Renewable Energy Applications Research Articles

Fabrication and characterization of Nickel-coated 3D printed electrodes for enhanced oxygen evolution reaction in acidic media at various temperatures

Additive manufacturing (AM), often referred to as 3D printing, is a new technology that allows objects to be created by adding layer by layer, as opposed to traditional manufacturing processes. Therefore, the rapid and cost-effective AM method can facilitate research and development efforts in the field of electrochemistry. In this study, oxygen evolution reaction (OER) electrodes are fabricated by a 3D printing technique utilizing carbon-based (PP filament) and graphene-based PLA filaments (BM filament). Electrodes are then coated with Ni by electrochemical deposition method at different temperatures. Electrochemical analysis of Ni coated PP and BM electrodes is made in acidic environments (0.5 M H2SO4) using different characterization techniques. The results indicate that PP/Ni-50 and BM/Ni-50 electrodes have the highest catalytic activity for OER in acidic media. PP/Ni-50 and BM/Ni-50 electrodes exhibited a superior double layer (Cdl) value than other Ni coated 3D printed electrode samples, and their active surface areas are calculated as 0.201 cm2 and 0.286 cm2, respectively. A larger active surface area leads to an increase in the number of active sites, causing the OER process to be more effective. Consequently, increasing the active surface area of electrode samples is crucial for improving the efficiency and effectiveness of the Oxygen Evolution Reaction (OER) process, especially in the context of renewable energy and sustainable technology applications.

The role of epoxidation process on improving the oxidative, thermal stability, and tribological performance of mustard oil nano lubricants

This study investigated the potential of inedible mustard oil (MO) (Brassica juncea) as a suitable bio-lubricant for enhanced thermal, oxidative stability, and tribological performance. To improve the MO's oxidative stability, it was subjected to a chemical epoxidation reaction, forming epoxidized mustard oil (EMO). The MO and EMO were evaluated for viscosity, functional groups, wettability, oxidative stability, thermal stability, friction, and wear performance. The epoxidation process resulted in nearly doubling the oxidative stability, as well as improving the thermal stability of the MO. Results showed that the EMO exhibited better lubricating properties than the MO, including lower friction and wear. To further improve the lubrication performance of the EMO, solid lubricant additives (SLAs) were incorporated in various concentrations. These SLAs were graphene nanoplatelets (GNP) and hexagonal boron nitride (hBN). The GNP and hBN nano lubricants reduced friction by 31% and 13% compared to the EMO. The same nano lubricants also lowered wear by 73% and 51% relative to the EMO. The underlying mechanisms behind the improvement in the friction and wear performance were discussed. The EMO can be considered as a suitable base stock for renewable and sustainable energy applications.

Analysis of coupling characteristics of clean heating systems based on complementary solar, geothermal, and wind energy

