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 Special Issue was organized on the application of advanced intelligent methods in vehicle-to-smart grid communications considering the security, reliability, and resiliency perspectives. We tried to bring together the ideas from researchers and experts on both transportation and smart grid areas to tackle the technological challenges in future power systems with more electric vehicles and intelligent systems. Theoretically, vehicles consist of different electrical and electronic hardware devices, which are connected with each other through the so-called ECUs being controlled by different software tools. As a well-perceived technology, all sensing instruments located at different points in an electric vehicle will direct their data to the ECU, wherein these data are analyzed and processed, and the demanding orders are sent to the pertinent actuators. With the advance of automobile technology, the electric vehicles are not only an electric load consumer anymore, but are appearing at different parts of human life, ranging from the smart homes and smart buildings to smart grid and smart cities. Owing to the complex and interconnected cyber-physical nature of electric vehicles, they can are getting a more appealing and accessible target for cyber hackers to penetrate the entire system through car hacking. This can result in long-term effects on the security and privacy of the electric vehicle owners, smart buildings, smart grids, and smart cities of the future. Be on that, this Special Issue scope includes methods, tools, applications, and solutions for analyzing, processing, and improving the electric vehicles as a complex cyber-physical system considering its direct or indirect effects on the modern human life factors. Among the high number of submissions received, 13 articles were finally chosen to be published in this Special Issue. The core ideas and concepts of the works are summarized in the rest.

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.

The microgrid (MG) is a complicated cyber-physical system that operates based on interactions between physical processes and computational components, which make it vulnerable to varied cyber-attacks. In this article, the impact of data integrity attack (DIA) has been considered, as one of the most dangerous cyber threats to MGs, on the steady-state operation of hybrid microgrids (HMGs). Additionally, a novel method based on the sequential hypothesis testing approach is proposed to detect DIA on the renewable energy sources’ metering infrastructure and improve the data security within the HMGs. The proposed method generates a binary sample, which is used to compute a test statistic that is further used against two thresholds to decide among three alternatives. The performance of the suggested method is examined using an IEEE standard test system. The results illustrated the acceptable performance of the proposed methodology in detection of DIAs. In addition, to evaluate the effect of DIA on the operation of the HMGs, DIAs with different severities are launched on the measured power generation of renewable energy resources like wind turbines. The results showed that a successful DIA on renewable units can severely affect the operation of electric grids and cause serious damage.

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called "preparation range" to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.

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 concept of smart cities has emerged as an ongoing research in recent years. In this case, there is a proven association between the smart cities and the smart devices, which have caused the power systems to become more flexible, controllable and detectable. Along with these promising results, many disputes have been generated over the cyber-attacks as unpredictable destructive threats, if not properly repelled, which could seriously endanger the power system. With this in mind, this paper explores a novel stochastic virtual assignment (SVA) method based on a directed acyclic graph (DAG) approach, where the essential data of the system sections are broadcasted decentralized through the data blocks, as a worthwhile step to deal with the cyber attacks’ risk. To do so, an additional security layer is added to the data blocks aiming to enhance the security of the data against the long lasting data sampling by virtually assigning the hash addresses (HAs) to the data blocks, which are randomly changed based on a stochastic process. The basic network architecture is based on a Provchain structure as a new framework to constantly monitor data operation. Two pivotal strategies also represented to deal with the energy and time needed for the HAs generation process, which have improved the proposed method. In this paper, the proposed security framework is implemented in a smart city environment to provide a secure energy transaction platform. Results show the authenticity of this model and demonstrate the effectiveness of the SVA method in decreasing the successful probability of cyber threat, increasing the time needed for the cyber attacker to decrypt and manipulate the data block.

Accurate prediction of solar energy is an important issue for photovoltaic power plants to enable early participation in energy auction industries and cost-effective resource planning. This article introduces a new deep learning-based multistep ahead approach to improve the forecasting performance of global horizontal irradiance (GHI). A deep convolutional long short-term memory is used to extract optimal features for accurate prediction of the GHI. The performance of such deep neural networks directly depends on their architectures. To deal with this problem, a swarm evolutionary optimization method, called the sine-cosine algorithm, is applied and advanced to automatically optimize the network architecture. A three-phase modification model is proposed to increase the diversity of population and avoid premature convergence in the optimization mechanism. The performance of the proposed method is investigated using three datasets collected from three solar stations in the east of the United States. The experimental results demonstrate the superiority of the proposed method in comparison to other forecasting models.

