Coordinated clustering algorithms to support charging infrastructure design for electric vehicles

The confluence of several developments has created an opportune moment for energy system modernization. In the past decade, smart grids have attracted many research activities in different domains. To realize the next generation of smart grids, we must have a comprehensive understanding of interdependent networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this paper, we develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. We demonstrate the multiple factors that can be simultaneously leveraged in our framework in order to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.

[1]  Xing Xie,et al.  Mining correlation between locations using human location history , 2009, GIS.

[2]  Xing Xie,et al.  T-drive: driving directions based on taxi trajectories , 2010, GIS '10.

[3]  Yogesh L. Simmhan,et al.  Improving Energy Use Forecast for Campus Micro-grids Using Indirect Indicators , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[4]  Kevin S. Blake,et al.  Measuring Overcrowding in Housing , 2007 .

[5]  Chris Bailey-Kellogg,et al.  Spatial data mining to support pandemic preparedness , 2006, SKDD.

[6]  Matthew Chalmers,et al.  Guest Editors' Introduction: Urban Computing , 2007, IEEE Pervasive Computing.

[7]  M. Shahriar Hossain,et al.  Unifying dependent clustering and disparate clustering for non-homogeneous data , 2010, KDD.

[8]  Tetsuro Morimura,et al.  Large-Scale Nonparametric Estimation of Vehicle Travel Time Distributions , 2012, SDM.

[9]  Kanad Ghose,et al.  Detecting and Tracking Coordinated Groups in Dense, Systematically Moving, Crowds , 2012, SDM.

[10]  Xing Xie,et al.  Discovering spatio-temporal causal interactions in traffic data streams , 2011, KDD.

[11]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[12]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[13]  Guangzhong Sun,et al.  Driving with knowledge from the physical world , 2011, KDD.

[14]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[15]  Sarvapali D. Ramchurn,et al.  Putting the 'smarts' into the smart grid , 2012, Commun. ACM.