ITR - \(ASE+NHS\) -\ (dmc+int\): Toward a Multi-Layered Architecture for Reliable and Secure Large-Scale Networks: The Case of an Electric Power Grid

ITR - \(ASE+NHS\) - (dmc+int\)：面向可靠和安全的大规模网络的多层架构：该项目开发了一个信息技术（IT）框架，用于解决关键国家基础设施中可能导致灾难性故障的基本问题，特别是当系统接近满负荷运行时。这些系统使用大量备用储备来管理操作条件和设备状态的变化。当系统面临压力时，储备弥补了灵活决策的不足。当前的建模、监视和决策工具假设系统对干扰的响应是局部的，并且在没有联机协调的情况下也是局部可管理的。虽然这些工具通常可以实现可靠的操作，但偶尔发生的代价高昂且危及生命的停电、爆炸和水管断裂证明，这些工具和“保守”的操作方法可能会失败。持续的增长、技术的变化、设备的老化和管理的变化改变了系统，使旧的模型和实践变得不那么可靠。操作这些基础设施系统以实现更高的性能以及更高的可靠性和安全性需要在建模、模拟和操作复杂系统方面取得进步。这个项目的重点是电网，使用广泛接受的表示。虽然简化了现实，但这些基础设施网格的表示是高度复杂的模型。项目研究处理了以下重要问题：(1)处理复杂性。该项目开发了更简单的模型层，这些模型可以实时运行，并捕获不同的时间和空间分辨率尺度，还有一个“系统管理器”层，用于纠正由近似分解引起的“外部性”。(2)监测。一个使用稀疏传感器的网络策略性地在撞击地点测量最相关的数据。(3)系统动力学表征。与当前电网中使用的最先进的静态状态估计器不同，该项目开发了一种“准动态状态估计器”（QDSE），它使用状态的当前知识（估计）来更新网络状态的当前估计。(4)制度环境。该项目旨在整合电力基础设施尚未整合的监管重组和运营模式。该项目(a)发展一个资讯科技架构，使复杂的网络基础设施在各种输入/干扰和拓扑变化的情况下，能顺利、可靠和有效地运作；(b)创建一系列模型，通过更好地表示网络状态和动态来做出明智的决策；(c)确定可靠和有效地运行网络所需的信息和模型，以及(d)寻求最具成本效益的方法来收集和处理所需的数据，并建立一个更好的系统动力学模型。为了简化计算并允许模型近乎实时地运行，使用“系统管理器”对模型进行分解，以纠正由于不精确分解而产生的外部性。物理、金融/经济、监管和IT信号的相互作用驱动着复杂的基础设施系统。这些一般概念适用于电力基础设施，而电力基础设施最近因缺乏可靠性和无法支持有效市场而受到挑战。该项目通过评估不断发展的电力行业的几个候选体系结构的性能来演示概述的技术。

ITR - \(ASE+NHS\) -\ (dmc+int\): Toward a Multi-Layered Architecture for Reliable and Secure Large-Scale Networks:This project develops an information technology (IT) framework that addresses basic problems in critical national infrastructures that can lead to catastrophic failures, especially when the system operates near full capacity. These systems use large stand-by reserves to manage the variations in operating conditions and equipment status. When the system is under stress, reserves make up for lack of flexible decision making. Current modeling, monitoring, and decision tools assume that system response to disturbances is local, and that it is also locally manageable without on-line coordination. While these tools generally result in reliable operations, the occasional costly and life-threatening blackouts, explosions, and water main breaks are proof that the tools and "conservative" operating practices can fail. Continued growth, technology change, aging equipment, and regulatory changes alter the system, making the old models and practices less reliable. Operating these infrastructure systems to achieve higher performance along with greater reliability and security requires advances in modeling, simulating, and operating complicated systems. This project focuses on the electricity grid, using the widely accepted representations. While simplifications of reality, these representations of the infrastructure grid are highly complex models. The project research treats the following important issues: (1) Handling Complexity. The project develops layers of simpler models that run in real time and capture different time and space resolution scales, with a "systems manager" layer that corrects for the "externalities" caused by approximate decomposition. (2) Monitoring. A network using sparse sensors strategically placed measures the most relevant data at the location of impact. (3) Characterization of the System Dynamics. Unlike the state-of-the-art static state estimators used in current power grids, this project develops a "quasi-dynamic state estimator" (QDSE) that uses the current knowledge (estimate) of the state to update the current estimate of the network state. (4) Institutional Environment. This project seeks to integrate the regulatory restructuring and operations models have not been integrated for the electricity infrastructure.The project (a) develops an IT framework for smooth, reliable, and efficient operation of a complex network infrastructure over a broad range of inputs/disturbances and topological changes; (b) creates a family of models that allow informed decisions through better representations of the network state and dynamics; (c) identifies the information and models required to run the network reliably and efficiently, and (d) seeks the most cost-effective way to gather and process the required data, and build a better model of system dynamics. To simplify computation and allow the model to run in near real time, models are decomposed using a "systems manager" to correct for the externalities arising from inexact decomposition. An Interplay of physical, financial/economic, regulatory, and IT signals drives the complex infrastructure system. These general concepts are applied to the electric power infrastructure, which has recently been challenged for its lack of reliability and inability to support efficient markets. This project demonstrates the outlined techniques by assessing the performance of several candidate architectures of the evolving electric power industry.