Generative AI Applications for Power Systems
A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".
Deadline for manuscript submissions: 25 August 2026 | Viewed by 1298
Editors
Interests: renewable energy; grid integration; wind power; solar power; HVDC; HVAC; microgrids; data-driven solutions for power systems; quantum computing for power systems
Special Issues, Collections and Topics in MDPI journals
Interests: AI for power systems; federated learning in smart grids; quantum computing applications in power systems; energy markets and flexibility; energy management systems and energy communities; peer-to-peer trading
Special Issue Information
Dear Colleagues,
The digital transformation of modern power systems has led to the acquisition of vast amounts of data driven by the widespread deployment of smart metering and sensing technologies. This explosion of data has enabled advanced analytics using AI techniques to enhance the observability, operation, and planning of electrical grids, especially with the increasing penetration of renewable energy sources. Generative AI further leverages these developments by enabling a range of applications. These include the creation of realistic synthetic scenarios and data augmentation through Generative Adversarial Networks (GANs); probabilistic load and generation forecasting via Variational Autoencoders (VAEs); state estimation and reconstruction of missing data using input masking pretraining; simulation of rare or extreme events not captured in historical records through normalizing flows; and cross-domain situational awareness facilitated by multi-modal transformers.
In addition to these capabilities, generative AI is increasingly being used as an intelligent advisor, capable of synthesizing diverse data streams to provide contextual, real-time assessments of grid conditions. By continuously analyzing historical and live data, generative models can identify emerging patterns, predict abnormal system behavior, and suggest proactive mitigation strategies. This advisory role enhances operator situational awareness, supports risk-informed decision-making, and enables more agile responses to volatility introduced by renewable sources and demand-side flexibility activation.
With these capabilities, AI and generative AI empower utilities to perform more accurate forecasting, optimize grid operations, enhance reliability, and support the integration of renewables. Generative AI is now transitioning from traditional data augmentation and scenario generation toward real-time grid control, operator support, and market optimization. Continued interdisciplinary collaboration between electrical engineering, software engineering, and data science is essential to harness the full potential of these methods for power systems.
We look forward to your contributions.
Dr. Mònica Aragüés-Peñalba
Dr. Sara Barja-Martinez
Guest Editors
Manuscript Submission Information
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Keywords
- AI
- generative AI
- GANs
- VAEs
- transformers
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