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.

In this article, an accurate secured framework to detect and stop data integrity attacks in wireless sensor networks in microgrids is proposed. An intelligent anomaly detection method based on prediction intervals (PIs) is introduced to distinguish malicious attacks with different severities during a secured operation. The proposed anomaly detection method is constructed based on the lower and upper bound estimation method to provide optimal feasible PIs over the smart meter readings at electric consumers. It also makes use of the combinatorial concept of PIs to solve the instability issues arising from the neural networks. Due to the high complexity and oscillatory nature of the electric consumers' data, a new modified optimization algorithm based on symbiotic organisms search is developed to adjust the NN parameters. The high accuracy and satisfying performance of the proposed model are assessed on the practical data of a residential microgrid.

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.

This paper proposes an accurate hybrid method based on support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to predict the tidal current speed and direction. In the proposed hybrid model, the ARIMA model captures the linear component of the tidal current, and the remained residual components are modeled by SVR. In order to capture the maximum linear components, the appropriate order of the ARIMA model is determined by the Akaike information criterion. Autocorrelation and partial autocorrelation functions are used to verify the stationary or nonstationary characteristics of tidal data. In order to adjust the optimal values of SVR parameters, a new optimization method based on the crow search algorithm is developed to search the problem space globally. In addition, a three-phase modification method is proposed to increase the diversity of the algorithm. The proposed hybrid model is highly accurate and outperforms the artificial neural network, ARIMA, genetic algorithm, and SVR. This model is applied on the practical data collected from the Bay of Fundy, NS, Canada, in 2008.

This paper develops a probabilistic model to predict the tidal current for modeling the prediction uncertainty, and thereby the forecast error. This requires the extension of the deterministic models from a point-by-point forecast to the probabilistic models with prediction intervals (PIs). The proposed model uses PIs to construct the bandwidth, which models the uncertainty of tidal current prediction properly. It uses the lower upper bound estimation method to train the neural network (NN) without making any assumption about the distribution of the forecast error. In order to adjust the weighting and biasing factors of NN, firefly algorithm with a new two-phase modification method is developed to search the problem space globally. Two benchmarks are used to show the search ability of the algorithm. The high accuracy of the proposed model is examined on the practical tidal data collected from the Bay of Fundy, NS, Canada.

This paper proposes a univariate prognostic approach based on wavelet transform and support vector regression (SVR) to predict the tidal current speed and direction with high accuracy. The proposed model decomposes the tidal current data into some subharmonic components. The details and approximation components are later fed to several SVR models to attend the prediction process. In order to increase the robustness of the model, the idea of combined prediction is used to model each subharmonic signal by several SVRs. The median operator is further used to determine the aggregated forecast tidal current data. Due to the high reliance of SVR model on the kernel function and hyperplane parameters, a new optimization method based on the bat algorithm is used to train the SVR model. The final forecast tidal current data are constructed using an aggregation operator in the output of the SVRs. The accuracy and satisfying performance of the proposed model are examined on the practical tidal data collected from the Bay of Fundy, NS, Canada. The experimental results reveal the high capability and robustness of the proposed hybrid model for the tidal current prediction.

Integration of plug-in electric vehicle (PEV) will influence distribution systems in various aspects from network loss and operating costs to system reliability. PEVs can be either mobile loads or mobile storages dispersed in the network. In this paper, distribution feeder reconfiguration (DFR) technique is employed as a reliability-enhancing strategy to coordinate vehicle-to-grid (V2G) provision of PEV fleets in a stochastic framework. Uncertainties associated with network load demand, energy price, wind power generation, and PEV fleet behavior are considered. The proposed stochastic optimization problem is solved with a self-adaptive evolutionary algorithm based on symbiotic organism search (SOS). Numerical studies on standard test system verify the efficacy of the proposed DFR to improve the system reliability and optimal dispatch of V2G.

