Evaluation of the Potential of Nanofluids Containing Different Ag Nanoparticle Size Distributions for Enhanced Solar Energy Conversion in Hybrid Photovoltaic-thermal (PVT) Applications

Hybridising photovoltaic and photothermal technologies into a single system that can simultaneously deliver heat and power represents one of the leading strategies for generating clean energy at more affordable prices. In a hybrid photovoltaic-thermal (PVT) system, the capability to modulate the thermal and electrical power output is significantly influenced by the spectral properties of the heat transfer fluid utilised. In this study, we report on one of the first experimental evaluations of the capability of a multimodal silver nanofluid containing various particle shapes and particle sizes to selectively modulate the solar energy for PVT applications. The diverse set of particle properties led up to a 50.4% enhancement in the solar energy absorbed by the nanofluid over the 300 nm – 550 nm spectral region, where silicon is known to exhibit poor photovoltaic conversion performances. This improved substantially the absorption of solar energy, with an additional 18 – 129 W m-2 of thermal power being generated by the PVT system. Along with the advancements made in the thermal power output of the PVT system, a decrease of 4.7 – 36.6 W m-2 in the electrical power generated by the photovoltaic element was noted. Thus, for every ~11 W m-2 increase of thermal power achieved through the addition of the nanoparticles, a reduction of ~3 W m-2 in the ability to generate clean electricity was sustained by the PVT. Despite the energy trade-offs involved under the conditions of the nanofluid, the PVT system cumulatively harvested 405 W m-2 of solar energy, which amounts to a total conversion efficiency of 45%. Furthermore, the economics of the additional energy harvested through merging of the two systems was found to reach an enhancement of 77% under certain European conditions.

Prediction of combined cycle power plant electrical output power using machine learning regression algorithms

In order to monitor the performance and related efficiency of a combined cycle power plant (CCPP), in addition to the best utilization of its power output, it is vital to predict its full load electrical power output. In this paper, the full load electrical power output of CCPP was predicted employing practically efficient machine learning algorithms, including linear regression, ridge regression, lasso regression, elastic net regression, random forest regression, and gradient boost regression. The original data came from an actual confidential power plant, which was working on a full load for 6 years, with four major features: ambient temperature, relative humidity, atmospheric pressure, and exhaust vacuum, and one target (electrical power output per hour). Different regression performance measures were used, including R2 (coefficient of determination), MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error). Research results revealed that the gradient boost regression model outperformed other models with and without using the dimensionality reduction technique (PCA) with the highest R2 of 0.912 and 0.872, respectively, and had the lowest MAPE of 0.872 % and 1.039 %, respectively. Moreover, prediction performance dropped slightly after using the dimensionality reduction technique almost in all regression algorithms used. The novelty in this work is summarized in predicting electrical power output in a CCPP based on a few features using simpler algorithms than reported deep learning and neural networks algorithms combined. That means a lower cost and less complicated procedure as per each, however, resulting in practically accepted results according to the evaluation metrics used.

The Effects of Photovoltaic Panel’s Temperature towards Photovoltaic Electrical Power Output

Photovoltaic (PV) system is recognized as one of the most current renewable energy types in producing electrical power. The theories that science explain related to the function of the sun is in accordance with what is also described in the Qur'an. There is one ayah Quran related to the Greatness of Allah and The Mercy which is “And from among His Signs are the night and the day, and the sun and the moon. Prostrate yourselves to Allah Who created them, if you (really) worship Him [Fussilat:37]. However, one of the main issues of PV system is that the performance of the system is highly dependent to environmental conditions such as weather and solar irradiance. Increases in temperature reduce the band gap of a semiconductor. The decrease in the band gap of a semiconductor with increasing temperature can be viewed as increasing the energy of the electrons in the material. The parameter most affected by an increase in temperature is the open circuit voltage. Temperature coefficient indicates how much will be the decrement in power output if PV module. Hence, it is important to predict the actual generating output power of PV systems. This study investigates the relationship between the temperatures of the PV panel with the PV power output. The PV systems installed at the rooftop of Mega label SDN. BHD. with type of poly-crystalline 405.72KWP has been chosen as the reference system in this study. The results have shown that the rise of PV panel’s temperature will make the value of the PV electrical power output decreases.

Experimentally Verified Analytical Models of Piezoelectric Cantilevers in Different Design Configurations

This paper deals with analytical modelling of piezoelectric energy harvesting systems for generating useful electricity from ambient vibrations and comparing the usefulness of materials commonly used in designing such harvesters for energy harvesting applications. The kinetic energy harvesters have the potential to be used as an autonomous source of energy for wireless applications. Here in this paper, the considered energy harvesting device is designed as a piezoelectric cantilever beam with different piezoelectric materials in both bimorph and unimorph configurations. For both these configurations a single degree-of-freedom model of a kinematically excited cantilever with a full and partial electrode length respecting the dimensions of added tip mass is derived. The analytical model is based on Euler-Bernoulli beam theory and its output is successfully verified with available experimental results of piezoelectric energy harvesters in three different configurations. The electrical output of the derived model for the three different materials (PZT-5A, PZZN-PLZT and PVDF) and design configurations is in accordance with lab measurements which are presented in the paper. Therefore, this model can be used for predicting the amount of harvested power in a particular vibratory environment. Finally, the derived analytical model was used to compare the energy harvesting effectiveness of the three considered materials for both simple harmonic excitation and random vibrations of the corresponding harvesters. The comparison revealed that both PZT-5A and PZZN-PLZT are an excellent choice for energy harvesting purposes thanks to high electrical power output, whereas PVDF should be used only for sensing applications due to low harvested electrical power output.

