A site selection framework for urban power substation at micro‐scale using spatial optimization strategy and geospatial big data

Abstract The world is facing more energy crises due to extreme weather and the rapidly growing demand for electricity. Siting new substations optimizing location of existing ones are necessary address crisis. current site selection lacks consideration spatial temporal heterogeneity in urban power demand, which results unreasonable transfer waste, leading outages some areas. Aiming maximize grid coverage transformer utilization, we propose a multi‐scene micro‐scale substation siting framework (UrbanPS): (1) uses multi‐source big data machine learning model estimate fine‐scale consumption different scenarios; (2) region algorithm used divide supply area substations; (3) set problem genetic introduced optimize location. UrbanPS was perform optimization 110 kV terminal Pingxiang City, Jiangxi Province. Results show that utilization rate under scenarios close 99%. We also found can be saved by dynamic regulation operation.