选址优化 | 局部地形气候变化对数据中心能耗和成本的影响
Dirk Turek\ Peter Radgen | March 5, 2021
在宏观气候层面上，我们已经详细研究了选址对数据中心能耗的影响。但数据中心要想利用这些影响，通常需要跨国选址。本国（德国）境内和小于100公里的小半径范围内位置变化的影响还没有被量化研究。为此，我们建立了与数据中心温度相关联的动态数学模型，并在德国240 × 215公里范围内研究了其对能耗的影响。结果表明，在半径10公里位范围内，即使很小的位置变化，循环冷水机组也可能达到9.12%(最大56.58%)的年平均节能率。在国家境内100公里半径的自由位置，年平均节能率甚至可能达到37.35%(最多76.11%)。因此，就对能耗的影响而言，选址的优化与地域和国家有关，而中尺度气候变化的影响应该是未来数据中心选址的基本考量部分。
The effect of the location on the energy consumption of data centers has already been studied in detail on the macro-climatic level. To take advantage of these effects, however, it is usually necessary for the location of data centers to cross international borders. The influence of site changes within national borders and in a small radius of < 100 km has not yet been quantified. To investigate this, a dynamic mathematical model of the temperature-dependent components of a reference data center was created and the influence on the energy consumption in an area of 240 × 215 km in Germany was investigated. It could be shown that even small changes of the location within a 10 km radius of a location lead to annual energy savings in the recirculating chiller of 9.12% on average (maximum 56.58%). With a freedom of location of 100 km within national borders, savings of 37.35% on average (maximum 76.11%) are even possible. Location-dependent optimizations are therefore also relevant at local and national level with regard to their influence on energy consumption, and the consideration of mesoclimatic aspects should be an elementary part of the site selection process for data centers in the future.
全球对计算能力的需求量处于稳步增长状态。这种需求的主要驱动力一是数字化，通过数字替代品来替代模拟系统和服务，二是大数据和人工智能等领域的总体技术进步。数据中心能源效率的提升并不能完全弥补不断增长的能源需求。数据中心的总能源需求正在稳步增长。2005年全球能源需求量为153 TWh，到2018年增加到了205TWh，相当于增加32 %。未来，数据中心各方面都需进一步优化才能减少行业不断增长的能源消耗量，并将数字化带来的气候影响降至最低。
The demand for computing power is growing steadily worldwide. The main drivers of this demand are digitization, the replacement of analog systems and services through digital alternatives, and general technical advancements in areas such as big data and artificial intelligence. Improvements in data center energy efficiency are not able to fully compensate for the rising energy demand. As a result, data centers are experiencing a steadily increasing overall energy demand. The energy demand was 153 TWh in 2005worldwide and increased to 205 TWh as of 2018, which equals an increase of 32%. Optimizations in all aspects of a data center are necessary in the future to reduce the increasing energy consumption of this industry and to minimize the climate footprint of digitization.
瑞典、挪威、芬兰等北半球国家因为气候寒冷，正在积极推广宣传将本国作为节能数据中心的选址位置。数据中心所处环境温度越低，就越有利于使用自然冷却，这种运行方式比压缩式制冷机的主动冷却更节能。高效冷却方式和低电价的双重优势相结合，使得五大巨头(GAFAM/谷歌，亚马逊，Facebook，苹果，微软)将他们面向欧洲市场的数据中心主要放在北欧大陆。目前已经有环境温度对数据中心运行能耗和成本的影响的相关研究。这些研究在宏观气候水平上考虑了当地气候的差异，比较的地点相距数百甚至数千公里，气候条件有显著差异。Depoorter等人比较了欧洲的五个不同位置，北到瑞典斯德哥尔摩，南至西班牙巴塞罗那。Song等人比较了美国的三个地点和瑞典的一个地点。Shehabi等人比较了美国的五个地点。所有研究结论都表明低环境温度和低运行能源需求之间确实存在相关性，寒冷气候更有利于对数据中心运行。
Northern hemisphere countries such as Sweden, Norway, and Finland are actively promoting their country as an energy-efficient data center location due to the cold climate. The colder the ambient temperature of a data center, the more often so-called “free cooling” can be used, which enables a much more energy-efficient operation compared with active cooling by a compression chiller. The energy- and cost-efficient cooling in combination with low electricity prices has led the Big Five (GAFAM/ Google, Amazon, Facebook, Apple, Microsoft) to place their data centers for the European market primarily in the northern half of the continent. The effect of the ambient temperature at a location on the energy consumption and cost of data center operation has already been studied. These studies considered differences in the local climate on the macro-climatic level meaning that compared locations are hundreds or even thousands of kilometers apart, resulting in significant difference in climate conditions.Depoorter et al. compared five different locations in Europa with Stockholm being the northernmost location and Barcelona being the southernmost. Song et al. compared three locations in the USA with one location in Sweden and Shehabi et al. considered five locations across the USA. All reviewed papers concluded a correlation between low ambient temperature and low operational energy demand, favoring colder climates for data center operation.
