Showing papers in "International Journal of Photoenergy in 2022"

A Review on Factors Influencing the Mismatch Losses in Solar Photovoltaic System

Abstract: In the last two decades, it is persistently emphasized to develop energy generation systems free from greenhouse gas emissions since these gases cause global warming, and it leads to unpredictable monsoons. Consequently, it might not be a conducive environment for human beings and animals to dwell. To ascertain the green environment for the next generations and reduce the use of fossil fuels, renewable energy sources are highly suggested to generate electrical energy. Solar photovoltaic is reckoned to be one of the promising methods to generate electricity; however, it has a lower conversion value due to various losses resulting from external and internal parameters. Among various losses that occurred in the solar photovoltaic system, mismatch loss is imperative, which causes the system to perform poorly. Solar photovoltaic systems have made topical advances in the use of highly effective solar cell materials to achieve high efficiency. In this analysis, performance parameters are influenced by the internal and external conditions of the solar photovoltaic systems and they lead to an increase in the loss of the system. The present review is focused to fetch fruitful information on the several studies that analyzed the effects on the solar photovoltaic systems of parasitic resistances, dust generated by tresses, clouds, solar radiation, temperature, relative humidity, different connection topologies, circuit implementation for partial shading, and remedies suggested by the potential authors.

Imperative Role of Photovoltaic and Concentrating Solar Power Technologies towards Renewable Energy Generation

Abstract: The United Nations Development Programme (UNDP) 2030 agenda illustrates the requirement of expanding infrastructure and advancing technology for delivering modern and sustainable energy services for all in developing countries. Moreover, UNDP also set a goal of increasing the renewable energy share in the global energy. Renewable energy resources are eco-friendly and widely available resources from nature for generating energy. Geothermal energy, wind energy, solar energy, tidal energy, and biomass energy are renewable energy sources. Solar energy is one of the renewable energy generation approaches that harvests energy widely from sun radiation. Photovoltaic (PV) and concentrating solar power (CSP) are the primary technologies to capture solar energy. This study presents the significance of utilizing solar energy for electricity generation globally using PV and CSP technologies. Furthermore, the distinct energy capturing and storing mechanisms of PV and CSP technologies are presented in detail. This article presents the significance and implementation of thermal energy storage for storing energy obtained through CSP technology. Finally, the study presents a considerable gap between PV and CSP in terms of development with future trends.

A Novel Approach in Hybrid Energy Storage System for Maximizing Solar PV Energy Penetration in Microgrid

Abstract: Nowadays, energy storage system is utilized in many countries for energy planning in the future. The changes in solar radiation lead to the overproduction of electricity in a solar photovoltaic generator. A hybrid energy storage system would play an important role in enhancing the reliability of power generation using the solar system. The microgrid is the indispensable infrastructure of the smart grid in photovoltaic systems. In this paper, the energy storage system within the microgrid of the PV system is analysed. The storage system configuration and topologies of the microgrid are analysed with power electronic interference, control scheme, and optimization of the renewable source and energy storage system. A general sizing technique for HESS in a PV system based on pinch analysis and design space. The size of HESS scales that connect generator ratings to storage capacity is created by utilizing the pinch analysis on load and resource data. The design space is a feasible combination of a short-, long-, and medium-sized energy storage system in a PV generator.

Role of Microbes and Nanomaterials in the Removal of Pesticides from Wastewater

Abstract: Pesticides are a class of xenobiotic compounds that are recalcitrant and show persistence in the environment for a longer period of time. Research studies have linked their potential for mutagenicity, teratogenicity, and carcinogenicity. The accumulation of pesticides in water sources due to runoff from agricultural lands has posed a serious threat to the biota of the water ecosystem as well as to the human population. Long-term exposure to pesticides can cause neurological disorders, reproductive complications, cancer, immunological, and pulmonary diseases. The use of pesticides has dramatically surged in agricultural as well as nonagricultural practices. Tons of pesticides are applied in the fields, but a limited amount reaches to the target organism while the rest is wasted and gets accumulated in soil or ends up in water sources like groundwater or river, which results in eradication of nontarget organisms. A variety of pesticides are used for pest management, such as organochlorine (DDT), carbamates (carbaryl), organophosphates (malathion), and pyrethroids (pyrethrins). These chemicals are highly toxic to flora and fauna because of their nonbiodegradable and persistence nature. Biomagnification of pesticides usually leads to cause various problems in human beings. Organochlorines like DDT have been banned in many developed countries due to these reasons. Therefore, the removal of pesticides from wastewater and natural water sources is of utmost importance. Conventional methods possess various limitations; therefore, there is a requirement of an alternative method which can efficiently remove these pollutants from the wastewater. In this review, environmental impacts and health-related complications of pesticides and microbial remediation approaches and use of different nanomaterials in the pesticide removal have been discussed.

