Tackling variable renewables: the role of artificial intelligence

ASEAN aims to achieve a 30% share of renewable energy in total primary energy supply by 2030. This requires a significant build-out of solar and wind given the limited remaining potential and high cost of the other renewable energies such as hydro, geothermal and biomass.

The variable renewable energy (VRE) share has grown from 2.3% in 2020 to roughly 5% in 2025 across ASEAN. Viet Nam leads the region with over 12% of electricity generated from solar and wind, followed by Cambodia (7.6%), Thailand (6.3%), the Philippines (4.8%), and Singapore (2.7%). Brunei and Timor Leste have yet to integrate VRE as of 2025.

The primary technical challenge of VRE integration stems from the stochastic nature of wind and solar, along with seasonal fluctuations, and variations over time and space. It requires power systems designed for dispatchable sources like coal and gas to balance not only variable demand but also intermittent supply.

However, there are various technologies to resolve the challenge. The future is promising as ASEAN Centre for Energy (ACE) projects that in all scenarios VRE share in ASEAN will reach around 42-47% by 2045. The International Energy Agency (IEA) even projects in the Announced Pledge Scenario (APS) that over 60% of electricity demand in ASEAN will be met by wind and solar by 2045.

Artificial intelligence (AI) can be a prominent catalyst for this transition, improving efficiency, enhancing cost-effectiveness, and reducing emissions across energy generation, transmission, distribution and consumption.

Large-scale VRE integration increases system complexity due to generation fluctuation, uncertainty, and locational specificity. Additionally, traditional data analysis and decision-making approaches lack real-time insights and predictive capabilities, limiting operational efficiency, flexibility, and system responsiveness.

Unlike traditional rule-based models that rely on simplified physics and assumptions, AI identifies patterns and relationships within data and uses these insights to support decision making in power systems. Through continuous feedback and learning, AI can optimise operational decisions and enhance performance as more data and computational resources become available.

AI applications span the entire energy value chain. In this report, we focus on five commercially proven applications that can have significant potential to accelerate the energy transition in ASEAN power systems. These applications have been deployed globally and validated in real world operations. In particular, they enhance variable renewable energy (VRE) integration by improving forecasting accuracy, enabling predictive maintenance, optimising dispatch decisions, and supporting advanced techniques such as dynamic line rating.

Enhance VRE forecast accuracy to lower reserve capacity and ramping needs

High uncertainty in VRE forecasts leads to inefficient day-ahead scheduling, excessive reserve procurement, and heightened ramping stress on conventional generators. When VRE generation is underestimated, system operators commit additional thermal units and reserves unnecessarily. In contrast, when it is overestimated, rapid intraday ramping or emergency dispatch becomes necessary, driving up fuel consumption, mechanical wear, and operating costs.

Large forecast errors can also trigger network congestion, voltage instability, and renewable curtailment, undermining both system reliability and economic efficiency.

AI based forecasting significantly improves short term prediction accuracy using large volumes of meteorological data, satellite imagery, sensors, and historical generation data. AI models typically have forecast accuracy improvements of about 25% compared to traditional numerical weather prediction methods, enabling system operators to reduce reserve margins, lower reliance on inefficient standby thermal generation, and minimise unnecessary starts, stops, and ramping.

These gains are not merely theoretical; they have been demonstrated in commercial operations. For example, Google DeepMind partnered with the UK’s National Grid to deploy deep learning models combining weather forecasts with historical turbine data. The system improved wind power forecast accuracy by up to 20% for 36-hour ahead predictions, translating into more efficient scheduling and reduced balancing costs.

A more accurate forecast enables more optimal unit commitment, reduces renewable curtailment, and supports better congestion and voltage management. It also allows more accurate bidding, reduces imbalance penalties, and improves planning across distributed solar assets. Successful implementers including the transmission network operator in Belgium, Elia, which has developed an AI-based tool that reduces the system imbalance forecast error by 41%. In the US, Xcel Energy uses AI forecasting tools, helping cut curtailment by 20%, saving millions in operational costs. Open Climate Fix with its Quartz Solar tool saves Great Britain’s grid operator $39 million annually through more accurate forecasting.

