• Energy Research
• 2016-2025close
• EG close
• Energy Research close

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H∞-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties.

In this paper, the mathematical analysis of the robot effective mass is presented. The calculation of this effective mass and its ellipsoid are included. The relationship between the robot effective mass and the external force (collision) affecting the robot end-effector is investigated. The effective mass is analyzed using different robot configurations and different end-effector positions. This analysis is conducted using 2-DOF and 3-DOF planar robots and executed using MATLAB. The results from this analysis prove that the robot effective mass depends on the its configurations and end-effector position. Effective mass can thus be considered as one of the criteria in optimizing robot kinematics and configuration.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

Abstractβ-Glucanase has received great attention in recent years regarding their potential biotechnological applications and antifungal activities. Herein, the specific objectives of the present study were to purify, characterize and immobilize β-glucanase from Aspergillus niger using covalent binding and cross linking techniques. The evaluation of β-glucanase in hydrolysis of different lignocellulosic wastes with subsequent bioethanol production and its capability in biocontrol of pathogenic fungi was investigated. Upon nutritional bioprocessing, β-glucanase production from A. niger EG-RE (MW390925.1) preferred ammonium nitrate and CMC as the best nitrogen and carbon sources, respectively. The soluble enzyme was purified by (NH4)2SO4, DEAE-Cellulose and Sephadex G200 with 10.33-fold and specific activity of 379.1 U/mg protein. Tyrosyl, sulfhydryl, tryptophanyl and arginyl were essential residues for enzyme catalysis. The purified β-glucanase was immobilized on carrageenan and chitosan with appreciable yield. However, the cross-linked enzyme exhibited superior activity along with remarkable improved thermostability and operational stability. Remarkably, the application of the above biocatalyst proved to be a promising candidate in liberating the associate lignocellulosic reducing sugars, which was utilized for ethanol production by Saccharomyces cerevisiae. The purified β-glucanase revealed an inhibitory effect on the growth of two tested phytopathogens Fusarium oxysporum and Penicillium digitatum.

Humidification dehumidification water desalination system using two cycles of air flow (open and closed-air cycles).

This paper studies the vibrational motion of a dynamical system connected to an electromagnetic device, which is one of the energy harvesting (EH) devices that transform the vibrational motion into electric energy. This system has three degrees-of-freedom (DOF) and consists of two linked parts attached together; one is a nonlinear Duffing oscillator, and the other is a nonlinear damping spring pendulum. The regulating equations of motion (EOM) are achieved utilizing Lagrange’s equations and solved analytically applying the approach of multiple scales (AMS) till the third order of approximation. The accuracy of the attained solutions has been examined by comparing them with the numerical ones of the EOM. The time histories of the solutions and the nonlinear stability analysis of the modulation equations are represented graphically in various plots. The Poincaré maps and phase portraits diagrams displayed the stable behavior of the studied dynamical system. In addition, the different ranges of the stabilities are examined and discussed. In the electromagnetic device, the output power and current time series are depicted as a function of different values of the damping coefficients, excitation amplitudes, and load resistance. It is noted that the output current and power are dropped when the damping coefficient is raised. On the other hand, the increment of the excitation has a positive effect on the electrical generation and produces increment of the output power and current. Furthermore, the output power grows when the total resistance increases to accommodate the applied load. The EH device generates high output current and power at low-frequency values. The significance of this work is limited to the numerous uses of its outcomes in everyday life, such as powering medical devices, serving as a power supply for sensors, and serving as a backup energy source for some electronic devices.

Abstract This article introduces an effective stochastic operation framework for optimal energy management of the shipboard power systems including large, nonlinear and dynamic loads. The proposed framework divides the ship power system into several agents, which coordinate with each other based on their demands/supplies until. The alternating direction method of multipliers (ADMM) is deployed as the multi-agent framework to solve the reformulated distributed energy management problem in the ship. Two types of turbo-generators are considered in the proposed system model, including single-shaft and twin-shaft models, to increase the part-load efficiency in certain times when facing variable speed operation. The proposed distributed framework is equipped with a recursive mechanism, which helps the ship system for running optimal load scheduling when facing insufficient power generation. In order to model the uncertainty effects associated with the forecast error in the interval-ahead load demand, a stochastic framework based on unscented transform is devised which can work in the nonlinear and correlated environments of shipboard power systems. Due to the nonlinear cost function in each agent, a powerful optimization algorithm based on modified θ-firefly algorithm (Mθ-FOA) is proposed. This is a phasor algorithm, which helps for escaping from premature convergence and getting trapped in local optima. The appropriate performance of the proposed stochastic model is examined on the real dataset of a ship power system. The simulation results show the high robustness, guarantied consensus, economic operation and feasible solution when power generation shortage based on load shedding in the system.