International Journal of Photoenergy

Providing sustainable electricity access to remote areas is critical for economic development and environmental preservation. This study investigates the performance of single-source and hybrid renewable energy systems for the town of Zahedan, Iran, which has significant solar and wind energy potential. Using TRNSYS software, eight configurations were simulated and analyzed, comprising two single-source (photovoltaic [PV] and wind turbine [WT]) and six hybrid systems incorporating combinations of PV panels, WTs, alkaline fuel cells, and diesel generators. The analysis revealed that hybrid systems, particularly those combining PV and WT, outperformed single-source configurations. For instance, a hybrid system with 800 kW of PV and a 50 kW WT reduced diesel consumption by 35% and CO2 emissions by 45% compared to a system relying solely on a diesel generator. Conversely, the configuration involving WTs, fuel cells, and diesel generators showed high energy dumping (1,821,776 kWh) and considerable diesel usage, underscoring the challenges of maintaining energy balance without solar integration. Overall, hybrid renewable systems generally provide enhanced reliability and environmental benefits, although their performance heavily depends on the specific energy source mix. This study offers insights into optimizing renewable energy systems for remote locations, highlighting the necessity of a balanced solar-wind combination to achieve optimal sustainability and cost-effectiveness. The findings are applicable to regions with similar climatic conditions and contribute to global sustainable energy solutions, providing crucial information for policymakers and investors focused on supporting sustainable energy projects in isolated areas.

In Ethiopia, where millions rely on biomass, charcoal, and animal dung for energy, the predominant use is in baking injera, constituting over 50% of energy consumption. This traditional practice adversely affects the health of women and children and hampers education. To address this, solar energy is explored as a sustainable alternative. However, the intermittent nature of solar power poses challenges. A solution involves integrating a thermal storage system, ensuring a consistent and reliable energy supply for the crucial task of baking injera, ultimately improving lives and empowering communities. This study investigates the thermal characteristics of the charging and discharge processes and main heat transfer processes in the injera baking system with PV which was integrated with the thermal storage system. Our study journey began with exploring relevant papers and system designs, collecting and analyzing data, and, in addition, performing mathematical and numerical models. A numerical simulation was conducted using a finite-difference computational model for the thermal storage containing PCM and thermal oil. The thermal oil was used to store energy and transfer heat; furthermore, the developed computational models were analyzed using MATLAB programming software. The numerical simulation result by using solar radiation data from Addis Ababa showed that the thermal storage has the capacity to store about 33.03 MJ during charging using constant heat flux which was from the PV. The amount of energy discharged from the PCM was 13.1 MJ, and from the thermia oil, it was 3.50 MJ using natural convection heat transfer, and the discharging and overall efficiency of the system were about 50.2% and 46.67%, respectively. Also, the baking pan surface temperature stayed between 220°C and 146.4°C for about 3 h. This result was compared with different papers, and it can be concluded that the numerically investigated solar-powered injera baking integrated with thermal storage showed a promising result.

Photovoltaic (PV) installations have become integral for harnessing solar energy, yet ensuring uninterrupted power generation remains crucial. This study addresses the challenge of maintaining reliability in PV systems by proposing a method to detect and identify simultaneous faults, using kernel principal component analysis (KPCA) and statistical metrics. The proposed method employs KPCA, a machine learning technique adept at identifying patterns in complex data. By utilizing statistical metrics in a feature space generated by KPCA, potential faults in PV system performance data are flagged. Unlike prior research that focused on single faults, this work extends the application of KPCA to detect and identify multiple faults occurring simultaneously, such as partial shading combined with open or short circuit faults. Through extensive simulations, including 100 samples of different faults under varying irradiance conditions, the method demonstrates high accuracy rates: 93.33% for partial shading, 100% for open circuit, 100% for short circuit, and 81.81% for combinations of partial shading with either open or short circuit faults. Results from a Matlab-Simulink model validate the effectiveness of KPCA in detecting both single and simultaneous faults in PV systems’ DC side.

