引用本文: | 刘 珂,顾雪平,白岩松,等.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.[点击复制] |
LIU Ke,GU Xueping,BAI Yansong,et al.Online dynamic partition restoration optimization method of a power system considering wind power uncertainty[J].Power System Protection and Control,2024,52(19):60-73[点击复制] |
|
摘要: |
规模风电的接入给电力系统运行与控制带来了很大不确定性,也给大停电后的系统恢复带来挑战。根据恢复过程中变化的风电出力场景动态调整分区恢复方案有助于提升恢复效率。在计及初始停电场景中风电不确定性的基础上,为进一步考虑恢复过程中风电出力的不确定性,提出了一种电力系统在线动态分区恢复优化方法。首先,建立风电出力的不确定场景集合,基于Wasserstein距离构建分布之间的测度,采用核密度估计求取风电出力预测误差的不确定集合。然后,刻画恢复模型约束、分区模型约束、动态分区约束,分别从系统网架和运行状态两个角度设立两阶段优化目标,建立两阶段动态分区恢复分布鲁棒优化模型,并采用对偶理论等实现模型的转化与求解。最后,新英格兰10机39节点系统和实际电网算例的仿真结果表明所提动态分区恢复方法能有效应对风电出力不确定性和提高系统恢复效率。 |
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 |
DOI:10.19783/j.cnki.pspc.240245 |
投稿时间:2024-03-04修订日期:2024-07-23 |
基金项目:国家自然科学基金项目资助(U22B2099) |
|
Online dynamic partition restoration optimization method of a power system considering wind power uncertainty |
LIU Ke1,GU Xueping1,BAI Yansong1,LI Shaoyan1,LIU Yan1,LIU Yutian2,WANG Hongtao2 |
(1. School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;
2. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education
(Shandong University), Jinan 250061, China) |
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. |
Key words: power system restoration partition restoration online decision wind power uncertainty distributed robust optimization |