Journal of Applied Science and Engineering

Published by Tamkang University Press

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Spatio-Temporal Analysis and Modeling of Distributed Photovoltaic Contributions to Distribution Network Safety

 2026

 Volume 34

Huan YAN1, Yuanyuan YUE1, Honggang JIA1, Zijia HUI1, and Hao ZENG2

1State Grid Shaanxi Electric Power Company Limited Research Institute, Xi’an, China

2Chongqing Electric Energy Star Co., Ltd, Chongqing, China

Received: April 26, 2025
Accepted: July 30, 2026
Publication Date: August 17, 2026

上傳圖片

Overall control scheme of the grid-connected DPV system. 

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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The large-scale integration of distributed photovoltaic (DPV) systems introduces significant spatio-temporal variability into distribution networks, posing challenges to voltage stability, line loading, and protection coordination. This paper proposes a spatio-temporal analysis and modeling framework to quantify the safety impacts of DPV contributions. By integrating spatial topology characteristics with temporal generation-load fluctuations, the proposed model evaluates dynamic safety margins under varying penetration scenarios. A composite load model incorporating ZIP components, induction motor dynamics, and a simplified PV subsystem is established, and a reinforcement learning-enhanced Grey Wolf Optimizer (RL-GWO) is developed for high-precision parameter identification. Case studies demonstrate its effectiveness in identifying high-risk nodes and revealing critical spatio-temporal coupling effects, providing analytical support for distribution network planning and secure operation, while showing that appropriate DPV penetration can significantly improve voltage profiles and reduce network losses.

Keywords: Distributed photovoltaic; Spatio-temporal Analysis; Parameter Identification; Reinforcement learning; Distribution Network Security; Grey Wolf Optimizer

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