文献类型：会议论文

英文题名：Demand Response Scheme: Achieving Sustainability in Distributed Data Processing

作者：Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei

第一作者：Zhai, Xueying

通信作者：Zhai, XY[1]

机构：[1]Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China;[2]Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing, Peoples R China;[3]Guizhou Inst Technol, Guizhou Key Lab Elect Power Big Data, Guizhou, Peoples R China;[4]Qilu Univ Technol, Key Lab Comp Power Network & Informat Secur, Minist Educ,Shandong Comp Sci Ctr, Natl Supercomputer Ctr Jinan,Shandong Acad Sci, Jinan, Peoples R China

第一机构：Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China

通信机构：corresponding author), Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China.

会议论文集：9th International Conference on Data Science in Cyberspace

会议日期：AUG 23-26, 2024

会议地点：Jinan, PEOPLES R CHINA

语种：英文

外文关键词：Demand response; Distributed data processing; Renewable energy source; Sustainability computing;

年份：2024

摘要：The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.