The penetration of renewable distributed generations (RDGs) into traditional distribution systems (TDSs) remedies many of its deficiencies and shortcomings. Also, it provides mutual technical, economic and environmental benefits for both electricity companies and their customers as well. With a 25% load increase for the standard IEEE 30-bus system, buses 19, 26 and 30 have the lowest voltage magnitudes among all buses. Therefore, these weak buses are selected initially to allocate RDGs. Three cases, namely, one RDG allocated, two RDGs allocated and three RDGs allocated, of RDGs insertion are covered. A novel crow search algorithm auto-drive particle swarm optimization (CSA-PSO) technique is proposed for the first time in this study to specify the optimal allocation, sizing, and number of RDGs based on the total cost and power losses minimization objectives. A new reduction percent formula is used to estimate the reduction in total cost and the total power losses. These will help us to discern between the best cases based on total cost minimization and those based on total power losses minimization to pick up the best among all best cases. In brief, RDGs allocated on buses 19 and 30 is the best among all cases based on total cost reduction and total power losses reduction. Therefore, buses 19 and 30 are recommended to allocate a wind farm and a solar photovoltaic, respectively based on technical and economic issues. Finally, the simulation findings revealed the superiority of the CSA-PSO algorithm in solving the optimal power flow problem with RDGs compared to the state-of-the-art metaheuristic techniques.

In this study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains a diesel generator, photovoltaics (PV), a wind turbine (WT), and batteries as a storage system. The proposed HRES aims to electrify a remote region in northern Saudi Arabia based on annualized system cost (ASC) minimization and power system reliability enhancement. To differentiate and evaluate the performance, the BO was compared to four recent metaheuristic algorithms, called big-bang–big-crunch (BBBC), crow search (CS), the genetic algorithm (GA), and the butterfly optimization algorithm (BOA), to find the optimal design for the proposed off-grid HRES in terms of optimal and worst solutions captured, mean, convergence rate, and standard deviation. The obtained results reveal the efficacy of BO compared to the other four metaheuristic algorithms where it achieved the optimal solution of the proposed off-grid HRES with the lowest ASC (USD 149,977.2), quick convergence time, and fewer oscillations, followed by BOA (USD 150,236.4). Both the BBBC and GA algorithms failed to capture the global solution and had high convergence time. In addition, they had high standard deviation, which revealed that their solutions were more dispersed with obvious oscillations. These simulation results proved the supremacy of BO in comparison to the other four metaheuristic algorithms.

In the case of partial shading conditions (PSCs), normal equations cannot be completely implemented. The mathematical model of the Photovoltaic (PV) array needs to be modified and re-established with the existence of bypass diodes connected to the PV module, which can alleviate the negative effects of the PSCs and generate several peaks on the PV output characteristics curve. The first aim of this study is to modify and re-establish the mathematical model of the PV array under PSCs. Second, it aims to improve and validate the reliable Cuckoo Search Algorithm (CSA) by integrating it with PID (hybrid CSA-PID) to diminish the impact of PSCs problems. The hybrid CSA-PID was proposed to both track the global maximum power point (GMPP) of PV systems and reduce the tracking time to eliminate the fluctuations around the GMPP. Further, the PID controller was used to eliminate the error percentage obtained by CSA under PSCs to generate the required duty cycle, which provides the required and desired maximum voltage accordingly. The proposed CSA-PID technique has been implemented using both Matlab/Simulink and Hardware-In-Loop experiments on the MT real-time control platform NI PXIE-1071. For validation, the Hybrid CSA-PID method is evaluated and compared with CSA, modified particle swarm optimization (MPSO), PSO, and modified perturb and observe (MP&O) methods under similar conditions. Finally, the obtained findings demonstrated the efficacy and superiority of the proposed hybrid CSA-PID technique, demonstrating its resilience, fast reaction, and good performance in terms of tracking time and GMPP tracking.

Electrification of remote rural areas by adopting renewable energy technologies through the advancement of smart micro-grids is indispensable for the achievement of continuous development goals. Satisfying the electricity demand of consumers while adhering to reliability constraints with docile computation analysis is challenging for the optimal sizing of a Hybrid Energy System (HES). This study proposes the new application of an Artificial Ecosystem-based Optimization (AEO) algorithm for the optimal sizing of a HES while satisfying Loss of Power Supply Probability (LPSP) and Renewable Energy Fraction (REF) reliability indices. Furthermore, reduction of surplus energy is achieved by adopting Demand Side Management (DSM), which increases the utilization of renewable energy. By adopting DSM, 28.38%, 43.05%, and 65.37% were achieved for the Cost of Energy (COE) saving at 40%, 60%, and 80% REF, respectively. The simulation and optimization results demonstrate the most cost-competitive system configuration that is viable for remote-area utilization. The proposed AEO algorithm is further compared to Harris Hawk Optimization (HHO) and the Future Search Algorithm (FSA) for validation purpose. The obtained results demonstrate the efficacy of AEO to achieve the optimal sizing of HES with the lowest COE, the highest consistent level, and minimal standard deviation compared with HHO and FSA. The proposed model was developed and simulated using the MATLAB/code environment.

The current work contributes an estimate of the time-frequency characteristics of a leakage current in assessing the health condition of a polluted polymeric insulator. A 33 kV polymer insulator string was subjected to a series of laboratory tests under a range of environmental conditions, including pollution, wetting rate (WR), non-soluble deposit density (NSDD), and non-uniform distribution pollution (F_T/B). The temporal and frequency features of the leakage current were then extracted and used as assessment indicators for insulator conditions based on laboratory test findings. Two indices were generated from the leakage current waveform in the time domain: the curve slope index (F₁), which is determined by measuring the inclination of the curve between two successive time peaks of the leakage current, and the crest factor indicator (F₂). The frequency domain of the leakage current signal was used to calculate the other two indices. These are the odd harmonic indicators derived from the odd frequency harmonics of the leakage current up to the 9th component (F₃) and the 5th to 3rd harmonics ratio (F₄). The findings showed that the suggested indicators were capable of evaluating insulator conditions. Finally, the confusion matrix for the experimental and prediction results obtained with the proposed indices was used to assess which indicator performed the best. Therefore, the analysis suggests an alternative and effective method for estimating the health condition of a polluted insulator through leakage current characteristics obtained in the time and frequency domains.