Privacy Impact on Generalized Nash Equilibrium in Peer-to-Peer Electricity Market

TL;DR

This paper models privacy concerns in peer-to-peer electricity markets as a noncooperative game, analyzing how agents balance privacy with market efficiency and characterizing the equilibrium with explicit formulas.

Contribution

It introduces a novel game-theoretic framework for privacy in P2P electricity markets, providing equilibrium analysis and explicit expressions for privacy valuation.

Findings

Unique variational equilibrium characterized

Closed-form privacy price derived

Impact of privacy-preserving noise quantified

Abstract

We consider a peer-to-peer electricity market, where agents hold private information that they might not want to share. The problem is modeled as a noncooperative communication game, which takes the form of a Generalized Nash Equilibrium Problem, where the agents determine their randomized reports to share with the other market players, while anticipating the form of the peer-to-peer market equilibrium. In the noncooperative game, each agent decides on the deterministic and random parts of the report, such that (a) the distance between the deterministic part of the report and the truthful private information is bounded and (b) the expectation of the privacy loss random variable is bounded. This allows each agent to change her privacy level. We characterize the equilibrium of the game, prove the uniqueness of the Variational Equilibria and provide a closed form expression of the privacy price. In addition, we provide a closed form expression to measure the impact of the privacy preservation caused by inclusion of random noise and deterministic deviation from agents' true values. Numerical illustrations are presented on the 14-bus IEEE network.