CAREER: From Reactive to Proactive Distribution Grid Risk Management

这个NSF职业项目旨在解决当今配电公用事业所面临的气候变化缓解和适应挑战。配电公用事业公司管理可持续但可变的分布式可再生能源的整合，这使得维持电能质量变得更加困难。它们还管理频繁的恶劣天气事件的影响，这些事件会引发引发致命和毁灭性大火的火花。目前的做法是在这些问题出现后作出反应，因为缺乏能够主动评估和减轻风险的方法。该职业生涯项目将通过开发风险评估和优化方法来弥补这一亟需的差距，这些方法将通过从被动的配电网风险管理转变为主动的配电网风险管理，为配电网运营带来变革性的变化。该项目的智力优势在于开发了新的风险评估方法，以量化和减轻因负荷变化、野火点燃和电力中断而造成的风险。该项目的更广泛影响包括改善电力质量，降低引发野火的风险，以及减少全美范围内的停电。该项目还将通过使用开源软件改善本科电力系统教育，并通过建立一个学习社区，促进助教公平和包容的教学实践，促进留住少数族裔本科生。该项目的目标是开发风险评估方法，以量化短期操作风险，以及相关的基于风险的方法和随机优化方法的公式和求解算法。这些数据驱动的技术将考虑多种迫在眉睫的威胁，包括可再生能源发电和野火点燃的多种情景，并确定在所有这些情景中提高配电网运行安全性和可靠性的控制行动(例如，分布式能源和远程开关的设置点)。例如，配电网建模需要考虑三相潮流计算和二元决策变量以确保径向拓扑，从而增加了相对于输电电网的模型复杂性。要解决这个复杂的、多场景的问题，将需要新的模型和解决方法，用于数据驱动的随机和基于风险的优化。因此，该提案的一个主要技术重点是开发易于计算的方法，以在优化框架内利用大量数据，并有效地进行接口模拟和优化。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 NSF CAREER project aims to address climate change mitigation and adaptation challenges facing today's electric distribution utilities. Distribution utilities manage the integration of sustainable yet variable distributed renewable energy resources, which makes it harder to maintain power quality. They also manage the impacts of frequent severe weather events causing sparks that ignite deadly and devastating fires. The current approach is to react to these problems after they arise because of the lack of methods that can proactively assess and mitigate risks. This CAREER project will bridge this much-needed gap by developing risk-assessment and optimization methods that will bring transformative change to distribution grid operation by moving from reactive to proactive distribution grid risk management. The intellectual merit of the project lies in developing new risk assessment methods to quantify and mitigate risk due to load variability, wildfire ignitions and electric outages. The broader impacts of the project include improvements in power quality, a reduction in the risk of wildfire ignitions and fewer power outages across the United States. The project will also improve undergraduate power system education through the use of open-source software, and contribute towards increased retention of minority undergraduate students by developing a learning community that promotes equitable and inclusive teaching practices among teaching assistants.The goal of the project is to develop risk assessment methods to quantify short-term operational risk as well as formulations and solution algorithms for the associated risk-based and stochastic optimization methods. These data-driven techniques will consider multiple imminent threats including multiple scenarios for renewable energy generation and wildfire ignitions, and identify control actions (e.g., setpoints for distributed energy resources and remote switches) that improve security and reliability of distribution grid operations across all these scenarios. Modeling distribution grids requires, e.g., the consideration of three-phase power flow calculations and binary decision variables to ensure radial topologies, thus increasing model complexity relative to transmission grids. Solving this complex, multi-scenario problem will require both new models and solution approaches for data-driven stochastic and risk-based optimization. A main technical focus of the proposal is, therefore, to develop computationally tractable approaches to leverage large amounts of data within an optimization framework, and effectively interface simulations and optimization.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.