Collaborative Research: Physics Informed Real-time Optimal Power Flow

该NSF项目旨在使用机器学习技术开发一个物理信息实时最优潮流模型，以解决为发电厂输出提供接近最优解决方案的差距，同时考虑实际动态约束，以避免频率波动和电网不稳定性。该项目的智力优势包括开发技术，将物理和动力学原理集成到机器学习管道中，以及确保最佳潮流问题的可扩展和可靠解决方案的方法。该项目的更广泛影响包括对电网的重大长期影响，减少碳排放和提高电网可靠性，特别是在极端天气下，需求增加以及间歇性发电的不确定性。PI还将与国家实验室和非营利组织合作，以确保开发的模型可供更广泛的社区使用，包括公用事业，政策制定者和研究人员。此外，该项目还将提供物理学、工程学和机器学习交叉领域的培训和教育机会，从而促进能源和可持续发展领域熟练劳动力的发展。该项目在科学和工程方面做出了四项关键贡献：（1）将物理信息神经网络与传统馈送相结合的进展前向神经网络实时预测最优潮流问题的解，在追求动态稳定性的同时也追求最优性。(2)确保学习嵌入中约束满足的新方法。(3)研究确保训练可扩展到实际规模的大型网络的技术。(4)通过评估测量噪声下的模型性能和分析模型可靠性来追求模型的鲁棒性，以深入了解最优潮流问题的高质量近似值。该模型有望加快将更多的可再生能源引入电网，减少因稳定性问题和传统启发式下垂控制导致的次优问题而导致的限电。该奖项反映了NSF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。

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.