Identification of Nodes Most Vulnerable to Voltage Collapse in Cascading Failures of Power Systems

Li, Meixuan Jade; Tse, Chi K.

doi:10.1109/iscas46773.2023.10181960

Cited by 4 publications

(1 citation statement)

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“…Robust optimization has gained significant popularity among scholars worldwide due to its ability to address operational constraints of stochastic scenarios, enhance power system stability, and eliminate the need for scenario acquisition and extensive data processing. In reference [19], a method is proposed to identify high-risk links in transmission lines based on the similarity of current transfer.…”

Section: Introductionmentioning

confidence: 99%

Two-stage robust reserve coordinated operation based on real-time price mechanism

Deng,

Hui,

Liang

et al. 2024

Preprint

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In order to cope with the impacts of uncertainty factors on the reliability and economy of system operation, a two-stage robust reserve optimization model is proposed to take into account the real-time price day-ahead and intraday. The proposed model consists of two phases: the first phase is the day-ahead decision-making phase, which improves the flexibility of grid operation by establishing a demand response model based on the load classification under real-time price; and the second phase is the intraday decision-making phase, which takes into account the uncertainty of the wind - photovoltaic power and the failure of the transmission line in order to improve the robustness of the grid in coping with the uncertainties. The day-ahead decision-making is a deterministic optimization problem, which is solved by a linear programming problem, while the intra-day decision-making is an adaptive robust optimization problem, which is solved by using the Strong Dyadic Theory (SDT) method. By combining the power data of wind and photovoltaic fluctuation intervals and load demand of a regional power grid, a simulation model is constructed to verify that the proposed robust model is effective in optimizing the reserve capacity of the system operation under multiple uncertainties, reducing the scheduling cost of the system in the worst scenario, and ensuring the safe and reliable operation of the system.

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Section: Introductionmentioning

confidence: 99%