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

doi:10.11930/j.issn.1004-9649.202304013

Abstract

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

CLC Number:

TM73

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.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202304013

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

Figures/Tables 15

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

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

Fig.3 Load peak shaving frequency modulation index

Fig.4 Energy storage peak shaving frequency modulation index

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

Fig.5 Wind power clustering results

Fig.6 Photovoltaic clustering results

Fig.7 Load clustering results

Fig.8 Energy storage clustering results

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

Fig.9 Active output after polymerization

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

Fig.10 Response time before and after aggregation

Fig.11 Response capacity before and after aggregation

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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