2016112707-appb-000016
根据上述步骤S40可以计算得知，机房R1、R2对应的能耗分摊值计算过程如下：
Figure PCTCN2016112707-appb-000017
Figure PCTCN2016112707-appb-000018
根据述计算结果机房R1制冷能耗分摊值是350kw.h，机房R2制冷能耗分摊值是650kw.h。
实施例二
如图2所示，在本实施例中，一种集中制冷能耗分摊装置，包括：
基础数据获取模块10，用于通过动力环境监控系统获取预设时间段内的数据中心的基础数据，所述数据中心包括若干个机房，所述机房包括若干个机柜；
机柜系数计算模块20，用于根据所述基础数据计算每个机柜的能耗分摊系数；
机房系数计算模块30，用于根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数；
能耗分摊值计算模块40，用于根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值。
在本实施例中，通过数据中心中每个机柜的降温效果和降温范围计算每个机柜的分摊系数，并根据该系数以机房为单位进行统一分摊，提高了能耗分摊的准确率，从而达到提高机房PUE精确度，为合理的监控、管理机房，实现节能减排提供了科学依据。
在本实施例中，所述预设时间段是一个时间范围，比如数据中心需要制定一个月内的能耗分摊计划，那么就将预设时间段设置为一个月，并读取该数据中心上一个月的历史基础数据，根据历史经验数据计算出数据中心内每个机房需要分配的能耗分摊值，并应用到下一个月的能耗分摊计划中。
作为另一种实施例，所述预设时间段也可以是一周、一个季度等。
在本实施例中，所述基础数据包括：机柜冷通道平均温度、机柜热通道平均温度、机柜冷热通道之间的距离和集中制冷总能耗；通过机柜冷热风通 道之间的温度差可以得知每个机柜的降温效果，通过机柜冷热风通道之间的距离可以得知机柜的降温范围。
在本实施例中，所述机柜系数计算模块具体为：
通过以下公式计算机柜的能耗分摊系数：
Figure PCTCN2016112707-appb-000019
其中，λi为第i个机柜Ci的能耗分摊系数，th为该机柜的机柜热通道平均温度，tc为该机柜的机柜热通道平均温度，di为机柜冷热通道之间的距离。
在本实施例中，所述机房系数计算模块具体为：
通过以下公式计算机房的能耗分摊系数：
Figure PCTCN2016112707-appb-000020
其中，θj为第j个机房Rj的能耗分摊系数，n为第j个机房Rj内包括的机柜的数量。
在本实施例中，所述能耗分摊值计算模块具体为：
通过以下公式计算每个机房的能耗分摊值：
Figure PCTCN2016112707-appb-000021
其中，Ej为第j个机房Rj的能耗分摊值，E为集中制冷总能耗，m为所述数据中心包括的机房的数量。
在本实施例中，数据中心的机房内机柜的数量比较多，通常在几十到几百之间，为了能够完整的描述具体实施方案，本实施例假设假设某数据中心有R1、R2两个机房，并且采用统一的集中制冷系统；R1中包括两个机柜C1、C2；R2中包括三个机柜C3、C4、C5。
首先，确定需要计算的时间范围是某个月内，从动力环境系统中收集到该月内总共制冷能耗E＝1000kw.h；C1、C2对应的冷热风通道之间距离d1、d2都是1.8米；C3、C4、C5对应的冷热风通道距离d3、d4、d5都是1.2米；根据动力环境监控系统收集到这5个机柜冷、热通道平均温度值如下表二所示：
机柜名称 热通道平均值(单位℃) 冷通道平均值(单位℃)
C1 17 15
C2 18 15
C3 20 17
C4 21 16
C5 21 15
表二
根据上述步骤S20可以计算得知，这5个机柜对应的能耗分摊系数λ1、λ2、λ3、λ4、λ5计算过程如下：
Figure PCTCN2016112707-appb-000022
Figure PCTCN2016112707-appb-000023
Figure PCTCN2016112707-appb-000024
Figure PCTCN2016112707-appb-000025
Figure PCTCN2016112707-appb-000026
根据上述步骤S30可以计算得知，机房R1、R2对应的能耗分摊系数θ1、θ2计算过程如下：
Figure PCTCN2016112707-appb-000027
Figure PCTCN2016112707-appb-000028
根据上述步骤S40可以计算得知，机房R1、R2对应的能耗分摊值计算过程如下：
Figure PCTCN2016112707-appb-000029
Figure PCTCN2016112707-appb-000030
根据述计算结果机房R1制冷能耗分摊值是350kw.h，机房R2制冷能耗分摊值是650kw.h。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本发明各个实施例所述的方法。
