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The unprecedented growth of Renewable Energy Sources (RES) positions solar power as a leading contender in the global energy mix. Solar energy offers a sustainable alternative to fossil fuels, mitigating carbon emissions and promoting environmental sustainability. This study explores the crucial role of forecasting algorithms within photovoltaic (PV) systems. We aim to provide a comprehensive understanding of methodologies, datasets, and recent advancements for enhancing predictive accuracy in solar power generation forecasting. While machine learning has dominated previous research, recent studies highlight challenges in achieving optimal efficiency and accuracy. A significant obstacle lies in the deficiency of real-world application for large-scale specifically for solar power generation forecasting. To address this gap, this study defines prevalent forecasting methodologies and illuminates datasets with diverse characteristics and their relevance. This study meticulously provides and explore recent advanced methods and datasets, emphasizing their impact on forecasting performance. This study not only deepens our understanding of existing methodologies but also provides valuable insights for future advancements in solar power generation forecasting.

Conventional methodologies such as Incremental Conductance (IC) and Perturbation and Observation (P&O) can be considered effective and low-cost solutions for PV Maximum Power Point Tracking (MPPT) problems in most cases. However, these methods fail to guarantee global maximum tracking in certain situations, such as multiple peak challenges caused by Partial Shading Conditions (PSCs). Therefore, metaheuristic algorithms, like Particle Swarm Optimization (PSO), are employed in literature to address the MPPT problems during PSCs. Nevertheless, traditional PSO encounters issues such as slow convergence and a high probability of failure in tracking the global maximum power point during complex PSCs, which cause a reduction of system efficiency. To address these issues, a modified PSO hybrid with a finite control set Model Predictive Control (MPSO-MPC) has been developed as a robust MPPT technique. The MPC is incorporated into the proposed method to increase the tracking speed. The proposed approach combines a new initialization scheme that ensures uniform initial population distribution across the P-I curve. Additionally, an innovative method is used to update the search space once the partial shading pattern is detected to include only the feasible solutions in the search process. Finally, Incremental Conductance (IC) is introduced to refine the tracking process of global peak and increase its efficiency. The proposed MPSO-MPC algorithm is implemented using dSPACE MicroLabBox for real-time applications. Comprehensive investigations through MATLAB/Simulink simulation and experimental studies validate that the developed method outperforms traditional PSO and Cuckoo Search (CS) algorithms, with a convergence time that does not exceed 0.35 s and a tracking efficiency above 99.5 % under various complex PSCs. Furthermore, the results demonstrate that the proposed technique outperforms both PSO and CS across a range of environmental conditions and load disturbances.

To address the intermittent nature of solar photovoltaic (PV) and wind energy systems, the deployment of multiple energy storage facilities has been significantly expanded, enhancing power system reliability and flexibility toward sustainable energy solutions. This paper focuses on analyzing energy systems that utilize different energy storage options, including battery energy storage system (BESS) and hydrogen energy storage (HES). In this context, this study aims to evaluate the techno-economic and environmental impacts of integrating a hydrogen energy storage (HES) facility comprising an electrolyzer, fuel cell, and hydrogen tank into a hybrid PV/wind/battery energy storage system (BESS). Three different systems have been considered in this analysis. In this paper, three systems are considered: a diesel generator, a hybrid renewable energy system without demand-side management (DSM), and a hybrid renewable energy system with DSM. This study aims to develop a model for electricity generation based on various combinations of hybrid renewable energy systems (HRES) using HOMER energy software in Upper Egypt, specifically focusing on Qena City and Hurghada City. The primary objectives are to identify the optimal configuration of a hybrid renewable energy system that reliably, continuously, and sustainably meets the load requirements of these locations, while minimizing the total system’s net present cost and the cost of energy (COE) through multi-objective analysis with HOMER Pro software. The simulation results using HOMER Pro software indicate that adopting demand-side management (DSM) results in the lowest net present cost (NPC) values for both Qena and Hurghada, at ${\$}$ 798,614 and ${\$}$ 646,046, respectively. Additionally, hydrogen production increases to approximately 953 kg/year in Qena and 850 kg/year in Hurghada, alongside reductions in post-DSM load and carbon dioxide ( ${CO}_{2}$ ) emissions. The comparative analysis shows that the diesel system contributes 124,654 kg/year of ${CO}_{2}$ emissions. In contrast, the proposed hybrid system with DSM significantly reduces ${CO}_{2}$ emissions to 0.275 kg/year in Qena and 0.0666 kg/year in Hurghada.

