A microgrid (MG) is a unique area of a power distribution network that combines distributed generators (conventional as well as renewable power sources) and energy storage systems. Due to the integration of renewable generation sources, microgrids have become more unpredictable. MGs can operate in two different modes, namely, grid-connected and islanded modes. MGs face various challenges of voltage variations, frequency deviations, harmonics, unbalances, etc., due to the uncertain behavior of renewable sources. To study the impact of these issues, it is necessary to analyze the behavior of the MG system under normal and abnormal operating conditions. Two different tools are used for the analysis of microgrids under normal and abnormal conditions, namely, power flow and short-circuit analysis, respectively. Power flow analysis is used to determine the voltages, currents, and real and reactive power flow in the MG system under normal operating conditions. Short-circuit analysis is carried out to analyze the behavior of MGs under faulty conditions. In this paper, a review of power flow and short-circuit analysis algorithms for MG systems under two different modes of operation, grid-connected and islanded, is presented. This paper also presents a comparison of various power flow as well as short-circuit analysis techniques for MGs in tabular form. The modeling of different components of MGs is also discussed in this paper.

The high penetration of renewables into the active distribution network (ADN) brings voltage deviation and difficulties to the optimal power flow (OPF) problem. The optimal operation of the distribution grid aims to efficiently manage the flow of electricity from sources to end-users, ensuring a resilient and sustainable grid. To perform the optimal operation, OPF plays a pivotal role in solving a complex optimization problem due to the system’s operational constraints and the provided OPF solutions. Implementing traditional OPF algorithms can be challenging for large-scale networks with complex topologies and constraints. The most recent advancement in learning-based models has shifted the paradigm towards data-driven approaches. This paper proposes a high-fidelity graph attention networks (GAT) model that leverages the attention mechanism and graph convolution feature mapping property to learn neighbor informative node representations for OPF solutions.We validated the proposed model on the IEEE-33, 69, and modified 123-bus power distribution networks. The proposed GAT model outperformed the state-of-the-art MPGCN and DNN models, achieving improvement of 86.33% and 62.71%, respectively, under 60% DG penetration condition. The robustness assessment of DNN, MPGCN, and GAT models are also compared in all three test cases. The GAT model exhibited less variability in its median error 0.22, 0.21, and 0.038, respectively, in each case. For computational efficiency analysis, the GAT model was processed on an IEEE-123 bus with 13,810 samples in 782 seconds, which remains within the steady-state OPF calculation time limit of 15 minutes (900 seconds). The proposed GAT model showcases its effectiveness and promises results for addressing the OPF problem in the distribution network, as evidenced by performance evaluation metrics.

The principal reason for why electric vehicles are required to serve as an alternative to the more widespread gasoline and petroleum-based vehicles used in modern times is due to the use of an environmentally conscious means of transportation or to circumvent the tumultuous economic dealings of the compressed natural gas and petroleum industries. There is a growing daily need for large, high-voltage e-mobilities, mostly driven by anticipated advancements in electric vehicle technology. Consequently, all of the various components of these vehicles must be able to be accommodated within a limited and compact space. The battery is an essential component in e-mobility. The insulation, health monitoring, and problem diagnostics of lithium-ion (Li-ion) batteries are of utmost importance in ensuring these vehicles’ safety and efficient functioning. Real-time and fast insulation detection techniques are required to ensure safety in high-voltage (HV) vehicles and to avoid insulation failure. This paper used the Recursive-Least-Squares (RLS) algorithm because it is computationally efficient for building the insulation detection system. Based on the RLS technique, we proposed field programmable gate array (FPGA)-based algorithms and implemented them using VHDL coding. The FPGA is very fast at detection, and the error is lower. We validated the FPGA results with MATLAB simulation results from the existing literature, and the errors are much less when using FPGAs. An experimental hardware platform was also created to validate the proposed FPGA technique with various motor and resistive loadings on electric vehicles (EVs).

Global warming and an impending energy problem have compelled nations to become greener and cleaner. Worldwide, interest in electric vehicles (EVs) is growing as a result of rising gasoline prices and environmental concerns. The need for electrical components and cabling networks is expanding along with the demand for electrified vehicles. The complexity of vehicle electrical, electronics, and control circuits is also rising. There are many wire and cable performance issues in modern e-mobility, including limited space, vibration, high-temperature variation, unfriendly fluids, and rising data transmission requirements. Thus, it is important to study and evaluate the thermal and electrical stresses in the electric vehicles cabling network to avoid insulation failure, which causes fire accidents in the electric components of EVs. An EV's entire electrical system gets hotter, and insulation strength is affected by both temperature and the electrical field. The insulation system is one of the most important components in any electrified vehicle. From a design and protection perspective, the computation of electric potential distribution and electric field within and around the cables of electric vehicles is very important. The strong non-linear dependency of the electrical conductivity of the insulation material on prevailing electrical stress and temperature makes the field problem not only coupled but also non-linear. Finite Difference Numerical (FDM) is used for non-linear field computation. In this paper, different electric vehicle cabling network layouts were studied and listed the challenges. Chosen the high voltage cables of electric vehicles from the cabling network and numerically computed the conductivity, temperature, and electrical fields of cable.