Management of Cascading Outage Risk Based on Risk Gradient and Markovian Tree Search

TL;DR

This paper introduces a risk management method for cascading outages in power systems using Markovian tree search and risk gradient optimization, demonstrating accuracy and efficiency on multiple test systems.

Contribution

It develops a novel risk management approach combining Markovian tree search with an iterative risk management method for effective cascading outage risk reduction.

Findings

Accurate risk gradient computation verified on RTS-96 system.

Effective risk reduction demonstrated through optimization.

Time performance shows suitability for online decision support.

Abstract

Since cascading outages are major threats to power systems, it is important to reduce the risk of potential cascading outages. In this paper, a risk management method of cascading outages based on Markovian tree search is proposed. With the tree expansion on the cascading outage risk, risk gradient is computed efficiently by a forward-backward tree search scheme with good convergence, and it is then employed in an optimization model to minimize control cost while effectively reducing the cascading outage risk. To overcome the limitation with linearization in computing risk gradient, an iterative risk management (IRM) approach is further developed. Tests on the RTS-96 3-area system verify the accuracy of the computed risk gradient and its effectiveness for risk reduction. Time performance of the proposed IRM approach is tested on the RTS-96 system, a 410-bus US-Canada northeast system and a 1354-bus Mid-European system, and demonstrates its potentials for decision support on practical power systems online or on hourly basis.