Anomaly detection in power systems is crucial for operational reliability and safety, often addressed through binary classification in existing research. However, a research gap exists in multiclass classification for enhanced reliability. To bridge this gap, this study employs four machine learning (ML) classifiers: Random Forest (RF), Decision Tree, Naive Bayes (NB), and Support Vector Machine (SVM) using comprehensive testing on a dataset comprising sixteen indices and their pair combinations (totaling 136 pairs). These classifiers, trained on a dataset derived from simulating a test system with hybrid DGs, exhibit superior anomaly detection, especially with the $\frac {dv}{dq}\& \frac {dv}{dp}$ pair. Among them, RF and DT classifier achieves precision, recall, and F score of unity and outperforming NB and SVM. The performance of the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair is compared with existing research papers in terms of accuracy and data division. The comparison shows that the proposed RF and DT classifiers with $\frac {dv}{dq}\& \frac {dv}{dp}$ pair achieve 100% accuracy even with 50% data division, whereas other techniques fail to achieve it even at 20% for testing and 80% for training. The study underscores the critical role of pair selection and classifier combinations in effective anomaly detection, facilitating the implementation of robust mitigating strategies for power system stability.

Proper surveillance and maintenance of photovoltaic (PV) systems are crucial to ensure continuous power generation and prevent operational downtimes. However, manual analysis of electroluminescence (EL) images is subjective, time-intensive, and requires significant expertise. To address this issue, a comprehensive deep learning architecture has been developed for the semantic segmentation of 29 different features and defects within EL images of PV panels. The SegNet architecture encoder has been replaced with the VGG16 encoder, which incorporates pre-trained weights to leverage transfer learning during the feature extraction stage. A Convolutional Block Attention Module (CBAM) block has also been introduced to enhance the decoder’s ability to generate fine-grained segmentations. Additionally, the suggested architecture has been evaluated through the application of three different loss functions: weighted categorical cross-entropy loss, categorical cross-entropy, and focal loss. The Attention-Based SegNet architecture proposed with a weighted categorical cross-entropy loss exhibits superior performance in terms of accuracy, F1 score, intersection over union (IoU), precision, recall, mean IoU (mIoU), specificity, Jaccard index, and Dice coefficient. It achieves a Dice coefficient of 0.9408 and an mIoU of 0.9101, outperforming the state-of-the-art SEiPV-Net trained on the same dataset by 8.77% and 4.97%, respectively.

As a result of advancements in technology and population growth, there has been a significant rise in global electrical demand. Consequently, the integration of renewable sources such as photovoltaic (PV) systems into distribution systems has gained popularity as an effective solution to meet the increasing load requirements. This research paper introduces an optimized approach for allocating PV systems at various penetration levels, utilizing a powerful optimization algorithm known as the modified Reptile Search Algorithm (MRSA). MRSA is an enhanced version of the Reptile Search Algorithm (RSA) that addresses issues related to local optima stagnation and premature convergence by incorporating the disperse ambush strategy and proportional selection method. To assess the efficacy of the proposed optimizer, a comprehensive set of comparative experiments was conducted using the CEC’2020 test suite. The experimental results consistently demonstrate that the proposed technique outperforms alternative methods in terms of both convergence speed and accuracy. Additionally, the MRSA algorithm was employed to determine the optimal allocation of PV systems, with the total power loss serving as a single objective function while considering equality and inequality constraints. The IEEE 33-bus RDS was employed as the test system. The obtained results provide evidence that incorporating multiple PV systems yields superior outcomes compared to a single PV system at various penetration levels within the RDS. Furthermore, integrating PV systems at higher penetration levels yields better results than incorporating them at lower penetration levels.

Large photovoltaic (PV) generations are vulnerable to thermal faults where the location is difficult to determine. The pre-existing thermal fault detection method (mostly manual visual inspection) is time-consuming and non-continuously monitoring. It may lead to more defects, such as degrading the faulted PV cell or putting the PV farm on fire. We propose a novel PV thermal fault monitoring and detection method using a catadioptric device (CD), which promises quick and continuous detection. In this paper, as an early stage in CD development, we focus on constructing a model involving some parameters to identify the object image formation. Then, through the model, a simulation case study of the PV farm monitoring proses was performed where the CD is ostensibly placed in front of the PV arrays. We variated the model parameters and selected the combination that resulted in a symmetrical and non-overlapped PV array image, which was then analyzed to see the sensor requirements of the CD. The results showed that the developed model could simulate PV image formation on CD with good validity. Also, the case result found that nine parameter combinations produce symmetrical and non-overlapped PV images where the image ratio is more affected by the camera position than the focal length. Finally, the minimum sensor requirement is determined by the center length of the farthest PV image to monitor all of the PV arrays.

