Distribution Network Resilience Enhancement with Topological Neural Networks

利用拓扑神经网络增强配电网弹性

• 批准号: 2229417
• 负责人: Baris Coskunuzer
• 金额: $ 34.99万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229417&HistoricalAwards=false
• 关键词: Distribution; Network; Resilience; Enhancement; Topological

The number of outages caused by severe weather as a result of climate change rose from about 50 annually nationwide in the early 2000s to over 100 annually on average over the past five years. With the increasing trend of cyber-physical threats and extreme weather events due to climate change, resilience of the power network has emerged as a problem of utmost societal importance. By facilitating the cross-disciplinary exchange of ideas, this project will develop a novel geometric deep learning model to significantly improve the security, reliability, and efficiency of the modern power grid by leveraging clean energy resources – thereby, impacting the safety and economic well-being of our society across a broad front. The project will provide training opportunities to graduate students. In particular, the PIs will develop a novel geometric deep learning model supported with topological data analysis tools for robust decision-making and efficient knowledge transfer for disruptions with time-dominant characteristics, particularly focusing on predicting the distribution network evolution and operational decisions (by leveraging distributed energy resources and clean energy resources) for resilience under disruptive events such as natural disasters and adversarial attacks. Envisioned gains in generalizability, robustness and learning efficiency via knowledge-transfer (across events and operation timescales) will be demonstrated by studying response and recovery operation problems in power grids. The developed model will be applied to enhance the resilience of the power distribution network under disruptive events, ranging from preparatory to restorative tasks. The pre-emptive measures adopted include (i) outage prediction and defensive islanding, while (ii) network reconfiguration is used as a restorative action.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.

随着网络物理威胁和极端天气事件的增加，气候变化导致的恶劣天气造成的停电数量从2000年代初期全国每年约50起增加到过去五年的平均每年100起以上。针对气候变化，电力网络的恢复力已成为一个最重要的社会问题，通过促进跨学科的思想交流，该项目将开发一种新颖的几何深度学习模型，以显着提高电力网络的安全性、可靠性和效率。现代电网由利用清洁能源，从而广泛影响我们社会的安全和经济福祉。该项目将为研究生提供培训机会，特别是，PI 将开发一种由拓扑数据支持的新型几何深度学习模型。用于针对具有时间主导特征的中断进行稳健决策和知识转移的分析工具，特别侧重于预测配电网络演变和运营决策（通过利用分布式能源和清洁能源），以在自然灾害等破坏性高效事件下实现恢复能力和对抗性攻击。通过研究电网的响应和恢复操作问题，将展示通过知识转移（跨事件和操作时间尺度）在通用性、鲁棒性和学习效率方面的预期收益，所开发的模型将应用于增强配电网络的弹性。所采取的先发制人措施包括（i）中断预测和防御性孤岛，而（ii）网络重新配置作为恢复行动。该奖项反映了 NSF 的法定措施。使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。