首页|基于多保真度模型的高比例新能源配电网潮流不确定性表征方法

基于多保真度模型的高比例新能源配电网潮流不确定性表征方法

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
在能源生产和消费低碳化转型的背景下，如何量化源-荷不确定性的影响，高效、准确地实现配电网潮流不确定性表征，对配电网的安全、可靠运行具有重要意义。该文从计算效率和准确性两方面出发，将现有基于概率潮流的不确定性表征方法分为高、低保真度模型两类，综合分析了两种方法存在的问题。在此基础上，提出一种基于多保真度模型的高比例新能源配电网潮流不确定性表征方法，以实现潮流状态变量矩信息的高精度估计与概率分布函数的刻画。在矩信息估计方面，提出结合高、低保真度模型特点的最优输入样本数量分配方法，在给定总计算负担下实现了输出变量矩信息的无偏估计。在概率分布函数刻画方面，提出基于综合启动函数的概率分布函数刻画方法，利用多保真度模型提供的先验信息，提升概率分布函数拟合的准确性。通过118节点配电网的仿真计算，验证所提方法的有效性。
Uncertainty Representation Method of Power Flow in Distribution Network With High Percentage of Renewable Energy Based on the Multi-fidelity Model
In the context of the decarbonization transition of energy production and consumption,it is important to quantify the impacts of source-load uncertainty and realize the uncertainty representation of power flow efficiently and accurately for the safe and reliable operation of the distribution network.This paper classifies the existing uncertainty representation methods into two categories including high and low fidelity models based on their computational efficiency and accuracy,and comprehensively analyzes the disadvantages of both methods.On this basis,an uncertainty representation method of power flow in distribution network with high percentage of renewable energy based on the multi-fidelity model is proposed to achieve high-precision estimation of the statistical moment of the power flow status variables and depict the probability distribution function.In terms of statistical moment estimation,an optimal input sample allocation method combining the characteristics of high and low fidelity models is proposed to achieve unbiased estimation of the statistical moment of output variable with a given computational budget.In terms of probability distribution function fitting,a method based on the comprehensive startup function is proposed to improve the accuracy of the fitting result by using the prior information provided by the multi-fidelity model.The effectiveness of the proposed method is verified by simulation calculations of 118-node distribution network.

uncertainty representationprobabilistic power flowmulti-fidelity modelstatistical momentprobability distribution function

胡喆、王晗、严正、徐潇源、陈玥、许少伦

展开 >

电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市 闵行区 200240

上海非碳基能源转换与利用研究院,上海市 闵行区 200240

香港中文大学机械与自动化工程系,香港特别行政区 999077

不确定性表征 概率潮流 多保真度模型 统计矩 概率分布函数

国家自然科学基金国家自然科学基金澳门特别行政区科学技术发展基金智慧城市物联网国家重点实验室(澳门大学)开放课题

52107116U2166201SKL-IOTSCUM-2024-2026SKL-IoTSCUM-2024-2026/ORP/GA03/2023

2024

10.13334/j.0258-8013.pcsee.230121

中国电机工程学报
中国电机工程学会

中国电机工程学报

CSTPCD北大核心
影响因子：2.712
ISSN：0258-8013
年,卷(期)：2024.44(8)
  • 35