NDLI: Scalable user assignment in power grids: a data driven approach

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Scalable user assignment in power grids: a data driven approach

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Scalable user assignment in power grids: a data driven approach

Content Provider	ACM Digital Library
Author	Li, Yanhua Liao, Yong Trapp, Andrew C. Xie, Haiyong Li, Shijian Fu, Jie Lyu, Bo
Abstract	The fast pace of global urbanization is drastically changing the population distributions over the world, which leads to significant changes in geographical population densities. Such changes in turn alter the underlying geographical power demand over time, and drive power substations to become over-supplied (demand << capacity) or under-supplied (demand ≈ capacity). In this paper, we make the first attempt to investigate the problem of power substation-user assignment by analyzing large-scale power grid data. We develop a Scalable Power User Assignment (SPUA) framework, that takes large-scale spatial power user/substation distribution data and temporal user power consumption data as input, and assigns users to substations, in a manner that minimizes the maximum substation utilization among all substations. To evaluate the performance of our SPUA framework, we conduct evaluations on real power consumption data and user/substation location data collected from a province in China for 35 days in 2015. The evaluation results demonstrate that our SPUA framework can achieve a 20%--65% reduction on the maximum substation utilization, and 2 to 3.7 times reduction on total transmission loss over other baseline methods.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450345897
DOI	10.1145/2996913.2996970
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User assignment Power grid Optimization
Content Type	Text
Resource Type	Article

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