合成空间嵌入式电力网格网络的生成
Generation of Synthetic Spatially Embedded Power Grid Networks
AI总结：
本文提出一种生成具有类似结构和空间特性的合成电力网格网络算法，通过高斯混合模型估计节点位置密度，并基于电网历史演变过程连接节点，首次考虑了节点和线路的空间分布对合成电网的重要性。
摘要：
开发用于提高电力网格韧性和效率的算法需要使用真实传输网络拓扑进行性能评估。然而，由于安全原因，此类拓扑及其特别是变电站和线路的位置通常不公开。因此，我们研究北美电网的结构特性，并提出一种生成具有类似属性的合成空间嵌入式网络的算法。该算法使用高斯混合模型（GMM）对节点位置进行密度估计，并生成具有类似空间分布的节点集合。然后，它使用两种受电网历史演变启发的程序连接节点。该算法具有多个可调参数，可以生成与任何给定电网相似的电网。特别是，我们将其应用于西部互联（WI）以及由SERC可靠性公司和佛罗里达可靠性协调委员会运营的电网，并展示了其生成的电网在结构和空间特性上与这些电网相似。据我们所知，这是首次考虑节点和线路的空间分布及其在生成合成电力电网中的重要性。
英文摘要：
The development of algorithms for enhancing the resilience and efficiency of the power grid requires performance evaluation with real topologies of power transmission networks. However, due to security reasons, such topologies and particularly the locations of the substations and the lines are usually not publicly available. Therefore, we study the structural properties of the North American grids and present an algorithm for generating synthetic spatially embedded networks with similar properties to a given grid. The algorithm uses the Gaussian Mixture Model (GMM) for density estimation of the node positions and generates a set of nodes with similar spatial distribution to the nodes in a given network. Then, it uses two procedures, which are inspired by the historical evolution of the grids, to connect the nodes. The algorithm has several tunable parameters that allow generating grids similar to any given grid. Particularly, we apply it to the Western Interconnection (WI) and to grids that operate under the SERC Reliability Corporation (SERC) and the Florida Reliability Coordinating Council (FRCC), and show that it generates grids with similar structural and spatial properties to these grids. To the best of our knowledge, this is the first attempt to consider the spatial distribution of the nodes and lines and its importance in generating synthetic power grids.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
