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Energy, Power, Control, and Networks

U.S. National Science Foundation · Opportunity PD-18-7607

Who can apply

• Unrestricted

About this opportunity

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

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