"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 work, 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 SPUA framework, we conduct evaluations on real power consumption data and user/substation location data collected from Xinjiang 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."
Worcester Polytechnic Institute
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Li, Shijian, "Scalable User Assignment in Power Grids: A Data Driven Approach" (2017). Masters Theses (All Theses, All Years). 1107.
ADMM, optimization, assignment, power grid