数据中心规划建设要点，优化数据中心选址-局部地形气候对数据中心能耗和成本的影响

优化数据中心选址-局部地形气候对数据中心能耗和成本的影响（2）

Optimized data center site selection—mesoclimatic effectson data center energy consumption and costs（2）

Dirk Turek\ Peter Radgen | March 5, 2021

前文提要

本篇文章为优化数据中心选址的系列文章，共三篇。本文为优化数据中心的第二篇文章，前篇定义了什么是‘mesoclimatic’气候。本篇文章我们会针对温度参考元件对数据中心选址的影响展开详细介绍。

前篇回顾《选址优化part1 | 局部地形气候变化对数据中心能耗和成本的影响》

方法论

Methodology

被用于量化制冷系统的能耗在所选地址的不同半径的影响。需要输入一些初始的数据如IT负载、制冷系统的运行特性和温度分布为下级的优化场景计算出一个基准。基于位置相关的能耗，定义了三种优化方案，以量化在10km, 25km或100km半径内迁址可能实现的节能。

To quantify the impact of different radii of site selection on the energy consumption of cooling systems, a three tier process is used (see Fig. 1). Initial data for the IT load, operation characteristic, and temperature profiles are needed to calculate a base line for the optimization scenarios. Based on the location-dependent energy consumption, three optimization scenarios are defined to quantify the energy saving that are possible by relocating within a 10-, 25-, or 100-km radius.一种三层的流程（见Fig.1）

数据中心必须将由IT硬件运行产生的热量散发出去，以避免元器件过热。从空调的角度来看，整个IT硬件可以理想化为具有可变输出的单一热源。以巴登-符腾堡州的某一数据中心一年内 IT 硬件功耗的真实测量值作为参考，并结合了具有2MW峰值负载的典型UPS的动态负载模型。图2显示了需要被空调带走的热负荷。热负荷可以用数值表L(k)表示，其中 k ∈ ℕ。负载特性与典型数据中心负载特性相似(Radgen 等， 2020年).

Data centers must dissipate heat, generated during IT hardware operation, to prevent components from overheating. From an air conditioning perspective, the entire IT hardware can be idealized as a single heat source with variable output. Real measurements of IT hardware power consumption over the course of 1 year in a data center in Baden-Württemberg were used as a reference and combined with a dynamic load model of a typical UPS with 2MVA peak load. Fig. 2 shows the resulting thermal load that needs to be removed by the cooling system. The thermal load can be represented as a value table L(k) with k ∈ ℕ. The load profile is similar to typical load profiles of data centers (Radgen et al. 2020).

为了散发数据中心中IT硬件产生的热量，有许多基本功能相似的空调概念。在本论文中，假定数据中心具有混合冷却塔和能够自然冷却的冷却系统。

To dissipate the heat generated by the IT hardware in the data center, there are a number of air conditioning concepts that are similar in their basic function. In this paper, a data center with a hybrid cooling tower and a cooling system capable of free cooling is assumed.

来自于IT硬件产生的热量被通常被称作空气处理单元（AHU）的基于空气的通风技术散发出去，随后将热量通过热交换器转移到第二个循环系统中。根据当前的环境温度，第二循环可分成自然冷却和压缩冷却两种运行模式。只有当环境温度低到足以在不使用压缩式制冷机的情况下将热负荷散发到环境中时，才能进行自然冷却（见图 3）。如果环境温度足够低，冷却剂在换热器处升温并被泵送到冷却塔（也称为再冷器），在那里冷却并再循环到换热器。这个过程的能源消耗相对较低，因为唯一的能源需求来自冷却塔中的风扇和循环冷却剂的泵。

The heat is removed from the IT hardware via air-based ventilation technology commonly referred to as air handling units (AHU) and transferred to a second circular system via a heat exchanger. Based on the current ambient temperature, this second circuit can be divided into the two operating modes of free cooling and compression cooling. Free cooling is only possible if the ambient temperature is low enough to dissipate the heat load to the environment without using a compression refrigeration machine (see Fig. 3). If the ambient temperature is low enough, the coolant warms at the heat exchanger and is pumped to a cooling tower (also referred to as a recooler) where it gets cooled and recirculated to the heat exchanger. This process has a relatively low energy consumption, as the only energy requirement comes from the fans in the cooling tower and the pumps to circulate the coolant.