Photovoltaic (PV) farms are subject to thermal faults that can degrade and reduce module efficiency. The currently used method of detecting thermal fault is time-consuming and hard to locate the fault position, especially in large PV areas. It can result in additional flaws, such as igniting the PV farm. We provide a new approach for monitoring and detecting PV thermal faults through a catadioptric device (CD) that offers fast and continual detection. As an early development stage, in this study, we concentrate on building a mathematical model that can determine the thermal fault coordinate based on two object images from different CD positions. The experiment then verifies the model, and the parameter variation is performed to find the coordinate prediction characteristic. Also, a case study simulation on large PV system monitoring was performed to figure out the thermal fault localization process. The result shows that the mathematical model can be used to determine the coordinate position of the thermal fault with acceptable measurement error. The parameter $x_{k}$ tends to affect the average measurement error of the coordinate prediction and the error gradient of each axes couple. Greater the $x_{k}$ , smaller measurement and gradient error can be achieved. In addition, the case study simulation result shows that the thermal fault position can be predicted with the worst percentage error (PE) of less than 10%, with a mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) in a reasonable value. Also, the sensitivity pattern can be used for CD condition monitoring.

The smart grid system is an ideal solution to overcome the limitations of traditional grid systems. A system is considered smart if it has smart in all its parts such as smart generation, smart transmission and distribution, smart control, smart metering, and...etc. In this paper, smart transmission and distribution have been presented to achieve the requirements of the smart grid system. By transferring to the smart grid, all utilities catch a lot of benefits such as increasing reliability, efficiency, and safety. On other hand, the operation cost will be decreased. In addition, fast dynamic response for fault detection and isolation based on automatic Internet of Things (IoT) and wireless control can be achieved. It should be noted that in the smart grids, the security of the system and occupational safety will be greatly increased. The system has been validated under different kinds of faults such as single-line to ground fault, and double-line to ground fault. In addition, the theoretical operation has been tested via MATLAB Simulink software.

Nowadays, the Overcurrent (OC) and Earth Fault (EF) relays coordination problem is one of the most complex and challenging concerns of power protection and network operators due to the high and volatile generation capacity of renewable energy sources in the grid. In this article, a new and dynamic optimal coordination scheme based on a novel hybrid tripping characteristic has been designed and developed for Over Current Relays (OCRs). Considering the impact of renewable energy sources such as the photovoltaic (PV) system on fault characteristic, this work presents and verifies a novel dynamic and hybrid tripping to achieve minimum tripping time and improve the OCR and EF relays coordination performance in terms of security, sensitivity, and selectivity. The proposed dynamic and hybrid scheme will help the OCRs to cover the EF events, and it has been tested under different fault scenarios compared to the literature. The IEEE-9 and IEEE-33 bus systems are implemented in the ETAP package to validate the effectiveness of the proposed hybrid characteristics against traditionally well-established IEC characteristics. Furthermore, the performance of the proposed advance and dynamic protection approach which doesn’t require a communication infrastructure is investigated for a power network with PV plants under different grid operation modes and topology to provide more robustness protection system. The results, as presented using Industrial software (ETAP), showed that the novel dynamic and hybrid tripping scheme improved the speed of the total time tripping different fault scenarios and location by more than 50% and covers all EF events compared to traditional OCR schemes from the literature. The proposed novel dynamic approach has superior performance in detecting high-impedance faults and significantly reducing the tripping time on the IEEE 33 bus network by 47%.

