Understanding the role of buildings in a smart microgrid

A ‘smart microgrid’ refers to a distribution network for electrical energy, starting from electricity generation to its transmission and storage with the ability to respond to dynamic changes in energy supply through co-generation and demand adjustments. At the scale of a small town, a microgrid is connected to the wide-area electrical grid that may be used for ‘baseline’ energy supply; or in the extreme case only as a storage system in a completely self-sufficient microgrid. Distributed generation, storage and intelligence are key components of a smart microgrid. In this paper, we examine the significant role that buildings play in energy use and its management in a smart microgrid. In particular, we discuss the relationship that IT equipment has on energy usage by buildings, and show that control of various building subsystems (such as IT and HVAC) can lead to significant energy savings. Using the UCSD as a prototypical smart microgrid, we discuss how buildings can be enhanced and interfaced with the smart microgrid, and demonstrate the benefits that this relationship can bring as well as the challenges in implementing this vision.

[1]  Gregory D. Abowd,et al.  At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award) , 2007, UbiComp.

[2]  Gregor P. Henze,et al.  The performance of occupancy-based lighting control systems: A review , 2010 .

[3]  Vishal Garg,et al.  Smart occupancy sensors to reduce energy consumption , 2000 .

[4]  Thomas Weng,et al.  Occupancy-driven energy management for smart building automation , 2010, BuildSys '10.

[5]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[6]  Simon Hay,et al.  The case for apportionment , 2009, BuildSys '09.

[7]  Han Zhao,et al.  Granger causality analysis on IP traffic and circuit-level energy monitoring , 2010, BuildSys '10.

[8]  Adam Dunkels,et al.  Efficient application integration in IP-based sensor networks , 2009, BuildSys '09.

[9]  David E. Culler,et al.  Experiences with a high-fidelity wireless building energy auditing network , 2009, SenSys '09.

[10]  Lun Jiang,et al.  SCOPES: Smart Cameras Object Position Estimation System , 2009, EWSN.

[11]  François Ingelrest,et al.  The hitchhiker's guide to successful wireless sensor network deployments , 2008, SenSys '08.

[12]  Xiaojiang Du,et al.  A survey of key management schemes in wireless sensor networks , 2007, Comput. Commun..

[13]  Kang G. Shin,et al.  LiteGreen: Saving Energy in Networked Desktops Using Virtualization , 2010, USENIX Annual Technical Conference.

[14]  Jan Kleissl,et al.  Cyber-physical energy systems: Focus on smart buildings , 2010, Design Automation Conference.

[15]  Shengwei Wang,et al.  CO 2-Based Occupancy Detection for On-Line Outdoor Air Flow Control , 1998 .

[16]  David E. Culler,et al.  Design and implementation of a high-fidelity AC metering network , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[17]  Rajesh Gupta,et al.  SleepServer: A Software-Only Approach for Reducing the Energy Consumption of PCs within Enterprise Environments , 2010, USENIX Annual Technical Conference.

[18]  Thomas Weng,et al.  The energy dashboard: improving the visibility of energy consumption at a campus-wide scale , 2009, BuildSys '09.

[19]  Karsten Schwan,et al.  VirtualPower: coordinated power management in virtualized enterprise systems , 2007, SOSP.

[20]  Mani B. Srivastava,et al.  ViridiScope: design and implementation of a fine grained power monitoring system for homes , 2009, UbiComp.

[21]  Andrew Warfield,et al.  Live migration of virtual machines , 2005, NSDI.

[22]  David E. Culler,et al.  sMAP: a simple measurement and actuation profile for physical information , 2010, SenSys '10.

[23]  Andreas Terzis,et al.  Surviving wi-fi interference in low power ZigBee networks , 2010, SenSys '10.