Enable predictive maintenance to reduce unplanned outages and extend asset lifetime

Traditional asset maintenance follows fixed schedules, which can miss early degradation signs and lead to premature replacement or unexpected failures. AI enables predictive maintenance (PM) by continuously analysing real time and historical data from sensors and operational systems, including vibration, temperature, electrical currents, voltages and weather data.

By detecting subtle patterns that precede failures, AI models can flag early-stage faults, weeks or months in advance, allowing utilities to plan maintenance proactively and avoid catastrophic failures and forced outages. This reduces operational costs, minimises downtime, and extends the availability and lifespan of high value assets such as wind turbines, inverters, battery systems, transformers and transmission equipment.

In practice, AI predictive maintenance has delivered measurable results. The German automation company Siemens reports up to 85% improvement in downtime forecasting and up to 50% reduction in unplanned machine downtime from a PM AI solution. AES – a US power generation company – has integrated AI for wind turbine maintenance and smart meter health, achieving around 90% accuracy in component failure prediction and reducing repair costs per job from roughly $100,000 to $30,000.

In solar PV systems, AI models can deliver a 10% increase in energy output and a 25% reduction in maintenance costs through early detection of panel degradation. Utilities are also applying AI to grid-wide asset management. A Portuguese electric utilities company uses AI-based inspection tools for overhead lines to improve maintenance efficiency and effectiveness, while the California Independent System Operator (CAISO) is piloting AI to improve coordination and verification of planned maintenance, enhancing situational awareness and decision-making capabilities for system operators.

AI predictive maintenance improves VRE integration by increasing the reliability and availability of renewable generation and grid assets. By preventing unexpected failures, AI reduces sudden loss of wind and solar output that would otherwise require costly reserves or emergency dispatch of thermal generation. This strengthens system reliability and enables higher levels of VRE penetration.

Facilitate dispatch optimisation to reduce overall system balancing costs

AI dispatch optimisation reduces grid balancing costs and supports higher VRE integration through faster, more accurate dispatch decisions that adapt to real-time grid conditions. Unlike traditional power dispatch models, which rely on simplified representation and can miss important grid dynamics, AI models capture non-linear factors like transmission losses, voltage limits, congestion, and uncertainty without slowing down decision-making, making them well suited for real-time grid operations.

By shifting computational effort offline through model training, AI dispatch tools demonstrate significant operational efficiency gains. This improved accuracy maximises renewable utilisation, reduces curtailment, manages congestion through optimal power flow rerouting, and minimises reliance on thermal generation, lowering both balancing costs and emissions. A study in the United States shows that AI’s initial training carbon footprint can be offset within minutes of operation.

AI also enhances dispatch outcomes by integrating market signals and distributed energy resources (DERs) flexibility. Probabilistic forecasting and reinforcement learning enable dispatch decisions to adapt continuously to forecast errors and system disturbances, while AI price and congestion forecasting can improve bidding strategies in electricity markets, delivering up to 1% fuel cost reductions and 5% efficiency gains through better market participation.

Beyond centralised operators, AI enables virtual power plants (VPPs) to aggregate DERs and demand response at scale by determining optimal charging and discharging schedules based on forecasts of demand, prices, renewable generation, user needs and grid constraint.

AI has gained significant results in dispatch optimisation. For example, Tata Power partnered with AutoGrid in India to deploy an AI energy management system that achieved 75 MW of peak demand reduction within six months, while utilities such as UK’s Octopus Energy have used AI platforms to orchestrate over 2 GW of flexible capacity from EVs, home batteries, and heat pumps helping to flatten peak demand. The company has expanded to China to develop the AI-driven electricity trading platform in an effort to liberalise the power market.

By coordinating millions of small, fast decisions across assets and consumers, AI-enabled dispatch reduces balancing costs, improves system flexibility, and enables the reliable integration of higher shares of variable renewable energy.

Real-time control to strengthen system security and reliability

As power systems become increasingly digitised, the surge of data from sensors, smart meters, and monitoring devices can overwhelm control centres and introduce new centralised failure risks. AI helps process raw data and even make decisions locally with the appropriate safeguards, for instance by edge computing. AI can make millions of decisions per second that human operators could not possibly manage manually.

