FairMarket-RL injects LLM-computed fairness scores into PPO rewards and reports 30-90 day P2P market simulations with roughly 54% local trade share, 27-30% consumer bill savings, and a profitable grid.
Distributionally Fair Peer-to-Peer Electricity Trading
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Peer-to-peer energy trading platforms enable direct electricity exchanges between peers who belong to the same energy community. In a semi-decentralized system, a community manager adheres to grid restrictions while optimizing social welfare. However, with no further supervision, some peers can be discriminated against from participating in the electricity trades. To solve this issue, this paper proposes an optimization-based mechanism to enable distributionally fair peer-to-peer electricity trading. For the implementation of our mechanism, peers are grouped by energy poverty level. The proposed model aims to redistribute the electricity trades to minimize the maximum Wasserstein distance among the transaction distributions linked to the groups while limiting the sacrifice level with a predefined parameter. We demonstrate the effectiveness of our proposal using the IEEE 33-bus distribution grid, simulating an energy community with 1600 peers. Results indicate that up to 70.1% of unfairness can be eliminated by using our proposed model, even achieving a full elimination when including a non-profit community photovoltaic plant.
citation-role summary
background 1
citation-polarity summary
fields
eess.SY 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets
FairMarket-RL injects LLM-computed fairness scores into PPO rewards and reports 30-90 day P2P market simulations with roughly 54% local trade share, 27-30% consumer bill savings, and a profitable grid.