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

基于直流调度的人工智能辅助自动交流潮流模型创建算法

• 批准号: 2243204
• 负责人: Yilu Liu
• 金额: $ 35万
• 依托单位: University of Tennessee Knoxville
• 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: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243204&HistoricalAwards=false
• 关键词: AI; Assisted; Algorithms; Automatic; AC

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.

该 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，适用于传统和可再生能源发电机比例可变的许多未来操作场景。该项目将通过显着缩短模型开发时间并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，结合基于物理的原理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）开发数据驱动的协助人类干预的问题发现算法。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）提供机会，包括实验室参观和演示，让 K-12 学生了解人工智能在实际电网中的潜在应用。可再生能源的更高渗透率导致日常发电组合的变化增加，因此，直流电源的交流功率流 (ACPF) 解决方案流动(DCPF)调度情况不再是获得下一个操作条件的ACPF解的良好初始化。目前还没有可靠的算法和厂商产品可以将DCPF调度案例自动转换为融合ACPF案例，因此转换需要人工分析和调优。该项目旨在通过多项创新来解决这个问题：1) 物理引导机器学习初始化器 (PMLI) 取代 ACPF 牛顿拉夫森求解方法中的平启动初始化，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）具有自动无功功率补偿选择的热启动增量算法。这项工作将为实现电网功能的实时模拟迈出重要一步。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，认为值得支持。

项目成果

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

牛顿-拉夫森交流潮流收敛的机器学习初始化器

• DOI: 10.1109/tpec60005.2024.10472261
• 发表时间: 2024-03
• 期刊: 2024 IEEE Texas Power and Energy Conference (TPEC
• 作者: Okhuegbe, Samuel N;Ademola, Adedasola A;Liu, Yilu
• 通讯作者: Liu, Yilu