中国电机工程学报2026，Vol.46Issue(9)：3564-3577,中插6,15.DOI:10.13334/j.0258-8013.pcsee.242774
联邦分割强化学习驱动的配电网-建筑群隐私安全协同运行方法
Federated Split Reinforcement Learning-driven Privacy-preserving Coordinated Operation Method for Distribution Network and Buildings
摘要
Abstract
As natural carriers of resources like heating,ventilation,air conditioning systems,and electric vehicles,buildings can provide significant flexibility for the distribution network operation.For the coordinated operation of the distribution network and buildings,although multi-agent deep reinforcement learning can address the parameter dependence of model-based methods,it faces the privacy leakage risk during centralized training.Thus,this paper proposes a federated split reinforcement learning method that avoids the interaction of privacy data among multiple agents.This method is implemented in a hierarchical solution framework that integrates optimization and reinforcement learning for coordinated scheduling on the premise of operational security and data privacy.Firstly,a model for the coordinated operation of the distribution network and buildings is established.Then,this paper constructs a hierarchical solution framework combining reinforcement learning and optimization to learn the decision policies of buildings while guaranteeing the safe and economic operation of the distribution network.Next,a federated reinforcement learning algorithm incorporating split learning is proposed,where split learning is used in deploying and optimizing the global value function in blocks via gradients,enabling privacy-preserving and model-free coordinated operation of buildings.Finally,the effectiveness of the proposed method is validated using an IEEE 3 3-bus test system with 20 connected buildings.关键词
建筑群/配电网/联邦强化学习/分割学习/隐私保护Key words
buildings/distribution network/federated reinforcement learning/split learning/privacy preservation分类
信息技术与安全科学引用本文复制引用
汤凌峰,谢海鹏,别朝红..联邦分割强化学习驱动的配电网-建筑群隐私安全协同运行方法[J].中国电机工程学报,2026,46(9):3564-3577,中插6,15.基金项目
国家自然科学基金项目(联合基金项目)(U24B6010).Project Supported by National Natural Science Foundation of China(Joint Fund Project)(U24B6010). （联合基金项目）
