Privacy-Preserving Decentralized Energy Management for Networked Microgrids via Objective-Based ADMM

TL;DR

This paper introduces a privacy-preserving decentralized energy management strategy for microgrids using an enhanced ADMM approach with a novel stopping criterion, achieving high-quality solutions while safeguarding microgrid privacy.

Contribution

It develops an objective-based ADMM method with a new stopping rule and a post-processing algorithm, improving decentralized microgrid energy management accuracy and privacy preservation.

Findings

The proposed OB-ADMM achieves solutions close to centralized benchmarks.

The method preserves microgrid privacy by only sharing network power exchange info.

Simulations validate the effectiveness and robustness of the approach.

Abstract

This paper proposes a decentralized energy management (DEM) strategy for a network of local microgrids, providing economically balanced energy schedules for all participating microgrids. The proposed DEM strategy can preserve the privacy of each microgrid by only requiring them to share network power exchange information. The proposed DEM strategy enhances the traditional alternating direction method of multipliers (ADMM) formulation for networks of microgrids by examining the global objective value as well as the solution quality. A novel stopping decision combining these two metrics is proposed for the enhanced objective-based ADMM (OB-ADMM) method. This paper also presents a centralized energy management (CEM) model as a benchmark, and a post-processing proportional exchange algorithm (PEA) to balance the economic benefit of each microgrid. The resulting proposed OB-ADMM model combined with the PEA delivers the final high-fidelity optimal solution for multiple microgrids in a grid-connected collaborative power exchange network. Moreover, the proposed decentralized operational strategy preserves the economic and privacy interests of individual microgrid participants. Different network cases are simulated to test the algorithm's performance, and the results validate the aforementioned claims.