A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

Abstract: Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. The results are been compared with results obtained by another machine learning algorithm such as polynomial regression and case-based reasoning. Further, a website is developed wherein the user can key in the date and time. The output of that transaction is predicted temperature, humidity, and forecasted power generation of the specific standalone photovoltaic system. The presented results and obtained characteristics confirm the superiority of the proposed techniques in predicting power generation.

Optimization of Solar Hybrid Power Generation Using Conductance-Fuzzy Dual-Mode Control Method

Abstract: The functioning of a solar hybrid power system is investigated in this research using a unique fuzzy control method. Turbines, solar photovoltaics, diesel engines, fuel cells, aqua-electrolyzes, and other autonomous generation products are used in the hybrid renewable energy system. Further energy storage components of the system include the batteries, turbine, and ultracapacitor. This research incorporates a supercapacitor hybrid energy storage system (HESS) into a solar hybrid power generating system, allowing the consumption and energy storage space and power output to be significantly increased. This study’s approach incorporates a decentralized power generation system with a HESS while increasing electrical output in phases utilizing a dynamic reactive power compensation scheme and a conductance-fuzzy dual-mode control strategy. Due to a nonlinear behavior of photovoltaic (PV) devices’ power output, maximum power point tracking (MPPT) methods must be used to create the greatest power. Infrequently developing atmospheric circumstances, traditional MPPT algorithms do not work adequately. Modeling is used to determine the microgrid’s power output to the photovoltaic hybrid power generating organization, as well as the optimization method for each device in the network. The dynamic power factor correction scheme and also the conductance-fuzzy dual-mode control approach are primarily used in this study to optimize the solar hybrid renewable energy system.

Multicluster Analysis and Design of Hybrid Wireless Sensor Networks Using Solar Energy

Abstract: A wireless touch network is a distributed, self-organizing network of multiple sensors and actuators in combination with multiple sensors and a radio channel. Also, the security area of such a network can be several meters to several meters. The main difference between wireless sensor networks from traditional computer and telephone networks is the lack of a fixed infrastructure owned by a specific operator or provider. Each user terminal in a touch network is capable of acting as a terminal device only. Despite the long history of sensor networks, the concept of building a sensor network is not finally imposed and expressed in some software and hardware (platform) solutions. In this paper, the design and analysis of multicluster model of the sensor nodes in wireless sensor network with the help of solar energy. This proposed model provides the required energy to transmit the information between two end nodes in different cluster. The communication between the end to end clusters was increased based on this design. The implementation of sensory networks at the current stage depends largely on the specific needs of the industrial problem. The architecture, software, and hardware implementation technology is at an intensive development stage, attracting the attention of developers looking for a technological niche of future makers.

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

Abstract: The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Li-ion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

Forecasting Solar Energy Production Using Machine Learning

Abstract: When it comes to large-scale renewable energy plants, the future of solar power forecasting is vital to their success. For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation. In order to improve the accuracy of the suggested model, an ensemble of machine learning models was used in this study. The results of the simulation show that the proposed method has reduced placement cost, when compared with existing methods. When comparing the performance of an ensemble model that integrates all of the combination strategies to standard individual models, the suggested ensemble model outperformed the conventional individual models. According to the findings, a hybrid model that made use of both machine learning and statistics outperformed a model that made sole use of machine learning in its performance.

