电力系统自动化2025，Vol.49Issue(13)：32-42,11.DOI:10.7500/AEPS20240707001
基于Copula时空相关性模型的新能源容量置信度准时序评估
Quasi-sequential Evaluation of Capacity Credit for Renewable Energy Based on Copula Spatio-temporal Correlation Model
摘要
Abstract
The increase in the penetration rate of renewable energy poses a challenge to the power supply guarantee capability of the power system.How to consider the multi-dimensional spatio-temporal correlation of renewable energy and accurately quantify its capacity credit is of great significance for the reliability analysis and generation planning of new power systems.Therefore,a temporal correlation model based on Copula transfer kernel-continuous state Markov chain and a spatio correlation model based on D-Vine Copula function are proposed to generate multi-dimensional spatio-temporal scenarios for renewable energy.On this basis,an improved quasi-sequential Monte Carlo simulation method of capacity credit for renewable energy cluster is proposed to accurately and efficiently characterize the power supply capability of wide-area renewable energy.The case study of RTS-GMLC cross-region power grid shows that the proposed method can accurately model the multi-dimensional spatio-temporal correlation of renewable energy,support the evaluation of capacity credit for renewable energy considering wide-area wind-photovoltaic complementarity and its influencing factor analysis,and provide the quantitative basis for optimizing wind-photovoltaic capacity ratio,improving source-load correlation and integrating energy storage to enhance capacity credit.关键词
新型电力系统/新能源/D-Vine Copula模型/时空相关性/容量置信度/蒙特卡洛模拟/储能Key words
new power system/renewable energy/D-Vine Copula model/spatio-temporal correlation/capacity credit/Monte Carlo simulation/energy storage引用本文复制引用
王世龙,王仁顺,耿光超,江全元..基于Copula时空相关性模型的新能源容量置信度准时序评估[J].电力系统自动化,2025,49(13):32-42,11.基金项目
国家重点研发计划资助项目(2022YFB2403000) （2022YFB2403000）
国家电网有限公司科技项目(522722230034). This work is supported by National Key R&D Program of China(No.2022YFB2403000)and State Grid Corporation of China(No.522722230034). （522722230034）