Indirect Lightning Stroke (ILS) is considered an urgent issue on overall power systems due to its sudden dangerous occurrence. A grid-connected solar Photovoltaic (PV) power plant of 1MW was considered and analyzed using PSCAD/EMTDC software. The effect of grounding grid resistance ( $R_{g}$ ) on the induced voltages caused by the indirect strokes was discussed. The Transient Grounding Potential Rise (TGPR) variation with $R_{g}$ was presented and discussed. Four different models were proposed and installed for the system under study, which includes a combination of the Externally Gapped Line Arrester (EGLA) with the Non-Gapped Line Arrester (NGLA). The results show that when the $R_{g}$ was reduced from 5 to 1 ohm, TGPR decreased by about 79.63%, whereas the peak value was reduced by about 91.3% nearby the striking position. Four models of EGLAs were proposed to reduce the induced transient overvoltage effectively. The four models showed a remarkable ability to reduce the backflow current (BFC) and, consequently, the induced overvoltage. The EGLA’s type with the composite air gap reduced the TGPR by about 77.04 % and reduced the induced overvoltage nearby the striking position by about 51.3%.

The identification of parameters in solar cell models is still a major challenge in photovoltaic (PV) system simulation and design. Because of its more basic ideas, efficiency, adaptability, swarm and evolutionary optimization algorithms, as well as simple procedural frameworks, have been generally used in industry with real-world problems. However, due to the nonlinearity and complication of the PV parameter identification, the obtained solutions from swarm and evolutionary optimizers were immature. An efficient metaheuristic approach for identifying PV model parameters based on the salp swarm algorithm (SSA) is presented in this paper. In the suggested modified salp swarm optimization (MSSA), the leaders and followers will be updated based on the new formulas. The algorithm’s exploration potential is increased by this modification while also preventing it from converge prematurely. The behavior of the suggested technique is verified using benchmark functions, and the outcomes are contrasted with those of SSA and other successful optimization approaches. The suggested MSSA detects numerous characteristics in the PV model include single diode, double diode, and PV modules, in the most efficient way possible. According to the simulation results, MSSA outperforms the competition and may produce better optimal solutions. The findings demonstrate that the best value of RMSE obtained by MSSA is up to 69 percent lower than other methods and is nearly 5.6 percent lower than that assessed by SSA.

As the transition to electric mobility is expanding at a rapid pace, operationally feasible and economically viable charging infrastructure is needed to support electrified fleets. This paper presents a co-simulation of optimal electric vehicle supply equipment (EVSE) and techno-economic system design models to investigate the behaviors of various EVSE configurations from cost and technical aspects. While the system design optimization is performed for a grid-tied PV system, the optimal EVSE model considers all EVSE options that are currently installed at workplaces. To investigate the impact of the EV utilization rate, three fleet sizes are considered, which are generated based on real EV fleet data. Furthermore, the impact of electricity rates is also explored through an innovative business EV-specific (BEV) rate and a conventional time-of-use (ToU) tariff. It is shown that investing in grid-tied renewable energy technologies for workplace charging infrastructure supply can lower charging costs. Cost savings differ from EVSE types and fleet size under the BEV rate, while EVSEs display similar cost-saving behavior under the ToU tariff irrespective of fleet size. DC Fast Charging (DCFC) EVSE is found to be highly sensitive to fleet size as compared to AC EVSEs. Moreover, DCFCs make better use of the BEV rate, which makes their economics competitive as much as AC EVSEs. Finally, it is found that the fleet size and AC EVSE types have a minor effect on the use of renewable energy in contrast to the DCFC case.

This paper proposes an Energy Management System (EMS) for domestic PV-battery applications with the aim of reducing the absolute net energy exchange with the utility grid by utilizing the two days-ahead energy forecasts in the optimization process. A Mixed-Integer Linear Programming (MILP) exploits two days-ahead energy demand and PV generation forecasts to schedule the day-ahead battery energy exchange with both the utility grid and the PV generator. The proposed scheme is tested using the real data of the Active Office Building (AOB) located in Swansea University, UK. Performance comparisons with state-of-the-art and the commercial EMS currently running at the AOB reveal that the proposed EMS increases the self-consumption of PV energy and at the same time reduces the total energy cost. The absolute net energy exchange with the grid and the total operating costs are reduced by 121% and 54% compared to the state-of-the-art and 194% and 8% when compared to the commercial EMS over a six-month period. Furthermore, the results show that the proposed method can reduce the energy bill by up to 46% for the same period compared to the state-of-the-art. The paper also investigates the effect of using different objective functions on the performance of the EMS and shows that the proposed EMS operate more efficiently when it is compared with another cost function that directly promotes reducing the absolute net energy exchange.