In order to overcome the limitations of traditional clean energy utilization methods, this paper proposed an innovative technical solution for a combined heating system that cleverly integrated solar, wind, and geothermal energy to achieve complementarity and synergized among them, thereby ensuring stable and efficient energy utilization. First, a comprehensive mathematical model was developed for the entire heating system, encompassing solar thermal subsystem, geothermal subsystem, wind power generation subsystem, and a second-stage reheating subsystem. Subsequently, Ebsilon simulation software was utilized to cleverly couple these subsystems together, with corresponding boundary conditions set to ensure the overall efficiency and stability of the system. Based on meteorological data and geothermal resource parameters from a typical heating season in Zhengzhou, Henan Province, China, this paper thoroughly analyzed the variations in key performance indicators such as the photothermal conversion efficiency of solar thermal subsystem and the heating capacity of geothermal subsystem. This provided valuable insight for optimizing the design of heating system. The results indicated that during the daylight hours of the heating season, both the photothermal conversion efficiency and the heat supply from the solar thermal subsystem exhibited an increasing trend as solar radiation increased. Among them, the photothermal conversion efficiency peaked at 76.013%, while the maximum heat supply output reached 40.311 kW. When solar direct radiation was relatively weak, the system primarily relied on the heat release process of the thermal storage tank to maintain heating, with a minimum heat supply of 27.268 kW. During nighttime hours of the heating season, the geothermal subsystem dominated the heating process, with a maximum heat supply of 125.556 kW. Additionally, for every 5 °C increased in geothermal water temperature, the heat supply from the geothermal subsystem increased by an average of 6.553 kW, demonstrating excellent heating response performance. Therefore, the integrated clean heating system that combines solar, geothermal, and wind energy not only significantly improves the utilization efficiency of clean energy but also enhances the heating stability of the integrated clean energy coupling system to a certain extent. The clean heating technical solution proposed in the paper had a theoretical total heating capacity of 19 680 kW during the heating season. When converted, this equates to a substitution of 6.9 tons of standard coal, resulting in a reduction of carbon dioxide emissions by 17.94 tons. This demonstrates the considerable cleanliness and environmental benefits of the proposed heating system. This study provides a valuable reference for the engineering application of renewable energy in the field of clean heating.

New Coupled-Inductor High-Gain DC/DC Converter With Bipolar Outputs

This paper presents a new bipolar step-up DC/DC converter with the capability of decreasing the input current ripple for renewable energy applications. Two power switches with the simultaneous operation are used in the proposed converter. The suggested topology can provide positive and negative output voltages in a quadratic form with the same and different voltage levels and common ground. The bipolar outputs of the presented topology can be varied independently using the duty cycle and tuning the turn ratio of the coupled inductor (CI). Moreover, the maximum voltage stress across the switching components is much lower than the output voltage. This topology operates under soft-switching conditions for the second switch and all diodes. The steady-state and the main operating equations of the converter are also derived. Finally, a 210 W prototype with 110 V, -110 V, and 220 V output voltages is implemented to justify the theoretical analysis and performance operation of the proposed converter.

Synergizing hybrid renewable energy systems and sustainable agriculture for rural development in Nigeria

This research explores the viability and importance of implementing Hybrid Renewable Energy Systems (HRES) in Nigeria's agriculture sector. Despite progress, most rural regions still struggle with unreliable electricity. HRES provides a way to power agricultural endeavors while promoting sustainability, economic viability, and ecological conservation. A case study involving renewable energy applications for powering a typical Nigerian farm facility is also presented. Based on the review's findings, while 53.1 % of the journals analyzed were primarily concerned with community load, the agricultural sector received much less coverage, especially in rural areas. For the case study, solar PV dominates the HRES (98.3 %) electricity production, with biogas and wind turbines contributing the rest. The project costs $1,782,602 over 25 years, with competitive energy costs and a discounted payback time of 7.22 years. Environmentally, carbon dioxide emissions are negligible (16 kg/yr), and excess electricity production offers income potential for the farm facility. Overall, HRES is presented as an environmentally beneficial and cost-effective way to change rural farm operations, emphasizing the critical need to integrate energy and agriculture. Still, challenges such as inaccurate data, inadequate infrastructure, and inconsistent policy dynamics remain. In addition, the work highlights an area where more study is needed, namely the use of MCDM analysis for selecting the optimal energy system based on multiple criteria. Utilizing HRES in agriculture is consistent with the SDGs and will increase agricultural productivity, self-sufficiency, and environmental friendliness in Nigeria.

