基于遗传算法和地图上输电线路寻路的城市小区变电站布局优化

E. G. Kozlov, S. Kosyakov
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引用次数: 0

摘要

在城市小区供电方案设计中,确定变电站的布置是一项具有挑战性的任务,因为在选择电力线路时需要考虑空间限制。因此,变电站配电的选择通常受到几种变型的比较的限制。在科学文献中,通常不考虑输电线路的实际环境限制来考虑优化问题。这大大降低了所用模型的充分性。因此,在城市小区供电方案设计中,研究在GIS环境下结合组合优化方法和构建最小成本路径选择变电站最优布局的可能性是有意义的。研究采用了图上最短路径的确定方法和遗传算法搜索。以伊万诺沃市电力线图为基础数据进行计算。为了在数字城市地图上为大量建筑物供电,提出了一种确定变电站最优数量和位置的新方法。采用成本面波动算法估算从用户到变电站任意位置铺设电缆输电线路的成本。采用遗传算法选择网络结构。研究结果证实了在给定空间限制条件下利用GIS确定最佳变电站分布的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of placement of transformer substations in urban neighborhoods using genetic algorithm and pathfinding of electric power transmission lines on the map
To define the placement of electric substations when designing power supply schemes in urban neighborhood is a challenging task since it is necessary to consider spatial restrictions when choosing routes for electric power lines. Thus, the selection of the substation distribution is usually limited by comparison of several variants. In the scientific literature, problems of optimization are usually considered without taking into account real environmental restrictions for the electric power transmission lines. And it significantly reduces the adequacy of the models used. Thus, the study of the possibilities to use both combinatorial optimization methods and the construction of minimum cost routes in the GIS environment to select the optimal layout of electrical substations when designing power supply schemes for urban neighborhood is relevant. The methods to determine the shortest paths on the graphs and the genetic algorithm search are used for the research. The map of the electric lines of Ivanovo city has been used as base data for calculation. A new method has been developed to determine the optimal number and placement of transformer substations to power a lot of buildings on the digital city map. A wave algorithm of cost surfaces is used to estimate the cost of laying cable electric power transmission lines from the consumer to any location of the transformer substation. A genetic algorithm is used to select the network structure. The research results have confirmed the possibility to determine the best transformer substation distribution using GIS with given spatial restrictions.
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