Artificial intelligence (AI) is emerging as a critical tool to keep Asia-Pacific's power systems resilient. As grids grow more complex with renewables feeding in from every direction, AI can forecast demand, prevent blackouts, and cut waste. But without shared standards, reliable data, and investment in skills, the promise of AI risks stalling. Regional cooperation will be key to making energy systems both smarter and more secure.
When you flip a switch, you expect the lights to turn on-a simple act powered by what seems like a simple system. For decades, electricity was thought of as a one-way street, flowing predictably from giant power plants to our homes. Not anymore. Across the APEC region, solar panels, offshore wind farms, and rooftop grids are feeding power into systems from every direction. The shift is vital for meeting climate targets, but it also makes energy systems more fragile-one weak point can ripple into blackouts across entire regions.
With the APEC region racing to hit its renewable energy targets by 2030 (Figure 1), these complexities are only set to grow. Alongside legacy power plants, more renewable energy sources such as solar farms, wind turbines, and small hydroelectric systems, are all integrated into grids in remote areas, offshore sites and even on rooftops. Energy is now flowing in every direction, reshaping how power systems must be managed.
The problem is that most grids were never built for this. Designed for one-way flows, they now struggle with weather-sensitive renewables and rising demand. The result is inefficiency and fragility: APEC economies account for nearly 70 percent of global power-sector emissions, while blackouts linked to extreme weather or grid failures have cost up to USD 1.6 billion in losses. Smarter, more resilient systems are urgently needed.
Why AI Matters for Energy Resilience
During ideal conditions, these newer energy sources can generate more electricity than the electric grid can handle. If the sun isn't shining or the wind isn't blowing, production drops.
This makes balancing supply and demand a challenge for grid operators, and in a world where power systems are increasingly interconnected across regions, one weak point can ripple into a much bigger problem.
This is exactly where AI comes in. By analyzing vast amounts of data, AI can predict electricity demand and supply more quickly and accurately than traditional methods. That means better decisions, fewer blackouts, less energy waste, and less curtailment of clean power.
One of the most immediate benefits of AI is optimizing existing energy infrastructure, where it could unlock up to 175 GW of additional transmission capacity. In economies where rising electricity demand is often outpacing the expansion of transmission infrastructure, this is a game changer.
AI is also changing how we approach grid resilience (Figure 2). Machine learning tools can detect unusual patterns in grid performance like sudden voltage drops or equipment strain before they turn into real problems. Some systems can even simulate stress events such as typhoons, heatwaves, or cyberattacks, giving operators time to prepare. During extreme weather, AI can help prioritize which areas to power down, to prevent wildfires or to ensure service to hospitals.
Beyond the grid, AI is improving energy efficiency across buildings, industries, and upstream mineral exploration. Smart building systems can adjust lighting, heating, and cooling in real time, cutting emissions and lowering bills. In industry, AI can deliver 2 to 6 percent energy savings by optimizing production in real time. Upstream, it is accelerating the discovery of critical minerals like lithium and nickel essential for EV batteries while reducing fieldwork and improving safety.
What's Holding AI Back in Energy Systems?
Despite its promise, deploying AI in energy systems isn't without challenges.
First is trust and transparency. Many AI systems, especially those using deep learning, operate as "black boxes", producing technically accurate decisions that are difficult to explain. In critical infrastructure like power grids, this raises questions of accountability and public trust. Who is responsible if AI makes a wrong call that leads to service interruption or worse?
Second is fragmented policy frameworks. There are no common standards for measuring AI's own energy use or environmental impact, and conflicting legal and technical standards create barriers to safe and effective operation particularly across borders. Without harmonized standards, interoperability suffers, reliability declines, and regional coordination becomes harder.
Third is the digital divides. Not all economies, regions, or communities have equal access to digital infrastructure, quality data, or skilled technical workforces. Without coordinated support, AI deployment risks reinforcing inequalities favoring well-resourced actors while leaving behind smaller utilities or vulnerable populations.
A key technical barrier is interoperability. Smart appliances or distributed generators often cannot "talk" to grid operators or price signals in real time, due to the lack of common data protocols and communication standards. That limits the effectiveness of AI in enabling smarter energy decisions at all levels.
Data access and quality are also fundamental. AI systems are only as good as the data they're trained on. Without timely, interoperable, and secure data from generation to end-use, AI cannot perform reliably.
Policies to Power Smart Grids
Even the smartest of AI won't succeed without the right systems in place. That's why policy frameworks and cooperation are as critical as the technology itself.
One priority for APEC economies is developing shared rules and standards. When economies align on how AI is managed, especially when it comes to transparency, data use, and accountability, it becomes easier to deploy new technologies safely and at scale, even across borders.
A practical way to get there? Regulatory sandboxes and joint pilot projects. These are safe spaces where governments and companies can test ideas starting with low-risk applications such as AI weather forecasting for renewables, before rolling them out more widely.
But good policy and standards alone won't be enough. AI depends on reliable, secure, and shared data to function. That means stronger regional coordination around open data standards, cybersecurity, and privacy protections is essential. Without this foundation, even the most advanced models won't deliver on their promise.
Finally, investment in AI must extend to people. Engineers, policymakers, and system operators need the right skills to design, use, and oversee AI responsibly. APEC could lead in creating region-wide training programs that connect technical know-how with real-world energy challenges.
In the end, AI is just one part of the puzzle. The real progress comes when economies work together in sharing knowledge, building trust, and making energy smarter for everyone.
Emmanuel A. San Andres is a senior analyst, Ashley Teshalonica Siagian is an intern at the APEC Policy Support Unit. For more on this topic, download the policy brief Using AI to Power Up Efficient and Resilient Energy Systems.
