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

Paper Submission

Research Paper Submission Tracks

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

Call for Papers Description

We invite original, unpublished full papers that present innovative research, methodologies, and applications of probabilistic methods in power systems.