Energy, Power, Control, and Networks (EPCN)

The Energy, Power, Control, and Networks (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