上海交通大学电子信息与电气工程学院,上海市 200240
汪岩佳(1996—),男,博士研究生,主要研究方向:能源政策、电力系统分析与控制。E-mail:wangyanjia1996@126.com
王宇川(1998—),男,硕士研究生,主要研究方向:综合能源系统优化运行。E-mail:yc_wang@sjtu.edu.cn
王西田(1973—),男,博士,副教授,主要研究方向:电力系统仿真与控制。E-mail:x.t.wang@sjtu.edu.cn
解大(1969—),男,通信作者,博士,教授,主要研究方向:新能源并网技术、综合能源系统优化运行。E-mail:xieda@sjtu.edu.cn
收稿:2025-02-05,
修回:2025-03-18,
网络首发:2025-05-29,
纸质出版:2025-10-25
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汪岩佳,王宇川,王西田等.考虑虚拟电厂分布式资源联动不确定性的优化调度策略[J].电力系统自动化,2025,49(20):103-112.
WANG Yanjia,WANG Yuchuan,WANG Xitian,et al.Optimal Scheduling Strategy Considering Linkage Uncertainty of Distributed Resources in Virtual Power Plant[J].Automation of Electric Power Systems,2025,49(20):103-112.
汪岩佳,王宇川,王西田等.考虑虚拟电厂分布式资源联动不确定性的优化调度策略[J].电力系统自动化,2025,49(20):103-112. DOI: 10.7500/AEPS20250205001.
WANG Yanjia,WANG Yuchuan,WANG Xitian,et al.Optimal Scheduling Strategy Considering Linkage Uncertainty of Distributed Resources in Virtual Power Plant[J].Automation of Electric Power Systems,2025,49(20):103-112. DOI: 10.7500/AEPS20250205001.
虚拟电厂中分布式资源之间的联动不确定性使得虚拟电厂响应电网调度的准确性面临巨大挑战。为此,提出一种考虑虚拟电厂分布式资源联动不确定性的优化调度策略,旨在提高虚拟电厂调度的经济性和响应的准确性。首先,通过季节性趋势分解和交叉相关性分析,刻画了分布式资源功率变化的联动不确定性,并构建了联动矩阵。其次,提出了一种综合评估方法。该方法综合考虑经济性和联动不确定性,优化了分布式资源在响应调度后的恢复过程,以最小化调度的经济费用。然后,提出了一种基于高斯Copula函数的动态调度策略,通过建立资源间的相关性模型来动态调整资源的调度过程,提升调度的准确性和经济性。最后,利用中国深圳市247个充电站的实际数据进行仿真验证。仿真结果表明,相比于现有方法,所提分布式资源调度策略能够降低10.09%的费用。
The linkage uncertainty among distributed resources in a virtual power plant (VPP) poses significant challenges to the accuracy of the response of the VPP to power grid scheduling. To address this issue
this paper proposes an optimal scheduling strategy considering the linkage uncertainty of distributed resources in a VPP
aiming to enhance the economic efficiency of VPP scheduling and the accuracy of response. Firstly
this paper portrays the linkage uncertainty of distributed resource power variations through seasonal trend decomposition and cross-correlation analysis
and constructs a linkage matrix. Secondly
a comprehensive assessment method is proposed. This method optimizes the recovery process of distributed resources after responding to scheduling to minimize the economic cost of scheduling
which holistically considers the economics and linkage uncertainty. Then
a dynamic scheduling strategy based on the Gaussian Copula function is introduced. By establishing a correlation model among resources
this strategy dynamically adjusts the scheduling process to improve both accuracy and economic efficiency. Finally
the real data from 247 charging stations in Shenzhen
China
are used for simulation validation. Simulation results demonstrate that
compared with existing methods
the proposed scheduling strategy of distributed resources can reduce the costs by 10.09%.
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