6 papers
Explicit Ensemble Learning Surrogate for Joint Chance-Constrained Optimal Power Flow
Amir Bahador Javadi, Amin Kargarian
The increasing penetration of renewable generation introduces uncertainty into power systems, challenging traditional deterministic optimization methods. Chance-constrained optimiz…
Data-driven Modeling of Grid-following Control in Grid-connected Converters
Amir Bahador Javadi, Philip Pong
As power systems evolve with the integration of renewable energy sources and the implementation of smart grid technologies, there is an increasing need for flexible and scalable mo…
Grid-forming Control of Converter Infinite Bus System: Modeling by Data-driven Methods
Amir Bahador Javadi, Philip Pong
This study explores data-driven modeling techniques to capture the dynamics of a grid-forming converter-based infinite bus system, critical for renewable-integrated power grids. Us…
Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment
Amir Bahador Javadi, Amin Kargarian, Mort Naraghi-Pour
The increasing penetration of renewable energy sources introduces significant uncertainty in power system operations, making traditional deterministic unit commitment approaches co…
Automatic Regression for Governing Equations with Control (ARGOSc)
Amir Bahador Javadi, Amin Kargarian, Mort Naraghi-Pour
Learning the governing equations of dynamical systems from data has drawn significant attention across diverse fields, including physics, engineering, robotics and control, economi…
A Review on Symbolic Regression in Power Systems: Methods, Applications, and Future Directions
Amir Bahador Javadi, Philip Pong
As power systems evolve with the increasing integration of renewable energy sources and smart grid technologies, there is a growing demand for flexible and scalable modeling approa…