Energy, Power, Control, and Networks
The Energy, Power, Control, and Networks grant supports research in networked systems, renewable energy integration, electric power, machine learning, adaptive systems, and emerging technologies like robotics and transportation with a focus on energy efficiency and grid management.
Funder: National Science Foundation
Due Dates: Proposals accepted anytime
Funding Amounts: No specified ceiling; typical NSF awards range from 1–3 years and vary by project scope
Summary: Supports innovative research in modeling, optimization, learning, adaptation, and control of networked systems, energy, power, and emerging technologies including machine learning, robotics, and renewable energy integration.
Key Information: No cost sharing required; open to a broad range of applicants; proposals must follow the current NSF Proposal & Award Policies & Procedures Guide.
Description
The Energy, Power, Control, and Networks (EPCN) program funds foundational and applied research in the areas of modeling, optimization, learning, adaptation, and control of networked multi-agent systems. The program emphasizes higher-level decision making, dynamic resource allocation, and risk management in the presence of uncertainty, failures, and stochastic disturbances. EPCN also supports advances in machine learning algorithms, adaptive dynamic programming, neuromorphic engineering, and brain-like networked architectures for real-time learning.
Research areas of interest include, but are not limited to:
- Control systems (including distributed control and optimization, networked multi-agent systems, stochastic/hybrid/nonlinear systems, cyber-physical systems)
- Energy and power systems (generation, transmission, storage, integration of renewables, grid monitoring and protection, microgrids, power grid cybersecurity, market design, consumer behavior, regulatory policy)
- Power electronics and electric machines (advanced power electronics, electric/hybrid vehicles, energy harvesting, grid-tied converters)
- Learning and adaptive systems (neural networks, data analytics, intelligent systems, machine learning applications)
- Applications in energy, transportation, robotics, and biomedical devices & systems
The program encourages proposals that address emerging technologies and cross-disciplinary challenges, particularly those that advance energy efficiency, grid resilience, and the integration of renewable energy sources.
