Solar Panels Research Articles

Mechanical Property Analysis of a Boom-Membrane Structure Used for Aerospace Technologies.

Traditional deployable truss space structures previously had upper limits on their key indicators, such as the deployed area, folded ratio and total weight, and hence, the application of new extendable mechanisms with novel deployment types is desired. Foldable extendable tape spring booms made from FRP (fiber-reinforced polymer) laminate composites and their corresponding boom-membrane structures were invented in recent years to satisfy the needs of the large-scale requirements of spacecraft, especially for antennas, solar sails and solar arrays. This paper aimed to analyze the properties of the deployed states of extendable tape spring booms and their boom-membrane structures. By establishing an analytical model of the boom and the structure, the bending stiffness, critical buckling load of the boom and the fundamental frequency of the membrane structure were acquired. To provide more guidance on the boom-membrane structure design, a geometric and material parametric study was carried out. Meanwhile, an experimental study to investigate the deployed properties of the booms and membrane structures was introduced to afford some practical verification.

Assessing the social life cycle impacts of the Spanish electricity mix: A decadal analysis

Power generation systems are crucial to national energy transitions, such as Spain's, which stands as a notable example. However, this profound transformation could have multifaceted implications, leading to unintended consequences on society. The present work is the first to understand the social impacts of the Spanish power sector and their technology supply chains using the social life cycle sssessment methodology. The functional unit is 1 kWh of electricity produced by the technologies in the Spanish electricity mix. A cradle-to-gate approach is taken using a supply chain protocol to complete the system boundaries. The social life cycle inventory, comprising data on national suppliers, working hours and social flows, was integrated into the PSILCA database to derive the social profile of each power technology and, consequently, to obtain a comprehensive view of the Spanish power sector. The results reveal that social impact associated with the Spanish electricity mix has increased or remained stable from 2010 to 2022. Analysis of four indicators (child labour, contribution of the sector to the economic development, frequency of forced labour and women in the sectoral labour force) reveals significant differences, highlighting three main social hotspots: i) solar PV panel production in East and Southeast Asia, particularly China, ii) natural gas extraction and refining in North Africa, concentrated in Algeria, for natural gas combined cycle and cogeneration plants, and iii) construction and operation of hydropower and nuclear plants in Spain. This study demonstrates that current strategies for Spain's power sector transition may not guarantee a favourable social performance, emphasizing the need for balanced environmental and social considerations in energy policy making, aligned with the Sustainable Development Goals.

Thermal performance of the photovoltaic module with evaporative cooling ventilated cavity

Abstract Energy is indispensable in modern life, and solar photovoltaic technology stands out for its substantial advantages. However, the current conversion rate remains suboptimal, ranging from 15% to 20%. Compounding this, a portion of solar energy undergoes conversion into thermal energy, resulting in an elevation of the PV (photovoltaic) module’s temperature and a subsequent reduction in electricity generation efficiency. In response to this challenge, a solution was conceived—a design featuring an evaporative cooling ventilated cavity crafted to alleviate the operating temperature of the photovoltaic module. This innovative system integrates a photovoltaic facade with an evaporative cooling ventilation cavity, encompassing crucial components such as solar photovoltaic panels, an evaporative cooling layer, and a ventilated cavity equipped with thermal regulation. An experimental system was meticulously developed. The results illuminate the system’s efficacy in temperature reduction: approximately 5°C for the PV back sheet, 5°C for the cavity back sheet, and 5.2°C inside the cavity. Furthermore, the system achieves a noteworthy average operating temperature reduction of about 14.1%, 20.2%, and 20.4%, respectively. These findings underscore the substantial impact of the evaporative cooling system on regulating and enhancing the thermal performance of PV modules.

