6 papers
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…
Impact of Data Poisoning Attacks on Feasibility and Optimality of Neural Power System Optimizers
Nora Agah, Meiyi Li, Javad Mohammadi
The increased integration of clean yet stochastic energy resources and the growing number of extreme weather events are narrowing the decision-making window of power grid operators…
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…
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets
Meiyi Li, Javad Mohammadi
Amid the increasing interest in the deployment of Distributed Energy Resources (DERs), the Virtual Power Plant (VPP) has emerged as a pivotal tool for aggregating diverse DERs and…