Energy, Power, Control, and Networks

The Energy, Power, Control, andNetworks (EPCN) Program supports innovative research in modeling, optimization, learning, adaptation, and control of networked multi-agent systems, higher-level decision making, and dynamic resource allocation, as well as risk management in the presence of uncertainty, sub-system failures, and stochastic disturbances. EPCN also invests in novel machine learning algorithms and analysis, adaptive dynamic programming, brain-like networked architectures performing real-time learning, and neuromorphic engineering. EPCN’s goal is to encourage research on emerging technologies and applications including energy, transportation, robotics, and biomedical devices & systems. EPCN also emphasizes electric power systems, including generation, transmission, storage, and integration of renewable energy sources into the grid; power electronics and drives; battery management systems; hybrid and electric vehicles; and understanding of the interplay of power systems with associated regulatory & economic structures and with consumer behavior.


Areas managed by Program Directors (please contact Program Directors listed in the EPCN staff directory for areas of interest):


Control Systems


Distributed Control and Optimization
Networked Multi-Agent Systems
Stochastic, Hybrid, Nonlinear Systems
Dynamic Data-Enabled Learning, Decision and Control
Cyber-Physical Control Systems
Applications (Biomedical, Transportation, Robotics)


Energy and Power Systems


Solar, Wind, and Storage Devices Integration with the Grid
Monitoring, Protection and Resilient Operation of Grid
Power Grid Cybersecurity
Market design, Consumer Behavior, Regulatory Policy
Microgrids
Energy Efficient Buildings and Communities


Power Electronics Systems


Advanced Power Electronics and Electric Machines
Electric and Hybrid Electric Vehicles
Energy Harvesting, Storage Devices and Systems
Innovative Grid-tied Power Electronic Converters


Learning and Adaptive Systems


Neural Networks
Neuromorphic Engineering Systems
Data analytics and Intelligent Systems
Machine Learning Algorithms, Analysis and Applications

The Energy, Power, Control, andNetworks (EPCN) Program supports innovative research in modeling, optimization, learning, adaptation, and control of networked multi-agent systems, higher-level decision making, and dynamic resource allocation, as well as risk management in the presence of uncertainty, sub-system failures, and stochastic disturbances. EPCN also invests in novel machine learning algorithms and analysis, adaptive dynamic programming, brain-like networked architectures performing real-time learning, and neuromorphic engineering. EPCN’s goal is to encourage research on emerging technologies and applications including energy, transportation, robotics, and biomedical devices & systems. EPCN also emphasizes electric power systems, including generation, transmission, storage, and integration of renewable energy sources into the grid; power electronics and drives; battery management systems; hybrid and electric vehicles; and understanding of the interplay of power systems with associated regulatory & economic structures and with consumer behavior.


Areas managed by Program Directors (please contact Program Directors listed in the EPCN staff directory for areas of interest):


Control Systems

• Distributed Control and Optimization


• Networked Multi-Agent Systems


• Stochastic, Hybrid, Nonlinear Systems


• Dynamic Data-Enabled Learning, Decision and Control


• Cyber-Physical Control Systems


• Applications (Biomedical, Transportation, Robotics)

Energy and Power Systems

• Solar, Wind, and Storage Devices Integration with the Grid


• Monitoring, Protection and Resilient Operation of Grid


• Power Grid Cybersecurity


• Market design, Consumer Behavior, Regulatory Policy


• Microgrids


• Energy Efficient Buildings and Communities

Power Electronics Systems

• Advanced Power Electronics and Electric Machines


• Electric and Hybrid Electric Vehicles


• Energy Harvesting, Storage Devices and Systems


• Innovative Grid-tied Power Electronic Converters

Learning and Adaptive Systems

• Neural Networks


• Neuromorphic Engineering Systems


• Data analytics and Intelligent Systems


• Machine Learning Algorithms, Analysis and Applications