Advanced AI models are already demonstrating substantial performance gains. For example, IBM’s GridFM model can cope with the increasing complexity and uncertainties stemming from the energy transition by providing a significant speed-up in computation of at least 3–4 orders of magnitude over conventional solvers. It addresses tasks such as contingency analysis, outage prediction, load and renewable forecasting, and dynamic optimal power flow, allowing operators to respond more effectively to real-time system conditions.

AI surrogate and anomaly detection models further enhance grid security by improving situational awareness and enabling self-healing capabilities. By rapidly ingesting high resolution operational and atmospheric data, AI can detect subtle shifts in system behaviour, localise faults and assist operators with real-time decision making and procedure selection. This allows operators to isolate faults quickly, reducing the extent and duration of outages. As a result, service can be restored faster with minimal disruption to consumers, improving overall grid reliability and customer satisfaction.

In practice, AI fault detection and localisation systems can identify grid faults within seconds and precisely pinpoint their location, reducing outage duration and improving system reliability. CAISO is also evaluating AI tools such as OATI Genie to analyse diverse datasets alongside operating procedures, helping streamline outage management and ensure best-practice responses.

AI real-time system control is essential to ensure grid stability and reliability as VRE penetration rises. These capabilities are particularly relevant for ASEAN, where extreme weather events and ageing infrastructure increase vulnerability to power outages.

Support dynamic line rating to avoid or defer new transmission investment

Traditional transmission planning relies on static line ratings based on worst case assumptions such as high ambient temperatures, low wind speeds, and peak demand, combined with conservative safety margins. While operationally simple, this approach underutilises existing transmission assets and constrains renewable integration.

Dynamic Line Rating (DLR) enables a more dynamic and granular assessment of grid capacity by combining real time weather data, sensor measurements, and power flow models to continuously adjust line ratings. It typically allows transmission lines to safely carry 10-30% additional capacity above their maximum rating for around 90% of the time. When conditions are favourable, DLR increases transfer capacity, reducing congestion, and improving system reliability without compromising security.

AI plays a critical role in operating DLR by turning raw, uncertain inputs into trusted, real time limits that grid operators can actually use. It compiles weather forecasts, line sensor data, and historical loading into continuously updated thermal and sag predictions, filters noise and detects faulty sensors with confidence bounds so operators can manage risk instead of guessing.

Real world experience demonstrates significant benefits. For instance, PPL Electric Utilities in the United States avoided $65 million in congestion costs on a single line within one year, while Belgium reported substantial socio-economic benefits of several thousands of euros in just hours when DLR enabled access to cheaper imports during supply constraints. A German study found that DLR could reduce total system costs by 3-5.5%, around €4 billion annually in high-renewables scenarios by improving asset utilisation and reducing the need for storage and new generation.

For ASEAN countries, where long rainy seasons and strong wind resources provide natural cooling and where permitting and land acquisition are slow, AI DLR offers a high impact solution to reduce renewable curtailment and unlock latent transmission capacity.

Other applications of AI in power sectors

Beyond forecasting, dispatch and grid operation support, AI use cases are expected to grow rapidly as power systems grow more complex, with high shares of DERs, bi-directional power flows, and tighter operational constraints.

For example, AI can enable the intelligent battery management system (BMS) for lifecycle optimisation, fault detection and predictive support. In hydropower systems, AI improves reservoir inflow modeling and load forecasting, enabling more efficient dispatch, improved water resource management, and lower balancing costs for system operators.

AI is also increasingly used to enhance system resilience and climate preparedness. Advanced AI models support long-term, seasonal, and extreme weather forecasting, enabling operators to anticipate large fluctuations in renewable output driven by climate phenomena such as the North Atlantic Oscillation, which helps to plan alternative resources accordingly.

AI spatial downscaling of climate and satellite data provides high-resolution risk indicators for flooding, wildfires, droughts, wind, and rainfall, supporting early warning systems and outage anticipation. In the ASEAN region, AI applications extend beyond terrestrial grids, with submarine cable sensors that can be used to detect early seismic activity in earthquake prone areas such as Indonesia, Malaysia, and the Philippines.

In addition, agentic AI enhances customer experience for utility retailers, while AI siting tools improve the planning of wind and solar installations by combining geospatial, climate, and infrastructure data to maximise generation potential and minimise curtailment and system impacts.