Fast Security-Constrained Optimal Power Flow through Low-Impact and Redundancy Screening

TL;DR

This paper introduces an algorithm that reduces the complexity of security-constrained optimal power flow problems by identifying redundant constraints, significantly decreasing computation time for large power systems.

Contribution

The paper presents a novel method for minimal constraint screening in SCOPF, improving computational efficiency while maintaining solution accuracy.

Findings

At least 95% of constraints are redundant in tested cases.

Significant reductions in SCOPF solve times achieved.

Method validated on multiple large-scale power system models.

Abstract

Determining contingency aware dispatch decisions by solving a security-constrained optimal power flow (SCOPF) is challenging for real-world power systems, as the high problem dimensionality often leads to impractical computational requirements. This problem becomes more severe when the SCOPF has to be solved not only for a single instance, but for multiple periods, e.g. in the context of electricity market analyses. This paper proposes an algorithm that identifies the minimal set of constraints that exactly define the space of feasible nodal injections for a given network and contingency scenarios. By internalizing the technical limits of the nodal injections and enforcing a minimal worst-case impact of contingencies to line flows, computational effort can be further improved. The case study applies and analyzes the methods on the IEEE 118 and A&M 2000 bus systems, as well as the German and European transmission systems. In all tested cases the proposed algorithm identifies at least 95% of the network and security constraints as redundant, leading to significant SCOPF solve time reductions. Scalability and practical implementation are explicitly discussed. The code and input data of the case study is published supplementary to the paper under an open-source license.