Disrupting the power grid via EV charging: The impact of the SMS Phishing attacks

2021 ◽  
Vol 26 ◽  
pp. 100477
Author(s):  
Elif Ustundag Soykan ◽  
Mustafa Bagriyanik ◽  
Gurkan Soykan
2021 ◽  
Vol 2 (2) ◽  
pp. 1-21
Author(s):  
Hossam ElHussini ◽  
Chadi Assi ◽  
Bassam Moussa ◽  
Ribal Atallah ◽  
Ali Ghrayeb

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.


Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1688 ◽  
Author(s):  
C. Birk Jones ◽  
Matthew Lave ◽  
William Vining ◽  
Brooke Marshall Garcia

An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.


Electricity ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 143-157
Author(s):  
Jovi Atkinson ◽  
Ibrahim M. Albayati

The operation and the development of power system networks introduce new types of stability problems. The effect of the power generation and consumption on the frequency of the power system can be described as a demand/generation imbalance resulting from a sudden increase/decrease in the demand and/or generation. This paper investigates the impact of a loss of generation on the transient behaviour of the power grid frequency. A simplified power system model is proposed to examine the impact of change of the main generation system parameters (system inertia, governor droop setting, load damping constant, and the high-pressure steam turbine power fraction), on the primary frequency response in responding to the disturbance of a 1.32 GW generation loss on the UK power grid. Various rates of primary frequency responses are simulated via adjusting system parameters of the synchronous generators to enable the controlled generators providing a fast-reliable primary frequency response within 10 s after a loss of generation. It is concluded that a generation system inertia and a governor droop setting are the most dominant parameters that effect the system frequency response after a loss of generation. Therefore, for different levels of generation loss, the recovery rate will be dependent on the changes of the governor droop setting values. The proposed model offers a fundamental basis for a further investigation to be carried on how a power system will react during a secondary frequency response.


2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xiaomin Xu ◽  
Dongxiao Niu ◽  
Yan Li ◽  
Lijie Sun

Considering that the charging behaviors of users of electric vehicles (EVs) (including charging time and charging location) are random and uncertain and that the disorderly charging of EVs brings new challenges to the power grid, this paper proposes an optimal electricity pricing strategy for EVs based on region division and time division. Firstly, by comparing the number of EVs and charging stations in different districts of a city, the demand ratio of charging stations per unit is calculated. Secondly, according to the demand price function and the principle of profit maximization, the charging price between different districts of a city is optimized to guide users to charge in districts with more abundant charging stations. Then, based on the results of the zonal pricing strategy, the time-of-use (TOU) pricing strategy in different districts is discussed. In the TOU pricing model, consumer satisfaction, the profit of power grid enterprises, and the load variance of the power grid are considered comprehensively. Taking the optimization of the comprehensive index as the objective function, the TOU pricing optimization model of EVs is constructed. Finally, the nondominated sorting genetic algorithm (NSGA-II) is introduced to solve the above optimization problems. The specific data of EVs in a municipality directly under the Central Government are taken as examples for this analysis. The empirical results demonstrate that the peak-to-valley ratio of a certain day in the city is reduced from 56.8% to 43% by using the optimal pricing strategy, which further smooth the load curve and alleviates the impact of load fluctuation. To a certain extent, the problem caused by the uneven distribution of electric vehicles and charging stations has been optimized. An orderly and reasonable electricity pricing strategy can guide users to adjust charging habits, to ensure grid security, and to ensure the economic benefits of all parties.


2021 ◽  
Vol 804 (3) ◽  
pp. 032005
Author(s):  
Yanfang Kang ◽  
Xiaohan Guo ◽  
Xiaojuan Xi ◽  
Lingyun Li ◽  
Dapeng Li
Keyword(s):  

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1869 ◽  
Author(s):  
Alexandre Lucas ◽  
Giuseppe Prettico ◽  
Marco Flammini ◽  
Evangelos Kotsakis ◽  
Gianluca Fulli ◽  
...  

Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO2eq/MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.


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