A capacity renting framework for shared energy storage considering peer-to-peer energy trading of prosumers with privacy protection

TL;DR

This paper introduces a capacity renting framework for shared energy storage that integrates peer-to-peer energy trading among prosumers, using game theory and privacy-preserving algorithms to enhance efficiency and protect data privacy.

Contribution

It develops a novel framework combining P2P energy trading with shared ESS, modeled via a generalized Nash game and solved with privacy-preserving ADMM methods.

Findings

P2P trading significantly impacts shared ESS operation.

The proposed algorithm effectively preserves prosumer privacy.

Numerical results validate the framework's efficiency and privacy protection.

Abstract

Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers. However, as DG penetration rates rise, spatial energy imbalances become increasingly significant, necessitating the integration of peer-to-peer (P2P) energy trading within the shared ESS framework. Two key challenges emerge in this context: the absence of effective mechanisms and the greater difficulty for privacy protection due to increased data communication. This research proposes a capacity renting framework for shared ESS considering P2P energy trading of prosumers. In the proposed framework, prosumers can participate in P2P energy trading and rent capacities from shared ESS. A generalized Nash game is formulated to model the trading process and the competitive interactions among prosumers, and the variational equilibrium of the game is proved to be equivalent to the optimal solution of a quadratic programming (QP) problem. To address the privacy protection concern, the problem is solved using the alternating direction method of multipliers (ADMM) with the Paillier cryptosystem. Finally, numerical simulations demonstrate the impact of P2P energy trading on the shared ESS framework and validate the effectiveness of the proposed privacy-preserving algorithm.