如果环境温度太高以至于自然冷却模式无法运行时，则可以使用压缩式制冷机（也称为直接膨胀 (DX) 制冷机）来捕获 IT 硬件的热流并通过冷却塔将其输送到温度更高的环境中（见图4）。相比自然冷却，这将消耗更多能量，因为必须消耗额外的能量来运行制冷机。

If the ambient temperature is so high that free cooling operation is not possible, a compression chiller (also referred to as direct expansion (DX) chiller) is used to capture the heat flow of the IT hardware and dissipate it at a higher temperature via the cooling tower (see Fig. 4). This requires more energy than free cooling, as additional energy must be used to operate the chiller.

两种运行模式之间的切换温度取决于数据中心可接受的温度以及制冷技术的质量。对于本文提及作为参考的数据中心，假设制冷剂的温度为25/20℃，这是欧洲数据中心的平均值且符合ASHRAE规定的数据中心温度指南(Avgerinou 等人 2017年; ASHRAE 2016年)。

The switching temperature between these two operating modes depends on the acceptable temperature in the data center and the quality of the cooling technology used. For the reference data center, a coolant temperature of 25/20 °C is assumed, which is average for a data center in Europe and within the guidelines for data center temperatures stated by ASHRAE (Avgerinou et al. 2017; ASHRAE 2016).

在这两种运行模式下，再冷器用于将热量散发到周围空气中（见图3和图4中的“冷却塔”）。干冷和混合冷却系统均可用于此目的。干冷系统通过强制对流将热量迁移到环境中，风扇负责空气传输。混合却冷系统还可以通过在换热器表面喷淋水来利用蒸发冷却的效果，这通过蒸发过程中的相变来冷却换热器。为了减少耗水量，可以仅向再冷器的局部喷水。

In both operating modes, a recooler is used to dissipate the heat to the ambient air (“cooling tower” in Figs. 3 and 4). Both dry and hybrid cooling systems can be used for this purpose. Dry cooling systems transmit the heat to the environment via forced convection, with fans being responsible for air transport. Hybrid cooling systems can additionally use the effect of evaporative cooling by applying water on the surface of the heat exchanger, which cools the heat exchanger by the phase change during evaporation. In order to reduce the water consumption, it is possible to only partially spray the recooler with water.

根据制造商数据、自行计算和来自 Koch (2020) 的数据，为参考数据中心定义了具有 1.000 kWth 制冷容量的混合制冷器的冷却系统的运行特性，如图 5 所示。操作特性可以表示为函数 K(t)。参考数据中心的自然冷却和压缩冷却之间的切换是在 16 °C 时完成的。

The operating characteristic of a cooling system with a hybrid chiller for a cooling capacity of 1.000 kWth, as seen in Fig. 5, was defined for the reference data center based on manufacturer data, own calculations, and data from Koch (2020). The operating characteristics can be represented as the function K(t). Switching between free cooling and compression cooling in the reference data center is done at 16 °C.

