Sustainable Energy & Power Systems
Self-organizing power systems for the renewable grid — a plug-and-play architecture for photovoltaics, battery storage, and grid-edge devices.
Overview
The lab is developing a comprehensive approach to system reliability and power quality under widespread renewable generation. By developing techniques for both centralized cloud-based coordination and distributed peer-to-peer networks, the group enables coordinated response of many local units to adjust consumption and generation, satisfy physical constraints, and provide ancillary services requested by grid operators.
The work applies nonlinear and robust control theory to design self-organizing power systems that effectively respond to grid events and variability. A key feature is a flexible plug-and-play architecture in which devices and small power networks can easily engage or disengage from other power networks or the grid. The design approach has been tested across many scenarios using more than 100 actual physical devices — photovoltaics, battery-storage inverters, and home appliances.
Current threads include grid-forming (GFM) and grid-following (GFL) inverter design with robust H-infinity control, distributed apportioning of energy resources for ancillary services, virtual-impedance shaping for low-voltage microgrids, power-hardware-in-the-loop validation, and non-intrusive load monitoring at high frequency. A growing cyber-physical-security subthread addresses resilience against false-data injection, malicious agents in consensus algorithms, and cyber-topology attacks on electricity markets.
The area is supported by ARPA-E (NODES program, Rapidly Viable Sustained Grid), DOE, NSF, and industry partnerships including NREL and Dynapower.
Cyber-Physical Security
Detection of malicious attacks on consensus algorithms in power networks; cyber-secure microgrid operation resilient to false-data injection; reinforcement-learning-based vulnerability analysis of electricity markets.
Recent publications in this area
See all 41 →-
Topology Change Estimation in Power Distribution Networks: A Machine Learning Approach
Dey, Arnab; Chakraborty, Soham; Salapaka, Murti V.
IEEE Transactions on Smart Grid 17, no. 4 (July 2026): 3445. · 2026
-
Learning Topology of Meshed Microgrids Using Voltage Magnitude.
Mohammed Tuhin Rana, and Murti V. Salapaka.
2025 IEEE Energy Conversion Conference Congress and Exposition (ECCE). IEEE · 2025
-
Risk Sharing Among Microgrids: Connectivity Requirements and Optimal Control.
Arnab Dey, Vivek Khatana, Ankur Mani and Murti V. Salapaka.
61st Allerton Conference on Communication, Control, and Computing Proceedings · 2025
-
A Distributed Malicious Agent Detection Scheme for Resilient Power Apportioning in Microgrids,
V. Khatana, S. Chakraborty, G. Saraswat, S. Patel and M. V. Salapaka
IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, IL, USA · 2024
-
A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters,
V. Khatana, S. Chakraborty and M. V. Salapaka
IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, IL, USA · 2024
Content coming in later sessions
Featured work
Flagship papers in this area. Session 3 (CV import).
Current members
People working in this area. Session 4.
Alumni placements
Where alumni who worked in this area now are. Session 4.
Recent news
Highlights tagged to this area. Session 5.
Facilities used
Equipment and testbeds enabling this area. Session 5.
This lab also works in
-
Theoretical Foundations
UmbrellaControl theory, network structure and causal discovery, distributed optimization, nonlinear dynamics, thermodynamics at the small scale.
-
Machine Learning
Data-driven modeling with physics priors; RL for grid security; sample-complexity-optimal learning of networks.
-
Nanoscience
AFM, optical tweezers, probe-based data storage, and quantitative real-time nano-imaging.
-
Single-Molecule Biophysics
Motor proteins, cargo transport, force spectroscopy of dystrophin and utrophin, and the biophysics of cellular systems studied one molecule at a time.
Interested in joining this area?
The lab welcomes prospective PhD students and postdocs.
Learn how to join