A Fully Decentralized Tuning-free Inexact Projection Method for P2P Energy Trading

TL;DR

This paper introduces a fully decentralized, tuning-free inexact projection algorithm for peer-to-peer energy trading, enhancing privacy and scalability without the need for parameter tuning or central coordination.

Contribution

It proposes a novel decentralized algorithm that does not require parameter tuning and ensures convergence across various P2P energy trading scenarios.

Findings

Algorithm converges without parameter tuning

Limited information sharing preserves privacy

Effective in IEEE 13 bus test system

Abstract

Agent-based solutions lend themselves well to address privacy concerns and the computational scalability needs of future distributed electric grids and end-use energy exchanges. Decentralized decision-making methods are the key to enabling peer-to-peer energy trading between electricity prosumers. However, the performance of existing decentralized decision-making algorithms highly depends on the algorithmic design and hyperparameter tunings, limiting applicability. This paper aims to address this gap by proposing a decentralized inexact projection method that does not rely on parameter tuning or central coordination to achieve the optimal solution for Peer-to-Peer (P2P) energy trading problems. The proposed algorithm does not require parameter readjustments, and once tuned, it converges for a wide range of P2P setups. Moreover, each prosumer only needs to share limited information (i.e., updated coupled variable) with neighboring prosumers. The IEEE 13 bus test system is used to showcase our proposed method's robustness and privacy advantages.