计及线路投运风险的电力系统恢复路径优化

SUN Lei

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计及线路投运风险的电力系统恢复路径优化

更新时间：2025-08-13

- 计及线路投运风险的电力系统恢复路径优化
- Automation of Electric Power Systems Vol. 39, Issue 23, Pages: 75-82(2015)
- 作者机构：
  
  浙江大学电气工程学院,浙江省,杭州市,310027
- 作者简介：
- 基金信息：
- DOI：
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- Published：2015
- 稿件说明：
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SUN Lei. [J]. Automation of electric power systems, 2015, 39(23): 75-82.
DOI：

SUN Lei. [J]. Automation of electric power systems, 2015, 39(23): 75-82. DOI：

摘要

投运空载线路是大停电后网架重构方案的主要步骤之一。线路能否投运成功直接影响整个恢复过程的进程和效率。在此背景下

发展了计及线路投运风险的恢复路径优化模型。首先分析了影响线路投运的因素

主要包括线路的充电电容和恢复时间

进而定义了线路投运失败的相对可能性。然后

基于线路投运失败的严重性指标

定义了线路的投运风险。考虑到线路恢复时间的不确定性会导致线路投运风险的不确定性

为此建立了分别适用于“串行”和“并行”恢复阶段、以线路投运风险之和最小为目标的鲁棒优化模型

以确定最优恢复路径。采用高效商业求解器CPLEX求解所发展的鲁棒优化模型。最后

以新英格兰10机39节点系统为例说明了所发展的模型和方法的基本特征。

Abstract

Putting unloaded transmission lines into operation is one of the main steps of network reconfiguration in power system restoration. Whether an unloaded transmission line could be restored successfully or not may have a significant impact on the speed of the whole restoration process. Given this background

an approach to determine optimal restoration paths for a power system is presented with the failure risk of restoring transmission lines taken into account. First

the factors having impacts on restoring transmission lines are analyzed

including the charging capacity and restoration time of the transmission lines. Then

the relative probability of line restoring failure is defined. Based on the severity of line restoring failure

the line restoring risk is defined. Considering that the uncertainty of the restoration time of the lines leads to the uncertainty of the line restoring outcome

two robust optimization models are presented to minimize the total line restoring risks

which are applied to the “series” and “parallel” restoration stages

respectively. The highly efficient commercial solver CPLEX 12.2 is next employed to solve the developed two robust optimization models. Finally

the New England 10-unit 39-bus power system is served for demonstrating the basic characteristics of the developed models and methods. This work is jointly supported by National High Technology Research and Development Program of China (863 Program) (No. 2015AA050202)

National Natural Science Foundation of China (No. 51377005)

Zhejiang Key Science and Technology Innovation Group Program (No. 2010R50004) and State Grid Corporation of China (No. 5211011306TB).

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