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The increasing level of air pollution caused by the transport sector necessitates countries to adopt Electric Vehicles (EVs). To espouse EVs, the charging infrastructures' location should be optimal to fulfill the mass‐market consumer needs and reduce the governmental expenses. In this work, the placement of two categories of charging infrastructures, specifically Charging Station (CS) and Dynamic Wireless Charging (DWC) infrastructure is planned in Dubai, United Arab Emirates (UAE) as a case study. For this study, Dubai is divided into 14 districts as per its new addressing system, and the allocation of the two types of charging infrastructures is based on the projection of population growth, EVs adoption forecasting, and other factors with the objective of meeting the consumers' needs and minimizing the government's expenditure. The proposal introduces a novel hybrid model for forecasting, integrating the strengths of the Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) model for capturing time‐series statistical characteristics, and the deep learning Attention‐based Convolutional Neural Network (ACNN) for modeling nonlinear relationships in time‐series data. The model's effectiveness was validated through comparative analyses against state‐of‐the‐art (SOTA) models on standard benchmarks, showing significant improvements: 29.70% reduction in Mean Absolute Error (MAE), and 19.15% reduction in Root Mean Square Error (RMSE). [ABSTRACT FROM AUTHOR]

The proportional integral derivative (PID) regulator is the most often utilized controller in the industry due to its benefits. It permits linear systems to operate well, but it causes non-linear behavior when the system is subjected to physical variable circumstances, such as temperature and saturation. A PID controller is insufficient in this case. The proportional integral (PI) controller inside the direct torque control (DTC) regulates the speed of the doubly fed induction motor (DFIM). However, the system consisting of DTC and a DFIM is non-linear due to its multivariable parameters, resulting in undesirable overshoots and torque ripples. As a result, several approaches are used to improve the DTC's robustness. The integration of optimization methods was discovered. These algorithms are used to provide gains that are near-optimal, bringing the system closer to its ideal state in order to accomplish effective torque and speed control. This article focuses on a comparative study of the different objective functions, in order to have very effective DFIM behaviors, by using a genetic algorithm. Agenetic algorithm (GA) is presented in this study for adjusting the optimal PID parameters in DTC to control the DFIM, utilizing objective functions such as integral square error (ISE), integral time absolute error (ITAE), and integral absolute error (IAE), employed independently and in a weighted combination. This article offers a comparison of several objective functions inside the DTC and DFIM, which will be utilized in future research into another optimization technique for this control type. Matlab/Simulink was used to construct the novel hybrid structure based on the GA-DTC intelligent control. The simulation results demonstrated the efficiency of the GA-DTC intelligent control with a weighted combination, providing acceptable performance with respect to rapidity, precision, and stability, as well as an improvement of 14.53% in the rejection time reduction, fewer torque ripples and flux ripples on the stator and rotor by 27.88%, 15.13%, and 4.375%, respectively, and respective increases of 32.45% and 71% in the THDs of the stator and rotor currents, which are acceptable. [ABSTRACT FROM AUTHOR]

This paper proposes a robust investment and operation model to attend the power and heat needs of a microgrid (MG) connected to the distribution system. The optimization algorithm decides on the best investment and operation of combined heat and power (CHP), boilers, PV power generation and battery energy storage systems (BESS). For the BESS, the algorithm estimates the optimal energy storage capacity (MWh) as well as the maximum hourly delivery capacity (MW). The non‐linear and non‐concave heat rate chart is recast by a mix‐integer linear model to have a tractable and precise model. The model considers the uncertain in some parameters using probability density function (pdf) to portrait its behavior. Thus, the problem has been modeled using stochastic programming approach, and its objective function is the expected value of the annual operational cost. The model is tested using a real case where two adjacent consumers share the power and heat facilities to minimize the overall up to 17% depending on the gas price scenario. The results demonstrate the benefits of employing different technologies and the synergies of all technologies operating together. [ABSTRACT FROM AUTHOR]