基于多保真度代理模型的概率静态电压稳定裕度计算方法Probabilistic static voltage stability margin calculation method based on multifidelity surrogate model
王耕宇,王晗,严正,王彦虹,任曦骏,李志伟
WANG Gengyu,WANG Han,YAN Zheng,WANG Yanhong,REN Xijun,LI Zhiwei
摘要(Abstract):
针对高渗透率可再生能源与新型负荷接入下电力系统SVSM(静态电压稳定裕度)计算问题,计及源荷多重不确定性的影响,提出了一种基于多保真度代理模型的概率SVSM计算方法。所提多保真度模型包括低保真度模型与校正函数两部分,低保真度模型以低精度样本为输入,通过稀疏多项式混沌展开方法,充分发挥了代理模型计算效率高的优势;校正函数采用少量高精度样本为输入实现关键多项式基函数待求系数修正,在提升计算准确性的同时尽可能减少计算负担。最后,采用含风光场站与随机负荷的IEEE 30节点系统和IEEE 118节点系统验证了所提方法的有效性,并分析了模型参数的影响。
To address the static voltage stability margin(SVSM) calculation problem in power systems with high penetration of renewable energy and emerging loads, this paper presents a probabilistic SVSM calculation method based on a multi-fidelity surrogate model that accounts for multi-source uncertainties from both generation and demand. The proposed multi-fidelity model consists of a low-fidelity model and a correction function. The low-fidelity model utilizes low-accuracy samples as input and leverages sparse polynomial chaos expansion to fully exploit the high computational efficiency of surrogate models. Concurrently, the correction function uses a small number of high-accuracy samples as input to modify the undetermined coefficients of key polynomial basis functions, thereby improving calculation accuracy while minimizing the computational burden. Finally, the validity of the proposed method is verified using the IEEE 30-bus and IEEE 118-bus systems, which incorporate wind and PV power plants and stochastic loads, and the impacts of model parameters are analyzed.
关键词(KeyWords):
静态电压稳定分析;多保真代理模型;稀疏多项式混沌展开;源荷不确定性
static voltage stability analysis;multi-fidelity surrogate model;sparse polynomial chaos expansion;generation and load uncertainty
基金项目(Foundation): 国家自然科学基金(U24B6009)
作者(Author):
王耕宇,王晗,严正,王彦虹,任曦骏,李志伟
WANG Gengyu,WANG Han,YAN Zheng,WANG Yanhong,REN Xijun,LI Zhiwei
DOI: 10.19585/j.zjdl.202607001
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- 静态电压稳定分析
- 多保真代理模型
- 稀疏多项式混沌展开
- 源荷不确定性
static voltage stability analysis - multi-fidelity surrogate model
- sparse polynomial chaos expansion
- generation and load uncertainty