Analytical method based on improved Gaussian mixture model for probabilistic load flow
DOI:10.19783/j.cnki.pspc.190778
Key Words:probabilistic load flow  Gaussian mixture model  genetic algorithm  correlation  joint distribution
Author NameAffiliationE-mail
LI Congcong State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China  
WANG Tong State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China  
XIANG Yuwei State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China xiangyw5517@163.com 
WANG Zengping State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China  
SHI Bonian Beijing Sifang Automation Co., Ltd., Beijing 100085, China  
ZHANG Yan State Grid Beijing Electric Power Research Institute, Beijing 100075, China  
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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).
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