Despite the widespread use of radio frequency identification and wireless connectivity such as near field communication in electric vehicles, their security and privacy implications in Ad-Hoc networks have not been well explored. This article provides a data protection assessment of radio frequency electronic system in the tire pressure monitoring system (TPMS). It is demonstrated that eavesdropping is completely feasible from a passing car, at an approximate distance up to 50 m. Furthermore, our reverse analysis shows that the staticn-bit signatures and messaging can be eavesdropped from a relatively far distance, raising privacy concerns as a vehicles’ movements can be tracked by using the unique IDs of tire pressure sensors. Unfortunately, current protocols do not use authentication, and automobile technologies hardly follow routine message confirmation so sensor messages may be spoofed remotely. To improve the security of TPMS, we suggest a novel ultralightweight mutual authentication for the TPMS registry process in the automotive network. Our experimental results confirm the effectiveness and security of the proposed method in TPMS.

This paper introduces a novel Internet of Thing (IoT) enabled approach for optimizing the operation costs and enhancing the network reliability incorporating the uncertainty effects and energy management in multi-carrier Energy Hub (EH) and integrated energy systems (IES) with renewable resources, Combined Heat and Power (CHP) and Plug-In Hybrid Electric Vehicle (PHEV). In the proposed model, the optimization process of different carriers of Multi Energy Hubs (MEH) energy considers a price-based demand response (DR) program with MEH electrical and thermal demands. During the peak period, energy carrier prices are calculated at high tariffs, and other power hubs can help to reduce hub energy costs. The proposed model can handle the random behavior of renewable sources in a correlated environment and find optimal solution for turbines' communication in EHs. The simulation results show the high performance of the proposed model by considering the dependency between wind turbines in MEH structure, power exchange and heat among the EHs.

Integrated around 2004, the concept of Energy Internet (EI) could provide new windows for the industrial power system society by incorporating the features of physical systems and cyber systems simultaneously. Technically, physical systems like electricity generation resources must be controlled and managed according to the instructions received from the cyber-systems. Being equipped by the smart grid and the internet idea, the EI can yield significant benefits such as higher reliability, improved power quality and mitigated the cost and losses. In the EI structure, the multiway flow of information and communication is monitored and controlled by the widespread and heterogeneous devices including the energy router, smart meters, etc. These technologies and devices bring many security concerns for the EI. Moreover, there are emerging concerns over the way to control, predict, manage, combine, and coordinate the energy resources in the EI. However, there exist many challenges that should be investigating and addressing before formal adoption of EI in industrial power and energy systems. To this end, this special issue invites the authors from both industry and academia to submit original research works on EI challenges in the power system.

This article proposes a two-layer in-depth secured management architecture for the optimal operation of energy internet in hybrid microgrids. In the cyber layer of the proposed architecture, a two-level intrusion detection system (IDS) is proposed to detect various cyber-attacks (i.e., Sybil attacks, spoofing attacks, false data injection attacks) on wireless-based advanced metering infrastructures. The sequential probability ratio testing approach is utilized in both levels of the proposed IDS to detect cyber-attacks based on a sequence of anomalies rather than only one piece of evidence. The process of making a decision in the proposed IDS is a random walk that starts from a point between two thresholds and moves toward one of them concerning received data samples. The feasibility and performance of the proposed architecture are examined on the IEEE 33-bus test system and the results are provided for both islanded and grid-connected operation modes.

The papers in this special section focus on applications of artificial intelligence in industrial power electronics and systems. The grid infrastructures and modernization, as well as the integration of renewable energies and using smart meters, can generate a large amount of data. These can lead to high complexity in the power system/electronics operation and control. Moreover, grid contingencies due to the natural disasters and cyber/physical attacks are highly unpredictable and costly preventable, which require fast and reliable data processing to preserve grid reliability and resiliency. Furthermore, the reliability of the power electronics devices and interfaces are very important, and can be improved by using the large data of measurements during long term operation. To this end, artificial intelligence (AI) techniques can potentially make it possible to provide new solutions to power electronics and power system operations and analysis. This special issue aims to investigate applications of AI in power system operation, analysis, planning, cybersecurity, as well as power electronics control, modulation techniques, reliability of the power electronics switches, and efficiency improvement in power electronics applications.

