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Towards Reliable Neural Optimizers: Permutation-Equivariant Neural Approximation in Dynamic Data Driven Applications Systems
Meiyi Li, Javad Mohammadi
Dynamic Data Driven Applications Systems (DDDAS) motivate the development of optimization approaches capable of adapting to streaming, heterogeneous, and asynchronous data from sen…
Learning to Optimize Joint Chance-constrained Power Dispatch Problems
Meiyi Li, Javad Mohammadi
The ever-increasing integration of stochastic renewable energy sources into power systems operation is making the supply-demand balance more challenging. While joint chance-constra…
Speeding Ticket: Unveiling the Energy and Emission Burden of AI-Accelerated Distributed and Decentralized Power Dispatch Models
Meiyi Li, Javad Mohammadi
As the modern electrical grid shifts towards distributed systems, there is an increasing need for rapid decision-making tools. Artificial Intelligence (AI) and Machine Learning (ML…
Towards Reliable Neural Optimizers: A Permutation Equivariant Neural Approximation for Information Processing Applications
Meiyi Li, Javad Mohammadi
The complexities of information processing across Dynamic Data Driven Applications Systems drive the development and adoption of Artificial Intelligence-based optimization solution…
Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mapping
Meiyi Li, Javad Mohammadi
The evolution towards a more distributed and interconnected grid necessitates large-scale decision-making within strict temporal constraints. Machine learning (ML) paradigms have d…
A Fully Decentralized Tuning-free Inexact Projection Method for P2P Energy Trading
Meiyi Li, Javad Mohammadi, Soummya Kar
Agent-based solutions lend themselves well to address privacy concerns and the computational scalability needs of future distributed electric grids and end-use energy exchanges. De…