| 引用本文: | 李聪聪,王彤,相禹维,等.基于改进高斯混合模型的概率潮流解析方法[J].电力系统保护与控制,2020,48(10):146-155.[点击复制] |
| LI Congcong,WANG Tong,XIANG Yuwei,et al.Analytical method based on improved Gaussian mixture model for probabilistic load flow[J].Power System Protection and Control,2020,48(10):146-155[点击复制] |
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| 摘要: |
| 大规模风电并网使电力系统的随机性问题日益突出，概率潮流分析是一种能够计及电力系统随机性的稳态运行分析重要工具。针对风电的随机性和多个风电场出力之间的相关性问题，提出利用遗传算法改进的高斯混合模型求解多个风电场出力的联合概率密度函数，利用联合概率密度函数对多个风电场出力的随机性和相关性进行刻画。在此基础之上，利用线性潮流方程计算多条线路和多个节点电压的联合概率分布，最终求解概率潮流的计算结果。仿真结果表明，所提方法计算精度高，速度快，利用联合概率密度函数和联合分布函数能够比边缘分布更精确地评估电力系统多条线路同时过载的风险。 |
| 关键词: 概率潮流 高斯混合模型 遗传算法 相关性 联合分布 |
| DOI：10.19783/j.cnki.pspc.190778 |
| 投稿时间：2019-07-04修订日期：2019-12-21 |
| 基金项目:国家自然科学基金项目资助(51637005); 中央高校基本科研专项资金资助(2018MS006); 国家电网公司科技项目资助(特大型电网系统级控制保护技术框架研究与设计)(SGBJDK00KJJS1900088) |
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| Analytical method based on improved Gaussian mixture model for probabilistic load flow |
| LI Congcong,WANG Tong,XIANG Yuwei,WANG Zengping,SHI Bonian,ZHANG Yan |
| (State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;Beijing Sifang Automation Co., Ltd., Beijing 100085, China;State Grid Beijing Electric Power Research Institute, Beijing 100075, China) |
| Abstract: |
| The random nature of a power system is accentuated by large-scale wind generation. Probabilistic load flow is an important tool for steady-state operation evaluation analysis that takes into account the random nature of the system. Considering the random nature and correlation of output power of several wind farms, a probability model based on Gaussian mixture model improved by genetic algorithm is proposed, which can exactly characterize the random nature and correlation of renewable generation. On this basis, the joint probability density function and joint cumulative distribution function of transmission lines are derived by a load flow equation, which obtains the results of probabilistic load flow. Simulation results demonstrate that the proposed method gives high accuracy and high speed. The method can assess the risk of multiple lines being overloaded simultaneously. This work is supported by National Natural Science Foundation of China (No. 51637005), Fundamental Research Funds for the Central Universities (No. 2018MS006), and Science and Technology Project of State Grid Corporation of China (Research and Design of System Level Control Protection Technology Framework for Extra Large Power Grid) (No. SGBJDK00KJJS1900088). |
| Key words: probabilistic load flow Gaussian mixture model genetic algorithm correlation joint distribution |
