Dye-sensitized solar cells (DSSCs) serve as low-costing alternatives to silicon solar cells because of their low material and fabrication costs. Usually, they utilize Pt as the counter electrode (CE) to catalyze the iodine redox couple and to complete the electric circuit. Given that Pt is a rare and expensive metal, various carbon materials have been intensively investigated because of their low costs, high surface areas, excellent electrochemical stabilities, reasonable electrochemical activities, and high corrosion resistances. In this feature article, we provide an overview of recent studies on the electrochemical properties and photovoltaic performances of carbon-based CEs (e.g., activated carbon, nanosized carbon, carbon black, graphene, graphite, carbon nanotubes, and composite carbon). We focus on scientific challenges associated with each material and highlight recent advances achieved in overcoming these obstacles. Finally, we discuss possible future directions for this field of research aimed at obtaining highly efficient DSSCs.
Here, we report that long-term stable and efficient organic solar cells (OSCs) can be obtained through the following strategies: i) combination of rapid-drying blade-coating deposition with an appropriate thermal annealing treatment to obtain an optimized morphology of the active layer; ii) insertion of interfacial layers to optimize the interfacial properties. The resulting devices based on poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b;4,5-b']dithiophene-2,6-diyl-alt-(4-(2-ethylhexyl)-3-fluorothieno[3,4-b]thiophene-2-carboxylate-2,6-diyl)] (PBDTTT-EFT):[6,6]-phenyl C71 butyric acid methyl ester (PC71 BM) blend as the active layer exhibits a power conversion efficiency (PCE) up to 9.57 %, which represents the highest efficiency ever reported for blade-coated OSCs. Importantly, the conventional structure devices based on poly(3-hexylthiophene) (P3HT):phenyl-C61 -butyric acid methyl ester (PCBM) blend can retain approximately 65 % of their initial PCE for almost 2 years under operating conditions, which is the best result ever reported for long-term stable OSCs under operational conditions. More encouragingly, long-term stable large-area OSCs (active area=216 cm2 ) based on P3HT:PCBM blend are also demonstrated. Our findings represent an important step toward the development of large-area OSCs with high performance and long-term stability.
Particle Swarm Optimization (PSO) is widely used in maximum power point tracking (MPPT) of photovoltaic (PV) energy systems. Nevertheless, this technique suffers from two main problems in the case of partial shading conditions (PSCs). The first problem is that PSO is a time invariant optimization technique that cannot follow the dynamic global peak (GP) under time variant shading patterns (SPs) and sticks to the first GP that occurs at the beginning. This problem can be solved by dispersing the PSO particles using two new techniques introduced in this paper. The two new proposed PSO re-initialization techniques are to disperse the particles upon the SP changes and the other one is upon a predefined time (PDT). The second problem is regarding the high oscillations around steady state, which can be solved by using fuzzy logic controller (FLC) to fine-tune the output power and voltage from the PV system. The new contribution of this paper is the hybrid PSO-FLC with two PSO particles dispersing techniques that is able to solve the two previous mentioned problems effectively and improve the performance of the PV system in both normal and PSCs. A detailed list of comparisons between hybrid PSO-FLC and original PSO using the two proposed methodologies are achieved. The results prove the superior performance of hybrid PSO-FLC compared to PSO in terms of efficiency, accuracy, oscillations reduction around steady state and soft tuning of the GP tracked.
A compact UHF antenna has been presented in this paper for nanosatellite space mission. A square ground plane with slotted rectangular radiating element have been used. Coaxial probe feeding is used to excite. The rectangular slot of the radiating patch is responsible for resonating at lower UHF bands. One of the square faces of the nanosatellite structure works as the ground plane for the slotted radiating element. The fabricated prototype of the proposed antenna has achieved an impedance bandwidth (S11< -10dB) of 7.0 MHz (398 MHz- 405 MHz) with small size of 97 mm× 90 mm radiating element. The overall ground plane size is 100 mm × 100 mm × 0.5 mm. The proposed antenna has achieved a gain of 1.18 dB with total efficiency of 62.5%. The proposed antenna addresses two design challenges of nanosatellite antenna, (a) assurance of the placement of solar panel beneath the radiating element; (b) providing about 50% open space for solar irradiance to pass onto the solar panel, enabling the solar panel to achieve up to 93.95% of power under of normal conditions.
