Risk Assessment of Multi-timescale Cascading Outages based on Markovian Tree Search

TL;DR

This paper introduces a novel risk assessment method for cascading outages using Markovian Tree Search, improving computational efficiency and modeling accuracy in power system risk analysis.

Contribution

It reformulates a Markovian tree model from a multi-timescale simulation and develops a tree search scheme with a risk estimation index to enhance efficiency and accuracy.

Findings

Effective in a 4-node power system

Demonstrates improved convergence and efficiency on RTS-96 system

Reduces duplicated simulations significantly

Abstract

In the risk assessment of cascading outages, the rationality of simulation and efficiency of computation are both of great significance. To overcome the drawback of sampling-based methods that huge computation resources are required and the shortcoming of initial contingency selection practices that the dependencies in sequences of outages are omitted, this paper proposes a novel risk assessment approach by searching on Markovian Tree. The Markovian tree model is reformulated from the quasi-dynamic multi-timescale simulation model proposed recently to ensure reasonable modeling and simulation of cascading outages. Then a tree search scheme is established to avoid duplicated simulations on same cascade paths, significantly saving computation time. To accelerate the convergence of risk assessment, a risk estimation index is proposed to guide the search for states with major contributions to the risk, and the risk assessment is realized based on the risk estimation index with a forward tree search and backward update algorithm. The effectiveness of the proposed method is illustrated on a 4-node power system, and its convergence profile as well as efficiency is demonstrated on the RTS-96 test system.