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.