Collaborative Research: Physics Informed Real-time Optimal Power Flow

合作研究：基于物理的实时最佳潮流

• 批准号: 2242930
• 负责人: Kyri Baker
• 金额: $ 22.5万
• 依托单位: University of Colorado at Boulder
• 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: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242930&HistoricalAwards=false
• 关键词: Collaborative; Research; Physics; Informed; Real

This NSF project aims to develop a physics-informed real-time optimal power flow model using machine learning techniques to address the gap in providing close to optimal solutions for power plant outputs while considering practical dynamical constraints to avoid frequency fluctuations and grid instabilities. The intellectual merits of the project include developing techniques to integrate physical and dynamical principles in machine learning pipelines and methods to ensure scalable and reliable solutions to optimal power flow problems. The broader impacts of the project include significant long-term impacts on power grids, reducing carbon emissions and increasing grid reliability, especially under extreme weather, increased demand, and uncertainty from intermittent generation. The PIs will also engage with national laboratories and non-profit organizations to ensure that the developed model is accessible and usable by the broader community, including utilities, policymakers, and researchers. Furthermore, the project will provide opportunities for training and education in the intersection of physics, engineering, and machine learning, thereby contributing to the development of a skilled workforce in the field of energy and sustainability.The project makes four key scientific and engineering contributions: (1) Advancements in combining physics-informed neural networks with conventional feed-forward neural networks to predict solutions to optimal power flow problems in real-time, pursuing dynamic stability while also optimality. (2) Novel approaches of ensuring constraint satisfaction in the learned embedding. (3) Investigation of techniques to ensure scalability of training to large, realistically-sized networks. (4) Pursuit of model robustness by assessing model performance under measurement noise and analyzing model reliability to develop insights into high-quality approximations of the optimal power flow problem. The proposed model holds the promise to expedite the adoption of increased renewable energy into the power grid, reducing curtailment resulting from stability concerns and suboptimalities resulting from conventional heuristic droop control.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项目旨在使用机器学习技术来开发具有物理信息的实时最佳功率流模型，以解决该差距，以便为发电厂输出提供最佳解决方案，同时考虑实用的动态约束，以避免频率波动和网格不稳定性。该项目的智力优点包括开发技术，以整合机器学习管道中的物理和动态原理，并确保可扩展和可靠的解决方案，以解决最佳功率流问题。该项目的更广泛影响包括对电网的重大长期影响，减少碳排放和增加网格可靠性，尤其是在极端天气下，需求增加以及间歇性产生的不确定性。 PI还将与国家实验室和非营利组织互动，以确保开发的模型可以由更广泛的社区（包括公用事业，政策制定者和研究人员）访问和使用。此外，该项目将为物理，工程和机器学习的交汇处提供培训和教育的机会，从而为能源和可持续性领域的熟练劳动力的发展做出贡献。该项目的四个关键科学和工程贡献使科学和工程的贡献是：（1）进步方面的进步，在相结合的物理网络方面，可以预测传统的馈送神经网络，以预测传统的神经网络，以预测型神经网络，以预测型神经网络，以实现范围，以预测型神经网络，并促进型神经网络，并实现了范围的范围。最佳性。 （2）确保在学习的嵌入中确保限制满意度的新方法。 （3）调查技术以确保训练对大型，实际尺寸的网络的可扩展性。 （4）通过评估测量噪声下的模型性能并分析模型可靠性，以发展对最佳功率流问题的高质量近似的见解，来追求模型鲁棒性。拟议的模型有望加快将可再生能源提高到电网中的提高，从而减少了由于常规启发式下垂控制所带来的稳定性关注和次要限制而导致的缩减。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的cribitia crietia cripitia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia criperia均值得通过评估。

项目成果

Gradient-enhanced physics-informed neural networks for power systems operational support

用于电力系统运行支持的梯度增强物理信息神经网络

• DOI: 10.1016/j.epsr.2023.109551
• 发表时间: 2023
• 期刊: Electric Power Systems Research
• 影响因子: 3.9
• 作者: Mohammadian, Mostafa;Baker, Kyri;Fioretto, Ferdinando
• 通讯作者: Fioretto, Ferdinando