Data-driven Calculation Method for Distributed Photovoltaic Hosting Capacity in Low Voltage Distribution Network

CHEN Yunfei; 1; ZHANG Yi; 1; OU Jieyu; 1; XIN Rong; 1; LIN Qinan; 1; MA Ming; 2

doi:10.13335/j.1000-3673.pst.2025.0237

您当前的位置：

首页 >

文章列表页 >

Data-driven Calculation Method for Distributed Photovoltaic Hosting Capacity in Low Voltage Distribution Network

更新时间：2026-03-13

- Data-driven Calculation Method for Distributed Photovoltaic Hosting Capacity in Low Voltage Distribution Network
- Vol. 49, Issue 11, Pages: 4759-4770(2025)
- 作者机构：
  
  福州大学电气工程与自动化学院,福建省,福州市,350108
- 作者简介：
- 基金信息：
- DOI：10.13335/j.1000-3673.pst.2025.0237
  CLC： TM615
- Published：2025
- 稿件说明：
移动端阅览
CHEN Yunfei, 1, ZHANG Yi, et al. Data-driven Calculation Method for Distributed Photovoltaic Hosting Capacity in Low Voltage Distribution Network[J]. 2025, 49(11): 4759-4770.
DOI：

CHEN Yunfei, 1, ZHANG Yi, et al. Data-driven Calculation Method for Distributed Photovoltaic Hosting Capacity in Low Voltage Distribution Network[J]. 2025, 49(11): 4759-4770. DOI： 10.13335/j.1000-3673.pst.2025.0237.

摘要

分布式光伏可承载能力常用于评估配电网在满足安全稳定运行约束下所允许接纳的光伏容量。然而，现有可承载能力计算方法多依赖于精确的网络拓扑与线路参数信息且存在场景模拟量大、耗时长等局限，难以应用于实际低压配电网。为此，该文提出一种基于数据驱动的低压配电网分布式光伏可承载能力计算方法。首先，应用显式神经网络拟合低压配电网下各用户功率与节点电压的映射关系并根据网络推理逻辑构建节点电压与用户功率的显式方程；其次，计算各相电压对节点功率的灵敏度，据此构建聚类性能指标以实现低压配电网非全相用户接入下的自适应分区；最后，依据潮流拟合模型以及分区赋值指标构建兼顾电压偏差与三相不平衡的低压配电网分布式光伏可承载能力计算模型，实现快速、高效的光伏可承载能力计算。通过IEEE-123节点模型与某实际配电网进行算例验证，结果表明，所提可承载能力计算方法不仅适用于实际低压配电网场景，且具备优良的鲁棒性，可在拓扑与线路参数信息不完备的条件下逼近低压配电网的理论接入容量。

Abstract

Distributed photovoltaic (PV) hosting capacity is commonly used to assess the PV capacity allowed to be accommodated under the premise that the distribution network satisfies safe and stable operation. However

the existing hosting capacity calculation methods mostly rely on accurate network topology and line parameter information and have the limitations of large scene simulation and long time-consuming

which are difficult to be applied to low voltage distribution network (LVDN). Therefore

this paper proposes a data-driven method for distributed PV hosting capacity in LVDN. Firstly

an explicit neural network is applied to fit the mapping relationship between the power of each consumer and the voltage of nodes in LVDN and construct explicit equations reflecting the connection between the power and the voltage of the nodes according to the inference logic of the network. Secondly

calculate the sensitivity of each phase voltage to the node power and construct partition assignment index to realize adaptive partition under non-all-phase user access in LVDN. Finally

based on the clustering performance index and power flow fitting model

a hosting capacity calculation model is constructed for the LVDN considering voltage deviation and three-phase unbalance

enabling fast and efficient calculation of PV hosting capacity. The IEEE-123 node model and an actual LVDN is verified and the results show that the proposed method is not only applicable to the actual LVDN scenario but also has excellent robustness

which can approach the theoretical hosting capacity of the LVDN under the condition of incomplete topology and line parameter information.

关键词

Keywords

references

Views

865

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Data-driven Adaptive Method for Selection of Efficient Unit Commitment Model

Earth Fault Detection Method for Low Voltage Multi-earthed Neutral Systems Based on Residual Current Time-domain Parameter Vectors

Virtual Twin Based Pilot Protection of AC Transmission Lines

Comprehensive Compensation System Combining Reactive Power Compensation and Harmonic Suppression for Large-Scale Electric Arc-Furnace

低压电网在1～30MHz频段的阻抗测量与特性分析

Related Author

江贤睿

高倩

杨知方

SUN Zhongyu

XU Bingyin

WANG Wei

Related Institution

输变电装备技术全国重点实验室(重庆大学)，重庆市沙坪坝区

School of Electrical Engineering, Shandong University

College of Electrical Engineering and Information Technology, Sichuan University

College of Information Science and Engineering,Central South University

National Engineering Research Center of Converters

AI问答

⁰