Solar Energy最新文献

Increasing solar-photovoltaic-power penetration necessitates the implementation of flexible power point tracking (FPPT) in solar farms. However, coordinating multiple solar photovoltaic (PV) power generation systems (SPVPGSs) poses a significant challenge due to their inherent intermittency and varying dynamic characteristics, complicating FPPT implementation. To overcome this challenge, this paper proposes a distributed economic model predictive control (DEMPC) scheme to achieve FPPT, while simultaneously enhancing overall economic performance in solar farms. By integrating solar farm control and local control into one optimal control framework, this scheme eliminates the need for power allocation, PV voltage reference calculation, and pulse width modulation. Leveraging the SPVPGS model and soft power constraint, DEMPC controllers are designed to achieve the economic targets of solar farms, which cooperate through a communication network to realize FPPT and economic optimization. Additionally, the strong nonlinearity of SPVPGS causes a non-convex mixed integer nonlinear programming (MINLP) problem, solved by a MINLP algorithm using finite converter switching states. Simulations under step-changed irradiance and power reference, as well as urgent maintenance conditions, demonstrate that the DEMPC-based FPPT scheme significantly outperforms the traditional hierarchical model predictive control-based FPPT scheme, presenting both superior dynamic response and enhanced economic performance in FPPT implementation.

This study aims to increase freshwater productivity and thermal performance while improving the economic determinant of the traditional square pyramid distiller. The study was conducted on four modified pyramid distillation systems and their impact on 4E (Energy, Exergy, Economic, and Environmental). The V-corrugated wick material was combined with the water basin in the first configuration (AHPSS-1). The second system uses the same components but integrates a Solar Air Heater (SAH) to raise the water temperature (AHPSS-2). In the third proposed system, a hybrid solar heating system was used by combining a PV-powered electric heater with the previous system (AHPSS-3). The fourth configuration was similar to the third but with the addition of titanium oxide nanoparticles in the water to augment heat transfer performance (AHPSS-4). Thermal analysis of the proposed systems was performed to evaluate their thermal performance in various aspects of energy and exergy efficiency relative to the Conventional Pyramid Distiller (CPSS). In addition, an economic and environmental analysis was conducted for each case of the proposed systems. The outcomes showed an apparent enhancement in thermal and economic analyses versus the CPSS for the four proposed systems. Moreover, there was an improvement in productivity by 27.6, 102.5, 147.8 and 161.5 % for AHPSS-1, AHPSS-2, AHPSS-3, and AHPSS-4 severally. Consequently, the performance of the fourth proposed system was optimum compared to other cases, where the total freshwater yield of the conventional and novel pyramid solar distiller reached 3.9 and 10.2 L/m². In addition, the daily thermal efficiency and the cost of one liter reached 69.8% and 0.011 USD. Furthermore, the annual CO₂ emissions for the modified configuration were estimated at 9.44 and 19.75 tons/year versus 7.48 tons/year for CPSS. Also, the enviroeconomic parameters ranged from 136.81 to 286.35 USD/year.

In this work, we delve into the realm of perovskite materials with a comprehensive analysis on its structural and thermodynamic stability. Employing a machine learning approach, our study focuses on three important features for stability prediction such as formation energy (E_f), energy above hull (E_hull), and tolerance factor (TF). These features act as key indicators, allowing us to understand the intricate balance of energy and thermodynamic stability in perovskite structures for solar energy applications. We achieve this by training machine learning models on datasets generated computationally using DFT. Understanding the structural prediction of perovskite materials (ABX₃, ABO₃, ABO₂X and ABOX₂), whether thermodynamically stable or unstable, is critical for assessing their suitability for photovoltaic or photocatalytic applications. This study examines 14,199 mixed perovskite halides, oxides, and oxynitrides in order to determine the relationship between the aforementioned parameters and perovskite material composition. When compared to other machine learning models, using the ExtraTrees regression algorithm results in a higher accuracy of approximately 93.6 %, 94.75 %, and 98.41 % in predicting E_f, E_hull, and TF, respectively. The proposed method not only predicts E_f, E_hull, and TF, but it also aids in the discovery of new materials. We are particularly interested in ABO₃ and ABO₂N compositions from this perovskite family. We have come up with 306 stable perovskite oxides and 311 stable oxynitrides using our prediction. Among these, we discovered 45 novel compositions of perovskite oxynitrides (ABO₂N) and two novel compositions of perovskite oxides (ABO₃) that are energetically, thermodynamically, and structurally stable which need experimental validation further. Our prediction represents a robust, quick, and cost-effective strategy for illuminating new avenues in materials science and improving the understanding of the structural and thermodynamic behavior of perovskite materials. Furthermore, we present feature ranking, correlation, and display feature importance graphs and SHapley Additive Explanations (SHAP) relevant to structural stability prediction.