Photovoltaic (PV) system researchers continue to be intrigued by innovative models that describe PV performance under a wide range of conditions. The development of a precise mathematical model using experimental measurements information from PV modules is a necessary component for constructing I-V and P-V dynamic characteristics using system parameters. This paper presents a new PV model using Hunter–Prey Optimization (HPO) and Wild Horse Optimizer (WHO). The parameters of photovoltaic panels are obtained using such an effective optimization technique and manufacturer datasheets as well as experimental data. In this topic, the HPO and WHO have been developed to extract the PV model parameters of solar cells or modules. The HPO and WHO parameters extraction can be employed in Three Diode Model (TDM). Furthermore, the Route Mean Square Error (RMSE) value between the data of the model’s optimised parameters and the measurement data of the photovoltaic modules is used as the objective function (OF). To verify the efficacy of the HPO and WHO in extracting photovoltaic characteristics, R.T.C. France Solar Cell, Photowatt-PWP201 panel, and thin-film PV systems were used as case studies with different circumstances, such as variations in solar irradiance and module temperature. The effectiveness and accuracy of the proposed HPO and WHO for parameter extraction of various photovoltaic panels throughout various operational circumstances are proven by simulation results as well as statistical measurements. The results show that, considering the case study of R.T.C. France solar cell as a reference case, the finest RMSE values are 7.56748 $\times $ 10.04 and 7.51198 $\times $ 10.04 for HPO and WHO, respectively. Moreover, the statistical results demonstrate that the efficiencies of the HPO and WHO algorithms are 98.37% and 91.06%, respectively. Furthermore, the PV characteristics of the estimated models and those of the datasheets are matched.

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.

Connecting different renewable energy sources (RESs) to the electrical grids is presently being urged to fulfill the enormous need for electric power and to decrease traditional sources' ecological related issues, the so-called hybrid systems. Unfortunately, these hybrid systems suffer from the possible negative environmental impacts of the wind gusts in wind energy conversion systems (WECSs) that may degrade the overall system performance. Additionally, various severe faults may disconnect some RESs from the hybrid system, like three-phase faults. In this paper, the static synchronous compensator (STATCOM) is considered for both improving the performance of a hybrid system, contains WECS and photovoltaics (PVs) against wind gusts and maintaining the continuous operations of RESs during three-phase fault occur at the point of common coupling (PCC) between the RESs and the grid. The STATCOM is stimulated by two PI controllers regulating the reactive power flow between the STATCOM and the hybrid system at PCC and, consequently, regulating the voltage at PCC. A metaheuristic optimizer optimally schedules these two PI controllers based on whale optimization algorithm (WOA). The impartial comparison between the WOA dynamic performance and the particle swarm optimization as another optimization algorithm verifies the efficiency of the WOA for the near-optimal gain scheduling of the PI controller gains.

Optimal planning of integration the Photovoltage Distributed Generation (PV-DG) and DSTATCOM is a crucial task due to the stochastic variations of PV output power and the load demand which are related to solar irradiance variations and the activities of the customers, respectively. In this article, the optimal planning problem of the PV-DG and DSTATCOM system is solved. The proposed model considers the uncertainties of the solar irradiance and the load demand for a multi-objective function, including the cost reduction, the voltage profile, and stability index improvement. Modified Ant Lion Optimizer (MALO) is proposed to enhance the basic ALO searching ability using two strategies. The first strategy is based on Levy Flight Distribution (LFD) to strengthen the exploration of the algorithm and avoid the premature of the basic ALO. In contrast, the second strategy is based on updating the solutions in a spiral orientation to improve the exploitation of the algorithm. The IEEE 69-bus and 118-bus radial distribution systems are used to demonstrate the effectiveness of the proposed method, and the yielded simulations are compared with the basic ALO and other well-known optimization techniques for power loss minimization under deterministic conditions. The simulation results demonstrate that the techno-economic benefits can be increased considerably by optimal inclusion of two PV-DGs and DSTATCOMs compared with a single system.

