基于分布式储能与光伏的虚拟电厂与配电网协同优化方法

doi:10.11930/j.issn.1004-9649.202407022

中国电力 ›› 2025, Vol. 58 ›› Issue (6): 1-9.DOI: 10.11930/j.issn.1004-9649.202407022

• 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

基于分布式储能与光伏的虚拟电厂与配电网协同优化方法

刘军会¹(), 龚健²(), 佟炳绅²(), 李松杰²(), 张艺涵¹, 崔世常³(), 田春筝¹, 张永斌¹

1. 国网河南省电力有限公司经济技术研究院，河南郑州　450052
2. 中国地质大学（武汉）自动化学院，湖北武汉　430074
3. 华中科技大学电气与电子工程学院，湖北武汉　430074

收稿日期:2024-07-03 发布日期:2025-06-30 出版日期:2025-06-28
作者简介:
刘军会（1988），男，高级工程师，从事电力需求侧响应与电力市场分析研究，E-mail：719814284@qq.com
龚健（2000），女，硕士研究生，从事配电网虚拟电厂协同调度研究，E-mail：GongJ620@cug.edu
佟炳绅（2001），男，博士研究生，从事电力与综合能源系统分布式优化运行研究，E-mail：m18627069602@163.com
李松杰（1999），男，硕士研究生，从事弹性配电网故障恢复研究，E-mail：songjay16@163.com
崔世常（1994），男，通信作者，助理研究员，从事负荷侧管理与虚拟电厂研究，E-mail：shichang_cui@hust.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:
国网河南省电力有限公司经济技术研究院科技项目（5217L0230019）。

Coordinated Optimization Method for Virtual Power Plants and Distribution Networks Considering Distributed Energy Storage and Photovoltaics

LIU Junhui¹(), GONG Jian²(), TONG Bingshen²(), LI Songjie²(), ZHANG Yihan¹, CUI Shichang³(), TIAN Chunzheng¹, ZHANG Yongbin¹

1. Economic and Technological Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China
2. School of Automation, China University of Geosciences, Wuhan 430074, China
3. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Received:2024-07-03 Online:2025-06-30 Published:2025-06-28
Supported by:
This work is supported by Science and Technology Project of Economic and Technological Research Institute of State Grid Henan Electric Power Company (No.5217L0230019).

摘要/Abstract

摘要：

随着光伏渗透率和用户用电量的不断提高，配电网经常出现源荷功率差异大的情况，进而导致部分节点电压上、下界严重越限问题，既增加电网功率和电能损耗，又影响用户正常用电。从配电系统供需平衡角度出发，构建了一种虚拟电厂和配电网的协同调度模型，利用虚拟电厂能聚合多种分布式能源的特性，通过提供虚拟电厂的有功/无功功率使其有效参与到配电市场的运行中，改善电压上、下界越限问题，提高电能质量和配电系统的安全可靠性，同时降低了配网总运行成本。仿真结果表明，构建的协同调度模型通过利用虚拟电厂的灵活性，有效缓解了配电网的电压上、下界越限问题，配电网总运行成本降低，安全稳定性能得到了提升。

关键词: 分布式储能, 分布式光伏, 配电网, 虚拟电厂, 电压越限

Abstract:

With the continuous increase in photovoltaic penetration and user electricity consumption, serious upper and lower voltage limit violations often occur at partial nodes of distribution networks due to significant power imbalances between the source side and the user side, which not only increases the power and energy loss of the grid, but also affects the normal electricity consumption of users. This study developed a coordinated dispatch models between virtual power plants (VPPs) and distribution networks from the perspective of supply-demand balance in power distribution systems. By leveraging the capability of VPPs to aggregate diverse distributed energy resources (DERs), the models enable VPPs to actively participate in distribution market operations through the provision of active and reactive power support. This approach effectively mitigates both the upper and lower voltage limit violations, thereby enhancing power quality and the security/reliability of the distribution systems, while simultaneously reducing the total operational costs of the grid. Simulation results demonstrate that the coordinated dispatch models developed in this paper effectively alleviate the upper and lower voltage limit violations in distribution networks by utilizing the flexibility of VPPs, reduce the total operating cost and enhance the overall safety and stability performance of the distribution networks.

Key words: distributed energy storage, distributed photovoltaic, distribution network, virtual power plant, voltage limit violation

刘军会, 龚健, 佟炳绅, 李松杰, 张艺涵, 崔世常, 田春筝, 张永斌. 基于分布式储能与光伏的虚拟电厂与配电网协同优化方法[J]. 中国电力, 2025, 58(6): 1-9.

LIU Junhui, GONG Jian, TONG Bingshen, LI Songjie, ZHANG Yihan, CUI Shichang, TIAN Chunzheng, ZHANG Yongbin. Coordinated Optimization Method for Virtual Power Plants and Distribution Networks Considering Distributed Energy Storage and Photovoltaics[J]. Electric Power, 2025, 58(6): 1-9.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202407022

https://www.electricpower.com.cn/CN/Y2025/V58/I6/1

图/表 10

图 1 VPP和配电网协同调度框架

Fig.1 VPP and distribution network collaborative dispatching framework

图 2 IEEE-33节点配电系统拓扑

Fig.2 Topology of the IEEE 33-bus distribution system

表 1 分布式储能和光伏的聚合参数

Table 1 Aggregation parameters for distributed ES and PV

对象	聚合物理量	聚合值
ES	$ {P}_{j,t}^{\rm ES,C,max}、{P}_{j,t}^{\rm ES,D,max} $/MW	0.02、0.02
	$ E_j^{\rm R} $/(MW·h)	0.4
	$ {S}_{j}^{\text{ES},\mathrm{min}}、{S}_{j}^{\text{ES},\mathrm{max}} $	0.2, 0.8
PV	$ P_{j,t}^{{\rm PV,pre}} $/MW	0.02
PV	$ {\alpha _j} $	0.95

图 3 算例1.1结果

Fig.3 Result of case 1.1

图 4 算例1.2的MT出力

Fig.4 MT output of case 1.2

图 5 PV调度前后出力

Fig.5 Output of PV before and after scheduling

图 6 调度前后ES出力

Fig.6 Output of ES before and after scheduling

表 2 配网各项成本

Table 2 Various costs of distribution network 单位：欧元

算例	上级电网购电成本	VPP成本		MT成本	总成本
算例	上级电网购电成本	PV成本	ES成本	MT成本	总成本
1.1	6858.948	0	0	78.8680	6937.8160
1.2	6713.011	18.0554	8.9131	4.7385	6744.6874
2	–513.307	1350.1760	9.3928	0	846.2619

图 7 调度前后PV出力（算例2）

Fig.7 Output of PV before ad after scheduling (case 2)

图 8 调度前后ES出力（算例2）

Fig.8 Output of ES before and after scheduling (case 2)

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基于分布式储能与光伏的虚拟电厂与配电网协同优化方法

Coordinated Optimization Method for Virtual Power Plants and Distribution Networks Considering Distributed Energy Storage and Photovoltaics

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基于分布式储能与光伏的虚拟电厂与配电网协同优化方法

Coordinated Optimization Method for Virtual Power Plants and Distribution Networks Considering Distributed Energy Storage and Photovoltaics

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