Opportunity number
National Science Foundation (NSF)
Energy Power Control and Networks (EPCN)
Due date
Proposals Accepted at any time
Advanced Manufacturing Big Data Economic Development Energy Information Technologies Innovation
Project funding
Depends on project
Program funding
Funding size
Project Dependent

Energy, Power, Control, and Networks (EPCN) Program

RFP Summary provided by the agency

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 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.

What is the mission and focus of the program: research, social, economic or others?

EPCN’s goal is to encourage research on emerging technologies and applications including energy, transportation, robotics, and biomedical devices & systems.

How do you submit to this opportunity?

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.

Proposers may opt to submit proposals in response to this Program Solicitation via Grants.gov or via Research.gov:


Who are the target applicants: cities, universities, companies, small business, nonprofits, or others?


Example project(s) summaries from past RFPs:

https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408141&HistoricalAwards=false Collaborative Research: An Intelligent Restoration System for a Self-healing Smart Grid (IRS-SG) Latest Amendment Date: July 23, 2018 Start Date: August 15, 2014, End Date: August 31, 2019 (Estimated) Awarded Amount to Date: $176,000, Sponsor: Clemson University How can we restore power more effectively after a major outage, such as a hurricane, a cascade blackout or a more local outage? Can we use modern computer-based methods to get better performance than what we have today, in a system which is mainly informal and based on guesswork under conditions of great stress and limited information? This new collaborative project will bring together an expert on power restoration with a pioneer of new intelligent computation methods for the power grid, in hopes of finding a more powerful and modern way to restore power more effectively.

The goals are;

  • to develop an online adaptive restoration tool using advanced scalable computational intelligence techniques.
  • investigate a novel scheme to use renewable resources in system restoration.
  • explore a blackstart unit investment strategy to improve the self-healing capability; and
  • real-time implementation and demonstration of the new system on benchmark and utility power systems.

The grant will include travel to New Zealand, to discuss use of the new system to help provide better response to events like earthquakes. It will also include outreach to Native Americans in the Dakotas.

The problem of efficient restoration is very difficult from a technical point of view. A small number of researchers, like the lead PI, have developed a few tools of practical use in this problem, but it is still largely an unsolved problem. The main justification for NSF involvement in this area, and for significant hope of success, is the use of intelligent systems methods far beyond what anyone has applied in the past to this problem, methods pioneered in the intelligent systems part of the EPCN program at NSF

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