AI to unlock the next wave of renewable integration in ASEAN

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AI projects in ASEAN’s power sectors

Energy companies in Malaysia, the Philippines, and Indonesia are using IBM platforms to improve reliability, maintenance optimisation, and safety.

Viet Nam’s Son La hydropower plant and EVNNPT are applying AI to reduce human exposure to hazards and enhance hydropower operations and transmission line management.

Thailand has launched a US$ 1.8 billion AI-powered smart grid initiative to optimise energy flows, strengthen cybersecurity, and drive long-term revenue growth, while Indonesia uses AI in isolated grids to cut diesel consumption.

Malaysia Sarawak launches the world’s first AI-based power conversion device.

In the Philippines, Aboitiz Power’s Project Arkanghel uses AI digital twins to improve predictive maintenance and decision-making at coal-fired power plants.

Methodology

The AI adoption curves from Deloitte were referred to evaluate different AI adoption scenarios. For ASEAN, AI adoption rates are approximated using the average of global and EU adoption trajectories. Under these assumptions, AI adoption index reaches approximately 55% in the Baseline scenario and 64% in the Widespread scenario by 2035.

Annual cost savings are calculated as the product of: (i) the assumed AI impact (%), (ii) the AI adoption rate (%), and (iii) the estimated baseline costs in the absence of AI adoption.

We assess AI impacts across both power generators and utilities, drawing on assumptions from the IEA and evidence from IBM survey of utilities. Three categories of cost benefits are considered: operations and maintenance (O&M) cost reductions (8–10%), CAPEX deferral (7–9%), and fuel savings (10%), alongside an assumed 10% reduction in emissions from AI increased operational efficiencies.

Benefits of AI adoption are evaluated under two policy pathways: Stated Policy Scenario (Low VRE) and Announced Pledge Scenario (High VRE) from the IEA Southeast Asia energy Outlook 2024. Therefore, four scenarios will be assessed in total.

Projected CAPEX, O&M costs, fuel expenditures, and emissions from the IEA report are also used to estimate AI-related cost savings for generators.

For utilities, O&M cost reductions are estimated as a share of total transmission and distribution (T&D) costs. Country-specific T&D cost ratios are derived from publicly available data for Viet Nam, Thailand, the Philippines, Malaysia, and Indonesia. Due to data limitations, Brunei, Cambodia, Lao PDR, Myanmar, and Timor Leste are assumed to follow the average ratio of Viet Nam, Thailand, Indonesia, and Malaysia, while Singapore’s ratio is proxied using data from the Philippines. Electricity tariffs for 2024 are calculated as an average across residential, commercial, and industrial customers. Overall, we assume O&M costs account for 30% of total T&D costs.

Acknowledgements

Contributors

Special thanks to our external reviewers: Dr. Daikichi Seki (aiESG), Dr. Pol Torres (EURECAT, Technology Centre of Catalonia), and Norul Rafiq Namas Khan, C. Eng. (Volta Forensic).

Thanks to Reynaldo Dizon for contributing to the report’s illustrations. Thanks to Shiyao Zhang and Ardhi Arsala Rahmani for their communications support. Thanks to Richard Black, Sam Hawkins, Paweł Czyżak for conducting the internal review and providing valuable suggestions.