Renewable Energy Production Technologies Research Articles

A Paris aligned 1.5 °C mitigation pathway for the chemical industry based on 100% renewable energy and novel production technologies

With renewables growing at an unprecedented pace and critical carbon budget deadlines approaching, research is shifting to the new frontier of Paris aligned 1.5 °C mitigation pathways for hard-to-abate sectors, including the chemical industry sector. This is a significant challenge with chemical products such as plastics, fertilizers and many more products intertwined with today’s society and with CO2 emissions originating both from energy and from chemical conversions. This paper presents a detailed bottom-up production-based energy and emission calculation to make a 1.5 °C compatible scenario for the chemical industry based on three main measures compared to a business-as-usual scenario. This research found that the holistic combination of demand and supply measures is critical to reduce the different types of emissions in the sector (energy and non-energy process emissions). An overall reduction of 82% of CO2 emissions in 2050 is calculated with a decrease in base chemical production growth (− 21%), 100% renewable energy for heat and electricity (− 50%) and the introduction of novel electrical-based and biomass-based production technologies for ethylene, propylene, methanol and ammonia (− 10%). Critical system developments include slowing chemical demand growth with recycling and circularity best practices, advanced electrification of heat supply and the substantial market penetration of the current most advanced sustainable production technologies for methanol, ammonia, and ethylene based on their reasonable technology maturation trajectories. Here, green methanol and ammonia will require 381 TWh in 2030 and 3232 TWh in 2050 for green hydrogen electrolysis, which will represent 1% and 4% of global electricity demand.

A statistical study for the impact of REMS and nuclear energy on carbon dioxide emissions reductions in G20 countries

Renewable and nuclear energy are seen as clean and eco-friendly substitutes for energy derived from fossil fuels. Which of the two is better, though, is still up for debate. In order to meet their combined global energy objectives and perhaps become carbon neutral or even carbon negative in the near future, a shift to additional renewable energy sources and nuclear energy will be necessary. One such method of improving energy usage through the implementation of a renewable energy management system (REMS) that will help attain a continuous and reliable source of energy, allowing for the proactive and effective management of renewable assets and dependable supply. This article investigates attitudes and opinions about nuclear and renewable energy production technologies by analyzing the dependence of carbon dioxide (CO2) emissions on non-renewable energy sources, renewable energy sources, and nuclear energy in the group of twenty (G20) countries, over the period from 2000 to 2021. This study employs advanced panel data econometric techniques in the presence of outliers in the G20 dataset, utilizing a robust random coefficient regression (RRCR) model via the M-estimation approach to study the impacts of non-renewable, renewable, and nuclear energy use with respect to CO2 emission mitigation in G20 countries. The overall conclusions of the econometric analysis confirm renewable and nuclear energy's importance in reducing CO2 emissions. We can conclude that the robust M-estimate approach is the best estimation technique for the G20 dataset, as compared to the classical estimation methods in the presence of outliers. The results show that for every percentage point of energy consumption that falls into the category of non-renewable energy, CO2 emissions will increase by 2.5737% for all G20 countries. Also, A 1% rise in renewable energy consumption reduces CO2 emissions by 1.0566% for all G20 countries. Similarly, a 1% increase in nuclear energy consumption will reduce CO2 emissions by 5.5703% for all G20 countries. Therefore, we can conclude that energy derived from renewable sources and nuclear energy greatly cuts CO2 emissions in all G20 countries. Total energy consumption, on the other hand, increases CO2 emissions in the long run. According to these findings, it is pivotal for the G20 countries to increase nuclear energy use and employ REMS to reduce the majority of G20 country's reliance on fossil fuels in order to minimize CO2 emissions. These findings are vital for understanding the relationship between these variables and the implementation of new or revised policies and initiatives supporting carbon neutrality at the G20 countries and national levels.