The incessantly growing demand for electricity in today's world claims an efficient and reliable system of energy supply. Distributed energy resources such as diesel generators, wind energy and solar energy can be combined within a microgrid to provide energy to the consumers in a sustainable manner. In order to ensure more reliable and economical energy supply, battery storage system is integrated within the microgrid. In this article, operating cost of isolated microgrid is reduced by economic scheduling considering the optimal size of the battery. However, deep discharge shortens the lifetime of battery operation. Therefore, the real time battery operation cost is modeled considering the depth of discharge at each time interval. Moreover, the proposed economic scheduling with battery sizing is optimized using firefly algorithm (FA). The efficacy of FA is compared with other metaheuristic techniques in terms of performance measurement indices, which are cost of electricity and loss of power supply probability. The results show that the proposed technique reduces the cost of microgrid and attain optimal size of the battery.
Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.
The behavior of solar cells and modules under various operational conditions can be determined effectively when their intrinsic parameters are accurately estimated and used to simulate the current-voltage (I-V) characteristics. This work proposed a new computational approach based on approximation and correction technique (ACT) for simple and efficient extraction of solar cells and modules parameters from the single-diode model. In this technique, an approximated value of series resistance (Rs) was first derived and used to determine the initial value of parallel resistance (Rp). Later, the final corrected values of Rs and Rp were obtained by resubstituting their approximated values in a five-loop iteration using the manipulated equations. For rapid evaluation and validation of the proposed technique, a software application was also created using MATLAB program. The correctness and robustness of the proposed technique was validated on five types of solar cells and modules operated at varied temperatures and irradiances. The lowest RMSE value was achieved for RTC France (7.78937E-4) and PVM 752 GaAs (2.10497E-4) solar cell. The legitimacy of ACT extracted parameters was established using a simple yet competitive implementation approach wherein the performance of the developed technique was compared with several state-of-the-art methods recently reported in the literature.
Matched MeSH terms: Solar Energy/statistics & numerical data*
The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.
This study investigates the effects of stirring duration on the synthesis of graphene oxide (GO) using an improved Hummers' method. Various samples are examined under different stirring durations (20, 40, 60, 72, and 80 h). The synthesized GO samples are evaluated through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. The GO sample with 72 h stirring duration (GO72) has the highest d-spacing in the XRD results, highest atomic percentage of oxygen in EDX (49.57%), highest intensity of oxygen functional group in FTIR spectra, and highest intensity ratio in Raman analysis (ID/IG = 0.756). Results show that GO72 with continuous stirring has the highest degree of oxidation among other samples. Electrochemical impedance spectroscopy analysis shows that GO72-titanium dioxide (TiO2) exhibits smaller charge transfer resistance and higher electron lifetime compared with the TiO2-based photoanode. The GO72 sample incorporating TiO2 nanocomposites achieves 6.25% photoconversion efficiency, indicating an increase of more than twice than that of the mesoporous TiO2 sample. This condition is fully attributed to the efficient absorption rate of nanocomposites and the reduction of the recombination rate of TiO2 by GO in dye-sensitized solar cells.
Photovoltaic (PV) systems need measurements of incident solar irradiance and PV surface temperature for performance analysis and monitoring purposes. Ground-based network sensor measurement is preferred in many near real-time operations such as forecasting and photovoltaic (PV) performance evaluation on the ground. Hence, this study proposed a Fuzzy compensation scheme for temperature and solar irradiance wireless sensor network (WSN) measurement on stand-alone solar photovoltaic (PV) system to improve the sensor measurement. The WSN installation through an Internet of Things (IoT) platform for solar irradiance and PV surface temperature measurement was fabricated. The simulation for the solar irradiance Fuzzy Logic compensation (SIFLC) scheme and Temperature Fuzzy Logic compensation (TFLC) scheme was conducted using Matlab/Simulink. The simulation result identified that the scheme was used to compensate for the error temperature and solar irradiance sensor measurements over a variation temperature and solar irradiance range from 20 to 60 °C and from zero up to 2000 W/m2. The experimental results show that the Fuzzy Logic compensation scheme can reduce the sensor measurement error up to 17% and 20% for solar irradiance and PV temperature measurement.
This paper examines the temperature profile of a building material and also a
built space. The study directly examines the influence of solar radiation on
building material and the heat it generated and diffuses into the built space.
Two experiments are presented. The first look at a simple technique for
evaluating heat performance of a building material, and the second evaluates
the performance of a cross-ventilated built space with respect to solar radiation.