Renewable Energy Application and Carbon Redution Study on the Campus Teaching Building

This work conducts carbon simulation and accounting of one public teaching building in South China. The calculation results show that the carbon emission of public teaching building is 2586.5 Tons of CO2 per year. Based on the simulation, Solar Photovoltaic (PV) system can achieve carbon reduction of 595.5 TCO2 per year, the total power of the PV system reaches 1201kWp, the average power generation of the system is 1025MWh/year, and the system efficiency is 68.1%. The PV system can reduce the carbon emissions of public teaching buildings by 23%. The utilization rate of the roof space of public teaching buildings reaches 64%. Based on the simulation results to analyse the models of colleges and universities participating in the carbon trading market, three models are proposed: unified integration into the national carbon trading market system, incomplete integration into the carbon trading market, and the establishment of a university carbon trading market

Extendable high gain low current/high pulse modified quadratic–SEPIC converter for water treatment applications

Substantial attention has been drawn over the past few years by high step-up dc-dc converters owing to their applications in a wide range. Apart from renewable energy applications, high voltage/ high pulse converters are efficiently used in water treatment applications. The converter suggested a combination of Quadratic and SEPIC converters with a diode-capacitor cell. This topology generates high-voltage repetitive pulses with a single semiconductor switch and reduced component count. The stress across the components is less than the high-gain converters reported in the literature. The topology has an extendable feature by increasing the number of diode-capacitor cells without affecting the stress. The superiority of the high pulse generating topology is validated with a similar converter in the literature. This paper discusses the nL5 simulator results for the proposed rated topology required for water treatment. A scaled-down 50 W prototype is tested for various input voltages to generate high voltage pulse, and the analytical study is validated.

Using MWCNT and titanium oxide as boosters in passive heat transfer methods for clean and renewable energy applications

Passive heat transfer (PHT) methods provide an energy-efficient and environmentally friendly approach for transferring heat between systems, making them a promising solution for renewable and clean energy utilization across various applications. Changing the performance of heat transfer systems can be achieved by manipulating fluid flows. In this study, an investigation was conducted on the use of four blocks with different positions and sizes as obstacles embedded within a square enclosure, which affected the heat transfer through natural convection. Additionally, the study explored the effectiveness of a hybrid nanofluid created by injecting two types of nanoparticles, titanium oxide (TiO2) and Multi-walled Carbon Nanotube (MWCNT), into the cooling fluid. Computational fluid dynamics (CFD) simulations were employed to examine the impact of the blocks, nanoparticle concentration, and Rayleigh number on heat transfer parameters. The findings revealed that the addition of a 6 % volume fraction of titanium oxide and MWCNT to the base fluid resulted in a notable increase of over 5 % but less than 30 % in the average Nusselt number. In contrast to expectations, embedding obstacles drooped off average Nusselt number by 9 to 30 % in Rayleigh numbers of 10,000 and 100,000, and had no effect in Rayleigh numbers of 100 and 1000.

A Theoretical Insight into the Physical Characteristics of Double Perovskite Rb2TlInBr6 for Renewable Energy Applications

AbstractMetal halide perovskites have gained prominence in optoelectronics recently, thanks to their unique optical and electrical properties, along with their adaptable morphologies. The current work reports the electronic, structural, mechanical, optical, and thermoelectric traits of Rb2TlInBr6 for the very first time. The confirmation of thermodynamic stability is evidenced by the negative value of the formation energy, while structural stability is established by calculating values for the tolerance factor and octahedral tilting. Elastic constants (Cij) and mechanical attributes are evaluated to assess perovskites’ ability to resist external strains. Electronic band structures computed with GGA and mBJ potentials depict a semiconducting nature containing indirect bandgap of 1.8 and 2.42 eV, respectively. Optical characteristics of Rb2TlInBr6 ensure that it can be used effectively for optoelectronic applications. An insight into the thermoelectric characteristic is assessed through BoltzTraP code. The electrical conductivity and ZT reveal the ability of Rb2TlInBr6 to be utilized in green energy devices.

