Quantifying and Monetizing Renewable Energy Resiliency

As part of the seed LDRD in 2017 we generated catastrophe models to look at the value of resiliency from an insurance perspective. These data sets reflect the inputs and outputs of that analysis.
- Originated 01/01/1970 by National Renewable Energy Laboratory.

3 Resources

	Name	Size	Type	Resource Description
	NYC_exp_S42xlsx.xlsx	2842937	data	Input data for site value (building, content, business interruption) that was used to populate the catastrophe model and set bounds on the insurance exposure
	BIaal.combined.guBase.xlsx	396079	data	Outputs from the catastrophe model using the baseline assumptions. The dataset reflects the annual average loss (AAL) for business loss in the study area.
	BIaal.combined.gu.withCC.xlsx	237362	data	Outputs from the catastrophe model using the sea level rise assumptions. The dataset reflects the annual average loss (AAL) for business loss in the study area.

Keywords

NREL energy data resiliency reopt New York LDRD 2017 USA catastrophe models solar energy wind energy insurance premiums hybrid systems

Submitted

National Renewable Energy Laboratory

by , 303.384.7487, Center 7A40

Authors

• Lars Lisell, National Renewable Energy Laboratory
• Kate Anderson, National Renewable Energy Laboratory
• Nick Laws, National Renewable Energy Laboratory
• Spencer Marr, City University of New York
• Dag Lohman, Katrisk LLC
• Xiangkun Li, National Renewable Energy Laboratory
• Tony Jimenez, National Renewable Energy Laboratory
• Dylan Cutler, National Renewable Energy Laboratory
• Tria Case, City University of New York

License

NREL Disclaimer

Cite this dataset:

Lisell, Lars et al. (2018): Quantifying and Monetizing Renewable Energy Resiliency . National Renewable Energy Laboratory. https://dx.doi.org/10.7799/1432836

About this dataset

DOE Project

Research Areas

Additional Subjects

Related datasets

Datasets associated with the same DOE project

	Submission Name	Data Resources	Submitted	Status