A Numerical Study of the Performance of Point Absorber Wave Energy Converters

Free-floating and submerged wave energy converters (SWECs) are regarded as promising technologies for renewable energy production. These converters rely on a heave-motion buoy to capture the kinetic energy of ocean waves and convert it into electrical energy through power conversion systems. To better understand the impact of various factors on power generation and efficiency, the effects of different buoy shapes (rectangular, circular cylinder, and trapezoidal fin), submergence depths (0, 0.1, and 0.2 m), wave heights (0.04, 0.06, and 0.1 m), and spring stiffness (50 and 100 N/m) were investigated. A 2D numerical wave tank with a buoy was simulated, and the results were validated against experimental data. Information on vorticity, vertical displacement, power absorption, and efficiency are provided. The findings indicate that the buoy shape and wave height significantly affect power absorption and efficiency. Additionally, this study reveals that increasing submergence leads to higher power absorption and lower conversion efficiency.

Improvement of Biogas Production Utilizing a Complex Anaerobic Digestion Model and Gradient-Based Optimization

Anaerobic digestion (AD) is a promising technology for renewable energy production from organic waste. In order to maximize the produced biogas quantity and quality, this paper deals with the optimization of the AD process in a CSTR bioreactor of a full-scale biogas plant. For this purpose, a novel approach was adopted coupling, a highly complex BioModel for AD simulation, and a gradient-based optimization method. In order to improve AD performance, the dosages of various types of biological additives, the dosages of inorganic additives, and the temperature in the bioreactor were optimized in three different scenarios. The best biogas quality was obtained using multi-objective optimization, where the objective function involves the following two conflicting objectives: the maximization of biogas production and minimization of the needed heating energy. The obtained results show that, potentially, the content of CH4 can be increased by 11%, while the contents of H2, H2S, and NH3 can be reduced by 30%, 20%, and 81% when comparing the simulation results with the experimental data. The obtained results confirm the usefulness of the proposed approach, which can easily be adapted or upgraded for other bioreactor types.

Overcoming Defect Limitations in Photocatalysis: Boron‐Incorporation Engineered Crystalline Red Phosphorus for Enhanced Hydrogen Production

AbstractPhotocatalytic hydrogen evolution (PHE) from water splitting is a promising technology for clean and renewable energy production. Elemental crystalline red phosphorus (CRP) is purposefully designed and developed for PHE reaction. However, the photocatalytic activity of CRP is limited by its intrinsic P vacancy (VP) defects, which lead to detrimental charge trapping at deep states and hence its severe recombination. To address this issue, a boron (B) incorporated CRP (B‐CRP) photocatalyst is tailored, synthesized via a simple and mild boric acid‐assisted hydrothermal strategy. The incorporation of B effectively fills the VP defects, reducing deep trap states (DTS) and introducing beneficial shallow trap states (STS) within the band structure of CRP. This defect engineering approach leads to enhanced photocatalytic activity, with B‐CRP achieving a PHE rate of 1392 µmol g−1 h−1, significantly outperforming most reported elemental photocatalysts in the literature. Density functional theory (DFT) simulations and ultrafast spectroscopy support the constructive role of B‐dopant‐induced STS in prolonging active charge carrier lifetimes, promoting more efficient photocatalytic reactions. The findings not only demonstrate the effectiveness of B‐CRP as a photocatalyst but also highlight the usefulness of dopant‐induced STS in advancing PHE technologies.

Current status on utilizing a life cycle system perspective to evaluate renewable energy production systems for achieving UN SDGs

Increased awareness of environmental issues and climate change leads to a proportional increase in research and development of “greener” technologies pertaining to renewable energy. In order to avoid simply creating more issues in the process of solving an outstanding problem, the implementation of proposed alternatives has to be studied methodically and holistically from a cradle-to-grave perspective. Currently, the United Nations’ Sustainable Development Goals provide guidelines for country-level analysis but many of the indicators may not be applicable for technology-, products-, services- and system-level evaluation. Additionally, there is a lack of comprehensive and easily comparable sustainability assessments that can be applied across the different Sustainable Development Goals as well as a paucity of relevant targets for renewable energy technologies. Life cycle sustainability assessment, an extension to the widely applied life cycle assessment, is presented herein as the frame of reference to objectively provide insight into the potential impacts of prospective production systems, such that the Sustainable Development Goals can be met in a truly sustainable manner. An overview of current literature (>80 studies) on research into renewable energy production technologies in the areas of wind, solar and biomass is provided, with possible areas of life cycle sustainability assessment utilization highlighted and applied comprehensively in relation to the relevant Sustainable Development Goals. It is thus demonstrated that a Life Cycle Perspective in the form of Life Cycle Sustainability Assessments can be utilized to evaluate renewable energy technologies in a manner that truly meets the United Nations’ Sustainable Development Goals, and the relevant methodology applied to products and services as well.

