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Microgrid Fast Charging Stations With Idaho National Laboratory

Microgrid Fast Charging Stations With Idaho National Laboratory

Xendee Corporation has partnered with Idaho National Laboratory to develop an optimized platform for designing Microgrid Fast Charging Stations (MFCS) quickly and effectively. This project is funded by the DOE Office of Electricity, Microgrid Program.

The platform will be developed alongside a detailed analysis of two test sites which will act as seed locations for fast-charging facilities across the United States.

Additionally, with the use of distributed energy technologies, these Microgrid Fast Charging Stations can enable a deeper market penetration of private electric and commercial vehicles by allowing for the optimal placement of stations even in remote areas unserved by a traditional utility provider.

In areas where a provider does exist, an optimized MFCS can help reduce distribution grid disturbances and costs for the entire energy market by supplementing the immense energy required for ultra-fast charging vehicles during peak hours, or by eliminating the need for upgrades to transmission lines.

State of California Energy Commission

State of California Energy Commission

The State of California Energy Commission is seeking a streamlined approach to Microgrid screening & siting to maximize locational DER and microgrid...

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Xendee, Converge Strategies, RMI, and Barrett Energy Launch Free Airport Microgrid Toolkit to Strengthen Operational Resilience and Reduce Carbon

Xendee, Converge Strategies, RMI, and Barrett Energy Launch Free Airport Microgrid Toolkit to Strengthen Operational Resilience and Reduce Carbon

The Project Team has launched a new Airport Microgrid Toolkit designed to expedite the transition of airports to secure on-site Microgrids and DERs.

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Microgrid Modeling with Small Modular Reactors

Microgrid Modeling with Small Modular Reactors

This report explores the decarbonization of the University of Illinois Urbana-Champaign (UIUC) campus microgrid through the optimal deployment of...

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