Learning operational compatibility in distribution networks under multi-step renewable dynamics

Area of Science:

• Electrical Engineering
• Power Systems
• Artificial Intelligence

Background:

• High renewable energy penetration causes operational challenges in power distribution networks.
• Traditional optimization methods struggle with real-time feasibility assessment due to rapid variability and uncertainty.

Purpose of the Study:

• To develop a learning-based framework for assessing operational compatibility under multi-step renewable dynamics.
• To enable real-time feasibility assessment for power distribution networks with high renewable penetration.

Main Methods:

• A compatibility learning model was constructed, mapping system states and renewable forecasts to a probabilistic compatibility score.
• A Multi-Step renewable dynamics representation was introduced to capture correlated variability across forecasting intervals.
• The learning architecture integrated network information and operational features to approximate the feasible operating manifold.

Main Results:

• The framework correctly identified feasible operating conditions in approximately 94.6% of scenarios.
• Feasibility evaluation time was reduced by over 82% compared to conventional methods.
• Compatibility prediction accuracy exceeded 90% even under severe renewable fluctuation conditions.

Conclusions:

• The proposed learning-based framework enhances the ability to rapidly filter infeasible dispatch candidates.
• It allows for secure network operation across multiple forecast horizons despite renewable energy uncertainty.
• This approach offers a computationally efficient solution for real-time operational compatibility assessment in modern power grids.