A distributed approach to emergency demand response in geo-distributed mixed-use buildings

被引:8
作者
Chuan Pham [1 ]
Tran, Nguyen H. [2 ,3 ]
Ren, Shaolei [4 ]
Hong, Choong Seon [3 ]
Nguyen, Kim Khoa [1 ]
Cheriet, Mohamed [1 ]
机构
[1] Univ Quebec, Synchromedia Ecole Technol Super, Ste Foy, PQ, Canada
[2] Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia
[3] Kyung Hee Univ, Dept Comp Sci & Engn, Seoul, South Korea
[4] Univ Calif Riverside, Dept Elect & Comp Engn, Riverside, CA 92521 USA
来源
JOURNAL OF BUILDING ENGINEERING | 2018年 / 19卷
基金
新加坡国家研究基金会; 加拿大自然科学与工程研究理事会;
关键词
Emergency demand response; Mixed-use building; Geographically distributed datacenters; ENERGY; FRAMEWORK;
D O I
10.1016/j.jobe.2018.06.004
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Emergency Demand Response (EDR) has attracted research attention in recent years with its critical role in smart grids. Even though there are numerous potential participants for EDR, we especially focus on EDR, especially within datacenters and buildings, due to their huge power consumption yet flexible control knobs for power shedding. To reduce the deployment cost, many edge datacenters now are co-located inside buildings, which are responsible for power and IT infrastructure (called mixed-use buildings). In this paper, we consider a scenario that has not been addressed in the literature, in which multiple loads in geographically Distributed Mixed-use Buildings (geo-MUBs) can team up to participate EDR. We then design a mechanism that can coordinate tenants and geo-distributed buildings to minimize the system cost for EDR based on a robustly distributed framework, Alternating Direction Method of Multipliers (ADMM). In this mechanism, we also design a privacy-preserving scheme to conceal all tenants' transactions by using a lightweight algorithm. Simulation results show that our proposed method can reduce the total cost by 48.8% compared to existing approaches while satisfying all tenants constraints.
引用
收藏
页码:506 / 518
页数:13
相关论文
共 57 条
[21]  
Fuller J.C., 2011, POWER ENERGY SOC GEN, P1, DOI DOI 10.1109/PES.2011.6039827
[22]  
Gandhi A, 2009, PERF E R SI, V37, P157
[23]  
Godo S, 2014, IEEE IND ELEC, P5344, DOI 10.1109/IECON.2014.7049316
[24]   The Cost of a Cloud: Research Problems in Data Center Networks [J].
Greenberg, Albert ;
Hamilton, James ;
Maltz, David A. ;
Patel, Parveen .
ACM SIGCOMM COMPUTER COMMUNICATION REVIEW, 2009, 39 (01) :68-73
[25]  
Hang-yat L.A., 2014, Proceedings of the 5th International Conference on Future Energy Systems, P133
[26]  
Hong Xu, 2013, Performance Evaluation Review, V41, P373
[27]  
Irwin D., 2011, BUILDSYS
[28]   Exploiting Spatio-Temporal Diversity for Water Saving in Geo-Distributed Data Centers [J].
Islam, Mohammad A. ;
Ahmed, Kishwar ;
Xu, Hong ;
Tran, Nguyen H. ;
Quan, Gang ;
Ren, Shaolei .
IEEE TRANSACTIONS ON CLOUD COMPUTING, 2018, 6 (03) :734-746
[29]   Human-Building Interaction Framework for Personalized Thermal Comfort-Driven Systems in Office Buildings [J].
Jazizadeh, Farrokh ;
Ghahramani, Ali ;
Becerik-Gerber, Burcin ;
Kichkaylo, Tatiana ;
Orosz, Michael .
JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2014, 28 (01) :2-16
[30]  
Li S, 2016, IEEE T POWER SYST, V31, P1170, DOI [10.1109/TPWRS.2015.2432057, 10.1109/PESGM.2016.7741282]