Optimized Defense Resource Allocation for Coupled Power-Transportation Networks Considering Information Security
Year: 2025
Type: article
Abstract: Electric vehicle charging stations (EVCSs) are critical interfaces between urban mobility and distribution grids and are increasingly exposed to false data that can mislead operations and degrade voltage quality. This study proposes a defense-planning framework that models how cyber manipulation propagates to physical impacts in a coupled transport–power system. The interaction is modeled as a tri-level defender–attacker–operator problem in which a defender hardens a subset of charging stations, an attacker forges measurements and demand, and an operator redispatches resources to keep the system secure. We solve this problem with a method that embeds corrective operation into the evaluation and uses improved implicit enumeration (IIE) with pruning to identify a small set of high-value stations to protect with far fewer trials than an exhaustive search. On a benchmark feeder coupled to a road network, protecting a few traffic-critical stations restores compliance with voltage limits under tested attack levels while requiring roughly an order of magnitude fewer evaluations than complete enumeration. Sensitivity analysis shows that the loss of reactive power from PV inverters (PV VARs) harms voltage profiles more than an equivalent reduction in distributed storage, indicating that maintaining local reactive capability reduces the number of stations that must be hardened to meet a given voltage target. These results guide utilities and city planners to prioritize protection at traffic-critical EVCSs and co-plan local Volt/VAR capability, achieving code-compliant voltage quality under adversarial conditions with markedly lower planning effort.
Source: Energies
DOI: 10.3390/en18215855
Authors: Yuheng Liu, Wenteng Liang, Jie Li, Yufeng Xiong, Yan Li, Qinran Hu, Tao Qian, Jinyu Yue
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4415990634
