About us

PMAPS 2026 invites original research contributions that explore probabilistic, statistical, and stochastic methods applied to power systems. This premier international forum brings together academics, industry experts, and policymakers to discuss innovations that enhance grid reliability, resilience, and sustainability. Conference Theme: Towards Reliable Power Grids in the Age of AI and Large Loads

Important Dates

Sun, Mar 15, 2026
Submission Deadline
Fri, May 15, 2026
Notification of Acceptance

Call For Paper

Demand, generation, and market forecasting and uncertainty modeling
Integration and coordination of inverter-based resources, distributed energy resources, and virtual power plants
AI for stochastic/probabilistic grid control
Emerging applications of generative AI, large language models (LLMs), foundation models, and federated learning
Characterization, modeling, and forecasting of large loads
Bulk power system operation and planning considering large loads
Power system adequacy analysis considering large loads integration
Stability analysis of grids with extensive large loads integration
Siting, permitting, and policy implications for new large loads and associated generation projects (e.g., nuclear and geothermal)
Market design and flexibility mechanisms for large load variability management
High-impact, low-probability event analysis
Grid resilience metrics, frameworks, and enhancements
Cascading failures in electric power systems
Event-driven planning and operational strategies
Compounding hazards modeling and mitigation
Outage data analysis and data-driven resilience assessment tools
Asset condition monitoring, data management, and analysis
Reliability-centred and condition-based maintenance and asset management
Power quality monitoring and enhancement tools
End-of-life prognosis and ageing assessment tools

We invite original, unpublished full papers that present innovative research, methodologies, and applications of probabilistic methods in power systems.

Committee

Organizing Committee