Efficiency of Vertically Installed Solar PV Panels

Efficiency of Vertically Installed Solar PV Panels

Maximizing off-grid solar photovoltaic system efficiency through cutting-edge performance optimization technique for incremental conductance algorithm

The maximum power point tracking (MPPT) algorithms are required to deliver the optimal energy from solar photovoltaic cells/array (PV) under numerous weather conditions. Therefore, MPPT circuits driven by defined rules called algorithms are designed. These algorithms range from simple to complex in design and implementation and are selected based on the scenarios of the surroundings. However, the incremental conductance (InC) MPPT algorithm is one of the market's most simple, easy to implement, and demanding algorithms. The drawback associated with the InC algorithm is its tracking speed. To overcome this weakness various researchers have made multiple improvements. Although the performance became better but was not satisfied. Further, the improvements introduce steady-state oscillations of the operating point around the MPP. So, the user needs to pick and choose based on demand. Keeping the target in focus, we have introduced a couple of modifications in the structure of INC. that remain fruitful. The proposed structure named the augmented InC algorithm has shown marvelous improvement in tracking speed and steady-state oscillations. The results have been compared with the conventional InC algorithm, where the proposed augmented InC algorithm has outperformed the conventional InC algorithm in tracking speed and steady-state oscillations. We have used the MATLAB script to code the conventional InC algorithm and proposed augmented InC algorithms based on their designed flowchart. Both algorithms have been applied to the standalone solar photovoltaic system composed of a solar photovoltaic array, DC/DC boost converter, illumination and temperature inputs, MPPT algorithm, and a DC load. The model is designed in Simulink/MATLAB.

Performance prediction and analysis of a solar assisted medium-deep geothermal heating system

Abstract The solar assisted medium-deep geothermal heating (SAMGH) system is a novel kind of heating system that can combine the benefits of geothermal and solar energy. However, the variations in borehole heat exchanger (BHE) performance and the intermittency of solar energy pose challenges for predicting the overall performance of the coupled system and designing the operational strategies. To conduct simulation on the SAMGH system for performance prediction and analysis, a coaxial medium-deep borehole heat exchanger coupled with the solar energy heating system for an office building in Xi’an was developed. The TRNSYS software was employed to establish the model of the coupled system. A ground source heat pump (GSHP) heating system was used for comparison. The simulation results showed that with the introduction of the solar energy and heat storage modules, the annual operating time of the geothermal system only accounts for 32.06%. The energy consumption of the coupled system can be reduced from 63585 kW to 44586 kW, and the energy consumption proportion of the geothermal system in total value decreased from 69.10% to 40.58%. Therefore, the average coefficient of performance (COP) of the heat pump and the system were improved by 63.71% and 91.77%, respectively. Moreover, because the solar energy is beneficial to the ground heat recovery, the average ground temperature increased from 42.5 °C to 43.88 °C after ten years of operation. The proposed design method and simulation results can serve as a reference for design method and performance analysis of the geothermal and solar coupled system.

CFD-DEM study of thermal behaviours of chip-like particles flow in a fluidized bed

Chip-like particles are increasingly practised in various thermal applications, yet the unique heat transfer characteristics are not well understood. In this work, a recently developed super-quadric computational fluid dynamics-discrete element method (CFD-DEM) is extended to couple with heat transfer sub-models to study the cooling behaviours of chip-like particles in a three-dimensional (3D) cylindrical fluidized bed. Then the effect of aspect ratio (AR) on the flow and thermal behaviours of chip-like particles are studied at the particle scale. The results show that the chip-like particles with an AR of 6 show the steepest temperature decrease and thus the fastest cooling process under the given conditions. The unimodal distributions of particle temperature for the small ARs (i.e., 2 and 4) but bimodal distributions for the large AR (i.e., 6) are captured. Further, a large convective heat flux appears in the dilute phase due to vigorous slip velocity and temperature difference while a small convective heat flux occurs in the dense phase where the chip-like particles tend to be in a close-packing status. Finally, the underlying mechanism is explored: the chip-like particles with larger ARs show better heat transfer performance because the AR enhances gas-solid relative movement intensity and the convective heat transfer flux, and the convective heat transfer dominates the whole particle cooling process. The fundamental study sheds light on the design and optimization of fluidized beds of chip-like particles including solar panel recycling and wood chip gasification.

