Autonomous policy evolution and decision robustness in hybrid learning-optimization frameworks for energy systems...

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence
• Optimization Theory

Background:

• Modern energy systems face challenges integrating high renewable penetration and managing operational uncertainties.
• Traditional optimization methods struggle with adaptive decision-making and robust risk management in dynamic environments.

Purpose of the Study:

• To develop a hybrid reinforcement learning-assisted distributionally robust optimization (RL-DRO) framework for resilient and low-carbon energy system operation.
• To integrate adaptive learning with conservative risk management for enhanced energy system performance under uncertainty.

Main Methods:

• A multi-agent reinforcement learning structure was combined with a Wasserstein-metric distributionally robust formulation.
• Reinforcement learning agents were trained to minimize a composite objective of expected cost and risk.
• The distributionally robust optimization layer ensured robustness against distributional ambiguity.

Main Results:

• The RL-DRO framework demonstrated smooth convergence within 4000 iterations.
• Achieved a 9.7% reduction in expected cost and a 28% improvement in robustness compared to stochastic optimization.
• Showcased clear compensatory dynamics between renewable curtailment and storage utilization, with emissions decaying from 200 to 140 tCO2.

Conclusions:

• The RL-DRO architecture effectively unifies data-driven learning and mathematical robustness for sustainable energy system operation.
• The framework enables distributed agents to achieve stable coordination and risk-aware, carbon-efficient decision-making.
• It provides a practical foundation for intelligent operation in modern power systems with high renewable penetration.