Solar Cell Parameters Estimation Using Simulated Annealing Algorithm

The paper presents a hierarchical polynomial chaos expansion-based probabilistic approach to analyze the single diode solar cell model under Gaussian parametric uncertainty. It is important to analyze single diode solar cell model response under random events or factors due to uncertainty propagation. The optimal values of five electrical parameters associated with the single diode model are estimated using six deterministic optimization techniques through the root-mean-square minimization approach. Values corresponding to the best objective function response are further utilized to describe the probabilistic design space of each random electrical parameter under uncertainty. Adequate samples of each parameter corresponding Gaussian uncertain distribution are generated using Latin hypercube sampling. Furthermore, a multistage probabilistic approach is adopted to evaluate the model response using low-cost polynomial chaos series expansion and perform global sensitivity analysis under specified Gaussian distribution. Coefficients of polynomial basis functions are calculated using least square and least angle regression techniques. Unlike the highly non-linear and complex single diode representation of solar cells, the polynomial chaos expansion model provides a low computational burden and reduced complexity. To ensure reproducibility, probabilistic output response computed using proposed polynomial chaos expansion model is compared with the true model response. Finally, a multidimensional sensitivity analysis is performed through Sobol decomposition of polynomial chaos series representation to quantify the contribution of each parameter to the variance of the probabilistic response. The validation and assessment result shows that the output probabilistic response of the solar cell under Gaussian parametric uncertainty correlates to a Rayleigh probability distribution function. Output response is characterized by a mean value of 0.0060 and 0.0760 for RTC France and Solarex MSX83 solar cells, respectively. The standard deviation of pm 0.0034 and pm 0.0052 was observed in the probabilistic response for RTC France and Solarex MSX83 solar cells, respectively.

Reliability analysis and design of a single diode solar cell model using polynomial chaos and active subspace

Solar cell/module modeling involves the formulation of the non-linear current versus voltage (I-V) curve. Determination of parameters plays an important role in solar cell/module modeling. This paper presents an application of the improved PSO search method and Particle Swarm Optimization technique for identifying the unknown parameters of solar cell and photovoltaic module models, namely, the series resistance, shunt resistance, generated photocurrent, saturation current, and ideality factor that govern the current-voltage relationship of a solar cell/module. For the confirmation of accuracy of the proposed method, a measurement data of 57 mm diameter commercial (R.T.C. France) silicon solar cell and a module consisting of 36 polycrystalline silicon cells (Photowatt-PWP 201) has been selected and the best optimal value of each parameter has been obtained using Improved PSO. Comparative study among different parameter estimation techniques is presented to demonstrate to verify the accurateness and the effectiveness of the proposed approach.

This paper presents an analysis of parameter variations of a single-diode solar cell model. The parameters analyzed are the series resistance, shunt resistance, temperature and radiation change. Model equations are derived and simulated. All simulations were performed in MatLab using looping iterative method. Results obtained show that an increase in series resistance causes a decrease in short-circuit current and output power. A decrease in shunt resistance also causes a decrease in short circuit current and output power. An increase in temperature above the nominal value of 25oC causes a significant decrease in the open circuit voltage. An increase in irradiance above a nominal value of 1000 W/m2 causes the short circuit current to increase from 8.21A at 1000 W/m2 to 10.67A at 1300W/m2. It can be seen that parameter variations have a net effect on the current-voltage (I-V) and power-voltage (P – V) characteristics.

A C++-based automatic executable program is implemented and provided in this article to integrate the current minus the short-circuit current, using the 3/8 integration rule, to compute the Co-Content function. The shunt ( Rsh ) and series resistance ( Rs ), photo ( Iph ) and saturation current ( Isat ), and ideality factor ( n ) (within the one-diode solar cell model) are then automatically calculated by the program, together with their standard deviations. When it is applied to simulated current voltage ( IV ) curves, the five solar cell parameters are extracted with less than 1% error, using only 26 points and in less than 1 s, in the case of noiseless IV curves, while in the case of 0.1% noise of the maximum current, less than 1% error is achieved with 51 points, and also in less than 1 s. The program is applied to measured IV curves in a commercial solar cell, and compared with the usual trapezoidal integration method and another method. All the solar cell parameters are reasonably extracted in under one second, whereas other techniques require at least 7 min.

