Grid-Aware Peer-to-Peer Energy Trading: A Learning-Augmented Framework

TL;DR

This paper introduces a learning-augmented P2P energy trading framework that predicts grid responses to improve local decision-making, efficiency, and privacy in active distribution networks.

Contribution

It develops a transformer-based predictive model enabling microgrids to anticipate DSO responses, enhancing P2P trading feasibility and reducing communication overhead.

Findings

The framework accurately predicts DSO responses in case studies.

It improves market efficiency and trade acceptance.

It reduces computational and communication burdens.

Abstract

Distribution networks are transitioning from passive to active systems due to the growing integration of distributed energy resources (DERs). Peer to Peer (P2P) energy trading has emerged as a viable framework that enables local energy exchange among participants, represented here as aggregated microgrids (MGs). Incorporating network constraints is essential to ensure that P2P transactions remain physically feasible and consistent with grid's operating limits. However, existing P2P frameworks still lack advanced predictive mechanisms that allow prosumers to anticipate network feasibility or the distribution system operator (DSO) response during trade formulation. This paper proposes a learning augmented P2P and DSO interface that predicts the DSOs response to the proposed P2P trades, allowing prosumers to self-assess and refine their trading decisions. A supervised transformer based regression model is trained to enable MGs to locally predict the DSOs response without sharing their proposed trades, thereby reducing transaction overhead, alleviating DSO burden, and preserving information privacy. The proposed framework is validated on the modified IEEE 33 bus distribution power system with interconnected microgrids. Case studies are presented to validate the effectiveness of the proposed model in terms of market efficiency, trade acceptance and computational burden.