Multi-agent reinforcement learning improves economic performance and fairness in P2P electricity markets, suggesting scalable solutions for diverse communities.
Key Points
The research focuses on improving fairness in peer-to-peer electricity markets using a multiagent reinforcement learning framework.
Developed FairMarket-RL, a fairness-aware multiagent RL framework guided by a large language model.
Implemented a continuous double auction model considering fairness scores in the bidding process.
Tested the framework with realistic residential load and photovoltaic profiles across various community sizes.
Framework shifts exchanges increasingly towards local P2P trades.
Lowered costs for consumers compared to traditional grid procurement.
Maintained robust fairness outcomes across participants in trials.