新型电力系统风险抑制下分布式光伏承载力概率评估方法

doi:10.11930/j.issn.1004-9649.202407042

中国电力 ›› 2025, Vol. 58 ›› Issue (6): 33-44.DOI: 10.11930/j.issn.1004-9649.202407042

• 基于人工智能驱动的低碳高品质新型配电系统 • 上一篇下一篇

新型电力系统风险抑制下分布式光伏承载力概率评估方法

王方敏¹(), 顼佳宇²(), 苏宁¹(), 牛焕娜³(), 袁嘉兴³, 门盼龙³

1. 北京电力经济技术研究院有限公司，北京　100055
2. 国网北京市电力公司，北京　100031
3. 中国农业大学信息与电气工程学院，北京　100083

收稿日期:2024-07-08 发布日期:2025-06-30 出版日期:2025-06-28
作者简介:
王方敏（1978），女，高级工程师，从事电网规划设计、新能源并网接入研究，E-mail：wangfm2024@163.com
顼佳宇（1992），女，博士，工程师，从事分布式光储规划配置与协同控制研究，E-mail：xujy321@163.com
苏宁（1983），男，硕士，高级工程师，从事电力系统规划设计及新能源并网研究，E-mail：jyysuning@163.com
牛焕娜（1976），女，博士，副教授，从事主动配电网/微电网能量管理、综合能源系统规划与运行、用户侧能效管理研究，E-mail：nhn@cau.edu.cn（第二十七届中国科协年会学术论文“配微储协同的低碳高品质新型配电系统”专题）
基金资助:
北京电力经济技术研究院有限公司资助项目（SGBJJY00SJJS2400033）；国家重点研发计划资助项目（2022YFE0129400）。

Probability Evaluation Method of Distributed Photovoltaic Carrying Capacity under Risk Suppression in New Power System

WANG Fangmin¹(), XU Jiayu²(), SU Ning¹(), NIU Huanna³(), YUAN Jiaxing³, MEN Panlong³

1. Beijing Electric Power Economic Research Institute Co., Ltd., Beijing 100055, China
2. State Grid Beijing Electric Power Company, Beijing 100031, China
3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Received:2024-07-08 Online:2025-06-30 Published:2025-06-28
Supported by:
This work is supported by Beijing Electric Power Economic Research Institute Co., Ltd. Technology Project (No.SGBJJY00SJJS2400033), National Key Research and Development Program of China (No.2022YFE0129400).

摘要/Abstract

摘要：

为了抑制新型电力系统背景下分布式光伏无序接入带来的安全稳定运行风险，提出了基于历史场景统计的光伏承载力分层分级概率评估方法。该方法建立了基于反向负载率的光伏承载力分级概率评估模型以及安全校核概率评估模型，给出了基于历史场景统计的光伏承载力薄弱环节分级概率评估流程，并通过构建基于百分位数统计的光伏承载力评估模型，最终形成了分布式光伏接入配电网承载力分层分级概率评估方法。通过改进导则算例和实际配电网案例验证了所提方法的有效性和普适性，实验表明该方法能够科学展示不同百分位数下各供电区域的光伏可新增容量，识别的光伏承载力薄弱程度更符合统计学意义。

关键词: 配电网, 光伏承载力, 历史场景统计, 分层分级

Abstract:

In order to suppress the safety and stable operation risks brought by the disorderly integration of distributed photovoltaics in the context of the new power system, a hierarchical probability evaluation method based on historical scene statistics is proposed. This method establishes a grading probability evaluation model of PV bearing capacity based on reverse load ratio and a safety check probability evaluation model. The grading probability evaluation process of PV bearing capacity weaknesses based on historical scene statistics is given. By constructing a PV bearing capacity evaluation model based on percentile statistics, a hierarchical probability evaluation method for the capacity of distributed PV access distribution network is finally formed. The effectiveness and universality of the proposed method are verified by the improved guideline example and the actual distribution network case. Experiments show that the method can scientifically show the new PV capacity of each power supply area under different percentiles, and the identified PV capacity weakness is more in line with statistical significance.

Key words: distribution grid, photovoltaic carrying capacity, historical scene statistics, hierarchical grading

王方敏, 顼佳宇, 苏宁, 牛焕娜, 袁嘉兴, 门盼龙. 新型电力系统风险抑制下分布式光伏承载力概率评估方法[J]. 中国电力, 2025, 58(6): 33-44.

WANG Fangmin, XU Jiayu, SU Ning, NIU Huanna, YUAN Jiaxing, MEN Panlong. Probability Evaluation Method of Distributed Photovoltaic Carrying Capacity under Risk Suppression in New Power System[J]. Electric Power, 2025, 58(6): 33-44.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202407042

https://www.electricpower.com.cn/CN/Y2025/V58/I6/33

图/表 16

图 1 基于二分法的光伏承载力静态评估流程

Fig.1 Static evaluation flow chart of photovoltaic carrying capacity based on binary method

图 2 含多元负荷型可上调调节资源的分布式光伏接入配电网的典型结构

Fig.2 Typical structure diagram of distributed photovoltaic access to distribution network with multi-load type up-regulation resources

图 3 基于历史场景统计的光伏承载力分级概率评估流程

Fig.3 Flowchart of PV bearing capacity grading probability evaluation based on historical scenario statistics

图 4 分布式光伏接入配电网承载力分层分级概率评估方法流程

Fig.4 Flow chart of the hierarchical hierarchical probability evaluation method of the carrying capacity of distributed photovoltaic access distribution network

