ERI: Resilient Operational Planning of Electricity Grid under the Risk of Wildfire

野火已成为世界许多地区生命、财产和生态系统的日益危险的威胁，特别是在易受极端天气影响的地区。公用事业公司面临着巨大的挑战，既要防止公用事业设备引发的野火，又要确保向客户连续供电。为了解决这个复杂的问题，该项目旨在开发一个敏捷的决策支持系统，以提高电网在极端野火风险期间的恢复能力。通过在防火措施和服务连续性之间实现微妙的平衡，这项研究有可能挽救生命，保护关键基础设施，并在高风险地区保持不间断的能源供应。该项目符合NSF的使命，即为国家福利做出贡献并解决关键的社会问题。其更广泛的影响将有助于公共安全，提高人们对在野火风险下运行电网所涉及的复杂性的认识和理解，并刺激未来对电力系统运营规划的研究。此外，该项目还具有促进跨学科合作的潜力，从而大大提高我们对野火风险管理的理解。该项目涉及开发一个科学框架，该框架利用可微分编程，考虑风速和湿度等气象数据以及结构特征，量化各个电力线路的野火点燃风险。一个物理启发的代理模型将被训练，以提供一个标准化的风险评分，该评分将被整合到一个敏捷的运营规划框架中，用于在野火风险下的极端天气条件下的电网。运营规划框架将考虑短期和长期的决策，包括个别电力线的断电，分布式能源的整合，以及电网组件的扩展或修改。鉴于高风险情况下决策的紧迫性，将采用先进的机器学习技术，通过预先分配一批决策变量，系统地加快解决问题的过程。预计这将导致更有效和更实际的解决方案。初步结果表明，解决方案的时间大大缩短，解决方案的质量下降幅度可以忽略不计，这使得该研究为电网运营规划的未来发展奠定了良好的基础。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Wildfires have become an increasingly dangerous threat to lives, property, and ecosystems in many parts of the world, particularly in regions prone to extreme weather. Utility companies are faced with the immense challenge of preventing wildfires triggered by utility equipment while ensuring the continuity of electricity supply to their customers. To address this complex issue, this project aims to develop an agile decision support system that enhances the resilience of the power grid during periods of extreme wildfire risk. By aiming to achieve a delicate balance between fire prevention measures and service continuity, this research has the potential to save lives, protect critical infrastructure, and maintain an uninterrupted energy supply in high-risk areas. The project is aligned with the NSF's mission to contribute to the national welfare and address critical societal issues. Its broader impact will contribute to public safety, increase awareness and understanding of the complexities involved in operating the electricity grid under the risk of wildfires, and stimulate future research in power system operational planning. Furthermore, the project has the potential to foster interdisciplinary collaboration, leading to significant advances in our understanding of wildfire risk management.This project involves developing a scientific framework that quantifies the risk of wildfire ignition for individual power lines using differentiable programming, taking into account meteorological data, such as wind speed and humidity, and structural characteristics. A physical-inspired surrogate model will be trained to provide a normalized risk score that will be integrated into an agile operational planning framework for the electricity grid during extreme weather conditions under the risk of wildfire. The operational planning framework will consider both short-term and long-term decisions, including the de-energizing of individual power lines, integration of distributed energy resources, as well as the expansion or modification of grid components. Given the urgent nature of decision-making during high-risk situations, advanced machine learning techniques will be employed to systematically speed up the problem-solving process by pre-assigning a batch of decision variables. This is expected to result in a more efficient and practical solution. Preliminary results indicate significant improvement in solution time with a negligible drop in solution quality, making this research a promising foundation for future advances in electricity grid operational planning.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.