Work

Two-Stage Stochastic Resilience Optimization of Converter Stations Under Uncertain Mainshock-Aftershock Sequences

Year: 2025

Type: article

Abstract: Reducing vulnerability and enhancing resilience of power systems, particularly converter stations, against earthquakes are crucial for maintaining their safe operation. Nevertheless, earthquakes are often accompanied by aftershocks and encompass plenty of uncertainties, posing significant challenges for the development of a joint pre-earthquake preparation and post-earthquake restoration strategy. In this article, a novel two-stage stochastic programming model is put forth to bolster the resilience of converter stations under uncertainties associated with mainshock-aftershock sequences. The model is bifurcated into two pillars: the first stage focuses on designing equipment hardening strategy (EHS) and spare parts strategy (SPS) for converter stations before earthquakes. The second stage is dedicated to the optimization of recovery scheduling (RS) in the wake of mainshock-aftershock sequences. To mitigate the endogenous uncertainties of the model, a tolerated random number generation method is employed to generate deterministic scenarios in the second stage. The resulting optimization model is, then, efficiently solved using a combination of the sample average approximation and progressive hedging algorithm. To evaluate the performance of the proposed resilience optimization method, comparative studies are carried out on a real ±800 kV converter station located in the city of Yibin, Sichuan Province, P.R. China. Results from this application demonstrate that the EHS pre-dominantly focuses on hardening the equipment of the same circuit to guarantee the low transmission capacity under diverse earthquake scenarios, while the SPS is proven to be more effective than the EHS in scenarios with high values of peak ground acceleration (PGA) of mainshock and occurrence intensity of aftershocks.

Source: IEEE Transactions on Power Systems

DOI: 10.1109/tpwrs.2025.3561890

Authors: Kai Wang, Zhihang Xue, Di Cao, Yu Liu, Yi-Ping Fang

Institutions: University of Electronic Science and Technology of China, Université Paris-Saclay, CentraleSupélec

Language: English

Topic: Infrastructure Resilience and Vulnerability Analysis

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: green

Funders: Agence Nationale de la Recherche, National Natural Science Foundation of China