A Novel Bi-Level VSC-DC Transmission Expansion Planning Method of VSC-DC for Power System Flexibility and Stability Enhancement  

作　　者：Weigang Jin Lei Chen Shencong Zheng Yuqi Jiang Yifei Li Hongkun Chen 

机构地区：[1]Support Center of Power System Technology,Central China Branch of State Grid Corporation of China,Wuhan,430077,China [2]Hubei Engineering and Technology Research Center forAC/DC Intelligent Distribution Network,School of Electrical Engineering and Automation,Wuhan University,Wuhan,430072,China

出　　处：《Energy Engineering》2024年第11期3161-3179,共19页能源工程（英文）

基　　金：supported by the Science and Technology Project of Central China Branch of State Grid Corporation of China under Grant 52140023000T.

摘　　要：Investigating flexibility and stability boosting transmission expansion planning(TEP)methods can increase the renewable energy(RE)consumption of the power systems.In this study,we propose a bi-level TEP method for voltage-source-converter-based direct current(VSC-DC),focusing on flexibility and stability enhancement.First,we established the TEP framework of VSC-DC,by introducing the evaluation indices to quantify the power system flexibility and stability.Subsequently,we propose a bi-level VSC-DC TEP model:the upper-level model acquires the optimal VSC-DC planning scheme by using the improved moth flame optimization(IMFO)algorithm,and the lower-level model evaluates the flexibility through time-series production simulation.Finally,we applied the proposedVSC-DC TEPmethod to the modified IEEE-24 and IEEE-39 test systems,and obtained the optimalVSCDC planning schemes.The results verified that the proposed method can achieve excellent RE curtailment with high flexibility and stability.Furthermore,the well-designed IMFO algorithm outperformed the traditional particle swarm optimization(PSO)and moth flame optimization(MFO)algorithms,confirming the effectiveness of the proposed approach.

关 键 词：VSC-DC transmission expansion planning renewable energy consumption line delivery flexibility short-circuit ratio improved moth flame optimization algorithm 

分 类 号：TM72[电气工程—电力系统及自动化]