Design and Analysis of ANFIS – Based MPPT Method for Solar Photovoltaic Applications

Abstract: The solar photovoltaic energy is becoming popular in the modern-day distribution networks due to the clean energy factor. The photovoltaic modules exhibit a nonlinearity in the output power concerning the environmental conditions. This work suggests an adaptive neuro-fuzzy inference system- (ANFIS-) based maximal power point tracker (MPPT) for the optimization of the solar photovoltaic system (SPVS). The controller modelled is utilized to optimize the output power of a DC-DC converter connected to a 400 W PV array. The entire model is analysed employing MATLAB/SIMULINK using primary features provided by the technical data. The behavior of the controller modelled is tested for various weather conditions and partial shading conditions. The findings show the controller’s tracking speed effectiveness and dynamic response in PSCs.

Hybrid Renewable Power Generation for Modeling and Controlling the Battery Storage Photovoltaic System

Abstract: A major portion of the global energy demand was likely to be fulfilled by an extensive supply of renewable power. Renewable energy outputs, on the other hand, are changeable due to the dynamic nature of their sources. The integration of these variable sources of power into current power grids is proving difficult for electrical power system operators all around the world. The fundamental issue with renewable energy systems is that, due to the stochastic nature of renewable power, electricity production varies from period to period. Recent research and development on renewable technologies can ensure the islands’ long-term electricity supply. Renewable energy sources, on the other hand, are limited by their unpredictable nature and significant reliance on weather conditions. To offset this disadvantage, several renewable energy sources and converters must be joined. To balance the power generation and load power, a hybrid renewable power generation for standalone application is proposed. The solar plant model is made up of a 170 W photovoltaic (PV) panel connected in series, and conversion of energy is done using the maximum power point tracking (MPPT) algorithm, which regulates a buck-boost converter modulation. The MPPT method used in the converter’s control step is based on perturb and observe (P&O) and enhanced with a PI controller. The bidirectional buck-boost DC-DC converters (BBDC) are utilized to preserve a DC-link voltage stable. This is also storing additional hybrid energy in a large battery and is distributed to the system load; then there is a shortage of hybrid power. The load current power is regulated in terms of the frequency and enables it to be achieved using three vector control technique voltage source inverters (VSI). The results were offered to demonstrate a hybrid performance of this organization.

Design and Implementation of a Hybrid Solar-Wind-Biomass Renewable Energy System considering Meteorological Conditions with the Power System Performances

Abstract: This paper presents a performance evaluation of an off-grid PV-wind-biomass hybrid energy system for a remote area named Kuakata in Bangladesh considering dispatch strategy-based control, power system response, and reliability analysis-based stability and feasibility study. The simulation and optimization of operations of the system have been done by the HOMER software using the real-time field data of solar radiation, wind speed, and biomass of that particular area for ensuring economical and environmental feasibility offering the least net present cost (NPC), cost of energy (COE), and CO2 emission. The result shows that NPC has been reduced by 88 percent, CO2 emissions have been reduced by 99 percent, the operating cost has been lowered by 99 percent, and COE has been reduced by 92 percent than another available work. Besides, in comparison to traditional power sources, COE has been reduced by 40 percent, NPC has been lowered by 90 percent, and CO2 emissions have been reduced by 99 percent. The proposed system has also been analyzed utilizing DIgSILENT PowerFactory software to find the power system responses, i.e., active, reactive powers, voltage, and frequency responses of the proposed microgrid in a per unit fashion on the occurrence of a three-phase fault. To establish the feasibility of the microgrid, a reliability study considering different reliability indices has also been done. The analyzed hybrid energy system might be applicable to other regions of the world where there are similar climatic conditions.

Improving the Efficiency of Photovoltaic Panels Using Machine Learning Approach

Abstract: Photovoltaic (PV) solar panels account for a major portion of the smart grid capacity. On the other hand, the accumulation of solar panels dust is a significant challenge for PV-based systems. The accumulation of solar panels dust results in a significant reduction in the amount of energy produced. Because of the country’s low wind velocity and rainfall, frequent cleaning of solar panels is necessary either by manual or automated means. Cleaning activities should only be initiated when absolutely essential to reduce maintenance costs and increase the power output of solar panels that have been projected to be affected by dust accumulation. In this paper, we develop a deep belief network model to detect the dust particles in the solar panels installed as a large unit. The study takes into account various input metrics that includes solar irradiance, temperature level, and dust level on the panels. These metrics are used for the estimation of the level of dust present in the atmosphere and how often the panels can be cleaned at regular intervals. The simulation is conducted to test the efficacy of the model in cleaning the panels. The results are estimated in terms of accuracy, precision, recall, and F-measure. The results of the simulation show that the proposed model achieves higher accuracy rate of more than 99% than other methods.