Experimental study of the impact of dust on azimuth tracking solar PV in Sharjah

<span>Dust is one of the significant constraints in utilizing solar photovoltaic systems under harsh weather conditions in the desert regions due to creating a shadow that blocks solar irradiance from reaching solar cells and consequently, significantly reducing their efficiency. In this research, experimental study was performed to comprehend the nature of dust particles and their impact on the electrical power output that is generated from azimuth tracking solar PV modules under Sharjah environmental conditions in winter season. According to laboratory experiments, the power losses are linearly related to the dust accumulated density on the surface of the solar panel with a slope of 1.27% per g/m2. The conducted Outdoor studies revealed that the absolute reduction in output power increased by 8.46% after 41 continuous days with one low-intensity rainy day. The linear relationship obtained from indoor experiments was applied later to estimate the dust deposited density on the outdoor setup. The results showed that a regular cleaning process every two weeks is recommended to maintain the performance and to avoid the soiling loss. This work will help engineers in the solar PV plants to forecast the dust impact and figure out the regularity of the cleaning process in case of single axis tracking systems.</span>

Comprehensive Analysis with Enhanced Resolution of HAWT Blade using CFD

Energy is an important aspect for all countries. Due to the overexploitation of resources, nonrenewable resources, such as fossil fuels, are depleting day by day. This calls for alternative power sources, such as wind energy. Wind energy is a clean and inexhaustible source of energy. One of the ways of harvesting this energy is using wind turbines, which transform the kinetic energy of wind into electrical power output. Wind turbines face many problems, such as low wind hours, design issues, and so on. The main focus of this work is to find the optimum blade angle of turbine blades, in order to produce the maximum power output, even at low wind hours. In this study, CFD analysis is done on a 5 MW wind turbine blade at wind velocities 3, 12.5 and 25 m/s, which are the cut in, rated, and cut out velocities of wind turbines, respectively. The range of angles under consideration varies from 20 to 89 °. A 3D model of the blade is analyzed using ANSYS Fluent 19.0. The optimum blade angle is identified, and the characteristics of the curves of blade angles, with respect to different parameters, are obtained. HIGHLIGHTS Optimum Blade angle for better moment Pressure and velocity Characteristics at various blade angles 5MW HAWT blade moments for wind velocity of 3,12.5 and 25 m/s The maximum moment is obtained at an angle 82 degree at various wind velocities GRAPHICAL ABSTRACT

Control of Wave Energy Converter With Losses in Electrical Power Take-Off System

A permanent magnet linear electrical machine power takeoff (PTO) unit is simulated on the direct drive wave energy converter in this paper, which is controlled to provide the required reactive power. A shape-based control is implemented to maximize the wave energy production (mechanical PTO) with the limiting constraints on the electric drive. Further, the linear electrical machine design is optimized such that the electrical power output is maximized (e.g., reduced power losses). The numerical simulations are conducted using MATLAB/Simulink and the Simscape toolbox. Linear wave theory is applied in modeling the buoy dynamics. Additionally, the PTO unit is composed of a linear electrical machine, an ideal inverter, and an ideal energy storage system. The results show the proposed PTO tracks the reference control accurately. The electrical power output is significantly improved by limiting the current in the PTO compared to a passive control.

Near-field thermophotovoltaics for efficient heat to electricity conversion at high power density

Thermophotovoltaic approaches that take advantage of near-field evanescent modes are being actively explored due to their potential for high-power density and high-efficiency energy conversion. However, progress towards functional near-field thermophotovoltaic devices has been limited by challenges in creating thermally robust planar emitters and photovoltaic cells designed for near-field thermal radiation. Here, we demonstrate record power densities of ~5 kW/m2 at an efficiency of 6.8%, where the efficiency of the system is defined as the ratio of the electrical power output of the PV cell to the radiative heat transfer from the emitter to the PV cell. This was accomplished by developing novel emitter devices that can sustain temperatures as high as 1270 K and positioning them into the near-field (<100 nm) of custom-fabricated InGaAs-based thin film photovoltaic cells. In addition to demonstrating efficient heat-to-electricity conversion at high power density, we report the performance of thermophotovoltaic devices across a range of emitter temperatures (~800 K–1270 K) and gap sizes (70 nm–7 µm). The methods and insights achieved in this work represent a critical step towards understanding the fundamental principles of harvesting thermal energy in the near-field.