The development of wearable electronic gadgets has spanned the research attention toward the design of flexible and high-performance organic solar cells. The complicated process and long data execution time have limited its research progress. In this project, the machine learning (ML) framework with different algorithm models and kernel functions was employed to predict the device performance of solution-processed SnO₂-based organic solar cells. The device performance of the SnO₂ prepared using different spinning rates was used as the training data for machine learning prediction. The accuracy of the prediction was controlled using the root-mean-square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²). The comparison between the measured and predicted value of the device parameters such as open circuit voltage (V_oc), short circuit current density (J_sc), fill factor (FF), and power conversion efficiency (PCE) was discussed. The radial basis support vector regression (SVR) integrated with particle swarm optimization (PSO) model showed the highest performance in predicting the PCE of SnO₂-based organic solar cells with R² of 99%, RMSE of 0.0119 and MAPE of 0.0075. This novel study demonstrated that support vector regression (SVR) integrated with the particle swarm optimization (PSO) model is an alternative method to predict the device performance in future organic solar cells.

The performances of a pilot scale packed bed thermal energy storage system filled with 162 kg of developed phosphate-based ceramic materials (cylinders of 1.5 cm × 4 cm) was experimentally investigated under different operating conditions of inlet air temperature for the charge (334 +/- 21 °C, 531 +/- 23 °C and 760 +/- 15 °C), air flowrate for charging and discharging (26.5 to 74 kg/h), as well as under consecutive cycles.

The packed bed performed well at different temperature and charge / discharge flowrate. The cylindrical shape of the produced clay/phosphate-based ceramic combined with the horizontal implementation of the storage tank did not provoke a significant preferential path for the air inside the storage medium and did not create a significant thermal de-stratification during the charging and discharging phases. Furthermore, under consecutive cycles, the TES system could be quickly stabilized demonstrating the robustness and flexibility of the developed TES system, which can cover a wide range of application cases. The production of the developed ceramics is mastered by ceramic industries allowing the availability at industrial scale worldwide with competitive cost and carbon footprint.

This work opens new prospects for using phosphates-based ceramics as alternative promising media to build new generation of flexible and reliable high temperature TES system for industrial assets decarbonation, grid services as well as renewable energies high penetration into the grid.

Perovskite solar cells have drawn global attention due to their low cost and comparable efficiency to that of conventional silicon-based solar cells. Moreover, the perovskite solar cells exhibit high efficiencies when spiro-OMeTAD has been used as the hole transport material (HTM). To attain higher PSC efficiency, spiro-OMeTAD must be in its pure form. However, the multistep synthetic protocols and purification methods required to produce high-purity spiro-OMeTAD render it economically unfeasible. Thus, there is a need to develop low-cost new organic HTMs through easy synthetic and purification methods having good solubility, good hole mobility, and thermal stability. Therefore, certain carbazole-based derivatives bearing 4,4′-dimethoxydiphenylamines (DMPA) have been investigated previously as the affordable organic HTMs alternative to the widely used spiro-OMeTAD. Thus, our current review systematically examines the most recent molecular design strategies, hole-transporting properties, power conversion efficiency, and thermal stability of organic HTMs that have been made of various carbazole derivatives bearing two, three, four, six, and eight DMPA units, as reported in the past five years.

Although perovskite solar cells (PSCs) have attracted significant attention due to their outstanding performance, the optimization of PSC fabrication remains challenging, requiring tremendous amounts of experiments because of the diversity in perovskite compositions and the different concentrations of charge transport layers. Herein, we introduced methodologies to effectively reduce the fabrication time of PSCs. We focused on each step: substrate washing, electron transport layer coating, and perovskite layer coating. Through these steps, we could successfully reduce the overall fabrication time to 49.2% of its previous duration. Furthermore, this method significantly reduces the defect rate from 18.6% to 6.3%, thereby improving the reproducibility and performance of PSCs simultaneously.

