Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty
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 adapta...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/8/2134 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714118179684352 |
|---|---|
| author | Chao Chen Weifeng Peng Cheng Xie Shufeng Dong Yibo Hua |
| author_facet | Chao Chen Weifeng Peng Cheng Xie Shufeng Dong Yibo Hua |
| author_sort | Chao Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9fabd004c1dd440da7e30c80a4900462 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-9fabd004c1dd440da7e30c80a49004622025-08-20T03:13:47ZengMDPI AGEnergies1996-10732025-04-01188213410.3390/en18082134Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load UncertaintyChao Chen0Weifeng Peng1Cheng Xie2Shufeng Dong3Yibo Hua4State Grid Zhejiang Electric Power Co., Ltd. Electric Power Science Research Institute, Hangzhou 310014, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Grid Zhejiang Electric Power Co., Ltd. Electric Power Science Research Institute, Hangzhou 310014, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Grid Zhejiang Electric Power Co., Ltd. Electric Power Science Research Institute, Hangzhou 310014, ChinaWith 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.https://www.mdpi.com/1996-1073/18/8/2134hosting capacity assessmentscenario generationsparrow search algorithmsource–load uncertaintyWGAN-GP |
| spellingShingle | Chao Chen Weifeng Peng Cheng Xie Shufeng Dong Yibo Hua Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty Energies hosting capacity assessment scenario generation sparrow search algorithm source–load uncertainty WGAN-GP |
| title | Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty |
| title_full | Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty |
| title_fullStr | Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty |
| title_full_unstemmed | Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty |
| title_short | Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty |
| title_sort | photovoltaic hosting capacity assessment of distribution networks considering source load uncertainty |
| topic | hosting capacity assessment scenario generation sparrow search algorithm source–load uncertainty WGAN-GP |
| url | https://www.mdpi.com/1996-1073/18/8/2134 |
| work_keys_str_mv | AT chaochen photovoltaichostingcapacityassessmentofdistributionnetworksconsideringsourceloaduncertainty AT weifengpeng photovoltaichostingcapacityassessmentofdistributionnetworksconsideringsourceloaduncertainty AT chengxie photovoltaichostingcapacityassessmentofdistributionnetworksconsideringsourceloaduncertainty AT shufengdong photovoltaichostingcapacityassessmentofdistributionnetworksconsideringsourceloaduncertainty AT yibohua photovoltaichostingcapacityassessmentofdistributionnetworksconsideringsourceloaduncertainty |