Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids

2026-04-10

AI Summary

This study addresses the heightened challenges in day-ahead scheduling caused by uncertainties in renewable generation and electricity demand, which hinder green energy curtailment reduction and intra-day power balancing. To tackle this issue, the paper proposes an incentive-compatible peer-to-peer (P2P) electricity trading mechanism integrated with a multi-agent reinforcement learning framework. This approach enables self-interested microgrids to autonomously optimize their bidding strategies and energy storage arbitrage under dynamic grid electricity prices. The mechanism jointly promotes individual economic gains and system-wide decarbonization objectives, achieving coordinated optimization that significantly enhances renewable energy utilization, reduces reliance on high-carbon power sources, and improves the overall economic welfare of the local energy community.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Uncertainties in renewable generation and demand dynamics challenge day-ahead scheduling. To enhance renewable penetration and maintain intra-day balance, we develop a multi-agent reinforcement learning framework for self-interested microgrids participating in peer-to-peer (P2P) electricity trading. Each microgrid independently bids both price and quantity while optimizing its own profit via storage arbitrage under time-varying main-grid prices. A market-clearing mechanism coordinating trades and promoting incentive compatibility is proposed. Simulation results show that the learned bidding policy improves renewable utilization and reduces reliance on high-carbon electricity, while increasing community-level economic welfare, delivering a win-win situation in emission reduction and local prosperity.

Problem

• multi-agent reinforcement learning
• peer-to-peer energy trading
• microgrids
• renewable uncertainty
• low-carbon electricity

Innovation

• multi-agent reinforcement learning
• peer-to-peer energy trading
• incentive compatibility
• microgrid
• low-carbon electricity