基于联邦学习 ADMM 的工业园区分层分布式需求响应策略研究

刘默涵; 崔明建; 顾梨婷; 王轶申

doi:10.19783/j.cnki.pspc.251247

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基于联邦学习 ADMM 的工业园区分层分布式需求响应策略研究

工程应用 | 更新时间：2026-07-27

- 基于联邦学习 ADMM 的工业园区分层分布式需求响应策略研究
- Research on a federated learning ADMM-based hierarchical distributed demand response strategy for industrial parks
- 电力系统保护与控制 2026年54卷第12期
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- DOI：10.19783/j.cnki.pspc.251247
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- 纸质出版：2026
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刘默涵, 崔明建, 顾梨婷, 等. 基于联邦学习 ADMM 的工业园区分层分布式需求响应策略研究[J]. 电力系统保护与控制, 2026,54(12).

LIU Mohan, CUI Mingjian, GU Liting, et al. Research on a federated learning ADMM-based hierarchical distributed demand response strategy for industrial parks[J]. Power System Protection and Control, 2026, 54(12).
刘默涵, 崔明建, 顾梨婷, 等. 基于联邦学习 ADMM 的工业园区分层分布式需求响应策略研究[J]. 电力系统保护与控制, 2026,54(12). DOI： 10.19783/j.cnki.pspc.251247.

LIU Mohan, CUI Mingjian, GU Liting, et al. Research on a federated learning ADMM-based hierarchical distributed demand response strategy for industrial parks[J]. Power System Protection and Control, 2026, 54(12). DOI： 10.19783/j.cnki.pspc.251247.

摘要

工业用户是激励型需求响应的主要参与者。然而，集中式求解其响应策略难以兼顾多主体利益，易引发隐私泄露风险。为此，提出了一种基于联邦学习交替方向乘子法 (federated learning alternating direction method of multipliers

FedADMM) 的工业园区分层分布式需求响应策略，在考虑聚合商和用户双方利益的同时，有效保护敏感数据。首先，建立兼顾多主体利益的分层响应模型。聚合商层以响应收益最大为目标优化用户级响应策略，用户层以额外收益最大为目标优化设备级响应策略。随后，提出改进的联邦学习交替方向乘子法 (improved FedADMM

IFedADMM)，通过对全局参数更新、传输信息加密、客户端迭代规则方面进行改进，实现了迭代过程隐私保护和计算效率的提高。接着，采用该方法分布式求解优化模型，双方动态更新补偿价格直至收敛至最优解。最后，通过某工业园区算例验证了所提方法在提高隐私保护和计算效率等方面的有效性。

Abstract

Industrial customers are the primary participants in incentive-based demand response programs. However

centralized optimization of their response strategies struggles to balance the interests of multiple stakeholders and is prone to privacy leakage risks. To address these issues

this paper proposes a hierarchical distributed demand response strategy for industrial parks utilizing the federated learning alternating direction method of multipliers (FedADMM). This approach simultaneously considers the interests of both aggregators and customers while effectively protecting sensitive data. First

a hierarchical response model is established

accounting for the benefits of multiple stakeholders. The aggregator layer optimizes customer-level response strategies to maximize demand response profits

while the customer layer optimizes device-level strategies to maximize additional revenues. Second

an improved FedADMM (IFedADMM) is proposed. By improving global parameter updating

information transmission encryption

and client-side iteration rules

the proposed method achieves privacy protection during the iterative process and enhances computational efficiency. The optimization model is then solved in a distributed manner

where both parties dynamically update compensation prices until convergence to the optimal solution. Finally

a case study of an industrial park is conducted to verify the effectiveness of the proposed method in improving privacy protection and computational efficiency.

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Keywords

references

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WANG Dongxia, LIN Yanli, PENG Xin, et al. Differential-privacy-preserving distributed ADMM using load-balanced sparse all reduce communication[J]. IEEE Transactions on Cognitive Communications and Networking, 2026, 12: 4361-4375.

GONG Yonghai, LI Yichuan, FRERIS N M. FedADMM: a robust federated deep learning framework via primal-dual optimization[J]. arXiv: 2204.03529, 2026.

GABAY D, MERCIER B. A dual algorithm for the solution of nonlinear variational problems via finite element approximation[J]. Computers Mathematics with Applications, 1976, 2(1): 17-40.

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