Climate Adaptation Unlocking Value Chains with the Power of Technology 2025

Four tech-driven use cases to help energy systems adapt to climate-related disruptions FIGURE 9 Source: World Economic Forum and BCG analysis. Climate impact modelling AI and digital twins simulate systems for real-time monitoring and help operators preemptively manage extr eme weather impacts on energy infrastructur e. Asset monitoring: AI & digital twins monitor and pr edict maintenance needs for key assets (e.g., turbines, dams, offshor e wind farms), r educing costs and extending asset lifespans. Grid modelling: AI & digital twins model vulnerabilities acr oss the grid, helping operators fortify weak points like substations and transmission lines befor e failur es occur , as well as fluctuations in energy supply and demand based on weather and climate event scenarios.1 Energy early warning systems Earth Observation and IoT sensors detect envir onmental risks such as wildfir es or storms, allowing operators to take preventive action befor e grid infrastructur e is compr omised. Extreme weathe r event monitoring: Real-time data from Earth Observatio n dete ct storms and other extreme weathe r events. Wildfi re risk monitoring: Sensors track environme ntal condi tions to detect wildfire risks, avoiding outages and damage. Flood detection and prevention: IoT sensors mon itor flood- prone areas, providing early alerts that allow operators to reroute power and take preventive measur es to avoid damage to the grid.Energy emergency response Microgrids and energy storage systems ensur e critical facilities maintain power during disasters, allowing them to operate independently from the main grid. Power continuity in disasters: Microgrids maintain power to essential services like hospitals during extreme weather. Smart battery and storage management: Smart storage systems efficiently manage energy supply , providing backup power during outages and supporting the grid during high- demand periods.3 4Real-time energy management AI and r eal-time data optimize energy use and storage, ensuring grid stability by balancing supply , demand, and renewable integration. Peak demand and grid stabilization: AI reduces energy consumption during peak times and leverages energy storage to supply power , preventing overloads and maintaining stability during high demand. Failur e avoidance and waste r eduction: Predictive AI identifies potential failur es early , allowing for pr oactive maintenance and r educing energy waste by balancing supply with demand, impr oving grid efficiency.2 Upstr eam input pr oviders Energy pr oducers Distributo rs and storage systems ConsumersValue chain stakeholde rs involve d Climate Adaptation: Unlocking Value Chains with the Power of Technology 20