ERI: Intelligent Modeling and Parameter Selection in Distributed Optimization for Power Networks

这项工程研究启动(ERI)拨款将通过资助研究来促进国家繁荣、经济福利和国家安全，以应对优化国家电力系统等大规模网络系统的挑战，由于可再生能源和电动汽车的日益普及，电力系统正在迅速发展。该奖项支持研究创新算法的开发，使多个代理能够在保护个人隐私的同时有效地沟通和协作解决复杂的网络优化问题。通过将数学优化和工程相结合，这个跨学科项目的结果将使学术和工业研究人员、数据科学家和政策制定者受益。教育和推广活动旨在激励STEM学生，特别是那些来自代表性不足群体的学生，通过暑期研究项目等倡议，在科学和工程领域探索数据驱动的方法。这项研究创建了智能建模和参数选择方法，以应对分布式优化(DO)中的重要挑战，对于快速发展的现代电网等广泛类别的复杂网络系统来说，这是一种很有前途的方法。本研究针对DO算法收敛速度慢的问题，设计了创新的分区技术。这种方法不仅通过减少子问题的数量来简化DO的定制，而且还对子问题施加了所需的结构。PI设计了自适应和基于学习的策略，以解决DO算法使用经典参数选择技术和基于学习的方法(如物理信息深度学习)进行参数选择的普遍挑战。该研究还将检验DO算法对数据不确定性和通信错误的稳健性。结果将通过开源优化软件包传播，促进这些创新算法在现实世界中的实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Engineering Research Initiation (ERI) grant will contribute to the advancement of national prosperity, economic welfare, and national security by funding research that addresses the challenge of optimizing large-scale networked systems such as nation’s power system, which is rapidly evolving due to the growing prevalence of renewable energy sources and electric vehicles. This award supports research examining the development of innovative algorithms enabling multiple agents to communicate and collaborate effectively in solving complex network optimization problems while safeguarding individual privacy. By integrating mathematical optimization and engineering, the results of this cross-disciplinary project will benefit academic and industrial researchers, data scientists, and policymakers. The educational and outreach activities seek to inspire STEM students, especially those from underrepresented groups, to explore data-driven methodologies in science and engineering through initiatives like summer research programs.This research creates intelligent modeling and parameter selection methods to address important challenges in distributed optimization (DO), a promising approach for a broad class of complex networked systems such as the quickly evolving modern power networks. This research designs innovative partitioning techniques to improve the slow convergence of DO algorithms. This approach not only simplifies the customization of DO by reducing the number of sub-problems but also imposes desirable structures on the sub-problems. The PI devises adaptive and learning-based strategies to address the pervasive challenge of parameter selection for DO algorithms using both classical parameter selection techniques and learning-based methods, such as physics-informed deep learning. The research will also examine the robustness of DO algorithms to data uncertainty and communication errors. Results will be disseminated through open-source optimization software packages, facilitating the real-world implementation of these innovative algorithms.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.