Distributed Optimization and Learning: A Paradigm Shift for Power Systems
Published in IEEE Systems Journal (accepted, 2025), 2025
This article surveys and unifies recent advances in distributed optimization and learning for modern power systems, with an emphasis on scalability, robustness, and data-driven operation.
Recommended citation: A. Al-Tawaha, E. Cibaku, S. Park, J. Lavaei, and M. Jin. "Distributed Optimization and Learning: A Paradigm Shift for Power Systems." IEEE Systems Journal, accepted, 2025.