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

ERI：电力网络分布式优化中的智能建模和参数选择

• 批准号: 2347120
• 负责人: Mehdi Karimi
• 金额: $ 19.75万
• 依托单位: Board of Trustees of Illinois State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2347120&HistoricalAwards=false
• 关键词: ERI; Intelligent; Modeling; Parameter; Selection

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.

这项工程研究启动（ERI）赠款将通过资助研究来促进国家繁荣，经济福利和国家安全的发展，该研究应对优化大规模网络系统（例如国家电力系统）的挑战，该系统由于可再生能源和电动汽车的普遍性而迅速发展。该奖项支持研究研究创新算法的开发的研究，使多个代理能够在保护复杂的网络优化问题的同时，在维护个人隐私方面进行有效的沟通和协作。通过整合数学优化和工程，该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。教育和宣传活动旨在通过夏季研究计划等计划来激发STEM学生，特别是来自人为群体不足的学生，以探索科学和工程学中的数据驱动方法。这项研究创造了智能建模和参数选择方法，以解决分布式优化（DO）的重要挑战，这是一种为复杂网络的诸如快速竞争的现代网络等广泛的网络类别的有希望的方法。这项研究设计了创新的分区技术，以改善DO算法的缓慢收敛性。这种方法不仅通过减少子问题的数量，而且在子问题上施加理想的结构来简化DO的自定义。 PI设计了使用经典参数选择技术和基于学习的方法，例如物理学深度学习，以解决DO算法的参数选择的普遍挑战，以解决自适应和学习的策略。该研究还将检查DO算法对数据不确定性和通信错误的鲁棒性。结果将通过开源优化软件包来传播，支持这些创新算法的现实实施。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的优点和更广泛的影响来评估NSF的法定任务，并被认为是宝贵的支持。