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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，支撑着美国所有的经济和社会活动。因此，始终保持其高效、可靠和安全的运行是至关重要的。然而，随着可再生能源和电动汽车的快速增长，以及对网络安全的担忧日益加剧，该系统正在经历前所未有的转型时期。因此，不仅对电网的高效安全运行提出了更高的要求，而且实现这一目标也变得更具挑战性。从计算的角度来看，这个问题尤其尖锐，因为更复杂的问题需要更频繁地解决。因此，解决电力系统安全运行问题的传统方法在快速发展的电网中面临着保持其有效性的重大而紧迫的挑战。为了克服这些挑战，该项目将开发新的基于机器学习的方法，以大大加快解决关键和大规模安全电力系统运行问题。值得注意的是，所开发的方法将数据驱动方法与电力系统的物理模型相结合。该项目的影响扩展到所有工程系统中的机器学习算法设计，其中可以结合物理系统模型的知识和算法设计中的传统智慧。在可再生能源高度普及的情况下，所开发的算法将大大提高电力系统的效率、可靠性和安全性，而无需建造更多的输电线路或购买更高的备用容量，从而为消费者带来巨大的经济节省。该项目还将通过培训下一代劳动力掌握机器学习和电力系统的跨学科专业知识，为行业迫切需要的教育需求做出贡献。各学院致力透过该计划，让少数族裔及弱势背景的学生参与，促进研究和教育的多元化。该项目将开发新的机器学习算法，利用并集成现有的计算工具，以大大提高解决具有挑战性的电力系统运行问题的计算效率。我们通过设计算法来实现这一点，这些算法使用数据来取代一些基于人类经验的现有启发式。我们使用自下而上的方法，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。这使我们能够设计出能够意识到问题的物理性质的算法，并补充该领域现有的工具。具体来说，我们追求三个研究重点：i)通过在现有算法中引入数据驱动组件来解决最佳的发电机调度水平；Ii)使用强化学习快速识别和量化有问题的偶发事件；iii)利用先前推力的结果找到最安全、最有效的发电机组承诺计划。这些算法可以直接集成到当前的求解器中，并且具有比现有方法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集成的创新方法，以及c)在整体框架中以更快的速度解决关键电力系统运行问题的潜在变革性工具。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。