基于地理信息融合和Voronoi-Kmeans混合分区的变电站选址定容

Substation Site Selection and Capacity Determination Based on Geographic Information Fusion and Hybrid Voronoi-Kmeans Partitioning

  • 摘要: 针对传统变电站规划中对地理信息利用不足与分区负荷不够均衡的问题,结合Voronoi图和K-means算法,提出融合地理信息系统(geographic information system,GIS)与Voronoi-Kmeans混合分区的变电站选址定容方法。首先,根据基于GIS的空间电力负荷和实际用地信息生成地价权重矩阵;其次,基于空间电力负荷网格化结果设计Voronoi-Kmeans混合算法,对空间电力负荷进行分区;然后,根据各区的总负荷以及容载比对变电站定容;最后,构建包含地价因素的变电站年费用最小规划模型并采用改进的遗传算法求解。算例分析表明,该选址定容方法可以提升变电站规划的准确性,减少电力系统的规划成本。

     

    Abstract: In response to the issues of inadequate utilization of geographic information and insufficiently balanced zoning load in traditional substation planning, a method for substation siting and capacity determination integrating geographic information system (GIS) and Voronoi-Kmeans hybrid zoning is proposed. Firstly, the land price weight matrix is generated based on the spatial power load and actual land information based on GIS. Secondly, based on the results of spatial power load gridding, the hybrid Voronoi-Kmeans algorithm is designed to partition the spatial power load. Subsequently, the substation capacity is determined based on the total load of each zone and the capacity ratio. Finally, an annual cost-minimization planning model for substations that incorporates land prices is constructed and solved using an improved genetic algorithm. The analysis of the arithmetic example demonstrates that the proposed site selection and capacity determination method improves the accuracy of substation planning and reduces the planning cost of the power system.

     

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