Energy, Power, Control, and Networks

The Energy, Power, Control, and Networks (EPCN) Program supports innovative research in various fields. These include the study of networked multi-agent systems, higher-level decision-making, dynamic resource allocation, and risk management in situations that might involve uncertainty, subsystem failure, and unpredictable disturbances. The EPCN program also delves into the development of novel machine learning algorithms, adaptive dynamic programming, real-time learning through brain-like networked architectures, and neuromorphic engineering.

The ultimate goal of the EPCN Program is to foster research on emerging technologies and their application. This can cover a broad range of areas including energy, transportation, robotics, and biomedical devices & systems. The program puts a strong emphasis on examining electric power systems. Aspects such as generation, transmission, storage, and the advancement of renewable energy sources and their synchronization with the grid are particularly important. Further, this program also looks into power electronics and drives; battery management systems; and hybrid and electric vehicles. The EPCN Program is also interested in understanding the interconnection between power systems and regulations, economic structures, and consumer behavior.

Areas of particular interest to Program Directors (who can be contacted via the EPCN staff directory) include 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).

For Energy and Power systems, key areas of investigation are 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.

For Learning and Adaptive Systems, focal points include Neural Networks; Neuromorphic Engineering Systems; Data analytics and Intelligent Systems; Machine Learning Algorithms, Analysis, and Applications.