CAREER: Towards Data-Driven and Field-Validated Microgrid Modeling and Analysis Techniques

职业：迈向数据驱动和现场验证的微电网建模和分析技术

• 批准号: 2237886
• 负责人: Mahmoud Kabalan
• 金额: $ 52.98万
• 依托单位: University of St. Thomas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237886&HistoricalAwards=false
• 关键词: CAREER; Towards; Data; Driven; Field

This NSF CAREER project aims to increase the reliability, security, and resiliency of the electric power grid via the use of microgrids. Microgrids are local electric energy systems that can operate with the grid and separate from the grid during emergencies. Microgrids can improve grid resiliency and sustainability, and accelerate disaster recovery. The project will bring transformative change to how microgrids are designed and operated by addressing the gap between theoretical studies and real-world applications. To achieve this goal, state-of-the-art data-driven and machine learning algorithms will be employed. The intellectual merits of the project include developing a new approach to accurately model real-world conditions, using machine learning to reduce model complexity, and creating and field-validating a microgrid stability prediction tool. The broader impacts of the project include an improved method to design and operate microgrids which would reduce implementation costs. By reducing costs, microgrids can be deployed faster in both developing and developed nations. This would quicken the electrification of historically marginalized communities and improve grid resiliency, robustness, and sustainability. The newly created knowledge would be disseminated through hands-on courses and workshops on power engineering. Stability prediction for microgrids require accurate mathematical modeling of the physical system to capture important dynamics and subtleties. Current modeling practices do not account for two critical real-world phenomena, namely, controller saturation and protection action, both of which have drastic effects on system stability. The first technical contribution of this project will address this gap by developing an approach to concurrently model those two phenomena. Additionally, microgrid stability studies are approached through linear or nonlinear techniques. Stability techniques can become too complex due to model order and number of nonlinearities. The second technical contribution will leverage advances in Scientific Machine Learning (SciML) to reduce a system’s model order by creating surrogates. The third technical contribution will be a microgrid stability prediction tool using SciML that will predict transient stability under different operating conditions and design factors. Data from an industry-grade microgrid and real-world equipment will be used to tune and confirm the accuracy of those surrogates and tools.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.

这个NSF职业项目旨在通过使用微电网来提高电力电网的可靠性，安全性和弹性。微电网是局部电能系统，可以在紧急情况下与网格合作并与网格分开。微电网可以提高电网弹性和可持续性，并加速灾难恢复。该项目将通过解决理论研究和现实世界应用之间的差距来使微电网的设计和操作方式带来变革性的变化。为了实现这一目标，将采用最先进的数据驱动和机器学习算法。该项目的智力优点包括开发一种新的方法来准确建模现实世界的条件，使用机器学习来降低模型的复杂性，并创建和现场验证微电网稳定性预测工具。该项目的更广泛影响包括一种改进的设计和操作微电网的方法，以降低实施成本。通过降低成本，可以在发展中国家和开发国家中更快地部署微电网。这将迅速对历史上边缘化的社区进行电气化，并提高网格弹性，鲁棒性和可持续性。新创建的知识将通过动手课程和动力工程研讨会来传播。微电网的稳定性预测需要对物理系统进行准确的数学建模，以捕获重要的动力学和细微趋势。当前的建模实践并未解释两个关键的现实现象，即控制器满意度和保护行动，两者都对系统稳定性产生巨大影响。该项目的第一个技术贡献将通过开发一种同时建模这两种现象的方法来解决这一差距。另外，通过线性或非线性技术进行了微电网稳定性研究。由于模型顺序和非线性数量，稳定技术可能变得过于复杂。第二种技术贡献将利用科学机器学习（SCIML）的进步来通过创建替代物来减少系统的模型顺序。第三个技术贡献将是使用SCIML的微电网稳定性预测工具，该工具将在不同的操作条件和设计因素下预测瞬态稳定性。来自行业级的微电网和现实设备的数据将用于调整和确认这些代理和工具的准确性。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估来获得的支持。