在图5中，运行模式清晰可辨。低于10℃情况下，系统运行在自然冷却模式下。能耗随着温度升高而增加，因为循环冷却器的风扇速度增加以继续将冷却剂冷却至 20 °C。循环泵的基本功耗与温度无关并保持恒定。高于10 °C，再冷器会额外喷水，这会降低风扇的速度，从而降低能量需求，但会导致持续的水消耗。当室外温度高于 16 °C 时，通过再冷器在回流中不再可能达到 20 °C 的温度。此时，压缩机开始运行，再冷器此时用作冷却过程的冷凝器。在这种情况下，只需将冷却介质冷却到 29°C，这可以通过较低的风扇转速来实现。换热器从19°C开始局部喷水，到24°C以上需要整体喷水，以最大限度地利用绝热冷却效果。

In Fig. 5, the operating modes are clearly recognizable. At temperatures below 10 °C, the system is operated with dry free cooling. Energy consumption increases with higher temperatures, as the fan speed of the recirculating chiller increases in order to continue cooling the coolant to 20 °C. The base load of the circulation pumps is temperature independent and remains constant. Above 10 °C, the recooler is additionally sprayed with water, which reduces the necessary speed of the fans and thus the energy requirement, but results in a constant water consumption. At an outside temperature above 16 °C, it is no longer possible to achieve a temperature of 20 °C in the return flow via the recooler. At this point, the compression chiller is operated and the recooler now serves as a condenser for the cooling process. In this case, it is only necessary to cool the cooling medium down to 29 °C, which can be accomplished with a lower fan speed. The heat exchanger is partially sprayed with water from 19 °C and the entire heat exchanger is sprayed with water above 24 °C in order to make maximum use of the adiabatic cooling effect.

在巴登符腾堡州，在确定数据中心冷却系统的尺寸和设计时可以考虑当地气候，根据 DIN 4710 使用六个不同的参考气候区(标准 DIN 4710 2003)，。当前正式的DIN 4710 版本于 2003 年发布，基于1961年至1990年期间从15个测量站收集的数据。但是，考虑到冷却系统能耗的地理差异，这种分类使用范围有限，因为气候带内的小空间波动无法考虑。然而，德国可以获得有关温度条件的空间高分辨率数据集（Krähenmann 等人，2016年）。德国气象局 (Deutscher Wetter Dienst DWD) 提供1995年至2012年公共数据集的平方公里级别每小时分辨率的温度数据。对于巴登-符腾堡州，这会产生 33,310 个单独的数据集，表明每平方千米的小时温度分布数据。2012 年被用作以下计算的数据基础，因为它是可用的最新数据集。

In Baden-Württemberg, the local climate can be taken into account for the dimensioning and design of data center cooling systems based on DIN 4710 using six different reference climate zones (Norm DIN 4710 2003). The currently valid version of DIN 4710 was published in 2003 and is based on the data collected from 15 measuring stations in the period from 1961 to 1990. For the consideration of local differences in the energy consumption of the cooling systems, this classification is, however, only of limited use, since small spatial fluctuations within the climate zones cannot be considered. However, spatially high-resolution data sets on temperature conditions are available for Germany (Krähenmann et al. 2016). The German Weather Service (Deutscher Wetter Dienst DWD) offers temperature data in hourly resolution at the square kilometer level as public data sets for the years 1995 to 2012. For Baden-Württemberg, this results in 33.310 individual data sets indicating the hourly temperature profile per square kilometer. The year 2012 was used as the data basis for the following calculations as it is the most recent data set available.

对于选定为参照的2012年，数据集可以表示为具有 2.926 亿个数据点的三维矩阵T ∈ℕ3。东西跨度在这里可以表示为横坐标 j，南北跨度可以表示为纵坐标 i，时间用系数k来表示。环境温度Ti,j,k 在矩阵中以0.1℃的步长给出。位置和时间离散功率P可以通过时间离散IT功率L(k)，IT最大功率Lmax，和冷却系统在在温度值Ti,j,k 的制冷系统的功率K(t)，使用公式（1）来计算。如图2 所示，IT 负载是恒定的，只有很小的波动。这种一致的负载曲线是数据中心的典型特征（Radgen 等人，2020年）。因此，当制冷系统接近其设计负载运行时，可以假设 IT 负载和制冷系统之间存在线性相关性。必须注意的是，这种相关性不适用于因白天/夜晚或工作日/周末波动而快速变化的负载曲线。

