Managing Risk and Catastrophe Modeling for the Growing Renewables Sector
Derek BlumJanuary 25, 2022
The demand for renewable sources of energy grows every year, driven by a range of factors including climate change, energy security concerns, and rising fossil fuel costs. In its Renewables 2021 report, the International Energy Agency stated that global renewable capacity was set to expand by over 60 percent in the next five years, adding over 1800 gigawatts of capacity by 2026.
During the past two years, RMS® has seen an increase in demand for more advanced insights and quantification of catastrophe risk for renewable energy facilities from (re)insurers and brokers, as well as the owners and investors of such facilities around the world. The importance of these insights will only increase as we progress through the energy transition.
So how does RMS build bespoke engineering views for renewables? Since 2003, we have provided specific engineering views of risk for industrial facilities in the RMS Industrial Facilities Model (IFM) which works alongside the various RMS peril models. The IFM initially focused on U.S. and Caribbean hurricane, and U.S. earthquake, but over the years RMS has continuously improved the model and expanded its region and peril coverage.
The IFM uses new science and loss experience, including on-the-ground reconnaissance from recent major events. It includes 51 distinct views for perils including wind, storm surge, inland flood, and earthquakes, and spans 165 peril model regions across the world. Renewables represent a unique and rapidly growing industrial risk now included in the RMS IFM.
Enhanced Risk Differentiation for Renewables
The IFM uses a component-based approach, where each element of a class of industrial facility is considered individually. The different damage mechanisms, by peril, are evaluated to define damage functions for each component and coverage: structure, machinery and equipment, and stock.
In addition, exposure weights are used to aggregate component-level damage functions to produce coverage-level damage functions for the facility. This generates a single damage function for the entire facility.
Let’s explore what this means for a renewables facility and examine a wind turbine as an example. There are four primary components that make up a wind turbine; foundation, supporting tower, nacelle (generator and housing), and rotor blades.
We can begin by considering the effect of an earthquake on the wind turbine. Analytical and experimental studies investigating the nonlinear behavior of supporting towers of wind turbines conclude that local buckling can result in tower collapse. Whereas, for a hurricane’s powerful wind speeds, damage to the nacelle and rotor blades is more likely. Damage mechanisms by peril help us to construct the resultant vulnerability curves used within the RMS peril models.
Now that we understand the damage potential to a single wind turbine, we can extrapolate to consider the overall facility, alongside additional components such as the on-site transmission and distribution systems. Additionally, we need to consider the geographic spread of the peril and changing site conditions.
To do this, RMS considers the typical size of the industrial facility versus the underlying hazard, as well as the percentage of the facility that will be impacted by the peril given the mean average and the uncertainty within the hazard footprint. That said, when a site is particularly large (approximately 1.2 miles by 1.8 miles/2 kilometers by 3 kilometers) and has large values, and when the peril in question has a high hazard gradient, we recommend separating the site into further detail to best utilize the model and gain improved accuracy.
This is achieved by dividing the industrial complex into different linked locations, so losses can be properly represented within the financial model. This will appropriately consider the correlation and allow for one overarching policy structure. Note that we recommend additional components – such as battery storage units or external transmission and distribution lines, are coded and modeled separately to ensure the intricacies of these risks are adequately considered.
The coding of these risks can have a significant impact on the analyzed result, making proper coding an important and worthwhile effort. As an example, Figure 1 highlights the impact of considering the flood defenses in the exposure coding of a site. Further information around the best practices of modeling industrial sites can also be found in the recent RMS blog Risk Modeling and the Rise of Renewables.
Better Risk Assessment for Renewables
There is no one hard-and-fast rule on how to model these sites. Instead, the intricacies of each site should be considered to ensure the most accurate result is returned. The principles discussed here allow for better assessment of catastrophe risk to not only a portfolio of renewable facilities and can also help organizations that are interested in assessing the risk of a single site or asset.
Overall, RMS has, and will continue, to put considerable effort into understanding how catastrophes impact renewable energy and other industrial facilities. We could not do this without our industry partners; if you are interested in better modeling for these risks, have feedback, or have any questions on how best to model a facility, please do reach out to us at email@example.com. Find more information about the RMS Industrial Facilities Model on rms.com.
Derek leads product management for emerging risks and specialty insurance models at RMS. This encompasses, among others, special insurance lines such as energy and other industrial facilities, builder’s risk, and inland marine. It also includes emerging risks such as cyber, terrorism, and infectious disease. In his role, Derek’s team sets the strategy and roadmap for these solutions, working closely with the industry to improve the quantification and management of catastrophic risk.
With over 18 years at RMS, Derek has worked in the catastrophe risk modeling market in a variety of capacities. Derek holds a bachelor’s degree in quantitative economics from Stanford University.