您当前的位置：
首页 >
文章列表页 >
Photovoltaic power probability forecasting based uncertainty quantification of node voltage in distribution system with renewable energy
KEY TECHNOLOGIES FOR NEW TYPE POWER SYSTEMS: RESILIENCE ASSESSMENT AND ENHANCEMENT TECHNOLOGIES FOR NEW URBAN POWER GRIDS（2） | 更新时间：2026-01-14

    • Photovoltaic power probability forecasting based uncertainty quantification of node voltage in distribution system with renewable energy

    • “Experts propose a voltage uncertainty quantification method based on photovoltaic power probability prediction to address the voltage security challenges of new energy access distribution systems, providing a solution to the short-term prediction problem of node voltage.”
    • Electric Power Automation Equipment   Vol. 46, Issue 1, Pages: 31-39(2026)

    • DOI：10.16081/j.epae.202509015    

      CLC： TM732

    • Received：27 January 2025，

      Revised：2025-09-02，

      Published：10 January 2026

    移动端阅览

  • SHEN Zhongkai,ZONG Xingchen,WAN Can,et al.Photovoltaic power probability forecasting based uncertainty quantification of node voltage in distribution system with renewable energy[J].Electric Power Automation Equipment,2026,46(01):31-39. DOI： 10.16081/j.epae.202509015.

  •  
  •  

0

Views

1

下载量

0

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Optimization scheduling and emission reduction cost evaluation of electricity-heat integrated energy system based on dimensionality reduction of KPCA and AHC for renewable energy
Load forecasting of distribution network based on learnable basis vector-based time-domain learning weighted iteration network
Service restoration strategy of distribution system considering dynamic islanding partition and merging
Self-adaptive multi-scale transient voltage stability assessment based on improved SK-BiLSTM
Deep reinforcement learning method for voltage control considering topology change of distribution system

Related Author

QIN Jinghui
PAN Xueping
SUN Xiaorong
GUO Jinpeng
CHEN Junyi
WANG Yiming
WANG Mingchen
SUN Kaiqi

Related Institution

School of Electrical and Power Engineering，Hohai University
School of Electrical Engineering，Shandong University
China Electric Power Research Institute
Institute of Electrical Engineering of Chinese Academy of Sciences
School of Electrical Engineering and Automation，Hefei University of Technology
0