A Rome district transition towards optimal and sustainable heat and power generation

In this work, a specific district of Rome energy metabolism has been studied to reduce the carbon footprint of electric and thermal energy generation. Two scenarios were investigated, one “ex-ante” with the district as it was, fully dependent on the national grid for electricity and methane-fuelled boilers for heating. And an “ex-post” scenario with the buildings retrofitted with more efficient thermal insulation, LED lights, photovoltaic panels and trigeneration systems. The energy metabolism of the district was modelled in TRNSYS in both cases. For the ex-post scenario, the digital twin implemented a control logic for energy flows with several functionalities including prioritizing the power flow, setting rules and boundaries on the operation of the co-generators, preventing batteries from overcharging/discharging. The sizing of PV panels, batteries, and cogeneration units in the ex-post scenario was optimized in terms of both energy and economic performance using a Python library with the Non-dominated Sorting Genetic algorithm (NSGA-II). In the ex-post scenario, the optimal energy system delivers significant reductions in energy costs and CO2 emissions. The energy transition results in a decrease in purchased electricity of 68.2%, CO2 emissions by 56% and generation costs by 48.9%. The Levelized Cost Of Energy (LCOE) of the district is estimated to be 196 €/MWh.

Study on the Optical Coupling Effect of Building-Integrated Photovoltaic Modules Applied with a Shingled Technology

Building-integrated photovoltaics (BIPV) comprise the integration of a solar power generation system into the exterior design and architectural elements of a building to produce electricity, which allows the building itself to generate electricity. By integrating shingled technology into the photovoltaic module with optimization of the optical effect, the output performance of the module can be increased while securing an aesthetic appeal as an architectural exterior material for the building simultaneously. In this research, we studied enhancing the performance of BIPV modules through an analysis of the optical coupling effect for shingled technology using PSpice simulation. Compared to the efficiency of 0.2 cm string spacing, the optical coupling effect was increased by 33.33%, 46.98%, 67.01%, and 193.49% according to the string spacing of 0.5 cm, 1 cm, 2 cm and 4 cm, respectively. To analyze this increase, we focused on studying the increase in current due to the reflection and re-absorption of light in the back layer of the solar cell as the cause of this output enhancement. Additionally, the coupling effect in accord with different layers showed that using white EVA to reflect the incident light from the top layer resulted in 117.14% and 521.90% enhancements in maximum output power (Pm) loss % compared to the conventional and black backsheet applied PV modules, respectively.

Life Cycle Assessment of Large-Scale Solar Photovoltaic Plant based in Malaysia

Abstract A cradle-to-grave life cycle assessment (LCA) has been performed on a Large Scale Solar Photovoltaic (LSSPV) plant based in Peninsular Malaysia to study the detailed environmental impact of its life cycle. The LSSPV, with a capacity of 78 MWdc, is currently one of Malaysia’s largest solar power plants. The different life cycle phases of the LSSPV, starting from resource manufacturing and transportation, through plant construction and electricity generation, and to decommissioning of the plant, were assessed using the SimaPro software with the Ecoinvent v3.8 database. Results show that the overall emission rate for the LSSPV is 0.0309 kgCO2eq/kWh. It is estimated that the construction phase of the power plant, which includes activities such as infrastructure works and production of PV modules and Balance of System, yields the largest amount of kgCO2eq/kWh emission at 85.7% and imposes the highest environmental impact as compared to the other phases. The second biggest contributor is attributed to the transportation phase, contributing to 14.0% of the total kgCO2eq/kWh emission, of which most of the emission is emitted during the sea freight transportation of PV components from China port to Malaysia port (12.4%). A sensitivity analysis was performed to compare the environmental impact between open ground installation and the slanted roof installation mounting system of LSSPV. The findings from LCA proposed that open-ground installation has a greater negative impact on the environment because it involves additional land clearing and infrastructure transformation. The findings from this study serve as a benchmark for the LSSPV system in Malaysia and present technical perspectives for the future development of PV projects considering environmental impacts across the project lifetime.

