Designing Fairness in Autonomous Peer-to-peer Energy Trading

TL;DR

This paper investigates how trading prices affect autonomous peer-to-peer energy markets, proposing a hierarchical game-theoretic approach and a scalable algorithm to ensure network-wide fairness and individual hub incentives.

Contribution

It introduces a hierarchical game-theoretic framework and a privacy-preserving algorithm for designing fair, locally-beneficial trading prices in autonomous energy markets.

Findings

Autonomous P2P trading benefits the entire network.

The proposed algorithm converges to fair pricing profiles.

Active participation of energy hubs is incentivized.

Abstract

Several autonomous energy management and peer-to-peer trading mechanisms for future energy markets have been recently proposed based on optimization and game theory. In this paper, we study the impact of trading prices on the outcome of these market designs for energy-hub networks. We prove that, for a generic choice of trading prices, autonomous peer-to-peer trading is always network-wide beneficial but not necessarily individually beneficial for each hub. Therefore, we leverage hierarchical game theory to formalize the problem of designing locally-beneficial and network-wide fair peer-to-peer trading prices. Then, we propose a scalable and privacy-preserving price-mediation algorithm that provably converges to a profile of such prices. Numerical simulations on a 3-hub network show that the proposed algorithm can indeed incentivize active participation of energy hubs in autonomous peer-to-peer trading schemes.