Renewable Energy Sources constrained Generation Scheduling of Thermal Units in a Regulated Power Industry

Renewable energy production technologies use resources directly from the environment to generate electric power. These energy sources such as sunshine, wind, tides and biomass are some of the more popular gifts from the nature. The renewable energy resources emit nothing into atmosphere or never produce green house gas or pollute the environment at all. Even if the renewable energy sources are safe to the atmosphere, these resources meet out smaller percent of power requirement internationally This paper a population based optimization algorithm of Corona Virus Herd Immunity Optimizer (CVHIO) is proposed to solve the renewable energy constrained generation scheduling problem. The objective function of the problem is to minimize the total operating cost of hybrid system with satisfying standard operating constrains of thermal, wind and solar power plants. Initially the variables of the problem are effectively optimized using CVHIO algorithm. . Numerical example with IEE 39 bus test system (ten thermal twenty four hour) with an equivalent Solar and Wind generators are considered to prove the performance of CVHIO approach.

Optimal Allocation of Storage Capacity in Distribution Network for Renewable Energy Expansion

Today the integration of renewable energy production technologies into power systems brings a new challenge in terms of optimal usage of renewable. It is known that due to the discontinuous nature of renewable energy, the challenge can be associated with the presence of unwanted voltage fluctuations and power losses in power grids. This study performs optimization of the calculation of hosting capacity to determine the maximum amount of renewable energy that can be further expanded. This can be possible by energy storage deployment on the same power grids. The battery storages are expected to have power injection and absorption associated with discharging and charging of energy storages, respectively, to solve voltage fluctuations and power disturbances. Thus, to enhance the probability of distributed generation penetration, it is necessary to have optimal allocation and size during the renewable energy penetration in low-voltage distribution networks. Global optimization as a genetic algorithm (GA) method is used during hosting capacity assessment. The results show that using GA-based optimization, total cost and loss can be reduced by 38.8 and 73%, respectively. The practical results are verified by the IEEE-33 bus system.

Regulating Lewis Acid‐Base Interactions to Enhance Stability of Tin Oxide for High‐Performance Perovskite Solar Cells

AbstractPerovskite solar cells are an attractive technology for renewable energy production. However, stability issues with the electron transport layer (ETL), particularly the colloidal tin oxide (SnO2) solution, can impact cell efficiency. In this study, a novel acidization treatment is introduced to reactivate long‐time stored SnO2 solutions, which previously led to low‐efficiency perovskite solar cells. The acidization treatment results in enhanced conductivity of the SnO2 layer, improved perovskite film quality, and ultimately increased efficiency. These findings show that a 1‐month stored SnO2 solution treated with acetic acid produces a device with a photoelectric conversion efficiency (PCE) of 20.9%, compared to 13.5% efficiency without treatment. With the addition of PEAI, the champion efficiency of the acetic acid‐treated device is 22.3%. This study provides a simple and effective engineering approach to fabricating high‐performance and stable ETLs for perovskite solar cells.

Bioelectrochemical Systems (BES) for Biomethane Production—Review

Bioelectrochemical systems (BESs) have great potential in renewable energy production technologies. BES can generate electricity via Microbial Fuel Cell (MFC) or use electric current to synthesize valuable commodities in Microbial Electrolysis Cells (MECs). Various reactor configurations and operational protocols are increasing rapidly, although industrial-scale operation still faces difficulties. This article reviews the recent BES related to literature, with special attention to electrosynthesis and the most promising reactor configurations. We also attempted to clarify the numerous definitions proposed for BESs. The main components of BES are highlighted. Although the comparison of the various fermentation systems is, we collected useful and generally applicable operational parameters to be used for comparative studies. A brief overview links the appropriate microbes to the optimal reactor design.

