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This paper presents a finite control-set model predictive control (FCS-MPC) based technique to reduce the switching loss and frequency of the on-grid PV inverter by incorporating a switching frequency term in the cost function of the model predictive control (MPC). In the proposed MPC, the control objectives (current and switching frequency) select an optimal switching state for the inverter by minimizing a predefined cost function. The two control objectives are combined with a weighting factor. A trade-off between the switching frequency (average) and total harmonic distortion (THD) of the current was utilized to determine the value of the weighting factor. The switching, conduction, and harmonic losses were determined at the selected value of the weighting factor for both the proposed and conventional FCS-MPC and compared. The system was simulated in MATLAB/Simulink, and a small-scale hardware prototype was built to realize the system and verify the proposal. Considering only 0.25% more current THD, the switching frequency and loss per phase were reduced by 20.62% and 19.78%, respectively. The instantaneous overall power loss was also reduced by 2% due to the addition of a switching frequency term in the cost function which ensures a satisfactory empirical result for an on-grid PV inverter.

The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT. By the initiation of Grid formation across the sensor nodes, which can simplify the cluster formation, the Cluster Head (CH) selection is accomplished using fish swarm optimization (FSO). The performance of the Graph-Grid-based Convolution clustered neural network with fish swarm optimization (GG-Conv_Clus-FSO) in energy optimization of the network is compared with existing state-of-the-art protocols, and GG-Conv_Clus-FSO outperforms the existing approaches, whereby the packet delivery ratio (PDR) is enhanced by 95.14%.

A conventional electrical grid mostly depends on the electrical power generated from fossil fuels. However, the pollutants from fossil fuels are the key factors for adverse climate change. Most of the developed countries of the world have already recognized the fact that the energy mix requires to be diversified by incorporating renewable energy. This is especially relevant for a good number of the member countries of the Organization of Islamic Cooperation (OIC), as much of their electric power is still generated from fossil fuels due to the relative abundance of fossil fuels in those countries. In order to integrate renewable energy sources into the hybrid energy mix, an existing conventional grid needs to undergo drastic changes. Alongside this, the population boom in the OIC member countries has caused higher demand for a steady supply of electricity that the conventional grids have long been struggling to cope with. With a view to solving this multifaceted problem, incorporation of the smart power management schemes is indispensable using a smart electrical grid, where information and communications technology is integrated into its major building blocks. This allows advanced applications of a grid, such as the formation of micro-grids, demand-side management, energy storage, high-tech power electronic converters, etc. As the smart grids are being adopted by many developed countries, it is high time for the OIC member countries to pay due attention to this development, if they have not already done so. This paper explains, with special focus on the OIC member countries, the various smart power management technologies, their operations and applications, and the benefits and challenges. Then, it goes on to carry out the Strength–Weakness–Opportunity–Threat with Analytical-Heuristic-Procedure (SWOT-AHP) analysis to evaluate its feasibility of incorporation and the underlying strategies appropriate for its implementation. Furthermore, a Hybrid Multi-Criteria-Decision-Making (MCDM) analysis is performed to evaluate the sequence of the emphasis that should be given on each of the technologies from those available for the smart power management initiative. Finally, the study reinforces the stance by drawing parallels from the UN Sustainable Development Goals (SDG) and highlights the importance of the smart grid in line with the global vision of SDG. This paper aims at assisting the decision-makers in implementing smart power management schemes in the OIC member countries, in particular, and other countries of the world, in general.

An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Harvesting energy from the fringing electric field of power lines is a topic of recent interest. So far, most of the reported energy harvesting processes used a fixed ground connection that makes the harvesting process immobile. This paper presents a new idea to avoid the fixed ground connection and make the extraction process mobile, which can lead to charging of wearable and mobile devices, reducing the frequency of battery charging and enhancing the longevity of the batteries. A comprehensive circuit model for the energy extraction system is still absent, and this research proposes a circuit model that forms a basis for the circuit design and prediction of the energy extraction efficacy. The circuit model provides the analytical basis to estimate charging time, optimum operating voltage and optimum power for such circuits. Experimental results are also presented to justify the circuit model. Although the actual amount of power extracted was very small, further research is needed to enhance the energy extraction efficacy.

This study investigates numerically the effect of the two-phase nanofluid model due to natural convection within a square cavity along with the existence of a conducting solid block, and a corner heater using the finite difference method (FDM). The top horizontal wall is retained at a cold temperature that is fixed as constant, while the isothermal heater is positioned at the bottom left corner within the square cavity. The remaining fractions of the right vertical wall and the heated wall are set to be adiabatic. The water-based nanofluid, together with Al 2 O 3 nanoparticles, have been evaluated by determining the following parameters: the volume fraction of nanoparticles, thickness of solid block, Rayleigh number, and the solid block thermal conductivity. As a result, the comparative evaluation with outputs reported in publications and prior experimental works has pointed out exceptional agreement with the findings retrieved in this study. The experimental outcomes are graphically illustrated in terms of the average and local Nusselt numbers, isotherms, distribution of nanoparticles, and the streamlines. The findings indicate that an elevation of the thermal conductivity in blocks with a similar size successfully increases the transfer rate of heat, wherein the dominance of conduction has been observed.