Bi-objective optimization modeling for biomass supply chain planning

Biomass energy plays an essential role in renewable energy for many reasons, such as reducing the dependence on fossil fuels and lowering greenhouse gas emissions, providing heat, electricity, and biofuels for various applications, and utilizing waste materials for helpful energy products. Besides, it can create employment opportunities and promote rural development, especially in developing countries where biomass resources are abundant and accessible. In the context of renewable energy research and application, this paper aims to develop a multi-objective mixed integer linear programming for designing multiple echelon biomass supply chain networks. The model is formulated to consider the economic costs and environmental impact of biomass distribution from the suppliers to the biomass plants. In this research, the Epsilon constraint method is adopted to generate Pareto fonts, which provides the trade-offs between two objectives. Moreover, sensitivity analysis is implemented to provide decision-makers with information about a network with changed parameters such as demand. Our model allows the decision maker to determine the capacity of warehouses and biomass power plants, inventory levels, type of trucks, etc. The proposed model is verified and evaluated using a practical dataset from Can Tho province, Central Mekong River Delta in Vietnam, generating several benefits for energy security and sustainability. Such a network includes 3 types of power plants, 3 scales of warehouses, 13 potential locations, and 41 suppliers. From the generated solutions, with the proportion of biomass electricity satisfaction varying from 5% to 30%, Hung Phu, O Mon, and Cai Rang industrial parks are the most suitable for power plants.

Solar and Wind Data Recognition: Fourier Regression for Robust Recovery

Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.

Energy Market Prediction and Risk Assessment Based on China's Rural Collective Economy

INTRODUCTION: Energy, as a core element supporting the functioning of modern society, is vital to the development of the rural collective economy. With the upgrading of the agrarian industrial structure and the improvement of rural electrification levels, the energy demand gradually increases. Therefore, for China's rural collective economy, an in-depth study of the forecasting and risk assessment of the energy market has essential theoretical and practical value for scientific planning of resource allocation and improving energy utilization efficiency.
 OBJECTIVES: This study aims to reveal the development trend and key influencing factors through an in-depth analysis of China's rural collective economy's energy market and to make scientific forecasts of the future development of the energy market. At the same time, through risk assessment, it proposes risk prevention and resolution countermeasures of the energy market for the rural collective economy to provide decision support for rural energy security and sustainable development.
 METHODS: This study adopts a comprehensive analysis approach, combining historical data, policy literature analysis, and expert interviews. First, a comprehensive analytical framework is established by combing the development history of the rural collective economy energy market over the past few years. Second, quantitative analysis models and numerical simulations are used to analyze the key factors affecting the energy market. Finally, expert interviews are conducted to obtain the views of experts in related fields on the future development and risks of the energy market to improve the research conclusions further.
 RESULTS: The results of the study show that the energy market of China's rural collective economy will show a trend of gradual growth, but it also faces multiple risk challenges, including market price fluctuations, policy adjustments, and an imbalance between supply and demand. In the future, with the deepening of green energy policies, rural collective economies will pay more attention to the application of clean and renewable energy.
 CONCLUSION: To summarize, this study provides a scientific reference for the energy strategy decision-making of rural collective economies by forecasting and assessing the risk of the energy market based on China's rural collaborative economies. In the future, it is necessary to pay more attention to the improvement of the policy system to promote the development of green energy, as well as the establishment of a sound market regulatory mechanism to reduce the uncertainty of the energy market and provide solid support for the sustainable development of the rural collective economy.

Cascaded H–Bridge Multilevel Inverter: Review of Topologies and Pulse Width Modulation

Multilevel inverters (MLIs) have become more popular for medium-voltage and high-power applications. The cascaded H-bridge multilevel inverter (CHBMLI) is one of the three most popular topologies of MLIs. It was more reliable due to its fewer components per level. The number of possible output voltage levels is more than twice the number of DC sources, the most suitable topology for integration with renewable energy sources, easy to design, and has good performance with modularity. The main disadvantage of CHBMLI is the need for separate DC sources for each H-bridge. However, this can be considered as an advantage to be employed in renewable energy applications. This paper provides a review of CHBMLI topologies and pulse width modulation (PWM) techniques, including fundamental and high switching frequency techniques, such as selective harmonic elimination (SHE), space vector modulation (SVM), nearest level modulation (NLM), and Carrier-Based PWM.

