Resilient Distribution Network Planning against Dynamic Malicious Power Injection Attacks

TL;DR

This paper proposes a novel grid-level planning approach to improve the resilience of distribution networks against dynamic malicious power injection attacks, balancing security and cost efficiency.

Contribution

It introduces a tractable optimization framework incorporating security constraints into distribution network planning, transforming an intractable problem into a solvable mixed-integer linear program.

Findings

29.3% increase in attack resiliency

Only 2.1% rise in economic cost

Attack severity depends on reactance sum along paths

Abstract

Active distribution networks facilitating bidirectional power exchange with renewable energy resources are susceptible to cyberattacks due to integration of a diverse array of cyber components. This study introduces a grid-level defense strategy aimed at enhancing attack resiliency based on distribution network planning. Our proposed framework imposes a security requirement into existing planning methodologies, ensuring that voltage deviation from its rated value remains within a tolerable range against dynamically and maliciously injected power at end-user nodes. Unfortunately, the formulated problem in its original form is intractable because it is an infinite-dimensional bi-level optimization problem over a function space. To address this complexity, we develop an equivalent transformation into a tractable form as mixed-integer linear program leveraging linear dynamical system theory and graph theory. Notably, our investigation reveals that the severity of potential attacks hinges solely on the cumulative reactances over the path from the substation to the targeted node, thereby reducing the problem to a finite-dimensional problem. Further, the bi-level optimization problem is reduced to a single-level optimization problem by using a technique utilized in solving the shortest path problem. Through extensive numerical simulations conducted on a 54-node distribution network benchmark, our proposed methodology exhibits a noteworthy 29.3% enhancement in the resiliency, with a mere 2.1% uptick in the economic cost.