图 5 待评估区域拓扑

Fig.5 Topology map of the area to be evaluated

表 1 评估区域1承载力分级评估等级结果

Table 1 Assessment area 1 bearing capacity grading evaluation grade result table

评估对象	安全校核评估	反向负载率分级概率	评估等级划分
3绕组220 kV主变1（110 kV母线1）	通过	$ \Pr \{ {\lambda _{c,k}} \leqslant {\lambda _k}(t) {{＜}} {\lambda _{r,k}}\} $为3.11%	Ⅱ级
3绕组220 kV主变1（35 kV母线1）	通过	$ \Pr \{ {\lambda _k}(t) {{＜}} 0\} $为100%	Ⅳ级
110 kV线路1 （110 kV母线2）	通过	$ \Pr \{ {\lambda _{c,k}} \leqslant {\lambda _k}(t) {{＜}} {\lambda _{r,k}}\} $为13.86%	Ⅱ级
3绕组110 kV主变1（35 kV母线2）	通过	$ \Pr \{ {\lambda _k}(t) {{＜}} 0\} $为100%	Ⅳ级
3绕组110 kV主变1（10 kV母线1）	通过	$ \Pr \{ {\lambda _{c,k}} \leqslant {\lambda _k}(t) {{＜}} {\lambda _{r,k}}\} $为15.02%	Ⅱ级

表 2 评估区域2承载力分级评估等级结果

Table 2 Assessment area 2 bearing capacity grading evaluation grade result table

评估对象	安全校核评估	反向负载率分级概率	评估等级划分
3绕组220 kV主变2（110 kV母线3）	通过	$ \Pr \{ 0 \leqslant {\lambda _k}(t) {{＜}} {\lambda _{c,k}}\} $为48.76%	Ⅲ级（蓝色）
3绕组220 kV主变2（35 kV母线3）	通过	$ \Pr \{ {\lambda _k}(t) {{＜}} 0\} $为100%	Ⅳ级（绿色）
35 kV线路2 （35 kV母线4）	通过	$ \Pr \{ {\lambda _{c,k}} \leqslant {\lambda _k}(t) {{＜}} {\lambda _{r,k}}\} $为17.77%	Ⅱ级（黄色）
35 kV变压器2（10 kV母线2）	通过	$ \Pr \{ {\lambda _{r,k}} \leqslant \lambda (t)\} $为0.23%	Ⅰ级（红色）

表 3 评估区域3承载力分级评估等级结果

Table 3 Assessment area 3 bearing capacity grading evaluation grade result table

评估对象	安全校核评估	反向负载率分级概率	评估等级划分
3绕组220 kV主变3（110 kV母线4）	通过	$ \Pr \{ {\lambda _k}(t) {{＜}} 0\} $为99.08%	Ⅳ级
3绕组220 kV主变3（10 kV母线3）	通过	$ \Pr \{ {\lambda _k}(t) {{＜}} 0\} $为100%	Ⅳ级
110 kV线路3 （110 kV母线5）	通过	$ \Pr \{ {\lambda _{c,k}} \leqslant {\lambda _k}(t) {{＜}} {\lambda _{r,k}}\} $为10.50%	Ⅱ级
3绕组110 kV主变3（35 kV母线5）	通过	$ \Pr \{ {\lambda _{c,k}} \leqslant {\lambda _k}(t) {{＜}} {\lambda _{r,k}}\} $为7.47%	Ⅱ级
3绕组110 kV主变3（10 kV母线4）	电压偏差校核不通过	—	Ⅰ级

表 4 评估区域4承载力分级评估等级结果表

Table 4 Assessment area 4 bearing capacity grading evaluation grade result table

评估对象	安全校核评估	反向负载率分级概率	评估等级划分
3绕组220 kV主变4（110 kV母线6）	—	—	Ⅰ级
3绕组220 kV主变4（10 kV母线5）	—	—	Ⅰ级
10 kV变压器4（0.4 kV母线1）	—	—	Ⅰ级

图 6 评估区域光伏承载力薄弱环节分级评估结果拓扑

Fig.6 Topology diagram of the grading assessment results of the weak links in the evaluation of regional photovoltaic carrying capacity

图 7 区域1各母线不同百分位数下光伏承载力结果

Fig.7 Results of photovoltaic capacity at different percentiles of each bus in region 1

图 8 区域2各母线不同百分位数下光伏承载力结果

Fig.8 Results of photovoltaic capacity at different percentiles of each bus in region 2

图 9 区域3各母线不同百分位数下光伏承载力结果

Fig.9 Results of photovoltaic capacity at different percentiles of each bus in region 3

图 10 某实际配电网案例网络拓扑

Fig.10 Network topology of an actual distribution network case

图 11 左区域不同百分位数下典型母线光伏承载力结果

Fig.11 Results of photovoltaic capacity of typical bus under different percentiles in the left region

图 12 右区域不同百分位数下典型母线光伏承载力结果

Fig.12 Results of photovoltaic capacity of typical bus under different percentiles in the right region

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新型电力系统风险抑制下分布式光伏承载力概率评估方法

Probability Evaluation Method of Distributed Photovoltaic Carrying Capacity under Risk Suppression in New Power System

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新型电力系统风险抑制下分布式光伏承载力概率评估方法

Probability Evaluation Method of Distributed Photovoltaic Carrying Capacity under Risk Suppression in New Power System

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