Scalable User-Substation Assignment with Big Data from Power Grids

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 $\ll$≪ capacity) or under-supplied (demand $\approx$≈ 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 control the assignments between users and 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 northwestern province in China for 35 days in 2015. The evaluation results demonstrate that our SPUA framework can achieve a 20–65 percent reduction on the maximum substation utilization, and 2 to 3.7 times reduction on total transmission loss over other baseline methods.