This paper presents a methodology for determining the optimal parameters of a photocatalysis process for treating pharmaceutical wastewater. Three parameters are considered: pH value, catalyst dosages, and reaction time to boost the amoxicillin degradation efficiency (ADE). The proposed methodology contains two stages: fuzzy modelling and a parameter determination process using the marine predators algorithm (MPA). Firstly, based on the experimental dataset of ADE in terms of pH value, catalyst dosages, and reaction time, a robust fuzzy model is produced to model the photocatalysis/ozonation process. The target is reducing the root mean square error (RMSE) between the actual data and the experimental dataset. Using fuzzy, the RMSE decreased from 2.0248 using ANOVA to 0.3148 using fuzzy (decreased by 84%). Next, using the MPA, the optimal parameters of pH value, catalyst dosages, and reaction time corresponding to maximum ADE are determined. The suggested strategy boosted the ADE from 88.23% to a rate of 11.68% compared with the experimental and RSM approaches. Under this condition, the optimal solutions are 11, 384 mg/L, and 33.615 min, respectively, for pH, catalyst dosages, and reaction time.

The amount of irradiance incident on a photovoltaic module and the module temperature are essential parameters to estimate its performance and forecast its energy output. These data are often available only where there are meteorological stations. Consequently, other methods are required to estimate these data. The objective of this work was to estimate the irradiance and temperature from different models and further estimate the annual energy output for Bambili, Cameroon. To this effect, mathematical models such as Angstrom–Prescott (A-P), Kaplanis, Duffie and Beckman, and Collares-Pereira and Rabl were employed for the estimation of irradiance, while the WAVE, SOYGRO, and Parton and Logan models were used for temperature computations. Collares-Pereira and Rabl irradiance models showed a lower percentage root mean square error (RMSE) value of 6.71 with respect to the National Aeronautics and Space Administration (NASA), while for temperature models, Parton and Logan performed better from 6 a.m. to noon and SOYGRO from 1 to 6 p.m. with percentage RMSE values of 7.17 and 9.86, respectively. With the estimated irradiance and temperatures from the models, the monthly and annual energy outputs were computed using mathematical models of the PV module. The percentage RMSE of the annual energy estimated with respect to the Photovoltaic Geographical Information System (PVGIS) was found to be 1.95 and 4.40 for BP3125 and BP3180 modules, respectively, while the percentage RMSE of the annual energy estimated with respect to Photovoltaic Software (PVsyst) was found to be 2.14 and 4.22 for BP3125 and BP3180 modules, respectively. In conclusion, the results showed that Collares-Pereira and Rabl, SOYGRO, and PV model equations can be employed with BP3125 and BP3180 PV modules for the estimation of energy output for Bambili and other locations with no meteorological stations or internet services.

Doping TiO2 with chalcogen (S, Se, and Te) decreases its band gap and enhances the photocatalytic activity. However, doping TiO2 could create mid-gap states that trap the electrons and reduce the photocatalytic activity. Therefore, more investigation is needed to extrapolate clear insights for technological development. Density functional theory (DFT) is used to investigate the effects of doping chalcogens (X) (X=S, Se, and Te) in the two layers (2L) and four layers (4L) anatase (101) TiO2 surfaces. The structural and electronic properties as well as the band gap are investigated for the doping of one or two chalcogens in the TiO2 surface. The hybridization between the p chalcogen and 3d Ti as well as the 2p O orbitals causes the narrowing of the band gap. The substitutional doping of chalcogen in the TiO2 surface introduces new states in the band gap region and predicts the enhancement of visible light photoactivity. Among all the substitutional doping chalcogens in TiO2, the Te/TiO2 band gap is significantly lower than in the Se- and S-doping systems. The effects of the chalcogen doping depend on the concentration of the chalcogen as well as the number of layers in TiO2 surfaces.

Today, the utilization of small-scale solar systems for houses has increased significantly around the globe. The rise of electricity prices due to elevated prices of fossil fuels coupled with the global concerns about the increased emission of greenhouse gases has fueled the increased application of renewable energy systems. However, such policies have been ineffective in particular countries where the electricity prices are kept too low to control social atmosphere and enhance the people’s well-being for political purposes—in this case, solar systems have witnessed no growth as they have rendered not economically justifiable. In this respect, the present research considers Iran as a country with very low electricity prices coupled with very large potentials for utilizing solar energy in the presence of imbalanced electricity supply and demand, with the research objective being an investigation of the required incentive policies to promote the utilization of distributed solar photovoltaic (DSPV) systems in the country. According to the results, it was figured out that, under the current conditions, escalating the price of electricity for houses (even to as high as 10 times the current prices) cannot cover the costs of the equivalent DSPV systems, indicating a need for another cluster of policies. As an option, one may consider buying the generated electricity by the customer (from renewable sources) at a higher price than that at which the electricity is offered to households via the grid (by a margin of 15 folds), which is supposed to provide the required incentive to expand the utilization of DSPV systems.