A single concentrator solar cell model with a heat sink is established to simulate the thermal performance of the system by varying the number, height, and thickness of fins, the base thickness and thermal resistance of the thermal conductive adhesive. Influence disciplines of those parameters on temperatures of the solar cell and heat sink are obtained. With optimized number, height and thickness of fins, and the thickness values of base of 8, 1.4 cm, 1.5 mm, and 2 mm, the lowest temperatures of the solar cell and heat sink are 41.7 °C and 36.3 °C respectively. A concentrator solar cell prototype with a heat sink fabricated based on the simulation optimized structure is built. Outdoor temperatures of the prototype are tested. Temperatures of the solar cell and heat sink are stabilized with time continuing at about 37 °C–38 °C and 35 °C–36 °C respectively, slightly lower than the simulation results because of effects of the wind and cloud. Thus the simulation model enables to predict the thermal performance of the system, and the simulation results can be a reference for designing heat sinks in the field of single concentrator solar cells.

In this study, solar cell parameters, the most basic material of solar energy that has become increasingly widespread in recent years, are determined by Particle Swarm Optimization and Firefly and algorithms. The previously proposed single-diode solar cell model was used as a base for solar cells used in series connection and then in parallel connection with each other. Integral Absolute Error (IAE) was chosen as error criterion in heuristic algorithm for the parameters of a single diode solar cell. With this work, the parameters of the solar cell are brought closer to the reality, which is important because it will change the maximum power and efficiency to be drawn from the panes by designing a more accurate controller. In this study, it has been seen that the firefly algorithm gives more successful results when firefly and particle swarm optimization algorithms compared to the previous studies.

It is known that compound parabolic concentrators (CPCs) can improve electrical performance of a photovoltaic (PV) flat-plate system. However, a lumped electrical model of a PV cell/module with CPC for assessing performance under different operating conditions is unavailable. In this paper, a six-parameter based model is developed and applied to a PV cell, two PV models with CPC, and a PV module with 2D asymmetric CPC (trough). For validation, CPC with a single PV cell and two CPC modules with 2×2 and 9×9 PV cells are fabricated and measured in an indoor laboratory under standard test conditions. Results show that the optimised algorithm precisely predicts the six model parameters. A sensitivity analysis is performed to identify the importance of each parameter in the model. Ideality factor, circuit current and reverse saturation current are found to be the most dominant factor, while shunt resistance is the least important with CPC gain coefficient and series resistance are in between. Transient performance of a PV cell with CPC under variable outdoor climate conditions is also examined by coupling optical, thermal and electrical effects.

Effect of Series and Shunt Resistance on Organic-Inorganic Hybrid Solar Cells Performance

Photovoltaic technology, which converts the sun’s light energy directly into electricity, can be used to make photovoltaic cells. The use of photovoltaic cells is centered on the idea of a low-carbon economy and green environmental protection, which effectively addresses the pollution problem in smart cities. Accurate identification of photovoltaic cell parameters is critical for battery life cycle and energy utilization. To accurately identify the single diode model (SDM), dual diode model (DDM), and three diode model (TDM) parameters of solar photovoltaic cells, and an improved honey badger algorithm (IHBA) is proposed in this paper. In the early stages of iteration, the IHBA uses the spiral exploration mechanism to improve the population’s global exploration ability. Furthermore, a density update factor that varies according to the quasi-cosine law is introduced to speed up the algorithm’s convergence speed and prevent the algorithm from falling into the local optimal value. Simultaneously, the pinhole imaging strategy is utilized to disturb the present optimal position to improve the algorithm’s optimization accuracy. The experimental comparison results of 18 benchmark test functions, Wilcoxon rank sum statistical test, and 30 CEC2014 test functions reveal that an IHBA shows remarkable performance in convergence speed, optimization accuracy, and robustness. Finally, the IHBA is used to identify the parameters of three kinds of commercial silicon R.T.C French solar photovoltaic cell models with a 57 mm diameter. In comparison to other algorithms, the IHBA can minimize the root mean square error (RMSE) between the measured current and estimated current at the fastest speed, demonstrating the practicality and superiority of the IHBA in tackling this problem.

Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm

Hydrogen energy is expected to contribute to a decarbonized society because it does not emit CO2 during combustion. In particular, CO2 emissions from hydrogen production can also be prevented by utilizing renewable energy such as solar light and wind power. As water electrolysis is one of major hydrogen production methods, combination of water electrolysis and solar cell can make hydrogen production without CO2 emission. However, single solar cells usually have not enough voltage to electrolyze water. Therefore, modularization of several solar cell units is needed to electrolyze water to hydrogen. We have fabricated organic thin-film solar cells using organic materials in the photoelectric conversion layer and have aimed to achieve highly efficient conversion of light energy to hydrogen energy through water electrolysis using these cells. In this study, we fabricated solar cell modules for water electrolysis using two donor polymers, P3HT and PBDB-T and fullerene derivative, PC61BM and two non-fullerene acceptors, ITIC and IT-4F. The solar cell architecture consisted of indium tin oxide (ITO) cathode/ZnO/photoactive layer/MoO3/Ag anode. ZnO was deposited by sol-gel method, polymer donor:fullerene or non-fullerene acceptor by spin coating method, and MoO3 and Ag by vacuum deposition method. First, three types of solar cells composed of A fullerene-based P3HT:PC61BM, non-fullerene-based PBDB-T:IT-4F and PBDB-T:ITIC were fabricated, and their performance was evaluated using a solar simulator. The short-circuit current density (Jsc) and open-circuit voltage (Voc) were increased by changing to the non-fullerene system, indicating that the non-fullerene system has longer wavelength absorption and larger energy gap between HOMO of donor polymer and LUMO of acceptor, respectively. In addition, the PBDB-T:ITIC system was expected to reduce the number of series solar cell units in the module for water electrolysis because Voc was higher than that of the PBDB-T:IT-4F system. The photovoltaic performance of the PBDB-T:ITIC system was then evaluated by changing film thickness in the PBDB-T:ITIC solar cell. Jsc reached its maximum at the film thickness indicating absorbance of 0.73 at 637 nm, while Voc did not depend on film thickness, and the fill factor (FF) decreased with increasing film thickness. As a result, the power conversion efficiency (PCE) was achieved to be 6.75%. Currently, the maximum output voltage (Vmax) is 0.63 V. Therefore, it was found that a module with three single solar cell units connected in series on the same substrate was required in order to achieve above the voltage of 1.23 V, which is thermodynamically necessary for water electrolysis.3-cell series module with an area of 1.16 cm2 per cell were fabricated and their performance was evaluated. As a result, ideally, a 3-cell series module would have similar Jsc to that of a single solar cell, and Voc becomes about 3 times that of a single solar cell. However, the 3-cell series module has a particularly low FF of 0.40 compared to 0.48 for the single solar cell, resulting in decrease in PCE to 4.38%. To investigate the effect of cell area, single-cell solar cells with 1.16 and 0.126 cm2 were then fabricated and evaluated for performance. Jsc and Voc are comparable whereas FF is 0.57 for the small area, while it is lower at 0.48 for the large one. This indicates that an increase in area leads to an increase in series resistance and a decrease in FF. In addition, to investigate the effect of module structure, we evaluated the performance of 3 single solar cells with a cell area of 1.16 cm2 connected in series and parallel by crocodile clip. As a result, a large decrease in FF was observed for both connections, suggesting that crosstalk happened, caused by that ZnO, the electron transport layer was uniformly coated on the substrate. We will examine whether the performance of single solar cells can be maintained by changing the module structure.

This work presents a Simulink-based model of a photovoltaic (PV) system using a single-diode and two-diode model of solar cell. A comparison between the two-diode and single-diode model of PV cell has been illustrated. In addition, the output of series-parallel connection of PV cells has been examined. In the model, series and shunt resistances are calculated by an efficient iteration method based on open-circuit voltage, short-circuit current and irradiance values. The PV module implemented in Simulink/MATLAB considers five parameters. The parameters are series and shunt resistance, reverse saturation current, photocurrent and ideality factor. Approximate parameters are obtained from the manufacturers datasheets. The model includes light intensity and ambient temperature as input. Power, cell temperature and voltage as well as any measurements of interests are the outputs. For the grid connection of solar cell, inverter, filter and a step-up transformer is utilized. The performance of the model is found satisfactory.

Impacts of temperature and irradiance on polycrystalline silicon solar cells parameters

The critical worldwide revolution towards clean energy has prompted the improvement of studies on the fabrication of high-performance solar cells. In this regard, rendering an accurate model of the solar cell for performance evaluation in the simulation could be essential. So far, several models have been proposed for the solar cell, including single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM). By increasing the number of diodes considered in the equivalent circuit in order to deliver a more accurate model, the number of unknown parameters which must be identified will be increased as well. Therefore, presenting an efficient algorithm to estimate these parameters becomes an interesting issue in recent years. In this study, an improved optimization algorithm, called springy whale optimization algorithm (SWOA), is proposed to estimate the model parameters of solar cells. SWOA is a generalization of the WOA and has the advantages of high convergence speed, global search capability, and high robustness over it. In order to inquire the efficiency of SWOA, this algorithm is posed to estimate the parameters of models of solar cells and photovoltaic (PV) modules as well; the simulation results authenticate the supremacy of the proposed algorithm. Furthermore, the effectiveness of SWOA algorithm in the practical application has been evaluated using commercial modules, including polycrystalline (SW255), multi-crystalline (KC200GT), and monocrystalline (SM55). This assessment is carried out for various operating conditions under different irradiance and temperature conditions, which yield variations in the parameters of the PV model. The results obtained from various experimental setups confirm the high performance and robustness of the proposed algorithm.