Contingency Analysis Based on Partitioned and Parallel Holomorphic Embedding

TL;DR

This paper introduces a robust, convergent holomorphic embedding-based method for steady-state contingency analysis in power systems, enhanced with partitioning and parallel computation to improve efficiency and scalability.

Contribution

It proposes a novel partitioned and parallel holomorphic embedding approach that improves robustness and reduces computation in large-scale power system contingency analysis.

Findings

Guarantees convergence if solutions exist

Reduces computation time in large systems

Demonstrates effectiveness on systems up to 21,447 buses

Abstract

In the steady-state contingency analysis, the traditional Newton-Raphson method suffers from non-convergence issues when solving post-outage power flow problems, which hinders the integrity and accuracy of security assessment. In this paper, we propose a novel robust contingency analysis approach based on holomorphic embedding (HE). The HE-based simulator guarantees convergence if the true power flow solution exists, which is desirable because it avoids the influence of numerical issues and provides a credible security assessment conclusion. In addition, based on the multi-area characteristics of real-world power systems, a partitioned HE (PHE) method is proposed with an interface-based partitioning of HE formulation. The PHE method does not undermine the numerical robustness of HE and significantly reduces the computation burden in large-scale contingency analysis. The PHE method is further enhanced by parallel or distributed computation to become parallel PHE (P${}^\mathrm{2}$HE). Tests on a 458-bus system, a synthetic 419-bus system and a large-scale 21447-bus system demonstrate the advantages of the proposed methods in robustness and efficiency.