RENEWABLE RESILIENCE: TACKLING CLIMATE CHANGE
As global demand for renewable energy rises, a concerning trend emerges—the decreasing asset valuations within the industry
In the last few years, more and more renewable energy companies have faced an unexpected decrease in their assets’ performance due to climate change. Thus, a comprehensive and meticulous analysis of both energy production and climate exposure is a must.
By embracing predictive climate intelligence, the renewable energy industry will gain crucial insights to not only respond but also adapt effectively to the challenges posed by climate change.
By embracing predictive climate intelligence, the renewable energy industry will gain crucial insights to not only respond but also adapt effectively to the challenges posed by climate change.
Reliable and Adjusted Energy Resource Assessment
Our predictive algorithms provide realistic and adjusted asset valuation for wind and solar farms, and hydroelectric power plants.
Currently, the industry value their assets by making estimations based on historical data (e.g. Annual Energy Production). The problem is that standard procedures do not respond to climate change. For example, according to Martinez et al., in wind power, minor alterations due to climate change can immensely affect the Levelized Cost Of Energy (LCOE) because wind power density is to the cube of wind speed, hence extremely sensitive to change.
At Geoskop, we developed our proprietary algorithms to respect the climate change trends as indicated by scientific models while providing operators and investors with highly accurate resource assessments.
Currently, the industry value their assets by making estimations based on historical data (e.g. Annual Energy Production). The problem is that standard procedures do not respond to climate change. For example, according to Martinez et al., in wind power, minor alterations due to climate change can immensely affect the Levelized Cost Of Energy (LCOE) because wind power density is to the cube of wind speed, hence extremely sensitive to change.
At Geoskop, we developed our proprietary algorithms to respect the climate change trends as indicated by scientific models while providing operators and investors with highly accurate resource assessments.
Exposure to Acute Climate Events
We generate probabilistic forecasts for the assets` exposure to acute or extreme climate events, such as 3-second wind gusts, storms, heatwaves or droughts, among others. Climate change’s economic impact over the industry is increasing over time, as are the threats to business continuity.
Take, for instance, the destruction of the solar farms in Zaragoza in 2019 by wind gusts up to 75 km/h — a consequence of predictions solely based on historical data. Nevertheless, we produce acute climate physical risk predictions based on scientific knowledge about climate change to support businesses adapting to climate change.
Take, for instance, the destruction of the solar farms in Zaragoza in 2019 by wind gusts up to 75 km/h — a consequence of predictions solely based on historical data. Nevertheless, we produce acute climate physical risk predictions based on scientific knowledge about climate change to support businesses adapting to climate change.
Scientific and Commercial Validation
Our proprietary algorithms have demonstrated outstanding performance and extensive testing with numerous renewable assets.
The predictions have undergone rigorous validation against climate change scenarios endorsed by the Intergovernmental Panel on Climate Change (IPCC—CMIP6), proving our reliability in multiple projects worldwide. As a result, we can assure you of the highest predictive quality, aligning consistently with the scientific evidence.
The predictions have undergone rigorous validation against climate change scenarios endorsed by the Intergovernmental Panel on Climate Change (IPCC—CMIP6), proving our reliability in multiple projects worldwide. As a result, we can assure you of the highest predictive quality, aligning consistently with the scientific evidence.