Carbon-based perovskite solar cells (PSCs) have the advantages of a long lifetime and are compatible with highly scalable manufacturing processes. The use of carbon electrodes and the absence of a hole selective layer (HSL) promote a simplified fabrication process. However, the efficiency of HSL-free carbon-based PSCs is inferior to PSCs that utilize metal electrodes and HSL due to poor hole extraction at the perovskite/carbon interface. To overcome those issues, researchers added an interlayer on the perovskite/carbon interface or incorporated additive material into the carbon layer. However, there is limited research on utilizing both strategies to improve carbon-based PSC performance. Here, we use CuSCN as an interlayer between the perovskite/carbon interface and a carbon additive in carbon-based PSCs. By utilizing both strategies, the charge transfer of carbon-based PSC was significantly improved compared to carbon-based PSC utilizing only one improvement strategy. Confirmed by photoluminescence (PL) and electrochemical impedance spectroscopy (EIS) characterizations, the CuSCN interlayer and CuSCN-incorporated carbon electrode enable more efficient hole transfer, resulting in an increased power conversion efficiency from 0.58% to 5.06%. The proposed PSC structure shows promising results for further improving carbon-based PSC performance.

The integration of microgrid (MG) and distribution static synchronous compensator (D-STATCOM) controller in power system has become crucial for enhancing voltage profiles, improving system reliability, and minimizing power losses in radial distribution networks. This study presents an innovative approach for the optimal allocation of MG and D-STATCOM within a radial distribution system (RDS), aimed at significantly improving voltage profiles. Particle swarm optimization (PSO), a powerful and efficient metaheuristic algorithm, is utilized to determine the optimal sizing of MG and D-STATCOM controllers. The loss sensitivity factor (LSF) technique is employed to find the optimal position for D-STATCOM, and the voltage stability index (VSI) is used to determine the optimal location for MG. To validate the effectiveness of the PSO-based approach, simulations are conducted on a standard IEEE 30 bus RDS. The results indicate that the strategic placement of MGs and D-STATCOMs markedly enhances the voltage profile across the distribution network, leading to improved power quality and reduced technical losses.

As a kind of inexhaustible renewable energy, solar energy is popular, and photovoltaic power generation has been paid attention to by all circles. However, determining the global maximum power point (GMPP) is difficult under external ambient temperature and light intensity change, and MPPT control technology becomes the key to research. Fast, accurate, and stable GMPP capture has become a hot research problem in PV power generation systems. The GWO algorithm incorporating the Levy flight function and the INC using the vertex as the dividing point with different step sizes on the left and right sides are combined and applied to the MPPT control strategy of PV systems. The global search is completed by IGWO first, and then the exact search is completed by the improved INC when it is close to the global optimum. The final tracking accuracy is above 99%, and compared with the GWO, INC, and ICS-IP&O algorithms, respectively, in the case of abrupt changes, the tracking time is accelerated by 0.021 s on average. The oscillation amplitude is smaller, and the voltage is more stable.

The present study investigates the performance of photovoltaic thermal (PVT) systems that employ silver, aluminum oxide, copper, and titanium dioxide nanoparticles with distilled water as a solvent. The volume portions of the nanoparticles considered are 2% and 5% by weight. The study employs an energy balance equation to encompass circular geometries for fluid flow channels and a flow velocity ranging from 1×10−4 to 3×10−4 m/s. A numerical model has been established to investigate the performance of the photovoltaic thermal system and obtained the highest performance in Cu/water nanofluid for a uniform mass flow rate of 0.0670 kg/s and volume portion of 5% compared to other nanofluids, and the average electrical, thermal, and overall performance achieved is 15.8%, 30.2%, and 45.3%, respectively. Moreover, an artificial neural network (ANN) was developed to predict the electrical and thermal efficiency of the PVT system, and the mean absolute percentage error (MAPE) between array error of the thermal and electrical efficiency of the system is 4.98% and 2.61%, respectively. This value shows the strong validation of the numerical and ANN simulation values.