This paper addresses impacts of hydrogen production and thermal energy recovery with an economic model of proton exchange membrane fuel cell power plant (PEMFCPP) on optimal operation management of renewable microgrids (MGs). A stochastic scenario-based approach is proposed to capture uncertainties of electrical and thermal loads, output power production of photovoltaic (PV) and wind turbine (WT) units, market price, natural gas price, hydrogen selling price, operating temperature of the stack of fuel cells (FCs), and pressure of hydrogen and oxygen. The proposed economic model is tested on an MG with different types of power sources including FCs, PV and WT units, and microturbines (MTs).

Stochastic charging behavior of plug-in hybrid electric vehicles (PHEVs) under different charging strategies brings new challenges for distribution networks such as feeder overloading and loss increase. In this way, the augmented penetration of these vehicles mandates employing new operative tools to inspect their impacts on electrical grids. Therefore, this paper proposes a novel optimal stochastic reconfiguration methodology to moderate the charging effect of PHEVs by changing the topology of grid using some remote controlled switches. Uncertainties associated with network demand, energy price, and PHEV charging behavior in different charging frameworks are handled with Monte Carlo simulation and the proposed stochastic problem is solved with krill herd optimization algorithm. Numerical studies on Tai-power distribution system verify the efficacy of proposed reconfiguration to improve the system performance considering PHEV charging loads.

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

In the new competitive electricity markets, the necessity of appropriate load forecasting tools for accurate scheduling is completely evident. The model which is utilised for the forecasting purposes determines how much the forecasted results would be dependable. In this regard, this paper proposes a new hybrid forecasting method based on the wavelet transform, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) for short-term load forecasting. In the proposed model, the autocorrelation function and the partial autocorrelation function are utilised to see the stationary or non-stationary behaviour of the load time series. Then, by the use of Akaike information criterion, the appropriate order of the ARIMA model is found. Now, the ARIMA model would capture the linear component of the load time series and the residuals would contain only the nonlinear components. The nonlinear part would be decomposed by the discrete wavelet transform into its sub-frequencies. Several ANNs are applied to the details and approximation components of the residuals signal to predict the future load sample. Finally, the outputs of the ARIMA and ANNs are summed. The empirical results show that the proposed hybrid method can improve the load forecasting accuracy suitably.

In this paper, a new hybrid method based on teacher learning algorithm (TLA) and artificial neural network (ANN) is proposed to develop an accurate model to investigate short-term load forecasting more precisely. In contrast to the other evolutionary-based training techniques, the proposed method utilises both the ability of ANNs to generate a non-linear mapping among different complex data as well as the powerful ability of TLA for global search and exploration. In addition, in an attempt to choose the most satisfying features from the set of input variables, a novel feature-selection approach based on fuzzy clustering and fuzzy set theory is proposed and utilised sufficiently. In order to improve the overall performance of TLA for optimisation applications, a new modification phase is proposed to increase the ability of the algorithm to explore the entire search space globally. The simulation results show the feasibility and the superiority of the proposed hybrid method over the other well-known methods in the area.

Accurate load-forecasting problem is a significant and vital issue, especially in the new competitive electricity market. The models that are employed for forecasting purposes would determine how reliable the last forecasted results are. Therefore, this paper proposes a new hybrid correction method based on autoregressive integrated moving average (ARIMA) model, support vector regression (SVR) and cuckoo search algorithm (CSA) to achieve a more reliable forecasting model. The proposed method gets use of the autocorrelation function (ACF) and the partial ACF to search the stationary or non-stationary behaviour of the investigated time series. In the case of non-stationary data, it will be differenced one or more times to become stationary. After that, Akaike information criterion is utilised to find the appropriate ARIMA model such that the linear component of the data would be captured. Therefore, the ARIMA residuals would contain the non-linear components that should be modelled by use of the SVR model. The role of CSA as a successful optimisation algorithm is to find the optimal SVR parameters for more accurate forecasting. Meanwhile, a novel self-adaptive modification method based on CSA is proposed to empower the total search ability of the algorithm effectively. The proposed method is applied to the empirical peak load data of Fars Electrical Power Company in Iran.