CAREER: Machine Learning Based 4D Decomposition and Distributed Optimization

分布式优化在工程系统（包括电力系统）中的广泛应用中出现。虽然分布式算法已经在文献中提出，全面的框架是不完整的分解优化问题的多维基础上，如空间和时间。此外，由于分解、协调和建模步骤中的缺陷和挑战，大多数现有算法缺乏可扩展性，并且在应用于大型现实问题时变得计算昂贵。该提案侧重于可扩展分布式优化的基础研究。提出了几种结合机器学习和数学模型和方法，以创建高度可扩展的，快速的，高效的四维分布式优化算法，用于电力系统的运行和规划。该项目将涉及不同的学生，特别是代表性不足的少数民族，并显着改善工程教育，STEM课程，劳动力培训和K12学生参与工程教育。这项研究将建立机器学习和基于几何的分解和分布式算法，它不仅改革了电力系统的运行和规划，而且为解决分布式计算的不足开辟了新的研究途径。优化.通过这个项目，将开发一个时间分解，然后将创建一个全面的四维分解。将开发基于机器学习的策略，以最佳地分解优化问题。此外，学习和数学方法将被设计来创建高效和可扩展的异步分布式算法。为了进一步降低计算成本，迭代方法被提出来利用分类和回归技术来减少优化问题的可行空间。此外，该项目小组将开发方法，使分布式算法对优化变量和目标函数的初始值的选择具有鲁棒性，并将设计创新的信息共享方法，以降低计算复杂性，并在集成到电力系统运行和规划时提高拟议算法的准确性。项目团队将在各种综合测试系统和数据集上实施开发的模型和算法，该项目由ECCS部门/ EPCN计划和刺激竞争研究的既定计划（EPSCOR）共同资助该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

Distributed optimization arises in a broad range of applications in engineering systems, including electric power systems. While distributed algorithms have been proposed in the literature, comprehensive frameworks are incomplete for decomposing optimization problems on a multi-dimensional basis, such as space and time. In addition, because of deficiencies and challenges in decomposition, coordination, and modeling steps, the majority of existing algorithms suffer from lack of scalability and become computationally expensive when applied to large real-world problems. This proposal focuses on fundamental research on scalable distributed optimization. Several combined machine learning and mathematical models and methods are proposed to create highly scalable, fast, and efficient four-dimensional distributed optimization algorithms for power systems operation and planning. The project will involve diverse students, particularly underrepresented minorities, and significantly improve engineering education, STEM curriculum, workforce training, and K12 students involvement in engineering education.This research will establish machine learning and mathematical-based decomposition and distributed algorithms, which not only reform power system operation and planning but also open new avenues of research to solve computational deficiencies of distributed optimization. Through this project, a temporal decomposition will be developed, and then a comprehensive four-dimensional decomposition will be created. Machine learning-based strategies will be developed to optimally decompose optimization problems. In addition, learning and mathematical approaches will be devised to create highly efficient and scalable asynchronous distributed algorithms. To further reduce computational costs, iterative methods are proposed to reduce the feasible space of optimization problems taking advantage of classification and regression techniques. Furthermore, the project team will develop methods to make distributed algorithms robust against the choice of initial values of optimization variables and objective functions and will devise innovative information-sharing approaches to reduce the computational complexity and enhance the accuracy of the proposed algorithms when integrated into power system operation and planning. The project team will implement the developed models and algorithms on various synthetic test systems and data sets, as well as real-world practical power grids.This project is jointly funded by the ECCS division / EPCN program and the Established Program to Stimulate Competitive Research (EPSCOR).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.