Evaluation of artificial neural network models for predictive monitoring of biogas production from cassava wastewater: A training algorithms approach

Anaerobic digestion (AD) is an established technology for resource recovery and renewable energy production. However, its performance is dictated by diverse variables including operating parameters and system design. Then, the development of modelling and optimization strategies, such as the application of machine learning (ML), to predict/maintain its performance have received significant attention. In this study, ML integrated based models for predicting cumulative biogas from cassava wastewater AD were developed using artificial neural networks (ANN). Biogas generation was investigated for 21 days with and without the addition of calcium particles from milled calcined chicken waste as a low-cost substitute for buffering. Digestion time, pH, and calcium eggshell-based concentration were selected as input features for biogas prediction through the implementation of the ANN approach. Moreover, seven different training algorithms were used to train the model, and a genetic algorithm (GA) was also implemented to optimize the ANN architecture, resulting in faster learning and higher prediction performance. Given the data set, the Levenberg-Marquardt algorithm with a hyperbolic tangent as a transfer function to the hidden and output layers was the most efficient model in predicting the biogas produced with an R-value of 0.9999.

Design of solar power plant and real-time voltage quality monitoring system using LabVIEW

This research presents the design of a solar power generation system in combination with energy storage unit. The objective of this research is to apply renewable energy production technology to reduce the use of external energy. this research was to design a solar power generation system for beef cattle breeding group, Na Yom sub-district, Mueang district, Phetchabun province. The solar power generation system is designed to be installed on the roof. In addition, this research study and development of innovative prototypes for real-time solar voltage quality monitoring system using the LabVIEW program. The study results showed that this solar power generation system in combination with the energy storage unit can supply the load covering the energy consumption of the beef cowshed, which can reduce the use of external electricity by 23.10% and the solar voltage quality monitoring system can display the results for the monitoring of the solar power generation system in conjunction with the real-time voltage quality. In addition to the objectives of this solar power generation system can be developed to further the innovation of renewable energy generation. If a group of farmers has built a widespread power generation system, including electricity generation from other types of renewable energy, it can be developed into a micro grid system for energy security

Photoelectrochemical Degradation of Organic Pollutants Coupled with Molecular Hydrogen Generation Using Bi2O3/TiO2 Nanoparticle Arrays

The growing world population is closely associated with the increased demand of safe drinking water and sustainable energy production. This drives the focus of the scientific community to work toward water remediation and clean energy generation. The combination of photoelectrooxidation of pollutants at the anode with simultaneous hydrogen gas production at the cathode is a smart strategy to address these problems. Herein, we have designed a bifunctional photoelectrocatalytic system consisting of a self-standing photoanode to degrade the water pollutant molecules with simultaneous production of molecular hydrogen at the cathode. The photoanode was prepared by coating Bi2O3 over the surface of self-standing TiO2 nanorods. Thus, prepared photoelectrodes show high degradation efficiency for rhodamine molecules, where direct oxidation of rhodamine by the holes generated under solar light illumination was detrimental for its activity. During simultaneous pollutant removal and energy production experiments, the anode shows 100% degradation of pollutant molecules while the cathode shows high hydrogen gas production (128 mM cm–2 h–1). The prepared composite showed higher efficiency of visible-light absorbance, high charge generation capability, and low charge transfer resistance at the interface as determined via several characterizations, compared to the bare titania. The catalyst is easy to prepare and robust in activity for several kinds of pollutant molecules tested. Its robust activity, high stability, and durability open up an avenue for the wastewater treatment with simultaneous renewable energy production technologies.

