Artificial intelligence (AI) has emerged as an important tool to keep the power systems in the Asia-Pacific region resilient. As grids become more complicated when renewable energy is delivered from all directions, AI can forecast demand, prevent power losses, and reduce waste. However, without investment in sharing standards, reliable data and skills, AI promises stall. Regional cooperation is key to making smarter and safer energy systems safer.
When you flip the switch, you're hoping to turn on a simple ACT driven by what makes the light seem like a simple system. For decades, electricity was thought to be one way from the huge power plant to our homes as expected. There's no more. Throughout the APEC area, solar panels, offshore wind farms and rooftop grids power the system from all directions. This shift is essential to achieving climate targets, but it could make energy systems more vulnerable and ripple overages across the region.
As the APEC Region competes to achieve its renewable energy targets by 2030 (Figure 1), these complexities are set only for growth. In addition to legacy power plants, more renewable energy sources, such as solar farms, wind turbines and small hydroelectric systems, are all integrated into remote areas, offshore sites, and even rooftop grids. Now, energy flows in all directions and is redesigning how power systems are managed.
The problem is that most grids were not built for this. Designed for one-way flows, they now suffer from weather-sensitive renewable energy and growing demand. As a result, there are inefficiencies and vulnerabilities. The APEC economy accounts for almost 70% of the global electricity sector's emissions, but the losses from blackouts related to extreme weather and grid failures are US$1.6 billion. A smarter, more resilient system is urgently needed.
Why AI is important for energy resilience
Under ideal conditions, these new energy sources can generate more power than the electrical grid can handle. If the sun doesn't shine or the wind isn't blowing, production will drop.
This creates challenges for grid operators in which supply balance and demand are challenged, and in a world where power systems are increasingly interconnected between regions, one weakness can ripple into much larger issues.
This is exactly where AI appears. By analyzing huge amounts of data, AI can predict power demand and supply faster and more accurately than traditional methods. That means better decisions, reduced power outages, reduced energy waste, and less cleaning power.
One of the most direct benefits of AI is optimizing your existing energy infrastructure, allowing you to unlock additional transmit capacity up to 175 GW. In an economy where increasing electricity demand is often outweighing the expansion of transmission infrastructure, this is a game changer.
AI is also changing the way grid approaches resilience (Figure 2). Machine learning tools can detect abnormal patterns of grid performance, such as sudden voltage drops and equipment tension, before they turn into real problems. Some systems can also simulate stressful events such as typhoons, heat waves, and cyberattacks, and also give operators time to prepare. During extreme weather, AI can prioritize which areas to power, prevent wildfires, or ensure service to hospitals.
Beyond the grid, AI is improving energy efficiency between buildings, industry, and upstream mineral exploration. Smart building systems can adjust lighting, heating and cooling in real time, reducing emissions and lowering bills. In the industry, AI can offer 2-6% energy savings by optimizing production in real time. Upstream, important minerals such as lithium and nickel accelerate the discovery of important minerals essential to EV batteries, reducing fieldwork and improving safety.
What energy systems contain AI?
Despite that promise, deploying AI in energy systems is not without challenges.
The first is trust and transparency. Many AI systems, especially those using deep learning, act as “black boxes” and produce technically accurate decisions that are difficult to explain. With critical infrastructures like the power grid, this creates issues of accountability and public trust. Who is responsible for the wrong call that leads to interruption or worsening of service?
The second is a fragmented policy framework. There are no general standards for measuring AI's own energy use or environmental impact, and conflicting legal and technical standards create barriers to safe and effective operations, especially across borders. Without harmonized standards, interoperability is reduced, reliability is reduced, and local coordination is difficult.
The third is digital division. Not all economies, regions, or communities have equal access to digital infrastructure, quality data, or skilled technological workforces. Without coordinated support, AI deployments risk strengthening inequality in favour of resourced actors, leaving smaller utilities or vulnerable groups.
An important technical barrier is interoperability. Due to the lack of common data protocols and communication standards, smart appliances or distributed generators often cannot “discuss” grid operators or price signals in real time. This limits the effectiveness of AI in enabling smarter energy decisions at all levels.
Data access and quality are also fundamental. AI systems are as good as the data they are trained. Without timely, interoperable, and secure data from generation to end use, AI cannot be reliably executed.
Policy for powering smart grids
Even the smartest AI can't succeed if the right system is not in place. Therefore, policy frameworks and collaboration are just as important as the technology itself.
One of the priorities of the APEC economy is to develop sharing rules and standards. When it comes to how AI is managed, especially transparency, data use, and accountability, it makes it easier to deploy new technologies safely and at scale across borders.
What is a practical way to get there? Regulatory Sandbox and a joint pilot project. These are safe spaces where governments and businesses can test ideas that begin with low-risk applications, such as AI weather forecasting for renewable energy.
However, good policies and standards alone are not enough. AI relies on trustworthy, secure, shared data. In other words, it is essential that local coordination is enhanced, focusing on open data standards, cybersecurity and privacy protection. Without this foundation, even the most advanced models will not fulfill their promises.
Finally, investment in AI needs to expand to people. Engineers, policy makers, and systems operators need the right skills to responsibly design, use and oversee AI. APEC could lead the creation of regional-wide training programs that connect technical know-how with real energy challenges.
Ultimately, AI is just part of the puzzle. True progress comes when the economy works together to share knowledge, build trust, and make energy smarter for all.
Emmanuel A. San Andres is a senior analyst and Ashley Tesharonic Casia Agian is an intern with the APEC Policy Support Unit. For more information on this topic, use AI to download policy briefs to enhance your efficient and resilient energy systems.
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