Tackling variable renewables: the role of artificial intelligence

The document examines the role of artificial intelligence (AI) in overcoming the technical challenges of integrating variable renewable energy (VRE) in Southeast Asia. It highlights five commercially proven AI applications: improving forecast accuracy to reduce reserve needs, enabling predictive maintenance to extend asset life, optimising dispatch to lower balancing costs, providing real-time control for grid security, and supporting dynamic line rating (DLR) to increase transmission capacity. The report provides global case studies—including examples from Google DeepMind, Siemens, and various utilities in the US, UK, and Belgium—to demonstrate how AI can reduce operational costs, curtailment, and carbon footprints while enhancing the reliability of power grids facing high VRE penetration.

• ASEAN's variable renewable energy (VRE) share grew from 2.3% in 2020 to approximately 5% in 2025, with Viet Nam leading the region at over 12% of electricity generation from solar and wind.
• AI-based forecasting can improve short-term prediction accuracy by about 25% compared to traditional numerical weather prediction, allowing operators to lower reserve margins and reduce reliance on standby thermal generation.
• Predictive maintenance powered by AI can significantly reduce downtime and costs; for example, Siemens reports up to 85% improvement in downtime forecasting and a 50% reduction in unplanned machine downtime.
• AI dispatch optimisation reduces grid balancing costs by capturing non-linear factors like transmission losses and congestion in real-time, and can facilitate the aggregation of distributed energy resources (DERs) via virtual power plants (VPPs).
• AI enhances real-time grid control and security; IBM's GridFM model provides a computational speed-up of at least 34 orders of magnitude over conventional solvers for tasks such as contingency analysis and outage prediction.
• Dynamic Line Rating (DLR) enabled by AI allows transmission lines to safely carry 10-30% additional capacity above their maximum rating for approximately 90% of the time, potentially reducing total system costs by 3-5.5% in high-renewables scenarios.
• Beyond grid operations, AI supports climate resilience through high-resolution risk indicators for flooding and wildfires, and in the ASEAN region, it can be used with submarine cable sensors to detect early seismic activity in Indonesia, Malaysia, and the Philippines.

APA

Ember (2026). Tackling variable renewables: the role of artificial intelligence. https://ember-energy.org/chapter/tackling-variable-renewables-the-role-of-artificial-intelligence/

Chicago

Ember. Tackling variable renewables: the role of artificial intelligence. 2026. https://ember-energy.org/chapter/tackling-variable-renewables-the-role-of-artificial-intelligence/.

Wikipedia

{{cite report |author=Ember |title=Tackling variable renewables: the role of artificial intelligence |date=3 March 2026 |url=https://ember-energy.org/chapter/tackling-variable-renewables-the-role-of-artificial-intelligence/ |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{ember2026tackling, author = {{Ember}}, title = {{Tackling variable renewables: the role of artificial intelligence}}, institution = {Ember}, year = {2026}, month = mar, url = {https://ember-energy.org/chapter/tackling-variable-renewables-the-role-of-artificial-intelligence/}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }