SGER: Characterizing Power Systems with Tools from Complex Networks

网络？国家的脆弱性和减轻风险措施的有效性在很大程度上取决于其结构。这项合作研究的目标是根据拓扑和电气特性为大规模电力网络开发新的结构措施，并找到电力网络结构和脆弱性之间的联系。由此产生的措施将使用两个主要的区域传输运营商的电力流数据进行验证。智力MeritResearch在复杂网络和图论领域的研究表明，一个网络？的拓扑结构会显著影响其性能和脆弱性。以前的研究试图测量电网的结构，并使用随机图，小世界和无标度网络文献中的现有工具来识别漏洞，但结果是混合的和不一致的。部分混乱的结果，从一个网格拓扑结构的重点，忽视基尔霍夫？s定律，它控制着电路中的电流。由于脆弱性在很大程度上是流行为的函数，因此一组正确的电力网络结构度量需要结合这些物理定律。这项工作将解决这一缺陷，在现有的文献中，通过发展的脆弱性措施，考虑网络拓扑结构和物理定律，管理流。更广泛的影响这项工作提出了一种全新的思路来衡量和检测电力网络的脆弱性。如果成功，这一努力将为有助于显著提高美国电力基础设施可靠性和效率的工具提供知识基础。此外，这项工作将提高佛蒙特大学和宾夕法尼亚州立大学在传统学科领域的电力系统和复杂网络教育能力。该项目还将支持在UVM开发一个新的跨学科能源系统实验室，该实验室旨在吸引更多样化的学生学习能源系统工程。

AbstractA network?s vulnerability and the effectiveness of risk mitigation measures depend highly on its structure. The goal of this collaborative research is to develop new structural measures for large-scale electric power networks, based on both topological and electrical properties, and to find connections between the structure and vulnerability of electricity networks. The resulting measures will be validated using power-flow data of two major regional transmission operators. Intellectual MeritResearch in the area of complex networks and graph theory shows that a network?s topology can dramatically affect its performance and vulnerability. Previous studies have attempted to measure the structure of power grids and identify vulnerabilities using existing tools from the literature on random graphs, small-world and scale-free networks, but the results have been mixed and inconsistent. Part of the confusion results from a focus on grid topology, ignoring Kirchoff?s Laws, which govern flows in electrical circuits. Since vulnerability is largely a function of flow behavior, a correct set of structural metrics for power networks needs to incorporate these physical laws. This work will address this shortcoming in the existing literature through the development of vulnerability measures that consider both network topology and the physical laws that govern flow. Broader ImpactsThis work proposes a fundamentally new way of thinking about measuring and detecting vulnerability in power networks. If successful, this effort will provide the intellectual foundation for tools contributing to significant improvement of the reliability and efficiency of the US electricity infrastructure. Additionally this work will increase the capacity for education in power systems and complex networks, across traditional disciplinary lines, at the University of Vermont and Penn State University. The project will also support the development of a new cross-disciplinary energy systems laboratory at UVM, which is being designed to attract a more diverse pool of students to energy systems engineering.