以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。
工业实用性
本发明提出的一种集中制冷能耗分摊方法及装置，该方法包括：通过动力环境监控系统获取预设时间段内的数据中心的基础数据，所述数据中心包括若干个机房，所述机房包括若干个机柜；根据所述基础数据计算每个机柜的能耗分摊系数；根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数；根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值，能够通过数据中心中每个机柜的降温效果和降温范围计算每个机柜的分摊系数，并根据该系数以机房为单位进行统一分摊，提高了制冷能耗分摊的准确率。

Claims (10)

  1. 一种集中制冷能耗分摊方法，包括：
    通过动力环境监控系统获取预设时间段内的数据中心的基础数据，所述数据中心包括若干个机房，所述机房包括若干个机柜；
    根据所述基础数据计算每个机柜的能耗分摊系数；
    根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数；
    根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值。
  2. 根据权利要求1所述的一种集中制冷能耗分摊方法，其中，所述基础数据包括：机柜冷通道平均温度、机柜热通道平均温度、机柜冷热通道之间的距离和集中制冷总能耗。
  3. 根据权利要求2所述的一种集中制冷能耗分摊方法，其中，所述根据所述基础数据计算每个机柜的能耗分摊系数具体为：
    通过以下公式计算机柜的能耗分摊系数：
    Figure PCTCN2016112707-appb-100001
    其中，λi为第i个机柜Ci的能耗分摊系数，th为该机柜的机柜热通道平均温度，tc为该机柜的机柜热通道平均温度，di为机柜冷热通道之间的距离。
  4. 根据权利要求3所述的一种集中制冷能耗分摊方法，其中，所述根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数具体为：
    通过以下公式计算机房的能耗分摊系数：
    Figure PCTCN2016112707-appb-100002
    其中，θj为第j个机房Rj的能耗分摊系数，n为第j个机房Rj内包括的机柜的数量。
  5. 根据权利要求4所述的一种集中制冷能耗分摊方法，其中，所述根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值具体为：
    通过以下公式计算每个机房的能耗分摊值：
    Figure PCTCN2016112707-appb-100003
    其中，Ej为第j个机房Rj的能耗分摊值，E为集中制冷总能耗，m为所述数据中心包括的机房的数量。
  6. 一种集中制冷能耗分摊装置，包括：
    基础数据获取模块，用于通过动力环境监控系统获取预设时间段内的数据中心的基础数据，所述数据中心包括若干个机房，所述机房包括若干个机柜；
    机柜系数计算模块，用于根据所述基础数据计算每个机柜的能耗分摊系数；
    机房系数计算模块，用于根据每个机房内所包括的每个机柜的能耗分摊系数计算所述机房的能耗分摊系数；
    能耗分摊值计算模块，用于根据所述数据中心的集中制冷总能耗得到每个机房的能耗分摊值。
  7. 根据权利要求6所述的一种集中制冷能耗分摊装置，其中，所述基础数据包括：机柜冷通道平均温度、机柜热通道平均温度、机柜冷热通道之间的距离和集中制冷总能耗。
  8. 根据权利要求7所述的一种集中制冷能耗分摊装置，其中，所述机柜系数计算模块具体为：
    通过以下公式计算机柜的能耗分摊系数：
    Figure PCTCN2016112707-appb-100004
    其中，λi为第i个机柜Ci的能耗分摊系数，th为该机柜的机柜热通道平均温度，tc为该机柜的机柜热通道平均温度，di为机柜冷热通道之间的距离。
  9. 根据权利要求8所述的一种集中制冷能耗分摊装置，其中，所述机房系数计算模块具体为：
    通过以下公式计算机房的能耗分摊系数：
    Figure PCTCN2016112707-appb-100005
    其中，θj为第j个机房Rj的能耗分摊系数，n为第j个机房Rj内包括的机 柜的数量。
  10. 根据权利要求9所述的一种集中制冷能耗分摊装置，其中，所述能耗分摊值计算模块具体为：
    通过以下公式计算每个机房的能耗分摊值：
    Figure PCTCN2016112707-appb-100006
    其中，Ej为第j个机房Rj的能耗分摊值，E为集中制冷总能耗，m为所述数据中心包括的机房的数量。
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