Perovskite solar cells (PSCs) have emerged as a promising alternative to traditional silicon solar cells due to their low cost of fabrication and high power conversion efficiency (PCE). The utilization of lead halide perovskites as absorber layers in perovskite solar cells has been impeded by two major issues: lead poisoning and stability concerns. These hindrances have greatly impeded the industrialization of this cutting-edge technology. In light of the harmful effects of lead in perovskite solar cells, researchers have shifted their attention to exploring lead-free metal halide perovskites. However, the present alternatives to lead-based perovskite exhibit poor performance, thus prompting further inquiry into this matter. The primary objective of this research is to investigate the use of Cu2O as a hole transport layer in combination with lead-free metal halide perovskite (CsSn0.5Ge0.5I3) to achieve superior performance. Through meticulous experimentation, the suggested model has achieved outstanding results by optimizing several key variables. These variables include the thickness of the absorber layer (CsSn0.5Ge0.5I3), defect density, and doping densities, as well as the back contact work function and the operating temperature associated with each layer. The proposed FTO/PC60BM/CsSn0.5Ge0.5I3/Cu2O/Au solar cell structure surpassed prior configurations by comprehensively examining key aspects such as absorber layer thickness and defect density, doping densities, and back contact work. The structure has been also compared with multiple electron transport elements and concluded that the proposed model functions superior due to the use of PC60BM as an electron transport layer and it has an improved electron extraction procedure. Finally, the proposed model has achieved the optimized values as Jsc of 31.56 mA/cm⁻², Voc of 1.12 V, FF of 81.47%, and PCE of 27.72%. As a consequence of this research, the investigated structure may be an excellent contender for the eventual creation of lead-free solar power cells made from perovskite.

In order to reduce current leakage and improve electron transfer in solar cells, charge transport layers (CTL), mainly hybrid electron transport layers (h-ETL), are considered as a solution. In this research contribution, computational analysis using SCAPS-1D software is performed to explore the output photovoltaic parameters of a Sb2S3-based solar cell with h-ETL. No theoretical works on this configuration have been previously reported. The main objectives of the present work are to propose a h-ETL with good band alignment with the Sb2S3 absorber, high transparency, and Cd free; to mitigate the instability and cost issues associated with using Spiro-OMeTAD HTL; and to optimize the solar cell. Thus, we calibrated the J-V characteristics and electrical parameters of the FTO/(ZnO/TiO2)/Sb2S3/Spiro-OMeTAD/Au solar cell by numerical simulation and compared them with those of the experiment. Subsequently, our simulations show that to replace the TiO2 ETL used in the experiment and to form the h-ETL with ZnO, IGZO is found to be a good candidate. It has better band alignment with the Sb2S3 absorber than TiO2 ETL, which reduces the trap states at the ETL/Sb2S3 interface; it has high transparency due to its wide bandgap; and an intense electric field is generated at the IGZO/Sb2S3 interface, which reduces the recombination phenomenon at this interface. MoO3, MASnBr3, Cu2O, CuI, and CuSCN HTL were also tested to replace the Spiro-OMeTAD HTL. Simulation results show that the cell with MoO3 HTL achieves higher performance due to its high hole mobility and high quantum efficiency in the visible region; it also allows the solar cell to have better thermal stability (TC=−0.32%/K) than the cell with Spiro-OMeTAD HTL (TC=−0.53%/K). The parameters that could improve the solar cell efficiency (η) obtained after these substitutions were also optimized. In particular, the parameters of the Sb2S3 absorber layer (thickness, defect density, and doping), ETL and HTL layer thicknesses, h-ETL/Sb2S3 interface defect density, and series and shunt resistances have been optimized. Finally, by combining high performance and thermal stability, the results show that the thermal stability of the solar cell depends on the back contact type; thus, nickel (Ni) was found to combine high performance and better thermal stability among the back contacts investigated. After these improvements, the efficiency of the Sb2S3-based solar cell increased from 5.08% (JSC=16.19 mA/cm², VOC=0.56 V, and FF=55.40%) to 15.43% (JSC=18.51 mA/cm², VOC=1.11 V, and FF=74.76%). This study proposes an approach to optimize the Sb2S3 upper subcell for tandem solar cells.

