Collaborative Research: An Intelligent Restoration System for a Self-healing Smart Grid (IRS-SG)

我们如何在大停电后更有效地恢复电力，如飓风、连锁停电或更局部的停电？在一个主要是非正式的、基于巨大压力和有限信息条件下的猜测的系统中，我们能否使用现代计算机方法来获得比我们今天更好的性能？这个新的合作项目将汇集一位电力恢复专家和一位电网新智能计算方法的先驱，希望找到一种更强大、更现代的方法来更有效地恢复电力。目标是：（1）利用先进的可扩展计算智能技术开发在线自适应恢复工具;（2）研究在系统恢复中使用可再生资源的新方案;（3）探索提高自愈能力的黑启动机组投资策略;以及（4）在基准和公用事业电力系统上实时实施和演示新系统。这笔赠款将包括前往新西兰，讨论使用新系统，以帮助更好地应对地震等事件。它还将包括与达科他州的美洲原住民的外联活动。从技术角度来看，有效恢复的问题非常困难。少数研究人员，如首席PI，已经开发了一些实用工具来解决这个问题，但它在很大程度上仍然是一个未解决的问题。美国国家科学基金会参与这一领域的主要理由，以及成功的重大希望，是使用智能系统方法，远远超出了过去任何人对这个问题的应用，这些方法是美国国家科学基金会EPCN计划智能系统部分的先驱（例如，在Frank刘易斯和Derong Liu编辑的《RLADP手册》中描述的）。来自同步相量等新来源的数据将是这项工作的一部分。底层算法从一开始就被设计为在大规模并行分布式系统上运行，例如细胞神经网络硬件，它允许比传统的顺序计算机和算法更快的实时计算。

How can we restore power more effectively after a major outage, such as a hurricane, a cascade blackout or a more local outage? Can we use modern computer-based methods to get better performance than what we have today, in a system which is mainly informal and based on guesswork under conditions of great stress and limited information? This new collaborative project will bring together an expert on power restoration with a pioneer of new intelligent computation methods for the power grid, in hopes of finding a more powerful and modern way to restore power more effectively. The goals are; (1) to develop an online adaptive restoration tool using advanced scalable computational intelligence techniques; (2) investigate a novel scheme to use renewable resources in system restoration; (3) explore a blackstart unit investment strategy to improve the self-healing capability; and (4) real-time implementation and demonstration of the new system on benchmark and utility power systems. The grant will include travel to New Zealand, to discuss use of the new system to help provide better response to events like earthquakes. It will also include outreach to Native Americans in the Dakotas. The problem of efficient restoration is very difficult from a technical point of view. A small number of researchers, like the lead PI, have developed a few tools of practical use in this problem, but it is still largely an unsolved problem. The main justification for NSF involvement in this area, and for significant hope of success, is the use of intelligent systems methods far beyond what anyone has applied in the past to this problem, methods pioneered in the intelligent systems part of the EPCN program at NSF(described for example in the book Handbook of RLADP edited by Frank Lewis and Derong Liu). Data from new sources such as synchrophasors will be part of this work. The underlying algorithms are designed from the start to run on massively paralleldistributed systems, such as Cellular Neural Network hardware, which allow much faster real-time computation than traditional sequential computers and algorithms.