Enhancing PV solar panel efficiency through integration with a passive Multi-layered PCMs cooling system: A numerical study

Photovoltaic (PV) solar cells are at the forefront of sustainable electricity generation technologies, yet they exhibit relatively low efficiency. Typically, less than 20 % of the solar energy absorbed is converted into electrical energy, with the remainder converts into heat. This heat increases the PV panel's operating temperature, negatively impacting its efficiency and life expectancy. To mitigate this, a novel approach using multi-layered phase change materials (PCMs) has been examined in this study. The approach involves integrating organic PCMs (OPCMs) with metallic PCMs (MPCMs), enhancing PV panel cooling efficiency by attaching the multi-layer PCM to the panel's rear. This study, employing ANSYS FLUENT software's solidification and melting model, explores the impact of multi PCM layers thickness and total multi layers thickness on PV cell temperature and electrical efficiency. The analysis reveals that a multi-layered PCMs configuration with a thickness ratio of 15:85 between MPCM (CERROLOW-117® alloy) and OPCM (RT44) significantly decreases temperature rise in PV cells, resulting in a temperature drop of 59.6 °C. This improvement boosts PV panel performance by an average of 35.8 % compared to panels lacking cooling systems during peak sun hours under hot climate conditions characterized by ambient temperatures of 40 °C and high solar radiation exceeding 1000 W/m². This suggests that a carefully optimized PCM layering approach could markedly improve PV system efficiency, offering a practical solution to the overheating problem and extending the operational life of PV installations.

Quantum maximum power point tracking (QMPPT) for optimal solar energy extraction

Solar energy is key to achieving a more environmentally responsible future. One way to exploit it is to use semiconductor technology through solar panels to generate clean, sustainable, and controllable energy. However, the use of such solutions must be optimised by methods such as maximum power point tracking (MPPT) to extract the maximum available solar energy. Although MPPT algorithms have been widely used and improved, the use of newer approaches, such as quantum computing, appears to hold the promise of achieving new performance levels, particularly for real-time MPPT implementation. The goal of this work is to develop and test a quantum algorithm for the photovoltaic (PV) energy MPPT problem using quantum particle swarm optimisation. The performance of the classic and quantum MPPT algorithms was evaluated under three main operating conditions: normal, high-temperature, and partial shading conditions. This represents a variety of environmental scenarios that can affect the efficiency of solar power generation. According to the study's results, the classical algorithm recorded 0.15% more power than the quantum algorithm in normal operating conditions, and the quantum algorithm generated 3.33% more power in higher temperature tests and 0.89% more power in the partial shading test. Moreover, the quantum algorithm recorded lower duty cycles for the three tests. While the classical algorithm may have a slight edge in power output under normal operation conditions, the quantum algorithm indicates superior performance in challenging conditions and consistently reveals more promising overall efficiency.

Industry CPD: Design of solar panel mounting structures made of cold-formed steel

This CPD module, sponsored by GRAITEC UK, explores the structural analysis and design of solar panel mounting structures made of cold-formed steel.

