ASEAN’s power systems are entering a structurally more complex phase of the energy transition.
Solar and wind generation have expanded from 2.3% of electricity supply in 2020 to around 5% in 2025, and long-term projections suggest variable renewable energy (VRE) could reach 42–47% of generation by 2045, with some scenarios exceeding 60%. As VRE penetration rises, power systems must manage greater variability, forecast uncertainty, congestion, and balancing requirements.
While higher VRE penetration increases system complexity, global evidence shows that such challenges are manageable. The energy transition remains promising, with a growing portfolio of solutions.
Artificial intelligence (AI) is increasingly applied in power systems globally to address operational challenges. AI models are currently used to improve renewable generation forecasting, enable predictive maintenance, optimise dispatch and unit commitment, support real-time grid control, and operate dynamic line rating. These applications have demonstrated measurable operational improvements in multiple jurisdictions, particularly in systems with growing renewable shares.
The potential economic and emissions implications of wider AI deployment in ASEAN’s power sector is enormous. Under widespread adoption, AI could deliver up to $67 billion USD in cost savings and reduce nearly 400 million tons of CO2 emissions between 2026 and 2035, on hHigh-VRE deployment pathways, compared to the estimated baseline costs in the absence of AI adoption.