In this study, a heterojunction (P+ a-SiC/i intrinsic/n-Si) solar cell has been examined and characterized using the Analysis of Microelectronics and Photonic Structures (AMPS-1D) simulator. In this heterojunction solar cell, an intrinsic layer is imposed to enhance the efficiency and performance. The optimum efficiency of 36.52% ( Voc = 1.714 V, Jsc = 27.006 mA/cm2, and FF = 0.789 ) has been achieved with this intrinsic layer. It has also been observed the solar cell without intrinsic layer. In this case, the maximum efficiency of 2.378% has been observed which is very poor. The heterojunction solar cell also has been investigated with electron blocking layer (EBL) and defect layer. In this case, the simulation result shows the lower efficiency (34.357%) than the previous. This research paper introduces an optimized model of a heterojunction solar cell enhanced with an intrinsic layer to improve efficiency. The proposed design shows significant promise in its theoretical framework. Looking forward, the design could be realized in laboratory settings and has the potential to be scaled up for broader applications.

Bi2O3-BaTiO3 heterojunction with high photocatalysis efficiency was directly synthesized by rapid thermal processing (RTP). Bi2O3 and BaTiO3 were mixed in ratio and treated by RTP and conventional thermal processing (CTP), respectively. RTP samples have obvious Bi2O3 diffraction peaks, while CTP samples show pure BaTiO3 tetragonal perovskite. More small particles and layered existed in RTP samples. Photodegradation of MB solutions shows that RTP can promote photocatalytic efficiency. Its main lies in the following points: RTP can remove grain boundary defects by strengthening the bond grains; RTP can limit the solution region of the two substances to a certain range to get the best built-in electric field width; and RTP can strengthen the tetragonal BaTiO3 phase to hasten ion movement. Therefore, RTP can achieve much higher photocatalytic efficiency by improving the build heterojunction. This work provides a direct and efficient route to get improvement and high performance of heterojunction.

An evolutionary algorithm was employed to locate the global minimum of Ti O 2 n nanoparticles with n = 2 – 20 . More than 61,000 structures were calculated with a semiempirical method and reoptimized using density functional theory. The exciton binding energy of TiO2 nanoparticles was determined through the fundamental and optical band gap. Frenkel exciton energy scales as E B eV = 8.07 / n 0.85 , resulting in strongly bound excitons of 0.132–1.2 eV for about 1.4 nm nanoparticles. Although the exciton energy decreases with the system size, these tightly bound Frenkel excitons inhibit the separation of photogenerated charge carriers, making their application in photocatalysis and photovoltaic devices difficult, and imposing a minimum particle size. In contrast, the exciton binding energy of rutile is 4 meV, where the Wannier exciton energy scales as E B eV = 13.61 μ / ε 2 . Moreover, the Wannier excitons in bulk TiO2 are delocalized according to the Bohr radii: 3.9 nm for anatase and 7.7 nm for rutile.

Inorganic photocatalytic materials exhibiting a highly efficient response to ultraviolet-visible light spectrum have become a subject of widespread global interest. They offer a substantial prospect for generating green energy and mitigating water pollution. Zinc oxide (ZnO), among various semiconductors, proves advantageous for water-splitting applications due to its elevated reactivity, chemical stability, and nontoxic nature. However, its efficacy as a photocatalyst is hindered by limited light absorption capacity and swift charge carrier recombination. To improve charge separation and enhance responsiveness to ultraviolet-visible light photocatalysis, the formation of a heterojunction with another suitable semiconductor is beneficial. Thus, we employed hydrothermal route for the synthesis of the samples, which is a high-pressure method. The formations of ZnO/NiO heterostructures were revealed by scanning electron microscopy, X-ray diffraction analysis, energy-dispersive X-ray spectroscopy, and Fourier transform infrared spectroscopy. The nanocomposites were discovered to have a substantially higher photocatalytic activity for the generation of H2. The H2 production rates show that ZnO (i.e., 168.91 μ molg-1 h-1) exhibits good H2 production rates as compared to NiO (i.e., 135.74 μ molg-1 h-1). The best production rates were observed for ZN-30 (i.e., 247.56 μ molg-1 h-1) which is 1.46 times greater than ZnO and 1.82 times greater than NiO. This enhanced photocatalytic activity for ZN-30 is because of the good electron-hole pair separation due to the formation of depletion layer, suppression of fast charge recombination, and overcoming resistance corrosion.

