Challenges
Increased Risk Exposure: The Mid-West and South-West regions, where solar farm development is booming, are prone to severe convective storms—including tornadoes—that threaten infrastructure and disrupt operations.
Rising Financial Vulnerability: As solar farms grow in size and cost, the financial stakes are higher, making them more susceptible to significant losses from weather-related events.
Insurance and Protection Gap: The expanding scale of solar investments is outpacing the availability of adequate insurance coverage, leading to a widening protection gap and increased financial uncertainty.
Product details
Risk covered
Tornado Risk starting from EF0
Client profile
Utility-scale Solar Farms >10MW
Insured location
Texas, United States
Index
Based on % of damaged surface
PAYOUT STRUCTURE
Based on % of damaged surface
Total Insured value: 200M USD
Limit: 25M USD
- Trigger 1: NOAA (even EF0)
- Trigger 2: Damage detection by satellite
Payout depends on percentage of surface affected and the limit:
- 15% of the site being affected
- The insured receives 3,750,000 USD
case study example
A solar farm owner reached out to Descartes after being affected by a weak EF-1 Tornado.
Problem
They were unable to obtain traditional insurance cover any longer. Whilst our typical parametric tornado product - based on EF scale only - is well-suited for large structural buildings in urban areas, it shows limits for solar farms, which are low-rise assets in remote areas. As a result, the modelled payouts of the EF scale parametric product could not match the historical loss of the client and would have left them underinsured. That’s why Descartes developed a new custom product for solar farms.
Solution
The alternative is a satellite-based tornado cover powered by very high-resolution imagery, utilizing the tornado track as first trigger and an area index based on the percentage of damaged surface to determine the payout. Were this client insured with Descartes, it would have resulted in adequate parametric payouts at an optimized premium. This approach brings a very high correlation between damage on the ground and the modelled payouts, effectively reducing the basis risk.
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