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，自动创建真实美国电网的真实快照，用于许多未来的运行场景，其中包括传统和可再生能源发电机的可变比例。该项目将通过大大缩短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革命性的变化。该项目的智力优势包括：1)部署多阶段方法，将基于物理的原理和基于人工智能的方法相结合，将电网发电机的调度场景转换为全功率系统案例；2)开发数据驱动的问题发现算法，以协助人工干预。该项目的更广泛影响包括：a)促进高可再生能源电网实现国家清洁能源目标，b)来自代表性不足群体的本科生和研究生的参与，c)为K-12学生提供机会，包括实验室参观和演示，以了解人工智能在实际电网中的潜在用途。可再生能源渗透率的提高导致日发电结构变化的增加，因此，直流潮流（DCPF）调度情况下的交流潮流（ACPF）解不再是获得下一运行工况ACPF解的良好初始化。目前，既没有可靠的算法，也没有供应商产品可以自动将DCPF调度用例转换为收敛的ACPF用例，因此，转换需要手工分析和调优。本项目旨在通过以下几个创新来解决这个问题：1)一个物理引导的机器学习初始化器（PMLI）来取代ACPF的Newton Raphson解方法中的平启动初始化，2)一个迁移学习过程来重用开发的PMLI在多个电力系统上，以及3)一个热启动增量算法与自动无功补偿选择。这项工作将为实现电网功能的实时仿真提供重要的一步。该奖项反映了美国国家科学基金会的法定使命，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.