Advancements in Multi-Agent Reinforcement Learning for Energy Trading and Microgrid Optimization

The field of energy trading and microgrid optimization is moving towards the integration of multi-agent reinforcement learning (MARL) and advanced forecasting techniques to enhance decision-making and optimize energy management. Researchers are exploring the use of large language models, hierarchical MARL frameworks, and uncertainty-aware prediction models to improve the efficiency and resilience of energy systems. Noteworthy papers in this area include: LLM-Enhanced Multi-Agent Reinforcement Learning, which proposes an integrated LLM-MARL framework for real-time P2P energy trading. Uncertainty-Aware Knowledge Transformers, which presents a novel framework for P2P energy trading that integrates uncertainty-aware prediction with MARL. Diffusion-Modeled Reinforcement Learning, which introduces a diffusion-modeled carbon- and risk-aware reinforcement learning algorithm for intelligent operation of multi-microgrid systems.

Sources

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading

Arbitrage Tactics in the Local Markets via Hierarchical Multi-agent Reinforcement Learning

Uncertainty-Aware Knowledge Transformers for Peer-to-Peer Energy Trading with Multi-Agent Reinforcement Learning

Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization

Towards Microgrid Resilience Enhancement via Mobile Power Sources and Repair Crews: A Multi-Agent Reinforcement Learning Approach

Two-Stage TSO-DSO Services Provision Framework for Electric Vehicle Coordination

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