Transmission Defense Hardening Against Typhoon Disasters Under Decision-Dependent Uncertainty

作     者：Zhang, Weixin Shao, Changzheng Hu, Bo Xie, Kaigui Siano, Pierluigi Li, Mushui Cao, Maosen 

作者机构：Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Sec Chongqing 400044 Peoples R China Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa State Grid Chongqing Changshou Elect Power Supply Chongqing 401220 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON POWER SYSTEMS》 (IEEE Trans Power Syst)

年 卷 期：2023年第38卷第3期

页      面：2653-2665页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：China Scholarship Council 

主　　题：Tropical cyclones Transmission line measurements Uncertainty Power transmission lines Costs Power systems Stochastic processes Decision-dependent uncertainty transmission defense hardening typhoon disasters sample average approximation stochastic programming 

摘      要：Transmission defense hardening (TDH) is an effective practice to ensure the safe operation of the power system during and after natural disasters, such as typhoons. However, the coupling among the typhoon disaster, hardening decision, and transmission line status is not addressed in the previous research works. Besides, the enhancement measures for different types of transmission line failures have not yet been assessed. This paper proposes a comprehensive framework to improve power transmission system resilience against typhoon disasters. Firstly, the motion path and the wind field of typhoons are simulated using Monte Carlo sampling to quantify the spatiotemporal impacts of wind speed on the transmission line status. The decision-dependent uncertainty (DDU), which reflects the effects of hardening decisions on the transmission lines, is addressed by sampling according to the corresponding failure probabilities. Then, the scenario simulation is integrated into the proposed two-stage stochastic mixed-integer programming (SMIP) model considering various enhancement measures for different failures. Since the way to handle the DDU needs a large number of scenarios, it leads to high computation complexity. Hence, the sample average approximation (SAA) algorithm is introduced to cope with it. Finally, the proposed framework and its solution algorithm are carried out on the modified IEEE RTS-79 system and the modified IEEE 118-Bus system. The significant cost saving and resilience improvement demonstrate the effectiveness of the proposed framework.