计及源荷复杂依赖结构的配电网光储协同优化配置

姚 婧; 黄 纯; 江亚群; 陶子谦; 林 峡; 刘毅力

doi:10.19783/j.cnki.pspc.260249

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计及源荷复杂依赖结构的配电网光储协同优化配置

应用研究 | 更新时间：2026-10-15

- 计及源荷复杂依赖结构的配电网光储协同优化配置
- Optimal configuration of PV-ESS in distribution networks considering source-load complex dependence structure
- 电力系统保护与控制 2026年54卷第20期
- 作者机构：
  
  湖南大学电气与信息工程学院,湖南,长沙,410082
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.260249
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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姚婧, 黄纯, 江亚群, 等. 计及源荷复杂依赖结构的配电网光储协同优化配置[J]. 电力系统保护与控制, 2026,54(20).

YAO Jing, HUANG Chun, JIANG Yaqun, et al. Optimal configuration of PV-ESS in distribution networks considering source-load complex dependence structure[J]. Power System Protection and Control, 2026, 54(20).
姚婧, 黄纯, 江亚群, 等. 计及源荷复杂依赖结构的配电网光储协同优化配置[J]. 电力系统保护与控制, 2026,54(20). DOI： 10.19783/j.cnki.pspc.260249.

YAO Jing, HUANG Chun, JIANG Yaqun, et al. Optimal configuration of PV-ESS in distribution networks considering source-load complex dependence structure[J]. Power System Protection and Control, 2026, 54(20). DOI： 10.19783/j.cnki.pspc.260249.

摘要

高比例分布式光伏接入显著增强了配电网源荷不确定性。若忽略光伏出力与负荷需求间的非线性依赖结构，边缘分布误差将经联合建模传播至规划决策。为此，提出一种计及源荷复杂依赖结构的光储协同优化配置方法。首先，采用混合概率模型描述光伏出力的零值集聚与正值长尾特性。其次，在有效日照时段的光伏正值子样本上，由候选库经赤池信息准则优选条件Copula模型，兼顾对称与非对称负相关结构，并结合K-Medoids聚类生成典型场景。进而，建立基于二阶锥松弛的配置模型，采用Benders分解算法求解。改进IEEE33与IEEE123节点系统仿真表明：较传统正态-高斯Copula方法，光储总投资分别降低17.9%与16.0%，样本外成本偏差控制在1%以内；较集中式方案，95%置信水平下的蒙特卡洛风险价值分别降低30.2%与23.7%；且计算时间随系统规模近线性增长，验证了方法的经济性、泛化能力与可扩展性。

Abstract

High-penetration of distributed photovoltaic (PV) significantly intensifies the source-load uncertainty of distribution networks. If the nonlinear dependence between PV output and load demand is ignored

marginal distribution errors can propagate through joint modeling into planning decisions. To address this issue

a PV-ESS coordinated optimal configuration method considering the complex source-load dependence structure is proposed. First

a mixed probability model is adopted to describe the zero-value clustering and positive long-tail characteristics of PV output. Second

on the positive PV subsamples of each effective daylight period

a conditional Copula is selected from a candidate library by the Akaike information criterion to capture both symmetric and asymmetric negative dependence

while typical scenarios are generated by K-Medoids clustering. Furthermore

a configuration model based on second-order cone relaxation is established and solved by Benders decomposition. Simulations on the modified IEEE33-bus and 123-bus systems show that

compared with the conventional normal-Gaussian Copula method

the total PV-ESS investment is reduced by 17.9% and 16.0%

respectively

with the out-of-sample cost deviation within 1%. Compared with the centralized scheme

the Monte-Carlo value at risk at the 95% confidence level is reduced by 30.2% and 23.7% for the 33-bus and 23-bus systems

respectively. In addition

the computation time grows near-linearly with system scale

verifying the economic performance

generalization capability

and scalability of the proposed method.

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Keywords

references

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