基于灰靶理论和谱聚类的虚拟电厂多形态柔性资源聚合模型

doi:10.11930/j.issn.1004-9649.202304013

中国电力 ›› 2023, Vol. 56 ›› Issue (11): 104-112.DOI: 10.11930/j.issn.1004-9649.202304013

• 新型电力系统低碳规划与运行 • 上一篇下一篇

基于灰靶理论和谱聚类的虚拟电厂多形态柔性资源聚合模型

刘向向¹(), 张森林²(), 朱思乔²(), 马瑞²()

1. 国网江西省电力有限公司供电服务管理中心，江西南昌　330001
2. 长沙理工大学电气与信息工程学院，湖南长沙　410114

收稿日期:2023-04-06 出版日期:2023-11-28 发布日期:2023-11-28
作者简介:刘向向（1987—），男，高级工程师，从事电力需求侧管理研究，E-mail： liuxiangx1987@126.com
张森林（1998—），男，硕士研究生，从事电力系统运行与控制研究，E-mail： 1772749721@qq.com
朱思乔（1999—），女，通信作者，硕士研究生，从事新能源及虚拟电厂运行规划研究，E-mail： zhusiqiao8223@163.com
马瑞（1971—），男，博士，教授，从事电力系统分析与控制、能源互联网和电力大数据挖掘等研究，E-mail： marui818@126.com
基金资助:
国家自然科学基金资助项目（51977012）；国网江西省电力有限公司科技项目（521852220005）。

Multi-form Flexible Resource Aggregation Model for Virtual Power Plant Based on Grey Target Theory and Spectral Clustering

Xiangxiang LIU¹(), Senlin ZHANG²(), Siqiao ZHU²(), Rui MA²()

1. Economics and Technology Research Institute of Jiangxi Electric Power Co., Ltd., Nanchang 330001, China
2. College of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China

Received:2023-04-06 Online:2023-11-28 Published:2023-11-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.51977012) and Science & Technology Project of State Grid Jiangxi Electric Power Co., Ltd. (No.521852220005).

摘要/Abstract

摘要：

柔性资源通常聚合为虚拟电厂的形式参与电网调度。如何根据各类资源的特性，选择其最佳调度方案，改善电网调度运行稳定性是亟须解决的问题。首先考虑新能源发电、分布式储能、柔性负荷的响应特性，从响应时间、响应容量及日负荷波动率3方面分别建立各柔性资源通用的调频性能指标和调峰性能指标；其次，结合灰靶理论、客观赋权法和谱聚类法，将虚拟电厂内的柔性资源分成调频资源和调峰资源；最后，分别搭建调频型虚拟电厂聚合模型和调峰型虚拟电厂聚合模型并研究其聚合后的响应特性。算例仿真证明：聚合后虚拟电厂的响应时间与日负荷波动率均有所降低。调峰场景下优先利用调峰型虚拟电厂。当调峰型虚拟电厂不满足调峰需求时，调频虚拟电厂也参与调峰，以提高不同场景下的利用效率。

关键词: 虚拟电厂, 谱聚类, 灰靶理论, 特性分析, 评价指标

Abstract:

Flexible resources are usually aggregated into virtual power plants to participate in power grid scheduling. A multi-form flexible resource aggregation model for virtual power plant is therefore proposed based on grey target theory and spectral clustering. Firstly, based on the response characteristics of new energy generation, distributed energy storage and flexible loads, the common frequency modulation performance indicators and peak shaving performance indicators are established for each flexible resource from three aspects, including response time, response capacity and daily load fluctuation rate. Secondly, based on the grey target theory, objective weighting method and spectral clustering, the flexible resources in the virtual power plant are classified into frequency modulation resources and peak shaving resources. Finally, a frequency modulation-type virtual power plant aggregation model and a peak shaving-type virtual power plant aggregation model are established respectively, and their post-aggregation response characteristics are studied. The numerical simulation shows that the response time and the daily load fluctuation rate of the post-aggregation virtual power plant are reduced. The peak shaving-type virtual power plants should be used preferentially under peak shaving scenarios. When the peak shaving-type virtual power plants can not meet the peak shaving requirements, the frequency-modulation virtual power plants can participate in peak shaving to improve the utilization efficiency in different scenarios.

Key words: virtual power plant, spectral clustering, grey target theory, characteristic analysis, evaluation indicator

中图分类号:

TM73

刘向向, 张森林, 朱思乔, 马瑞. 基于灰靶理论和谱聚类的虚拟电厂多形态柔性资源聚合模型[J]. 中国电力, 2023, 56(11): 104-112.

Xiangxiang LIU, Senlin ZHANG, Siqiao ZHU, Rui MA. Multi-form Flexible Resource Aggregation Model for Virtual Power Plant Based on Grey Target Theory and Spectral Clustering[J]. Electric Power, 2023, 56(11): 104-112.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202304013

https://www.electricpower.com.cn/CN/Y2023/V56/I11/104

图/表 15

图 1 风电厂调峰调频指标

Fig.1 Peak shaving and frequency regulation indicators for wind power plants

图 2 光伏电厂调峰调频指标

Fig.2 Peak shaving and frequency modulation indicators for photovoltaic power plants

图 3 负荷调峰调频指标

Fig.3 Load peak shaving frequency modulation index

图 4 储能调峰调频指标

Fig.4 Energy storage peak shaving frequency modulation index

表 1 柔性资源调峰调频指标权重

Table 1 Weight of flexible resource peak shaving and frequency modulation indicators

资源名称	调频指标权重	调峰指标权重
风电场	0.52	0.48
光伏电站	0.47	0.53
柔性负荷	0.70	0.30
分布式储能	0.59	0.41

图 5 风电聚类结果

Fig.5 Wind power clustering results

图 6 光伏聚类结果

Fig.6 Photovoltaic clustering results

图 7 负荷聚类结果

Fig.7 Load clustering results

图 8 储能聚类结果

Fig.8 Energy storage clustering results

表 2 柔性资源 NJW谱聚类结果

Table 2 NJW spectral clustering results for flexible resources

资源名称	调频资源个数	调峰资源个数
风电场	21	9
光伏电站	12	19
柔性负荷	14	17
分布式储能	14	17

图 9 聚合后的有功出力

Fig.9 Active output after polymerization

表 3 聚合前后单个资源整体响应时间

Table 3 Overall response time of a single resource before and after aggregation

主体类型	响应
主体类型	调频	调峰	聚合前
风电	65.7500	57.5800	94.8900
光伏	29.7600	31.2900	40.0900
负荷	19.0300	23.0000	31.1500
储能	0.4902	0.4944	0.7600

图 10 聚合前后响应时间

Fig.10 Response time before and after aggregation

图 11 聚合前后响应容量

Fig.11 Response capacity before and after aggregation

表 4 聚合前后柔性资源日负荷波动率

Table 4 Daily load fluctuation rate of flexible resources before and after aggregation

主体类型	负荷波动率
主体类型	调频日	调峰日	聚合前日
风电	0.64	0.35	0.87
光伏	1.20	1.21	1.30
负荷	0.10	0.13	0.15
储能	0.09	0.13	0.15

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基于灰靶理论和谱聚类的虚拟电厂多形态柔性资源聚合模型

Multi-form Flexible Resource Aggregation Model for Virtual Power Plant Based on Grey Target Theory and Spectral Clustering

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基于灰靶理论和谱聚类的虚拟电厂多形态柔性资源聚合模型

Multi-form Flexible Resource Aggregation Model for Virtual Power Plant Based on Grey Target Theory and Spectral Clustering

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