In this article, a new solar PV fed Dynamic Voltage Restorer (DVR) based on Trans-Z-source Inverter (TransZSI) is proposed to improve the power quality of on-grid Photovoltaic (PV) systems. DVR is a power electronic compensator using for injecting the desired voltage to the Point of Common Coupling (PCC) as per the voltage disturbance. In the proposed DVR, in place of traditional VSI, TransZSI with outstanding merits of buck/boost, a broader range of voltage boost gain, fewer passive components, and lower voltage stress, is put forth. For efficient detection, accurate voltage disturbances mitigation, and also lessening the injected voltage harmonics, a hybrid Unit Vector Template with Maximum Constant Boost Control (UVT-MCBC) method is proposed for TransZSI-DVR. The performance of the proposed TransZSI-DVR with UVT-MCBC has been analyzed under severe sag, slight sag with harmonics, swell, and interruption. The comparative studies and simulation results have shown the effectiveness of the proposed TransZSI-DVR, as opposed to traditional ZSI-DVR and VSI-DVR. The TransZSI-DVR in the PV system has mitigated voltage sag/swell/interruption. It has also improved the power quality of both the injected voltage to the PCC and PV system's output voltage.

This paper introduces a multifunction interactive PV system that has the ability to feed a nonlinear local load in either grid-tied topology (GTT) or off-grid topology (OGT) and transits between the two topologies are seamless. The interactive PV system utilizes a sliding Fourier transform-based phase-locked loop (SFT-PLL) as an adaptive and robust control technique for extracting the fundamental load current. Further, the SFT-PLL is also used to synchronize the inverter output voltage with the grid during the GTT. The proposed synchronization technique has a superior immunity to the weak grid problems. The SFT-PLL based control technique has a very good ability to mitigate the nonlinear load harmonics and to deliver active current to the grid with a unity power factor. Moreover, the proposed control technique is able to keep a balanced grid current when the connected load is unbalanced whether the PV source is available or not. The DC-link voltage is controlled by the controller of the voltage source inverter (VSI) during GTT. During the OGT, the AC load voltage is regulated by the VSI controller while the DC-link voltage is regulated by the boost converter controller to satisfy the power requirements of the connected load. The simulation and experimental results show the effectiveness of the proposed control technique in feeding a balanced current with THD adheres to the IEEE-519 standard at different operating conditions. The performance of the proposed method is compared with recently published adaptive filtering techniques to show its effectiveness especially when the load current has a DC-offset.

This paper proposes an optimal design of Shunt-Resonance Fault Current Limiter (SRFCL) to enhance the Fault Ride-Through (FRT) capability and improve the transient stability of a grid-connected hybrid PV/wind power system. The design parameters of the SRFCL are optimized by using Particle Swarm Optimization (PSO) technique. The proposed SRFCL topology is designed in such a way that it can provide superior protection capability for limiting the fault current and supporting the grid voltage than the conventional Bridge Fault Current Limiter (BFCL). The effectiveness of the SRFCL in supporting the dynamic performance and improving the transient stability of the hybrid energy system is validated during both symmetrical and unsymmetrical faults in the electrical utility. Moreover, its credibility is evaluated compared with that of the BFCL and the FRT control schemes. Simulations have been performed using the MATLAB/SIMULINK software. The results illustrate that the proposed SRFCL augments significantly the dynamic behavior and the transient stability of the hybrid power system during the fault events. Also, when the optimal SRFCL is employed, the injected active power by the hybrid system and the grid voltage profile are improved considerably under the grid disturbances. Furthermore, the comparison confirms the superiority of the SRFCL performance to both the BFCL topology and the FRT control scheme in every aspect.

One of the important tasks for increasing the efficiency of photovoltaic (PV) system is the development and improvement of the maximum power point tracking algorithms (MPPT). These MPPT algorithms lead to the ability to catch efficiently the global maximum power point of the partially shaded PV array. One of these trackers is the particle swarm optimization (PSO) algorithm which is one of the Soft computing techniques. The conventional PSO based trackers have many advantages such as the simplicity of hardware implementation and independence from the installed system. The actual problem of the practical application of PSO is the determination of its parameters to ensure high effectiveness of extracting the global MPP. Analysis of scientific papers devoted to the PSO algorithm has shown that there is currently no methodology for the optimal parameters' selection of PSO algorithm based maximum power trackers for the PV system. This paper aims to create a convenient and reasonable method for choosing the optimal parameters of the PSO algorithm, taking into account the topology and parameters of the DC-DC converter and the configuration of solar panels. A new method for selecting the parameters of a buck converter connected to a battery has been presented. The optimal value of the sampling time for the digital MPP controllers, providing their maximum performance; has been determined based on a new methodology. Matlab/Simulink software package is used as the main research tool. The prominent outcomes identify that the modified PSO and its designed parameters best meet the requirements of the MPPT controller for the PV system.