In recent years, solar photovoltaic (PV) energy, as a clean energy source, has received widespread attention and experienced rapid growth worldwide. However, the rapid growth of PV power deployment also brings important challenges to the maintenance of PV panels, and in order to solve this problem, this paper proposes an innovative algorithm based on PA-YOLO. First, we propose to use PA-YOLO’s asymptotic feature pyramid network (AFPN) instead of YOLOv7’s backbone network to support direct interactions of nonadjacent layers and avoid large semantic gaps between nonadjacent layers. For the occlusion problem of dense targets in the dataset, we introduce a repulsive loss function, which successfully reduces the occurrence of false detection situations. Finally, we propose a customized convolutional block equipped with an EMA mechanism to enhance the perceptual and expressive capabilities of the model. Experimental results on the dataset show that our proposed model achieves excellent performance with an average accuracy (mAP) of 94.5%, which is 6.8% higher than YOLOv7. In addition, our algorithm also succeeds in drastically reducing the model size from 71.3 MB to 48.4 MB, which well demonstrates the effectiveness of the model.

This paper proposes a diagnosis method based on time series and support vector machine (SVM) to improve the timeliness, accuracy, and feasibility of fault diagnosis for photovoltaic (PV) arrays. It obtains the nominal output power of the PV array based on real-time collected data such as voltage, current, radiation, and temperature and normalizes the power values at different time points throughout the day to form a time series. Using the time series values as input data for a “one-to-one” multiclass classifier, we can identify and classify typical operational faults such as random shading, fixed shading, and aging degradation of PV arrays. The developed algorithmic model is trained and tested for different fault conditions using the data sets generated by the PV array simulation device. The experimental results show that our model has fairly good reliability and accuracy, and to some extent, it solves the problem of classifying shading and aging faults, two of which exhibit rather similar degradation characteristics.

The sun is an abundantly available and clean renewable energy source. Therefore, solar energy offers significant potential for mitigating climate change and reducing emissions from burning fossil fuels in the future. Solar chimney power plants (SCPPs) have a technical capability for meeting the massive sustainable power production. Basic parts of SCPP system are the chimney, turbine, and collector. The geometric dimensions of the components are the crucial factors for improving the solar chimney efficiency. The goal of this work is to analyse the influences of the inclination angle (θ) at chimney inlet on performance characteristics of the system by employing RNG k − ε turbulence model coupled with discrete ordinate (DO) solar ray tracing method via ANSYS Fluent CFD software. The model is built by taking into consideration geometric parameters of Manzanares plant and verified with its measurements. The innovative chimney entrance configurations are produced by altering the chimney entrance slope (θ = 50°–80°) with the geometrical dimensions of the chimney, collector, and fillet keeping constant. The computational results display that the new chimney configurations improve the maximum velocity, system power output, and turbine pressure drop. The peak velocity of 18.1 m/s is gained for the configuration with θ = 80° compared to that of 14.3 m/s obtained for the base model having θ = 45° at 1000W/m2. Besides, this configuration enhances power output to 61.5kW with a rise of 24.5% compared to the base model with a power output of 49.1kW at 1000W/m2.

The findings of the experimental study into optimizing the heat transfer rate of a PCM-based heat sink for high-power LEDs are presented in this work. The study investigated five heat sink types, with and without PCM. The LED case and junction temperatures, LED module temperatures, heat storage and release rate analyses, analyses of three types of cyclic operation modes, luminous flux, and heat sink thermal resistance were all examined independently. The results indicated that the PCM-based LED heat sink had improved thermal performance. The LED junction temperature of the PCM-equipped E-20 heat sink is nine degrees Celsius lower at 10 W than that of the heat sink without PCM. Furthermore, the E-20 heat sink with PCM extends the LED module’s critical lifespan. As a bonus, the E-20 with PCM had a 38.19 percent lower thermal resistance at 10 W than the E-20 without PCM. According to these results, the heat sink E-20 emits 715 lm at 10 W when operated without a phase-change material (PCM). With the same input power, the luminous flux of an E-20 equipped with a heat sink and a phase-change material (PCM) is 750 lm, a gain of 4.7%. Finally, clearly recommend the heat E-20 sink with PCM suitable for high-power LED thermal management system.