The fabrication of TiO2 nanotubes (TNT) was carried out by electrochemical anodization of Ti in aqueous electrolyte containing NH4F. The effect of electrolyte pH, applied voltage, fluoride concentration and anodization duration on the formation of TNT was investigated. It was observed that self-organized TNT can be formed by adjusting the electrolyte to pH 2-4 whereby applied voltage of 10-20 V can be performed to produce highly ordered, well-organized TNT. At 20 V, TNT can be fabricated in the concentration range of 0.07 M to 0.20 M NH4F. Higher fluoride concentration leads to etching of Ti surface and reveals the Ti grain boundaries. The prepared TNT films also show an increase in depth and in size with time and the growth of TNT films reach a steady state after 120 minutes. The morphology and geometrical aspect of the TNT would be an important factor influencing the photoelectrochemical response, with higher photocurrent response is generally associated with thicker layer of TNT. Consequently, one can tailor the resulting TNT to desired surface morphologies by simply manipulating the electrochemical parameters for wide applications such as solar energy conversion and photoelectrocatalysis.
The development of Transparent Solar Cells (TSC) has become the main focus of solar energy research in recent years. The TSC has a number of applications and make use of unexploited space such as skyscraper windows. In this paper, TSC is fabricated using commercially available titanium dioxide (TiO2) P25 to make a paste, which is deposited on FTO glass using screen printing and spin coating methods. The effects of the thickness of the TiO2 film on transparency are examined. The paste is synthesised in the Cleanroom and used in both methods of deposition. The final cell fabrication is a Dye sensitised solar cell (DSSC). The obtained transparency of the FTO glass is 83%, and after the deposition of TiO2 it is reduced to less than 80%. The overall transparency of the DSSC, which was made using the spin coating method, is 70% with an Isc of 9.5 mA and Voc 853mV.
This paper discusses the effect of jet impingement of water on a photovoltaic thermal (PVT) collector and compound parabolic concentrators (CPC) on electrical efficiency, thermal efficiency and power production of a PVT system. A prototype of a PVT solar water collector installed with a jet impingement and CPC has been designed, fabricated and experimentally investigated. The efficiency of the system can be improved by using jet impingement of water to decrease the temperature of the solar cells. The electrical efficiency and power output are directly correlated with the mass flow rate. The results show that electrical efficiency was improved by 7% when using CPC and jet impingement cooling in a PVT solar collector at 1:00 p.m. (solar irradiance of 1050 W/m² and an ambient temperature of 33.5 °C). It can also be seen that the power output improved by 36% when using jet impingement cooling with CPC, and 20% without CPC in the photovoltaic (PV) module at 1:30 p.m. The short-circuit current ISC of the PV module experienced an improvement of ~28% when using jet impingement cooling with CPC, and 11.7% without CPC. The output of the PV module was enhanced by 31% when using jet impingement cooling with CPC, and 16% without CPC.
The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.
In this study, nitrogen doped titanium dioxide-based dye-sensitised solar cell was successfully fabricated
using screen printing technique to discover the optimisation of process parameters for the solar cell
efficiency using response surface methodology (RSM). Parameter optimisation has been a major concern
in solar cell fabrication. The selected parameters were: nitrogen concentration (15-25 mg of urea), the
film thickness (25-60 µm) and dye loading time (12-24 hours), the optimum condition which yields the
highest efficiency of 3.5% was at 15 mg nitrogen concentration, 25 µm film thickness and 24-hours dye
loading time. Film thickness was found to have a significant influence on efficiency while the loading
time exceeding 18 hours has the least significant effect.
2D perovskites is one of the proposed strategies to enhance the moisture resistance, since the larger organic cations can act as a natural barrier. Nevertheless, 2D perovskites hinder the charge transport in certain directions, reducing the solar cell power conversion efficiency. A nanostructured mixed-dimensionality approach is presented to overcome the charge transport limitation, obtaining power conversion efficiencies over 9%.
Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.
The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.
Matched MeSH terms: Solar Energy/statistics & numerical data
In this day and age, with the ever-growing population and energy demand, we should take the renewable option route in our energy source. We should also keep in mind that said energy should not cause any lasting environmental damage, one of the perfect example being solar energy. A country that is hot and sunny all year long is the perfect contributor to solar energy, case in point, Malaysia. With that in mind Solar Tree is designed and developed to facilitate consumers who need electric power at any place, anytime, anywhere. The objective of this study is to assess a mini project in the likes of Solar Tree that can generate electricity without harming the environment, despite the weather. Intended specifically to be a mini project, it is understandable that electricity generated is limited, with only up to 500W in total. As a trial, two electronic devices were tested, specifically a mobile phone and a laptop, as both devices are used almost every day. The data collected is then tabulated and analysed. It was concluded the solar tree developed proved efficient in charging both devices and will continue to do so given enough sunlight.