Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms

The widespread deployment of "smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. The connectivity features of smart EVCSs can be utilized to schedule EV charging operations while respecting user preferences, thus avoiding synchronous charging from a large number of customers and relieving grid congestion. However, the communication and connectivity requirements involved in smart charging raise cybersecurity concerns. In this work, we investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. The objective of CMAs is to shift the EV aggregator's demand across different times of the day. The proposed CMAs can bypass existing protection mechanisms in EV communication protocols. We quantify the impact of CMAs on the EV aggregator's economic profit by modeling their participation in the day-ahead (DA) and real-time (RT) electricity markets. Finally, we propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging. We extensively analyze the attack impact and the efficiency of the proposed detection on real-world EV charging datasets. The results highlight the vulnerabilities of smart charging operations and the need for a monitoring mechanism to detect malicious CMAs

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come.Peer ReviewedPostprint (author's final draft

Data‐driven detection and identification of IoT‐enabled load‐altering attacks in power grids

Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large‐scale IoT‐based load‐altering attacks (LAAs) can seriously impact power grid operations, such as destabilising the grid's control loops. Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations. In this work, two data‐driven algorithms are proposed to detect and identify compromised nodes and the attack parameters of the LAAs. The first method, based on the Sparse Identification of Nonlinear Dynamics approach, adopts a sparse regression framework to identify attack parameters that best describe the observed dynamics. The second method, based on physics‐informed neural networks, employs neural networks to infer the attack parameters from the measurements. Both algorithms are presented utilising edge computing for deployment over decentralised architectures. Extensive simulations are performed on IEEE 6‐, 14‐, and 39‐bus systems to verify the effectiveness of the proposed methods. Numerical results confirm that the proposed algorithms outperform existing approaches, such as those based on unscented Kalman filter, support vector machines, and neural networks (NN), and effectively detect and identify locations of attack in a timely manner

Investigating the Injuries of Murder in Prohibition in Iranian Laws

AbstractThe issue of murder in marriage is regulated in Article 179 of the Criminal Code before the revolution originated in Article 324 of the French Criminal Code, and was previously reflected in Article 630 of the Islamic Criminal Code after the amendment in 1996. This problem has been criticized in terms of jurisprudence and validity. Deficiencies appear to be seen as weaknesses of a legitimate defense theory as a justified basis for verdicts, weaknesses in jurisprudence, and opportunities for misuse of sanctions for murder in marriage. Most of the objections are caused by; First, the lack of attention to place the problem into the realm of legal certainty, not the realm of proof, and second: the lack of careful analysis of the principles of jurisprudence and only refers to the views of some legal experts. This study uses a qualitative research method with the statutory approach. The results of the study stated that murder in marriage is relative in terms of defense and deserves the death penalty. In this study, the authors examined the damage to victims, the views of well-known legal experts, and an analysis of Iran's statute law with an analytical approach.Keywords: Damage, murder in marriage, statutory regulations AbstrakMasalah pembunuhan di dalam perkawinan diatur dalam Pasal 179 KUHP sebelum revolusi berasal dari Pasal 324 dari KUHP Perancis, dan sebelumnya tercermin dalam Pasal 630 KUHP Islam setelah perubahan pada tahun 1996. Masalah ini, mengalami kritik dalam hal yurisprudensi dan validitas. Kekurangan tampak terlihat seperti kelemahan teori pertahanan yang sah sebagai dasar yang dibenarkan untuk vonis, kelemahan yurisprudensi, dan peluang penyalahgunaan sanksi pembunuhan di dalam perkawinan. Sebagian besar keberatan  disebabkan oleh; Pertama, kurangnya perhatian untuk menempatkan masalah ke ranah kepastian hukum, bukan ranah pembuktian, dan kedua: kurangnya kecermatan analisis pada prinsip-prinsip yurisprudensi dan hanya merujuk kepada pandangan beberapa ahli hukum saja. Penelitian ini menggunakan metode penelitian kualitatif dengan pendekatan peraturan perundang-undangan. Hasil penelitian menyatakan bahwa pembunuhan di dalam pernikahan bersifat relatif dalam hal pembelaan dan layak mendapatkan hukuman mati. Dalam penelitian ini, penulis melakukan pemeriksaan pada kerusakan korban, pandangan terkenal para ahli hukum, dan analisis pada hukum statuta Iran dengan pendekatan analitis.Kata kunci: Kerusakan, pembunuhan di dalam nikah, peraturan perundang-undang АннотацияПроблема убийства в браке регулируется статьей 179 Уголовного кодекса до революции, возникшей в статье 324 Уголовного кодекса Франции, и ранее была отражена в статье 630 Исламского уголовного кодекса после внесения поправки в 1996 году. Эта проблема подвергнута критике с точки зрения юриспруденции и обоснованности. Недостатки, по-видимому, рассматриваются как слабые стороны законной теории защиты в качестве оправданной основы для вынесения вердиктов, слабые стороны в юриспруденции и возможности неправильного применения санкций за убийство в браке. Большинство претензий вызваны: во-первых, недостаточным вниманием, чтобы поместить проблему в область юридической однозначности, а не в область доказывания; во-вторых: недостаточным тщательным анализом принципов юриспруденции и отношением только к мнениям некоторых судебных экспертов. В данном исследовании используется качественный метод исследования с законодательным подходом. Результаты исследования показали, что убийство в браке является относительным с точки зрения защиты и заслуживает смертной казни. В этом исследовании авторы провели расследование ущерба, нанесенного жертвам, мнения известных судебных экспертов и анализ статутного права Ирана с аналитическим подходом.Ключевые слова: Ущерб, Убийство в браке, Законодательные нормативно-правовые акт