The large-scale application of wind power and photovoltaic power solves the energy crisis and alleviates the environmental problems caused by the use of conventional energy. However, they are at risk of being randomly tripped from the network when faced to voltage sag and severe fault events, which will lead to a sudden reduction of active power output and also complicates fault recovery process of the whole system. Moreover, it may also aggravate failures and lead to large-scale power outages, which stimulates a growing interest in analyzing the low-voltage ride-through (LVRT) capabilities of the renewable energy systems (RES) and improving the performance through developing various mathematical models and analysis tools. In this paper, a systematical overview of cause, classification of voltage sag phenomena and voltage sag emulating techniques is presented, and four voltage sag generators (VSGs) are discussed and compared, which include generator based-VSG, shunt impedance based-VSG, transformer based-VSG and full converter based-VSG. Furthermore, a closed-loop detection platform based on real-time digital simulator (RTDS) for the converter controller of a permanent magnet synchronous generator (PWSG) set is introduced, to investigate the LVRT performance of the WT system under grid voltage sag conditions. Finally, the application of VSG in RES are presented and the future research directions are also discussed.

The potential for utilizing clean energy technologies in Egypt is excellent given the abundant solar irradiation and wind resources. This paper provides detailed design, control strategy, and performance evaluation of a grid-connected large-scale PV/wind hybrid power system in Gabel El-Zeit region located along the coast of the Red Sea, Egypt. The proposed hybrid power system consists of 50 MW PV station and 200 MW wind farm and interconnected with the electrical grid through the main Point of Common Coupling (PCC) busbar to enhance the system performance. The hybrid power system is controlled to operate at the unity power factor and also the Maximum Power Point Tracking (MPPT) technique is applied to extract the maximum power during the climatic conditions changes. Modeling and simulation of the hybrid power system have been performed using MATLAB/SIMULINK environment. Moreover, the paper presented a comprehensive case study about the realistic monthly variations of solar irradiance and wind speed in the study region to validate the effectiveness of the proposed MPPT techniques and the used control strategy. The simulation results illustrate that the total annual electricity generation from the hybrid power system is 1509.85 GWh/year, where 118.15 GWh/year (7.83 %) generates from the PV station and 1391.7 GWh/year (92.17%) comes from the wind farm. Furthermore, the hybrid power system successfully operates at the unity power factor since the injected reactive power is kept at zero.

This paper proposes a novel and optimal battery sizing procedure for the primary frequency control (PFC) of islanded microgrid (MG). The Battery Energy Storage System (BESS), Photovoltaic (PV) systems and LED Lighting Loads (LEDLLs) are coordinated to quickly intercept frequency deviation in the stage of PFC. The PVs decrease their power generation in the case of surplus of power generation. The LEDLLs decrease their power consumption in the case of power shortage. The BESS participates in PFC in both cases by injecting/absorbing power. Some batteries with overloading characteristics are capable of fast discharge/charge for a short period, which can be used to reduce the required battery size for PFC application. The conventional overloading characteristics is based on constant power discharge/charge, but the BESS power varies in response to frequency deviation. To overcome this problem, a modified overloading characteristic is presented based on variable power discharge/charge, which is used to propose a battery sizing algorithm. The Genetic Algorithm is used to optimally determine the frequency controllers' coefficients of the BESS, PVs and LEDLLs to minimize the required battery size while maintaining the MG frequency within safe operational limits. The proposed battery sizing procedure is evaluated on the CIGRE low voltage benchmark system using simulation in MATLAB/Simulink software. The results show that beside the overloading characteristics of the battery, the participation of PVs and LEDLLs in PFC also reduces the required battery size, because their participation reduces the share of BESS participation in PFC.

This paper proposes a new interleaved non-isolated high step-up dc-dc converter for interfacing renewable energy applications. The proposed converter achieves a very high step-up voltage gain by using two coupled inductors and a voltage multiplier cell. This topology utilizes the interleaved boost converter in the input side, and the input current is shared with low ripple. Moreover, a voltage multiplier cell with the secondary windings of the coupled inductors is employed in the output side to achieve the interleaved energy storage. The voltage stress on the semiconductor switches and the passive components is significantly reduced and lower than the output voltage. The aforementioned converter can be operated without an extreme duty cycle or a high turns ratio. The reverse recovery problem of the diodes is mitigated, and the leakage energy is recycled. Furthermore, by implementing low-voltage-rated MOSFETs with a small ON-resistance, the conduction losses can be reduced, and the efficiency can be improved. The topology is fed by a single input voltage, and the mathematical expression is methodically explored. The operation principle of the proposed converter and the comparison between the proposed converter with other topologies are discussed. The design, parameters selection, and experimental results are thoroughly introduced. A 32 to 800 V-dc is verified and simulated by using PLECS. Consequently, a 400 W hardware prototype is verified to validate the theory and the design.

