{"title":"分布式系统中聚合小型互联网数据中心的需求灵活性容量价值评估","authors":"Bo Zeng, Xinzhu Xu, Fulin Yang","doi":"10.1016/j.gloei.2024.08.013","DOIUrl":null,"url":null,"abstract":"<div><div>With the advent of the digital economy, there has been a rapid proliferation of small-scale Internet data centers (SIDCs). By leveraging their spatiotemporal load regulation potential through data workload balancing, aggregated SIDCs have emerged as promising demand response (DR) resources for future power distribution systems. This paper presents an innovative framework for assessing capacity value (CV) by aggregating SIDCs participating in DR programs (SIDC-DR). Initially, we delineate the concept of CV tailored for aggregated SIDC scenarios and establish a metric for the assessment. Considering the effects of the data load dynamics, equipment constraints, and user behavior, we developed a sophisticated DR model for aggregated SIDCs using a data network aggregation method. Unlike existing studies, the proposed model captures the uncertainties associated with end tenant decisions to opt into an SIDC-DR program by utilizing a novel uncertainty modeling approach called Z-number formulation. This approach accounts for both the uncertainty in user participation intentions and the reliability of basic information during the DR process, enabling high-resolution profiling of the SIDC-DR potential in the CV evaluation. Simulation results from numerical studies conducted on a modified IEEE-33 node distribution system confirmed the effectiveness of the proposed approach and highlighted the potential benefits of SIDC-DR utilization in the efficient operation of future power systems.</div></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"8 3","pages":"Pages 460-473"},"PeriodicalIF":2.6000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the capacity value of demand flexibility from aggregated small Internet data centers in power distribution systems\",\"authors\":\"Bo Zeng, Xinzhu Xu, Fulin Yang\",\"doi\":\"10.1016/j.gloei.2024.08.013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>With the advent of the digital economy, there has been a rapid proliferation of small-scale Internet data centers (SIDCs). By leveraging their spatiotemporal load regulation potential through data workload balancing, aggregated SIDCs have emerged as promising demand response (DR) resources for future power distribution systems. This paper presents an innovative framework for assessing capacity value (CV) by aggregating SIDCs participating in DR programs (SIDC-DR). Initially, we delineate the concept of CV tailored for aggregated SIDC scenarios and establish a metric for the assessment. Considering the effects of the data load dynamics, equipment constraints, and user behavior, we developed a sophisticated DR model for aggregated SIDCs using a data network aggregation method. Unlike existing studies, the proposed model captures the uncertainties associated with end tenant decisions to opt into an SIDC-DR program by utilizing a novel uncertainty modeling approach called Z-number formulation. This approach accounts for both the uncertainty in user participation intentions and the reliability of basic information during the DR process, enabling high-resolution profiling of the SIDC-DR potential in the CV evaluation. Simulation results from numerical studies conducted on a modified IEEE-33 node distribution system confirmed the effectiveness of the proposed approach and highlighted the potential benefits of SIDC-DR utilization in the efficient operation of future power systems.</div></div>\",\"PeriodicalId\":36174,\"journal\":{\"name\":\"Global Energy Interconnection\",\"volume\":\"8 3\",\"pages\":\"Pages 460-473\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Energy Interconnection\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2096511725000416\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Energy Interconnection","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096511725000416","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Assessing the capacity value of demand flexibility from aggregated small Internet data centers in power distribution systems
With the advent of the digital economy, there has been a rapid proliferation of small-scale Internet data centers (SIDCs). By leveraging their spatiotemporal load regulation potential through data workload balancing, aggregated SIDCs have emerged as promising demand response (DR) resources for future power distribution systems. This paper presents an innovative framework for assessing capacity value (CV) by aggregating SIDCs participating in DR programs (SIDC-DR). Initially, we delineate the concept of CV tailored for aggregated SIDC scenarios and establish a metric for the assessment. Considering the effects of the data load dynamics, equipment constraints, and user behavior, we developed a sophisticated DR model for aggregated SIDCs using a data network aggregation method. Unlike existing studies, the proposed model captures the uncertainties associated with end tenant decisions to opt into an SIDC-DR program by utilizing a novel uncertainty modeling approach called Z-number formulation. This approach accounts for both the uncertainty in user participation intentions and the reliability of basic information during the DR process, enabling high-resolution profiling of the SIDC-DR potential in the CV evaluation. Simulation results from numerical studies conducted on a modified IEEE-33 node distribution system confirmed the effectiveness of the proposed approach and highlighted the potential benefits of SIDC-DR utilization in the efficient operation of future power systems.