We develop a data-driven approach to Pareto optimal control of large-scale systems, where decision makers know only their local dynamics. Using reinforcement learning, we design a control strategy that optimally balances multiple objectives. The proposed method achieves near-optimal performance and scales well with the total dimension of the system. Experimental results demonstrate the effectiveness of our approach in managing multi-area power systems. / Master of Science / We have developed a new way to manage complex systems—like power networks—where each part only knows about its own behavior. By using a type of artificial intelligence known as reinforcement learning, we've designed a method that can handle multiple goals at once, ensuring that the entire system remains stable and works efficiently, no matter how large it gets. Our tests show that this method is particularly effective in coordinating different sections of power systems to work together smoothly. This could lead to more efficient and reliable power distribution in large networks.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119384 |
| Date | 10 June 2024 |
| Creators | Tajik Hesarkuchak, Saeed |
| Contributors | Electrical Engineering, Boker, Almuatazbellah M., Eldardiry, Hoda Mohamed, Mili, Lamine M. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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