{"title":"通过联合工作量分担和碳配额交易实现数据中心的低碳运营","authors":"Dongxiang Yan;Mo-Yuen Chow;Yue Chen","doi":"10.1109/TCC.2024.3396476","DOIUrl":null,"url":null,"abstract":"Data centers (DCs) have witnessed rapid growth due to the proliferation of cloud computing and internet services. The huge electricity demand and the associated carbon emissions of DCs have great impacts on power system reliability and environmental sustainability. This paper proposes a bilevel model for low-carbon operation of DCs via carbon-integrated locational marginal prices (CLMPs). In the upper level, the power system operator sequentially solves the optimal power flow and the carbon emission flow problems to determine the CLMPs. In the lower level, a joint workload sharing and carbon trading model for DCs is developed to minimize their overall operation cost while keeping each DC's carbon footprint within its carbon allowance. To solve the bilevel model and preserve the privacy of DCs, we propose a bisection-embedded iterative method. It can tackle the issue of oscillation, thereby ensuring convergence. In addition, a filtering mechanism-based distributed algorithm is proposed to solve the lower-level DC problem in a distributed manner with much reduced communication overhead. Case studies on both small-scale and large-scale systems demonstrate the effectiveness and benefits of the proposed method.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"750-761"},"PeriodicalIF":5.3000,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low-Carbon Operation of Data Centers With Joint Workload Sharing and Carbon Allowance Trading\",\"authors\":\"Dongxiang Yan;Mo-Yuen Chow;Yue Chen\",\"doi\":\"10.1109/TCC.2024.3396476\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data centers (DCs) have witnessed rapid growth due to the proliferation of cloud computing and internet services. The huge electricity demand and the associated carbon emissions of DCs have great impacts on power system reliability and environmental sustainability. This paper proposes a bilevel model for low-carbon operation of DCs via carbon-integrated locational marginal prices (CLMPs). In the upper level, the power system operator sequentially solves the optimal power flow and the carbon emission flow problems to determine the CLMPs. In the lower level, a joint workload sharing and carbon trading model for DCs is developed to minimize their overall operation cost while keeping each DC's carbon footprint within its carbon allowance. To solve the bilevel model and preserve the privacy of DCs, we propose a bisection-embedded iterative method. It can tackle the issue of oscillation, thereby ensuring convergence. In addition, a filtering mechanism-based distributed algorithm is proposed to solve the lower-level DC problem in a distributed manner with much reduced communication overhead. Case studies on both small-scale and large-scale systems demonstrate the effectiveness and benefits of the proposed method.\",\"PeriodicalId\":13202,\"journal\":{\"name\":\"IEEE Transactions on Cloud Computing\",\"volume\":\"12 2\",\"pages\":\"750-761\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Cloud Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10518095/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cloud Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10518095/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
由于云计算和互联网服务的激增,数据中心(DC)见证了快速增长。数据中心巨大的电力需求和相关的碳排放对电力系统的可靠性和环境可持续性产生了巨大影响。本文提出了一种通过碳整合区位边际价格(CLMP)实现直流电低碳运行的双层模型。在上层,电力系统运营商依次求解最优电力流和碳排放流问题,以确定 CLMPs。在下层,为直流电开发了一个联合工作量分担和碳交易模型,以最大限度地降低其总体运营成本,同时将每个直流电的碳足迹控制在其碳限额内。为了解决双层模型并保护 DC 的隐私,我们提出了一种分段嵌入迭代法。它可以解决振荡问题,从而确保收敛。此外,我们还提出了一种基于过滤机制的分布式算法,以分布式方式解决底层 DC 问题,大大减少了通信开销。对小型和大型系统的案例研究证明了所提方法的有效性和优势。
Low-Carbon Operation of Data Centers With Joint Workload Sharing and Carbon Allowance Trading
Data centers (DCs) have witnessed rapid growth due to the proliferation of cloud computing and internet services. The huge electricity demand and the associated carbon emissions of DCs have great impacts on power system reliability and environmental sustainability. This paper proposes a bilevel model for low-carbon operation of DCs via carbon-integrated locational marginal prices (CLMPs). In the upper level, the power system operator sequentially solves the optimal power flow and the carbon emission flow problems to determine the CLMPs. In the lower level, a joint workload sharing and carbon trading model for DCs is developed to minimize their overall operation cost while keeping each DC's carbon footprint within its carbon allowance. To solve the bilevel model and preserve the privacy of DCs, we propose a bisection-embedded iterative method. It can tackle the issue of oscillation, thereby ensuring convergence. In addition, a filtering mechanism-based distributed algorithm is proposed to solve the lower-level DC problem in a distributed manner with much reduced communication overhead. Case studies on both small-scale and large-scale systems demonstrate the effectiveness and benefits of the proposed method.
期刊介绍:
The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.