Novel feature selection based ANN for optimal solar panels tilt angles prediction in micro grid

Predicting PV system electricity output is necessary for daily operational management and annual power system planning when integrating solar collector-based photovoltaic (PV) stations into micro grids. Tilting the panels at the ideal angle to maximize solar energy capture is necessary to maximize PV station production. This optimal tilt angle (OTA) must be predicted as it is a nonlinear function of the total solar radiation, diffuse solar radiation, and direct solar radiation. This research explores the use of feature selection-based artificial neural networks (ANN) with various machine learning algorithms to predict the OTA for PV systems at specific locations, aiming to maximize PV output in micro grids. The study identifies global solar radiation, diffuse solar radiation, clarity index, and global solar radiation on inclined surfaces as the most critical inputs for predicting OTA, while extraterrestrial radiation is deemed the least significant. Implementing the appropriate input variables significantly enhanced prediction accuracy from 38.59% to 90.72%. Among the neural networks evaluated, the Elman neural network demonstrated the greatest improvement.

Adaptive extreme learning machine using soft computing fuzzy propositions—Validating operating state of solar energy system

In this paper soft computing fuzzy proposition/rule based adaptive extreme learning is proposed. This article illustrates the impact of agricultural solar panel (AgSP) and its operating condition/state during the farming seasons and hence safety of energy system sustained. Extreme learning machine (ExLM) used as learning machine (LM) for artificial intelligence (AI) training to predict the operating state of applied AgSP green energy unit. soft computing Fuzzy rules are framed for every instant of leaning machine to identifying the lower mismatch conditions and avoid the false prediction. Higher and lower limits of adaptive resonance theory (ART) support LM (ART-LM) is properly used by providing unusual dust formation (UnDFR) data and usual/normal dust formation (UsDFR) data for AI training. Outcome of AI neural net is determined by resonant neuron net (RSNN). Predicted variable from ART-LM is calculated with higher and lower limits of dataset captured from applied energy common node (CN). Many test conditions of dust formation on solar photovoltaic (PV) has been validated to predict the state of applied energy system. For any UnDFR confirmation, entire AgSP based distributed generator (DG) unit will isolate from common node/utility grid. Proposed ART-LM based AI algorithm has been validated even for charging state events of electrical vehicle (EV) during the abnormal conditions.

Forecasting daily solar radiation: An evaluation and comparison of machine learning algorithms

Rising energy demand, resource depletion, and environmental issues tied to fossil fuels demand a transition to renewable energy. Solar power, abundant and well-established, presents a promising solution to address our expanding energy requirements. The sun radiates an astonishing amount of energy every second, far more than humanity’s current and future energy needs. Accurate solar radiation prediction is crucial for optimizing solar panel design, placement, and grid integration. This paper aims to predict daily global solar radiation data for six Pakistani cities: Karachi, Lahore, Islamabad, Quetta, Peshawar, and Multan. It highlights the importance of advanced algorithms and introduces an innovative data collection method using pyranometer sensors and microcontrollers, making data storage and analysis more affordable and efficient while reducing the financial burdens associated with traditional equipment. Focusing on Pakistan’s diverse solar radiation potential, this research evaluates eight machine learning algorithms using seven key statistical metrics to understand and compare their performance in predicting solar radiation. Four algorithms, k-nearest neighbors, Random Forest Regression, Gradient Boosting Regression, and Support Vector Regression (SVR), consistently exhibit remarkable precision, achieving outstanding R2 values of up to 99%. This highlights the crucial role of algorithm selection in solar radiation prediction, with SVR emerging as the top choice. SVR’s precise and reliable forecasts empower renewable energy planning and decision-making. This study provides valuable guidance for decision-makers to optimize solar energy utilization across diverse geographical regions and contributes invaluable insights to the field of renewable energy forecasting.

