{"id":10176,"date":"2026-08-17T21:24:06","date_gmt":"2026-08-17T13:24:06","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=10176"},"modified":"2026-08-17T23:54:09","modified_gmt":"2026-08-17T15:54:09","slug":"jase-202611-34-045","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-045","title":{"rendered":"Spatio-Temporal Analysis and Modeling of Distributed Photovoltaic Contributions to Distribution Network Safety"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Huan YAN1<\/sup>, Yuanyuan YUE1<\/sup>, Honggang JIA1<\/sup>, Zijia HUI1<\/sup>, and Hao ZENG2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>State Grid Shaanxi Electric Power Company Limited Research Institute, Xi\u2019an, China<\/p>\n\n\n\n

2<\/sup>Chongqing Electric Energy Star Co., Ltd, Chongqing, China<\/p>\n<\/div>\n\n\n\n

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Received: April 26, 2025
Accepted: July 30, 2026
Publication Date: August 17, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Overall control scheme of the grid-connected DPV system. <\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.045<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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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.<\/p>\n\n\n\n

Keywords: Distributed photovoltaic; Spatio-temporal Analysis; Parameter Identification; Reinforcement learning; Distribution Network Security; Grey Wolf Optimizer<\/em><\/p>\n\n\n\n

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