CSN OPINION:
As APEC nations accelerate their pursuit of 2030 renewable energy goals, artificial intelligence emerges as a crucial tool to modernise electricity grids, but challenges like data transparency and cybersecurity threaten to disrupt progress. Regional cooperation and investment in human capital are vital to harness AI’s full potential for a resilient, inclusive energy future.
As the Asia-Pacific Economic Cooperation (APEC) region rushes towards its ambitious 2030 renewable energy targets, the structure of electricity grids is undergoing a profound transformation. The traditional one-way flow of electricity—from large, centralised power plants to consumers—is evolving into a complex, bi-directional system. Increasing inputs from solar panels, offshore wind farms, rooftop installations, and small-scale hydroelectric systems are feeding power into the grid from myriad points, sometimes in remote or offshore locations. While this decentralised approach is crucial for meeting climate objectives, it introduces new vulnerabilities, with fragile energy networks increasingly susceptible to disruptions that could cascade into widespread blackouts.
Legacy grids, originally designed for straightforward, one-directional power flows, find themselves ill-equipped to cope with the variability and intermittency that renewables bring. Weather-sensitive energy generation, such as solar and wind, fluctuates, creating significant challenges for balancing supply and demand. The consequences are stark—APEC economies contribute roughly 70% of global power sector emissions, and recent blackouts linked to extreme weather or grid failures have inflicted losses estimated at USD 1.6 billion. Against this backdrop, the deployment of artificial intelligence (AI) emerges as a pivotal strategy to enhance grid resilience and efficiency.
AI’s strength lies in its ability to process and analyse large data sets rapidly and with high accuracy. This capability enables precise predictions of electricity supply and demand, optimising grid operations and reducing blackouts and energy waste. For example, AI can unlock up to 175 gigawatts of additional transmission capacity by making existing infrastructure operate more efficiently—a crucial advantage in regions where power demand outpaces infrastructure expansion. Moreover, AI enhances grid resilience by detecting anomalies such as sudden voltage drops or equipment stress before they trigger outages. Advanced machine learning models can simulate stress events like typhoons, heatwaves, or cyberattacks, providing operators valuable time to pre-emptively manage the grid and prioritise power supply to critical areas, such as hospitals, during crises.
Beyond grid management, AI is revolutionising energy use in buildings and industry by enabling real-time adjustments to lighting, heating, and cooling, delivering energy savings of 2 to 6%. In critical upstream activities, AI expedites the discovery of essential minerals like lithium and nickel, which are vital for electric vehicle batteries, while reducing manual labour and improving safety.
Nonetheless, AI’s integration into energy systems faces a suite of challenges. One major concern is the “black box” nature of many AI systems, which, while technically accurate, often lack transparency in decision-making. This opacity raises accountability questions—who bears responsibility if an AI error causes a service interruption or worse? Fragmented policy frameworks further complicate matters. The absence of common standards for AI’s energy consumption, environmental impact, and operational safety limits interoperability, particularly across borders, hindering regional coordination.
Digital divides compound difficulties; unequal access to digital infrastructure, quality data, and skilled personnel risks exacerbating disparities, favouring larger, well-resourced utilities while leaving smaller operators and vulnerable communities behind. Technical interoperability also suffers due to incompatible data protocols, impeding real-time communication between distributed energy resources and grid operators—a critical limitation for AI-enabled smart energy management.
Data quality and availability are fundamental to AI’s success, yet problems such as synchronization errors, data loss, and latency—especially within synchrophasor applications used for real-time grid monitoring—pose significant risks to AI system reliability. Cybersecurity vulnerabilities add another layer of concern, necessitating robust protections to safeguard critical infrastructure.
Addressing these hurdles calls for comprehensive policy action. Developing shared regulatory frameworks and standards across APEC economies is essential to facilitate transparency, data sharing, accountability, and cross-border AI deployment. Regulatory sandboxes and joint pilot projects offer practical pathways for safely testing AI innovations in low-risk contexts, such as AI-assisted weather forecasting for renewable energy. Stronger regional coordination on open data standards, cybersecurity measures, and privacy protections will underpin reliable AI functioning.
Investment in human capital is equally critical. Engineers, policymakers, and system operators require targeted training programs to develop the skills necessary to design, deploy, and oversee AI technologies responsibly within complex energy systems. The Republic of Korea exemplifies this approach by promoting AI diffusion, enhancing energy data access, and supporting AI-focused energy companies, fostering regional cooperation toward smarter, secure grids.
AI represents a transformative tool, but it is not a silver bullet. Its full potential to keep the lights on and accelerate the clean energy transition hinges on collaborative efforts—sharing knowledge, building trust, harmonising policy, and ensuring equitable access to technology. By doing so, APEC economies can harness AI not just as a technical upgrade but as a foundation for a resilient, efficient, and inclusive energy future.