Two Types of Asymmetric Switched-Capacitor Five-Level Single-Phase DC-AC Inverters for Renewable Energy Applications

Two types of asymmetric switched-capacitor five-level single-phase DC-AC inverters are presented based on the clamping half-bridge circuit and the output half-bridge circuit. Furthermore, the switches of the two proposed circuits can be driven by half-bridge gate drivers and can be modularized. Moreover, the detailed analysis of the operation principle, design of clamping capacitor and output filter of these two inverters are presented. Finally, the feasibility and validity of the proposed structures are verified by PSIM-simulated results and experimental results using FPGA as the control kernel, respectively.

Sulfur Vacancy Related Optical Transitions in Graded Alloys of MoxW1‐xS2 Monolayers

AbstractEngineering electronic bandgaps is crucial for applications in information technology, sensing, and renewable energy. Transition metal dichalcogenides (TMDCs) offer a versatile platform for bandgap modulation through alloying, doping, and heterostructure formation. Here, the synthesis of a 2D MoxW1‐xS2 graded alloy is reported, featuring a Mo‐rich center that transitions to W‐rich edges, achieving a tunable bandgap of 1.85 to 1.95 eV when moving from the center to the edge of the flake. Aberration‐corrected high‐angle annular dark‐field scanning transmission electron microscopy showed the presence of sulfur monovacancy, VS, whose concentration varied across the graded MoxW1‐xS2 layer as a function of Mo content with the highest value in the Mo‐rich center region. Optical spectroscopy measurements supported by ab initio calculations reveal a doublet electronic state of VS, which is split due to the spin‐orbit interaction, with energy levels close to the conduction band or deep in the bandgap depending on whether the vacancy is surrounded by W atoms or Mo atoms. This unique electronic configuration of VS in the alloy gave rise to four spin‐allowed optical transitions between the VS levels and the valence bands. The study demonstrates the potential of defect and optical engineering in 2D monolayers for advanced device applications.

Size tuning of Pt nanoparticles on ZrO2: Optimizing catalytic performance for hydrogen evolution and water-splitting reactions - A DFT study

In heterogeneous catalysis, determining the optimal metal particle size is challenging due to the intricate relationship between particle size, electronic structure, and activity. Here, we investigate the size-dependent activity of Pt nanoparticle (PtNP)-based ZrO2 catalyst for hydrogen evolution reaction (HER) and water-splitting reaction (WSR) using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. By analyzing different Pt nanoparticle sizes (Pt4, Pt7, Pt11, P15, Pt19, and Pt25) supported on ZrO2, we determined their optimized geometries, and electronic structures, and calculated the hydrogen adsorption energies (ΔGH) for HER and activation energies barrier (Δ‡G) for WSR. The calculated results demonstrate that Pt11 is the optimal size for HER, exhibiting the lowest ΔGH (−0.066 eV), while Pt15 shows the lowest Δ‡G (0.24 eV) for WSR. The density of states (DOS) shows that the occupied Pt states fill the gap of ZrO2 (3.72 eV), significantly reducing the band gap (Eg) of the PtNP/ZrO2 composites. The most favorable configuration of the PtNP on ZrO2 for HER and WSR is determined to be a three-Pt-layer structure with rooflike edges which corresponds to a nanoparticle size of ∼0.70–1.55 nm for experiments. These findings can be regarded as an effort toward the design and optimization of metal-based oxide catalysts for renewable energy applications.

