Search for: All records

False data injection (FDI) attacks targeting under-load tap changing (ULTC) transformers pose a significant threat to smart distribution networks by exploiting vulnerabilities in the volt-var optimization (VVO) process, leading to potential undervoltage and voltage collapse. The increased integration of renewable energy and cyber-physical systems has expanded the attack surface, making traditional detection methods inadequate. For example, in 2023, attacks on utilities and decentralized components in the United States rose by 200%, with overall cyber threats increasing by 104%, highlighting growing vulnerabilities in distribution systems. To this end, this article proposes a two-stage remediation framework for decentralized FDI (DFDI) attacks targeting ULTC transformers. In the attack stage, vulnerabilities in ULTCs and voltage regulators are scrutinized, risking voltage collapse or blackouts in the distribution system. In the remediation stage, the distribution system operator focuses on non-attacked ULTCs, voltage regulators, distributed generation (DG) units, and smart homes to minimize reliance on compromised components. In this regard, a distinctive formulation of distribution network resilience and load management (DNRLM) problem is introduced to identify a resilient network topology and determine a situational power balance strategy. The proposed framework focuses on minimizing the system's reliance on the attacked ULTCs and voltage regulator components, thereby avoiding the intended voltage collapse caused by such DFDIs. The simulation results verify that the proposed method reduces the voltage collapse proximity index by over 60%, enhancing system resilience under DFDI attacks. 

This article investigates the impacts of coordinated false data injection attacks (FDIAs) on voltage profiles in smart microgrids integrated with renewable-based distributed energy resources (DERs), a critical component of urban energy infrastructure in smart cities. By leveraging simulation and experimental methods, a coordinated framework is developed for understanding and mitigating these threats, ensuring the stability of renewable-based DERs integral to modern urban systems. In the examined framework, a team of attackers independently identify the optimal times of two different cyberattacks leading to undervoltage and overvoltage in a smart microgrid. The objective function of each model is to increase the voltage violation in the form of either overvoltage or undervoltage caused by the corresponding FDIA. In such a framework, the attackers design a multi-objective optimization problem (MOOP) simultaneously resulting in voltage violations in the most vulnerable regions of the targeted microgrid. Considering the conflict between objective functions in the developed MOOP, a Pareto-based solution methodology is utilized to obtain a set of optimal solutions, called non-dominated solutions, as well as the best compromise solution (BCS). The effectiveness of the unified FDIA is verified based on simulation and experimental validations. In this regard, the IEEE 13-node test feeder has been modified as a microgrid for the simulation analysis, whereas the experimental validation has been performed on a lab-scale hybrid PV/wind microgrid containing renewable energy resources. 

This paper proposes a framework to optimally employ static VAR compensators (SVCs) within a customized reconfiguration of system topology, leading to remediation of voltage violations caused by false data injection (FDI) cyberattacks targeting smart distribution grids. The designed framework contains formulations associated with planning and operation phases. In the planning phase, the scrutinized system, modified by photovoltaic (PV) units, is enhanced by optimally allocating static VAR compensators (SVCs) to keep the unity power factor throughout the system. Then, distribution system operator (DSO), being in attacker’s shoe, examines relevant cyberattack scenarios leading to voltage violations within the distribution system. Finally, in the operation phase, DSO takes advantage of the optimally planned SVCs to identify proper vectors (i.e., remedial actions) to cope with such potential scenarios of cyberattacks. These (to be recognized) vectors are associated with the variable shunt susceptance of the mentioned SVCs, which will be identified by solving a customized distribution feeder reconfiguration (DFR) problem in the operation phase. The main objective of the customized DFR is to maximize the contributions of SVCs through enhancing the voltage profile of the targeted system. This will enable DSO to mitigate the negative impacts of the FDI attacks and recover the voltage profile of the smart distribution network. The effectiveness of the proposed RAS is validated on three different smart test systems (i.e., 33-bus, 95-bus, and 136-bus systems), which are modified to contain SVC components and renewable-based distributed generation (DG) units. 

This paper proposes an on-line remedial action scheme (OLRAS) in order to mitigate the voltage violations caused by false data injection attacks (FDIAs) targeting under load tap changing (ULTC) transformers in smart distribution systems. The FDIA framework contains two different phases. In the attack phase, distribution system operator (DSO), being in attacker's shoe, considers cyberattack scenarios through compromising the results of volt-var optimization problem in a radial distribution grid modified with distributed energy resources (DERs) such as photovoltaic (PV) units and wind turbines (WTs). The outcome of the attack phase will be the compromised voltage profile of the distribution grid showing different rates of voltage violations. In the reaction phase, the DSO rapidly identifies a customized distribution feeder reconfiguration (CDFR) in order to update the flows of active and reactive power throughout the targeted distribution system and recover the voltage profile. The objective functions of the proposed CDFR are defined to minimize the impacts of such cyberattacks targeting ULTCs within distribution grids. This will empower DSOs to react to severe cyberattacks, bypassing the detection stage, and address the voltage violations in a timely manner. The effectiveness of the proposed OLRAS is validated on an IEEE test system. 

This paper presents a false data injection (FDI) attack model to target a selection of plugged-in electric vehicles (EVs) in a smart power distribution system resulting in a range of operational issues including but not limited to voltage collapse. To reduce the total cost and difficulty of the cyberattack, attacker utilizes a pre-attack analysis via generating PV and VQ curves for the buses of the distribution system in order to precisely recognize the weakest buses of the system (i.e., the most vulnerable ones) and also the required active and reactive power to be injected into the targeted buses to result in voltage collapse. The effectiveness of the proposed attack model is validated on an IEEE test distribution system modified to contain distributed generation (DG) and EV aggregators. 

In the process of protecting power systems against different types of cyberattacks, the primary step is to precisely model such frameworks from attacker's perspective. This paper investigates a false data injection (FDI) attack framework, which can target under-load tap changing (ULTC) transformers, resulting in manipulated voltage profile in radial smart distribution networks. The developed FDI model compromises the voltage profile of a distribution feeder through misleading the volt/var optimization, that optimally manages system-wide voltage profile and flow of reactive power. The presented attack model is formulated as a bi-objective optimization problem. The objective functions from the attacker's point of view are 1) minimizing the level of false data to be injected into the smart meters associated with load data and 2) maximizing the voltage deviation of the distribution grid. Negative impacts of such a cyberattack model have been validated and discussed in this work on an IEEE distribution test system, necessitating proper remedial actions, which will be elaborated in the next step of this research.