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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 <a href="https://www.nsf.gov/staff/staff_list.jsp?orgId=5915&subDiv=y&org=ECCS&from_org=ECCS">EPCN staff directory</a> for areas of interest):
Control Systems <ul> <li>Distributed Control and Optimization</li> <li>Networked Multi-Agent Systems</li> <li>Stochastic, Hybrid, Nonlinear Systems</li> <li>Dynamic Data-Enabled Learning, Decision and Control</li> <li>Cyber-Physical Control Systems</li> <li>Applications (Biomedical, Transportation, Robotics)</li> </ul> Energy and Power Systems <ul> <li>Solar, Wind, and Storage Devices Integration with the Grid</li> <li>Monitoring, Protection and Resilient Operation of Grid</li> <li>Power Grid Cybersecurity</li> <li>Market design, Consumer Behavior, Regulatory Policy</li> <li>Microgrids</li> <li>Energy Efficient Buildings and Communities</li> </ul> Power Electronics Systems <ul> <li>Advanced Power Electronics and Electric Machines</li> <li>Electric and Hybrid Electric Vehicles</li> <li>Energy Harvesting, Storage Devices and Systems</li> <li>Innovative Grid-tied Power Electronic Converters</li> </ul> Learning and Adaptive Systems <ul> <li>Neural Networks</li> <li>Neuromorphic Engineering Systems</li> <li>Data analytics and Intelligent Systems</li> <li>Machine Learning Algorithms, Analysis and Applications</li> </ul>
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 <a href="https://www.nsf.gov/staff/staff_list.jsp?orgId=5915&subDiv=y&org=ECCS&from_org=ECCS">EPCN staff directory</a> for areas of interest):
Control Systems <ul> <li>Distributed Control and Optimization</li> <li>Networked Multi-Agent Systems</li> <li>Stochastic, Hybrid, Nonlinear Systems</li> <li>Dynamic Data-Enabled Learning, Decision and Control</li> <li>Cyber-Physical Control Systems</li> <li>Applications (Biomedical, Transportation, Robotics)</li> </ul> Energy and Power Systems <ul> <li>Solar, Wind, and Storage Devices Integration with the Grid</li> <li>Monitoring, Protection and Resilient Operation of Grid</li> <li>Power Grid Cybersecurity</li> <li>Market design, Consumer Behavior, Regulatory Policy</li> <li>Microgrids</li> <li>Energy Efficient Buildings and Communities</li> </ul> Power Electronics Systems <ul> <li>Advanced Power Electronics and Electric Machines</li> <li>Electric and Hybrid Electric Vehicles</li> <li>Energy Harvesting, Storage Devices and Systems</li> <li>Innovative Grid-tied Power Electronic Converters</li> </ul> Learning and Adaptive Systems <ul> <li>Neural Networks</li> <li>Neuromorphic Engineering Systems</li> <li>Data analytics and Intelligent Systems</li> <li>Machine Learning Algorithms, Analysis and Applications</li> </ul>
Who's Eligible
Obtain Full Opportunity Text:
NSF Program Desccription PD-18-7607
Additional Information of Eligibility:
Applicants MUST be invited by DHS to apply.
Full Opportunity Web Address:
http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505249
Contact:
Agency Email Description:
If you have any problems linking to this funding announcement, please contact
Agency Email:
Date Posted:
2018-02-23
Application Due Date:
Archive Date:
2025-12-03
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