[论文解读] Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency

Mert Korkali, Jason G. Veneman+2 more|arXiv (Cornell University)|Oct 24, 2014

Complex Network Analysis Techniques被引用 8

一句话总结

本文挑战了主流观点，即基础设施网络之间的相互依赖性增加会提高故障风险，表明基于真实物理模型的电力与通信网络反而显示，耦合可降低级联故障的脆弱性。与预测耦合会增加风险的拓扑传播模型不同，基于物理的仿真表明，智能电网与通信网络的集成可增强系统鲁棒性，尤其在正常运行条件下表现更优。

ABSTRACT

Increased coupling between critical infrastructure networks, such as power and communication systems, will have important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several have studied interdependent network models and reported that increased coupling can increase system vulnerability. However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communication networks. This paper reports on two sets of experiments that compare the network vulnerability implications resulting from simple topological models and models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid shows a much higher level of vulnerability, relative to the contagion model. Second, we compare a model of topological cascades in coupled networks to three different physics-based models of power grids coupled to communication networks. Again, the more accurate models suggest very different conclusions. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the coupled topological model, in which zero coupling is optimal. Finally, an extreme case in which communication failures immediately cause grid failures, suggests that if systems are poorly designed, increased coupling can be harmful. Together these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems.

研究动机与目标

• 研究真实电力系统动态对互联系基础设施网络中级联故障风险的影响。
• 挑战基于拓扑传播模型的假设，即网络间耦合程度越高，脆弱性越大。
• 比较抽象拓扑模型与基于物理的潮流模型中的故障传播机制。
• 评估电力与通信网络间不同耦合水平对系统鲁棒性的影响。
• 识别可降低互联系关键基础设施系统级联故障风险的设计原则。

提出的方法

• 使用基于物理的潮流模型，在五种网络拓扑（网格、随机、随机正则、无标度及波兰电网）中模拟级联故障。
• 将拓扑传播模型作为基线进行比较，其中故障仅向直接连接节点传播。
• 使用耦合参数 q 对电力与通信网络之间的相互依赖性进行建模，取值范围为 0（无耦合）至 1（完全耦合）。
• 采用两种鲁棒性度量指标：Pr(|GC| > 0.5n) —— 巨大连通组件保持大于网络规模一半的概率 —— 以及 p∞，即级联故障后巨大连通组件的平均大小。
• 定义网络脆弱性指数 β 为故障概率曲线积分的负对数，以实现跨模型比较。
• 在完全耦合（q=1）与半耦合（q=0.5）配置下进行实验，以评估对耦合程度的敏感性。

实验结果

研究问题

• RQ1 真实电力网络中的故障传播机制与拓扑传播模型有何不同？
• RQ2 在真实电力系统模型中，电力与通信网络之间的耦合程度增加，是提高还是降低级联故障的脆弱性？
• RQ3 在基于物理的模型中，耦合强度（q）的变化对系统鲁棒性的影响，与拓扑模型相比有何差异？
• RQ4 在何种条件下，尽管存在潜在益处，网络间的耦合仍可能产生负面影响？
• RQ5 是否可使用统一的脆弱性指数 β 来比较不同网络模型与拓扑的鲁棒性？

主要发现

• 基于物理的电力网络模型在随机故障下表现出显著更高的脆弱性，尤其在网格和随机网络拓扑中，高于拓扑传播模型。
• 除最极端情况（即‘脆弱智能电网’模型）外，电力与通信网络间增加耦合可降低级联故障风险，与拓扑模型预测相反。
• 耦合的拓扑模型始终显示，更高的耦合度（q）会降低鲁棒性，且性能随 q 增加而单调下降。
• 理想与非理想智能电网模型均表明，增加耦合可提升鲁棒性，且脆弱性指数 β 随耦合度增加而降低。
• 脆弱性指数 β 显示，在基于物理的模型中，波兰电网拓扑最为鲁棒，而网格拓扑最脆弱，两种度量指标结果一致。
• 结果在不同鲁棒性度量下均具稳健性：Pr(|GC| > 0.5n) 与 p∞ 均一致表明，基于物理模型的耦合具有显著优势。