电力系统保护与控制2024，Vol.52Issue(19)：60-73,14.DOI:10.19783/j.cnki.pspc.240245
考虑风电不确定性的电力系统在线动态分区恢复优化方法
Online dynamic partition restoration optimization method of a power system considering wind power uncertainty
摘要
Abstract
The access of large-scale wind power brings great uncertainty to the operation and control of a power system,and also brings challenges to the system restoration after blackout.Dynamically adjusting the partition restoration scheme according to the changing wind power output scenarios during the restoration process helps to improve restoration efficiency.Based on the uncertainty of wind power in the initial outage scenario,an online dynamic partition restoration optimization method for a power system is proposed to further consider the uncertainty of wind power output during the restoration process.First,the uncertain scenario set of wind power output is established,and the measure between distributions is constructed based on Wasserstein distance.Kernel density estimation is used to obtain the uncertain set of wind power output prediction error.Secondly,the restoration model,partition model and dynamic partition constraints are characterized.Two-stage optimization objectives are set up from the perspectives of grid topology and operation state,and the two-stage dynamic partition restoration distributed robust optimization model is established.The dual theory is used to realize the transformation and analysis of the model.Finally,a simulation of the New England 10-machine 39-bus system and a real power system verify that the dynamic partition restoration method proposed can effectively deal with the uncertainty of wind power output and improve system restoration efficiency.关键词
电力系统恢复/分区恢复/在线决策/风电不确定性/分布鲁棒优化Key words
power system restoration/partition restoration/online decision/wind power uncertainty/distributed robust optimization引用本文复制引用
刘珂,顾雪平,白岩松,李少岩,刘艳,刘玉田,王洪涛..考虑风电不确定性的电力系统在线动态分区恢复优化方法[J].电力系统保护与控制,2024,52(19):60-73,14.基金项目
This work is supported by the National Natural Science Foundation of China(No.U22B2099). 国家自然科学基金项目资助(U22B2099) （No.U22B2099）
