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Division of Electrical, Communications and Cyber Systems
Energy, Power, Control, and Networks (EPCN)
|Radhakishan Bahetifirstname.lastname@example.org||(703) 292-8339|
|Alireza Khalighemail@example.com||(703) 292-8339|
|Anthony Kuhfirstname.lastname@example.org||(703) 292-8339|
|Anil Pahwaemail@example.com||(703) 292-2285|
Apply to PD 18-7607 as follows:
For full proposals submitted via FastLane: standard NSF Proposal & Award Policies & Procedures Guide proposal preparation guidelines apply.
For full proposals submitted via Grants.gov: the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov Guidelines applies. (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: http://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide)
Important Information for Proposers
A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 19-1), is effective for proposals submitted, or due, on or after February 25, 2019. Please be advised that, depending on the specified due date, the guidelines contained in NSF 19-1 may apply to proposals submitted in response to this funding opportunity.
Full Proposal Accepted Anytime
For additional information regarding the removal of deadlines for this program, please refer to the Dear Colleague Letter [https://www.nsf.gov/pubs/2018/nsf18082/nsf18082.jsp] and Frequently Asked Questions [https://www.nsf.gov/pubs/2018/nsf18083/nsf18083.jsp].
Proposals submitted to other program announcements and solicitations, including the Faculty Early Career Development Program (CAREER), must meet their respective deadlines; please refer to the deadline dates specified in the appropriate announcement or solicitation. Proposals for EArly-concept Grants for Exploratory Research (EAGER) or Rapid Response Research (RAPID) can be submitted at any time but Principal Investigators must contact the cognizant program director prior to submission. Proposals for supplements or workshops can be submitted at any time, and PIs are encouraged to contact the cognizant PD prior to submission.
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):
- 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
- 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