Noorazar H, Srivastava A, Pannala S, K Sadanandan Sajan
School of Electrical Engineering and Computer Science Washington State University Pullman Washington USA.
J Eng (Stevenage). 2021 Nov;2021(11):665-684. doi: 10.1049/tje2.12065. Epub 2021 Aug 9.
Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID-19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi-fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML-driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID-19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID-19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID-19 pandemic.
电能是现代生活的重要组成部分，即使在不利事件（如乌克兰网络攻击、玛丽亚飓风）期间，对电网弹性以实现持续可靠能源供应的期望也大幅提高。全球新冠疫情因潜在的劳动力中断、供应链中断以及网络安全威胁增加，提高了电能可靠性风险。此外，疫情在存在包括老化电网、分布式发电高度普及、市场机制和有源配电网等其他挑战的情况下，给电网运行带来了很大程度的不确定性。这种情况增加了采取电网弹性措施的必要性，以减轻疫情以及包括网络攻击和恶劣天气事件在内的同时发生的极端事件的影响。应对这种不利情况的解决方案将是多方面的：（a）应急规划和组织支持，（b）遵循安全协议，（c）利用增强的自动化和传感实现态势感知，以及（d）集成先进技术和数据点以提供机器学习驱动的增强决策支持。增强的数字化和自动化在发电、输电和配电等各个层面带来了更好的网络可视性。这些数据或信息可用于利用先进的机器学习技术实现自动化并提高电网弹性。本文探讨了面对疫情时电网的弹性，并讨论了有助于增强人工操作员能力的各种机器学习工具：（a）回顾新冠疫情对电网运行的影响以及操作员/组织为尽量减少新冠疫情影响而采取的行动，（b）介绍最近开发的可应用于在正常和极端情况（如新冠疫情）下提高电力系统弹性的机器学习和人工智能工具及概念。