Smart Integrated Decentralization Strategies of Solar Power System in Buildings

Abstract: This study signifies the need for a smart integrated decentralized solar energy system in Pakistan. Since the outlook of energy is highly dominated by its power sector, policy measures must be adopted to ensure its penetration in the system of any country. After the industrial, the housing sector is the major energy-consuming sector. The goal of this study is to assess energy generation through a smart integrated decentralized solar energy system in the power hub of a commercial area in Taxila, Pakistan. Model development involves a hypothetical model built on LabVIEW which allows the user interface a way to intermingle with the source code. It permits the user to the transformation of the values sent to the source code and sees the information that the source code calculates. The proposed system is a collaborative sharing integrated decentralized solar system that credits sunlight-based energy framework proprietors for the power they add to different buildings due to the collaborative sharing mechanism at Rs.10 per kWh. This low-cost electricity is available at your doorstep that you can share according to the collaborative sharing basis that will not range any certain variable. Results from the literature describe that 30% of the cost associated with the commercial price of electricity amounts to distribution cost. This system of the utilization of energy would be applied at a local level to achieve the maximum power generation from solar panels through blockchain use of solar systems, especially in regions that have no entrance to traditional power with little odds of getting associated in the next 5-10 years.

Investigation of the Effect of Physical Factors on Exergy Efficiency of a Photovoltaic Thermal (PV/T) with Air Cooling

Abstract: Thermal photovoltaic systems are used to harness solar energy to generate electricity and thermal at the same time. In this technology, electrical efficiency is very low compared to thermal efficiency; as the cell surface temperature rises, the electrical efficiency decreases, so one of the ways to achieve high efficiency is exergy analysis. Exergy analysis of a process or system shows how much of the ability to perform the work or input exergy has been consumed by that process or system. In this research, an ordinary thermal photovoltaic panel with air cooling has been examined for exergy. To do this, it has identified the effective performance variables from a mechanical point of view, which are inlet air temperature, inlet air flow, and length (number of modules that are connected in series). The effect of changing each of the variables based on Saveh weather conditions has been simulated using MATLAB software. The results show that the exergy efficiency of the panel decreases with the inlet air temperature increasing. It was also observed that the optimal airflow is 0012 (kg/s) and will have the highest efficiency per 8.8 m length.

Effect of Tin Oxide/Black Paint Coating on Absorber Plate Temperature for Improved Solar Still Production: A Controlled Indoor and Outdoor Investigation

Abstract: The ever-increasing water stress and availability of fresh drinking water are becoming a major challenge in rural and urban communities. The current high-end and large-scale technologies are becoming way more expensive and not friendly to the environment. In this regard, solar still is becoming a prominent and promising future technology due to its environment-friendly nature, less maintenance and operational costs, and simple design. The technological challenge regarding solar still is its low distillate yield. In this study, an attempt has been made to investigate the effect of tin oxide (SnO2) on the absorption surface of solar still towards improvement in sunlight absorption, which would lead to high distillate production rates. Various concentrations of SnO2, i.e., 0.5wt%, 1 wt%, 3 wt%, 5 wt%, 7 wt%, 10 wt%, 15 wt%, and 20 wt%, have been mixed in black and applied on the absorber plate to further optimize the suitable concentration. The experiments have been performed in both indoor (simulated) and outdoor conditions. An increase in surface temperature of absorber plate has been observed with increasing concentration of SnO2 under both the indoor and outdoor conditions, which is due to high solar spectrum absorption properties of SnO2 in the ultraviolet (UV) and near to far-infrared (IR) regions. The highest surface temperature of 101.61°C has been observed for specimens containing 15 wt% SnO2 in black paint under indoor conditions at 1000W/m2 irradiation levels, which is 53.67% higher compared to bare aluminum plate and 16.91% higher compared to only black paint coated aluminum plate. On the other hand, the maximum temperature of 74.96°C has been recorded for the identical specimens containing 15 wt% SnO2 under uncontrolled outdoor conditions. The recorded temperature is 47.96% higher than the bare aluminum plate and 14.88% higher than the black paint-coated aluminum plate. The difference of maximum temperatures under indoor and outdoor conditions is due to uncontrolled outdoor conditions and convective losses.