但不是所有的计算业务都可以跨国外包，比如为国家政府及其机构服务的数据中心。对于一国政府来说，要想实现数字主权独立，用于国家运营的基础设施就必须设置在本国境内。法律要求警察机构、财政部或联邦最高法院等当局使用的数据中心必须部署在本国境内，不得跨境。这就把数据中心选址限制在了他们自己的国家。例如，由于联邦政府的原因，德国国家当局的数据中心选址可能仅限于德国境内，甚至进一步缩小潜在地点范围至联邦州。这种限制同样适用于企业，因为有些应用程序在区域数据中心可以确保低延迟，并且对于中小型企业来说，留在国内可以减少管理成本。
But not all computing workload can be outsourced across national borders. This includes data centers that perform tasks for a country’s government and its agencies. For a country’s government, digital sovereignty is only possible if the infrastructure necessary for state operation remains within its borders. Cross-border placement of data centers is legally impossible for authorities such as the police, the Ministry of Finance or the Federal Supreme Court and therefore requires a data center within national borders. This limits the choice of location to their own country. Due to the federal administration, this can for example limit the choice of location for state authorities in Germany not only to Germany but also to the federal state, narrowing down the potential locations even further. These limitations can also be applicable to businesses as some applications require regional data centers to assure low latency, or to small and medium businesses that want to stay within a country to reduce administrative burden.
数据中心的最优选址是一个复杂的决策过程，需考虑许多与位置相关的因素，如营业税、土地价格、人员可用性、基础设施、土地使用计划和电价。必须对尽可能多的因素进行统一评估后才可能做出最合理的决策。最常用的量化标准就是货币单位，因为它可以用于经济评价且具有可比性。上面提到的许多区位因素都可以以货币形式评估，例如与贸易税有关的土地价格。但局部地域范围内的微小地域因素变化的影响还无法被量化。因此，在对局部区域内的多个地点作比较时，目前还不能将当地温度作为金融变量来考虑。
Choosing the optimal location for a data center is a highly complex decision process, where many locationdependent factors must be taken into account, such as business taxes, land prices, staff availability, infrastructure, land use plans, and electricity prices. In order to make the most rational decision in the choice of location, it is necessary to evaluate as many factors as possible uniformly. The most commonly used unit is the monetary unit as this can be used for economic evaluation and comparability. Many of the location factors mentioned above can be assessed in monetary terms. This includes, for example, the consideration of the land price in relation to the trade tax. The effect of small location variations within a regional area has not yet been quantified. It is therefore currently not possible to take the local temperature as a monetary variable into account when comparing multiple locations within a small area.
本文建立了与数据中心温度相关联的动态数学模型。冷却系统的能耗根据德国联邦境内的一平方公里分辨率来计算，并定义了三个不同大小的范围来说明数据中心在潜在位置方面可能具有的不同自由度，即分别研究了小半径(10公里)、中半径(25公里)和大半径(100公里)的站点差异对数据中心能耗的影响。能源消费的差异可以通过能源价格来进行货币比较，并且根据电力转化的二氧化碳强度，也可以评估地理位置对气候的影响。本次研究地点选择了德国巴登-符腾堡州（Baden-Württemberg），因为该州在短距离内兼具高山和峡谷的地形差异。这种地区的气候变化可以称为中尺度气候变化，介于全球(大气候)和局部(小气候)之间。本文量化了中尺度气候温差对数据中心能耗的影响，从而可能降低运营成本。该结果可作为未来数据中心选址在能源及金融量化方面的决策参数。
In this paper, a dynamic mathematical model of the temperature-dependent components of a reference data center is created. The energy consumption of the cooling system is then calculated depending on the location in a square kilometer resolution in the territory of one federal state of Germany. To account for the different degrees of freedom a data center might have in terms of potential locations, three ranges are defined. The effects of site differences in a small (10 km), medium (25 km), and large (100 km) radius on the energy consumption of the reference data center can thus be investigated. The difference in energy consumption can be compared in monetary terms via the energy price, and with the CO2 intensity of the electricity mix, the climate impact can also be evaluated in dependence of the location. The federal state of Baden-Württemberg was selected for this study because it has topographically relevant variability in the form of mountain regions and large valley zones. Variations in climate in an area like this can be referred to as a mesoclimate, being between global (macroclimate) and local (microclimate). This paper quantifies the impact of mesoclimatic temperature differences on data center energy consumption and thus potential reductions in operating costs. The result can be used as an energetically and monetarily quantifiable decision parameter for the site selection of future data centers.
翻译：
徐霄燕
北京德利迅达科技有限公司 给排水工程师
DKV（Deep Knowledge Volunteer）创始成员
校对：
钱涛
上海哔哩哔哩科技有限公司 高级运维工程师
DKV（Deep Knowledge Volunteer）普通成员