The electric vehicles' (EVs) rapid growth can potentially lead power grids to face new challenges due to load profile changes. To this end, a new method is presented to forecast the EV charging station loads with machine learning techniques. The plug-in hybrid EVs (PHEVs) charging can be categorized into three main techniques (smart, uncoordinated, and coordinated). To have a good prediction of the future PHEV loads in this article, the Q-learning technique, which is a kind of the reinforcement learning, is used for different charging scenarios. The proposed Q-learning technique improves the forecasting of the conventional artificial intelligence techniques such as the recurrent neural network and the artificial neural network. Results prove that PHEV loads can accurately be forecasted by using the Q-learning technique under three different scenarios (smart, uncoordinated, and coordinated). The simulations of three different scenarios are obtained in the Keras open source software to validate the effectiveness and advantages of the proposed Q-learning technique.

This paper proposes a resilient and secure configuration for coastal distribution grids by integrating the security constraint unit commitment (SCUC) and mobile marine microgrids (MMMGs). In the proposed configuration, MMMGs can be connected to the coastal distribution grids in both normal and post-disaster operations. It is assumed that both MMMGs and SCUC networks include both dispatchable (e.g., gas turbines and diesel generators) and nondispatchable generators (e.g., photovoltaics and wind turbines). The proposed problem consists of realistic formulations that seek to minimize the total MMMGs and SCUC operation costs, while maximizing distribution grid resiliency. A heuristic technique, known as the collective decision optimization algorithm, is employed to address the complexity and nonlinearity of the formulated problem. Moreover, the unscented transform technique is adopted to model the uncertainties associated with renewable energy sources output and load demand. To show the effectiveness and merits of the proposed configuration, the IEEE 69-bus distribution network is selected and tested for both normal and post-disaster operations.

This paper investigates the impact of uncoordinated, coordinated, and smart charging of plug-in hybrid electric vehicles (PHEVs) on the optimal operation of microgrids (MGs) incorporating the dynamic line rating (DLR) security constraint. The DLR constraint, particularly in the islanding mode, influences the ampacity of MG feeders, when distribution lines reach their maximum capacity. To overcome any line outage or contingency situation, smart PHEVs are utilized to help improve the grid security. However, using PHEVs can cause higher power losses and feeder overloading issues. To address these concerns, a reconfiguration technique is employed in this paper. A heuristic algorithm, known as the collective decision-based optimization algorithm, is utilized to overcome the non-convexity and nonlinearity of the problem. The unscented transform technique is employed to model DLR uncertainties caused by solar radiation, load demand, and weather temperature, as well as PHEVs' uncertainties caused by varying charging strategies, numbers of PHEVs being charged, charging start time, and charging duration. Moreover, a deep learning gated recurrent unit technique is designed to forecast renewable power output for mitigating the uncertainties in renewable energy components. A modified IEEE 33-bus test network is deployed to evaluate the efficiency and performance of the proposed model.

This article proposes a deep learning based approach for cyber attack detection in the vehicles. The proposed method is constructed based on generative adversarial network (GAN) classification to assess the message frames transferring between the electric control unit (ECU) and other hardware in the vehicle. To this end, two networks called generator (G) and discriminator (D) will run an adversarial game to fool each other. In such a process, the most optimal structure is found which distinguish between the model normal behavior and abnormalities. Due to the instabilities existing in the GAN model, a new optimization method based on firefly algorithm is proposed to create a class of generators in a feasible region, i.e. the discriminator D. A three-stage modification method is also devised to increase the algorithm population diversity and reduce the possibility of falling in local optima. The performance of the model is assessed on the experimental dataset recorded from the OBD-II port of an undefined vehicle.

This paper aims to investigate the optimal scheduling of stochastic reconfigurable hybrid ac-dc microgrid (MG) in the presence of renewable energies and also considering dynamic line rating (DLR) constraint. DLR is a practical limitation that can potentially affect the ampacity of lines, particularly in the islanded mode when the lines reach their maximum capacity in lack of main generation source at the point of interconnection with the utility. In order to prevent overloading of the lines, the reconfiguration technique is developed to change the topology of the network by some prelocated switches. A linearization technique is adapted to address the nonlinearity of both nodal ac power flow and the DLR constraints. The unscented transform technique is utilized to model uncertainties including renewable energy generations, hourly load demands, and hourly market prices along with the DLR uncertainties such as solar radiation, wind speed, and ambient temperature. Finally, a sensitivity analysis is performed to see the effect of wind speed and solar radiation on the energy management of hybrid ac-dc MG. The performance of the proposed methodology is examined on a modified IEEE-33 bus test system, which demonstrates the high efficiency and importance of the proposed techniques in minimizing the hybrid ac-dc MG operation cost while all of the constraints of the network are satisfied.