Preventing wide-area blackouts through adaptive islanding of transmission networks

最近的停电和干扰表明，以下两个驱动因素：a）更好地利用输电和配电网络的商业压力和B）分布式发电（DG）渗透率的增加可能会降低安全裕度并导致停电的可能性更高。这一跨学科项目涉及电力工程、图论和运筹学，将研究在级联故障即将发生时通过预防性地将大型网络分裂成孤岛来限制停电发生和停电成本的方法。所形成的岛应保持良好的需求/发电平衡，而不违反任何传输约束并避免任何发电机的机电不稳定性。在这个项目中解决的挑战包括识别条件时，可以安全地激活预防性孤岛，建立技术形成岛屿和/或隔离网络的一个生病的部分，并展示创新的方法来控制岛屿的高渗透率的DG。该提案适用于复杂性科学的要求，因为我们努力了解电网元素的小规模局部行为如何影响整个电网的弹性，并有助于防止灾难性后果。电力系统的孤岛决策通常是在系统处于高扰动状态且其实际模型与正常模型有很大差异时做出的。因此，我们建议基于从广域测量系统（WAMS）收集的实际测量结果进行分析，这些系统预计将在未来广泛使用，作为实现低碳愿景所需的下一代监测和控制技术的一部分。基于可再生能源的流动。这个项目的两个方面特别具有冒险性：其独特的跨学科性将数学、运筹学和电力工程联系在一起，并将其面向未来的应用应用于具有高DG渗透率的网络，即实现2020/2050年的碳减排目标，而不牺牲安全性和供电质量。从数学的角度来看，孤岛问题以一种令人兴奋的方式与度量空间的现代分析联系在一起。考虑到电网电抗可以在电网上产生一个新的度量结构，我们将在大量研究的等周问题的数学背景下分析孤岛问题。我们的主要工具将是离散拉普拉斯算子的谱分析，该算子将被加权以适应物理信息，并用于识别平衡岛的可能候选者。我们将使用离散版本的微积分和离散莫尔斯理论来捕获有关潮流的信息，开发用于识别平衡孤岛的替代技术，并评估断开孤岛对网络其余部分的影响。与通常的图划分问题不同，在图划分问题中，划分标准是先验已知的（并且保持不变），孤岛电力传输的困难来自于需要根据最近观察到的网络状态来调整划分。结果，已知的图划分技术（其在稀疏矩阵重新排序或超大规模集成设计的背景下被证明是如此成功）不能直接应用。我们提出的解决方案是扩展现有的技术，考虑到测量为基础的（因此动态变化）的图形属性和设计/实现新的图形分区算法。新技术将联合收割机图形解剖学与随机化相结合，并将由拉普拉斯算子的谱分析的理论工具指导。

Recent blackouts and disturbances have shown that the twin drivers of: a) commercial pressures for better utilisation of transmission and distribution networks and b) increased penetration of Distributed Generation (DG) are likely to reduce security margins and lead to a higher probability of blackouts. This interdisciplinary project, involving power engineering, graph theory and operational research, will investigate methodologies to limit the occurrence and cost of blackouts through preventive splitting of large networks into islands when a cascade fault is imminent. The formed islands should preserve a good demand/generation balance, without violating any transmission constraint and avoiding electromechanical instability of any generator. The challenges addressed in this project include identification of conditions when preventive islanding can safely be activated, establishing techniques for forming islands and/or isolating a sick part of the network, and demonstrating innovative methods for control of islands with a high penetration of DG. The proposal applies to Complexity Science call, as we strive to understand how small-scale local behaviour of elements of an electrical grid influences the resilience of the grid as a whole, and helps to prevent catastrophic consequences.A major novelty of the proposed approach is that it is measurement, rather than model based. The decision to island a power system is usually taken when the system is in a highly disturbed state and its actual model is significantly different from the normal one. Hence, we propose to base the analysis on actual measurements gathered from Wide Area Measurement Systems (WAMS), which are expected to become widely available in the future, as a part of the next-generation monitoring and control technologies necessary for enabling the vision of low-carbon renewable-based energy flows. Two aspects of this project are particularly adventurous: its unique interdisciplinarity linking mathematics, operational science and power engineering, and its future-oriented application to networks with a high DG penetration, i.e. achieving 2020/2050 carbon reduction targets without sacrificing security and quality of supply.From a mathematical point of view, problem of islanding connects in an exciting way with modern analysis of metric spaces. Taking the view that grid reactances can give rise to a new metric structure on the grid, we will analyse the problem of islanding in the mathematical context of much studied isoperimetric problems. Our main tool will be spectral analysis of the discrete Laplace operator, which will be weighted to accommodate the physical information and used to identify possible candidates for balanced islands. We will employ discrete version of calculus and discrete Morse theory to capture the information about the power flows, to develop alternative techniques for identifying balanced islands, and to assess the effect of disconnecting an island on the rest of the network.In practical terms, the islanding of transmission network is a challenging graph partitioning problem. Unlike the usual graph partitioning problems, in which the partitioning criteria are known a priori (and remain constant), the difficulty in islanding electricity transmission comes from the need to adjust the partitioning to the very recently observed state of the network. As a result, the known graph partitioning techniques, which have proved so successful in the context of sparse matrix reordering or very large scale integration design, are not directly applicable. The solution we propose is to extend the existing techniques to take into account measurement-based (hence dynamically changing) graph properties and design/implement novel graph partitioning heuristics. The new techniques will combine graph dissection heuristics with randomisation and will be guided by the theoretical tool of spectral analysis of the Laplace operator.