Recent development and cost down of PV(Photovoltaic) technology drive change of power system structure. The participation of PV generation is increasing, and the size of each PV farm is getting larger. In order to integrate large PV farms into the main grid, substation for interconnection needs to be sized properly. Unlike substations for load and conventional generators, PV farm substation has an uneven utilization ratio due to characteristics of solar radiation. With proper sizing method for the capacity of the substation can reduce the building cost of facilities. A combination of an energy storage system can further reduce the capacity of the substation. Battery energy storage system (BESS) can shift the peak production of PV during the daytime to midnight. According to market circumstances, BESS can reduce further construction costs by producing profit based on time difference of electric cost. For proper sizing of substation capacity, several factors must be considered including environmental factors, market structure and BESS in the system. In this article, a series of assessment methodology is introduced to calculate the optimized capacity of substation and BESS for PV farm interconnection. The long-term solar radiation data is analyzed for a given site of the PV farm. Based on market structure, the operation of BESS is optimized to make maximum profit during operation. The iterative calculation of each step results in the calculation of the optimized capacity of BESS and substation for given PV farm size.

Large-scale renewable energy-based power plants are becoming attractive technically and economically for generation mix around the world. Nevertheless, network operation has significantly changed due to the rapid integration of renewable energy in supply side. The integration of more renewable resources, especially inverter-based generation, deteriorates power system resilience to disturbances and substantially affects stable operations. The dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs) due to the limited capabilities of inverter-based resources (IBRs). A heavily loaded and stressed renewable rich grid is susceptible to fault-induced delayed voltage recovery. Hence, it is crucial to examine the system response upon disturbances, to understand the voltage signature, to determine the optimal location and sizing of grid-connected IBRs. Moreover, the IBRs fault contribution mechanism investigation is essential in adopting additional grid support devices, control coordination, and the selection of appropriate corrective control schemes. This article utilizes a comprehensive assessment framework to assess power systems' dynamic voltage signature with large-scale PV under different realistic operating conditions. Several indices quantifying load bus voltage recovery have been used to explore the system' s steady-state, transient response, and voltage trajectories. The recovery indices help extricate the signature and influence of IBRs. The proposed framework's applicability is carried out on the New England IEEE - 39 bus test system using the DIgSILENT platform.

In this paper, an adaptive global fast terminal sliding mode control method using fuzzy-neural-network (FNN) is proposed for a single-phase photovoltaic (PV) grid-connected transformerless system that is mainly composed of a boost chopper and a dc-ac inverter. A maximum power point tracking is accomplished in the boost part in order to extract the maximum power from the PV array. A global fast terminal sliding mode control strategy is proposed for an H-bridge inverter so that the tracking error between a grid reference voltage and the output voltage of the inverter can converge to zero in finite time. FNN is used to estimate the uncertainties of the system in real time since uncertainties in the system are difficult to obtain. The network weights are updated according to the adaptive law in real time to adapt to the variations of system uncertainties, enhancing the robustness of the system. Finally, a PV grid-connected system model is built in Simulink to verify the effectiveness of the proposed adaptive global fast terminal sliding mode control method.

This paper introduces a hybrid maximum power point tracking (MPPT) technique for photovoltaic (PV) arrays working under partial shading conditions. This new algorithm can combine a traditional MPPT algorithm, such as perturb and observe, or incremental conductance, with the artificial neural network (ANN) technique. The proposed hybrid MPPT algorithm is based on the ANN and used to predict the global MPP region by estimating its voltage boundaries. Consequently, the conventional MPPT algorithm searches for the MPP in the predicted region. The proposed technique is modeled and simulated using MATLAB/Simulink. The results show the effectiveness of the proposed hybrid MPPT technique to track the global MPP accurately with a rapid response comparing to the ANN; this increases the output power level of the PV array under various shading patterns.