TitleA site selection framework for urban power substation at micro-scale using spatial optimization strategy and geospatial big data
AuthorsYao, Yao
Feng, Chenqi
Xie, Jiteng
Yan, Xiaoqin
Guan, Qingfeng
Han, Jian
Zhang, Jiaqi
Ren, Shuliang
Liang, Yuyun
Luo, Peng
AffiliationChina Univ Geosci, Sch Geog & Informat Engn, Wuhan, Peoples R China
Univ Tokyo, Ctr Spatial Informat Sci, Chiba, Japan
State Grid Pingxiang Power Supply Co, Pingxiang, Peoples R China
Tech Univ Munich, Chair Cartog & Visual Analyt, Munich, Germany
Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China
Univ Oxford, Sch Geog & Environm, Oxford, England
Issue Date2023
AbstractThe world is facing more energy crises due to extreme weather and the rapidly growing demand for electricity. Siting new substations and optimizing the location of existing ones are necessary to address the energy crisis. The current site selection lacks consideration of spatial and temporal heterogeneity in urban power demand, which results in unreasonable energy transfer and waste, leading to power outages in some areas. Aiming to maximize the grid coverage and transformer utilization, we propose a multi-scene micro-scale urban substation siting framework (UrbanPS): (1) The framework uses multi-source big data and the machine learning model to estimate fine-scale power consumption for different scenarios; (2) the region growing algorithm is used to divide the power supply area of substations; and the (3) location set coverage problem and genetic algorithm are introduced to optimize the substation location. The UrbanPS was used to perform siting optimization of 110 kV terminal substations in Pingxiang City, Jiangxi Province. Results show that the coverage and utilization rate of the optimization results under different power consumption scenarios are close to 99%. We also found that the power can be saved by dynamic regulation of substation operation.
Appears in Collections:地球与空间科学学院

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