AdaptiveGrid: Self-Evolving Microgrids with Test-Time Reinforcement Learning
Abstract
A novel approach combining test-time reinforcement learning (TTRL) with microgrid energy management to create self-optimizing power systems. The system continuously learns from operational data without requiring explicit labels, enabling real-time adaptation to changing conditions while maintaining grid stability and optimizing energy usage patterns.
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Research Gap Analysis
Current microgrid management systems lack real-time adaptation capabilities and require extensive labeled data for training. No existing solution combines test-time reinforcement learning with energy systems for continuous self-optimization.
AdaptiveGrid: Self-Evolving Microgrids with Test-Time Reinforcement Learning
Motivation
Current microgrid energy management systems rely on pre-trained models that struggle to adapt to rapidly changing conditions, new energy sources, or evolving consumption patterns. While reinforcement learning has shown promise in energy management, existing approaches require extensive labeled training data and cannot easily adapt to new scenarios in real-time. Recent advances in test-time reinforcement learning (TTRL) have demonstrated the ability to learn from unlabeled data during inference, opening new possibilities for adaptive energy systems.
Proposed Approach
AdaptiveGrid introduces a novel framework that combines TTRL with traditional microgrid control systems to create self-evolving energy management solutions. The system operates in three main phases:
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Initial Deployment: A base model is trained using conventional RL techniques on historical data, establishing fundamental operating parameters and safety constraints.
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Continuous Adaptation: During operation, the TTRL component monitors system performance using multiple metrics (efficiency, stability, cost) to generate implicit reward signals. These signals guide real-time model updates without requiring ground truth labels.
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Safety-Aware Evolution: A hierarchical control structure ensures that model adaptations remain within safe operating bounds while optimizing for emerging patterns and opportunities.
The system employs majority voting mechanisms similar to those used in TTRL for LLMs but adapted for time-series energy data. Multiple prediction heads generate diverse energy management strategies, with successful outcomes reinforcing beneficial adaptations.
Expected Outcomes
- Reduced energy costs through continuous optimization of storage and distribution patterns
- Improved grid stability through rapid adaptation to changing conditions
- Enhanced integration of renewable energy sources by learning optimal usage patterns
- Reduced need for manual system tuning and expert intervention
- Generation of valuable insights into long-term energy consumption patterns
Potential Applications
- Smart city energy management
- Industrial microgrids with variable loads
- Renewable energy integration
- Electric vehicle charging networks
- Remote community power systems
The framework can be extended to other complex control systems requiring continuous adaptation without explicit supervision.
Proposed Methodology
Implement a hierarchical system combining TTRL with traditional microgrid control, using implicit reward signals derived from operational metrics to guide real-time model updates while maintaining safety constraints.
Potential Impact
Could revolutionize microgrid management by enabling autonomous adaptation to changing conditions, reducing costs, improving stability, and accelerating renewable energy integration. The approach could be generalized to other complex control systems.