Fast Power System Cascading Failure Path Searching with High Wind Power Penetration

TL;DR

This paper introduces a fast, efficient framework for analyzing cascading failures in power systems with high wind power penetration, using Markov chain search and dictionary-based acceleration to handle complex scenario simulations.

Contribution

It proposes a novel Markov chain-based method combined with a line status dictionary to significantly reduce computation time in cascading failure analysis under high wind power integration.

Findings

Effective in reducing computation time for cascading failure path searching.

Validated on IEEE RTS-79 test system and China's Henan Province power system.

Demonstrates feasibility of rapid failure analysis in complex, high-renewable power grids.

Abstract

Cascading failures have become a severe threat to interconnected modern power systems. The ultrahigh complexity of the interconnected networks is the main challenge toward the understanding and management of cascading failures. In addition, high penetration of wind power integration introduces large uncertainties and further complicates the problem into a massive scenario simulation problem. This paper proposes a framework that enables a fast cascading path searching under high penetration of wind power. In addition, we ease the computational burden by formulating the cascading path searching problem into a Markov chain searching problem and further use a dictionary-based technique to accelerate the calculations. In detail, we first generate massive wind generation and load scenarios. Then, we utilize the Markov search strategy to decouple the problem into a large number of DC power flow (DCPF) and DC optimal power flow (DCOPF) problems. The major time-consuming part, the DCOPF and the DCPF problems, is accelerated by the dynamic construction of a line status dictionary (LSD). The information in the LSD can significantly ease the computation burden of the following DCPF and DCOPF problems. The proposed method is proven to be effective by a case study of the IEEE RTS-79 test system and an empirical study of China's Henan Province power system.