Mixed-Integer Optimization for Bio-Inspired Robust Power Network Design

TL;DR

This paper introduces a bio-inspired mixed-integer nonlinear programming approach to enhance power grid resilience by balancing efficiency and redundancy, leading to more reliable and robust energy delivery during severe disturbances.

Contribution

It proposes a novel optimization framework inspired by ecological robustness to improve power network design for resilience against natural and cyber threats.

Findings

Increased system robustness under N-x contingencies.

Reduced violations and improved reliability.

Effective balance between efficiency and redundancy.

Abstract

Power systems are susceptible to natural threats including hurricanes and floods. Modern power grids are also increasingly threatened by cyber attacks. Existing approaches that help improve power system security and resilience may not be sufficient; this is evidenced by the continued challenge to supply energy to all customers during severe events. This paper presents an approach to address this challenge through bio-inspired power system network design to improve system reliability and resilience against disturbances. Inspired by naturally robust ecosystems, this paper considers the optimal ecological robustness that recognizes a unique balance between pathway efficiency and redundancy to ensure the survivability against disruptive events for given networks. This paper presents an approach that maximizes ecological robustness in transmission network design by formulating a mixed-integer nonlinear programming optimization problem with power system constraints. The results show the increase of the optimized power system's robustness and the improved reliability with less violations under N-x contingencies.