For the selected reference year 2012, the data set can be represented as a three-dimensional matrix T ∈ℕ3 with 292.6 million data points. The east-west expansion can be expressed here as abscissa j, the north-south expansion as ordinate i, and the temporal expansion as appliqué k. The ambient temperature Ti,j,k is given in this matrix in steps of 0.1 °C. The location- and time-discrete power consumption P of the cooling system can be calculated with the time-discrete IT power L(k), the IT maximum power Lmax, and the power consumption of the cooling system K(t) at the temperature point Ti,j,k using formula (1). As seen in Fig. 2, the IT-Load is constant with only small fluctuations. This consistent load profile is typical for data centers (Radgen et al. 2020). Therefore, a linear correlation between the IT-Load and the cooling system can be assumed as the cooling system operates close to its design load. It has to be noted that this correlation is not applicable to load profiles with rapid changes due to day/night or workday/weekend fluctuations.

位置和时间离散的功耗可以通过应用k相加，以便将研究区域的年能耗表示为位置离散的能耗 Ei,j（参见公式（2））。

The location- and time-discrete power consumption can then be summed up via the applications k, in order to indicate, as location-discrete energy consumption Ei,j the annual energy consumption in the study area (see formula (2)).

图6显示的是数值区域0到550MWh之间具有色标的二维可视化的能源消耗Ei,j图。再冷器的能耗Ei,j 根据不同温度处于202.55和553.82MWh/a之间。能耗的算术平均值为420.47MWh/a。就能耗来看，在位置E134,122 的首府Stuttgart被视为最不具备地理优势的数据中心位置之一（见图6）。

The two-dimensional visualization of Ei,j with a color scale in the value space between 0 and 550 MWh is shown in Fig. 6. The energy consumption Ei,j of the recooler lies between 202.55 and 553.82 MWh/a in the state due to the different temperatures. The arithmetic mean of the energy consumption is 420.47 MWh/a. In terms of energy, the state capital Stuttgart at the location E134,122 can be identified as one of the most disadvantageous data center locations (see Fig. 6).

为了确定位置变化的影响，定义了三个半径形式的自由度。对应为10公里、25公里 和 100 公里的半径。在这些半径内，对于每平方公里的点（对于 10 公里半径，请参见公式 (3)）计算相应半径内位置变化可能带来的最优的潜在的 Vi,j值。

To determine the influence of location variation, three degrees of freedom in the form of radii were defined. These correspond to a radius of 10, 25, and 100 km. Within these radii, the optimization potential Vi,j that would be possible by a change of location within the corresponding radius is calculated for each square kilometer point (see formula (3).) for the 10-km radius).

为了将能源价值转换为货币和生态价值，定义了 与地点无关的电价0.16 欧元/千瓦时和与地点无关的二氧化碳排放因子401 gCO2/kWh。0.16 欧元/千瓦时的电价对应于 2019 年德国的平均工业电价 (Bundesnetzagentur 2020)。401 gCO2/kWh的的 CO2 排放因子对应于 2019 年德国的平均电力碳排放 (Icha 2020)。

In order to convert the energetic values into monetary and ecological values, a location-independent electricity price of 0.16 €/kWh and a location-independent CO2-emission factor of 401 gCO2/kWh is defined. The electricity price of 0.16 €/kWh corresponds to the average industrial electricity price in Germany in 2019 (Bundesnetzagentur 2020). The CO2 emission factor of 401 gCO2/kWh corresponds to the average electricity mix in Germany in the year 2019 (Icha 2020).

未完待续

翻译：

沈文伟 David Shen

威图电子机械技术（上海）有限公司数据中心事业部产品经理

DKV(Deep Knowledge Volunreet)计划普通成员

校对：

刘海峰

Senior Research Analyst

451 Research/S&P Global Market Intelligence

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