Sustainable Nanomaterial-Based technologies for renewable energy production and efficient storage based on Machine learning techniques

This research proposes novel technique in nanomaterial based renewable energy production and efficient storage based on machine learning techniques. The renewable energy production and storage has been carried out using heuristic smart grid based energy storage system with gradient boosting auto-encoder. Since the simple machine learning (ML) approach is only capable of analysing simple raw data, it cannot perform the learning process. The experimental analysis has been carried out in terms of the Root mean square error (RMSE), accuracy, energy storage capacity, electricity cost, performance and accountability reporting (PAR) and carbon emission. The proposed technique attained RMSE of 63%, accuracy of 99%, energy storage capacity of 94%, electricity cost of 56%, PAR of 58, carbon emission of 39% which will improve the renewable energy production and storage using heuristic smart grid based energy storage system with gradient boosting auto-encoder.

Development prospects for energy communities in the EU identifying best practice and future opportunities using a morphological approach

The 2018/19 Clean Energy Package of the European Union introduced a wide range of policy measures to further advance the Energy Transition, with one focus being low-carbon, renewable energy (RE) to replace fossil-based sources. RE production is typically disaggregated locally, legally, and in terms of ownership of the production facilities. Moreover, RE production technologies are various, and so are their respective usage, investment schemes, investors, geographic and financial properties, and, as a result, their support measures. With a focus on the new regulatory framework of the Directive on the Internal Energy Market (IEMD) and the recast of the Renewable Energy Directive (RED II), we analyse best practice business models for Energy Communities (ECs). To capture the multitude of pluri-dimensional design perspectives for ECs we develop and employ a new morphological approach, i.e., a morphological box for ECs (https://kelso-institute-europe.de/projects/energy-communities). This approach enables researchers and policy makers to identify the constellations for participation models that best fit the new EU policy strategy and to show opportunities with regard to combinations underused or not at all exploited yet. Based on this assessment we formulate policy recommendations and suggestions for the further development of Energy Transition participation models in general and ECs in particular.

Structure and oxygen vacancy engineered CuCo-layered double oxide nanotube arrays as advanced bifunctional electrocatalysts for overall water splitting.

In recent years, as a green renewable energy production technology, electrochemical water splitting has demonstrated high development potential. Many materials have been reported as successful catalysts in the water-splitting field. However, it is still a huge challenge to produce bifunctional electrocatalysts for the efficient and sustainable generation of hydrogen and oxygen simultaneously. Herein, we successfully developed oxygen vacancies abundant CuCo layered double oxide (Ov-CuCo-LDO) hollow nanotube arrays (HNTAs) loaded on nickel foam as advanced electrocatalysts for total water splitting. When the current density was 10 mA cm-2, the Ov-CuCo-LDO HNTAs exhibited outstanding onset overpotentials of 53.9 and 72.5 mV for the hydrogen evolution and oxygen evolution reactions (HER and OER) in alkaline medium, respectively, because of the bimetallic synergistic effect between the cobalt and copper and the unique hollow porous structure. In addition, an as-assembled Ov-CuCo-LDO||Ov-CuCo-LDO electrolytic cell showed a small potential of 1.55 V to deliver a current density of 10 mA cm-2. Moreover, it also showed remarkable durability after long-term overall water splitting for more than 20 h. The research results in this paper are of great interest to practical applications of the water decomposition process, providing clear and in-depth insights into preliminary robust and efficient multifunctional electrocatalysts for overall water splitting.

Decentralized robust optimal dispatch of user-level integrated electricity-gas-heat systems considering two-level integrated demand response

With the change of users’ energy consumption concept, the users are no longer rigid as the traditional inelasticity but can be flexible to carry out integrated demand response (IDR). The load has also been transformed from a traditional purely consumptive load to a new type of load that combines production and consumption with the improvement and popularization of renewable energy production technologies such as wind power and photovoltaic. In this paper, considering the IDR of loads and the uncertainty of renewable energy output, a decentralized robust optimal dispatch study is conducted on user-level integrated electricity-gas-heat systems (IEGHSs) composed of energy hubs (EHs) and some users. This paper firstly developed the comprehensive model of the user-level IEGHS, including the detailed mathematical model of EH, IDR, and users. Then, based on the established model, an optimal dispatching model is established with the goal of the lowest operating cost for the system. In order to cope with the uncertainty of the output of renewable energy equipment while protecting the security and privacy of different participants in the integrated energy system (IES), a decentralized robust algorithm is used to solve the model. Finally, the proposed model is analyzed and verified by an IES example composed of one EH and three users with the ability of IDRs, and the feasibility of the proposed model and algorithm is verified.

