• Energy Research
• 2021-2025close
• AE close
• Applied Sciences close

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

This paper presents a method for coordinated network expansion planning (CNEP) in which the difference between the total cost and the flexibility benefit is minimized. In the proposed method, the generation expansion planning (GEP) of wind farms is coordinated with the transmission expansion planning (TEP) problem by using energy storage systems (ESSs) to improve network flexibility. To consider the impact of the reactive power in the CNEP problem, the AC power flow model is used. The CNEP constraints include the AC power flow equations, planning constraints of the different equipment, and the system operating limits. Therefore, this model imposes hard nonlinearity onto the problem, which is linearized by the use of first-order Taylor’s series and the big-M method as well as the linearization of the circular plane. The uncertainty of loads, the energy price, and the wind farm generation are modeled by scenario-based stochastic programming (SBSP). To determine the effectiveness of the proposed solution approach, it is tested on the IEEE 6-bus and 24-bus test systems using GAMS software.

Plastics are predominant in numerous sectors like packaging, agriculture, hardware, electronics, and many others. Annual plastic demand has been rapidly growing in the last few decades because of the increasing dependency on plastics. As a consequence, massive amounts of plastic waste are being generated every year. These plastic wastes are non-biodegradable, and hence their disposal poses a serious threat to the ecosystem and causes significant environmental problems such as endangering the safety of marine life, wildlife, air, water, and soil, etc. A large portion of plastic waste ends up in landfills, and only a small fraction is recycled. The continuous dependence on landfills as the main disposal method for plastic waste is costly and ineffective. Common solutions to plastic waste management are incineration and recycling; however, incineration emits harmful pollutants and greenhouse gases that contribute to ozone layer depletion and global warming; moreover, recycling is expensive and inefficient. As an alternative to recycling and incineration, the pyrolysis process can convert plastic wastes into more valuable fuel products. Pyrolysis is a thermal process that converts raw material into pyrolysis liquid, solid wax, and non-condensable gases in the absence of oxygen. This process is attractive because it is economical and energy-efficient, and it can be used to convert various types of plastic waste into valuable products. In recent years, there have been significant developments in pyrolysis applications in liquid fuel production from plastic wastes. This work reviews recent advances in and challenges for the pyrolysis process for converting plastic wastes into a valuable alternative fuel, focusing on studies of advanced pyrolysis processes published over the last five years. The paper also highlights the numerical modeling of pyrolysis of plastic wastes and the potential impact of pyrolysis on the future of sustainable waste-management practices of plastics.

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.

The United Arab Emirates (UAE) experiences up to 50% power losses in photovoltaic (PV) panels caused by frequent dust accumulation over the panels trailed by extreme temperature. Compositional and morphological insights into dust particle can potentially help design PV cleaning mechanisms inclusive of self-cleaning explored in the current article. Five different locations were studied to discover potential differences in dust samples. The collected samples were characterised employing Optical Microscopy, Scanning Electron Microscopy (SEM), X-ray Powder Diffraction (XRD), and Elemental Composition Analysis (Energy Dispersive Spectrometry, EDS). The micrographs revealed that the majority of particles were irregularly shaped, providing interlocking for the dust to stay over the surface. The particle size ranged from 0.01 to 300 µm, and some of the collected dust exhibited cavities. XRD analyses revealed variations in the chemical composition among the samples studied. Elemental Composition Analysis via EDS revealed both consistent patterns and variations in element presence among the dust samples, highlighting specific detections of chlorine (Cl) at some sites.

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

We numerically investigate mixed convective heat and mass transport in incompressible nanofluid flow through an exponentially stretching sheet with temperature-dependent viscosity. The fluid flow equations are transformed to a system of non-linear ordinary differential equations using appropriate similarity transformations and solved numerically by using the multi-domain bivariate spectral quasi-linearization technique. The fast convergence of the method is shown by demonstrating that the error is exponentially small for a finite number of iterations. The significance and impact of different fluid parameters are presented and explained. For engineering relevance, the entropy generation number has been calculated for different fluid parameter values.