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DOE OSTI · 1669459

Predictive Analytics for Behind-the-Meter Resources

Abstract

This presentation discusses the development of the predictive analytics for behind-the-meter resources.

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BibTeXRIS

Yang, Rui, Zhang, Yingchen (ORCID:0000000255590971), Shaffery, Peter, Yu, Nanpeng, Kabir, Farzana. 2020-07-23. Predictive Analytics for Behind-the-Meter Resources. https://www.osti.gov/biblio/1669459

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Welcome to the Special Issue on Grid-Edge Computing With Behind-the-Meter Resources [Guest Editorial]

The integration of distributed energy resources (DERs), such as solar photovoltaic systems, as well as other synergistic assets, including electric vehicles, energy storage, and smart appliances, in electric power systems has been dramatically increasing in the past few years. These assets have the capability to provide much-needed flexibility to electric power systems for improved grid reliability, resilience, and economic efficiency; however, most of these resources are located behind the meter (BTM) on customer premises, and their flexibility is not fully used in current grid operations. Grid-edge computing plays an important role in unlocking the great benefits and potential that BTM resources could provide to electric power systems by enhancing visibility and controllability at the grid edge.

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