Aranya Chakrabortty
Bio
Aranya Chakrabortty received his PhD in Electrical Engineering from Rensselaer Polytechnic Institute, Troy, NY in 2008, following which he was a postdoctoral research associate at the University of Washington, Seattle, WA. In 2010, he joined the Electrical and Computer Engineering department at North Carolina State University, where he is currently a professor. His research interests lie at the intersection of control theory and electric power systems, focusing on various research problems pertaining to integration of renewable energy (wind, solar, energy storage) in the power grid, and their impacts of dynamics, system identification, and control. Recently, he has also started taking an active research interest in the area of real-time data-driven monitoring and control of power grids, especially along the lines of multi-agent reinforcement learning and game theory. He is a Senior Member of IEEE, and a 2019 NCSU Faculty Scholar. He received the NSF CAREER award in 2011. He is currently serving as a program director at the US National Science Foundation (NSF) where he manages a wide range of research portfolio on energy, infrastructures, and climate change solutions.
SHORT DESCRIPTION OF INTERESTS:
Within this coastal resilience group, besides my usual research in power systems and renewables, I am interested in working on the following two topic areas, and would like to establish collaborations along these lines:
1. Distributed multi-agent optimization and control algorithms, both deterministic and stochastic, and model-based and data-driven, for control of power grids with wind, solar, and energy storage and electrified transportation, especially peer-to-peer communication-based control using emerging wireless communication technologies.
2. Modeling and control of water networks, applications of data-driven methods for understanding water networks, and how the cross-coupling between water and energy infrastructures can be made more efficient and robust.
https://people.engr.nscu.edu/achakra2/
Publications
- Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent , IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2024)
- Climate Change Mitigation, Adaptation, and Resilience: Challenges and Opportunities for the Control Systems Community , IEEE CONTROL SYSTEMS MAGAZINE (2024)
- Distributed Multiagent Reinforcement Learning Based on Graph-Induced Local Value Functions , IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2024)
- Optimal Charging Control and Incentivization Strategies for Electric Vehicles Considering Grid Dynamical Constraints , 2024 American Control Conference (ACC) (2024)
- Physics-aware Regression for DER Dispatch with Topological Reconfigurations of Radial Feeder , IEEE Transactions on Industry Applications (2024)
- A Generative Adversarial Network Approach for Identification and Mitigation of Cyber-Attacks in Wide-Area Control of Power Systems , AI for Energy Innovation’ workshop (2023)
- Data-Driven DER Dispatch in Distribution Networks using Multi-Stage Regression , IFAC World Congress (2023)
- Data-Driven Optimal Power Dispatch for Distributed Energy Resources in Radial Feeder using Multi-Stage Regression , IFAC PAPERSONLINE (2023)
- Distributed Reinforcement Learning for Networked Dynamical Systems , IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS (2023)
- Game-Theoretic Mixed H2/H∞ Control with Sparsity Constraint for Multi-Agent Control Systems , 2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC (2023)