Contingency-Risk Informed Power System Design

TL;DR

This paper introduces a mixed-integer programming approach for designing cost-effective power systems that meet the generalized N-k-e survivability criterion, efficiently handling complex contingency analysis.

Contribution

It presents a novel algorithm that efficiently identifies vulnerabilities to avoid combinatorial explosion in contingency analysis during power system design.

Findings

Successfully solved large-scale problems for k=4 within 2 minutes

Outperformed existing methods by significant computational speedup

Validated approach on IEEE benchmark systems

Abstract

We consider the problem of designing (or augmenting) an electric power system at a minimum cost such that it satisfies the N-k-e survivability criterion. This survivability criterion is a generalization of the well-known N-k criterion, and it requires that at least (1- e_j) fraction of the total demand to be met after failures of up to j components, for j=1,...,k. The network design problem adds another level of complexity to the notoriously hard contingency analysis problem, since the contingency analysis is only one of the requirements for the design optimization problem. We present a mixed-integer programming formulation of this problem that takes into account both transmission and generation expansion. We propose an algorithm that can avoid combinatorial explosion in the number of contingencies, by seeking vulnerabilities in intermediary solutions and constraining the design space accordingly. Our approach is built on our ability to identify such system vulnerabilities quickly. Our empirical studies on modified instances from the IEEE 30-bus and IEEE 57-bus systems show the effectiveness of our methods. We were able to solve the transmission and generation expansion problems for k=4 under 2 minutes, while other approaches failed to provide a solution at the end of 2 hours.