Federated duelling deep Q-network based collaborative energy scheduling for a power distribution network

Yanhong Yang; Wei Pei; Tianyi Xu; Dawei Wang; Abdelbari Redouane

doi:10.1049/enc2.70012

您当前的位置：

首页 >

文章列表页 >

Federated duelling deep Q-network based collaborative energy scheduling for a power distribution network

更新时间：2026-03-24

- Federated duelling deep Q-network based collaborative energy scheduling for a power distribution network
- Energy Conversion and Economics Vol. 6, Issue 3, (2025)
- 作者机构：
  
  1. Institute of Electrical Engineering, Chinese Academy of Sciences,Beijing,China
  2. College of Intelligence and Computing, Tianjin University,Tianjin,China
  3. Global Energy Interconnection Development and Cooperation Organization,Beijing,China
  4. National Higher School of Mines of Rabat,Rabat,Morocco
- 作者简介：
- 基金信息：
- DOI：10.1049/enc2.70012
  CLC：
- Published：2025
- 稿件说明：
移动端阅览
Yanhong Yang, Wei Pei, Tianyi Xu, et al. Federated duelling deep Q-network based collaborative energy scheduling for a power distribution network[J]. Energy Conversion and Economics, 2025, 6(3).
DOI：

Yanhong Yang, Wei Pei, Tianyi Xu, et al. Federated duelling deep Q-network based collaborative energy scheduling for a power distribution network[J]. Energy Conversion and Economics, 2025, 6(3). DOI： 10.1049/enc2.70012.

摘要

Abstract

The collaborative energy scheduling of source-load-energy storage has great potential to meet the active control requirements of power-distribution networks. In this study

a federated deep reinforcement learning framework was developed to facilitate collaborative energy scheduling and maximize the total economic benefit in a distribution network. Then

considering the application of Markov decision processes for energy scheduling

a spatial temporal graph convolutional network transformer based power generation packaging model for renewable energy sources was presented

and a collaborative energy scheduling strategy based on a federated duelling deep Q-network was designed. The simulation results indicate that the developed collaborative scheduling strategy can maximize the economic benefits of a power distribution network while ensuring data privacy.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Overview of collaborative response between the power distribution network and urban transportation network coupled by electric vehicle cluster under unconventional events

Related Author

Ying Wang

Related Institution

School of Electrical Engineering, Beijing Jiaotong University, Haidian District

AI问答

⁰