Amidst an ever-growing demand for electricity, artificial intelligence (AI) is stepping in to mitigate power disruptions.

Aseef Raihan vividly recalls a chilling night in February 2021 in San Antonio, Texas, during winter storm Uri. As temperatures plunged to -19°C, Texas faced an unprecedented surge in electricity demand to combat the cold. 

However, the state's electricity grid faltered, with frozen wind turbines, snow-covered solar panels, and precautionary shutdowns of nuclear reactors leading to widespread power outages affecting over 4.5 million homes and businesses. Raihan's experience of enduring cold nights without power underscored the vulnerability of our electricity systems.

The incident in Texas highlights a global challenge as countries witness escalating electricity demands due to factors like the rise in electric vehicle usage and increased adoption of home appliances like air conditioners. Simultaneously, many nations are transitioning to renewable energy sources, which pose challenges due to their variable nature. For instance, electricity production from wind and solar sources fluctuates based on weather conditions.

To bolster energy resilience, countries like the UK are considering the construction of additional gas-powered plants. Moreover, integrating large-scale battery storage systems into the grid has emerged as a solution. In Texas, significant strides have been made in this regard, with over five gigawatts of battery storage capacity added within three years following the storm.

However, the effectiveness of these batteries hinges on their ability to predict optimal charging and discharging times. This is where AI steps in. Tech companies like WattTime and Electricity Maps are leveraging AI algorithms to forecast electricity supply and demand patterns, enabling batteries to charge during periods of surplus energy and discharge when demand peaks. 

Additionally, AI is enhancing the monitoring of electricity infrastructure, with companies like Buzz Solutions employing AI-powered solutions to detect damage and potential hazards such as overgrown vegetation and wildlife intrusion, thus mitigating the risk of power outages and associated hazards like wildfires.