The 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.

中文翻译：

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基于空间优化策略和地理空间大数据的微观城市变电站选址框架

由于极端天气和快速增长的电力需求，世界正面临更多的能源危机。为了解决能源危机，有必要选址新变电站并优化现有变电站的位置。目前选址缺乏考虑城市电力需求时空异质性，导致能源不合理转移和浪费，导致部分地区停电。为了最大限度地提高电网覆盖范围和变压器利用率，我们提出了多场景微型城市变电站选址框架（UrbanPS）：（1）框架利用多源大数据和机器学习模型来估算不同场景的精细功耗；(2)采用区域生长算法划分变电站供电区域；(3)引入位置集覆盖问题和遗传算法来优化变电站选址。UrbanPS用于江西省萍乡市110kV终端变电站的选址优化。结果表明，不同功耗场景下优化结果的覆盖率和利用率均接近99%。我们还发现，通过动态调节变电站运行可以节省电力。UrbanPS用于江西省萍乡市110kV终端变电站的选址优化。结果表明，不同功耗场景下优化结果的覆盖率和利用率均接近99%。我们还发现，通过动态调节变电站运行可以节省电力。UrbanPS用于江西省萍乡市110kV终端变电站的选址优化。结果表明，不同功耗场景下优化结果的覆盖率和利用率均接近99%。我们还发现，通过动态调节变电站运行可以节省电力。