In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is obtained. Moreover, it reduces the PAR and discomfort index by 15.11% and 16.67%, respectively, in a 24 h time span. Furthermore, the home has home to grid (H2G) capability as it sells the surplus energy, and the total cost is further reduced by 29.41%.

The accurate prediction of aerodynamic properties is an essential requirement for the design of applications that involve fluid flows, especially in the aerospace industry. The aerodynamic characteristics of fluid flows around a wing or an airfoil are usually forecasted using the numerical solution of the Reynolds-averaged Navier–Stokes equation. However, very heavy computational expenses and lengthy progression intervals are associated with this method. Advancements in computational power and efficiency throughout the present era have considerably reduced these costs; however, for many practical applications, performing numerical simulations is still a very computationally expensive and time-consuming task. The application of machine learning techniques has seen a sharp rise in various fields over recent years, including fluid dynamics, and they have proved their worth. In the present study, a famous machine learning model that is known as the back-propagation neural network was implemented for the prediction of the aerodynamic coefficients of airfoils. The most important aerodynamic properties of the coefficient of lift and the coefficient of drag were predicted by providing the model with the name, flow Reynolds number, Mach number and the angle of attack of the airfoils with respect to the incoming flows as input parameters. The dataset for the current study was obtained by performing CFD simulations using the RANS-based Spalart–Allmaras turbulence model on four different NACA series airfoils under varying aerodynamic conditions. The data that were obtained from the CFD simulations were divided into two subsets: 70% were used as training data and the remaining 30% were used as validation and testing data. The BPNN showed promising results for the prediction of the aerodynamic coefficients of airfoils under different conditions. An RMSE value of 3.57 $\times$ 10⁻⁷ was achieved for the best performance validation case with 28 epochs when there were 10 neurons in the hidden layer. The regression plot also depicted a close to perfect fit between the predicted and actual values for the regression curves.

There is a need for the optimal positioning of protective devices to maximize customers satisfaction per their demands. Such arrangement advances the distribution system reliability to maximum achievable. Thus, radial distribution system (RDS) reliability can be improved by placing reclosers at suitable feeder sections. This article presents comprehensive details of an attempt to determine the reclosers’ optimal location in an RDS to maximize the utility profit by reliability improvement. Assessment of different reliability indices such as SAIDI, SAIFI, CAIFI, CAIDI, etc., with recloser placement, exhibits a considerable improvement in these indices in contrast with the absence of recloser. Consequently, a new bidirectional formulation has been proposed for the optimized arrangement of reclosers’. This formulation efficiently handles the bidirectional power flow, resulting from distributed generation (DG) unit (s) in the system. The proposed model has been solved for a test system by utilizing the Genetic algorithm (GA) optimization method. Later, test results conclude that reclosers’ optimal placement contributes significantly towards utility profit with minimum investment and outage costs.

The extensive employment of power semiconductor devices in multilevel inverters (MLIs) has the consequence of increased failure probabilities. With numerous applications demanding highly reliable inverters, several fault-tolerant schemes have been devised to address switch open-circuit faults. This paper analyzes a multilevel inverter topology for IGBT modules undergoing open-circuit faults, a major impediment to reliable operation within a power converter. Reconfiguration of modulation is performed post-fault. A modulation scheme is implemented across failure modes as a hybrid of nearest level control and selective harmonic elimination. Reliability assessment of the topology is performed, including a comparison with previous literature in terms of component requirements and reliability. Simulation results validate the proposed solutions.

Saudi Arabia has a 2030 Vision in which sustainability is a central pillar. Sustainability in manufacturing organizations has become a topic of discussion as a potential solution to help them survive and prosper in a competitive market. Here, the objective is to establish a sustainability indexing model for Saudi manufacturing organizations that will help organizations to prepare their sustainability reports in line with international sustainability standards. This study will also help Saudi manufacturing organizations evaluate their level of sustainability and identify barriers to improving their sustainability. The model presented has three sustainability dimensions, 12 criteria, and 29 sub-criteria. The model was implemented in a Saudi manufacturing organization as a case study. To improve the organization’s sustainability level, sustainability barriers were identified and analyzed. The model indicates that eight sub-criteria scored below the threshold value, which was found to be 0.19. These sub-criteria were further considered as sustainability barriers in the case organization.