Through recent acquisitions, a solar energy company purchased a 70 megawatt solar farm spanning 400 acres in Utah. However, increasing insurance costs, including for an excess hail policy, caused the forecast profitability of the new acquisition to steadily decrease.
The challenge: securing required coverage in a changing insurance market
As with most projects that require debt financing, the company’s new solar farm was subject to specific insurance requirements, which in this case had been agreed to by the prior owner-developer. While the insurance pricing seemed reasonable when the financing agreements were finalized, many lines of required coverage, especially those related to natural catastrophe perils, had since become significantly more expensive.
Amidst a significantly more challenging insurance market, it became more difficult to secure the required limits at a reasonable cost. Deductibles and retentions also increased substantially and some coverage thresholds, outlined in financing agreements, were no longer commercially reasonable or, in some cases, even available.
The solar energy company faced a dilemma and questioned whether to:
- Sell the project due to long-term profitability concerns.
- Purchase available coverage within the established budget, knowing that it would result in default of its financing requirements.
- Purchase coverage consistent with the financing requirements, regardless of the actual exposures, above the budgeted cost.
- Negotiate new coverage requirements with financing parties.
The company’s leadership contacted Marsh’s Energy and Power Practice to explore options.
The solutions: advanced risk modeling techniques identify substantial savings
Marsh’s renewable energy specialists analyzed the project’s existing coverage and exposures, thoroughly reviewed financing documents, and took stock of the insurance requirements. These were organized and catalogued, providing a comprehensive view of the minimum coverage that the client was obligated to purchase. Marsh's review found that the natural catastrophe sublimits in their current program seemed high for the site’s location and exposures; the required insurance was not defined by a set dollar amount, but by a formula, a common occurrence in financing agreements.
Next, Marsh’s risk engineering team ran new and more advanced modeling intended to provide a more accurate risk profile. Although the original modeling inputs were not available to Marsh specialists or to the client, the results appeared to have been calculated using a single coordinate as a proxy for the entire site. This approach can be misleading because the accuracy of the model varies depending on which coordinate is selected and how well it reflects the project’s exposures. The sheer size of the solar energy company’s operation meant that even if a destructive hailstorm or tornado destroyed part of the project site, it was unlikely to damage all, or even most, of the solar panels across the entire property.
The Marsh team used more granular CAT modeling techniques that evaluated aggregate locations and considered the project’s engineering information. The results showed that current limits were based on an overestimate of potential losses from rare weather events; the new probable maximum loss (PML) estimates were less than half the original.
Results: lender meetings lead to acceptance of new insurance coverage
The renewable energy brokerage team sought new coverage based on the updated modeling results and scheduled meetings with the lenders to explain the rationale. It was important to explain that while there was a dramatic decrease in the amount of coverage being sought, it was sufficient for the project’s exposures.
More importantly, from the lenders’ perspective, the underlying requirements had not changed. For example, the new insurance that the client planned to purchase would still cover 125% of the PML from a significant event caused by a natural catastrophe. However, the underlying values had decreased based on probable losses calculated by the new models.
The team also explained why certain coverage required by the lending agreement was no longer available. For example, the contracts established that property deductibles could not be greater than $100,000; available coverage had a minimum deductible of $250,000.
Following the meetings, the lenders agreed to the insurance purchasing plan outlined by Marsh.
Because the severe convective storm limits provided by the new policy were sufficient to cover the exposures of the project, the client was able stop purchasing an expensive excess policy. This, together with other savings made possible by the new program, changed the long-term profitability of the asset and led the client to reconsider selling the project.
Key takeaways
Engage with insurance and risk management advisors throughout the project lifecycle to help identify and receive guidance on potential red flags.
Lending agreements are often in place years before work on a project starts. What is considered “reasonable” or “commercially available” in the insurance market is likely to change many times throughout the course of a contract, even though insurance requirements remain static.
Ensure that risks are equitably allocated by connecting with a broker who can review agreements.
Start the insurance purchasing process early to allow enough time for coverage negotiations with insurers and discussions with your lending partners about potential discrepancies from contracted insurance requirements. Communicating early and often about shifts in the market can ease the renewal process and streamline future waiver requests and/or negotiations.
Use advanced modeling techniques to gain the most accurate assessment of your project’s exposures.
This data can be used to calculate the appropriate insurance coverage and can help you justify changes in insurance coverage to lenders.
For risk management and insurance guidance to support your renewable energy projects, contact Marsh's energy & power team.
*This case study is based on Marsh's experience with various clients. Individual results may vary.
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