# Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets

arXiv: https://arxiv.org/abs/2508.18610 · HTML: https://mlsanity.com/paper/2508.18610/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Peer-to-peer and community-based markets: A comprehensive review](https://mlsanity.com/paper/1810.09859/) (2018)
- [Peer-to-Peer Energy Systems for Connected Communities: A Review of Recent Advances and Emerging Challenges](https://mlsanity.com/paper/2011.11118/) (2020)
- [Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms](https://mlsanity.com/paper/2405.12716/) (2024)
- [A Game-Theoretic Approach to Energy Trading in the Smart Grid](https://mlsanity.com/paper/1310.1814/) (2013)
- [Peer-to-Peer Electricity Market Analysis: From Variational to Generalized Nash Equilibrium](https://mlsanity.com/paper/1812.02301/) (2018)
- [Designing Fairness in Autonomous Peer-to-peer Energy Trading](https://mlsanity.com/paper/2302.04771/) (2023)

## Built on by (most cited first, 1 of 1)

- [Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Markets](https://mlsanity.com/paper/2609.01918/) (2026)

## Abstract (arXiv)

Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems as rooftop PV and home energy management systems become pervasive, yet most existing market and reinforcement learning designs emphasize efficiency or private profit and offer little real-time guidance to ensure equitable outcomes under uncertainty. To address this gap, a fairness-aware multiagent reinforcement learning framework, FairMarket-RL, is proposed in which a large language model (LLM) critic shapes bidding policies within a continuous double auction under partial observability and discrete price-quantity actions. After each trading slot, the LLM returns normalized fairness scores Fairness-to-Grid (FTG), Fairness-Between-Sellers (FBS), and Fairness-of-Pricing (FPP) that are integrated into the reward via ramped coefficients and tunable scaling, so that fairness guidance complements, rather than overwhelms, economic incentives. The environment models realistic residential load and PV profiles and enforce hard constraints on prices, physical feasibility, and policy-update stability. Across a progression of experiments from a small pilot to a larger simulated community and a mixed-asset real-world dataset, the framework shifts exchanges toward local P2P trades, lowers consumer costs relative to grid-only procurement, sustains strong fairness across participants, and preserves utility viability. Sensitivity analyses over solar availability and aggregate demand further indicate robust performance, suggesting a scalable, LLM-guided pathway to decentralized electricity markets that are economically efficient, socially equitable, and technically sound.