Collaborative Research: Learning for Faster Computations to Enhance Efficiency and Security of Power System Operations

协作研究：学习更快的计算以提高电力系统运行的效率和安全性

基本信息

批准号：
2023531
负责人：
Baosen Zhang
金额：
$ 23万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2023531&HistoricalAwards=false
关键词：
Collaborative Research Learning Faster Computations

项目摘要

The electric grid is a complex critical infrastructure system that underpins all economic and social activities in the US. It is thus of utmost importance to maintain its efficient, reliable and secure operation at all times. The system, however, is undergoing an unprecedented period of transformation with rapid growths in renewable energy and electric vehicles, as well as increasing concerns of cyber security. Consequently, not only there is a higher requirement for efficient and secure operation of the grid, but also achieving it becomes much more challenging. The issue is especially acute from a computational perspective, as problems of much greater complexity need to be solved more frequently. As such, conventional approaches for solving secure power system operation problems face major and pressing challenges in maintaining their efficacy in the rapidly evolving power grids. To overcome these challenges, this project will develop novel machine-learning-based methods to greatly accelerate solving key and large-scale secure power system operation problems. Notably, the developed methods will integrate data-driven methods with the physical models of power systems. The impact of the project extends to machine learning algorithm design in all engineering systems where knowledge from physical system models and conventional wisdom in algorithm design can be incorporated. The developed algorithms will lead to greatly enhanced efficiency, reliability and security of power systems in the presence of high penetration of renewable energy and without the need of building more transmission lines or procuring much higher reserve capacity, resulting in tremendous economic savings for consumers. The project will also contribute to the much-demanded educational needs in the industry by training the next generation workforce to master interdisciplinary expertise of machine learning and power systems. The PIs are committed to promote diversity in research and education through the project by engaging students of minorities and from under-privileged backgrounds. This project will develop new machine learning algorithms, both leveraging and integrated with existing computational tools, to greatly improve the computational efficiency of solving challenging power system operation problems. We accomplish this by designing algorithms that use data to replace some of the existing heuristics based on human experience. We use a bottom-up approach by carefully formulating the problems to determine the best interface between the physical system and machine learning. This allows us to design algorithms that are aware of the physics of the problems and complement existing tools in the field. Specifically, we pursue three research thrusts: i) solving for optimal generator dispatch levels by introducing a data-driven component to the existing algorithms; ii) enabling fast identification and quantification of problematic contingencies using reinforcement learning; and iii) finding the most secure and efficient generation unit commitment schedule utilizing the results from the previous thrusts. These algorithms can be directly integrated into current solvers and have the potential of providing orders of magnitude speedup over existing methods. As such, this project offers a) new machine learning paradigms and algorithms, b) innovative ways of integrating machine learning methods with physical model-based optimization algorithms, and c) potentially transformative tools that solve key power system operation problems in a holistic framework with much faster speeds.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.

电网是一个复杂的关键基础设施系统，它为美国的所有经济和社会活动提供了基础。因此，始终保持其高效，可靠和安全的操作至关重要。然而，该系统正在经历前所未有的转型时期，可再生能源和电动汽车的快速增长，以及对网络安全的关注。因此，不仅有更高的要求对网格的高效和安全操作的要求，而且还需要实现它的挑战。从计算的角度来看，问题尤其是严重的，因为需要更频繁地解决复杂性的问题。因此，解决安全的电力系统操作问题的常规方法面临着主要的挑战，并在迅速发展的电网中保持了效力。为了克服这些挑战，该项目将开发基于机器学习的新方法，以极大地加速解决密钥和大规模的安全电源系统操作问题。值得注意的是，开发的方法将将数据驱动的方法与电源系统的物理模型集成在一起。项目的影响扩展到机器学习算法设计中的所有工程系统中，可以合并物理系统模型的知识和算法设计中的常规智慧。开发的算法将在可再生能源高渗透的情况下，无需建立更多的输电线路或采购更高的储备能力，从而极大地提高电力系统的效率，可靠性和安全性，从而为消费者带来了巨大的经济节省。该项目还将通过培训下一代劳动力来掌握机器学习和电力系统的跨学科专业知识，从而为行业中广泛的教育需求做出贡献。 PI致力于通过与少数民族的学生和贫困背景的学生吸引该项目来促进研究和教育的多样性。该项目将开发新的机器学习算法，包括利用并与现有的计算工具集成，以极大地提高解决挑战性电源系统操作问题的计算效率。我们通过设计使用数据来替代基于人类经验的某些现有启发式方法来实现这一目标。我们通过仔细制定问题来确定物理系统和机器学习之间的最佳接口来使用自下而上的方法。这使我们能够设计意识到问题物理的算法并补充现有的现有工具。具体来说，我们提出三个研究作用：i）通过向现有算法引入数据驱动的组件来解决最佳发电机调度级别； ii）使用强化学习来快速识别和量化有问题的偶然性； iii）使用先前的推力结果找到最安全，最有效的生成单位承诺时间表。这些算法可以直接集成到当前求解器中，并具有在现有方法上提供数量级加速的潜力。因此，该项目提供了a）新的机器学习范式和算法，b）将机器学习方法与基于物理模型的优化算法整合的创新方式，以及c）在整体框架中解决关键的电力系统操作问题的潜在变革性工具，并以更快的速度进行了速度，这些奖项反映了NSF的法定任务和范围的范围。标准。