Marine Predators Algorithm with Deep Learning based Solar Photovoltaic System Modelling and Optimization of Green Hydrogen Production

Green hydrogen production depends on solar energy contains employing renewable solar power to purpose the electrolysis of water, causing the separation of hydrogen and oxygen molecules. This method contains a high potential to address the energy transition challenges caused by weather changes and vital carbon-neutral alternatives. Photovoltaic (PV) based combined energy systems performance as a potential new technological solution for clean and affordable green hydrogen production. Optimizing solar PV systems for the effectual generation of green hydrogen includes maximizing the energy output of solar panels and increasing the entire hydrogen production method. Several researchers and scientists are paid attention to the optimizer and modelling of many blocks developing the PV electrolysis method to acquire the optimum solution. With this motivation, the study presents a new Marine Predator Algorithm with Deep Learning-based Modelling and Optimization of the Green Hydrogen Production (MPADL-MOGHP) technique. The MPADL-MOGHP technique can be employed for determining the optimum operational variables of the water electrolysis procedure relevant to hydrogen (H2) production. Catalyst amount (μg), electrolysis time (min), and electric voltage (V) are the three controlling factors that should be properly detected to increase hydrogen production. The MPADL-MOGHP technique comprises two major stages of operations such as modelling and optimization. Primarily, the DBN model is applied for simulating the water electrolysis procedure designed based on electric voltage, quantity of catalyst, and electrolysis time. Next, the MPA is applied for determining the optimum parameters of the water electrolysis process to maximize the generation rate of the hydrogen. The quantity of catalyst, the electrolysis time, and the electric voltage are applied as decision parameters during the optimization process. The performances of the MPADL-MOGHP system are tested on different aspects. The experimental values highlighted the promising results of the MPADL-MOGHP method over other existing techniques.

Blade-Coating of High Crystallinity Cesium-Formamidinium Perovskite Formulations.

Up-scalable coating processes need to be developed to manufacture efficient and stable perovskite-based solar modules. In this work, we combine two Lewis base additives (N,N'-dimethylpropyleneurea and thiourea) to fabricate high-quality Cs0.15FA0.85PbI3 perovskite films by blade-coating on large areas. Selected-area electron diffraction patterns reveal a minimization of stacking faults in the α-FAPbI3 phase for this specific cesium-formamidinium composition in both spin-coated and blade-coated perovskite films, demonstrating its scaling potential. The underlying mechanism of the crystallization process and the specific role of thiourea are characterized by Fourier transform infrared spectroscopy and in situ optical absorption, showing clear interaction between thiourea and perovskite precursors and halved film-formation activation energy (from 114 to 49 kJ/mol), which contribute to the obtained specific morphology with the formation of large domain sizes on a short time scale. The blade-coated perovskite solar cells demonstrate a maximum efficiency of approximately 16.9% on an aperture area of 1 cm2.

Surface Engineering of Tin Oxide Nanoparticles by pH Modulation Facilitates Homogeneous Film Formation for Efficient Perovskite Solar Modules (Adv. Energy Mater. 25/2024)

Surface Engineering of Tin Oxide Nanoparticles by pH Modulation Facilitates Homogeneous Film Formation for Efficient Perovskite Solar Modules (Adv. Energy Mater. 25/2024)

An Assessment of the Impact of Accumulated Dust on Efficiency and Performance Output of Solar Photovoltaic Panels

Part of the major causes of low performance and output of solar photovoltaic (PV) systems is dust accumulation on the panel. The radiation getting to the solar cells of the panel can be decreased due to the accumulation of dust on the surface of the solar PV panel. Dust decreases the amount of radiation on solar cells of the panel, and also reduces the performance output of the solar PV panel. The environment is one of the contributing parameters which directly affect the photovoltaic performance. In this research work, the influence of dust, on the efficiency and performance output of solar PV panels is investigated to determine the efficiency of the solar PV panel, determine the energy power output of the solar PV system. Three identical panels are used to measure voltage, current and temperature. The result obtained showed that the meanfficiency for the clean panel is 14.98%, the mean efficiency for panel with artificial dust is 11.19% and that of the panel with natural dust is 13.25%. The mean power for the clean panel is 245.04 W, for panel with artificial dust is 183.20 W and for panel with natural dust is 216.86 W. This research shows the correlation between dust accumulation and reduced maximum power output due to the drop in both voltage and current values for the panel with dust.