The Smart Grid is a modern power grid that relies on advanced technologies to provide reliable and sustainable electricity. However, its integration with various communication technologies and IoT devices makes it vulnerable to cyber-attacks. Such attacks can lead to significant damage, economic losses, and public safety hazards. To ensure the security of the smart grid, increasingly strong security solutions are needed. This paper provides a comprehensive analysis of the vulnerabilities of the smart grid and the different approaches for detecting cyber-attacks. It examines the different vulnerabilities of the smart grid, including system vulnerabilities and cyber-attacks, and discusses the vulnerabilities of all its elements. The paper also investigates various approaches for detecting cyber-attacks, including rule-based, signature-based, anomaly detection, and ma-chine learning-based methods, with a focus on their effectiveness and related research. Finally, prospective cybersecurity approaches for the smart grid, such as AI approaches and blockchain, are discussed along with the challenges and future prospects of cyberattacks on the smart grid. The paper’s findings can help policymakers and stakeholders make informed decisions about the security of the smart grid and develop effective strategies to protect it from cyber-attacks.

Natural selection has inspired researchers to develop and apply several intelligent optimization techniques in the past few decades. Generally, in artificial intelligence optimization, the particles follow a local or global best particle until finding an acceptable solution. In well- developed optimization techniques, such as swarm optimization (PSO) and the firefly algorithm (FA), getting around the initial optimal value of the group and randomly checking the effect of the surrounding points may lead to a better solution than the initial optimal value. The present work was inspired by the fascinating movement of Garra Rufa fish between two immersed legs during a regular "fish massage session." A new optimization approach is proposed and modeled based on the movements of Garra Rufa fish, in which the particles are separated into groups, and the best optimal value leads each group for the group. Also, some of these particles are allowed to change groups depending on the fitness of the leaders of the groups. The suggested strategy is then compared with PSO and FA using multiple test optimization functions, such as the Ackley, Hartmann, Michalewicz, Shubert, Easom, Bohachevsky, and Rastrigin functions. Also, a multiobjective real issue in power system is tested using the proposed methods where the objectives were cumulative voltage deviation and power losses of three weight sets during the selection allocation of distribution generators. The results show that the proposed method provides good data and greater convergence to the optimal point compared with the classical methods for most of the functions tested.

Distributed generation (DG) refers to small generating plants that usually develop green energy and are located close to the load buses. Thus, reducing active as well as reactive power losses, enhancing stability and reliability, and many other benefits arise in the case of a suitable selection in terms of the location and the size of the DGs, especially in smart cities. In this work, a new nature-inspired algorithm called Garra Rufa optimization is selected to determine the optimal DG allocation. The new metaheuristic algorithm stimulates the massage fish activity during finding food using MATLAB software. In addition, three indexes which are apparently powered loss compounds and voltage profile, are considered to estimate the effectiveness of the proposed method. To validate the proposed algorithm, the IEEE 30 and 14 bus standard test systems were employed. Moreover, five cases of DGs number are tested for both standards to provide a set of complex cases. The results significantly show the high performance of the proposed method especially in highly complex cases compared to particle swarm optimization (PSO) algorithm and genetic algorithm (GA). The DG allocation, using the proposed method, reduces the active power losses of the IEEE-14 bus system up to 236.7873%, by assuming 5DGs compared to the active power losses without DG. Furthermore, the GRO increases the maximum voltage stability index of the IEEE-30 bus system by 857% in case of the 4DGs, whereas GA rises the reactive power of 5DGs to benefit the IEEE-14 bus system by 195.1%.

Now days, it is vital to use electric vehicles (EVs) instead of traditional cars with internal combustion engines (ICEs) in order to reduce the high level of pollution in the environment, and many researchers are investigating the possible improvements on these vehicles. The main component of EVs is the electric motor and the selection of a motor with high efficiency, excellent dynamic response and high starting torque has a strong effect on the performance of EVs. In addition to that a reasonable price for the electric motor is required. This work focuses on the selection of the most suitable electric motor for EVs. Therefore a detailed study to compare between the performance of the major types of electric motors that are used in EVs is addressed in this paper. The results of this comparative study is tabulated and by careful consideration for all these results, the appropriate electric motor for EVs has been chosen. From the other hand, the artificial intelligent (AI) techniques play a crucial role in the EVs technologies, and several kinds of AI techniques used in EVs applications are overviewed in this work.

Optimization technologies have drawn considerable interest in power system research. The success of an optimization process depends on the efficient selection of method and its parameters based on the problem to be solved. Firefly algorithm is a suitable method for power system operation scheduling. This paper presents a modified firefly algorithm to address unit commitment issues. Generally, two steps are involved in solving unit commitment problems. The first step determines the generating units to be operated, and the second step calculates the amount of demand-sharing among the units (obtained from the first step) to minimize the cost that corresponds to the load demand and constraints. In this work, the priority list method was used in the first step and the second step adopted the modified firefly algorithm. Ten generators were selected to test the proposed method, while the values of the cost function were regarded as criteria to gauge and compare the modified firefly algorithm with the classical firefly algorithm and particle swarm optimization algorithms. Results show that the proposed approach is more efficient than the other methods in terms of generator and error selections between load and generation.

The accuracy of the power system model is important in investigating the transient phenomena of load frequency control (LFC). In this paper, Segmentation Particle Swarm Optimization (SePSO) method is proposed for governor-turbine model identification of single area power plant. The method is acquired based on a combination of segmentation and Particle Swarm Optimization (PSO) algorithms, in which the segmentation is used to recognize the local and global optimal point problem of PSO. The tests of the investigated governor system performed to obtain the step response to identify all parameters of the governor-turbine model. Finally, to verify the effectiveness of the proposed identification, three disturbance cases and three control parameters were implemented by comparing with the common PSO and GA-PSO algorithms. The results show that the proposed method performed better in terms of accuracy and computation time.