The investiture in Distributed Generations (DGs) technologies have begun to monopolies transmission system's architecture instantaneously after announcing its liberation and deregulation of the energy market to the public. Such movement has propelled vicious competitions among power generation companies to initiate superior innovations in meeting consumers' needs, administering high power quality yet economical. With the integration of DG technologies, power grid operations have dynamically benefited from such commendable services which apprehend power failures from transpiring; securing power quality, accommodating capricious demand curve, fault compensatory applications and other auxiliary services. Subsequently, the presence of DGs have revolutionised power grid management to conceive off-grid and self-sustaining criterion where microgrid/islanding topology adaptations are commonly affiliated. However, employing islanding-mode proposition, potential exposure to unplanned islanded paradox due to ineffectual detection control scheme is tenable. Prolonged accidental islanding-mode operation can afflict undesirable annihilation of utility's resources and deemed economically unprofitable which results in compromising safety and serviceability. Thus, this paper demonstrates contrasting dynamics in adopting either passive or active islanding detection approaches based on selected algorithms; Over/Under Voltage and Frequency detection (Passive) or Active Frequency Drift and Sandia Frequency Shift detection (Active). Comparisons of the proposed methodologies are presented using MATLAB/Simulink to view superiority and applicability in determining the status of the grid's operating mode.

In recent years, power grid infrastructures have paved way for decentralized topological layout where distributed power generations are surfaced at the point of consumption to gain higher sustainability factor while truncating power outage problems. In consequence, researches have begun infusing various methodologies with several advance heuristic optimization algorithms to provide superior power delivery solutions. Thus, Accelerated Lambda Iteration algorithm is conceptualized based from equal incremental cost criterion and Linear Extrapolation to solve linear Economic Dispatch problems which abrogate ambiguity in initializing initial lambda, alleviate the search space boundary and aggressive exploitation towards finding the optimal solution within minimal iterations. Alongside, transmission line losses managerialism is modeled using decentralized off-grid Renewable Energy Sources and a dedicated thermal generating unit serving as an auxiliary to compensate the grid's estimated power mismatch between generations and demands. Computations were initiated on standard case system to evaluate the proposed methodology's versatility, feasibility and cost-effectiveness in solving ED problems in comparison to other traditional method such as Lambda Iteration Method.

This paper presents the design and simulation of a piezoelectric cantilever beam for energy harvesting based on ambient mechanical vibration. The energy harvesting process is facilitated by the forward piezoelectric effect, which converts mechanical energy into electrical energy during detection of ambient vibration by the cantilever beam. The cantilever beam model consists of two piezoelectric layers and an elastic layer clamped to a vibrating host structure at one end, as well as a tip mass mounted at the other end. Using COMSOL Multiphysics, a comparative analysis of the cantilever beam's simulated output characteristics was conducted. These characteristics were analyzed with respect to varying input parameters such as resonant frequency, load resistance and length-width ratio of the cantilever beam. Uased on the simulation results, an optimized model of the cantilever beam was eventually developed. A maximum output power of 4.4 mW and DC output voltage of10.1 V was generated by the model at 4 kΩ load resistance, at a mechanical acceleration of 1 g and a resonant frequency of 75 Hz.

This paper presents both the theoretical approach and the HOMER software approach for the planning of optimum long-term microgrid. The proposed microgrid system comprises of solar, wind, energy storage, diesel generator and converter systems. The microgrid objectives of this work are to be at lowest net present costs and emissions. Hence, the theoretical approach will be using modelling equations for the distributed energy resources to generate possible configurations for a preferred configuration of an optimum microgrid. As for the software approach, will be through HOMER, a microgrid simulation software developed by National Renewable Energy Laboratory, which generates optimised low net present costs configurations for the microgrid. In addition, in this paper, the importance of microgrid planning was highlighted alongside with the relevant parameters which affect the overall feasibility of the microgrid. The results obtained presents a comparison between the theoretical and HOMER approach. Followed by case studies of Australia and Ireland simulated by HOMER to determine the cause and effect of the location chosen for the microgrid.

Nonlinear characteristics of solar photovoltaic (PV) and nonuniform surrounding conditions, including partial shading conditions (PSCs), are the major factors responsible for lower conversion efficiency in solar panels. One major condition is the cause of the multiple peaks and oscillation around the peak point leading to power losses. Therefore, this study proposes a novel hybrid framework based on an artificial neural network (ANN) and fractional order PID (FOPID) controller, where new algorithms are employed to train the ANN model and to tune the FOPID controller. The primary aim is to maintain the computed power close to its true peak power while mitigating persistent oscillations in the face of continuously varying surrounding conditions. Firstly, a modified shuffled frog leap algorithm (MSFLA) was employed to train the feed-forward ANN model using real-world solar PV data with the aim of generating a reference solar PV peak voltage. Subsequently, the parameters of the FOPID controller were tuned through the application of the Sanitized Teacher–Learning-Based Optimization (s-TLBO) algorithm, with a specific focus on achieving maximum power point tracking (MPPT). The robustness of the proposed hybrid framework was assessed using two different types (monocrystalline and polycrystalline) of solar panels exposed to varying levels of irradiance. Additionally, the framework’s performance was rigorously tested under cloudy conditions and in the presence of various partial shading scenarios. Furthermore, the adaptability of the proposed framework to different solar panel array configurations was evaluated. This work’s findings reveal that the proposed hybrid framework consistently achieves maximum power point with minimal oscillation, surpassing the performance of recently published works across various critical performance metrics, including the $MP{P}_{efficiency}$, relative error (RE), mean squared error (MSE), and tracking speed.