Maximum Power Point Tracking of PV Grids Using Deep Learning

Abstract: In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This deep learning model aims to maximise the output power from the solar grids when the panels are connected with the boost converter under different variable load conditions. BPNN-DL enables the prediction of reference voltage at different weather conditions for severing the various output power that ensures maximum power with stable output voltage. The proposed BPNN-DL is tested under different conditions to estimate the robustness of the modules under internal/external interferences. The results of the simulation show that the proposed method achieves maximum output power from each panel compared with existing methods in terms of regression analysis on training, testing, and validation.

Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

Abstract: The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.

Deep Learning Model on Energy Management in Grid-Connected Solar Systems

Abstract: Because of increased electricity consumption and the inherent limitations of fossil fuel ability to replenish themselves in the future, a shift to renewable energy sources is unavoidable. Although renewable energy sources are afflicted by intermittency, this problem can be alleviated by combining them with other sources of electricity. As a result of the above situation, the secondary source will take over if the primary source is unable to match the load demand. In this paper, we develop a hybrid renewable source that is connected with grids in an optimal way for the prediction of energy using an energy management system (EMS). The study is aimed at optimal handling of energy production, grid interaction, and the storage system, all of which must be accomplished simultaneously. The current state information from the battery, as well as control objectives, is used in this study to design control actions that maximise the amount of electricity injected into the grid. During the prediction window, it is assumed that the control inputs received at the start of the window will remain consistent throughout the duration of the window. The results of RMSE show errors lesser than 0.3% that shows improved rate of accuracy using EMS.

Thermal Performance Study of Solar Air Dryers for Cashew Kernel: A Comparative Analysis and Modelling Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Abstract: In this study, a new procedure for drying of cashew kernels is proposed. The new methodology involves the use of solar air dryers to remove the dark sticky coating called testa on the surface of the cashew kernel to get the final product. There are several challenges in the new procedures, especially with respect to the moisture level to be retained in the cashew kernel post drying for facilitating the optimal peeling activity. In this regard, thermal performance is carried out through a set of experimental and computational trials. The experiments have revealed that the natural and forced convection solar dryers with drying speeds of 1.0 kg/h and 1.66 kg/h, respectively, showed drying efficiencies of 51.7% and 50.9%, which is within the permissible acceptable limit, thus ascertaining a moisture reduction of 40 to 42% in each of case and keeping the moisture content within the band of approximately 5%, required for effective peeling of the testa from the cashew kernel to obtain the final product. This new method has resulted in batch drying of cashew kernels of up to 30 kg capacity in a time span of 360 minutes of solar irradiation with an average consumption of 255 kJ. The results of the experiments are also validated from the artificial neural network (ANN) and response surface methodology (RSM) modelling, resulting in better prediction and optimization of the performance parameters. The error between the actual and experimental values is well within the permissible limit of +/-5%, thereby correlating the experimental and statistical values for deriving an ANN model for predicting the results for different time duration and solar irradiation. In this way, the thermal analysis of the solar dryers for drying cashew kernels has resulted in findings, which shall be utilized to improve the overall performance efficiency and the methodology adopted for maximum yield.

