The electric grid is a complex critical infrastructure system that underpins all economic and social activities in the US. It is thus of utmost importance to maintain its efficient, reliable and secure operation at all times. The system, however, is undergoing an unprecedented period of transformation with rapid growths in renewable energy and electric vehicles, as well as increasing concerns of cyber security. Consequently, not only there is a higher requirement for efficient and secure operation of the grid, but also achieving it becomes much more challenging. The issue is especially acute from a computational perspective, as problems of much greater complexity need to be solved more frequently. As such, conventional approaches for solving secure power system operation problems face major and pressing challenges in maintaining their efficacy in the rapidly evolving power grids. To overcome these challenges, this project will develop novel machine-learning-based methods to greatly accelerate solving key and large-scale secure power system operation problems. Notably, the developed methods will integrate data-driven methods with the physical models of power systems. The impact of the project extends to machine learning algorithm design in all engineering systems where knowledge from physical system models and conventional wisdom in algorithm design can be incorporated. The developed algorithms will lead to greatly enhanced efficiency, reliability and security of power systems in the presence of high penetration of renewable energy and without the need of building more transmission lines or procuring much higher reserve capacity, resulting in tremendous economic savings for consumers. The project will also contribute to the much-demanded educational needs in the industry by training the next generation workforce to master interdisciplinary expertise of machine learning and power systems. The PIs are committed to promote diversity in research and education through the project by engaging students of minorities and from under-privileged backgrounds.

This project will develop new machine learning algorithms, both leveraging and integrated with existing computational tools, to greatly improve the computational efficiency of solving challenging power system operation problems. We accomplish this by designing algorithms that use data to replace some of the existing heuristics based on human experience. We use a bottom-up approach by carefully formulating the problems to determine the best interface between the physical system and machine learning. This allows us to design algorithms that are aware of the physics of the problems and complement existing tools in the field. Specifically, we pursue three research thrusts: i) solving for optimal generator dispatch levels by introducing a data-driven component to the existing algorithms; ii) enabling fast identification and quantification of problematic contingencies using reinforcement learning; and iii) finding the most secure and efficient generation unit commitment schedule utilizing the results from the previous thrusts. These algorithms can be directly integrated into current solvers and have the potential of providing orders of magnitude speedup over existing methods. As such, this project offers a) new machine learning paradigms and algorithms, b) innovative ways of integrating machine learning methods with physical model-based optimization algorithms, and c) potentially transformative tools that solve key power system operation problems in a holistic framework with much faster speeds.

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.

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State University New York Stony Brook
Stony Brook
United States
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