A Privacy-Preserving Decentralized Partition-Autonomous Planning Method for Interconnected Multi-Microgrid Distribution Systems Driven by Federated Proximal Policy Optimization

INTRODUCTION: The rapid integration of distributed renewable energy and ICT infrastructure has made interconnected multi-microgrid distribution systems essential for low-carbon power grids, yet centralized coordination exposes sensitive operational data to privacy risks, including eavesdropping and membership inference attacks under 5G and edge computing environments.

Objectives

This study resolves the tension between data privacy protection and decentralized collaborative planning by developing a unified federated reinforcement learning framework with formal privacy guarantees.

Methods

A privacy-preserving decentralized partition-autonomous planning method driven by federated proximal policy optimization is proposed. The network is divided into autonomous units, each performing local proximal policy optimization on local observations. A proximal term constrains update steps against Non-IID drift. A time-period adaptive differential privacy budget mechanism dynamically adjusts noise according to gradient sensitivity, complemented by a Shamir secret sharing-based secure aggregation protocol forming dual-layer protection.

Results

Experiments on the Baran-Wu 33-node test system show daily operational cost within 3.0% of the centralized benchmark, per round communication reduced by approximately 80% to 9.6 MB, voltage compliance at 98.4%, and membership inference attack AUC suppressed to 0.521DISCUSSION: The adaptive privacy budget reduces utility loss by 3.2 percentage points under strict privacy constraints versus fixed schemes, while secure aggregation maintains reliability under one PAU dropout with only 0.38% deviation.

Conclusion

The proposed framework provides a feasible technical path for secure collaborative planning of large-scale interconnected multi-microgrid systems under practical ICT constraints.