Articles
Sustainable and Resilient Infrastructure (23789689)10(1)pp. 40-62
Extreme dust storms (EDSs) are high-impact low-probability natural disasters, and their occurrence in humid climates can damage the power distribution systems (PDSs) as a critical infrastructure. In this paper, proposed a bi-level stochastic framework for simultaneously hardening substations and distribution lines. In the first level, total capital cost is addressed for PDS hardening under the financial constraints, while in the second level, the expected operating costs are minimized in the case of an EDS under the operating constraints. In the proposed model, the location of remote-controled switches (RCSs) is determined based on the PDS hardening planning results, and the decisions at each level depend on the planning results of the other level. The simulation results at different budget levels show that simultaneous hardening planning of distribution lines and substations considering network reconfiguration can not only reduce expected operating costs, but also can reducing total capital cost to PDS resilience enhancement. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Electric Power Systems Research (03787796)
Today, the expansion utilization of telecommunication systems results in the power systems having a physical-cyber structure. Despite the advantages of this structure, one of the main challenges is the impact of cyber on physical and vice versa. This study focuses on False Data Injection (FDI) attacks, where the attacker's aim is to overload a transmission line, leading to load shedding. It is proven that the attacker can bypass Bad Data Detection (BDD) module in DC state estimation (DC-SE) by considering both voltage phase angle and magnitude, even when they only know network parameters and can only inject false data in local area. The proposed method is formulated as a bi-level max-min optimization problem where the load shedding is maximized and minimized from the attacker's and operator's perspectives, respectively. To demonstrate the effectiveness of the proposed method, simulations are carried out on IEEE 39 Bus New England System using DIgSILENT PowerFactory 2021 SP2. © 2024 Elsevier B.V.
International Journal of Critical Infrastructure Protection (18745482)44
In the recent years, dust storms (DSs) pose a serious threat to critical infrastructure such as power distribution networks (PDNs). During DSs, the contamination of insulators, increases the possibility of damage to the PDNs insulation system and flashover induced power outage may occur. Power outages disrupt the performance of other urban infrastructures and, in addition to heavy financial losses, cause public dissatisfaction. Although this issue is of particular importance in areas with humid climate, a few studies have been reported on PDNs resilience improvement against DSs. This paper proposes a novel cost-based optimization model to make PDNs more resilient to DSs considering uncertainties. The proposed model is based on the two-stage stochastic mixed-integer programming (SMIP). In the first stage, decisions are made to equip repair crews (RCs) with insulator washing machines, hardening distribution lines with silicone-rubber insulators (SIs), and deploy backup distributed generators (DGs). Decisions in the second stage include network reconfiguration, RCs routing, DGs power dispatch, and load shedding as the critical options for PDN outage management during/after DSs. Case studies are evaluated in the IEEE 69-bus test system and a real 209-bus PDN in Khuzestan province, a coastal province in southwestern Iran. The simulation results at different budget levels have confirmed the efficiency of the proposed model for cost-optimal resilience enhancement planning of PDNs against DSs. © 2023 Elsevier B.V.