A deep learning-based solution for securing the power grid against load altering threats by IoT-enabled devices

The growing integration of high-wattage Internet-of-Things (IoT)-enabled electrical appliances at the consumer end has created a new attack surface that an adversary can exploit to disrupt power grid operations. Specifically, dynamic load-altering attacks (D-LAAs), accomplished by an abrupt or strategic manipulation of a large number of consumer appliances in a botnet-type attack, have been recognized as major threats that can potentially destabilize power grid control loops. This paper introduces a novel approach based a multi-output network (two-dimensional convolutional neural networks classifier and reconstruction decoder)-called “2DR-CNN”-to detect and localize D-LAAs with high resolution. To achieve this, we leverage the frequency and phase angle data of the generator buses monitored by phasor measurement units (PMUs) installed in the power grid. To verify the effectiveness of the proposed method, simulations are conducted on IEEE 14-and 39-bus systems. The performance of the 2DR-CNN method is compared against several benchmark machine learning-based approaches. The results confirm that the proposed method outperforms other techniques in detection and localizing D-LAAs with high resolution in a number of practical scenarios, including PMU measurement noises and missing measurements

Economic assessment of multi-operator virtual power plants in electricity market : a game theory-based approach

In recent years, the penetration of distributed energy resources (DERs) has increased significantly due to their tremendous effect on network flexibility, economic indicators, and power loss. On the contrary, a diverse assortment of DERs can lead to some challenges in controlling these resources in the power grid. To acquire the maximum benefit of DERs and overcome their challenges the concept of virtual power plants (VPPs) has been suggested. Due to the ability of VPPs to participate in electricity markets and the competition of VPPs to gain more profit we are facing deregulated multi-operator markets, and it is necessary to define VPPs as price maker units. The optimal economic assessment of VPPs in a multi-operator market depends on two folds: modeling inner cooperation between its components and managing external competition with other VPPs. To this end, in this paper, a new framework for optimal economic assessment of a multi-operator VPP system is proposed by considering a combination of non-cooperative and cooperative game theory-based approaches. In the proposed methodology, VPPs compete with other rivals to determine the amount of power exchange and offer prices based on supply function equilibrium. Due to incomplete information of VPPs about other opponents and market construction, a combination of particle swarm optimization and genetic algorithm is proposed to find the Nash equilibrium point. Also, the Shapely value concept is used for fair distribution of shared profit among VPPs components. The effectiveness of the proposed method has been verified in two case studies for a multi-operator VPP with a diverse assortment of DERs. The results show that VPP profit and electricity market prices directly relate to the diversity of resources in VPP. In this regard, the mark-up coefficient of the VPP with a greater number of DERs is about 16% and 32% larger than the two other VPPs which leads to more profit for this VPP and resources in its coalition

A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.An accurate electricity price forecasting (EPF) plays a vital role in the deregulated energy markets and has a specific effect on optimal management of the power system. Considering all the potent factors in determining the electricity prices—some of which have stochastic nature—makes this a cumbersome task. In this article, first, Grey correlation analysis is applied to select the effective parameters in EPF problem and eliminate redundant factors based on low correlation grades. Then, a deep neural network with stacked denoising auto-encoders has been utilized to denoise data sets from different sources individually. After that, to detect the main features of the input data and putting aside the unnecessary features, dimension reduction process is implemented. Finally, the rough structure artificial neural network (ANN) has been executed to forecast the day-ahead electricity price. The proposed method is implemented on the data of Ontario, Canada, and the forecasted results are compared with different structures of ANN, support vector machine, long shortterm memory—benchmarking methods in this field—and forecasting data reported by independent electricity system operator (IESO) to evaluate the efficiency of the proposed approach. Furthermore, the results of this article indicate that the proposed method is efficient in terms of reducing error criterion and improves the forecasting error about 5–10 percent in comparison with IESO. This is a remarkable achievement in EPF field.Peer ReviewedPostprint (author's final draft

Correlation Between Biodemographic Parameters and the Size of Inferior Vena Cava and Collapsibility Index Using Ultrasound in Children: Biodemographic Parameters in Ultrasound in Children

Background and Aim:There is a concern regarding the relationship between biodemographicparameters at different ages and the size of inferior vena cava (IVC) and the collapsibilityindex (CI). Due to the lack of normative data on these parameters in children, we aimed touse ultrasound to determine the mean sizes of IVC in healthy children and calculate the CI.Methods: In this analytical cross-sectional study, we measured the IVC diameter in euvolemicchildren aged four weeks to 12 years. The maximum IVC diameter was recorded duringthe exhalation phase of the respiratory cycle, while the minimum diameter was recordedduring the inhalation phase using M-mode. Additionally, we calculated the CI by dividing thedifference between the maximum and minimum IVC diameters by the maximum diameter.Results: In this study, 534 euvolemic healthy children with a mean age of 6.77±3.22 yearswere assessed. The mean diameter of the maximum IVC during exhalation was 5.26±4.70and the mean diameter of the minimum IVC during inspiration was 2.96±2.89 mm. Themean CI in the present study was 0.5±0.13. Ultrasound measurements of IVC diameterduring exhalation, unlike IVC diameter during inhalation, were positively correlated withage, weight, and height. The mean IVC and CI had a direct and significant correlation withbiodemographic parameters, such as age, height, weight, and body mass index.Conclusion: Evaluating intravascular volume status holds significant clinical relevance,particularly in pediatric patients. Utilizing ultrasound to assess the IVC allows for swift and noninvasive analysis of an individual’s hemodynamics, impacting clinical decision-making positively.Establishing normative IVC measurements in healthy and euvolemic children can serve as valuablereference data for clinicians and help them accurately assess fluid status in unwell pediatric patients