LLM-Driven Grid Planner
The LLM-Driven Grid Planner demonstrates how Large Language Models (LLMs) can serve as adaptive reasoning agents in smart grid management.
It integrates natural-language-guided reinforcement learning (RL) for real-time grid optimization, enabling interpretable and human-aligned decision support for operators.
Framework
Traditional grid control systems rely on predefined rule-based logic or black-box deep RL agents.
In contrast, this framework introduces a language-driven layer, allowing system operators to provide high-level natural-language goals, which the LLM translates into structured RL objectives or safety constraints.
This synergy creates an interpretable control loop, where human intentions, grid dynamics, and agent behavior are aligned through continuous dialogue and reasoning.
Core Components
- LLM Planner: Parses operator input and converts it into structured optimization or control policies.
- RL Agent: Learns to execute the translated goals using algorithms like PPO, DDPG, or SAC.
- Smart Grid Environment: Simulates volt-VAR, power flow, and frequency management scenarios.
- Explainability Layer: Generates step-by-step rationales behind control decisions for improved trust and transparency.
Objectives
- Enable human-in-the-loop grid management using natural language.
- Improve safety and interpretability in reinforcement learning decisions.
- Support scalable grid optimization through AI-assisted planning and control.
Broader Impact
The LLM-Driven Grid Planner exemplifies a step toward explainable, trustworthy, and interactive AI for smart infrastructure.
By merging language reasoning with reinforcement learning, it establishes a foundation for next-generation AI-assisted energy systems that are safer, transparent, and easier to govern.
