@misc{6d2a8e396b4349bf858e70fe9016f793,
title = "AI-Driven Smart Community Control for Accelerating PV Adoption and Enhancing Grid Resilience",
abstract = "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.",
keywords = "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, PV, smart community, smart home",
author = "Xin Jin and Fei Ding and Chris Bilby and Dan Forman and Mark Kovscek and Rajendra Adhikari",
year = "2022",
language = "American English",
series = "NREL Webinar presented 30 March 2022",
type = "Other",
}
