AI-Assisted Algorithms for Automatic AC Power Flow Model Creation based on DC Dispatch

这个NSF项目旨在开发用户友好的算法，自动创建真实的美国电网的真实快照，用于许多未来的操作场景，其中传统和可再生能源发电机的比例可变。该项目将通过显著减少模型开发时间和增加可考虑的运行条件，为电网风险识别和缓解过程带来变革性变化。该项目的智力优势包括：1）部署一个多阶段的方法，结合基于物理的原则和基于人工智能的方法，将电网发电机的调度场景转换为完整的电力系统案例，2）开发一个数据驱动的问题发现算法，以帮助人类干预。该项目的广泛影响包括：a）促进高度可再生能源电网的建设，以实现国家清洁能源目标，B）让代表性不足群体的本科生和研究生参与，c）提供机会，包括实验室图尔斯参观和演示，对于K-12名学生了解人工智能在真实的电网中的潜在用途。可再生能源资源的更高渗透率导致在日常发电组合的变化，因此，交流潮流（ACPF）解决方案的直流潮流（DCPF）调度情况下，不再是一个很好的初始化，以获得下一个操作条件的ACPF解决方案。目前，既没有可靠的算法，也没有供应商的产品来自动将DCPF调度案例转换为融合的ACPF案例，因此，转换需要手动分析和调整。该项目旨在使用几项创新来解决这个问题：1）物理引导的机器学习初始化器（PMLI），以取代ACPF的牛顿-拉夫森求解方法中的平坦启动初始化，2）迁移学习过程，以在多个电力系统上重用开发的PMLI，以及3）具有自动无功补偿选择的热启动增量算法。这项工作将为真实的电网功能的模拟提供重要的一步。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。

This NSF project aims to develop user-friendly algorithms that automatically create realistic snapshots of real U.S. electric grids for many futuristic operating scenarios with variable proportions of conventional and renewable energy generators. This project will bring transformative change to the process of risk identification and mitigation of electric grids by significantly reducing model development time and increasing the operating conditions that can be considered. The intellectual merits of the project include: 1) deployment of a multi-stage approach that combines physics-based principles and artificial intelligence-based methods to convert dispatch scenarios of grid generators to full power system cases, 2) development of a data-driven problem discovery algorithm to assist human intervention. The broader impacts of the project include: a) facilitation of high-renewable energy power grids towards the achievement of national clean energy goals, b) involvement of undergraduate and graduate students from underrepresented groups, c) provision of opportunities, including lab tours and presentations, for K-12 students to learn about the potential use of artificial intelligence in real power grids.Higher penetration of renewable energy resources has led to increased variations in daily generation mix, thus, the AC power flow (ACPF) solution of a DC power flow (DCPF) dispatch case is no longer a good initialization to obtain the ACPF solution of the next operating condition. Currently, there are neither reliable algorithms nor vendor products to automatically convert DCPF dispatch cases to converged ACPF cases, thus, conversion requires manual analysis and tuning. This project aims to solve this problem using several innovations: 1) a physics-guided machine learning initializer (PMLI) to replace flat start initialization in Newton Raphson solution method for ACPF, 2) a transfer learning process to reuse developed PMLI on multiple power systems, and 3) a hot-start incremental algorithm with automatic reactive power compensation selection. This work will provide an important step to make simulation of the power grid functions in real time a reality.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.