Effect of bandgap tunability on the physical attributes of potassium-based K2CuBiX6 (X = I, Br, Cl) double perovskites for green technologies

Potassium-based perovskites hold significant potential to revolutionize renewable technology by enabling more cost-effective and sustainable energy devices. The DFT incorporated in the Wein2K interface is used to provide an extensive investigation of the structural, mechanical, electrical, optical, and thermoelectric behavior of double perovskites K2CuBiX6 (X = I, Br, Cl). Structural stability is confirmed through optimization curves, tolerance factor and octahedral tilting. Mechanical properties support the ductile character of K2CuBiX6 (X = I, Br, Cl). Electronic band structures reveal that perovskite K2CuBiI6 initially possesses a bandgap of 0.6 eV. This bandgap is increased to 0.97 eV when (I) atom is replaced with (Br) atom. Furthermore, a prominent shift in the bandgap value occurs when the bromine (Br) atom is replaced with a chlorine (Cl) atom, resulting in a bandgap of 1.3 eV. The Kramer-Kronig equations are used to evaluate the optical characteristic, which shows significant absorption in the visible range for all structures, allowing them to be utilized in optoelectronics. Numerous thermoelectric characteristics are calculated using the Boltzmann semi-classical theory. The perovskites exhibit substantial potential for renewable energy applications, as they demonstrate significant ZT values, along with elevated Seebeck coefficients and electrical conductivities, which enhance their suitability for utilization in clean energy technologies.

Enhancing Microbial Fuel Cell Performance Prediction and IoT Integration Using Machine Learning and Renewable Energy

This study aims to enhance Microbial Fuel Cells (MFCs) reliability for remote environmental monitoring, emphasizing unexplored facets of accurate energy prediction and the integration of renewable energy-powered Internet of Things (IoT) devices. Following comprehensive research, design, and component procurement, an innovative and cost-effective IoT system was developed, leveraging renewable energy from MFCs. Using an Arduino UNO-WiFi, data was collected and showcased on a web page while logged in a Google Firebase database, with an Android app created for intuitive smartphone visualization. Over four months, sensor data was accumulated. An Artificial Intelligence (AI) model, employing Autoregressive Integrated Moving Average (ARIMA), precisely forecasted MFC energy production (RMSE: 0.0119 and 0.0113 for trials 1 and 2). Despite the initial energy production surge, a subsequent decline occurred due to organic matter depletion. This prototype represents an affordable and sustainable solution for cloud-based IoT environmental monitoring with AI-driven energy forecasts, embodying innovation in renewable energy applications and sustainable practices.

Leveraging Silicon Carbide Power Semiconductor Devices for Renewable Energy Applications

Leveraging Silicon Carbide Power Semiconductor Devices for Renewable Energy Applications

An Intelligent Classification Framework for Complex PQDs Using Optimized KS-Transform and Multiple Fusion CNN

Intelligent classification of multiple power quality disturbances (PQDs) is a top priority in pollution control of the power grid. However, the large-scale application of renewable energy introduces lots of nonlinear and impact loads, which makes the PQDs more complex and challenges the effectiveness of conventional detection frameworks. In this paper, a novel framework based on optimized Kaiser window-based S-transform (OKST) and multiple fusion convolutional neural network (MFCNN) is proposed to identify multiple complex PQDs. First, the OKST is used for the time-frequency positioning of PQDs, where an improved control function is proposed to meet different detection requirements of time-frequency. Additionally, the parameters of the control function are adjusted automatically using maximum energy concentration. Then, the MFCNN based on residual networks (ResNets) is further proposed to extract and classify these time-frequency features automatically. In MFCNN, feature information is fused using different convolution kernels at a 2-dimensional level, which can effectively reduce information loss and improve classification performance. The network model is set up using the Pytorch platform, and the dataset containing 28 types of PQDs and 2 types of nonlinearly mixed PQDs is built to test our framework. The result shows that the proposed OKST-MFCNN obtains an average accuracy of 99.38% under the 20 dB noise level, which is more accurate and robust than some advanced PQDs detection frameworks. Moreover, the accuracy of 97.94% is achieved with satisfactory real-time performance in hardware platform experiments, proving its superior identification performance for complex PQDs.