Brayton technology for Concentrated Solar Power plants: Comparative analysis of central tower plants and parabolic dish farms

Concentrated solar power plants intend to be key in the pool of renewable energy production technologies in the next future because of their versatility and high efficiency. In this work a comparative study between two promising technologies is developed. A central tower receiver surrounded by a heliostat field and a farm of parabolic dishes, both coupled to a hybrid Brayton cycle, are considered. Two power scales are surveyed (between 5 and 20 MW) at three different locations with quite different latitudes (between Sahara desert and medium European latitudes) and meteorological conditions. A modelling scheme developed by our group that allows to obtain the expected thermodynamic and thermo-economic plant records is applied. Key indicators like efficiency, net generated energy, levelized cost of electricity, and specific plant investment are calculated and analysed. Variability of natural gas prices and also land cost uncertainty are reflected on levelized cost of electricity range. Among the plants compared, minimum values are found within the interval [135–163]USD/MWh for central towers at the highest power considered and southern latitudes. In addition, the area needed for the installation of the plants and the influence of CO2 taxes is also analysed. Displayed cashflows show larger investment costs for central tower than for dish farms. Finally, those systems are put into the context of other concentrated solar power, other renewable, like wind or photovoltaic, and other conventional power plants.

Review on validation techniques of blade element momentum method implemented in wind turbines

There are two broad kinds of energy sources: renewable and non-renewable. In the renewable energy sector, significant advancements have been made in wind energy, the world’s fastest-growing renewable energy production technology. Despite this rapid development, the wind industry must still strive to ensure that any device designed for commercial use is reliable and its performance satisfies its economic projections. The critical factor in achieving this target is rotor aerodynamics because the rotor is the part that mainly reacts with the wind. When designing a wind turbine, the assessment of the aerodynamic characteristics of the blades must be accurate. Errors may result in wrong estimates of the turbines’ performance and economic projections. If experimental measurements cannot determine these characteristics, a designer will use alternative methods. Several modeling methods have proven viable solutions for design and analysis purposes, like Blade Element Momentum (BEM) methods. BEM methods analyze the aerodynamic performance of wind turbines, but it requires validation to examine the accuracy of its results. This paper highlights and reviews BEM validation techniques implemented in wind turbine aerodynamics analyses. BEM is validated against experimental data and other advanced models, namely, higher-order vortex and CFD (Computational Fluid Dynamics) models. Nevertheless, the experimental data are essential in wind turbine studies for both understanding the aerodynamic mechanisms and validating all these models.

Advancements in Bioelectricity Generation Through Nanomaterial-Modified Anode Electrodes in Microbial Fuel Cells

The use of nanotechnology in bioelectrochemical systems to recover bioelectricity and metals from waste appears to be a potentially appealing alternative to existing established procedures. This trend exactly characterizes the current renewable energy production technology. Hence, this review focuses on the improvement of the anode electrode by using different functional metal oxide-conducting polymer nanocomposites to enhance microbial fuel cell (MFC) performance. Enhancement of interfacial bioelectrocatalysis between electroactive microorganisms and hierarchical porous nanocomposite materials could enhance cost-effective bioanode materials with superior bioelectrocatalytic activity for MFCs. In this review, improvement in efficiency of MFCs by using iron oxide- and manganese oxide-based polypyrrole hybrid composites as model anode modifiers was discussed. The review also extended to discussing and covering the principles, components, power density, current density, and removal efficiencies of biofuel cell systems. In addition, this research review demonstrates the application of MFCs for renewable energy generation, wastewater treatment, and metal recovery. This is due to having their own unique working principle under mild conditions and using renewable biodegradable organic matter as a direct fuel source.