RAPID: A Cross-Infrastructure Data-driven Approach to Modeling and Simulation of the 2021 Texas Power Outage

这一快速反应研究补助金(RAPID)项目将收集跨域数据，并开发一个开放源代码的合成电网模型，该模型具有现成的数据集，用于2021年2月德克萨斯州停电的事件模拟和定量评估。2021年2月的极端冬季风暴和相关的电力中断估计已造成德克萨斯州70多人死亡和1500亿美元的经济损失。鉴于实际电网模型和相关信息的复杂性和保密性，对于更广泛的研究界来说，就这一事件发生的原因、原因和方式制定量化评估和可信的见解变得非常具有挑战性，更重要的是，未来可以做些什么来防止它再次发生。这笔赠款将开发一种开放源码的跨域方法，以建立一个现实的合成模型，该模型具有现成的数据集，用于停电模拟和更广泛的基础设施研究界对纠正措施的定量评估。这个项目的智力价值如下。首先，这个团队将收集及时的数据，并开发一个开源的大规模合成网格模型，通过严格的校准为事件评估量身定做。其次，我们将沿着时间表整合跨基础设施停机数据，以便为更广泛的研究社区提供科学模拟。这一现成的跨域停电数据集将涵盖沿时间线的事件细节，包括电力系统和天然气系统。最后但并非最不重要的一点是，该团队将为潜在纠正措施的有效性及其综合影响的量化研究执行一致的模拟方法和指标。这个团队计划在这个项目中积极地让代表性不足的群体参与进来。我们将通过广泛的场所积极宣传这一点，包括德克萨斯农工智能电网中心和德克萨斯农工能源研究所网络研讨会和研讨会系列。这个项目的发现将被纳入国际电力协会新开发的关于现代电力系统的数据科学和应用课程。该团队在电力行业的数据科学和可靠性评估方面拥有面向行业的短期课程的记录。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Grants for Rapid Response Research (RAPID) project will collect cross-domain data and develop an open-source synthetic grid model with a ready-to-use dataset for event simulation and quantitative assessment of the February 2021 Texas power outages. The extreme winter storm and associated electricity outages in February 2021 are estimated to have caused more than 70 deaths and $150 billion economic loss in Texas. Given the complexity and confidentiality of the actual electric grid model and relevant information, it becomes very challenging for the broader research community to develop quantitative assessments and credible insights on what, why, and how this event occurred, and more importantly, what could be done in the future to prevent it from happening again. This grant will develop an open-source cross-domain approach to build a realistic synthetic model with a ready-to-use dataset for the power outage simulation and quantitative assessments of corrective measures for the broader infrastructure research community. The intellectual merit of this project is as follows. First, this team will collect timely data and develop an open-source large-scale synthetic grid model tailored for the event assessment via rigorous calibration. Second, we will integrate cross-infrastructure outage data along the timeline that enable scientific simulation for the broader research community. This ready-to-use cross-domain blackout dataset will cover the details of the event along the timeline, including both the electric power system and the natural gas system. Last but not least, this team will perform consistent simulation methods and metrics for the quantitative studies on the effectiveness of potential corrective measures and their combined effects. This team plans to actively engage under-represented groups in this project. We will actively disseminate this through a wide range of venues, including a Texas A&M Smart Grid Center and Texas A&M Energy Institute webinar and workshop series. The finding of this project will be incorporated in the PI's newly developed course on Data Sciences and Application for Modern Power Systems. The team has a track record of industry-oriented short courses on data sciences and reliability assessment for the power industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.