A Guide to Reducing Carbon Emissions through Data Center Geographical Load Shifting
A Guide to Reducing Carbon Emissions through Data Center Geographical Load Shifting
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DOI：
10.1145/3447555.3466582
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
J. Lindberg;Yasmine Abdennadher;Jiaqi Chen;B. Lesieutre;Line A. Roald
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作者：
J. Lindberg;Yasmine Abdennadher;Jiaqi Chen;B. Lesieutre;Line A. Roald
最近的计算需求导致技术公司开发大规模，高度优化的数据中心。这些数据中心代表了电力网络上的大负载，这些电力网络具有在地理上和时间上转移负载的独特灵活性。本文重点介绍数据中心如何利用其地理负载灵活性，通过与电力市场的巧妙互动来减少碳排放。由于电力市场清算考虑了电网中的拥塞和电力流物理，因此与电力使用相关的碳排放在（可能地理上接近的）位置之间存在差异。利用我们对这一过程的了解，我们提出了一个新的和改进的指标来指导地理负荷转移，我们称之为位置边际碳排放λCO2。我们比较了这一指标和其他三个指标在一年内减少碳排放和发电成本的能力。我们的分析表明，λCO2在减少碳排放方面比不考虑电网具体情况的更常见的指标更有效。
Recent computing needs have lead technology companies to develop large scale, highly optimized data centers. These data centers represent large loads on electric power networks which have the unique flexibility to shift load both geographically and temporally. This paper focuses on how data centers can use their geographic load flexibility to reduce carbon emissions through clever interactions with electricity markets. Because electricity market clearing accounts for congestion and power flow physics in the electric grid, the carbon emissions associated with electricity use varies between (potentially geographically close) locations. Using our knowledge about this process, we propose a new and improved metric to guide geographic load shifting, which we refer to as the locational marginal carbon emission λCO2. We compare this and three other shifting metrics on their ability to reduce carbon emissions and generation costs throughout the course of a year. Our analysis demonstrates that λCO2 is more effective in reducing carbon emissions than more commonly proposed metrics that do not account for the specifics of the power grid.