Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty

Chao Chen, Weifeng Peng, Cheng Xie, Shufeng Dong () and Yibo Hua
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Chao Chen: State Grid Zhejiang Electric Power Co., Ltd. Electric Power Science Research Institute, Hangzhou 310014, China
Weifeng Peng: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Cheng Xie: State Grid Zhejiang Electric Power Co., Ltd. Electric Power Science Research Institute, Hangzhou 310014, China
Shufeng Dong: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yibo Hua: State Grid Zhejiang Electric Power Co., Ltd. Electric Power Science Research Institute, Hangzhou 310014, China

Energies, 2025, vol. 18, issue 8, 1-21

Abstract: With the continuous expansion of distributed photovoltaic (PV) integration, the hosting capacity of distribution networks has become a critical issue in power system planning and operation. Under varying meteorological and load fluctuation conditions, traditional assessment methods often face adaptability and uncertainty handling challenges. To enhance the practical applicability and accuracy of hosting capacity evaluations, this paper proposes a PV hosting capacity assessment model that incorporates source–load uncertainty and constructs an alternative scenario optimization evaluation framework driven by target-oriented scenario generation. The model considers system constraints and employs the sparrow search algorithm (SSA) to optimize the maximum PV hosting capacity. On the source side, PV output scenarios with temporal characteristics are generated based on the mapping relationship between meteorological factors and PV power. On the load side, historical data are employed to extract fluctuation ranges and to introduce random perturbations to simulate load uncertainty. In addition, a PV power scenario generation method based on the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is proposed, integrating physical-data dual-driven strategies to enhance the physical consistency of generated data, while incorporating a target-driven weighted sampling mechanism to improve its learning ability for key features. Case studies verify that the proposed method demonstrates strong adaptability and accuracy under varying meteorological and load conditions.

Keywords: hosting capacity assessment; scenario generation; sparrow search algorithm; source–load uncertainty; WGAN-GP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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