Solar energy is among the clean, ecofriendly, and reliable energies. Standalone PV plants have great potential to fulfill specific load demands in remote villages in Rwanda. However, owing to the scarcity of information on solar energy potentials in some areas, lack of accurate load demands, and overlooking energy consumption by farming activities, PV plants can be hardly optimally sized, developed, or utilized. This study proposes and characterizes the PV plant model based on precisely quantified load demands including the energy needed for e-farming. The technoeconomic performance of these PV plants was analyzed using PVSyst software. The results confirm availability of solar resources enough to steadily satisfy the loads in the communities. Nevertheless, several factors were seen to induce energy losses for the developed PV systems, among which the heating owing to the rise of temperature being the major factor of energy loss. In fact, the solar radiation intensity exceeds 1800 kW/m2/year, and the heating occurring at the surface of the panels causes energy losses of up to 9.46%. Also, the findings suggested that the investors will gain the financial benefits for 10 out of 25 years while the energy’s price would drop from 0.252 EUR/kWh to 0.180 EUR/kWh. These findings are significant as they provide information that planners and investors could use to make informed decisions. Future studies may need to use such results to quantify the contribution of available subsidies and incentive reduction on cost of solar energy and adoption of PV plants.

The nonlinearities present in photovoltaic (PV) generator models can significantly impact the performance of PV systems, leading to decreased system efficiency and reduced profitability. This paper is aimed at addressing these challenges by developing a novel control algorithm based on a high-performance adaptive control method for photovoltaic systems. The proposed algorithm is designed to effectively track set points and optimize power extraction even in the presence of disturbances. The key contribution of this work lies in the application of this control strategy specifically to PV systems to achieve optimal performance. When compared to traditional control methods, the proposed approach demonstrates significant improvements, notably in terms of power extraction efficiency and system loss reduction. Moreover, the control algorithm effectively ensures accurate tracking of set points. These outcomes underscore the notable performance enhancements facilitated by the proposed algorithm. In conclusion, the developed control algorithm offers superior performance in optimizing power extraction and maintaining precise set point tracking for PV systems, leading to improved system efficiency and increased profitability.

The photovoltaic (PV) industry is constantly striving to increase module power output while decreasing costs. Importantly, the performance and reliability of cost-saving products should be evaluated before being launched into the PV market to avoid any unnecessary side effects. This study investigated the performance of a monofacial module employing a cost-saving bifacial cell installed at a carport in South Korea. The bifacial cell reduces costs, compared to monofacial cell, by using a lower quantity of aluminum paste on its rear; consequently, it has become a popular product in the PV industry. The monofacial module employing the bifacial cell showed an improved voltage temperature coefficient and low-light performance over monofacial cell-based module. Our field data highlight three conclusions from the bifacial cell-based module; it showed (1) different voltage temperature coefficients with better performance at lower temperatures, (2) better low-light performance owing to high series resistance, and (3) high current owing to its bifaciality. Notably, under high irradiance and temperature conditions, the bifacial cell performed worse than the monofacial cell. We concluded that this type of cell may perform well under northern European climatic conditions, though further investigation is required to optimize cell performance under various weather conditions.

Today, the desire to use renewable energy as a source of clean and available energy in the grid has increased. Due to the unpredictable behavior of renewable resources, it is necessary to use energy storage resources in the microgrid structure. The power generation source and the storage source in microgrids should be selected in such a way that it has the ability to respond to the maximum demand in the state connected to the grid and operate independently. In this article, the optimal capacity and economic performance of a microgrid based on photovoltaic and battery system have been investigated. In this way, first, using the iterative optimization method, the optimal microgrid capacity has been obtained. Then, the dynamic planning method has been used for optimal microgrid energy management. The simulation results show the accuracy and efficiency of the proposed solutions. The proposed controller, while automatically and dynamically adapting to the solar cell output changes, is capable of responding to external requests, such as price signals or satisfying power system constraints or operator requests. In addition, the results indicate that by using the proposed energy management system, the microgrid system can regain stability during one to two cycles, during the occurrence of PV system radiation changes as well as ESS charge changes. And also, according to the ESS charge changes, the voltage changes should be within the defined permissible range between 0.95 and 1.05 pu, which is the result of the unique efficiency of the proposed energy management system.