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The science of predicting a perfect storm

Renewable energy risk engineering

The growth of renewable energy assets over the last 20 years has been propelled by three main factors: the race to decarbonize; maturing technology; and improved return on investment as the cost differential narrowed between baseload and distributed generation. But prime development sites, with minimal natural hazard exposures, good solar and wind yields, and access to existing infrastructure have been snapped up. Today, developers are increasingly forced to consider more remote locations with higher exposure to natural perils. This is affecting the insurability of assets, adding cost and more restrictive terms and conditions, and increasing the complexity for developers to secure finance.

The increased frequency and severity of extreme weather events, and the devastation they cause, is leading developers, their financiers, and insurers to make overly conservative risk estimates to ensure sufficient protection. This often translates to higher insurance costs at a time when coverage premiums are already increasing substantially. Natural catastrophe insurance costs have increased three-fold over recent years (depending on the geography). For even a small 100 megawatt project, this could double the original premium budget. In some cases it is a deal breaker. If this cost is not factored into the project financials, it could eat away at forecast revenue and profit, making a project unprofitable.

At a time when the social, political, and corporate consciousness is compelling exponential interest and growth in cleaner energy sources, investors need to use data and risk engineering solutions to better mitigate their risks and protect their investments. The unpredictability of climatic conditions in relation to renewable energy investments in particular, has seen the insurance industry struggle to adapt fast enough to be able to use scientific probability data of natural perils to calibrate the pricing of risk transfer options.

Mother nature can’t be controlled, but improving storm data and sophisticated engineering models that take into consideration additional variables are providing renewable energy investors with more accurate and higher confidence risk estimates. The bottom line is a more realistic budget for all project stakeholders.

The Energy & Power Newsletter

July 2021 Energy & Power Newsletter considering the insurance trends over the last quarter.

Single-location risk models lack accuracy

Risk modeling for wind or solar farms has traditionally taken into consideration the location of the insurable entity, but the models rarely account for the large areas covered by a single operation. Further, traditional models often base their estimates exclusively on location without considering other attributes that could affect a structure’s resilience during a storm.

Take the example of a solar farm that spans over 100 acres in an area prone to convective storms. A traditional model will consider the likelihood that the solar farm is hit by a hailstorm, and estimate the potential damage, often in the tens of millions of dollars. But the storm’s intensity is unlikely to be uniform across such a large area. While some solar panels may be damaged by hail, others may be completely unscathed.

Similarly, a flood may not lead to the same water elevations across a large insured property, with some areas experiencing water damage and others left completely dry.

Single-location risk models tend to provide an all-or-nothing result that may not reflect a property’s real exposure.

Greater clarity and cost management, through data and improved risk modeling techniques

The use of meteorological data, combined with more granular risk modeling techniques, provides deeper analysis for locations with a large footprint. Engineering information for specific assets, such as construction material, occupancy, layout, and elevation, allows for more accurate risk loss estimates based on the main exposures across a larger geography.

The analysis of statistical weather data alongside engineering models, produces greater clarity around risk exposure. Presenting this information to insurers could enable them to reduce their pricing by 25-35% for rare weather events that cause the biggest damage and loss. The cumulative savings for investors over the life cycle of an asset can total three to four years of annual insurance premium.

Early intervention and consultation with risk engineers can also enable a project developer to make the best decision about the right equipment at the design phase of a project. For example, solar panel A might be 25% more expensive than solar panel B, but be 20% cheaper to insure due to its design and construction. Adding insurability premium to capex cost can help to inform better design and purchasing decisions.

Sophisticated risk engineering solutions can support:

  • Risk mitigation and control: Applying best practices on loss control measures can reduce the frequency and severity of outages or losses.
  • Cost of risk reduction: Accurate risk evaluations and loss modeling supports companies’ risk mitigation and retention strategies, helping risk managers make the best risk transfer choices.

As renewable technologies evolve, and investors take greater risks, advanced risk engineering strategies can greatly improve and help manage hazard exposures, ultimately protecting their balance sheet.

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