A Comparative Thermal Performance Assessment of Various Solar Collectors for Domestic Water Heating

Abstract: Growing population, depleting fossil fuels, economic expansions, and energy intensive life style demand are resulting in higher energy prices. We use energy as of heat and electricity, which can directly be obtained from sun using thermal collectors and solar cells. Solar thermal systems are gaining attention for water and space heating applications due to green aspects of solar energy. A solar thermal collector is a vital part of solar thermal energy system to absorb radiant energy from the sun. In this study, a solar water heating (SWH) system has been designed and simulated in the TRNSYS ® software using thermal and chemical properties of heat transfer fluids using REFPROP for dwellings located on ±31° latitudes (+31 Lahore in Pakistan and -31° Perth in Australia). We present an efficiency parametric optimization-based model for water and space heating. Simulation results for four types of solar thermal collectors are presented, and performance is analyzed on the basis of output temperature ( T out ), solar fraction ( f ), and collector efficiency ( η ). This study evaluates the comparative performance of evacuated tube collector (ETC), flat-plate collector (FPC), compound parabolic concentrator (CPC), and thermosiphon-driven systems. Our findings conclude the evacuated glass tube collector achieves the highest solar fraction, i.e., 50% of demand coverage during August in Pakistan and February in Australia, with an overall average of 43% annually.

Design and Implementation of a Floating PV Model to Analyse the Power Generation

Abstract: The floating photovoltaic (FPV) system is a revolutionary power production technology that has gotten a lot of interest because of its many benefits. Aside from generating electricity, the technology can also prevent the evaporation of water. The electrical and mechanical structures of FPV power stations must be studied to develop them. Much research on FPV technologies has already been undertaken, and these systems have been evaluated from many perspectives. Many problems, including environmental degradation and electricity generation, fertile soils, and water management, are currently limiting societal growth. Floating photovoltaic (PV) devices save a great of land and water resources and have a greater energy conversion efficiency than standard ground power systems. A performance investigation of photovoltaic (PV) installations set on a moving platform is carried out. The paper presents and discusses various design alternatives for boosting the profitability and efficiency of floating photovoltaic (FPV) systems. Especially, FPV systems that take advantage of increasing capabilities like monitoring, conditioning, and attention were included. Although researchers have agreed on the benefits of floating systems, there has been little in-depth research on the parameters of floating photovoltaic systems. The results of this research tests were performed, and these reveal that the beneficial monitoring and conditioning impacts result in a significant gain in performance. The effects of using flat reflections on improvements are also investigated. As a result, this research examines the evolution of photovoltaic systems, then investigates the power generation capacity of floating photovoltaic systems, and then examines the benefits and possibilities of floating PV systems in depth. The concept of developing an integrated air storage system using a floating building on waters is discussed.

Ultrasensitive and Selective Electrochemical Detection of Dopamine Based on CuO/PVA Nanocomposite-Modified GC Electrode

Abstract: At present, the determination of dopamine (DA) is enormously necessary for the human body. Since then, it has played a crucial role in the brain that affects mood, sleep, memory, learning, and concentration. Dopamine insufficiency is a threat to human health. Dopamine recognition is important to avoid this problem. Copper oxide (CuO) nanoparticles are one of the potentials which can be used in the detection of dopamine level in the sample. In this work, CuO was synthesized by a simple chemical precipitation technique and modified by polyvinyl alcohol (PVA) as a capping agent. The nanomaterials manufactured are used for the detection of dopamine in 0.1 M PBS medium at room temperature. The CuO/PVA-modified electrode shows better electrocatalytic activity than CuO/GCE (glassy carbon electrode). The constructed dopamine biosensor of copper oxide-PVA nanocomposites also has extraordinary selectivity, stability, sensitivity (183.12 μA mM-1 cm-2), and a minimum level detection limit of 0.017 μM, is inexpensive, and has minimal effort and rapid detection of dopamine.

System Performance and Economic Analysis of a Phase Change Material Based Cold Energy Storage Container for Cold Chain Transportation

Abstract: We studied a shipping container integrated with phase change material (PCM) based thermal energy storage (TES) units for cold chain transportation applications. A 40 ft container was used, which was installed with ten plate-like TES units containing PCM and a charging loop. An appropriate PCM was selected for meeting the requirement of the transportation of fresh vegetables (7-12°C). The charging loop was linked to a separate charging facility via quick coupling valves. The discharging performance of the container under dynamic conditions was investigated. The COP of the system was estimated to be 1.73. Economic analyses showed that energy and operation costs of the PCM-based container were, respectively, 71.3% and 85.6% lower than the same container but powered by a diesel engine (called reefer container). The results also showed that the PCM-based container was able to maintain not only the temperature range (7-12°C) but also the humidity range (85-95%), leading to better quality and longer shelf-life of the goods.

