AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience
Rapid deployment of residential photovoltaic (PV) systems helps decarbonize our electricity supplies, but under certain circumstances, high-penetration PV may pose challenges to the electrical distribution grid. In a project funded by the U.S. Department of Energy's Solar Energy Technologies Office and Building Technologies Office, the National Renewable Energy Laboratory and its partners studied how flexible building loads and battery storage, when coordinated at home-level and community-level scales, can be used to address those challenges and enhance grid resilience. In this webinar, we will discuss the methodology, simulation and field pilot results, insights from partners, and lessons learned from the project.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
- DOE Contract Number:
- AC36-08GO28308; AC36-08GO28308
- OSTI ID:
- 1866761
- Report Number(s):
- NREL/PR-5500-82576; MainId:83349; UUID:fbf770aa-27d7-4ce2-ada2-d03d9311b93f; MainAdminID:64448
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
PV
artificial intelligence
building-grid integration
control
demand response
distributed energy resources
field deployment
grid reliability
grid resilience
grid-interactive efficient building
home energy management system
machine learning
smart community
smart home