文章摘要
孙磊,刘伟佳,林振智,等.计及线路投运风险的电力系统恢复路径优化[J].电力系统自动化,2015,39(23):75-82. DOI: 10.7500/AEPS20150214006.
SUN Lei,LIU Weijia,LIN Zhenzhi, et al.Determination of Optimal Restoration Paths for Power Systems Considering Failure Risk of Restoring Transmission Lines[J].Automation of Electric Power Systems,2015,39(23):75-82. DOI: 10.7500/AEPS20150214006.
计及线路投运风险的电力系统恢复路径优化
Determination of Optimal Restoration Paths for Power Systems Considering Failure Risk of Restoring Transmission Lines
DOI:10.7500/AEPS20150214006
关键词: 系统恢复  线路投运  风险评估  鲁棒优化  电力系统
KeyWords: system restoration  transmission line restoration operation  risk evaluation  robust optimization  power systems
上网日期:2015-12-01
基金项目:国家高技术研究发展计划(863计划)资助项目(2015AA050202);国家自然科学基金资助项目(51377005);浙江省重点科技创新团队项目(2010R50004);国家电网公司科技项目(5211011306TB)
作者单位E-mail
孙磊 浙江大学电气工程学院, 浙江省杭州市 310027  
刘伟佳 浙江大学电气工程学院, 浙江省杭州市 310027  
林振智 浙江大学电气工程学院, 浙江省杭州市 310027 zhenzhi.lin@gmail.com 
文福拴 浙江大学电气工程学院, 浙江省杭州市 310027; 文莱科技大学电机与电子工程系, 斯里巴加湾市 BE1410, 文莱  
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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