The escalation of implementing photovoltaic (PV) power generation has paved the road to innovative remarkable applications. The technology of utilizing electroluminescence imaging (EL) has aided the early identification of faults and rapid classification of solar cells in PV panels. Recently, deep learning neural networks (DNNs) has been extensively utilized in the field of PV fault detection and classification. Despite of the good achievements in the field of DNN-based approaches, however, there is still a potential for further developments. This includes better data preparation, proper dataset categorization and designing of efficient light-weight DNNs. In this work, an efficient approach is proposed to be used for defect detection and malfunctions’ classification in PV cells, based on utilizing EL-based imaging analysis. Here, three approaches were developed using multi-scale convolutional neural network (CNN) models, the former is based on deploying the pretrained SqueezeNet and the GoogleNet, in a transfer learning fashion, whereas the latter is a light-weight CNN approach (denoted as LwNet). The experiments were elaborated on the ELPV dataset after being properly modified and categized. Two scenarios were adopted: 4-class- and 8-class-classification procedures. Experimental validation of the developed CNNs have demonstrated very promising performances, especially when adopting the 8-class approach. An average accuracy of about 94.6%, 93.95%, and 96.2% was obtained using GoogleNet, SqueezeNet and LwNet, respectively. A privilege has been granted to LwNet over SqueezeNet and GoogleNet, in terms of classification performance and time saving efficiency.

Dust deposition on the surfaces of Photovoltaic (PV) arrays during their operation markedly affects their power generation efficiency. Previous research has overlooked the impact of the row spacing of PV modules on the actual dust deposition on PV arrays. This study investigates the dust deposition process and its behavior on PV arrays considering variations in row spacings, inlet wind speeds, dust particle sizes, and dust particle counts. By employing commercial Computational Fluid Dynamics (CFD) software, incorporating the SST $k-\omega$ turbulence model and discrete particle model, numerical simulations were performed to analyze the airflow field, dust particle trajectories, dust deposition patterns, and deposition rates on the PV array through grid-independent verification and numerical validation. At the same time, we performed a comparative analysis of the deposition rate considering the rebound of dust particles on the PV module surface versus the case where rebound is not considered. Results revealed that the maximum dust deposition rates at different inlet wind speeds were 6.84 %, 8.84 %, 11.0 %, and 14.6 %, corresponding to dust sizes of 50 μm, 100 μm, 120 μm, and 300 μm, respectively. Smaller dust particles exhibited lower deposition rates, while larger particles were influenced more by mass inertia and gravity, leading to predominant deposition on the front row of PV modules. Larger dust particles are more likely to deposit primarily on the front row of PV modules in a PV array. The tilt angles of 30°, 45°, and 60° were chosen to study the effects of different tilt angles on the dust deposition of PV arrays, and the results show that the dust deposition rate decreases as the tilt angle increases, and it is worth noting that the larger the tilt angle, the larger the dust deposition rate is when the dust particles are especially small as 5 μm. When wind speeds are low and dust particles are small, the dust deposition rate gradually rises as the row spacing increases. However, at higher wind speeds with small dust particles, the row spacing has minimal impact on the dust deposition rate. Conversely, with larger dust particles, increasing the row spacing results in a lower dust deposition rate. These findings underscore the significance of optimizing PV array design for enhanced power generation efficiency.

Accurate assessment of wind loads on PV modules is crucial for the economic efficiency and safety of PV power stations. Most of these studies focused on the PV arrays installed on flat ground, whereas research on the PV arrays installed on hillsides has been lacking. This paper carried out CFD simulations of single-row PV modules and arrays on a two-dimensional hillside. The results show that the slope can either enhance or weaken the wind load. The enhancement and weakening effects become stronger with larger slope. When the slope is 30°, the wind load of the single row of PV modules at the bottom of the hillside can be reduced by up to 80%, the load of the first row of the array can be reduced by up to 25%, the load of the single row of the PV modules at the top of the hillside can be enhanced by up to 150%, and the load of the last row of the array can be enhanced by up to 280%. The wind field in the range of 0.2 times the hillside length from the top of the hillside is more complex, and the wind load is quite different from that of the flat ground, which should receive special attention in the design process.