Design of Boosted Multilevel DC-DC Converter for Solar Photovoltaic System

Abstract: Integration of renewable energy sources to the grid-connected system has influenced scholarly research in recent times to evolve solutions for power electronic conversion. Particularly, solar photovoltaic (SPV), being a resource available throughout the year, demands needful research to meet the demand for industrial applications. To facilitate SPV, multilevel inverters (MLIs) and cascaded H-bridge inverters (CHBIs) are proposed in the literature to meet the power requirement. However, these circuits suffer from efficiency loss, economic aspects of DC sources usage, and switching losses. Hence, in this research, a new power converter topology is projected to improve the overall efficiency of SPV systems. Further, a three-level approach involving (i) SPV Panel-Temperature Reduction (SPV-PTR) Setup, (ii) Boost Multilevel Direct Current Link Converter (BMLDCLC), and (iii) use of effective snubber modules (SM) are effectively handled to promote the industry readiness of the proposed system. From a detailed system investigation, it is seen that the proposed arrangement has minimized the power loss to ensure better quality in output. Furthermore, the software-based results and hardware setup of the planned comprehensive converter have shown promising results in terms of (i) reduced voltage stress, (ii) reduced total harmonic distortion (THD) without filter component, and (iii) reduced power loss. It is observed that the experimental setup has reported a 12.9% of excess heat removal, 5% decrease in harmonics, and 33% switch reduction than the existing MLI schemes. In addition, the proposed setup is suggested to apply for industrial purposes indicate its efficacy to be a solution in real time.

A Novel Topology to Improve PV Module Efficiency under Partial Shading Conditions

Abstract: In this paper, we investigate the power degradation in a novel photovoltaic (PV) cell reconfiguration named KenDoKu (KDK) topology under different shading patterns. We analyze how a modification in the linkage between the PV cells within a shaded PV module can affect its effectiveness. The proposed approach relocates the physical position of the PV cells within the PV module without any change in electrical connections and redistributes the shading effects over the PV module for the improvement of the power generation. To achieve this purpose, modeling and simulation are performed for a set of various shading patterns such as homogeneous, sectional, and scattered shadings. The simulation model used is a combination of two-diode model and Bishop’s model. This model is applied to a PV module and is implemented in LTSpice software to quantify the impact of shading on P-V characteristics. The performance of the KDK topology is compared to other optimized configurations such as total-cross-tied (TCT), bridge link-total cross tied (BL-TCT), honey comb-bridge link (HC-BL), series parallel-total cross tied (SP-TCT) and existing odd-even (OE) and Latin square (LS) schemes of interconnection. The effectiveness of the KDK approach is evaluated in terms of the characteristics of P-V curves, global maximum power (GMP), mismatch power loss MPL (%), fill factor FF (%), and performance ratio PR (%). The simulated results revealed that the KDK configuration scheme performs better in terms of generating maximum power under the considered partial shading conditions. The proposed approach reduces the maximum power losses (MPL) and improves the fill factor (FF) with respect to OE and LS configurations in the most of the cases. Moreover, experimental verification is also carried out. The obtained results show that the KDK configuration outperforms the other analyzed PV cell rearrangement in terms of increased power.

An Enhanced P&O MPPT Algorithm for PV Systems with Fast Dynamic and Steady-State Response under Real Irradiance and Temperature Conditions

