A photovoltaic array (PVA) simulation model to be used in Matlab-Simulink GUI environment is developed and presented in this paper. The model is developed using basic circuit equations of the photovoltaic (PV) solar cells including the effects of solar irradiation and temperature changes. The new model was tested using a directly coupled dc load as well as ac load via an inverter. Test and validation studies with proper load matching circuits are simulated and results are presented here.

In recent years, there has been a proliferation of studies focusing on sustainable energy sources aimed at delivering reliable power with reduced greenhouse gas emissions. Among these endeavors, electrification of urban areas through incorporation of renewable energy sources (RESs) stands out as a pivotal application of RES. Increasingly, the integration of RESs like PV and wind into the power grid is being pursued as an alternative to traditional centralized power stations, offering a means to ensure continuous power supply during peak demand periods while simultaneously managing unit costs, thereby providing a systematic and readily accessible power supply to consumers. This paper presents a comprehensive examination of hybrid wind and PV with focus on achieving consistent DC bus-bar voltage through integration with microcontroller system via buck-boost converter. Additionally, hybrid system’s output voltage is tailored to accommodate necessary battery connections. However, given influence of environmental variables like fluctuations in solar irradiation and wind velocity on power output of PV and wind seamless integration of these sources becomes paramount. To address this challenge MATLAB model is developed to simulate grid integration of hybrid wind and PV, enabling a detailed analysis of integrated grid’s performance. Simulation results are then proposed with those obtained from conventional fossil fuel-based power generation methods. In this research underscores importance of integrating RES into existing power grids and highlights efficacy of hybrid wind and PV in meeting this objective. Findings presented herein contribute to advancing discourse on sustainable energy solutions, offering valuable insights for policymakers, industry stakeholders, and researchers.

An efficient self-adjusting flexible metaheuristic is introduced to develop automatic voltage and load frequency regulation schemes for multi-area power system. It employs non-linear threshold accepting algorithm to find the best combinations of PID control gains using innovative nonlinear search function. This approach outperformed well established heuristics and other conventional internal model control approaches in terms of the dynamic response, robustness in uncertain dynamical environments, and quality of the solutions.

Recently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility‐owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost‐effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day‐ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self‐adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K‐means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE‐24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near‐optimal day‐ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR.

The paper presents the application of Neural Network technique as a pattern recognition to high impedance faults (HIFs). The relay is based on a novel low-frequency (3rd and 5th harmonic feature diagnostic vector). The currents and voltages are used as a featured extracted signals for fault discrimination. The focus of this paper is to design a robust ANN-based relay, which can determine the high impedance low current faults on distribution radial electrical systems. A variety of faults and system conditions have been simulated to evaluate the reliability and sensitivity of the proposed technique.