Abstract: This paper presents an enhanced perturb and observe (P&O) method for reconciling the trade-off problem between the dynamic response and steady-state oscillations in maximum power point tracking (MPPT). The constraint of having to sacrifice either the dynamic response or the steady-state oscillations has been solved. The method uses the relationship between the open-circuit voltage and maximum power voltage from the fractional open-circuit voltage (FOCV) MPPT method to establish a valid, reduced, and confined search space within which an enhanced P&O via dynamic adaptive step size terminates the search for the maximum power point. The feasibility of the proposed method has been validated by comparing its performance with the conventional P&O algorithm. It was noted that the proposed method increased the operational efficiency of the PV module to 99.89%, reduced the tracking time to 1.8 ms, and preserved the good steady-state response with a power attenuation of less than 0.10 W or relative 0.16% under MATLAB environment. An experimental setup was used to collect real irradiance and temperature data which was used in real-time simulations. The enhanced P&O method was able to resist abrupt changes in irradiance and temperature as it effectively and efficiently followed the maximum power point (MPP). Finally, to appreciate the supremacy of the proposed method, it was compared to nineteen different MPPT methods from literature. The comparison showed that the enhanced P&O MPPT method is highly efficient and effective for MPPT in photovoltaic (PV) generation systems.

Fabrication and Performance Evaluation of Solar Tunnel Dryer for Ginger Drying

Abstract: A series of tests were conducted to investigate the performance of a solar tunnel dryer for drying ginger. To supply hot air to the dryer, two axial flow fans with a power rating of 28 W, a supply voltage of 220 V, and a 50 W photovoltaics (PV) module were employed. By dividing the 8.5-meter-long solar tunnel dryer into four equal portions every thirty minutes, solar radiation, dry air temperature, ambient temperature, relative humidity, and air velocity were measured at five solar tunnel dryer stations. The hot air temperature at the collector output grew from 34°C to 65.5°C for an 8-hour operation in the no-load condition when the solar radiation was changed between 540 and 820 W/m2. At 9:00 a.m., the average maximum temperature was 30°C. During the loading operation, the temperature was 77°C at 1:00 p.m. The moisture content of sliced ginger was reduced from 90.4 to 11.8% on a wet basis using the solar tunnel dryer. With a solar collector area of 6 m2, open sun drying takes 40 hours to achieve the same wet basis condition. A total of eight experiments were carried out, both with and without loads. The dry air temperature at the collector outlet ranged from 34.0 to 65.5 °C. As the drying efficiency, collector area, and time savings improve, the drying time decreases. The ginger is kept in a controlled area, resulting in high-quality dried ginger. The solar tunnel dryer showed a net saving in drying time of 40% over open sun drying.

Deep Learning-Based Smart Hybrid Solar Water Heater Erection Model to Extract Maximum Energy

Abstract: Currently, we are trying to get electricity in alternative ways. Many solar powered water heaters have come up to use water heaters. However, these tools are not 100 percent fully effective. The device we have manufactured is an automatic device that runs in the direction of sunlight. The device runs automatically in the morning facing east and in the evening facing west. In this instrument, the defective one-inch tube lamp and the three-quarter-inch tube lamp are put together and connected in series. In this paper, a smart deep learning model was proposed to improve the performance of the solar water heater. The gap between the tube lights is filled with methane gas, and the tube inside is filled with water. The water thus filled is heated by sunlight. Methane gas acts as a fast conductor of solar heat. An electronic control device is placed to determine the temperature of the hot water and to expel the hot water. This device can heat at least 10 liters of water in 15 minutes. Increasing the number of incandescent tube lights can heat up a large amount of water when this device is set up, or it can be designed by replacing tube lights with a series of large glass tubes using the same technology. This tool can be manufactured at low cost so that people from all walks of life can use it.

Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network

Abstract: In the recent decade, it is possible to use electric vehicles in a safe, cost-effective, and environmentally friendly manner, but only if accurate and trustworthy state parameter predictions are produced prior to their disposal. The state of health (SOH) of the lithium-ion batteries (LIBs) must be precisely forecasted in order to ensure that the LIB can operate safely. The inability of physical SOH estimators to cope with the dynamic character of SOH when operating in a highly nonlinear environment is a common limitation when operating in nonlinear environments. Traditional SOH estimation techniques have demonstrated that they have limits that can be overcome by data-driven methods. TCN, a new machine learning technique, combines the advantages of residual neural networks (ResNet) with the computing efficiency of neural networks to produce a technique that is both efficient and effective. The results of rgw simulation show that the proposed method has reduced placement cost, and also a TCN can accurately estimate the SOH of a LIB with an MSE error of less than 1% over the LIB lifetime. The performance of an electric car battery, which are numerous and diverse, can be anticipated more precisely using this approach.